Opportunities and challenges in long-read sequencing data analysis

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

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<tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data

Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364

limma powers differential expression analyses for RNA-sequencing and microarray studies

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458

featureCounts: an efficient general purpose program for assigning sequence reads to genomic features

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Salmon provides fast and bias-aware quantification of transcript expression

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Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement

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Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads

Wick, R. R., Judd, L. M., Gorrie, C. L., & Holt, K. E. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005595 · Cited by 8,728

Canu: scalable and accurate long-read assembly via adaptive k -mer weighting and repeat separationPreprint

Koren, S., Walenz, B. P., Berlin, K., Miller, J. R., Bergman, N. H., & Phillippy, A. M. (2017) · Genome Research · doi:10.1101/gr.215087.116 · Cited by 7,260

Assembly of long, error-prone reads using repeat graphs

Kolmogorov, M., Yuan, J., Lin, Y., & Pevzner, P. A. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0072-8 · Cited by 5,986

Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks042 · Cited by 5,195

YaHS: yet another Hi-C scaffolding tool

Zhou, C., McCarthy, S. A., & Durbin, R. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac808 · Cited by 2,440

Nanopore sequencing technology, bioinformatics and applications

Wang, Y., Zhao, Y., Bollas, A., Wang, Y., & Au, K. F. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-01108-x · Cited by 1,863

Next-Generation Sequencing Technology: Current Trends and Advancements

Satam, H., Joshi, K., Mangrolia, U., Waghoo, S., Zaidi, G., Rawool, S., Thakare, R. P., Banday, S., Mishra, A. K., Das, G., et al. (2023) · Biology · doi:10.3390/biology12070997 · Cited by 1,299

The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans

The Tabula Sapiens Consortium*, Jones, R. C., Karkanias, J., Krasnow, M. A., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., et al. (2022) · Science · doi:10.1126/science.abl4896 · Cited by 1,056

hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data

Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E., & Swarup, V. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100498 · Cited by 773

Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues

Pham, D., Tan, X., Balderson, B., Xu, J., Grice, L. F., Yoon, S., Willis, E. F., Tran, M., Lam, P. Y., Raghubar, A. M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43120-6 · Cited by 757

Statistics or biology: the zero-inflation controversy about scRNA-seq data

Jiang, R., Sun, T., Song, D., & Li, J. J. (2022) · Genome Biology · doi:10.1186/s13059-022-02601-5 · Cited by 614

scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data

Yang, F., Wang, W., Wang, F., Fang, Y., Tang, D., Huang, J., Lu, H., & Yao, J. (2021) · doi:10.1101/2021.12.05.471261 · Cited by 617

Joint probabilistic modeling of single-cell multi-omic data with totalVI

Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2021) · Nature Methods · doi:10.1038/s41592-020-01050-x · Cited by 592

Improved transcriptome assembly using a hybrid of long and short reads with StringTie

Shumate, A., Wong, B. Y., Pertea, G. M., & Pertea, M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009730 · Cited by 488

Rapid and sensitive detection of genome contamination at scale with FCS-GX

Astashyn, A., Tvedte, E. S., Sweeney, D., Sapojnikov, V., Bouk, N., Joukov, V., Mozes, E., Strope, P. K., Sylla, P. M., Wagner, L., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03198-7 · Cited by 502

Examining horizontal gene transfer in microbial communities

Brito, I. (2021) · Nature Reviews Microbiology · doi:10.1038/s41579-021-00534-7 · Cited by 482

Signatures of plasticity, metastasis, and immunosuppression in an atlas of human small cell lung cancer

Chan, J. M., Quintanal-Villalonga, À., Gao, V. R., Xie, Y., Allaj, V., Chaudhary, O., Masilionis, I., Egger, J. V., Chow, A. T., Walle, T., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.09.008 · Cited by 464

Pangenome graph construction from genome alignments with Minigraph-Cactus

Hickey, G., Monlong, J., Ebler, J., Novak, A. M., Eizenga, J. M., Gao, Y., Human Pangenome Reference Consortium, Abel, H. J., Antonacci-Fulton, L. L., Asri, M., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01793-w · Cited by 343

Machine learning in plant science and plant breeding

van Dijk, A. D. J., Kootstra, G., Kruijer, W., & de Ridder, D. (2020) · iScience · doi:10.1016/j.isci.2020.101890 · Cited by 340

plotsr: visualizing structural similarities and rearrangements between multiple genomes

Goel, M., & Schneeberger, K. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac196 · Cited by 328

Transcriptome variation in human tissues revealed by long-read sequencing

Glinos, D. A., Garborcauskas, G., Hoffman, P., Ehsan, N., Jiang, L., Gokden, A., Dai, X., Aguet, F., Brown, K. L., Garimella, K. V., et al. (2022) · Nature · doi:10.1038/s41586-022-05035-y · Cited by 318

Epigenetic patterns in a complete human genome

Gershman, A., Sauria, M. E. G., Guitart, X., Vollger, M. R., Hook, P. W., Hoyt, S. J., Jain, M., Shumate, A., Razaghi, R., Koren, S., et al. (2022) · Science · doi:10.1126/science.abj5089 · Cited by 297

DestVI identifies continuums of cell types in spatial transcriptomics data

Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01272-8 · Cited by 276

Identifying biases and their potential solutions in human microbiome studies

Nearing, J. T., Comeau, A. M., & Langille, M. G. I. (2021) · Microbiome · doi:10.1186/s40168-021-01059-0 · Cited by 240

rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data

Wang, Y., Xie, Z., Kutschera, E., Adams, J. I., Kadash-Edmondson, K. E., & Xing, Y. (2024) · Nature Protocols · doi:10.1038/s41596-023-00944-2 · Cited by 296

Diversity and ecology of protists revealed by metabarcoding

Burki, F., Sandín, M. M., & Jamy, M. (2021) · Current Biology · doi:10.1016/j.cub.2021.07.066 · Cited by 247

Curated variation benchmarks for challenging medically relevant autosomal genes

Wagner, J., Olson, N. D., Harris, L., McDaniel, J., Cheng, H., Fungtammasan, A., Hwang, Y.-C., Gupta, R., Wenger, A. M., Rowell, W. J., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01158-1 · Cited by 241

DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation

Liu, Y., Rosikiewicz, W., Pan, Z., Jillette, N., Wang, P., Taghbalout, A., Foox, J., Mason, C. E., Carroll, M. P., Cheng, A. W., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02510-z · Cited by 260

Semi-automated assembly of high-quality diploid human reference genomes

Jarvis, E. D., Formenti, G., Rhie, A., Guarracino, A., Yang, C., Wood, J., Tracey, A., Thibaud-Nissen, F., Vollger, M. R., Porubsky, D., et al. (2022) · Nature · doi:10.1038/s41586-022-05325-5 · Cited by 209

BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters

Kautsar, S. A., van der Hooft, J. J. J., de Ridder, D., & Medema, M. H. (2021) · GigaScience · doi:10.1093/gigascience/giaa154 · Cited by 235

Third-Generation Sequencing: The Spearhead towards the Radical Transformation of Modern Genomics

Athanasopoulou, K., Boti, M. A., Adamopoulos, P. G., Skourou, P. C., & Scorilas, A. (2021) · Life · doi:10.3390/life12010030 · Cited by 253

A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data

Yang, C., Chowdhury, D., Zhang, Z., Cheung, W. K., Lü, A., Bian, Z., & Zhang, L. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.11.028 · Cited by 242

Multi-omics data integration considerations and study design for biological systems and disease

Graw, S., Chappell, K., Washam, C. L., Gies, A. J., Bird, J. T., Robeson, M. S., & Byrum, S. D. (2020) · Molecular Omics · doi:10.1039/d0mo00041h · Cited by 231

Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing

Tian, L., Jabbari, J. S., Thijssen, R., Gouil, Q. A., Amarasinghe, S. L., Voogd, O., Kariyawasam, H., Du, M. R. M., Schuster, J., Wang, C., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02525-6 · Cited by 215

Chromosome-scale and haplotype-resolved genome assembly of a tetraploid potato cultivar

Sun, H., Jiao, W., Krause, K., Campoy, J. A., Goel, M., Folz‐Donahue, K., Kukat, C., Hüettel, B., & Schneeberger, K. (2022) · Nature Genetics · doi:10.1038/s41588-022-01015-0 · Cited by 221

Current and emerging trends in techniques for plant pathogen detection

Venbrux, M., Crauwels, S., & Rediers, H. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1120968 · Cited by 228

Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities

Chiara, M., D’Erchia, A. M., Gissi, C., Manzari, C., Parisi, A., Resta, N., Zambelli, F., Picardi, E., Pavesi, G., Horner, D. S., et al. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa297 · Cited by 214

Perspectives and Benefits of High-Throughput Long-Read Sequencing in Microbial Ecology

Tedersoo, L., Albertsen, M., Anslan, S., & Callahan, B. J. (2021) · Applied and Environmental Microbiology · doi:10.1128/aem.00626-21 · Cited by 217

Microbiome diversity and dysbiosis in aquaculture

Villamil, S. I., Huerlimann, R., & Jerry, D. R. (2020) · Reviews in Aquaculture · doi:10.1111/raq.12513 · Cited by 211

Chromatin accessibility profiling methods

Minnoye, L., Marinov, G. K., Krausgruber, T., Pan, L., Marand, A. P., Secchia, S., Greenleaf, W. J., Furlong, E. E. M., Zhao, K., Schmitz, R. J., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00008-9 · Cited by 206

Mini review: Genome mining approaches for the identification of secondary metabolite biosynthetic gene clusters in Streptomyces

Lee, N., Hwang, S., Kim, J., Cho, S., Palsson, B. Ø., & Cho, B. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.024 · Cited by 196

Nanopore sequencing and its application to the study of microbial communities

Ciuffreda, L., Rodríguez‐Pérez, H., & Flores, C. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.02.020 · Cited by 209

Benchmarking of Nanopore R10.4 and R9.4.1 flow cells in single-cell whole-genome amplification and whole-genome shotgun sequencing

Ni, Y., Liu, X., Simeneh, Z. M., Yang, M., & Li, R. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.03.038 · Cited by 196

A deep siamese neural network improves metagenome-assembled genomes in microbiome datasets across different environments

Pan, S., Zhu, C., Zhao, X., & Coelho, L. P. (2022) · Nature Communications · doi:10.1038/s41467-022-29843-y · Cited by 203

Discovering multiple types of DNA methylation from bacteria and microbiome using nanopore sequencing

Tourancheau, A., Mead, E. A., Zhang, X., & Fang, G. (2021) · Nature Methods · doi:10.1038/s41592-021-01109-3 · Cited by 192

The application of long-read sequencing in clinical settings

Oehler, J. B., Wright, H., Stark, Z., Mallett, A. J., & Schmitz, U. (2023) · Human Genomics · doi:10.1186/s40246-023-00522-3 · Cited by 189

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Gaudêncio, S. P., Bayram, E., Bilela, L. L., Cueto, M., Díaz‐Marrero, A. R., HAZNEDAROĞLU, B. Z., Jiménez, C., Mandalakis, M., Pereira, F., Reyes, F., et al. (2023) · Marine Drugs · doi:10.3390/md21050308 · Cited by 209

Illuminating the dark side of the human transcriptome with long read transcript sequencing

Kuo, R. I., Cheng, Y., Zhang, R., Brown, J. W. S., Smith, J., Archibald, A. L., & Burt, D. W. (2020) · BMC Genomics · doi:10.1186/s12864-020-07123-7 · Cited by 171

Detection of aberrant splicing events in RNA-seq data using FRASER

Mertes, C., Scheller, I. F., Yépez, V. A., Çelik, M. H., Liang, Y., Kremer, L. S., Gušić, M., Prokisch, H., & Gagneur, J. (2021) · Nature Communications · doi:10.1038/s41467-020-20573-7 · Cited by 187

Full-length transcript sequencing of human and mouse cerebral cortex identifies widespread isoform diversity and alternative splicing

Leung, S. K., Jeffries, A. R., Castanho, I., Jordan, B. T., Moore, K., Davies, J. P., Dempster, E. L., Bray, N. J., O’Neill, P., Tseng, E., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110022 · Cited by 178

A simple guide to de novo transcriptome assembly and annotation

Raghavan, V., Kraft, L., Mesny, F., & Rigerte, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab563 · Cited by 191

Single-cell RNA sequencing reveals SARS-CoV-2 infection dynamics in lungs of African green monkeys

Speranza, E. E., Williamson, B. N., Feldmann, F., Sturdevant, G. L., Pérez-Pérez, L., Meade-White, K. D., Smith, B. J., Lovaglio, J., Martens, C., Munster, V. J., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abe8146 · Cited by 160

Features and applications of haplotypes in crop breeding

Bhat, J. A., Yu, D., Bohra, A., Ganie, S. A., & Varshney, R. K. (2021) · Communications Biology · doi:10.1038/s42003-021-02782-y · Cited by 182

The giant diploid faba genome unlocks variation in a global protein crop

Jayakodi, M., Golicz, A. A., Kreplak, J., Fechete, L. I., Angra, D., Bednář, P., Bornhofen, E., Zhang, H., Boussageon, R., Kaur, S., et al. (2023) · Nature · doi:10.1038/s41586-023-05791-5 · Cited by 170

Marine mammal conservation: over the horizon

Nelms, S. E., Alfaro‐Shigueto, J., Arnould, J., Ávila, I. C., Nash, S. B., Campbell, E., Carter, M., Collins, T., Currey, R. J. C., Domit, C., et al. (2021) · Endangered Species Research · doi:10.3354/esr01115 · Cited by 182

Toward a genome sequence for every animal: Where are we now?

Hotaling, S., Kelley, J. L., & Frandsen, P. B. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109019118 · Cited by 167

Cobolt: integrative analysis of multimodal single-cell sequencing data

Gong, B., Zhou, Y., & Purdom, E. (2021) · Genome Biology · doi:10.1186/s13059-021-02556-z · Cited by 168

Long-Read DNA Sequencing: Recent Advances and Remaining Challenges

Warburton, P. E., & Sebra, R. P. (2023) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-101722-103045 · Cited by 163

Plant pan-genomics and its applications

Shi, J., Tian, Z., Lai, J., & Huang, X. (2022) · Molecular Plant · doi:10.1016/j.molp.2022.12.009 · Cited by 164

isoCirc catalogs full-length circular RNA isoforms in human transcriptomes

Xin, R., Gao, Y. P., Gao, Y., Wang, R., Kadash-Edmondson, K. E., Liu, B., Wang, Y., Lin, L., & Xing, Y. (2021) · Nature Communications · doi:10.1038/s41467-020-20459-8 · Cited by 169

Integrating spatial transcriptomics data across different conditions, technologies and developmental stages

Zhou, X., Dong, K., & Zhang, S. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00528-w · Cited by 160

Determining Hosts of Antibiotic Resistance Genes: A Review of Methodological Advances

Rice, E. W., Wang, P., Smith, A. L., & Stadler, L. B. (2020) · Environmental Science & Technology Letters · doi:10.1021/acs.estlett.0c00202 · Cited by 159

The role of alternative splicing in adaptation and evolution

Verta, J., & Jacobs, A. (2021) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2021.11.010 · Cited by 152

Complete sequencing of ape genomes

Yoo, D., Rhie, A., Hebbar, P., Antonacci, F., Logsdon, G. A., Solar, S. J., Antipov, D., Pickett, B. D., Safonova, Y., Montinaro, F., et al. (2025) · Nature · doi:10.1038/s41586-025-08816-3 · Cited by 171

Comparison of Transformations for Single-Cell RNA-Seq Data

Ahlmann-Eltze, C., & Huber, W. (2021) · doi:10.1101/2021.06.24.449781 · Cited by 148

pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq preprocessing tools

Germain, P., Sonrel, A., & Robinson, M. D. (2020) · Genome biology · doi:10.1186/s13059-020-02136-7 · Cited by 157

RNA-seq data science: From raw data to effective interpretation

Deshpande, D., Chhugani, K., Chang, Y., Karlsberg, A., Loeffler, C., Zhang, J., Muszyńska, A., Munteanu, V., Yang, H. T., Rotman, J., et al. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.997383 · Cited by 158

In vivo hypermutation and continuous evolution

Molina, R. S., Rix, G., Mengiste, A. A., Álvarez, B., Seo, D., Chen, H., Hurtado, J. E., Zhang, Q., García-García, J. D., Heins, Z., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00119-5 · Cited by 144

PBSIM2: a simulator for long-read sequencers with a novel generative model of quality scores

Ono, Y., Asai, K., & Hamada, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa835 · Cited by 140

Evolutionary and functional genomics of DNA methylation in maize domestication and improvement

Xu, G., Lyu, J., Li, Q., Liu, H., Wang, D.-F., Zhang, M., Springer, N., Ross-Ibarra, J., & Yang, J.-L. (2020) · Nature Communications · doi:10.1038/s41467-020-19333-4 · Cited by 109

EnvRtype : a software to interplay enviromics and quantitative genomics in agriculture

Costa‐Neto, G., Galli, G., Carvalho, H. F., Crossa, J. L., & Fritsche‐Neto, R. (2021) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkab040 · Cited by 151

SARS-CoV-2 growth, furin-cleavage-site adaptation and neutralization using serum from acutely infected hospitalized COVID-19 patients

Klimstra, W. B., Tilston-Lunel, N. L., Nambulli, S., Boslett, J., McMillen, C. M., Gilliland, T., Dunn, M. D., Sun, C., Wheeler, S. E., Wells, A., et al. (2020) · Journal of General Virology · doi:10.1099/jgv.0.001481 · Cited by 137

Metatranscriptomics for the Human Microbiome and Microbial Community Functional Profiling

Zhang, Y., Thompson, K. N., Branck, T. A., Yan, Y., Nguyen, L. H., Franzosa, E. A., & Huttenhower, C. (2021) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-031121-103035 · Cited by 150

Genetic testing and counseling for the unexplained epilepsies: An evidence‐based practice guideline of the National Society of Genetic Counselors

Smith, L. A., Malinowski, J. R., Ceulemans, S., Peck, K., Walton, N., Sheidley, B. R., & Lippa, N. C. (2022) · Journal of Genetic Counseling · doi:10.1002/jgc4.1646 · Cited by 133

Integrating multi-omics data for crop improvement

Scossa, F., Alseekh, S., & Fernie, A. R. (2020) · Journal of Plant Physiology · doi:10.1016/j.jplph.2020.153352 · Cited by 134

An Introduction to Nanopore Sequencing: Past, Present, and Future Considerations

Mackenzie, M. E., & Argyropoulos, C. (2023) · Micromachines · doi:10.3390/mi14020459 · Cited by 159

A survey of k-mer methods and applications in bioinformatics

Moeckel, C., Mareboina, M., Konnaris, M. A., Chan, C. S. Y., Mouratidis, I., Montgomery, A., Chantzi, N., Pavlopoulos, G. A., & Georgakopoulos-Soares, I. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.05.025 · Cited by 131

Unraveling metagenomics through long-read sequencing: a comprehensive review

Kim, C., Pongpanich, M., & Porntaveetus, T. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-04917-1 · Cited by 129

Integrated multi-omics analyses of microbial communities: a review of the current state and future directions

Arıkan, M., & Muth, T. (2023) · Molecular Omics · doi:10.1039/d3mo00089c · Cited by 133

Advances in optical mapping for genomic research

Yuan, Y., Chung, C., & Chan, T. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.07.018 · Cited by 121

Population-level integration of single-cell datasets enables multi-scale analysis across samples

Donno, C. D., Hediyeh-zadeh, S., Moinfar, A. A., Wagenstetter, M., Zappia, L., Lotfollahi, M., & Theis, F. J. (2023) · Nature Methods · doi:10.1038/s41592-023-02035-2 · Cited by 136

Multi-omic integration of microbiome data for identifying disease-associated modules

Muller, E., Shiryan, I., & Borenstein, E. (2024) · Nature Communications · doi:10.1038/s41467-024-46888-3 · Cited by 145

Computational analysis of cancer genome sequencing data

Cortés‐Ciriano, I., Gulhan, D., Lee, J. J.-K., Melloni, G. E. M., & Park, P. J. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00431-y · Cited by 118

Systematic comparison of tools used for m6A mapping from nanopore direct RNA sequencing

Zhong, Z.-D., Xie, Y.-Y., Chen, H.-X., Lan, Y.-L., Liu, X., Ji, J.-Y., Wu, F., Jin, L., Chen, J., Mak, D. W., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37596-5 · Cited by 119

Genome-wide detection of cytosine methylations in plant from Nanopore data using deep learning

Ni, P., Huang, N., Nie, F., Zhang, J., Zhang, Z., Wu, B. M., Bai, L., Liu, W., Xiao, C., Luo, F., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26278-9 · Cited by 128

GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis

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Single-cell transcriptomes of developing and adult olfactory receptor neurons in Drosophila

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