ATAC-seq

Last updated
The mechanism of identifying chromatin accessibility using the Tn5 transposase. a Open and closed status of chromatin. b When the chromatin accessibility is increased, the Tn5 transposase transpose in the open chromatin more often than in the inaccessible chromatin. The green/red symbols represents adapters. Tn5 Transposase in ATAC-seq.webp
The mechanism of identifying chromatin accessibility using the Tn5 transposase. a Open and closed status of chromatin. b When the chromatin accessibility is increased, the Tn5 transposase transpose in the open chromatin more often than in the inaccessible chromatin. The green/red symbols represents adapters.

ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) is a laboratory technique used in molecular biology to assess genome-wide chromatin accessibility. [1] The technique was first described in 2013 as an alternative approach to MNase-seq, FAIRE-Seq and DNase-Seq [1] but providing faster turnaround time, simplified protocol, and lower DNA input amount. [2] [3] [4]

Contents

Procedure

ATAC-seq identifies accessible DNA regions by probing open chromatin with hyperactive mutant Tn5 Transposase that inserts sequencing adapters into open regions of the genome. [2] [5] While naturally occurring transposases have a low level of activity, ATAC-seq employs the mutated hyperactive transposase. [6] In a process called "tagmentation", Tn5 transposase cleaves and tags double-stranded DNA with sequencing adaptors in a single enzymatic step. [7] [8] The tagged DNA fragments are then purified, PCR-amplified, and sequenced using next-generation sequencing. [8] Sequencing reads can then be used to infer regions of increased accessibility as well as to map regions of transcription factor binding sites and nucleosome positions. [2] The number of reads for a region correlate with how open that chromatin is, at single nucleotide resolution. [2]

ATAC-seq requires no sonication or phenol-chloroform extraction like FAIRE-seq; [9] no antibodies like ChIP-seq; [10] and no sensitive enzymatic digestion like MNase-seq or DNase-seq. [11] ATAC-seq preparation can be completed in under three hours. [12]

Applications

Comparative epigenomics applications of ATAC-seq such as cancer profiling, identifying cellular subtypes, and cell differentiation analysis. ATAC-Seq application v2.pdf
Comparative epigenomics applications of ATAC-seq such as cancer profiling, identifying cellular subtypes, and cell differentiation analysis.

ATAC-Seq analysis is used to investigate a number of chromatin-accessibility signatures. The most common use is nucleosome mapping experiments, [3] but it can be applied to mapping transcription factor binding sites, [13] adapted to map DNA methylation sites, [14] or combined with sequencing techniques. [15]

The utility of high-resolution enhancer mapping ranges from studying the evolutionary divergence of enhancer usage (e.g. between chimps and humans) during development [16] and uncovering a lineage-specific enhancer map used during blood cell differentiation. [17]

ATAC-Seq has also been applied to defining the genome-wide chromatin accessibility landscape in human cancers, [18] and revealing an overall decrease in chromatin accessibility in macular degeneration. [19] Computational footprinting methods can be performed on ATAC-seq to find cell specific binding sites and transcription factors with cell specific activity. [13]

ATAC-seq has found increasing applications in clinical research and disease studies. EPIC-ATAC has been developed as a deconvolution framework to quantify cell-type heterogeneity in bulk tumor ATAC-seq data, enabling analysis of regulatory processes underlying tumor development and correlation with clinical variables in cancer research. [20] [21] In immunology, ATAC-seq has been used to characterize dynamic epigenetic changes in T cell exhaustion, revealing that exhausted T cells possess unique chromatin accessibility patterns compared to naive, effector, and memory T cells, with implications for cancer immunotherapy. [22] The Cancer Genome Atlas has generated genome-wide chromatin accessibility profiles of 410 tumor samples spanning 23 cancer types, identifying 562,709 transposase-accessible DNA elements and revealing genetic risk loci of cancer predisposition as active DNA regulatory elements. [23] Integrative analysis combining ATAC-seq with RNA-seq has been used to identify novel oncogenes and elucidate regulatory mechanisms in hepatocellular carcinoma. [24]

Single-cell ATAC-seq

Modifications to the ATAC-seq protocol have been made to accommodate single-cell analysis. Microfluidics can be used to separate single nuclei and perform ATAC-seq reactions individually. [12] With this approach, single cells are captured by either a microfluidic device or a liquid deposition system before tagmentation. [12] [25] An alternative technique that does not require single cell isolation is combinatorial cellular indexing. [26] This technique uses barcoding to measure chromatin accessibility in thousands of individual cells; it can generate epigenomic profiles from 10,000-100,000 cells per experiment. [27] But combinatorial cellular indexing requires additional, custom-engineered equipment or a large quantity of custom, modified Tn5. [28] Recently, a pooled barcode method called sci-CAR was developed, allowing joint profiling of chromatin accessibility and gene expression of single cells. [29]

Computational analysis of scATAC-seq is based on construction of a count matrix with number of reads per open chromatin regions. Open chromatin regions can be defined, for example, by standard peak calling of pseudo bulk ATAC-seq data. Further steps include data reduction with PCA and clustering of cells. [25] scATAC-seq matrices can be extremely large (hundreds of thousands of regions) and is extremely sparse, i.e. less than 3% of entries are non-zero. [30] Therefore, imputation of count matrix is another crucial step performed by using various methods such as non-negative matrix factorization. As with bulk ATAC-seq, scATAC-seq allows finding regulators like transcription factors controlling gene expression of cells. This can be achieved by looking at the number of reads around TF motifs [31] or footprinting analysis. [30]

Spatial ATAC-seq

Spatial ATAC-seq combines chromatin accessibility profiling with spatial information, enabling researchers to map epigenetic landscapes while preserving tissue architecture. This method combines in situ Tn5 transposition chemistry with microfluidic deterministic barcoding to perform spatially resolved chromatin accessibility analysis on tissue sections at the cellular level and genome scale. [32] [33] The technique has been applied to co-profiling of the epigenome and transcriptome, facilitating investigation of the correlation between accessible peaks and expressed genes pixel by pixel in the tissue context. [32] Recent developments include SPACE-seq (SPatial assay for Accessible chromatin, Cell lineages, and gene Expression with sequencing), which enables simultaneous analysis of gene expression, chromatin accessibility, and mitochondrial DNA mutations using commercially available spatial transcriptomics platforms. [34] Laser capture microdissection coupled to ATAC-seq (LCM-ATAC-seq) has also been developed for targeted chromatin accessibility analysis of discrete contiguous or scattered cell populations in tissues, enabling analysis at mini-bulk resolution with the possibility to integrate cellular or morphological stainings. [35]

Multimodal ATAC-seq

Recent advances have enabled simultaneous profiling of chromatin accessibility alongside other molecular modalities in the same cells or tissue sections. Spatial ATAC–RNA-seq and spatial CUT&Tag–RNA-seq allow co-profiling of genome-wide chromatin accessibility or histone modifications in conjunction with whole transcriptome on the same tissue section at near-single-cell resolution. [32] ISSAAC-seq (In Situ Sequencing of chromatin Accessibility And Cellular transcriptomes) represents a multimodal update to ATAC-seq, providing a powerful method for investigating gene expression and chromatin accessibility within the same cell at high sensitivity and lower cost than commercially available kits. [36] These multimodal approaches have led to the development of computational tools like SCRIPro, which combines transcription factor-target importance from epigenomic data with transcription factor-target expression from transcriptomic data to construct gene regulatory networks from single-cell and spatial multiomics data. [37]

Computational Tools and Analysis

ATAC-seq data analysis presents unique methodological challenges due to data sparsity and the need for specialized bioinformatics tools. The major steps include pre-analysis (quality check and alignment), core analysis (peak calling), and advanced analysis (peak differential analysis and annotation, motif enrichment, footprinting, and nucleosome position analysis). [38] [39] MACS2 (Model-based Analysis of ChIP-seq 2) remains the most widely used peak caller for ATAC-seq data analysis, serving as the default peak caller in the ENCODE ATAC-seq pipeline. Originally developed for ChIP-seq, MACS2 has been adapted for ATAC-seq analysis and performs well for identifying regions of enriched transposase accessibility, though it requires parameter optimization for ATAC-seq-specific characteristics. [40] [41] Standardized analysis workflows have been developed, including the nf-core/atacseq pipeline, which provides a comprehensive, reproducible framework for ATAC-seq data processing from raw reads to final peak calls and quality control metrics. This Nextflow-based pipeline incorporates best practices for adapter trimming, alignment, duplicate removal, peak calling, and downstream analysis, facilitating standardized processing across different research groups and computational environments. [42] [43]

See also

References

  1. 1 2 Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ (December 2013). "Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position". Nature Methods. 10 (12): 1213–8. doi:10.1038/nmeth.2688. PMC   3959825 . PMID   24097267.
  2. 1 2 3 4 Buenrostro JD, Wu B, Chang HY, Greenleaf WJ (January 2015). "ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide". Current Protocols in Molecular Biology. 109: 21.29.1–21.29.9. doi:10.1002/0471142727.mb2129s109. PMC   4374986 . PMID   25559105.
  3. 1 2 Schep AN, Buenrostro JD, Denny SK, Schwartz K, Sherlock G, Greenleaf WJ (November 2015). "Structured nucleosome fingerprints enable high-resolution mapping of chromatin architecture within regulatory regions". Genome Research. 25 (11): 1757–70. Bibcode:2015GenRe..25.1757S. doi:10.1101/gr.192294.115. PMC   4617971 . PMID   26314830.
  4. ↑ Song L, Crawford GE (February 2010). "DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells". Cold Spring Harbor Protocols. 2010 (2): pdb.prot5384. doi:10.1101/pdb.prot5384. PMC   3627383 . PMID   20150147.
  5. ↑ Bajic M, Maher KA, Deal RB (2018). "Identification of Open Chromatin Regions in Plant Genomes Using ATAC-Seq". Plant Chromatin Dynamics. Methods in Molecular Biology. Vol. 1675. pp. 183–201. doi:10.1007/978-1-4939-7318-7_12. ISBN   978-1-4939-7317-0. ISSN   1064-3745. PMC   5693289 . PMID   29052193.
  6. ↑ Reznikoff WS (2008). "Transposon Tn5". Annual Review of Genetics. 42 (1): 269–86. doi:10.1146/annurev.genet.42.110807.091656. PMID   18680433.
  7. ↑ Adey, Andrew (December 2010). "Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition". Genome Biology. 11 (12) R119. doi: 10.1186/gb-2010-11-12-r119 . PMC   3046479 . PMID   21143862.
  8. 1 2 Picelli S, Björklund AK, Reinius B, Sagasser S, Winberg G, Sandberg R (December 2014). "Tn5 transposase and tagmentation procedures for massively scaled sequencing projects". Genome Research. 24 (12): 2033–40. doi:10.1101/gr.177881.114. PMC   4248319 . PMID   25079858.
  9. ↑ Simon JM, Giresi PG, Davis IJ, Lieb JD (January 2012). "Using formaldehyde-assisted isolation of regulatory elements (FAIRE) to isolate active regulatory DNA". Nature Protocols. 7 (2): 256–67. doi:10.1038/nprot.2011.444. PMC   3784247 . PMID   22262007.
  10. ↑ Savic D, Partridge EC, Newberry KM, Smith SB, Meadows SK, Roberts BS, et al. (October 2015). "CETCh-seq: CRISPR epitope tagging ChIP-seq of DNA-binding proteins". Genome Research. 25 (10): 1581–9. doi:10.1101/gr.193540.115. PMC   4579343 . PMID   26355004.
  11. ↑ Hoeijmakers WA, Bártfai R (2018). "Characterization of the Nucleosome Landscape by Micrococcal Nuclease-Sequencing (MNase-seq)". Chromatin Immunoprecipitation. Methods in Molecular Biology. Vol. 1689. pp. 83–101. doi:10.1007/978-1-4939-7380-4_8. ISBN   978-1-4939-7379-8. ISSN   1064-3745. PMID   29027167.
  12. 1 2 3 Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. (July 2015). "Single-cell chromatin accessibility reveals principles of regulatory variation". Nature. 523 (7561): 486–90. Bibcode:2015Natur.523..486B. doi:10.1038/nature14590. PMC   4685948 . PMID   26083756.
  13. 1 2 Li, Zhijian; Schulz, Marcel H.; Look, Thomas; Begemann, Matthias; Zenke, Martin; Costa, Ivan G. (26 February 2019). "Identification of transcription factor binding sites using ATAC-seq". Genome Biology. 20 (1): 45. doi: 10.1186/s13059-019-1642-2 . PMC   6391789 . PMID   30808370.
  14. ↑ Spektor R, Tippens ND, Mimoso CA, Soloway PD (June 2019). "methyl-ATAC-seq measures DNA methylation at accessible chromatin". Genome Research. 29 (6): 969–977. doi:10.1101/gr.245399.118. PMC   6581052 . PMID   31160376.
  15. ↑ Hendrickson DG, Soifer I, Wranik BJ, Botstein D, Scott McIsaac R (2018), "Simultaneous Profiling of DNA Accessibility and Gene Expression Dynamics with ATAC-Seq and RNA-Seq", Computational Cell Biology, Methods in Molecular Biology, vol. 1819, Springer New York, pp. 317–333, doi:10.1007/978-1-4939-8618-7_15, ISBN   9781493986170, PMID   30421411
  16. ↑ Prescott SL, Srinivasan R, Marchetto MC, Grishina I, Narvaiza I, Selleri L, et al. (September 2015). "Enhancer divergence and cis-regulatory evolution in the human and chimp neural crest". Cell. 163 (1): 68–83. doi:10.1016/j.cell.2015.08.036. PMC   4848043 . PMID   26365491.
  17. ↑ Lara-Astiaso D, Weiner A, Lorenzo-Vivas E, Zaretsky I, Jaitin DA, David E, et al. (August 2014). "Immunogenetics. Chromatin state dynamics during blood formation". Science. 345 (6199): 943–9. doi:10.1126/science.1256271. PMC   4412442 . PMID   25103404.
  18. ↑ Corces MR, Granja JM, Shams S, Louie BH, Seoane JA, Zhou W, et al. (October 2018). "The chromatin accessibility landscape of primary human cancers". Science. 362 (6413) eaav1898. Bibcode:2018Sci...362.1898C. doi:10.1126/science.aav1898. PMC   6408149 . PMID   30361341.
  19. ↑ Wang J, Zibetti C, Shang P, Sripathi SR, Zhang P, Cano M, et al. (April 2018). "ATAC-Seq analysis reveals a widespread decrease of chromatin accessibility in age-related macular degeneration". Nature Communications. 9 (1) 1364. Bibcode:2018NatCo...9.1364W. doi:10.1038/s41467-018-03856-y. PMC   5893535 . PMID   29636475.
  20. ↑ Gabriel, Aurélie AG; Racle, Julien; Falquet, Maryline; Jandus, Camilla; Gfeller, David (2024-08-02), Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data, bioRxiv, doi:10.1101/2023.10.11.561826 , retrieved 2025-08-19
  21. ↑ Corces, M. Ryan; Granja, Jeffrey M.; Shams, Shadi; Louie, Bryan H.; Seoane, Jose A.; Zhou, Wanding; Silva, Tiago C.; Groeneveld, Clarice; Wong, Christopher K.; Cho, Seung Woo; Satpathy, Ansuman T.; Mumbach, Maxwell R.; Hoadley, Katherine A.; Robertson, A. Gordon; Sheffield, Nathan C. (2018-10-26). "The chromatin accessibility landscape of primary human cancers". Science. 362 (6413): eaav1898. Bibcode:2018Sci...362.1898C. doi:10.1126/science.aav1898. PMC   6408149 . PMID   30361341.
  22. ↑ Chen, Chufeng; Liu, Jiaying; Chen, Yidong; Lin, Anqi; Mou, Weiming; Zhu, Lingxuan; Yang, Tao; Cheng, Quan; Zhang, Jian; Luo, Peng (January 2023). "Application of ATAC-seq in tumor-specific T cell exhaustion". Cancer Gene Therapy. 30 (1): 1–10. doi:10.1038/s41417-022-00495-w. ISSN   1476-5500. PMC   9842510 . PMID   35794339.
  23. ↑ Taavitsainen, S.; Engedal, N.; Cao, S.; Handle, F.; Erickson, A.; Prekovic, S.; Wetterskog, D.; Tolonen, T.; Vuorinen, E. M.; Kiviaho, A.; Nätkin, R.; Häkkinen, T.; Devlies, W.; Henttinen, S.; Kaarijärvi, R. (2021-09-06). "Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse". Nature Communications. 12 (1): 5307. Bibcode:2021NatCo..12.5307T. doi:10.1038/s41467-021-25624-1. ISSN   2041-1723. PMC   8421417 .
  24. ↑ "Library QC for ATAC-Seq and CUT&Tag".
  25. 1 2 Mezger A, Klemm S, Mann I, Brower K, Mir A, Bostick M, et al. (September 2018). "High-throughput chromatin accessibility profiling at single-cell resolution". Nature Communications. 9 (1) 3647. Bibcode:2018NatCo...9.3647M. doi:10.1038/s41467-018-05887-x. PMC   6128862 . PMID   30194434.
  26. ↑ Cusanovich, Darren (May 2015). "Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing". Science. 348 (6237): 910–914. Bibcode:2015Sci...348..910C. doi:10.1126/science.aab1601. PMC   4836442 . PMID   25953818.
  27. ↑ Lareau CA, Duarte FM, Chew JG, Kartha VK, Burkett ZD, Kohlway AS, Pokholok D, Aryee MJ, et al. (2019). "Droplet-based combinatorial indexing for massive scale single-cell epigenomics". bioRxiv. doi: 10.1101/612713 .
  28. ↑ Chen X, Miragaia RJ, Natarajan KN, Teichmann SA (December 2018). "A rapid and robust method for single cell chromatin accessibility profiling". Nature Communications. 9 (1) 5345. Bibcode:2018NatCo...9.5345C. doi:10.1038/s41467-018-07771-0. PMC   6297232 . PMID   30559361.
  29. ↑ Cao, Junyue; Cusanovich, Darren A.; Ramani, Vijay; Aghamirzaie, Delasa; Pliner, Hannah A.; Hill, Andrew J.; Daza, Riza M.; McFaline-Figueroa, Jose L.; Packer, Jonathan S.; Christiansen, Lena; Steemers, Frank J. (2018-09-28). "Joint profiling of chromatin accessibility and gene expression in thousands of single cells". Science. 361 (6409): 1380–1385. Bibcode:2018Sci...361.1380C. doi: 10.1126/science.aau0730 . ISSN   0036-8075. PMC   6571013 . PMID   30166440.
  30. 1 2 Li Z, Kuppe C, Cheng M, Menzel S, Zenke M, Kramann R, et al. (2021). "Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen". Nature Communications. 12 (1): 865931. Bibcode:2021NatCo..12.6386L. doi: 10.1038/s41467-021-26530-2 . PMC   8568974 . PMID   34737275.
  31. ↑ Schep AN, Wu B, Buenrostro JD, Greenleaf WJ (October 2017). "chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data". Nature Methods. 14 (10): 975–978. doi:10.1038/nmeth.4401. PMC   5623146 . PMID   28825706.
  32. 1 2 3 Zhang, Di; Deng, Yanxiang; Kukanja, Petra; Agirre, Eneritz; Bartosovic, Marek; Dong, Mingze; Ma, Cong; Ma, Sai; Su, Graham; Bao, Shuozhen; Liu, Yang; Xiao, Yang; Rosoklija, Gorazd B.; Dwork, Andrew J.; Mann, J. John (April 2023). "Spatial epigenome-transcriptome co-profiling of mammalian tissues". Nature. 616 (7955): 113–122. Bibcode:2023Natur.616..113Z. doi:10.1038/s41586-023-05795-1. ISSN   1476-4687. PMC   10076218 . PMID   36922587.
  33. ↑ "Spatial ATAC-Seq - CD Genomics". www.spatial-omicslab.com. Retrieved 2025-08-19.
  34. ↑ Huang, Yung-Hsin; Belk, Julia A.; Zhang, Ruochi; Weiser, Natasha E.; Chiang, Zachary; Jones, Matthew G.; Mischel, Paul S.; Buenrostro, Jason D.; Chang, Howard Y. (2025-04-22). "Unified molecular approach for spatial epigenome, transcriptome, and cell lineages". Proceedings of the National Academy of Sciences. 122 (16): e2424070122. Bibcode:2025PNAS..12224070H. doi:10.1073/pnas.2424070122. PMC   12037033 . PMID   40249782.
  35. ↑ Carraro, Caterina; Bonaguro, Lorenzo; Srinivasa, Rachana; Uelft, Martina van; Isakzai, Victoria; Schulte-Schrepping, Jonas; Gambhir, Prerna; Elmzzahi, Tarek; Montgomery, Jessica V.; Hayer, Hannah; Li, Yuanfang; Theis, Heidi; Kraut, Michael; Mahbubani, Krishnaa T.; Aschenbrenner, Anna C. (2023-10-23). "Chromatin accessibility profiling of targeted cell populations with laser capture microdissection coupled to ATAC-seq". Cell Reports Methods. 3 (10) 100598. doi:10.1016/j.crmeth.2023.100598. ISSN   2667-2375. PMC   10626193 . PMID   37776856.
  36. ↑ Atkinson, Stuart P. (2022-10-10). "Spatial and Multimodal Updates to Single-cell ATAC-seq". EpiGenie | Epigenetics, Stem Cell, and Synthetic Biology News. Retrieved 2025-08-19.
  37. ↑ Chang, Zhanhe; Xu, Yunfan; Dong, Xin; Gao, Yawei; Wang, Chenfei (2024-07-01). Nikolski, Macha (ed.). "Single-cell and spatial multiomic inference of gene regulatory networks using SCRIPro". Bioinformatics. 40 (7) btae466. doi:10.1093/bioinformatics/btae466. ISSN   1367-4811. PMC   11288411 . PMID   39024032.
  38. ↑ Yan, Feng; Powell, David R.; Curtis, David J.; Wong, Nicholas C. (2020-02-03). "From reads to insight: a hitchhiker's guide to ATAC-seq data analysis". Genome Biology. 21 (1): 22. doi: 10.1186/s13059-020-1929-3 . ISSN   1474-760X. PMC   6996192 . PMID   32014034.
  39. ↑ Long, Yahui; Ang, Kok Siong; Sethi, Raman; Liao, Sha; Heng, Yang; van Olst, Lynn; Ye, Shuchen; Zhong, Chengwei; Xu, Hang; Zhang, Di; Kwok, Immanuel; Husna, Nazihah; Jian, Min; Ng, Lai Guan; Chen, Ao (September 2024). "Deciphering spatial domains from spatial multi-omics with SpatialGlue". Nature Methods. 21 (9): 1658–1667. doi:10.1038/s41592-024-02316-4. ISSN   1548-7105. PMC   11399094 .
  40. ↑ Zhang, Yong; Liu, Tao; Meyer, Clifford A.; Eeckhoute, Jérôme; Johnson, David S.; Bernstein, Bradley E.; Nusbaum, Chad; Myers, Richard M.; Brown, Myles; Li, Wei; Liu, X. Shirley (2008-09-17). "Model-based Analysis of ChIP-Seq (MACS)". Genome Biology. 9 (9): R137. doi: 10.1186/gb-2008-9-9-r137 . ISSN   1474-760X. PMC   2592715 . PMID   18798982.
  41. ↑ Veerappa, Avinash M.; Rowley, M. Jordan; Maggio, Angela; Beaudry, Laura; Hawkins, Dale; Kim, Allen; Sethi, Sahil; Sorgen, Paul L.; Guda, Chittibabu (2024-07-23). "CloudATAC: a cloud-based framework for ATAC-Seq data analysis". Briefings in Bioinformatics. 25 (Supplement_1): bbae090. doi:10.1093/bib/bbae090. ISSN   1477-4054. PMC   11264300 . PMID   39041910.
  42. ↑ Ewels, Philip A.; Peltzer, Alexander; Fillinger, Sven; Patel, Harshil; Alneberg, Johannes; Wilm, Andreas; Garcia, Maxime Ulysse; Di Tommaso, Paolo; Nahnsen, Sven (March 2020). "The nf-core framework for community-curated bioinformatics pipelines" . Nature Biotechnology. 38 (3): 276–278. doi:10.1038/s41587-020-0439-x. ISSN   1546-1696.
  43. ↑ Chen, Huidong; Lareau, Caleb; Andreani, Tommaso; Vinyard, Michael E.; Garcia, Sara P.; Clement, Kendell; Andrade-Navarro, Miguel A.; Buenrostro, Jason D.; Pinello, Luca (2019-11-18). "Assessment of computational methods for the analysis of single-cell ATAC-seq data". Genome Biology. 20 (1): 241. doi: 10.1186/s13059-019-1854-5 . ISSN   1474-760X. PMC   6859644 . PMID   31739806.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.