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The 2022 Nucleic Acids Research database issue and the online molecular biology database collection

Affiliations.

1 Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK.
2 Institut Curie, 25 rue d'Ulm, 75005 Paris, France.
PMID: 34986604
PMCID: PMC8728296
DOI: 10.1093/nar/gkab1195

The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focus specifically on COVID-19 and SARS-CoV-2, including SCoV2-MD, the first of the Issue's Breakthrough Articles. Major nucleic acid databases reporting updates include MODOMICS, JASPAR and miRTarBase. The AlphaFold Protein Structure Database, described in the second Breakthrough Article, is the stand-out in the protein section, where the Human Proteoform Atlas and GproteinDb are other notable new arrivals. Updates from DisProt, FuzDB and ELM comprehensively cover disordered proteins. Under the metabolism and signalling section Reactome, ConsensusPathDB, HMDB and CAZy are major returning resources. In microbial and viral genomes taxonomy and systematics are well covered by LPSN, TYGS and GTDB. Genomics resources include Ensembl, Ensembl Genomes and UCSC Genome Browser. Major returning pharmacology resource names include the IUPHAR/BPS guide and the Therapeutic Target Database. New plant databases include PlantGSAD for gene lists and qPTMplants for post-translational modifications. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Our latest update to the NAR online Molecular Biology Database Collection brings the total number of entries to 1645. Following last year's major cleanup, we have updated 317 entries, listing 89 new resources and trimming 80 discontinued URLs. The current release is available at http://www.oxfordjournals.org/nar/database/c/.

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Nucleic Acids Research is a peer-reviewed scientific journal published by Oxford University Press. It covers research on nucleic acids, such as DNA and RNA, and related work. Some of its content is available under an open access license. According to the Journal Citation Reports, the journal s 2010 impact factor is 7.836. The journal publishes two yearly special issues, one dedicated to biological databases, published in January since 1993 and the other on biological web servers, published in July since 2003.

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Protein-nucleic acid hybrid nanostructures for molecular diagnostic applications

Review Article
Published: 24 August 2024

Cite this article

Noah R. Sundah 1 , 2 ,
Yuxuan Seah 2 ,
Auginia Natalia 1 , 2 ,
Xiaoyan Chen 1 , 2 ,
Panida Cen 1 , 2 , 3 ,
Yu Liu 1 , 2 &
Huilin Shao 1 , 2 , 4 , 5

Molecular diagnostic technologies empower new clinical opportunities in precision medicine. However, existing approaches face limitations with respect to performance, operation and cost. Biological molecules including proteins and nucleic acids are being increasingly adopted as tools in the development of new molecular diagnostic technologies. In particular, leveraging their complementary properties—the functional diversity of proteins and the precision programmability of nucleic acids—a wide range of protein-nucleic acid hybrid nanostructures have been developed. These hybrid structures take diverse forms, ranging from one-dimensional to three-dimensional hybrids, as static assemblies to dynamic machines, and possess myriad functions to recognize target biomarkers, encode vast information and execute catalytic activities. Motivated by recent advances in this area of molecular nanotechnology, we review the state-of-art design and application of various types of protein-nucleic acid hybrid nanostructures for molecular diagnostics, and present an outlook on the challenges and opportunities for emerging pre-clinical and clinical applications, highlighting the promise for earlier detection, more refined diagnosis and highly tailored treatment decision that ultimately lead to improved patient outcomes.

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Acknowledgements

This work was supported in part by funding from National University of Singapore (NUS), NUS Research Scholarship, Ministry of Education, Institute for Health Innovation & Technology, Ministry of Education, National Research Foundation, and National Medical Research Council.

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Institute for Health Innovation and Technology, National University of Singapore, Singapore, 117599, Singapore

Noah R. Sundah, Auginia Natalia, Xiaoyan Chen, Panida Cen, Yu Liu & Huilin Shao

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Noah R. Sundah, Yuxuan Seah, Auginia Natalia, Xiaoyan Chen, Panida Cen, Yu Liu & Huilin Shao

Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore, 119077, Singapore

Agency for Science, Technology and Research, Institute of Molecular and Cell Biology, Singapore, 138673, Singapore

Huilin Shao

Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore

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Sundah, N.R., Seah, Y., Natalia, A. et al. Protein-nucleic acid hybrid nanostructures for molecular diagnostic applications. Nano Res. (2024). https://doi.org/10.1007/s12274-024-6925-6

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Nucleic acid chemistry

Since the discovery of the double-helical structure of DNA and postulation of the central dogma of molecular biology, stating that the flow of genetic information goes from DNA to RNA to protein, the field of nucleic acid chemistry has expanded dramatically.

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Synthesis and modification of nucleic acids

Generation of DNA oligomers with similar chemical kinetics via in-silico optimization

Networks of interacting DNA oligomers have various applications in molecular biology, chemistry and materials science, however, kinetic dispersions during DNA hybridization can be problematic for some applications. Here, the authors reveal that limiting unnecessary duplexes using in-silico optimization can reduce in-vitro kinetic dispersions by as much as 96%.

Michael Tobiason
Bernard Yurke
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The selection of a hydrophobic 7-phenylbutyl-7-deazaadenine-modified DNA aptamer with high binding affinity for the Heat Shock Protein 70

DNA aptamers can be selected against a wide range of therapeutic targets, however, the success rate of selective binding remains low due to the highly hydrophilic nature of the DNA backbone. Here, the authors design a hydrophobic 7-phenylbutyl-7-deazaadenine-modified DNA aptamer showing high binding affinity for the heat shock protein 70.

Catherine Mulholland
Ivana Jestřábová
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Evaluation of 3′-phosphate as a transient protecting group for controlled enzymatic synthesis of DNA and XNA oligonucleotides

Controlled enzymatic DNA synthesis represents an alternative synthetic methodology that circumvents the limitations of traditional soild-phase synthesis. Here, the authors explore the use of 3’-phosphate as a transient protecting group for the controlled enzymatic synthesis of DNA and XNA oligonucleotides.

Marie Flamme
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Structure and function of nucleic acids

Structure of a 10-23 deoxyribozyme exhibiting a homodimer conformation

RNA-cleaving DNAzymes exhibit potential as biosensors and in vivo knockdown agents, however, the structures of DNAzymes remain underexplored. Here, the authors report the 2.7 Å X-Ray crystal structure of a 10-23 DNAzyme–substrate complex in a homodimer conformation.

Evan R. Cramer
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i-Motif folding intermediates with zero-nucleotide loops are trapped by 2′-fluoroarabinocytidine via F···H and O···H hydrogen bonds

The oligonucleotide d(TC 5 ) forms a well-characterized tetrameric i-motif in solution; however, the isolation of dimeric and trimeric intermediates remains challenging. Here, the authors report that 2′-deoxy-2′-fluoroarabinocytidine substitutions can prompt TC 5 to form dimeric i-motif folding intermediates through fluorine and oxygen hydrogen bonds.

Roberto El-Khoury
Veronica Macaluso
Masad J. Damha

Intrastrand backbone-nucleobase interactions stabilize unwound right-handed helical structures of heteroduplexes of L- a TNA/RNA and SNA/RNA

Serinol nucleic acid and L-threoninol nucleic acid can bind to RNA and DNA, endowing them with potential as nucleic acid-based drugs. Here the authors prepare single crystals of L- a TNA/RNA and SNA/RNA heteroduplexes to further our structural understanding of how synthetic nucleic acids hybridize with natural nucleic acids.

Yukiko Kamiya
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Hiroyuki Asanuma

Applications of nucleic acids in chemical biology and medicinal chemistry

DNA-encoded chemical libraries yield non-covalent and non-peptidic SARS-CoV-2 main protease inhibitors

Conventional structure-based design of M pro inhibitors of SARS-CoV-2 often starts from the structural information of M pro and their binders; however, the continual rise of resistant strains requires innovative routes to discover new inhibitors. Here, the authors develop a DNA-encoded chemical library screening to produce non-covalent, non-peptidic small molecule inhibitors for SARS-CoV-2 M pro independently of preliminary knowledge regarding suitable starting points.

Ravikumar Jimmidi
Srinivas Chamakuri
Damian W. Young

Accessible light-controlled knockdown of cell-free protein synthesis using phosphorothioate-caged antisense oligonucleotides

Light-activated antisense oligonucleotides have been developed to induce gene knockdown in living cells, however, their synthesis remains challenging and application in cell-free systems is underexplored. Here, the authors report a one-step method for selectively attaching photocages onto phosphorothioate linkages of antisense oligonucleotides that can knockdown cell-free protein synthesis using light.

Denis Hartmann
Michael J. Booth

Advanced preparation of fragment libraries enabled by oligonucleotide-modified 2′,3′-dideoxynucleotides

Next-generation genome sequencing technologies have revolutionized the life sciences, however all sequencing platforms require nucleic acid pre-processing to generate suitable libraries for sequencing. Here, oligonucleotide-tethered 2′,3′-dideoxynucleotide terminators bearing universal priming sites are synthesised and incorporated by DNA polymerases, allowing integration of the fragmentation step into the library preparation workflow while also enabling the obtained fragments to be readily labeled by platform-specific adapters.

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Žana Kapustina
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Published: 18 August 2024

Development and validation of a rapid five-minute nucleic acid extraction method for respiratory viruses

Yu Wang 1 ,
Yuanyuan Huang 1 ,
Yuqing Peng 1 ,
Qinglin Cao 1 ,
Wenkuan Liu 2 ,
Zhichao Zhou 2 ,
Guangxin Xu 1 ,
Lei Li 3 , 4 &
Rong Zhou 1 , 2

Virology Journal volume 21 , Article number: 189 ( 2024 ) Cite this article

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The rapid transmission and high pathogenicity of respiratory viruses significantly impact the health of both children and adults. Extracting and detecting their nucleic acid is crucial for disease prevention and treatment strategies. However, current extraction methods are laborious and time-consuming and show significant variations in nucleic acid content and purity among different kits, affecting detection sensitivity and efficiency. Our aim is to develop a novel method that reduces extraction time, simplifies operational steps, and ensures high-quality acquisition of respiratory viral nucleic acid.

We extracted respiratory syncytial virus (RSV) nucleic acid using reagents with different components and analyzed cycle threshold (Ct) values via quantitative real-time polymerase chain reaction (qRT-PCR) to optimize and validate the novel lysis and washing solution. The performance of this method was compared against magnetic bead, spin column, and precipitation methods for extracting nucleic acid from various respiratory viruses. The clinical utility of this method was confirmed by comparing it to the standard magnetic bead method for extracting clinical specimens of influenza A virus (IAV).

The solution, composed of equal parts glycerin and ethanol (50% each), offers an innovative washing approach that achieved comparable efficacy to conventional methods in a single abbreviated cycle. When combined with our A Plus lysis solution, our novel five-minute nucleic acid extraction (FME) method for respiratory viruses yielded superior RNA concentrations and purity compared to traditional methods. FME, when used with a universal automatic nucleic acid extractor, demonstrated similar efficiency as various conventional methods in analyzing diverse concentrations of respiratory viruses. In detecting respiratory specimens from 525 patients suspected of IAV infection, the FME method showed an equivalent detection rate to the standard magnetic bead method, with a total coincidence rate of 95.43% and a kappa statistic of 0.901 ( P < 0.001).

Conclusions

The FME developed in this study enables the rapid and efficient extraction of nucleic acid from respiratory samples, laying a crucial foundation for the implementation of expedited molecular diagnosis.

Introduction

Acute respiratory virus infection is a prevalent human disease, particularly affecting children, the elderly, and immunocompromised individuals, with a high incidence during certain seasons [ 1 , 2 ]. Common respiratory viruses include respiratory syncytial virus (RSV), adenovirus (ADV), influenza virus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [ 3 ]. Among them, influenza A virus (IAV) and SARS-CoV-2 have caused global pandemics, posing a serious threat to human health and social public safety [ 4 , 5 ]. Currently, ongoing research and development for RSV and SARS-CoV-2 vaccines are being conducted worldwide; however, achieving herd immunity will still be a prolonged process due to challenges such as low vaccination rates and virus mutations in low- and middle-income countries [ 6 , 7 ].

The nucleic acid amplification test (NAAT) and its detection of specific DNA is a powerful tool widely employed in various fields, including disease diagnosis, gene functional analysis, and mutation screening. In diagnostic applications, NAAT-based analysis offers several advantages over traditional enzyme or antibody-based methods, including higher sensitivity, faster sample-to-result processing time, and greater flexibility in target detection selection, which enables rapid adaptation to various emerging challenges [ 8 ]. However, the major bottleneck preventing the rapid and large-scale implementation of molecular diagnostics is the separation and purification of nucleic acid from samples, which is a complex process that traditionally requires skilled technicians and involves numerous manual pipetting steps [ 9 ].

Isolating high-quality nucleic acid while preserving its purity and integrity is an essential prerequisite for downstream molecular applications, including quantitative real-time polymerase chain reaction (qRT-PCR), isothermal amplification technologies (IATs), next-generation sequencing (NGS), and microarrays [ 10 , 11 , 12 , 13 ]. Although there are several methods for nucleic acid isolation, they can be primarily categorized into three groups: liquid-phase extraction utilizing phenol and guanidine thiocyanate (GTC), solid-phase adsorption employing silica membrane spin columns, and superparamagnetic beads. The methods all follow a common sequence: first, cell lysis and inhibition of ribonuclease P (RNase P) activity with subsequent separation of nucleic acids from the lysed mixture; then, washing of the nucleic acids; and, ultimately, recovery of purified nucleic acids [ 14 ]. After phenol-based TRIzol reagent is used to extract nucleic acids, they undergo separation and purification through precipitation. This method is widely used for RNA extraction from tissues and cells; however, TRIzol requires a duration exceeding 70 min for extraction, the recovered RNA often suffers from contamination due to residual organic matter [ 15 ]. The spin column method is based on the binding of nucleic acids to a solid-phase silica carrier under a high salt concentration, followed by a series of washing and centrifugation steps to eliminate contaminants, and finally elution of the nucleic acids from the silica using a low-salt solution [ 16 ]. The spin column method involves numerous operational steps, with a duration of approximately 40–60 min, and requires multiple centrifugations during extraction, which increases the risk of nucleic acids breakage and degradation. The magnetic bead method employs paramagnetic beads coated with various functionalized surface chemicals to efficiently capture and purify nucleic acids. In addition, the magnetic beads are separated from the liquid during washing and elution steps by utilizing a magnet to attract the beads [ 17 , 18 ]. The magnetic bead method only requires 25–30 min, but the recovery rate of nucleic acid in the eluent is relatively low, and the presence of residual magnetic beads also exerts an inhibitory effect on subsequent PCR [ 19 ]. The extraction process can be broadly divided into manual and automatic processes, with many DNA/RNA extraction kits designed for manual operation. However, with the increasing demand for high-throughput analysis, there is a growing popularity of kits specifically designed for automatic operation.

Given the instability of RNA and the ubiquitous presence of RNA enzymes, it is crucial to select an RNA assay kit that can simultaneously meet quality, purity, and integrity requirements while minimizing the time required. In this study, we have developed a rapid respiratory virus nucleic acid extraction method that can be completed within 5 min. The method not only ensures the quality of the extracted nucleic acid but also reduces processing time, thereby facilitating prompt and efficient downstream applications based on nucleic acid analysis.

Materials and methods

Cells, viruses, and clinical specimens.

AD293 cells (from American Type Culture Collection) were cultured in Dulbecco’s modified Eagle medium (DMEM; Gibco, Grand Island, NY, USA) supplemented with 10% fetal bovine serum (FBS; Gibco, Grand Island, NY, USA), 1% penicillin, and 1% streptomycin (Gibco, Grand Island, NY, USA) at 37°C in an incubator with a 5% CO 2 atmosphere. RSV (RSV-A2), ADV (ADV4), IAV (H1N1), herpes simplex virus 1 (HSV-1), and human coronavirus 229E (HCoV-229E) were obtained from the State Key Laboratory of Respiratory Disease. A pseudovirus of SARS-CoV-2 was purchased from Sansure Biotech Inc (Changsha, China). Frozen clinical specimens from 525 suspected IAV-infected patients were obtained from the First Affiliated Hospital of Guangzhou Medical University. This project was approved by the biosafety committee, and all experiments were performed in accordance with biosafety regulations.

DNA and RNA extraction

We have developed a five-minute nucleic acid extraction (FME) reagent (China Food and Drug Administration (CFDA) Certification Class I, No. 20230661). The reagents comprised a lysis solution containing GTC, sodium citrate tribasic dihydrate (sodium citrate), sodium lauroyl sarcosine (sarkosyl), dithiothreitol (DTT), polyethylene glycol 6000 (PEG 6000), and isopropyl alcohol (IPA); a washing solution consisting of a mixture of glycerin and ethanol (EtOH) in equal proportions; an elution solution composed of Tris–HCl (pH 8.0) and ethylene diamine tetraacetic acid (EDTA); and magnetic beads purchased from BayBio Bio-tech Co., Ltd (Guangzhou, China). A total of 40 μL of magnetic beads and 500 μL of lysis solution were added to a 1.5 mL centrifuge tube, followed by the addition of 200-μL samples that were mixed by vortexing for 1 min, and subsequently all the supernatant was removed using a magnetic separator. The magnetic beads were washed with 300 μL of washing solution, vortexed for 1 min, and subjected to magnetic separation again. The supernatant was discarded. Then, 100 μL of elution solution was added, the mixture was incubated at 56°C for 1 min and magnetically separated, and all the supernatant containing the extracted nucleic acid was transferred. Alternatively, an automated nucleic acid extractor can be used by adding a 200-μL sample to a preloaded well plate containing reagents and placing it into an E-Five nucleic acid extractor (HuYanSuo Medical Technology Co., Ltd., Guangzhou, China). The machine was operated according to the manufacturer’s instructions, with the entire process taking approximately 5 min. Upon completion of the extraction, 100 μL of eluent was stored at −80°C for further use.

To compare the efficiency of extraction, the same sample was extracted according to the manufacturer’s instructions for each commercial reagent kit while maintaining consistent loading and elution volumes. The extraction kits utilized in this study included the following: LemnisCare (LC) Viral DNA/RNA Extraction kit (LemnisCare Medical Technology Co., Ltd., Shenzhen, China), referred to as LC; Magen Total DNA/RNA kit (Magen Biotechnology Co., Ltd., Guangzhou, China), referred to as Magen; Baypure Viral DNA/RNA Extraction kit (BayBio Bio-tech Co., Ltd., Guangzhou, China), referred to as Baypure; HR Fast-Virus DNA/RNA kit (Huirui Biotechnology Co., Ltd., Zhuhai, China), referred to as HR; Viral Nucleic Acid Isolation kit (Jiangsu BioPerfectus Technologies Co., Ltd., Taizhou, China), referred to as BioPerfectus; TIANamp Virus DNA/RNA kit (Tiangen Biotech Co., Ltd., Beijing, China), referred to as TIANamp; ABT Nucleic Acid Extraction kit (Applied Biological Technologies Co., Ltd., Beijing, China), referred to as ABT; GIRM Nucleic Acid (DNA/RNA) Extraction kit (HuYanSuo Medical Technology Co., Ltd., Guangzhou, China), referred to as GIRM; and TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA), referred to as TRIzol.

Nucleic acid concentration and integrity

The concentration and purity of RNA from each kit were assessed by analyzing 1 μL of extracted nucleic acid using the NanoDrop One (Thermo Fisher Scientific, Waltham, MA, USA). A known concentration of plasmids was added to the FME to compare the amount of DNA recovered from the elution solutions while concurrently assessing the integrity of the DNA fragments through 1.5% agarose gel electrophoresis.

Quantitative real-time PCR

The qRT-PCR experiment was conducted using a SLAN-96P real-time PCR detection system (Hongshi Medical Technology Co., Ltd., Shanghai, China). The RSV, ADV, IAV, and pseudovirus of SARS-CoV-2 were analyzed with a respiratory syncytial virus Nucleic Acid Diagnostic kit (HuYanSuo Medical Technology Co., Ltd., Guangzhou, China), human adenovirus Nucleic Acid Diagnostic kit (HuYanSuo Medical Technology Co., Ltd., Guangzhou, China), SARS-CoV-2 and influenza A/B Virus Nucleic Acid Diagnostic kit (Sansure Biotech Inc., Changsha, China), and 2019-nCoV Nucleic Acid Diagnostic kit (Sansure Biotech Inc., Changsha, China), respectively. The reaction system and procedures of the four commercially available test kits were followed according to the instructions. HSV-1 and HCoV-229E were detected using SYBR Green dye (Tsingke Biotech Co., Ltd., Beijing, China). The nucleic acid of HCoV-229E required an additional reverse transcription step of 25°C for 5 min, activation at 50°C for 15 min, and 85°C for 2 min (Vazyme Biotech Co., Ltd., Nanjing, China). The reaction system consisted of 10 μL of ArtiCan ATM SYBR qPCR Mix (Tsingke Biotech Co., Ltd., Beijing, China), 1.5 μL of forward and reverse primers (Additional file 1 : Table S1), and 7 μL of DNA template. The PCR cycling program was as follows: 95°C for 1 min; and 40 cycles of 95°C for 10 s, 60°C for 20 s, followed by melting curve analysis.

Clinical performance of the FME

Frozen respiratory tract specimens (pharyngeal swabs) were collected from suspected IAV-infected patients, and the FME and standard magnetic bead method (BioPerfectus) were used for nucleic acid extraction. Both extraction methods ensured consistent sample volumes of 200 μL and elution volumes of 100 μL. The SARS-CoV-2 and Influenza A/B Virus Nucleic Acid Diagnostic kits (Sansure Biotech Inc., Changsha, China) were used for qRT-PCR detection, and specifically for the detection of IAV clinical specimens. Briefly, 20 μL of nucleic acid was added to a mixture containing 26 μL of a PCR buffer mixture (dNTPs, MgCl 2 , primer, and probe) and 4 μL of an enzyme mixture (reverse transcriptase and Taq polymerase). The PCR cycling program was as follows: 50°C for 4 min and 95°C for 30 s, followed by 45 cycles of 95°C for 2 s and 60°C for 20 s. The SLAN-96P system (Hongshi Medical Technology Co., Ltd., Shanghai, China) was used for qRT-PCR. A VIC channel cycle threshold (Ct) value of ≤ 40 and a simultaneous Cy5 channel Ct value of ≤ 40 indicated positivity for IAV. If at least one of the two extraction methods yielded a positive result for IAV, the sample was classified as positive for IAV; otherwise, it was considered negative.

Quantitative analysis of proteins

Modified Bradford reagent (Sangon Biotech Co., Ltd., Shanghai, China) was used to quantify the protein content in the washing solution after a magnetic bead wash. In brief, the reagent was used to measure the OD595 of bovine serum albumin (BSA) with a range of known concentrations. A standard curve correlating the absorbance with protein concentration at 595 nm was constructed. Subsequently, the OD595 of the test sample was measured in the same manner to determine its protein concentration by referencing the standard curve.

Statistical analysis

SPSS Statistics 20 software (IBM, Armonk, NY, USA) was used to calculate the percentage of positive and negative agreements between the FME and standard magnetic bead methods, and Cohen’s kappa statistic was calculated [ 20 ]. Statistical graphs were generated using GraphPad Prism 8 (GraphPad Software Inc., San Diego, CA, USA). Statistical analysis was performed with Student’s t tests, and P < 0.05 (two tailed) was considered statistically significant. The data presented are representative of at least three independent experiments.

The combination of glycerin and EtOH can efficiently purify nucleic acid

Nucleic acid extraction by the magnetic bead method involves three steps: lysis, washing, and elution (Fig. 1 A). To develop a rapid and efficient method for nucleic acid extraction, we have explored a novel composition of lysate and washing solution with magnetic beads. First, the LC kit was selected as the control group due to its efficiency being comparable to that of other commercial kits for RSV and ADV extraction (Additional file 2 : Figure S1), and a novel washing solution, glycerin, was substituted for the one in the LC kit to compare its washing efficacy to that of this LC washing buffer. Glycerin, a commonly employed protein stabilizer and storage buffer component, was identified as a viable washing solution for the purification of nucleic acid on magnetic beads in our study. As the glycerin concentration increased gradually from 20% to 80%, the extraction efficiency of RSV gradually improved, with no significant difference between 80% glycerin and the LC washing buffer (Fig. 1 B). Considering the high viscosity of concentrated glycerin, we chose 50% glycerin and gradually increased the EtOH concentration on this basis. The results showed that compared with 50% glycerin, the higher the EtOH content, the better the extraction efficiency of RSV. When the proportion of EtOH reached 30%, its washing effect surpassed that of the LC washing buffer, reaching optimal performance when the EtOH concentration increased to 50% (Fig. 1 C). Subsequently, we gradually increased the glycerin content while maintaining 50% EtOH and observed a corresponding gradual enhancement in extraction efficiency as the glycerin increased from 10% to 50% (Fig. 1 D). This showed that the combination of 50% glycerin and 50% EtOH allowed glycerin and EtOH to effectively perform their respective washing functions and that the solution with this ratio possessed an optimal viscosity for facilitating the subsequent steps. Furthermore, it was surprising to observe that using 50% glycerin and 50% EtOH for one wash yielded better results than washing two or three times (Fig. 1 E). This indicated that a single wash with 50% glycerin and 50% EtOH effectively eliminated the majority of impurities, while increasing the number of washes may result in the loss of nucleic acids. To further explore the efficacy of glycerin in removing impurities, we quantitatively measured the protein content of the novel washing solution after the washing process. The presence of 50% glycerin was found to exhibit a certain protein elution effect; however, as the proportion of EtOH gradually increased and reached 30%, the efficiency of the protein elution was significantly enhanced (Fig. 1 F). The proportion of glycerin was gradually increased while maintaining 50% EtOH, but it was found that the washed protein content remained relatively stable (Fig. 1 G), suggesting that while glycerin removed impurities, the primary ability to wash proteins was due to EtOH. Simultaneously, we also found that washing only once with 50% glycerin and 50% EtOH effectively eliminated the majority of protein impurities, while subsequent rounds of washing yielded less protein removal.

Glycerin and EtOH combination for nucleic acid purification. A Schematic diagram of the nucleic acid extraction magnetic bead method. B Using the LC reagent as a control group, varying glycerin concentrations (80%, 70%, 60%, 50%, 40%, 30%, and 20%) and diethyl pyrocarbonate (DEPC) H 2 O were used to replace the LC kit washing buffer for low-concentration RSV nucleic acid extraction. One washing was performed, and qRT-PCR Ct values were used to assess the washing solution effect. C Using the LC reagent as a control group, different glycerin–EtOH combinations (50% glycerin and 50% EtOH, 50% glycerin and 40% EtOH, 50% glycerin and 30% EtOH, 50% glycerin and 20% EtOH, 50% glycerin and 10% EtOH, and 50% glycerin) were used to replace the LC kit washing buffer for high-concentration RSV nucleic acid extraction. One washing was performed, and qRT-PCR Ct values were used to assess the washing solution effect. D EtOH-glycerin solutions (50% EtOH and 50% glycerin, 50% EtOH and 40% glycerin, 50% EtOH and 30% glycerin, 50% EtOH and 20% glycerin, 50% EtOH and 10% glycerin, and 50% EtOH) were used to replace the LC kit washing buffer for high-concentration RSV nucleic acid extraction. One washing was performed, and qRT-PCR Ct values were used to assess the washing solution effect. E High-concentration RSV nucleic acid extraction using 50% glycerin and 50% EtOH washing solution, followed by one, two, or three rounds of purification in the washing step. Efficiency was evaluated based on qRT-PCR Ct values. F – H Wash solutions obtained from the wash steps in C–E were analyzed for the protein concentration. Data are presented as means ± SD for three independent biological replicates. Statistical significance was calculated using t tests; ns P > 0.05, * P < 0.05, ** P < 0.01, *** P < 0.001

The effect of A Plus lysis solution

In addition to the washing solution, we also developed a novel lysis solution termed A Plus (AP). When using AP or other commercial lysis buffers combined with 50% glycerin and 50% EtOH to extract high-concentration RSV, the lysis effect of AP was essentially comparable or even slightly superior to that of other brands of lysis buffer (Fig. 2 A), indicating the superior effectiveness of AP compared to the lysate in other commercially available kits. To investigate the key components involved in AP lysis, we used AP that lacked individual components to re-extract RSV. The results indicate that, compared with the intact AP, the reduction of each component in the AP lysis solution led to a decrease in extraction efficiency, among which the most significant impact was caused by reducing GTC or IPA (Fig. 2 B), indicating that the reduction of any constituent of the AP lysis solution adversely affects the final extraction effect, with the absence of GTC and IPA having the most pronounced impact. This suggested that GTC and IPA play a pivotal role in the lysis effect of AP solution. If a lysis solution is not thoroughly washed during the extraction process, it may have a significant impact on subsequent experiments, such as PCR amplification. Therefore, we also investigated whether adding various components of AP to the elution solution affected qRT-PCR performance. We found that the amplification of qRT-PCR was completely inhibited after the direct addition of AP lysis solution or GTC, and sarkosyl also had a significant inhibitory effect ( P < 0.01). Although the degree of inhibition was not obvious with sodium citrate, it decreased the fluorescence normalized reporter (Rn) value of qRT-PCR (Additional file 3 : Figure S2A and S2B). Further research showed that the inhibitory effect of sodium citrate, sarkosyl, and GTC on qRT-PCR disappeared when their concentration in the elution solution was reduced to 1.25, 1, and 0.1 mM, respectively (Additional file 3 : Figure S2C–S2H). This implied that while sodium citrate, sarkosyl, and GTC are the primary components in the lysis process, they must be removed during the washing stage to ensure that their concentration in the elution solution remains below inhibitory levels.

The AP solution demonstrates high efficacy in sample lysis. A During the extraction of high-concentration RSV nucleic acid, the lysis solution was prepared using either AP, phosphate-buffered saline (PBS), or a commercially available nucleic acid extraction kit lysis buffer. The washing solution consisted of a mixture of 50% glycerin and 50% EtOH, and the efficiency of each lysis solution was determined by analyzing the qRT-PCR Ct values. B AP lysis solution was prepared lacking specific components, and these lysis solutions or PBS were used to extract RSV viral nucleic acid at high concentrations. Subsequently, a wash step was performed using 50% glycerin and 50% EtOH, followed by the determination of qRT-PCR Ct values to assess the efficiency of each lysis solution. Data are presented as means ± SD for three independent biological replicates. Statistical significance was calculated using t tests; ns P > 0.05, * P < 0.05, ** P < 0.01, *** P < 0.001

Extraction effect of the FME

We used AP lysis solution, washing solution (50% glycerin and 50% EtOH), magnetic beads, and an elution solution separately and sequentially to form the FME. The concentration and purity of RNA are two important criteria for any RNA extraction process. We found that the concentration of RNA obtained after applying the FME method to AD293 cells was higher than that of other traditional methods, and the purity (260/280 ratio) remained between 1.9 and 2.0 (Table 1 ). Recovery and integrity are metrics used to evaluate the effectiveness of nucleic acid extraction systems. To evaluate the extraction efficiency of the FME on DNA, we added known amounts of DNA to AP and compared it with the amount of DNA in the elution after extraction. The results showed that there was a strong linear correlation between the amounts of DNA added to the FME and the amounts of DNA recovered from the system (Fig. 3 A) and that complete nucleic acid fragments were obtained through extraction using the FME (Fig. 3 B). Subsequently, we also conducted a comparative analysis of the RSV extraction efficiency using the FME and several commercially available kits, including magnetic bead (Magen and LC), spin column (ABT and TIANamp), and precipitation methods (GIRM and TRIzol). The results showed that both the FME and GIRM precipitation methods yielded the highest RSV nucleic acid concentrations, with FME demonstrating a shorter processing time, and the FME was superior to the magnetic bead method and spin column method reagents used as a comparison (Fig. 3 C). This suggested that the FME could obtain a high RNA concentration from viral samples in a short period of time.

Performance analysis of the FME method. A There was a strong linear correlation (Pearson’s R , R 2 = 0.9969) between the amount of DNA added to the FME and the amount of DNA recovered from the FME (μg). B DNA integrity was analyzed by agarose gel electrophoresis. Line 1: DNA Marker, Line 2: DNA before being added to the FME, Line 3: DNA after being added to the FME. C The FME and six different nucleic acid extraction kits were used to extract high-concentration RSV, and the Ct values obtained through qRT-PCR served as the criteria for evaluating the efficiency of each extraction kit. D–F PBS was used for fivefold gradient dilutions of RSV, and each gradient was subjected to nucleic acid extraction using either manual or automatic methods. The Ct values were used to assess the consistency between automatic and manual operations. D: Amplification curve, E: histogram, F: linear regression curve, manually extracted R 2 = 0.9921, and automatically extracted R 2 = 0.9922. Data are presented as means ± SD for three independent biological replicates. Statistical significance was calculated using t tests; ns P > 0.05, * P < 0.05, ** P < 0.01, *** P < 0.001

Automation and performance evaluation of the FME

Automation and high throughput are two of the biggest advantages of the magnetic bead method [ 21 ]. Considering the differences in RNA yield between manual and automatic extraction, we paired the FME with an automatic nucleic acid extraction instrument and implemented specific procedures to achieve a 5-minute extraction (Additional file 1 : Table S2) and then compared the differences between the manual and automatic FME. As a result, it was observed that there was no significant difference in the efficacy of the manual or automatic FME when using fivefold diluted RSV samples sequentially (Fig. 3 D and E), and the regression curves obtained from both approaches exhibited a high degree of overlap (Fig. 3 F). This showed that after using a specific program, the FME could be adapted to currently available instruments and consumables, achieving high-quality automatic nucleic acid extraction within 5 min.

To confirm the ability of automatic FME to extract actual samples, we selected several respiratory viruses commonly used in laboratories and compared the FME method with a representative magnetic bead method (LC), spin column method (TIANamp), and precipitation method (GIRM). The results showed that both the FME and the precipitation method exhibited the most effective extraction of samples with high, medium, and low concentrations of RSV, and the FME was superior to the spin column method and magnetic bead method (Fig. 4 A). When extracting ADV, the traditional precipitation method was confirmed to be the most effective, followed by the FME and spin column, although the FME still outperformed the magnetic bead method at all concentrations (Fig. 4 B). For IAV extraction, except for a slightly better performance of the precipitation method at the highest concentration compared with the FME, both the FME and precipitation method displayed optimal effects at all concentrations, surpassing other techniques (Fig. 4 C). Regarding HSV-1 extraction, no significant difference was observed among the FME, precipitation, and spin column methods; however, all three were marginally more effective than the magnetic bead method (Fig. 4 D). For high-concentration pseudovirus in a SARS-CoV-2 extraction, the precipitation method was the best, followed by the FME and spin column methods, while the magnetic bead method was the least effective. At a medium concentration, there was no significant difference among the FME, precipitation, and spin column methods, and all were superior to the magnetic bead method. At a low concentration, there was no significant difference among the four methods (Fig. 4 E). In terms of high, medium, and low concentrations of HCoV-229E sample extractions, the FME exhibited the highest extraction effect, followed by the precipitation, magnetic bead, and spin column methods (Fig. 4 F). This showed that FME achieved a superior or second-best outcome compared with commonly used nucleic acid extraction kits available on the market, regardless of whether the process involved extracting DNA or RNA viruses, or enveloped or non-enveloped viruses.

Effects of the FME on the extraction of multiple respiratory viruses. The efficiency of each method was evaluated by extracting high, medium, and low concentrations of respiratory viruses using the FME, magnetic bead (LC), spin column (TIANamp), and precipitation (GIRM) methods, followed by obtaining Ct values through qRT-PCR. A Effect of RSV nucleic acid extraction. B Effect of ADV nucleic acid extraction. C Effect of IAV nucleic acid extraction. D Effect of HSV-1 nucleic acid extraction. E Effect of SARS-CoV-2 pseudovirus nucleic acid extraction ( n gene). F Effect of HCoV-229E nucleic acid extraction. Data are presented as means ± SD for three independent biological replicates. Statistical significance was calculated using t tests; ns P > 0.05, * P < 0.05, ** P < 0.01, *** P < 0.001

Frozen clinical specimens from 525 suspected IAV-infected patients were simultaneously extracted using both the standard magnetic bead method and the FME. The Ct values obtained by the standard magnetic bead method and FME were compared to evaluate the clinical performance of the FME. The detection performance of the FME for IAV, compared with that of the standard magnetic bead method, was as follows: sensitivity, 97.00% (323/333); specificity, 92.71% (178/192); positive predictive value, 95.85% (323/337); negative predictive value, 94.68% (178/188); and total coincidence rate (323+178)/525 = 95.43% (Table 2 ). The consistency between the two methods was good, with a Kappa statistic of 0.901 ( P < 0.001). In addition, we analyzed the Ct value distribution of the clinical specimens. The average Ct values of IAV extracted by the standard magnetic bead method and FME were 31.49 and 31.16, respectively. There was no significant difference in the Ct value distribution between the two extraction methods. However, the Ct values obtained through the FME were broader in the upper and lower quartiles than those obtained by the magnetic bead method (Fig. 5 A). Further analysis was conducted on specimens with Ct values > 35 to evaluate the limit of detection (LoD) of the FME assay in actual clinical samples. It was found that there was no significant difference in the Ct distribution between the FME and standard magnetic bead method in low-concentration specimens (Fig. 5 B).

Distribution of Ct values for clinical specimens using the FME and standard magnetic bead method. A Distributions of Ct values obtained from IAV clinical specimens using the FME and standard magnetic bead extraction methods. B Distribution of Ct values for low-concentration specimens (Ct values > 35 for results of both tests). Lines within boxes represent medians. Upper and lower boundaries of boxes represent upper and lower quartiles, respectively. Bars represent minimum and maximum values. Dashed lines indicate the detection limit. ND, not detected. Statistical significance was calculated using t tests; ns P > 0.05

We noticed that a total of 24 specimens had inconsistent results after being extracted using the FME and standard magnetic bead method. Among them, 14 cases were positive for FME detection and negative for standard magnetic bead method detection; 10 cases were negative for FME detection (including one case with a Ct value > 40, which was judged to be negative due to the interpretation criteria) and positive for standard magnetic bead method detection (Additional file 1 : Table S3). All these inconsistent results had Ct values > 35, and 21 had Ct values > 37. Clearly, these specimens were borderline positive results. In addition to the difference in extraction efficiency, the detection performance of the qRT-PCR instrument on the LoD also affects the detection rate of the results. Overall, the effect of the FME on clinical specimen extraction was comparable to that of the standard magnetic bead method.

In recent years, the field of pathogen detection technology has witnessed an unprecedented pace of development. Compared with lateral flow immunochromatographic assays (LFIAs), enzyme-linked immunosorbent assays (ELISAs), plaque reduction neutralization tests (PRNTs), and other technologies, NAAT-based detection methods exhibit superior sensitivity, specificity, and speed of detection [ 22 , 23 , 24 ]. Among all NAATs, qRT-PCR is the most widely used and has emerged as the preferred method for detecting human pathogens [ 25 , 26 ]. However, a major choke point of qRT-PCR diagnosis is that it relies on the extraction and purification of nucleic acids from samples, which is crucial for achieving optimal sensitivity, but also a relatively time-consuming and laborious process [ 27 , 28 ]. It has been reported that extraction-free methods can serve as an alternative procedure, alleviating the supply bottleneck of extraction reagents [ 29 ]. However, these strategies are incapable of eliminating PCR inhibitors in the specimen, leading to a significant reduction in sensitivity when processing samples with low viral loads [ 30 , 31 ]. Therefore, there is an urgent need for novel technology capable of rapidly extracting high-quality nucleic acid.

Glycerin functions as a humectant, solvent, plasticizer, adhesive, and binding agent. Despite its extensive utilization in the fields of food, cosmetics, medicine, and chemical synthesis, glycerin has not yet been employed as the primary raw material in nucleic acid purification reagents [ 32 ]. In this study, we demonstrated that glycerin, which is miscible with water in any proportion, can purify nucleic acids, and the higher the concentration of glycerin, the better the purification effect (Fig. 1 B). Furthermore, after adding a certain proportion of EtOH to 50% glycerin, the effect of washing with 50% glycerin and 50% EtOH for a short duration was superior to that of thoroughly washing by the conventional magnetic bead method three times (Fig. 1 C). However, we found that the elution of miscellaneous proteins using 50% glycerin and 50% EtOH primarily relied on the presence of EtOH (Fig. 1 F and G); the specific role of glycerin in nucleic acid purification remains to be investigated.

To release nucleic acids from the sample, physical, chemical, or enzymatic methods are usually used to lyse samples [ 9 ]. In clinical laboratories, nucleic acid lysis commonly involves mixing the sample with a chemical lysis solution, subjecting it to high-temperature heating, and supplementing with proteinase K [ 21 , 33 ]. In this study, we developed a chemical-based lysis solution AP, which exhibited a superior lysis effect compared with the majority of commercially available lysis reagents when combined with 50% glycerin and 50% EtOH for nucleic acid extraction (Fig. 2 A). The FME, composed of AP, 50% glycerin and 50% EtOH, exhibited superior efficacy in RSV extraction compared with conventional methods (Fig. 3 C). Moreover, the extracted RNA from AD293 cells demonstrated above-average levels of concentration and purity (Table 1 ). It is worth pointing out that commercially available kits have been modified to ensure consistent sample loading and elution volumes with FME, thus the above results may not have necessarily reflected the optimal efficiency of these kits; however, these findings highlight the advantages of FME in terms of speed and performance. We also explored the inhibition of the PCR reaction when there was residual AP lysis solution during elution when the concentration of each AP component reached a certain threshold. After the concentrations of GTC, sodium citrate, and sarkosyl reached a level that inhibited PCR amplification, an exponential increase in the Ct values resulted (Additional file 3 : Figure S2). Subsequently, by diluting the FME elution solution 10-fold, we observed a sequential increase of approximately 3.4 in the detected Ct values (Additional file 4 : Figure S3), indicating the absence of inhibitory residues within the elution. Although concentration and purity are important parameters for evaluating the quality of nucleic acid extraction, the ultimate indicator lies in its functionality in downstream applications [ 34 ].

Compared with other nucleic acid extraction techniques, the magnetic bead method offers several advantages, including unrestricted sample volumes, easy removal from sample suspensions, and suitability for large-scale automated operations. Moreover, various types of magnetic particles can be utilized for the separation and purification of DNA, RNA, and plasmid DNA [ 35 , 36 ]. We equipped the FME with a small automatic nucleic acid extractor to automate the FME in the same manner as the manual operation in terms of extraction time, quality, and stability (Fig. 3 F). We observed that the FME demonstrated superior or comparable efficacy in extracting respiratory viruses compared with magnetic bead, spin column, and precipitation methods (Fig. 4 ). We hypothesized that the disparity in extraction efficiency among different viruses was associated with the presence of a specific secondary structure and envelope. It should be noted that, in terms of time requirements, the magnetic bead method, when automated, required approximately 25–30 min, while both the spin column method and precipitation method, being manual processes, necessitated close to 1 h. Furthermore, both the spin column and precipitation methods require manual operations, which places high demands on the skills and proficiency of the operators. By contrast, the automatic FME not only extracted high-quality nucleic acid but also required less time with no technical requirements for personnel. In addition, our research showed that the FME had excellent clinical performance when analyzing specimens from individuals suspected to have IAV infection. The distribution of Ct values detected by FME in 525 clinical specimens was similar to that observed using the standard magnetic bead method (Fig. 5 A), and there was no significant difference between the two methods in the analysis of low-concentration specimens close to the LoD (Fig. 5 B), although FME identified more positive cases (Additional file 1 : Table S3). In spite of this, it is essential to observe that the aforementioned clinical specimens refer to pharyngeal swabs, and other samples such as sputum, stool, and blood cannot be directly processed using the FME method. In the case of sputum and stool, pre-treatment is required prior to extraction, which typically takes a minimum of 30 min [ 37 , 38 ]. Additionally, whole blood and plasma samples are highly viscous, and therefore require additional time for lysis, washing and magnetic bead magnetization. Consequently, when extracting viruses from complex samples, FME is not able to produce high-quality nucleic acid within 5 min. Collectively, these findings highlight the promising clinical applicability of FME for analyzing non-complex samples.

Recently, numerous innovative techniques for nucleic acid extraction have been reported, including the use of cellulose-based membranes to extract nucleic acid without the requirement of a separate elution step, enabling direct amplification from the membrane [ 39 ]. A polytetrafluoroethylene (PTFE)-based nucleic acid extraction system achieved a fully enclosed sample extraction process that seamlessly integrated with droplet digital PCR (ddPCR) [ 40 ]. Using an integrated microfluidic system, the process of sample extraction, enrichment, and detection was completed within a small chip [ 41 ]. Although these novel methodologies exhibited rapidity and compactness, their applicability remained confined to specific scenarios. To achieve widespread adoption in scientific research and clinical testing, challenges pertaining to automation, cost-effectiveness, and processing of intricate samples still need to be addressed. The results of qRT-PCR detection depend on a number of factors, including the specimen type, the timing of collection, the quality and quantity of viral RNA, the primer and probe design for viral RNA targets, the reagents and instruments used for detection, as well as the signals and cut-off values employed for result interpretation [ 42 , 43 ]. Among them, obtaining high-quality nucleic acid is fundamental for accurate detection. However, when changing the RNA extraction kits from one type to other, particularly those based on different chemical components, the reliability of both the quantity and quality of extracted nucleic acid becomes uncertain [ 44 , 45 ]. How to detect viruses with high Ct values and low loads stably, while avoiding false negatives, remains a challenging problem.

In summary, this study demonstrates that glycerin served as an effective washing solution for nucleic acid purification and presented a novel nucleic acid extraction technology based on glycerin with a remarkable processing time of only 5 min. The developed technology exhibited notable features, including rapidity, cost-effectiveness, automation, and high-quality yields of extracted nucleic acids. Consequently, this study significantly contributes to the reduction of sample pre-processing time in NAATs and lays a foundation for the realization of rapid molecular diagnosis.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Abbreviations

Respiratory syncytial virus

Severe acute respiratory syndrome coronavirus 2

Influenza A virus

Nucleic acid amplification test

Quantitative real-time polymerase chain reaction

Isothermal amplification technologies

Next-generation sequencing

Guanidine thiocyanate

Ribonuclease P

Dulbecco’s modified Eagle medium

Fetal bovine serum

Herpes simplex virus 1

Human coronavirus 229E

Five-minute nucleic acid extraction

China Food and Drug Administration

Sodium citrate tribasic dihydrate

Sodium lauroyl-sarcosine

Dithiothreitol

Polyethylene glycol 6000

Isopropyl alcohol

Ethylene diamine tetraacetic acid

Cycle threshold

Bovine serum albumin

A Plus lysis solution

Normalized reporter

Limit of detection

Lateral flow immunochromatographic assays

Enzyme-linked immunosorbent assays

Plaque reduction neutralization tests

Polytetrafluoroethylene

Droplet digital PCR

Diethyl pyrocarbonate

Phosphate-buffered saline

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Acknowledgments

We would like to thank all the staff members of the molecular diagnostic PCR team for their support and valuable discussion.

This work was supported by the Research and Development Project in the Key Areas of Guangdong Province (2022B1111020003) and the Emergency Key Program of Guangzhou Laboratory (EKPG21-13). The funders had no input into the study design, data collection, or interpretation, or the decision to submit the work for publication.

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Yu Wang, Yuanyuan Huang, Yuqing Peng, Qinglin Cao, Guangxin Xu & Rong Zhou

State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China

Wenkuan Liu, Zhichao Zhou & Rong Zhou

School of Pharmacy, Tongji Medical College, Huazhong University of Science of Technology, Wuhan, China

GIRM Biosafety (Guangzhou) Co., Ltd, Guangzhou, China

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Contributions

YW: conceptualization, methodology, investigation, data curation, formal analysis, writing–original draft. YYH: investigation, data curation, formal analysis, writing–original draft. YQP: investigation, data curation, validation. QLC: investigation, data curation. WKL: methodology, investigation, resources, validation. ZCZ: methodology, resources. GXX: investigation, data curation. LL: methodology, funding acquisition, resources, supervision, writing–reviewing and editing. RZ: conceptualization, funding acquisition, project administration, writing–reviewing and editing. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Lei Li or Rong Zhou .

Ethics declarations

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This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The research protocol was reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (No. 2020-77). All enrolled patients obtained written informed consent from their guardians.

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Supplementary Information

Additional file 1. , 12985_2024_2381_moesm2_esm.tif.

Additional file 2: Figure S1. The LC exhibits an extraction efficiency similar to the magnetic bead method. A and B RSV and ADV nucleic acid were extracted using LC, Baypure, and HR magnetic bead extraction kits, and qRT-PCR Ct values were used to assess the efficiency of each method for extracting nucleic acid. A: Effect of RSV nucleic acid extraction. B: Effect of ADV nucleic acid extraction. Data are presented as means± SD for three independent biological replicates. Statistical significance was calculated using t tests; ns P > 0.05, * P < 0.05, ** P < 0.01.

12985_2024_2381_MOESM3_ESM.tif

Additional file 3: Figure S2. Effect of residual AP lysis solution on PCR amplification. RSV nucleic acid was extracted at a high concentration by the FME. A and B In the RSV reaction system, 4 μL of RSV nucleic acid was added, along with an additional 1 μL each of DEPC H 2 O, 25 mM sodium citrate, 20 mM sarkosyl, 2.5% PEG 6000, 1 M DTT, and 4 M GTC, IPA, or AP lysis solution. qRT-PCR Ct values were used to assess the effect of the residual AP lysis solution components on amplification. C and D 4 μL of RSV nucleic acid was added, followed by an additional 1 μL of sodium citrate diluted in a twofold gradient. Ct values were used to assess the effect of residual sodium citrate on PCR amplification. E and F 4 μL of RSV nucleic acid was added, followed by an additional 1 μL of sarkosyl diluted in a twofold gradient. Ct values were used to assess the effect of residual sarkosyl on PCR amplification. G and H 4 μL of RSV nucleic acid was added, followed by an additional 1 μL of GTC in a twofold gradient. Ct values were used to assess the effect of GTC on PCR amplification. Solid lines indicate the median, and dashed lines indicate the detection limit. Data are presented as means ± SD for three or four independent biological replicates.

12985_2024_2381_MOESM4_ESM.tif

Additional file 4: Figure S3. The Ct values of the eluted nucleic acid were determined following a 10-fold gradient dilution. High concentrations of RSV were extracted by the FME, and the nucleic acid in the elution was sequentially diluted using a 10-fold gradient with DEPC H 2 O. The Ct values were determined by qRT-PCR for each dilution gradient, with each measurement repeated three times. A Amplification curve. B Linear regression curve, R 2 = 0.9993.

Additional file 5.

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Wang, Y., Huang, Y., Peng, Y. et al. Development and validation of a rapid five-minute nucleic acid extraction method for respiratory viruses. Virol J 21 , 189 (2024). https://doi.org/10.1186/s12985-024-02381-3

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Published : 18 August 2024

DOI : https://doi.org/10.1186/s12985-024-02381-3

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iSN04: A novel nucleic acid drug for the treatment of vascular diseases

by Shinshu University

Atherosclerosis, a major cause of mortality worldwide, involves an overgrowth of vascular smooth muscle cells in the blood vessels, constraining blood flow and potentially causing cardiovascular diseases.

Against this backdrop, researchers from Shinshu University have developed a DNA aptamer called iSN04 that targets and counteracts with the protein nucleolin in smooth muscle cells . This anti-nucleolin aptamer helps maintain smooth muscle cells in a differentiated state, offering new treatment potential for atherosclerosis and other vascular diseases.

Their findings were published on 15 June 2024 in Volume 14 of the journal Biomolecules . Ms. Mana Miyoshi, affiliated with the Department of Agriculture, Graduate School of Science and Technology, Shinshu University, contributed to the study as the first author.

Heart diseases and strokes are on the rise worldwide, with atherosclerosis being a leading contributor. Atherosclerosis involves the buildup of plaques—composed of fat, cholesterol, calcium, and other substances found in the blood—inside the arteries.

Over time, this buildup can lead to the hardening and narrowing of the arteries, restricting blood flow . Notably, this condition involves vascular smooth muscle cells (VSMCs) in the arterial walls.

VSMCs can switch between a contractile state (ideal for blood vessel function) and a proliferative state (capable of contributing to plaque formation). During atherosclerosis, the switch from a contractile to a proliferative state can lead to plaque instability and rupture, underscoring the importance of a corresponding therapeutic strategy.

Traditional treatments for atherosclerosis typically focus on lowering cholesterol levels and managing risk factors like high blood pressure. However, a novel approach involves directly targeting VSMCs to stabilize plaques and prevent their rupture.

This innovative strategy has led researchers from Shinshu University, Japan, to develop a novel nucleic acid drug called iSN04. Associate Professor Tomohide Takaya, from the Faculty of Agriculture, Shinshu University led the study.

iSN04 belongs to a group of nucleic acid drugs called DNA aptamer—a short, single-stranded DNA molecule capable of selectively binding to a specific target of interest. iSN04 interacts with a protein called nucleolin in VSMCs.

Nucleolin plays a role in the de-differentiation (loss of specialized function) and proliferation of VSMCs, contributing to plaque formation and instability. By targeting nucleolin with iSN04, the researchers aimed to keep VSMCs in their contractile state, thereby reducing plaque formation and promoting plaque stability.

Interestingly, the same research group laid the groundwork through multiple previous studies for the current study.

Dr. Takaya says, "Our anti-nucleolin DNA aptamer, iSN04, was originally identified as an inducer of skeletal muscle differentiation in 2021. Subsequently, we found that iSN04 also promotes cardiac muscle differentiation in 2023. Therefore, we hypothesized that iSN04 could promote smooth muscle differentiation."

He adds, "This study showed that iSN04 indeed induces vascular smooth muscle cell differentiation, resulting in the inhibition of angiogenesis."

The study showed that iSN04 can help maintain VSMCs in their contractile, differentiated state. The researchers interestingly found that iSN04 can effectively enter VSMCs without any carriers. Once inside, iSN04 reduced VSMC proliferation and increased the levels of a protein marker called α-smooth muscle actin, helping VSMCs in achieving a contractile state.

Another concern the researchers sought to address was angiogenesis within plaques. Angiogenesis refers to new blood vessel formation within plaques that can lead to their instability and rupture. The study demonstrated that iSN04 could inhibit angiogenesis in an experimental model using mouse aortic rings, indicating its potential to stabilize plaques.

Ms. Miyoshi said, "Given our anti-nucleolin DNA aptamer inhibits angiogenesis by inducing VSMC differentiation, it can find applications as a nucleic acid drug for pathological angiogenesis involved in atherosclerosis, cancer, and retinopathy."

The breakthrough development of iSN04 marks a promising new chapter in the fight against atherosclerosis, and could revolutionize its treatment and improve patient outcomes globally.

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Cellular DNA damage response pathways might be useful against some disease-causing viruses

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The UB research is applicable to two types of small DNA viruses: papillomaviruses, such as HPV, and polyomaviruses, such as the one shown here in a 3D print. Polyomaviruses infect most people without causing serious disease but they can lead to serious diseases and some cancers in immunologically weakened individuals. Photo: NIAID

By ELLEN GOLDBAUM

Published August 22, 2024

New research reveals that triggering a cell’s DNA damage response could be a promising avenue for developing novel treatments against several rare but devastating viruses for which no antiviral treatments exist, possibly including human papilloma virus (HPV), which causes cancer.

Published online on Aug. 10 in Nucleic Acids Research , the paper focuses on the DNA damage response pathway and demonstrates how this pathway can reduce the function of a viral enzyme, a helicase, resulting in suppressing viral replication.

“This research is significant both for understanding how cells respond to DNA damage, to prevent them from becoming cancerous in the first place, how targeting this pathway can be used in new cancer treatments, and because it now opens up possibilities for new approaches to treating some rare but devastating viral infections,” says Thomas Melendy, senior author on the paper and associate professor of microbiology and immunology in the Jacobs School of Medicine and Biomedical Sciences at UB.

How replication slows in response to DNA damage

The research focuses on a process called the DNA damage response, part of which has evolved to stop or slow DNA synthesis whenever cellular DNA damage occurs. “These pathways are important for preventing exacerbation of DNA damage that can lead to either cell death or cancer,” explains Rama Dey-Rao, research assistant professor of microbiology and immunology in the Jacobs School and joint first author on the paper with Caleb Hominski, a previous student in the lab.

When these pathways are activated, DNA replication is suppressed at sites in the genome called origins; at the same time, the progression of DNA replication forks also slows down. Replication forks, so-called because their structure resembles a fork, are where large groups of proteins coordinate genome replication through the unwinding and synthesis of DNA.

Melendy says that while quite a bit is known about how DNA damage response causes cells to stop DNA replication origins from “firing,” it’s been much harder to figure out how the progression of replication forks slows down in response to DNA damage.

“Researchers have been very interested in how that slowing occurs because it’s so dramatic,” says Melendy. “DNA damage response pathways cause replication forks to slow down progression by about ten-fold. This ten-fold slowdown means that synthesis of the cell’s genome, which usually takes about 12 hours, would take nearly five days, greatly increasing the time cells have to repair DNA damage.”

Viral connection

For years, Melendy and his colleagues have been studying two types of small DNA viruses: papillomaviruses, such as HPV, and polyomaviruses, which infect most people without causing serious disease but can lead to serious diseases and some cancers in immunologically weakened individuals. A rare cancer caused by a polyomavirus caused the death of musician Jimmy Buffett in 2023.

“We previously showed that in response to DNA damage, HPV does not stop or slow its DNA replication, while polyomaviruses do stop or slow their DNA replication,” says Melendy, “so by comparing and contrasting these two virus types we can gain insights into how polyomavirus DNA replication is slowed in response to DNA damage, which in turn provides us insights into how human cells slow replication forks.”

In the current research, they demonstrate that a phosphorylation site — where a phosphate is added to a molecule — on the major polyomavirus DNA replication and transcription protein is highly conserved in polyomaviruses across many animal species.

“The conservation of this phosphorylation/modification across polyomaviruses that have evolved to infect many different species of mammals suggested it was likely important,” says Melendy.

To study the effects of this, the UB researchers made a mutation at the specific amino acid residue on the viral protein where this phosphorylation occurs to mimic the addition of a phosphate group being there.

When they expressed this mutant viral protein in human cells using a system to evaluate polyomavirus DNA replication, they found the virus’s genome replication was decreased by 10-fold. However, viral transcription was unaffected, indicating that phosphorylation on that amino acid residue has a highly specific effect on viral DNA replication, but didn’t affect other functions of that protein.

Role for DNA helicase

In comparing the wild-type and mutant proteins, they found the only function it was compromised for was the ability to act as a DNA helicase, unwinding DNA strands to facilitate entry of DNA synthesis enzymes.

“This is the first demonstration that it might be possible to use phosphorylation as a ‘switch’ on a DNA helicase to dial down replication speed,” explains Melendy.

Evidence suggests a similar phosphorylation can occur in human DNA helicases as well.

“For many cancers, if we selectively inhibit the DNA damage checkpoints they still retain, and simultaneously treat with lower than normal amounts of DNA-damaging chemotherapeutics, then we might be able to selectively damage cancer cells while leaving non-cancerous cells intact, greatly enhancing cancer cell killing while simultaneously reducing toxic side effects.”

This is an ongoing area of study by the UB researchers with their collaborators at Roswell Park Comprehensive Cancer Center.

Based on the current study, these DNA damage checkpoints may now be relevant to treating viral infections of the small DNA viruses under investigation at UB.

“Because they rely almost exclusively on host cell enzymes to synthesize their viral genomes, these small DNA viruses have been very resistant to anti-viral therapeutics,” says Melendy. “We currently have no antiviral treatments for HPV or polyomaviruses. By triggering the DNA damage response in a patient, this could dramatically slow viral DNA replication, suppressing the infection, providing us with a novel avenue for possible antiviral treatments of these as-of-yet untreatable viral infections.”

Shichen Shen and Jun Qu, both of the School of Pharmacy and Pharmaceutical Sciences, are co-authors. The work was funded by the National Institutes of Health and the NIH Training Grant in Microbial Pathogenesis.

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Article Contents

Introduction, materials and methods, data availability, supplementary data, methods for constructing and evaluating consensus genomic interval sets.

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Julia Rymuza, Yuchen Sun, Guangtao Zheng, Nathan J LeRoy, Maria Murach, Neil Phan, Aidong Zhang, Nathan C Sheffield, Methods for constructing and evaluating consensus genomic interval sets, Nucleic Acids Research , 2024;, gkae685, https://doi.org/10.1093/nar/gkae685

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The amount of genomic region data continues to increase. Integrating across diverse genomic region sets requires consensus regions, which enable comparing regions across experiments, but also by necessity lose precision in region definitions. We require methods to assess this loss of precision and build optimal consensus region sets. Here, we introduce the concept of flexible intervals and propose three novel methods for building consensus region sets, or universes: a coverage cutoff method, a likelihood method, and a Hidden Markov Model. We then propose three novel measures for evaluating how well a proposed universe fits a collection of region sets: a base-level overlap score, a region boundary distance score, and a likelihood score. We apply our methods and evaluation approaches to several collections of region sets and show how these methods can be used to evaluate fit of universes and build optimal universes. We describe scenarios where the common approach of merging regions to create consensus leads to undesirable outcomes and provide principled alternatives that provide interoperability of interval data while minimizing loss of resolution.

Advancements in high-throughput sequencing technologies have resulted in a vast amount of diverse epigenomic data that has given us tremendous insight into genome function. Epigenomic data are often summarized into genomic region sets stored in BED files. Through the work of hundreds of individual labs and projects such as ENCODE ( 1 ), the NCBI Gene Expression Omnibus ( 2 ) now contains almost 100 000 BED files ( 3 ). The volume of data has made integration challenging.

For an analysis that spans several genomic interval sets, one of the first steps is to define a consensus region set, or region universe , upon which the diverse sets can be interpreted ( 4–10 ). Such universe region sets have many common practical use cases. For example, they define genomic intervals for differential peak analysis ( 11 ); they form the regions of interest in a count matrix in single-cell epigenome analysis ( 11 , 12 ); they are used as a background for statistical region enrichment analysis ( 13–16 ); and they are a region vocabulary in vector representation approaches ( 17–20 ).

For many tools, the choice of universe is critical. It defines the features to which data will be projected. Currently, there are different ways of choosing a universe for analysis. Simple approaches include tiling the genome into fixed-size bins ( 12 , 15 ), or using intersection or union operations on a collection of region sets ( 11 ). Some methods have been developed to create better-fitting universes for specific downstream use cases ( 21 , 22 ). An alternative is to use a predefined universe from an external source; for example, the ENCODE consortium curated a registry of candidate cis-regulatory elements accessible through the SCREEN webserver ( 1 ), and the Ensemble Regulatory Build is a central, reusable source of regulatory region definitions ( 23 ). The choice of universe matters because universes can be a poor fit to data, and if a universe does not fit the data well, it can lead to incomplete or incorrect results ( 5 , 14 , 15 ). However, despite the importance of selecting a universe, it is often done ad hoc , and there are few approaches to assess the fit of a universe to a collection of region sets.

Here, we address these limitations by introducing novel concepts for constructing and evaluating region universes. First, we introduce the idea of flexible genomic intervals, which represent region boundaries by intervals instead of points, allowing us to summarize many fixed regions into fewer flexible regions without loss of information. Next, we propose three methods for constructing flexible region universes: a coverage cutoff universe, a maximum likelihood universe, and a Hidden Markov Model (HMM). Finally, we propose three methods to evaluate the fit of a universe to a collection of region sets: (i) the base-level F 10 -score; (ii) a Region Boundary Distance score ( RBD ); and (iii) a likelihood model score that assesses the likelihood that the proposed universe was drawn from the given distribution of region sets.

To assess our universes and evaluation methods, we compared our methods against alternatives and predefined universes. We show that flexible universes can capture information from complex data collections into one well-defined universe. Moreover, we show how our assessment metrics provide complementary measures of assessing universe fit and we prove the relevance of these measures. We show that the union universe has many downsides and propose the HMM universe as a generally useful approach for defining well-fit universes. To demonstrate how these universes could affect downstream analysis, we conclude with an application of region set enrichment analysis, where we show how the results are affected by choice of universe. Overall, our results demonstrate the importance of considering region universe and provide promising new tools to construct better-fitting universes for a variety of use cases.

To integrate genomic interval data, we first require a consensus set of intervals, or a universe. We may select a predefined universe from an external source or define one from a collection of input region sets using a consensus algorithm (Figure 1A ). With a universe in hand, we can then ‘tokenize’ the original regions (Figure 1B ). Tokenization redefines them into universe regions, normalizing differences in region boundaries to transform similar regions into a single representation. The most basic tokenization method is simple interval overlap. This approach works well if a universe approximates the original data well; otherwise, this may result in loss of precision. A universe may not be a good fit to a collection for a variety of reasons (Figure 1C ); for example, (i) a region can be shifted; (ii) two neighboring regions may be merged, making them indistinguishable in downstream analysis ( 5 ); (iii) a universe may omit important intervals, leading to loss of information ( 5 ); or (iv) a universe may contain extraneous regions that do not reflect genome coverage, adding noise and compute time ( 14 ). If a universe is a poor fit, it can affect downstream analysis negatively; for example, a differential accessibility analysis wouldn’t even test a locus that had been dropped from the universe. It could also miss a significant locus if it were merged with an abutting locus that lacked differential signal. Or, a motif analysis based on universe regions that had been shifted could miss enriched sequence features that were present in the part of the region left out of the universe.

$Overview of the concept of a universe. (A) A consensus algorithm takes a region set collection $\mathbb {R} = [\mathcal {R}_1, \mathcal {R}_2, ...]$ and builds a consensus representation called universe $\mathcal {U}$. (B) We ‘tokenize’ raw regions into universe $\mathcal {U}$ by redefining them as universe regions, creating a more uniform collection. (C) Universes may poorly represent region sets by shifting, merging, dropping or adding extraneous regions.$

Overview of the concept of a universe. ( A ) A consensus algorithm takes a region set collection |$\mathbb {R} = [\mathcal {R}_1, \mathcal {R}_2, ...]$| and builds a consensus representation called universe |$\mathcal {U}$|⁠ . ( B ) We ‘tokenize’ raw regions into universe |$\mathcal {U}$| by redefining them as universe regions, creating a more uniform collection. ( C ) Universes may poorly represent region sets by shifting, merging, dropping or adding extraneous regions.

To address these issues, we developed three new approaches for constructing a universe that is a good fit to the original data: first the ‘coverage cutoff universe’; second, the ‘maximum likelihood universe’; and finally, an ‘HMM universe’. To assess them, we also developed three universe fit metrics. Finally, we applied these to a variety of real datasets.

Methods for building optimal universes

The coverage cutoff universe.

The simplest example of a universe built from the data is a ‘union universe’, in which a collection of region sets is merged. This method is often done for differential analysis of ATAC-Seq data ( 11 ). The union universe by definition covers all bases from the original collection; however, it can also lead to very large regions, particularly if the number of input region sets is large. Another simple alternative is using an intersection operation, which would include only bases covered in every region set in the collection, but this has the opposite problem: it leads to very sparse universes.

We reasoned that a hybrid approach may achieve a better result. First, we conceptualize a collection of region sets as a coverage signal track across all input region sets. Then, similar to a peak calling approach, we choose a cutoff x such that universe includes only positions with coverage greater or equal to x (Figure 2A ). Setting the cutoff to one corresponds to a union universe, and setting the cutoff equal to the number of input region sets corresponds to an intersection universe. Setting a cutoff in between the two balances these extremes and provides a tunable parameter that may be adjusted depending on the needs of downstream tasks. We call the resulting universe a coverage cutoff (CC) universe. A principled approach to selecting the cutoff is to use a simple likelihood model that calculates the probability of appearing in a collection. With this model, we can calculate an optimal cutoff according to Eq. ( 1 ) (see Supplementary methods for details).

$Different approaches to building universes. (A) Coverage-based universes are derived from the genome coverage of a collection of region sets. Examples include intersection $\mathcal {U}_{int}$, coverage cutoff $\mathcal {U}_{CC}$, and union universe $\mathcal {U}_{union}$. (B) A flexible region in contrast to fixed region can represent boundaries of many variable regions. (C) The flexible coverage cutoff (CCF) universe is based on coverage of the genome by a collection. It uses two cutoff values: the lower defines flexible boundaries and the upper defines the region core. (D) A collection of genomic region sets is aggregated, and region starts, core (overlap), and ends are counted, creating signal tracks. (E) Maximum likelihood universe is derived from three signal tracks. Using a likelihood model, we build a scoring matrix that assesses the probability of each position being a given part of a flexible region. Next, we find the most likely path, which represents the maximum likelihood universe. (F) The HMM universe treats signal tracks representing genome coverage by different parts of a region as emissions of hidden states that correspond to different parts of flexible regions.$

Different approaches to building universes. ( A ) Coverage-based universes are derived from the genome coverage of a collection of region sets. Examples include intersection |$\mathcal {U}_{int}$|⁠ , coverage cutoff |$\mathcal {U}_{CC}$|⁠ , and union universe |$\mathcal {U}_{union}$|⁠ . ( B ) A flexible region in contrast to fixed region can represent boundaries of many variable regions. ( C ) The flexible coverage cutoff (CCF) universe is based on coverage of the genome by a collection. It uses two cutoff values: the lower defines flexible boundaries and the upper defines the region core. ( D ) A collection of genomic region sets is aggregated, and region starts, core (overlap), and ends are counted, creating signal tracks. ( E ) Maximum likelihood universe is derived from three signal tracks. Using a likelihood model, we build a scoring matrix that assesses the probability of each position being a given part of a flexible region. Next, we find the most likely path, which represents the maximum likelihood universe. ( F ) The HMM universe treats signal tracks representing genome coverage by different parts of a region as emissions of hidden states that correspond to different parts of flexible regions.

Here, S c is a sum of genome coverage by collection and g is the size of the genome.

We realized that, in a sense, the CC universe is a point estimate of a more complex distribution of possible universes. We reasoned that we may gain some insight by modeling the boundaries of the consensus regions as intervals, rather than points. To do this, we developed a new concept of a genomic region we call a flexible region . In contrast to fixed interval that is defined by two fixed points ( start and end ), a flexible interval is defined by four ( start start , start end , end start and end end ) (Figure 2B ). A flexible interval can model many region variations into one well-defined flexible interval.

A simple approach to constructing a flexible region universe is to define two cutoff values instead of one: the looser represents the cutoff for boundaries, and the stricter for the region core (Figure 2C ). This way, positions with coverage between those two points will be assigned to flexible region boundaries and positions with coverage higher than the second cutoff will be assigned to core of the region. Using this idea, we built a confidence interval around the optimal cutoff value, which extends the CC universe into the coverage cutoff flexible (CCF) universe.

Maximum likelihood universe

While flexible intervals more naturally represent collections of overlapping region sets than fixed intervals, we reasoned that they still suffer from the possibility of merging neighboring regions when collections are large. To address this issue, we need information about not just the coverage of regions in a collection, but also the region start and end positions. To compute this information, we developed a fast plane-sweep algorithm (see Supplementary methods for details). With this tool we can quickly and efficiently calculate three tracks representing aggregate start, end, and coverage values of a region set collection at base-pair resolution (Figure 2D ). We reasoned that a model that could incorporate all of these signals may improve universe resolution.

We can conceptualize a flexible universe as a path through the genome that assigns either start, core, or end state to each position. Using a universe scoring model and optimization algorithm, we can build the best path through the genome (Figure 2E ). As a scoring model, we next developed a complex likelihood model, an extension of the simple likelihood model introduced earlier for CC universe, which considers not just the coverage (core), but also in region start and end signal tracks. This model describes for each position the probability of it being a region start, core, or end. We thus build the maximum likelihood universe (LH) in 3 steps: (i) compute the three signal tracks; (ii) use a likelihood model to build a scoring matrix; (iii) find the maximum likelihood path through the genome (see Supplementary methods for details).

Hidden Markov model universe

The maximum likelihood universe provides a simple and principled model for optimal flexible universes. However, a disadvantage is that it provides no tunability, since the likelihood scores are determined purely from the data. We reasoned that this may lead to results depending on input collection. To address this, we sought a more tunable model using a Hidden Markov Model (HMM).

An HMM models a hidden processes using (i) a matrix of transition probabilities between hidden states and (ii) emission probabilities of observations from hidden states. In our model, there are three observed sequences: the number of starts, overlaps, and ends at a given position. The hidden variable corresponds to the different parts of the flexible segment (Figure 2F and Supplementary Figure S1 ). We can tune transition probabilities, which can be chosen in a way that will prevent unnecessary segmentation, and emission matrix, which describes the relationship between observations and hidden states (see Supplementary methods for details).

Methods for evaluating universe fit

Having developed several new approaches to construct universes, we next sought to evaluate these universes and compare them to other common approaches. Because the choice of universe can dramatically affect downstream analyses, it is important to choose a universe deliberately. However, there are no well-established methods for assessing universe fit to data. Furthermore, different analyses may be better served by a different types of universe, indicating that there really is no generally optimal universe, but the idea of what makes a ‘good’ universe depends on the downstream analysis. For example, a differential accessibility analysis should prioritize sensitivity over specificity; in this case, it is not a major problem if the universe includes many regions present in only a few samples, since the cost of extra comparisons is lower. On the other hand, the cost of excluding a region that could be a significant differential locus would be high. In contrast, a word-based deep learning task that trains a model with input dimensions equal to the size of the universe may elect a more specific universe, at the cost of discarding some regions that are present in a few of the samples, because otherwise the training could be intractable. Thus, the question of universe optimality depends on the use case, and therefore, we require methods of evaluating universe fit that can be tuned to a research question. This problem is similar to the comparison of two generic interval sets, for which several methods have been developed ( 24 ), with two key differences: first, we want to compare a universe region set not only to one other region set, but to a collection of them; and second, the question is not symmetric: it is generally more important that a universe not miss information (regions), even at the cost of some extra regions – and the desired level of asymmetry can vary. Therefore, we developed three methods for assessing universe fit to data: (i) a base-level overlap score; (ii) a region boundary distance score and (iii) the universe likelihood.

Base-level overlap score

Our first metric is based on base coverage. We consider the universe as a prediction of whether a given genomic position is present in a given region set from the collection. Treating each region set from the collection as a query, we can then conceptualize matches and mismatches as true positives (covered in both universe and query), false positives (covered in universe, but not in query), or false negatives (covered in query, but not in universe) (Figure 3A ). This allows us to calculate common classification evaluation measures such as precision and recall. Precision counts the number of true positives, so a low precision indicates presence of unimportant positions in the universe; recall measures how much of the universe is in a query. To combine precision and recall, we use the F 10 -score, a weighted version of the traditional F -score that pays 10 more times attention to recall than precision. This asymmetry captures our goal to prioritize sensitivity over specificity: by prioritizing recall, we indicate that it’s better to have a few extra, noisy regions that to exclude something important. The F 10 -score results in values between 0 and 1, with a perfect fit approaching 1. An alternative approach to base-level overlap score would be Jaccard similarity, however it does not account for asymmetry of the comparison.

$Different approaches to assess how well the universe represents the data. (A) The base-level overlap measure considers the universe as a prediction of a region set and based on that it calculates number of false positives (FP), true positives (TP), and false negatives (FN), and from that derives recall (R) and precision (P), which are combined into the F10-score. (B) The region boundary distance (RBD) score assesses how well a universe represents start and end positions, by calculating distance from region set to universe, and from universe to region set; those two metrics are combined into a region boundary score by calculating their reciprocal, weighted harmonic mean. (C) Likelihood assessment uses a likelihood model based on signal tracks representing genome coverage by different parts of a region to calculate universe likelihood as a combination of likelihoods of all three signals tracks. D) A complete analysis example comparing a collection of region sets against 4 proposed universes: $\mathcal {U}_1$ a precise universe, $\mathcal {U}_2$ a sensitive universe, $\mathcal {U}_3$ a fragmented universe, and $\mathcal {U}_4$ well-fit universe. For each universe, all 3 metrics are calculated. The F10-score and RBD score assess individual region sets. The final score for a collection is their average. In contrast, the likelihood is calculated directly for the whole collection.$

Different approaches to assess how well the universe represents the data. ( A ) The base-level overlap measure considers the universe as a prediction of a region set and based on that it calculates number of false positives (FP), true positives (TP), and false negatives (FN), and from that derives recall ( R ) and precision ( P ), which are combined into the F 10 -score. ( B ) The region boundary distance (RBD) score assesses how well a universe represents start and end positions, by calculating distance from region set to universe, and from universe to region set; those two metrics are combined into a region boundary score by calculating their reciprocal, weighted harmonic mean. ( C ) Likelihood assessment uses a likelihood model based on signal tracks representing genome coverage by different parts of a region to calculate universe likelihood as a combination of likelihoods of all three signals tracks. D) A complete analysis example comparing a collection of region sets against 4 proposed universes: |$\mathcal {U}_1$| a precise universe, |$\mathcal {U}_2$| a sensitive universe, |$\mathcal {U}_3$| a fragmented universe, and |$\mathcal {U}_4$| well-fit universe. For each universe, all 3 metrics are calculated. The F 10 -score and RBD score assess individual region sets. The final score for a collection is their average. In contrast, the likelihood is calculated directly for the whole collection.

Region boundary distance score

One disadvantage of the base overlap score is that it is unaware of region boundaries. A universe region that covers two abutting query regions would get a perfect score. This can be highly problematic in downstream applications; for example, in a differential analysis, lumping two distinct loci together could dilute differential signal. To address this, we sought a measure that would consider region starts and ends (Figure 3B ). We calculate the distance between each boundary of each region in the query and the closest corresponding boundary in the universe. Universes with boundaries that are near the query boundaries would have shorter distances, indicating better universe fit. However, highly fragmented universes with many unnecessary boundaries would have very small distances from query to universe. To account for this, we also calculate the inverse distance: from boundaries in universe to the nearest boundaries in the query. Finally, we combine those two metrics into a region boundary distance score ( RBD ) by taking their reciprocal, weighted harmonic means. With this score we describe the universe’s ability to conserve information about starts and ends, with a score of one representing a perfect representation of boundary locations. However, we do not incorporate any information about collection coverage in this score.

For fixed universes, the start and end point are well-defined, however for flexible regions they are intervals. Therefore, for flexible universes, we modify the RBD score to set distance equal to zero if a boundary query region is inside the universe’s boundary interval.

Universe likelihood

Finally, we sought a metric that incorporates both information about region boundaries as well as genome coverage. We propose here a universe likelihood score (Figure 3C ). We first calculate three signal tracks representing genome coverage by start, core, and end of the regions in the collection. Then for each signal track we make a separate model, which results in three separate models for different parts of a region. Each of these models describes the probability of a given position being a given part of a region, depending on the signal strength (see Supplementary methods for details). That results in a complex, probabilistic description of a region set collection. Next, we use this model to calculate the likelihood of the universe, which we can compare between universes. We make two versions of likelihood calculations, one suited for fixed universes and one for flexible universes. We use log likelihood, so our values range from minus infinity (low) to zero (high). Finally, to increase interpretability, we normalize the scores by subtracting the likelihood of an empty universe (one that contains no regions). Thus, a positive final score reflects a given universe that is more likely than having no regions at all, while a negative score means the universe is less likely than the empty universe.

To accommodate flexible universes, we adapted the likelihood score by calculating the boundary likelihood as if the whole flexible interval could contain a boundary position, rather than a single fixed point (see Supplementary methods for details).

Assessing region sets collections

Having developed three assessment methods, we can use them to compare competing universes to assess which universe is the best fit for a collection of region sets. We do this by computing the scores for each universe and comparing among universes (Figure 3D ). The scores assess different aspects of the universe fit; the F 10 -score promotes sensitive universes over specific ones, RBD score penalizes sparse universes, and likelihood provides complex universe assessment. Although the likelihood score incorporates information about boundaries as well as how well the universe covers the collection, it can penalize sensitive universes because it is not intentionally biased toward asymmetry the way the previous scores are. Thus, by computing all these scores, we reason that we get a complete picture that can guide decisions for selecting a universe for a collection of region sets.

Evaluation on real data

Next, we developed an evaluation strategy to test our universe building and assessment methods on real data. We assembled five diverse collections of region sets representing different biological problems (Figure 4A ): (i) CTCF ChIP small , a small random collection of CTCF region sets ( n = 40) from the ENCODE database ( 1 ); (ii) CTCF ChIP large , CTCF ChIP-seq datasets ( n = 877); (iii) TF ChIP , ChIP-seq experiments for diverse transcription factors (TFs) ( n = 8503); (iv) B-LCL ATAC , a small set of ATAC-seq files from B-Lymphoblastoid cell lines (B-LCL; n = 400) from ChIP-Atlas ( 25 ); (v) a Random ATAC , random ATAC-seq results ( n = 5000). These datasets vary in data type, collection size, and level of heterogeneity of input regions across region sets.

Overview of evaluation approach. ( A ) Five collections representing different biological problems used for assessment. ( B ) For each collection, we compared it to five data-driven universes and three predefined universes. The data-driven universes are tailored to the input collection, but the predefined universes do not vary by collection. ( C ) We assessed the fit of each universe to each collection using our three assessment methods.

We also assembled universes to assess. First, we obtained three universes that do not depend on analyzed data, which we call predefined universes: (i) the tiles universe, which bins the genome into non-overlapping 1000 bp tiles; (ii) the SCREEN universe, which consists of predefined cis-regulatory elements from ENCODE ( 1 ); and (iii) the Regulatory Build (RB) universe, consisting of pre-defined regulatory elements from Ensembl ( 23 ). In addition to these three external universes, we also built 5 data-driven universes that are specific to each region set collection. These include: (i) the union universe; (ii) the CC universe; (iii) the CCF universe; (iv) the LH universe; and (v) the Hidden Markov Model (HMM) universe. This led to 28 universes and 40 pairwise comparisons of universe-to-collection (Figure 4B ). For each comparison, we computed our three assessment methods (Figure 4C ). This gives us a comprehensive evaluation of both externally sourced and data-driven universes, tested on diverse query region set collections.

Data overview

To explore the differences in our five region set collections, we first computed general coverage statistics ( Supplementary Table S1 ). The smallest region set, B-LCL ATAC , contains ≈700 000 regions and covers 0.2% of the genome, whereas the largest, TF ChIP , contains ≈1.5 billion regions and covers 91% of genome. We also observed that the ATAC-seq collections have smaller regions on average than the ChIP-seq collections.

Universe overview

The universes also have very different characteristics, with some requiring additional filtering based on region likelihood and size (see Supplementary methods for details, Supplementary Figures S2 and S3 ). For example, for the CTCF ChIP large collection, the eight universes have different levels of precision and fragmentation (Figure 5A ). The universes differed in average region size, number of regions, and percent of genome covered ( Supplementary Figure S4 , Supplementary Table S2 ). Having assembled the universes, we next computed our three assessment methods.

$Universes overview and results of base-level overlap score. (A) Example of universes assessed for the CTCF ChIP large collection, including the 3 constant external universes, and 5 data-driven universes built from the input collection. (B) Different universes represent genome coverage by the collection to a different extent, example from the Random ATAC collection. Collection $\mathbb {R}$ consists of many different files, which are represented by the core signal track. Regions in $\mathcal {R}_1, \mathcal {R}_2, \mathcal {R}_3$ are best represented by CC, CCF and LH universes in terms of overlap. (C) Precision and recall distribution for each collection and universes assessment. (D) Average F10-score for each collection and universes assessment.$

Universes overview and results of base-level overlap score. ( A ) Example of universes assessed for the CTCF ChIP large collection, including the 3 constant external universes, and 5 data-driven universes built from the input collection. ( B ) Different universes represent genome coverage by the collection to a different extent, example from the Random ATAC collection. Collection |$\mathbb {R}$| consists of many different files, which are represented by the core signal track. Regions in |$\mathcal {R}_1, \mathcal {R}_2, \mathcal {R}_3$| are best represented by CC, CCF and LH universes in terms of overlap. ( C ) Precision and recall distribution for each collection and universes assessment. ( D ) Average F 10 -score for each collection and universes assessment.

Assessment 1: base-level overlap F 10 -score

Region sets in a collection can differ widely; our first assessment method assesses fit by quantifying the degree of overlap between each region set and the universe. In example data from the Random ATAC collection, we observe that some universes cover many bases present in only few of the collection’s region sets, while other universes are more stringent (Figure 5B ). To assess this globally, we first computed precision and recall for each comparison (Figure 5C ). We observed that the tiling universe and union universe both have perfect recall for all tested collections, consistent with how these universes are constructed; the tiling universe covers the entire (mappable) genome, and the union universe by definition covers every base contained in the collection. In contrast, recall is lower for the more stringent data-driven universes; the CC, CCF and LH universes exclude positions with low coverage, especially for large collections; the HMM universe has higher recall in general, indicating that it contains most positions covered by the collection. Finally, the lowest recall scores are assigned to the external universes, SCREEN and RB, which is consistent with these universes being built from other data sources. This highlights an advantage of building bespoke universes tailored to a collection: recall is superior. On the precision side, the worst performer overall is the tiles universe, consistent with many tiles in the universe that do not reflect coverage in the collection. In contrast, SCREEN and RB had generally higher precision, especially for ChIP-Seq collections.

In general, data-driven universes tend to represent collections well with good precision and recall. This is most apparent for the B-LCL ATAC collection for which data-driven universes are much better than predefined. For likelihood universes (CC, CCF, and LH universes) built from large ChIP-Seq collections ( CTCF ChIP large and TF ChIP ) we observe the worst recall among data-driven universes. This is the consequence of Eq. ( 1 ), from which we observed that, for ChIP-Seq collections, the optimal cutoff value is higher. On the other hand, both the HMM universe and the union universe have high recall and low precision. In general, we see that universes with high recall have lower precision, reflecting the delicate balance between including complete information in universes without adding too much noise.

To propose a balance between precision and recall, we next calculated the F 10 -score, which assigns more weight to recall than precision (Figure 5D ). For predefined universes, we see that the tiles universe scores well for ChIP-seq collections, which cover more of the genome, but poorly for ATAC-seq collections, which have lower coverage. In general, both RB and SCREEN are outperformed by data-driven universes, especially for the B-LCL ATAC collection, for which they contain too much noise. In general, for ChIP-Seq collections, the union universe is the best for these metrics, consistent with the weighting we chose that gives 10 times the weight to recall. Interestingly, for the TF ChIP collection, the HMM universe outperforms likelihood universes; on the other hand, for ATAC-Seq collections, the CCF universe outperforms both union and HMM universes. Overall, we conclude that computing precision, recall, and F 10 provide useful insight into assessing universe fit. They provide a way to quantify the advantages of a data-driven universe and assess how much information is lost by an external universe.

Assessment 2: region boundary distance score

Next, we sought to address the major weakness we see in the base-level overlap score: that it does not consider region boundaries. Assessing boundaries is important because of how it affects downstream analysis. If two regulatory elements with distinct behavior are merged into a single region in a universe, then all downstream analyses will essentially evaluate the average of the two signals. But different universes have different sensitivities to boundary points (Figure 6A ); for example, anecdotally, the union universe is not sensitive at all: it contains few boundaries, especially for larger collections. It will clearly merge together many neighboring regions, even if they have distinct patterns across input sets. The CC and CCF universes are more sensitive but still miss out on many boundaries for bigger collections; the LH universe is very sensitive to boundaries, but has other weaknesses (it tends to exclude positions with low coverage); all of those problem are solved with HMM universe, which is very sensitive and also is able to represent regions with low coverage. To assess boundaries globally, we turned to the region boundary distance score. First, for each region set, for each region, for each boundary, we calculated the distance to the nearest corresponding universe boundary (See Methods; Figure 6B ). For all collections, the distance from collection to RB universe was very high. Similarly, the union universe performs very poorly in this metric, particularly for larger collections, consistent with intuition that the union regions lose boundary precision as the number of regions increases. We also computed the inverse: distances from query to universe (Figure 6C ). Interestingly, for ATAC-Seq collections, we observe a small distance from query to universe but a high distance from universe to query. This indicates that all universes have many boundaries that are not present in the raw data, but at the same time boundaries present in the queries are well-reflected by universes.

Results of region boundary distance score. ( A ) Different universes represent region boundaries to a different extent. Three signal tracks provide summarized description of the whole collection. Both LH and HMM universe are most sensitive to region boundaries. ( B ) Distribution of median distances from query to universe. ( C ) Distribution of median of distance from universe to query. ( D ) Average RBD score for each collection and universe comparison.

To summarize both distance directions, we calculated their reciprocal, weighted harmonic mean, the Region Boundary Distance score ( RBD ) (Figure 6D ). The average RBD score shows that the RB universe is a very poor fit to all collections, likely because it contains few, large regions. On the other hand, tiles universe has similar scores for all collections, which is good for big ChIP-Seq collections compared to other universes, but bad for ATAC-Seq collections. Interestingly, the SCREEN universe seems to be a good fit for all collections, and the best fit for ATAC-Seq collections, even outperforming the data-driven ones for this metric. Among data-driven universes, the HMM universe performs the best, with LH in second place for all collections except B-LCL ATAC . However, for this collection all data-driven universes have similar scores. Coverage-based universes (CC, CCF) perform well for ATAC-Seq collections, but not for large ChIP-Seq collections ( CTCF ChIP large and TF ChIP ) compared to other data-driven universes. As expected, the RBD score reflects the poor performance of the union universe for large ChIP-Seq collections; the merging leads to poor reflection of interval boundaries.

Assessment 3: likelihood calculation

Finally, we calculated the likelihood score for each comparison (Figure 7A ). In general, predefined universes are a worse fit to the collections than an empty universe, with exception of SCREEN for CTCF ChIP large and TF ChIP collections. Among data-driven universes, the union universe performs very poorly, achieving negative scores for all collections. As expected, CC, CCF, and LH universes outperform the empty universe for all collections, as these were designed to optimize likelihood in some way. The HMM universe performs well overall; however, it is worse than empty universe for CTCF ChIP large and TF ChIP collections. A more detailed look reveals that the low HMM likelihood scores in these scenarios are driven by region coverage tracks, not boundaries, suggesting that for these collections, our current HMM parameterization may yield a universe with too much noise (see Materials and Methods; Supplementary Figure S5 ).

Results of universe likelihood and comparison between fixed and flexible scores. ( A ) Likelihood of each universe given collection. ( B ) Change of RBD score when we account for flexibility. ( C ) Change of likelihood, when we account for flexibility.

Comparing flexible to fixed universes

So far, our assessments have not taken into account that some universes can be flexible. We believe that flexible universes provide several advantages, and sought to assess them. Flexible intervals don’t quite fit into the standard 3-column BED format; however, they can be stored using optional fields of an extended BED format. In this approach, sequence name, start, and end of the flexible region are represented by the first three columns, and thickStart and thickEnd columns hold the information about end of the flexible start and start of the flexible end. To assess this, we applied our flexible-aware version of the RBD score. We observed that RBD score improves for all flexible universes (Figure 7B ). The change is less significant for CCF universe; however for LH and HMM universes, the new score is close to one, with the HMM universe performing slightly better.

We computed a version of the likelihood score that considers universe flexibility. This score shows a significant improvement; for all flexible universes, all collection scores change by an order of magnitude (Figure 7C ). Likelihood values that consider flexibility are similar for all universes; however, the HMM universe performs slightly worse for large ChIP-Seq collections ( CTCF ChIP large , TF ChIP ). This reflects that, unlike the CCF and LH universes, the HMM universe does not explicitly optimize likelihood. A more detailed look into likelihood showed that although the HMM universe has the best likelihood of cores of the regions, it performs less well for boundary positions (see Methods; Supplementary Figure S6 ).

Assessing across metrics

Since each metric assesses different aspects of universe fit, considering them independently limits the scope of assessment. For a holistic view, we summarized the scores of each metric into normalized heatmaps, allowing comparison within and across metrics (Figure 8 ). We observed several informative cross-score patterns: First, the F 10 -score is the most consistent metric across universes, indicating that all these universes cover the collections to similar extent (Figure 8A ). In contrast, the RBD indicates more variation in how well universes represent region boundaries (Figure 8B ). This is consistent with our intuition that matching boundaries is a more difficult task, since it requires ensuring large regions are split well. The disparity between F 10 -scores and RBD score demonstrates their combined utility. For example, the union universe has perhaps the overall best F 10 -scores across universes, but has low RBD score. Inversely, the SCREEN universe has the best RBD score for the B-LCL ATAC collection, but is significantly worse than any data-driven universe for F 10 -score. In likelihood scores, the CC, CCF, LH universes outperformed other universes to similar extent, while tiles, RB and union were universally poor fits for all universes (Figure 8C ). The HMM has much better RBD score than CC, but a worse likelihood, reflecting that likelihood considers more than boundary positions. Additionally, the likelihood is stricter in boundary assessments: while for RBD score we take a median of actual values, for likelihood we use probability of a given position being a boundary. Comparison of flexible and fixed versions of RBD score and likelihood highlights the value of flexible regions (Figure 8D , E ); RBD score for LH and HMM improved significantly after accounting for flexibility.

Results of universe comparison using different scores. ( A ) Row normalized F 10 -score of each universe given collection. ( B ) Row normalized RBD score of each universe given collection. ( C ) Row normalized likelihood of each universe given collection. ( D ) Row normalized flexible version of RBD score of each universe given collection. ( E ) Row normalized flexible version of likelihood of each universe given collection.

Application on downstream analysis

To demonstrate how universe affects downstream analyses and how our universe building and evaluation methods can be applied, we performed a region set enrichment analysis using LOLA, a tool for statistical region enrichment analysis ( 15 ). The goal is to take some demo region sets and then use them to search a database of region sets to find similar regions, and explore how the choice of universe affects the results. We constructed two different experiments. For our first experiment, we used the Random ATAC collection as a database. We used three predefined universes – tiles, RB and SCREEN – as well as data-driven universes built from the Random ATAC and B-LCL ATAC collections. To see how the universes based on rare cell types perform, we also added data-driven universes built from fifteen Glia ATAC-Seq files. We queried the database with fifteen files that were not present in the database and were not used for universe construction, representing three different cell types: five files from A549, five from B-LCL, and five from Glia samples. For our second experiment, we used 623 ChIP-Seq files representing different TFs to build a database and data-driven universes. We queried the database with thirty files representing different TFs: 10 files from EZH2, 10 files from POLR2A, 10 files from YY1. For both experiments, we assessed performance of the region enrichment analysis with R -precision (rPPV), which measures the precision based on the top R results, where R is equal to the number of correct results in the whole database.

Our results demonstrate clear impact of the universe on analysis performance (Figure 9A ). In the first experiment, the data-driven universes were the best performers for the specific questions; Glia ATAC queries (gray dots) performed best under the tailored Glia data-driven universes, followed by the Random ATAC data-driven universes, and performed poorly with any pre-defined universes or with the B-LCL data-driven universes. The B-LCL queries (yellow dots) also performed best with the tailored B-LCL-driven universes, and performed reasonably well with predifined or Random ATAC data-driven universes, but poorly with the Glia data-driven universe. Finally, A549 samples performed equally well with predefined or Random ATAC data-driven universes, and poorly with the universes built on the other data types. This shows that using universes based on a specific cell type increase the performance of downstream analysis for this type. Our second experiment, based on TF data, shows a clear difference between the union universe and other more complex data driven universes. We also see an imbalance among the predefined universes, with the SCREEN universe outperforming tiles and RB in general. In conclusion, for ATAC-Seq data, rPPV is higher for data driven universes than predefined ones; for TF data rPPV is higher for more complex data-driven universes (CC, CCF, ML, HMM) than union universes. Overall, these results demonstrate that choosing the right universe is a complex task. In our experiments, the more complex data-driven universes (CC, CCF, LH and HMM) are always either comparable or superior to simpler data-driven or pre-defined universes, although the exact performance depends on the both the initial data and the downstream task. Most importantly, this analysis suggests that the assessment methods we present can be helpful in choosing the right universe.

Results of downstream enrichment analysis depends on universe. ( A ) R -precision (rPPV) of query files depending on universe. ( B ) Median of different universe, depending on the collection used for their construction.

Many integrative epigenome analyses require the data to be defined on a set of consensus regions, or universe. This universe is critical for analysis because it determines the precision of regions assessed. Our experiments highlight how different universes can have different levels of fit to collections and may therefore be useful for different tasks. Despite the importance of this choice, few approaches have been developed to aid analysts in building appropriate universes or assessing the fit of an existing universe. In this study, we have addressed these issues by presenting several novel methods to build universes from collections of region sets, as well as new ways to assess the fit of a universe to a collection of region sets. We also introduced the concept of flexible segments, and proposed several methods for constructing universes that can use either traditional fixed boundaries or flexible interval boundaries.

In general, data-driven universes outperformed predefined ones. However, the data-driven universes also have a weakness: by definition, they change with the underlying collection, and therefore cannot support an integrative analysis that spans collections. If results need to be compared across collection, then a shared universe is required. There are a few options for analysis in this case, all of which are facilitated by our work: First, a custom data-driven universe that spans all included collections can be built. Since we have described several ways to create well-performing data-driven universes, it would be easy to just design a bespoke universe for a given comparative analysis. However, this may not always be possible or convenient, and at some point, an external universe may be preferred. In this case, a trade-off is required: fit of the universe must be sacrificed to increase interoperability with other collections. Our assessment methods now provide a principled way to assess this trade-off and inform research decisions.

Among the non-data-driven universes we tested, SCREEN performed well for the collections we tested. However, there are almost certainly other collections or use cases for which the tiles, RB, or other external universes would be a better choice. Along these lines, we propose that our methods for building universes can be used in the future to create predefined universes from large collections, thereby creating even better global universes that can be re-used for integrative analysis. In the future, a centralized repository of universes, built using different methods and for different target use cases, could be a useful resource; a given collection of region sets could be represented into different universes based on the balance of fit and need for integration.

Based on our results, we propose that the union universe, though widely used, does not represent ChIP-Seq data well, particularly for large collections. Instead, we propose the HMM universe as a good all-around option that solves many of the issues with simpler universes. It has the highest sensitivity to boundary positions and good recall. It also provides adjustable parameters; by setting emission and transition probabilities, users may adjust model sensitivity and keep it consistent across collections. Still, the final choice of universe should consider the needs of downstream analysis. Our results show that assessing universe fit is a complex question, with many features to optimize. Even with our assessment metrics, it is difficult to claim an optimal universe for a given collection; instead, the answer depends on the downstream analysis priorities. For example, in general, the CCF and LH universe represent properties of a whole collection well, but they exclude infrequent regions; thus, they may be useful for NLP analysis of the genome but could lead to losing information about rare cell types in single cell analysis. In contrast, the union universe by definition covers all bases found in the region set collection, and therefore has a great F 10 -score; however, it also merges regions, which is reflected by poor RBD score and likelihood scores, indicating that it would not be a good fit for an application that requires high region resolution. Therefore, multiple perspectives must be considered for a holistic assessment of universe fit.

One advantage of our new universe construction methods is that they naturally create flexible universes. Flexible universes are a new way of summarizing information from large collections with less loss of information. We showed that proposed approaches of making flexible universes improve results over inflexible universes. We see flexible regions as a powerful new concept that can modify our current way of thinking about universes. Furthermore, we expect that using them for differential peak analysis, statistical region enrichment analysis and NLP approaches has potential to improve results. Flexible regions will become more useful as we and others develop the necessary tooling to work with them; for example, we will require tokenization methods that can project a traditional region set into a flexible universe quickly and accurately, while considering the universe flexibility.

In conclusion, this research provides new concepts, methods, and insight that will help researchers to determine the best analysis path for many types of genomic region analysis.

Software is available at https://github.com/databio/geniml .

Supplementary Data are available at NAR Online.

National Human Genome Research Institute [R01-HG012558]; National Institute of General Medical Sciences [R35-GM128636]. Funding for open access charge: NIH.

Conflict of interest statement . N.C.S. is a consultant for InVitro Cell Research, LLC.

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The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focu …
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NEW AND UPDATED DATABASES. In its 30th incarnation, the Nucleic Acids Research Database Issue once again ranges across biology with a total of 178 papers. Table Table1 1 lists the 90 new databases included, a recent record number, and there are 82 update papers from resources previously covered by NAR. Finally, six databases most recently published elsewhere contribute updates (Table (Table2). 2).
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Nucleic Acids Research is a peer reviewed fully open access journal publishing 24 issues per year online and in print. All papers published in the Journal are made freely available online under open access publishing agreements, with applicable charges.
Nucleic acids
Read the latest Research articles in Nucleic acids from Nature. ... Four different XNAs — polymers with backbone chemistries not found in nature, namely, arabino nucleic acids, 2 ...
Phys.org
Nucleic Acids Research. Nucleic Acids Research is a peer-reviewed scientific journal published by Oxford University Press. It covers research on nucleic acids, such as DNA and RNA, and related ...
Journal Citation Reports
Learn about the journal profile, impact factor, ranking, and citation analysis of Nucleic Acids Research, a leading journal in the Web of Science Core Collection.
Protein-nucleic acid hybrid nanostructures for molecular diagnostic
Molecular diagnostic technologies empower new clinical opportunities in precision medicine. However, existing approaches face limitations with respect to performance, operation and cost. Biological molecules including proteins and nucleic acids are being increasingly adopted as tools in the development of new molecular diagnostic technologies. In particular, leveraging their complementary ...
The 2021 Nucleic Acids Research database issue and the online molecular
NEW AND UPDATED DATABASES. The 28th annual Nucleic Acids Research Database Issue contains 189 papers spanning, as usual, a wide range of biology. Unsurprisingly, COVID-19 casts a long shadow over the Issue. Seven new databases specifically address the pandemic and the SARS-CoV-2 virus responsible (Table (Table1) 1) but new and returning databases in all areas have rushed to support research ...
Issues
Browse the latest articles on nucleic acid research, including breakthrough, critical, and review articles, as well as data resources and analyses. Topics include gene regulation, chromatin, epigenetics, RNA, DNA, and more.
Nucleic acid chemistry
Nucleic acids have become important diagnostic markers for many diseases, enabled by breakthroughs in synthesis and sequencing technologies. ... The Collection primarily welcomes original research ...
Development and validation of a rapid five-minute nucleic acid
The rapid transmission and high pathogenicity of respiratory viruses significantly impact the health of both children and adults. Extracting and detecting their nucleic acid is crucial for disease prevention and treatment strategies. However, current extraction methods are laborious and time-consuming and show significant variations in nucleic acid content and purity among different kits ...
Precise Preparation of Supramolecular Spherical Nucleic Acids for
Angewandte Chemie International Edition is one of the prime chemistry journals in the world, publishing research articles, highlights, communications and reviews across all areas of chemistry. Molecular spherical nucleic acids (m-SNAs) are a second generation of spherical nucleic acids (SNAs), which are of significance in potential application ...
The 2022 Nucleic Acids Research database issue and the online molecular
The 29th annual Nucleic Acids Research Database Issue contains 185 papers covering topics from across biology and beyond. The ongoing COVID-19 pandemic continues to play a major role, inspiring the construction of seven new databases (Table (Table1). 1). The reader will also find its impact obvious in papers describing other new and returning ...
Volume 51 Issue 6
Publishes the results of leading edge research into physical, chemical, biochemical and biological aspects of nucleic acids and proteins involved in nucleic acid metabolism and/or interactions. Fully open access.
iSN04: A novel nucleic acid drug for the treatment of vascular diseases
This innovative strategy has led researchers from Shinshu University, Japan, to develop a novel nucleic acid drug called iSN04. Associate Professor Tomohide Takaya, from the Faculty of Agriculture ...
Archive of "Nucleic Acids Research".
Nucleic Acids Res; Nucleic Acids Research Vols. 1 to 52; 1974 to 2024; Vol. 52 2024: v.52(D1): D1-D1702 2024 Jan 5: v.52(1): 1-509 2024 Jan 11: v.52(2): 511-1003 ... Articles from Nucleic Acids Research are provided here courtesy of Oxford University Press. Follow NCBI. Connect with NLM National Library of Medicine 8600 Rockville Pike ...
Cellular DNA damage response pathways might be useful against some
Published online on Aug. 10 in Nucleic Acids Research, ... "This research is significant both for understanding how cells respond to DNA damage, to prevent them from becoming cancerous in the first place, how targeting this pathway can be used in new cancer treatments, and because it now opens up possibilities for new approaches to treating ...
Volume 51 Issue 2
Nucleic Acids Research | 51 | 2 | January 2023. Cover: Zalpha (Zα) domains bind to left-handed Z-DNA and Z-RNA. The Zα domain protein family includes cellular (ADAR1, ZBP1 and PKZ) and viral (vaccinia virus E3 and cyprinid herpesvirus 3 (CyHV-3) ORF112) proteins.
Methods for constructing and evaluating consensus genomic interval sets
Here, S c is a sum of genome coverage by collection and g is the size of the genome. We realized that, in a sense, the CC universe is a point estimate of a more complex distribution of possible universes. We reasoned that we may gain some insight by modeling the boundaries of the consensus regions as intervals, rather than points.

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Protein-nucleic acid hybrid nanostructures for molecular diagnostic applications

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Nucleic acid chemistry

Andrea Rentmeister, PhD

Michal Hocek, PhD, DSc

Synthesis and modification of nucleic acids

Generation of DNA oligomers with similar chemical kinetics via in-silico optimization

The selection of a hydrophobic 7-phenylbutyl-7-deazaadenine-modified DNA aptamer with high binding affinity for the Heat Shock Protein 70

Evaluation of 3′-phosphate as a transient protecting group for controlled enzymatic synthesis of DNA and XNA oligonucleotides

Structure and function of nucleic acids

Structure of a 10-23 deoxyribozyme exhibiting a homodimer conformation

i-Motif folding intermediates with zero-nucleotide loops are trapped by 2′-fluoroarabinocytidine via F···H and O···H hydrogen bonds

Intrastrand backbone-nucleobase interactions stabilize unwound right-handed helical structures of heteroduplexes of L- a TNA/RNA and SNA/RNA

Applications of nucleic acids in chemical biology and medicinal chemistry

DNA-encoded chemical libraries yield non-covalent and non-peptidic SARS-CoV-2 main protease inhibitors

Accessible light-controlled knockdown of cell-free protein synthesis using phosphorothioate-caged antisense oligonucleotides

Advanced preparation of fragment libraries enabled by oligonucleotide-modified 2′,3′-dideoxynucleotides

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Development and validation of a rapid five-minute nucleic acid extraction method for respiratory viruses

Conclusions

Introduction

Materials and methods

DNA and RNA extraction

Nucleic acid concentration and integrity

Quantitative real-time PCR

Clinical performance of the FME

Quantitative analysis of proteins

Statistical analysis

The combination of glycerin and EtOH can efficiently purify nucleic acid

The effect of A Plus lysis solution

Extraction effect of the FME

Automation and performance evaluation of the FME

Availability of data and materials

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Supplementary Information

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