HTSeq—a Python framework to work with high-throughput sequencing data

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

Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106

Trimmomatic: a flexible trimmer for Illumina sequence data

Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548

The Sequence Alignment/Map format and SAMtools

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041

<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

BEDTools: a flexible suite of utilities for comparing genomic features

Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437

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

Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219

Differential expression analysis for sequence count data

Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581

Bioconductor: open software development for computational biology and bioinformatics

Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 12,082

The NumPy Array: A Structure for Efficient Numerical Computation

van der Walt, S., Colbert, S. C., & Varoquaux, G. (2011) · Computing in Science & Engineering · doi:10.1109/mcse.2011.37 · Cited by 9,381

Biopython: freely available python tools for computational molecular biology and bioinformatics

Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp163 · Cited by 6,080

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

Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106

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

Two new Later Stone Age sites from the Final Pleistocene in the Falémé Valley, eastern Senegal

Ndiaye, M., Lespez, L., Tribolo, C., Rassé, M., Hadjas, I., Davidoux, S., Huysecom, É., & Douze, K. (2024) · Monash University Research Portal (Monash University) · doi:10.3929/ethz-b-000667478 · Cited by 24,214

Near-optimal probabilistic RNA-seq quantification

Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723

The single-cell transcriptional landscape of mammalian organogenesis

Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490

Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

Soneson, C., Love, M. I., & Robinson, M. D. (2015) · F1000Research · doi:10.12688/f1000research.7563.1 · Cited by 3,929

Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834

A pathology atlas of the human cancer transcriptome

Uhlen, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., Benfeitas, R., Arif, M., Liu, Z., Edfors, F., et al. (2017) · Science · doi:10.1126/science.aan2507 · Cited by 3,371

Recognition of RNA N6-methyladenosine by IGF2BP proteins enhances mRNA stability and translation

Huang, H., Weng, H., Sun, W., Qin, X., Shi, H., Wu, H., Zhao, B. S., Mesquita, A., Liu, C., Yuan, C. L., et al. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0045-z · Cited by 2,997

The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads

Liao, Y., Smyth, G. K., & Shi, W. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz114 · Cited by 2,878

A survey of best practices for RNA-seq data analysis

Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794

B cells and tertiary lymphoid structures promote immunotherapy response

Helmink, B. A., Reddy, S. M., Gao, J., Zhang, S., Basar, R., Thakur, R., Yizhak, K., Sade-Feldman, M., Blando, J., Han, G., et al. (2020) · Nature · doi:10.1038/s41586-019-1922-8 · Cited by 2,731

Single-cell reconstruction of the early maternal–fetal interface in humans

Vento-Tormo, R., Efremova, M., Botting, R. A., Turco, M. Y., Vento-Tormo, M., Meyer, K. B., Park, J.-E., Stephenson, E., Polański, K., Goncalves, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0698-6 · Cited by 2,358

A circadian gene expression atlas in mammals: Implications for biology and medicine

Zhang, R., Lahens, N. F., Ballance, H. I., Hughes, M. E., & Hogenesch, J. B. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408886111 · Cited by 2,304

Succinate Dehydrogenase Supports Metabolic Repurposing of Mitochondria to Drive Inflammatory Macrophages

Mills, E. L., Kelly, B., Logan, A., Costa, A. S. H., Varma, M., Bryant, C. E., Tourlomousis, P., Däbritz, J. H. M., Gottlieb, E., Latorre, I., et al. (2016) · Cell · doi:10.1016/j.cell.2016.08.064 · Cited by 2,291

Fusobacterium nucleatum Promotes Chemoresistance to Colorectal Cancer by Modulating Autophagy

Yu, T., Guo, F., Yu, Y., Sun, T., Ma, D., Han, J., Qian, Y., Kryczek, I., Sun, D., Nagarsheth, N., et al. (2017) · Cell · doi:10.1016/j.cell.2017.07.008 · Cited by 2,198

RNA sequencing: the teenage years

Stark, R., Grzelak, M., & Hadfield, J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0150-2 · Cited by 2,041

A living biobank of breast cancer organoids captures disease heterogeneity

Sachs, N., de Ligt, J., Kopper, O., Gogola, E., Bounova, G., Weeber, F., Balgobind, A. V., Wind, K., Gracanin, A., Begthel, H., et al. (2018) · Cell · doi:10.1016/j.cell.2017.11.010 · Cited by 1,973

Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences

Cox, L. M., Yamanishi, S., Sohn, J., Alekseyenko, A. V., Leung, J. M., Cho, I., Kim, S. G., Li, H., Gao, Z., Mahana, D., et al. (2014) · Cell · doi:10.1016/j.cell.2014.05.052 · Cited by 1,859

Scater: Pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw777 · Cited by 1,852

Astrocyte scar formation aids central nervous system axon regeneration

Anderson, M. A., Burda, J. E., Ren, Y., Ao, Y., O’Shea, T. M., Kawaguchi, R., Coppola, G., Khakh, B. S., Deming, T. J., & Sofroniew, M. V. (2016) · Nature · doi:10.1038/nature17623 · Cited by 1,851

Translation of CircRNAs

Pamudurti, N. R., Bartok, O., Jens, M., Ashwal-Fluss, R., Stottmeister, C., Ruhe, L., Hanan, M., Wyler, E., Perez-Hernandez, D., Ramberger, E., et al. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.02.021 · Cited by 1,810

Determinants of response and resistance to CD19 chimeric antigen receptor (CAR) T cell therapy of chronic lymphocytic leukemia

Fraietta, J. A., Lacey, S. F., Orlando, E. J., Pruteanu-Malinici, I., Gohil, M., Lundh, S., Boesteanu, A. C., Wang, Y., O’Connor, R. S., Hwang, W.-T., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0010-1 · Cited by 1,785

Dietary Fiber-Induced Improvement in Glucose Metabolism Is Associated with Increased Abundance of Prevotella

Kovatcheva-Datchary, P., Nilsson, A., Akrami, R., Lee, Y. S., De Vadder, F., Arora, T., Hallen, A., Martens, E., Björck, I., & Bäckhed, F. (2015) · Cell Metabolism · doi:10.1016/j.cmet.2015.10.001 · Cited by 1,731

Intratumoral Tcf1+PD-1+CD8+ T Cells with Stem-like Properties Promote Tumor Control in Response to Vaccination and Checkpoint Blockade Immunotherapy

Siddiqui, I., Schaeuble, K., Chennupati, V., Fuertes Marraco, S. A., Calderon-Copete, S., Pais Ferreira, D., Carmona, S. J., Scarpellino, L., Gfeller, D., Pradervand, S., et al. (2019) · Immunity · doi:10.1016/j.immuni.2018.12.021 · Cited by 1,679

Chromosomal instability drives metastasis through a cytosolic DNA response

Bakhoum, S. F., Ngo, B., Laughney, A. M., Cavallo, J.-A., Murphy, C. J., Ly, P., Shah, P., Sriram, R. K., Watkins, T. B. K., Taunk, N. K., et al. (2018) · Nature · doi:10.1038/nature25432 · Cited by 1,593

m 6 A mRNA methylation facilitates resolution of naïve pluripotency toward differentiation

Geula, S., Moshitch-Moshkovitz, S., Dominissini, D., Mansour, A. A., Kol, N., Salmon‐Divon, M., Hershkovitz, V., Peer, E., Mor, N., Manor, Y. S., et al. (2015) · Science · doi:10.1126/science.1261417 · Cited by 1,583

Identification of the tumour transition states occurring during EMT

Pastushenko, I., Brisebarre, A., Sifrim, A., Fioramonti, M., Revenco, T., Boumahdi, S., Keymeulen, A. V., Brown, D. V., Moers, V., Lemaire, S., et al. (2018) · Nature · doi:10.1038/s41586-018-0040-3 · Cited by 1,556

Using clusterProfiler to characterize multiomics data

Xu, S., Hu, E., Cai, Y., Xie, Z., Luo, X. M., Zhan, L., Tang, W., Wang, Q., Liu, B., Wang, R., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-01020-z · Cited by 1,528

Designer matrices for intestinal stem cell and organoid culture

Gjorevski, N., Sachs, N., Manfrin, A., Giger, S., Bragina, M. E., Ordóñez-Morán, P., Clevers, H., & Lutolf, M. P. (2016) · Nature · doi:10.1038/nature20168 · Cited by 1,426

Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402

Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data

Racle, J., de Jonge, K., Baumgaertner, P., Speiser, D. E., & Gfeller, D. (2017) · eLife · doi:10.7554/elife.26476 · Cited by 1,389

Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma

Cloughesy, T. F., Mochizuki, A. Y., Orpilla, J. R., Hugo, W., Lee, A. H., Davidson, T. B., Wang, A. C., Ellingson, B. M., Rytlewski, J. A., Sanders, C. M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0337-7 · Cited by 1,382

SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data

Varet, H., Brillet-Guéguen, L., Coppée, J., & Dillies, M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157022 · Cited by 1,375

Transcriptomics technologies

Lowe, R. G. T., Shirley, N. J., Bleackley, M. R., Dolan, S., & Shafee, T. M. A. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005457 · Cited by 1,307

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

Loss of a mammalian circular RNA locus causes miRNA deregulation and affects brain function

Piwecka, M., Glažar, P., Hernandez-Miranda, L. R., Memczak, S., Wolf, S. A., Rybak-Wolf, A., Filipchyk, A., Klironomos, F., Cerda Jara, C. A., Fenske, P., et al. (2017) · Science · doi:10.1126/science.aam8526 · Cited by 1,210

Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis

Reyfman, P. A., Walter, J. M., Joshi, N., Anekalla, K. R., McQuattie-Pimentel, A. C., Chiu, S., Fernandez, R., Akbarpour, M., Chen, C.-I., Ren, Z., et al. (2019) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201712-2410oc · Cited by 1,203

Two independent modes of chromatin organization revealed by cohesin removal

Schwarzer, W., Abdennur, N., Goloborodko, A., Pekowska, A., Fudenberg, G., Loe-Mie, Y., Fonseca, N. A., Huber, W., Haering, C. H., Mirny, L., et al. (2017) · Nature · doi:10.1038/nature24281 · Cited by 1,196

Computational and analytical challenges in single-cell transcriptomics

Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170

High-definition spatial transcriptomics for in situ tissue profiling

Vickovic, S., Eraslan, G., Salmén, F., Klughammer, J., Stenbeck, L., Schapiro, D., Äijö, T., Bonneau, R., Bergenstråhle, L., Navarro, J. F., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0548-y · Cited by 1,150

Analysing high-throughput sequencing data in Python with HTSeq 2.0

Putri, G., Anders, S., Pyl, P. T., Pimanda, J. E., & Zanini, F. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac166 · Cited by 1,150

5-methylcytosine promotes mRNA export — NSUN2 as the methyltransferase and ALYREF as an m5C reader

Yang, X., Yang, Y., Sun, B., Chen, Y., Xu, J., Lai, W., Li, A., Wang, X., Bhattarai, D. P., Xiao, W., et al. (2017) · Cell Research · doi:10.1038/cr.2017.55 · Cited by 1,141

Developmental Heterogeneity of Microglia and Brain Myeloid Cells Revealed by Deep Single-Cell RNA Sequencing

Li, Q., Cheng, Z., Zhou, L., Darmanis, S., Neff, N. F., Okamoto, J., Gulati, G., Bennett, M. L., Sun, L. O., Clarke, L. E., et al. (2019) · Neuron · doi:10.1016/j.neuron.2018.12.006 · Cited by 1,129

CEL-seq2: sensitive highly-multiplexed single-cell RNA-Seq

Hashimshony, T., Senderovich, N., Avital, G., Klochendler, A., de Leeuw, Y., Anavy, L., Gennert, D., Li, S., Livak, K. J., Rozenblatt-Rosen, O., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0938-8 · Cited by 1,125

Gene co-expression analysis for functional classification and gene–disease predictions

van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 995

Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy

Gide, T. N., Quek, C., Menzies, A. M., Tasker, A. T., Shang, P., Holst, J., Madore, J., Lim, S. Y., Velickovic, R., Wongchenko, M., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.01.003 · Cited by 1,047

The dynamic N1-methyladenosine methylome in eukaryotic messenger RNA

Dominissini, D., Nachtergaele, S., Moshitch-Moshkovitz, S., Peer, E., Kol, N., Ben-Haim, M. S., Dai, Q., Segni, A. D., Salmon‐Divon, M., Clark, W. C., et al. (2016) · Nature · doi:10.1038/nature16998 · Cited by 1,047

Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas

Moncada, R., Barkley, D., Wagner, F., Chiodin, M., Devlin, J. C., Baron, M., Hajdu, C. H., Simeone, D. M., & Yanai, I. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0392-8 · Cited by 1,045

Cellular senescence drives age-dependent hepatic steatosis

Ogrodnik, M., Miwa, S., Tchkonia, T., Tiniakos, D., Wilson, C. L., Lahat, A., Day, C. P., Burt, A., Palmer, A., Anstee, Q. M., et al. (2017) · Nature Communications · doi:10.1038/ncomms15691 · Cited by 1,044

A single-cell molecular map of mouse gastrulation and early organogenesis

Pijuan-Sala, B., Griffiths, J. A., Guibentif, C., Hiscock, T. W., Jawaid, W., Calero-Nieto, F. J., Mulas, C., Ibarra-Soria, X., Tyser, R. C. V., Ho, D. L. L., et al. (2019) · Nature · doi:10.1038/s41586-019-0933-9 · Cited by 1,033

Macrophages Facilitate Electrical Conduction in the Heart

Hulsmans, M., Clauss, S., Xiao, L., Aguirre, A. D., King, K. R., Hanley, A., Hucker, W. J., Wülfers, E. M., Seemann, G., Courties, G., et al. (2017) · Cell · doi:10.1016/j.cell.2017.03.050 · Cited by 1,011

Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma

Braun, D. A., Hou, Y., Bakouny, Z., Ficial, M., Sant’ Angelo, M., Forman, J., Ross-Macdonald, P., Berger, A. C., Jegede, O. A., Elagina, L., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0839-y · Cited by 992

Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition

Hata, A. N., Niederst, M. J., Archibald, H. L., Gomez-Caraballo, M., Siddiqui, F. M., Mulvey, H. E., Maruvka, Y. E., Ji, F., Bhang, H.-E. C., Krishnamurthy Radhakrishna, V., et al. (2016) · Nature Medicine · doi:10.1038/nm.4040 · Cited by 985

Negative feedback control of neuronal activity by microglia

Badimon, A., Strasburger, H. J., Ayata, P., Chen, X., Nair, A., Ikegami, A., Hwang, P., Chan, A. T., Graves, S. M., Uweru, J. O., et al. (2020) · Nature · doi:10.1038/s41586-020-2777-8 · Cited by 979

p53 inhibits CRISPR–Cas9 engineering in human pluripotent stem cells

Ihry, R. J., Worringer, K. A., Salick, M. R., Frias, E., Ho, D., Theriault, K., Kommineni, S., Chen, J., Sondey, M., Ye, C., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0050-6 · Cited by 978

RNA editing by ADAR1 prevents MDA5 sensing of endogenous dsRNA as nonself

Liddicoat, B., Piskol, R., Chalk, A. M., Ramaswami, G., Higuchi, M., Hartner, J. C., Li, J. B., Seeburg, P. H., & Walkley, C. R. (2015) · Science · doi:10.1126/science.aac7049 · Cited by 977

m6A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways

Li, H., Tong, J., Zhu, S., Batista, P. J., Duffy, E. E., Zhao, J., Bailis, W., Cao, G., Kroehling, L., Chen, Y. G., et al. (2017) · Nature · doi:10.1038/nature23450 · Cited by 974

The landscape of kinase fusions in cancer

Stransky, N., Cerami, E., Schalm, S., Kim, J. L., & Lengauer, C. (2014) · Nature Communications · doi:10.1038/ncomms5846 · Cited by 968

How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

Schurch, N. J., Schofield, P., Gierliński, M., Cole, C., Sherstnev, A., Singh, V., Wrobel, N., Gharbi, K., Simpson, G. G., Owen-Hughes, T., et al. (2016) · RNA · doi:10.1261/rna.053959.115 · Cited by 966

Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures

Şenbabaoğlu, Y., Gejman, R. S., Winer, A. G., Liu, M., Allen, E. M. V., de Velasco, G., Miao, D., Ostrovnaya, I., Drill, E. N., Luna, A., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1092-z · Cited by 969

Resetting Transcription Factor Control Circuitry toward Ground-State Pluripotency in Human

Takashima, Y., Guo, G., Loos, R., Nichols, J., Ficz, G., Krueger, F., Oxley, D., Santos, F., Clarke, J., Mansfield, W. M., et al. (2014) · Cell · doi:10.1016/j.cell.2014.08.029 · Cited by 959

Root microbiota drive direct integration of phosphate stress and immunity

Castrillo, G., Teixeira, P. J. P. L., Paredes, S. H., Law, T. F., de Lorenzo, L., Feltcher, M. E., Finkel, O. M., Breakfield, N. W., Mieczkowski, P. A., Jones, C. D., et al. (2017) · Nature · doi:10.1038/nature21417 · Cited by 962

edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets

Chen, Y., Chen, L., Lun, A. T. L., Baldoni, P. L., & Smyth, G. K. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf018 · Cited by 951

Human Tumor-Associated Macrophage and Monocyte Transcriptional Landscapes Reveal Cancer-Specific Reprogramming, Biomarkers, and Therapeutic Targets

Cassetta, L., Fragkogianni, S., Sims, A. H., Swierczak, A., Forrester, L. M., Zhang, H., Soong, D. Y. H., Cotechini, T., Anur, P., Lin, E. Y., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.02.009 · Cited by 946

Joint profiling of chromatin accessibility and gene expression in thousands of single cells

Cao, J., Cusanovich, D. A., Ramani, V., Aghamirzaie, D., Pliner, H. A., Hill, A. J., Daza, R. M., McFaline-Figueroa, J. L., Packer, J. S., Christiansen, L., et al. (2018) · Science · doi:10.1126/science.aau0730 · Cited by 938

Multi-stage Differentiation Defines Melanoma Subtypes with Differential Vulnerability to Drug-Induced Iron-Dependent Oxidative Stress

Tsoi, J., Robert, L., Paraiso, K., Galvan, C., Sheu, K. M., Lay, J., Wong, D. J. L., Atefi, M., Shirazi, R., Wang, X., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.03.017 · Cited by 938

Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer

Lee, H., Hong, Y., Etlioglu, H. E., Cho, Y. B., Pomella, V., den Bosch, B. V., Vanhecke, J., Verbandt, S., Hong, H.-K., Min, J.-W., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0636-z · Cited by 934

Human haematopoietic stem cell lineage commitment is a continuous process

Velten, L., Haas, S. F., Raffel, S., Blaszkiewicz, S., Islam, S., Hennig, B. P., Hirche, C., Lutz, C., Buss, E. C., Nowak, D., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3493 · Cited by 925

TOX reinforces the phenotype and longevity of exhausted T cells in chronic viral infection

Alfei, F., Kanev, K., Hofmann, M., Wu, M., Ghoneim, H. E., Roelli, P., Utzschneider, D. T., von Hoesslin, M., Cullen, J. G., Fan, Y., et al. (2019) · Nature · doi:10.1038/s41586-019-1326-9 · Cited by 930

The genomic landscape of pediatric and young adult T-lineage acute lymphoblastic leukemia

Liu, Y., Easton, J., Shao, Y., Maciaszek, J., Wang, Z., Wilkinson, M. R., McCastlain, K., Edmonson, M., Pounds, S. B., Shi, L., et al. (2017) · Nature Genetics · doi:10.1038/ng.3909 · Cited by 918

Indoleacrylic Acid Produced by Commensal Peptostreptococcus Species Suppresses Inflammation

Wlodarska, M., Luo, C., Kolde, R., d'Hennezel, E. B., Annand, J. W., Heim, C. E., Krastel, P., Schmitt, E. K., Omar, A. S., Creasey, E. A., et al. (2017) · Cell Host & Microbe · doi:10.1016/j.chom.2017.06.007 · Cited by 918

Bacterial metabolism of bile acids promotes generation of peripheral regulatory T cells

Campbell, C., McKenney, P. T., Konstantinovsky, D., Isaeva, O. I., Schizas, M., Verter, J., Mai, C., Jin, W.-B., Guo, C.-J., Violante, S., et al. (2020) · Nature · doi:10.1038/s41586-020-2193-0 · Cited by 901

Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization

Baccin, C., Al-Sabah, J., Velten, L., Helbling, P. M., Grünschläger, F., Hernández-Malmierca, P., Nombela‐Arrieta, C., Steinmetz, L. M., Trumpp, A., & Haas, S. F. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0439-6 · Cited by 894

Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression

Schmiedel, B. J., Singh, D., Madrigal, A., Valdovino-Gonzalez, A. G., White, B. M., Zapardiel-Gonzalo, J., Ha, B., Altay, G., Greenbaum, J. A., McVicker, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.022 · Cited by 867

ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases

Shen, L., Shao, N., Liu, X., & Nestler, E. J. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-284 · Cited by 866

Acetylation of Cytidine in mRNA Promotes Translation Efficiency

Arango, D., Sturgill, D., Alhusaini, N., Dillman, A. A., Sweet, T. J., Hanson, G., Hosogane, M., Sinclair, W. R., Nanan, K. K., Mandler, M. D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.030 · Cited by 862

Gene expression across mammalian organ development

Cardoso-Moreira, M., Halbert, J., Valloton, D., Velten, B., Chen, C., Shao, Y., Liechti, A., Ascenção, K., Rummel, C., Ovchinnikova, S., et al. (2019) · Nature · doi:10.1038/s41586-019-1338-5 · Cited by 838

An organoid platform for ovarian cancer captures intra- and interpatient heterogeneity

Kopper, O., de Witte, C. J., Lõhmussaar, K., Valle-Inclan, J. E., Hami, N., Kester, L., Balgobind, A. V., Korving, J., Proost, N., Begthel, H., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0422-6 · Cited by 834

Multi-omics analysis identifies ATF4 as a key regulator of the mitochondrial stress response in mammals

Quirós, P. M., Prado, M. A., Zamboni, N., D’Amico, D., Williams, R. W., Finley, D., Gygi, S. P., & Auwerx, J. (2017) · Journal of Cell Biology · doi:10.1083/jcb.201702058 · Cited by 832

Th17 cells transdifferentiate into regulatory T cells during resolution of inflammation

Gagliani, N., Vesely, M. C. A., Iseppon, A., Brockmann, L., Xu, H. Y., Palm, N. W., de Zoete, M. R., Licona-Limón, P., Paiva, R., Ching, T. H., et al. (2015) · Nature · doi:10.1038/nature14452 · Cited by 830

Major Impacts of Widespread Structural Variation on Gene Expression and Crop Improvement in Tomato

Alonge, M., Wang, X., Benoit, M., Soyk, S., Pereira, L., Zhang, L., Suresh, H., Ramakrishnan, S., Maumus, F., Ciren, D., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.021 · Cited by 824

E-cadherin is required for metastasis in multiple models of breast cancer

Padmanaban, V., Krol, I., Suhail, Y., Szczerba, B. M., Aceto, N., Bader, J. S., & Ewald, A. J. (2019) · Nature · doi:10.1038/s41586-019-1526-3 · Cited by 818

5-methylcytosine promotes pathogenesis of bladder cancer through stabilizing mRNAs

Chen, X., Li, A., Sun, B.-F., Yang, Y., Han, Y.-N., Yuan, X., Chen, R.-X., Wei, W.-S., Liu, Y., Gao, C., et al. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0361-y · Cited by 827

Integrative functional genomic analysis of human brain development and neuropsychiatric risks

Li, M., Santpere, G., Imamura Kawasawa, Y., Evgrafov, O. V., Gulden, F. O., Pochareddy, S., Sunkin, S. M., Li, Z., Shin, Y., Zhu, Y., et al. (2018) · Science · doi:10.1126/science.aat7615 · Cited by 810

Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities

Vasaikar, S., Huang, C., Wang, X., Petyuk, V. A., Savage, S. R., Wen, B., Dou, Y., Zhang, Y., Shi, Z., Arshad, O. A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.03.030 · Cited by 799

Metabolic network analysis reveals microbial community interactions in anammox granules

Lawson, C. E., Wu, S., Bhattacharjee, A. S., Hamilton, J. J., McMahon, K. D., Goel, R. K., & Noguera, D. R. (2017) · Nature Communications · doi:10.1038/ncomms15416 · Cited by 794

The cohesin release factor WAPL restricts chromatin loop extension

Haarhuis, J. H. I., van der Weide, R. H., Blomen, V. A., Yáñez-Cuna, J. O., Amendola, M., van Ruiten, M. S., Krijger, P. H. L., Teunissen, H., Medema, R. H., van Steensel, B., et al. (2017) · Cell · doi:10.1016/j.cell.2017.04.013 · Cited by 793

Impaired HLA Class I Antigen Processing and Presentation as a Mechanism of Acquired Resistance to Immune Checkpoint Inhibitors in Lung Cancer

Gettinger, S., Choi, J., Hastings, K., Truini, A., Datar, I., Sowell, R., Wurtz, A., Dong, W., Cai, G., Melnick, M. A., et al. (2017) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-0593 · Cited by 787

Aerobic glycolysis promotes T helper 1 cell differentiation through an epigenetic mechanism

Peng, M., Yin, N., Chhangawala, S., Xu, K., Leslie, C. S., & Li, M. O. (2016) · Science · doi:10.1126/science.aaf6284 · Cited by 787

Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package

Tarazona, S., Furió‐Tarí, P., Turrà, D., Pietro, A. D., Nueda, M. J., Ferrer, A., & Conesa, A. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv711 · Cited by 787

The Stress Granule Transcriptome Reveals Principles of mRNA Accumulation in Stress Granules

Khong, A., Matheny, T., Jain, S., Mitchell, S. F., Wheeler, J. R., & Parker, R. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.10.015 · Cited by 787

Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets

Zhao, J. H., Stacey, D., Eriksson, N., Macdonald-Dunlop, E., Hedman, Å. K., Kalnapenkis, A., Enroth, S., Cozzetto, D., Digby-Bell, J. L., Marten, J., et al. (2023) · Nature Immunology · doi:10.1038/s41590-023-01588-w · Cited by 781

An atlas of genetic influences on osteoporosis in humans and mice

23andMe Research Team, Morris, J. A., Kemp, J. P., Youlten, S. E., Laurent, L., Logan, J. G., Chai, R. C., Vulpescu, N. A., Forgetta, V., Kleinman, A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0302-x · Cited by 773

Neural Circuit-Specialized Astrocytes: Transcriptomic, Proteomic, Morphological, and Functional Evidence

Chai, H., Diaz-Castro, B., Shigetomi, E., Monte, E., Octeau, J. C., Yu, X., Cohn, W., Rajendran, P. S., Vondriska, T. M., Whitelegge, J. P., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.029 · Cited by 769

variancePartition: interpreting drivers of variation in complex gene expression studies

Hoffman, G. E., & Schadt, E. E. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1323-z · Cited by 769

A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation

Nestorowa, S., Hamey, F. K., Pijuan Sala, B., Diamanti, E., Shepherd, M., Laurenti, E., Wilson, N. K., Kent, D. G., & Göttgens, B. (2016) · Blood · doi:10.1182/blood-2016-05-716480 · Cited by 768

mRNA circularization by METTL3–eIF3h enhances translation and promotes oncogenesis

Choe, J., Lin, S., Zhang, W. C., Liu, Q., Wang, L., Ramírez-Moya, J., Du, P., Kim, W., Tang, S., Piotr Sliz, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0538-8 · Cited by 766

A human cell atlas of fetal gene expression

Cao, J., O’Day, D. R., Pliner, H. A., Kingsley, P. D., Deng, M., Daza, R. M., Zager, M. A., Aldinger, K. A., Blecher-Gonen, R., Zhang, F., et al. (2020) · Science · doi:10.1126/science.aba7721 · Cited by 764

A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart

Asp, M., Giacomello, S., Larsson, L., Wu, C., Fürth, D., Qian, X., Wärdell, E., Custodio, J., Reimegård, J., Salmén, F., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.025 · Cited by 754

Analysing high-throughput sequencing data in Python with HTSeq 2.0

Putri, G., Anders, S., Pyl, P. T., Pimanda, J. E., & Zanini, F. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac166 · Cited by 1,150

pySeqRNA: An Automated Python Package for Advanced RNA Sequencing Data Analysis and Annotation

Duhan, N. (2021) · Digital Commons - USU (Utah State University)

HTSstation: A Web Application and Open-Access Libraries for High-Throughput Sequencing Data Analysis

David, F. P. A., Delafontaine, J., Carat, S., Ross, F. J., Lefebvre, G. C., Jarosz, Y., Sinclair, L., Noordermeer, D., Rougemont, J., & Leleu, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085879 · Cited by 100

A Simple Process of RNA-Sequence Analyses by Hisat2, Htseq and DESeq2

Wen, G. (2017) · doi:10.1145/3143344.3143354 · Cited by 71

From RNA-seq reads to differential expression results

Oshlack, A., Robinson, M. D., & Young, M. D. (2010) · Genome biology · doi:10.1186/gb-2010-11-12-220 · Cited by 733