featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
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MOTIVATION: Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. RESULTS: We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. AVAILABILITY AND IMPLEMENTATION: featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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Raw read counts are assembled outside limma using tools such as featureCounts, HTSeq-counts or RSEM.
“Raw read counts are assembled outside limma using tools such as featureCounts (29), HTSeq-counts (30) or RSEM (31).”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [29]
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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 · see citing works
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607 · see citing works
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 · see citing works
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849 · see citing works
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556 · see citing works
The Human Genome Browser at UCSC
Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, A. D. (2002) · Genome Research · doi:10.1101/gr.229102 · Cited by 10,400 · see citing works
Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066 · see citing works
NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy
Pruitt, K. D., Tatusova, T., Brown, G. R., & Maglott, D. R. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1079 · Cited by 1,149 · see citing works
Normalization, testing, and false discovery rate estimation for RNA-sequencing data
Li, J., Witten, D. M., Johnstone, I. M., & Tibshirani, R. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr031 · Cited by 369 · see citing works
Nookaew, I., Papini, M., Pornputtapong, N., Scalcinati, G., Fagerberg, L., Uhlén, M., & Nielsen, J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks804 · Cited by 309 · see citing works
De novo high-coverage sequencing and annotated assemblies of the budgerigar genome
Ganapathy, G., Howard, J., Koren, S., et al. (2013) · GigaDB · doi:10.5524/100059 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Reyes,A. et al. (2013) Drift and conservation of differential exon usage across tissues in primate species. Proc. Natl Acad. Sci. USA, 110, 15377-15382.
Cited by
Sorted by citations
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 · see citing works
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464 · see citing works
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155 · see citing works
Self-citationDifferential 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 · see citing works
Metabolic regulation of gene expression by histone lactylation
Zhang, D., Tang, Z., Huang, H., Zhou, G., Cui, C., Weng, Y., Liu, W., Kim, S., Lee, S., Perez-Neut, M., et al. (2019) · Nature · doi:10.1038/s41586-019-1678-1 · Cited by 4,371 · see citing works
Genomic analyses identify molecular subtypes of pancreatic cancer
Bailey, P., Chang, D. K., Nones, K., Johns, A. L., Patch, A.-M., Gingras, M.-C., Miller, D. K., Christ, A. N., Bruxner, T. J. C., Quinn, M. C., et al. (2016) · Nature · doi:10.1038/nature16965 · Cited by 3,621 · see citing works
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 · see citing works
Opportunities and challenges in long-read sequencing data analysis
Amarasinghe, S. L., Su, S., Dong, X., Zappia, L., Ritchie, M. E., & Gouil, Q. (2020) · Genome Biology · doi:10.1186/s13059-020-1935-5 · Cited by 2,530 · see citing works
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416 · see citing works
McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw777 · Cited by 1,852 · see citing works
The neuroactive potential of the human gut microbiota in quality of life and depression
Valles-Colomer, M., Falony, G., Darzi, Y., Tigchelaar, E. F., Wang, J., Tito, R. Y., Schiweck, C., Kurilshikov, A., Joossens, M., Wijmenga, C., et al. (2019) · Nature Microbiology · doi:10.1038/s41564-018-0337-x · Cited by 1,963 · see citing works
Mahajan, A., Taliun, D., Thurner, M., Robertson, N. R., Torres, J. M., Rayner, N. W., Payne, A. J., Steinthorsdottir, V., Scott, R. A., Grarup, N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0241-6 · Cited by 1,744 · see citing works
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 · see citing works
Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma
Zhang, Q., He, Y., Luo, N., Patel, S. J., Han, Y., Gao, R., Modak, M., Carotta, S., Haslinger, C., Kind, D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.10.003 · Cited by 1,624 · see citing works
Human chimeric antigen receptor macrophages for cancer immunotherapy
Klichinsky, M., Ruella, M., Shestova, O., Lu, X. M., Best, A., Zeeman, M., Schmierer, M., Gabrusiewicz, K., Anderson, N. R., Petty, N. E., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0462-y · Cited by 1,495 · see citing works
Human primary liver cancer–derived organoid cultures for disease modeling and drug screening
Broutier, L., Mastrogiovanni, G., Verstegen, M. M., Francies, H. E., Gavarró, L. M., Bradshaw, C. R., Allen, G. E., Arnes-Benito, R., Sidorova, O., Gaspersz, M. P., et al. (2017) · Nature Medicine · doi:10.1038/nm.4438 · Cited by 1,471 · see citing works
Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates
Simoni, Y., Becht, E., Fehlings, M., Loh, C. Y., Koo, S.-L., Teng, K. W. W., Yeong, J. P. S., Nahar, R., Zhang, T., Kared, H., et al. (2018) · Nature · doi:10.1038/s41586-018-0130-2 · Cited by 1,370 · see citing works
L1 drives IFN in senescent cells and promotes age-associated inflammation
De Cecco, M., Ito, T., Petrashen, A. P., Elias, A. E., Skvir, N. J., Criscione, S. W., Caligiana, A., Brocculi, G., Adney, E. M., Boeke, J. D., et al. (2019) · Nature · doi:10.1038/s41586-018-0784-9 · Cited by 1,255 · see citing works
“Counts mapping to the genome were determined using featureCounts 43 .”
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 · see citing works
Autophagy promotes immune evasion of pancreatic cancer by degrading MHC-I
Yamamoto, K., Venida, A., Yano, J., Biancur, D. E., Kakiuchi, M., Gupta, S., Sohn, A. S. W., Mukhopadhyay, S., Lin, E. Y., Parker, S. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2229-5 · Cited by 1,240 · see citing works
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146 · see citing works
Normal aging induces A1-like astrocyte reactivity
Clarke, L. E., Liddelow, S. A., Chakraborty, C., Münch, A. E., Heiman, M., & Barres, B. A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800165115 · Cited by 1,237 · see citing works
Gene expression elucidates functional impact of polygenic risk for schizophrenia
Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091 · see citing works
Lin, Y.-T., Seo, J., Gao, F., Feldman, H. M., Wen, H.-L., Penney, J., Cam, H. P., Gjoneska, E., Raja, W. K., Cheng, J., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.05.008 · Cited by 1,125 · see citing works
TOX is a critical regulator of tumour-specific T cell differentiation
Scott, A. C., Dündar, F., Zumbo, P., Chandran, S. S., Klebanoff, C. A., Shakiba, M., Trivedi, P., Menocal, L., Appleby, H., Camara, S., et al. (2019) · Nature · doi:10.1038/s41586-019-1324-y · Cited by 1,106 · see citing works
The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight
Garrett-Bakelman, F. E., Darshi, M., Green, S. J., Gur, R. C., Lin, L., Macias, B. R., McKenna, M. J., Meydan, C., Mishra, T., Nasrini, J., et al. (2019) · Science · doi:10.1126/science.aau8650 · Cited by 1,128 · see citing works
Sensory Neurons Co-opt Classical Immune Signaling Pathways to Mediate Chronic Itch
Oetjen, L. K., Mack, M. R., Feng, J., Whelan, T. M., Niu, H., Guo, C. J., Chen, S., Trier, A. M., Xu, A. Z., Tripathi, S. V., et al. (2017) · Cell · doi:10.1016/j.cell.2017.08.006 · Cited by 1,092 · see citing works
Modulation of Myelopoiesis Progenitors Is an Integral Component of Trained Immunity
Mitroulis, I., Ruppova, K., Wang, B., Chen, L.-S., Grzybek, M., Grinenko, T., Eugster, A., Troullinaki, M., Palladini, A., Kourtzelis, I., et al. (2018) · Cell · doi:10.1016/j.cell.2017.11.034 · Cited by 1,100 · see citing works
Senolytic CAR T cells reverse senescence-associated pathologies
Amor, C., Feucht, J., Leibold, J., Ho, Y.-J., Zhu, C., Alonso-Curbelo, D., Mansilla-Soto, J., Boyer, J. A., Li, X., Giavridis, T., et al. (2020) · Nature · doi:10.1038/s41586-020-2403-9 · Cited by 1,098 · see citing works
SEQC/MAQC-III Consortium (2014) · Nature Biotechnology · doi:10.1038/nbt.2957 · Cited by 983 · see citing works
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 1,086 · see citing works
Xiong, Y., Liu, Y., Cao, L., Wang, D., Guo, M., Jiang, A., Guo, D., Hu, W., Yang, J., Tang, Z., et al. (2020) · Emerging Microbes & Infections · doi:10.1080/22221751.2020.1747363 · Cited by 1,076 · see citing works
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091 · see citing works
Hobit and Blimp1 instruct a universal transcriptional program of tissue residency in lymphocytes
Mackay, L. K., Minnich, M., Kragten, N. A. M., Liao, Y., Nota, B., Seillet, C., Zaid, A., Man, K., Preston, S., Freestone, D., et al. (2016) · Science · doi:10.1126/science.aad2035 · Cited by 1,010 · see citing works
Self-citationSala Frigerio, C., Wolfs, L., Fattorelli, N., Thrupp, N., Voytyuk, I., Schmidt, I., Mancuso, R., Chen, W.-T., Woodbury, M. E., Srivastava, G., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.03.099 · Cited by 926 · see citing works
Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
Ding, J., Adiconis, X., Simmons, S. K., Kowalczyk, M. S., Hession, C. C., Marjanovic, N. D., Hughes, T. K., Wadsworth, M. H., Burks, T., Nguyen, L. T., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0465-8 · Cited by 871 · see citing works
The bone marrow microenvironment at single-cell resolution
Tikhonova, A. N., Dolgalev, I., Hu, H., Sivaraj, K. K., Hoxha, E., Cuesta-Domínguez, Á., Pinho, S., Akhmetzyanova, I., Gao, J., Witkowski, M., et al. (2019) · Nature · doi:10.1038/s41586-019-1104-8 · Cited by 936 · see citing works
Molecular Identity of Human Outer Radial Glia during Cortical Development
Pollen, A. A., Nowakowski, T. J., Chen, J., Retallack, H., Sandoval-Espinosa, C., Nicholas, C. R., Shuga, J., Liu, S. J., Oldham, M. C., Diaz, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.004 · Cited by 908 · see citing works
A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain
Davie, K., Janssens, J., Koldere, D., De Waegeneer, M., Pech, U., Kreft, Ł., Aibar, S., Makhzami, S., Christiaens, V., Bravo González-Blas, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.057 · Cited by 840 · see citing works
Single-cell genomics identifies cell type–specific molecular changes in autism
Velmeshev, D., Schirmer, L., Jung, D., Haeussler, M., Perez, Y., Mayer, S., Bhaduri, A., Goyal, N., Rowitch, D. H., & Kriegstein, A. R. (2019) · Science · doi:10.1126/science.aav8130 · Cited by 927 · see citing works
Splatter: simulation of single-cell RNA sequencing data
Zappia, L., Phipson, B., & Oshlack, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1305-0 · Cited by 905 · see citing works
Ruiz de Galarreta, M., Bresnahan, E., Molina-Sánchez, P., Lindblad, K. E., Maier, B., Sia, D., Puigvehi, M., Miguela, V., Casanova-Acebes, M., Dhainaut, M., et al. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0074 · Cited by 912 · see citing works
Winkler, E. S., Bailey, A. L., Kafai, N. M., Nair, S., McCune, B. T., Yu, J., Fox, J. M., Chen, R. E., Earnest, J. T., Keeler, S. P., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0778-2 · Cited by 901 · see citing works
Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma
Zhao, J., Chen, A. X., Gartrell, R. D., Silverman, A. M., Aparicio, L., Chu, T., Bordbar, D., Shan, D., Samanamud, J., Mahajan, A., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0349-y · Cited by 903 · see citing works
Reprogramming to recover youthful epigenetic information and restore vision
Lu, Y., Brommer, B., Tian, X., Krishnan, A., Meer, M., Wang, C., Vera, D. L., Zeng, Q., Yu, D., Bonkowski, M. S., et al. (2020) · Nature · doi:10.1038/s41586-020-2975-4 · Cited by 820 · see citing works
Microbiome Influences Prenatal and Adult Microglia in a Sex-Specific Manner
Thion, M. S., Low, D., Silvin, A., Chen, J., Grisel, P., Schulte-Schrepping, J., Blecher, R., Ulas, T., Squarzoni, P., Hoeffel, G., et al. (2018) · Cell · doi:10.1016/j.cell.2017.11.042 · Cited by 779 · see citing works
Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., Mason, C. E., Socci, N. D., & Betel, D. (2013) · Genome Biology · doi:10.1186/gb-2013-14-9-r95 · Cited by 733 · see citing works
Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease
Kinchen, J., Chen, H. H., Parikh, K., Antanaviciute, A., Jagielowicz, M., Fawkner-Corbett, D., Ashley, N., Cubitt, L., Mellado-Gomez, E., Attar, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.08.067 · Cited by 745 · see citing works
Genome-wide programmable transcriptional memory by CRISPR-based epigenome editing
Nuñez, J. K., Chen, J., Pommier, G. C., Cogan, J. Z., Replogle, J. M., Adriaens, C., Ramadoss, G. N., Shi, Q., Hung, K. L., Samelson, A. J., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.025 · Cited by 760 · see citing works
Readhead, B., Haure-Mirande, J.-V., Funk, C. C., Richards, M. A., Shannon, P., Haroutunian, V., Sano, M., Liang, W. S., Beckmann, N. D., Price, N. D., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.05.023 · Cited by 708 · see citing works
Yan, H. H. N., Siu, H. C., Law, S., Ho, S. L., Yue, S. S. K., Tsui, W. Y., Chan, D., Chan, A. S., Ma, S., Lam, K. O., et al. (2018) · Cell Stem Cell · doi:10.1016/j.stem.2018.09.016 · Cited by 724 · see citing works
TREM2 regulates microglial cholesterol metabolism upon chronic phagocytic challenge
Nugent, A. A., Lin, K., van Lengerich, B., Lianoglou, S., Przybyla, L., Davis, S. S., Llapashtica, C., Wang, J., Kim, D. J., Xia, D., et al. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.007 · Cited by 745 · see citing works
Transcriptomic analysis of purified human cortical microglia reveals age-associated changes
Galatro, T. F., Holtman, I. R., Lerario, A. M., Vainchtein, I. D., Brouwer, N., Sola, P. R., Veras, M. M., Pereira, T. F., Leite, R. E. P., Möller, T., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4597 · Cited by 741 · see citing works
Clark, S. J., Argelaguet, R., Kapourani, C.-A., Stubbs, T. M., Lee, H. J., Alda-Catalinas, C., Krueger, F., Sanguinetti, G., Kelsey, G., Marioni, J. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03149-4 · Cited by 657 · see citing works
Unexpected role of interferon-γ in regulating neuronal connectivity and social behaviour
Filiano, A. J., Xu, Y., Tustison, N. J., Marsh, R. L., Baker, W., Smirnov, I., Overall, C. C., Gadani, S. P., Turner, S. D., Weng, Z., et al. (2016) · Nature · doi:10.1038/nature18626 · Cited by 697 · see citing works
An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
Angelidis, I., Simon, L. M., Fernandez, I. E., Strunz, M., Mayr, C. H., Greiffo, F. R., Tsitsiridis, G., Ansari, M., Graf, E., Strom, T.-M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08831-9 · Cited by 637 · see citing works
Denisenko, E., Guo, B. B., Jones, M., Hou, R., de Kock, L., Lassmann, T., Poppe, D., Clément, O., Simmons, R. K., Lister, R., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-02048-6 · Cited by 686 · see citing works
Innate Immune Training of Granulopoiesis Promotes Anti-tumor Activity
Kalafati, L., Kourtzelis, I., Schulte-Schrepping, J., Li, X., Hatzioannou, A., Grinenko, T., Hagag, E., Sinha, A., Has, C., Dietz, S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.058 · Cited by 648 · see citing works
IL-11 is a crucial determinant of cardiovascular fibrosis
Schafer, S., Viswanathan, S., Widjaja, A. A., Lim, W.-W., Moreno-Moral, A., DeLaughter, D. M., Ng, B., Patone, G., Chow, K., Khin, E., et al. (2017) · Nature · doi:10.1038/nature24676 · Cited by 639 · see citing works
Interferon-γ Drives Treg Fragility to Promote Anti-tumor Immunity
Overacre-Delgoffe, A. E., Chikina, M., Dadey, R. E., Yano, H., Brunazzi, E. A., Shayan, G., Horne, W., Moskovitz, J. M., Kolls, J. K., Sander, C., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.005 · Cited by 627 · see citing works
Thiol-linked alkylation for the metabolic sequencing of RNA
Herzog, V. A., Reichholf, B., Neumann, T., Rescheneder, P., Bhat, P., Burkard, T. R., Wlotzka, W., von Haeseler, A., Zuber, J., & Ameres, S. L. (2017) · doi:10.1101/177642 · Cited by 622 · see citing works
<i>Ex Vivo</i> Profiling of PD-1 Blockade Using Organotypic Tumor Spheroids
Jenkins, R. W., Aref, A. R., Lizotte, P. H., Ivanova, E., Stinson, S., Zhou, C. W., Bowden, M., Deng, J., Liu, H., Miao, D., et al. (2018) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-0833 · Cited by 594 · see citing works
Zarrinpar, A., Chaix, A., Xu, Z. Z., Chang, M. W., Marotz, C. A., Saghatelian, A., Knight, R., & Panda, S. (2018) · Nature Communications · doi:10.1038/s41467-018-05336-9 · Cited by 546 · see citing works
Association of COVID-19 inflammation with activation of the C5a–C5aR1 axis
Carvelli, J., Demaria, O., Vély, F., Batista, L., Chouaki Benmansour, N., Fares, J., Carpentier, S., Thibult, M.-L., Morel, A., Remark, R., et al. (2020) · Nature · doi:10.1038/s41586-020-2600-6 · Cited by 526 · see citing works
BET inhibitor resistance emerges from leukaemia stem cells
Fong, C. Y., Gilan, O., Lam, E. Y. N., Rubin, A. F., Ftouni, S., Tyler, D., Stanley, K., Sinha, D., Yeh, P., Morison, J., et al. (2015) · Nature · doi:10.1038/nature14888 · Cited by 545 · see citing works
Loss of the FAT1 Tumor Suppressor Promotes Resistance to CDK4/6 Inhibitors via the Hippo Pathway
Li, Z., Razavi, P., Li, Q., Toy, W., Liu, B., Ping, C., Hsieh, W., Sanchez-Vega, F., Brown, D. N., Da Cruz Paula, A. F., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.11.006 · Cited by 508 · see citing works
The histone mark H3K36me2 recruits DNMT3A and shapes the intergenic DNA methylation landscape
Weinberg, D. N., Papillon-Cavanagh, S., Chen, H., Yue, Y., Chen, X., Rajagopalan, K. N., Horth, C., McGuire, J. T., Xu, X., Nikbakht, H., et al. (2019) · Nature · doi:10.1038/s41586-019-1534-3 · Cited by 524 · see citing works
Reliability of human cortical organoid generation
Yoon, S.-J., Elahi, L. S., Pașca, A. M., Marton, R. M., Gordon, A., Revah, O., Miura, Y., Walczak, E. M., Holdgate, G. M., Fan, H. C., et al. (2019) · Nature Methods · doi:10.1038/s41592-018-0255-0 · Cited by 507 · see citing works
Erny, D., Dokalis, N., Mezö, C., Castoldi, A., Mossad, O., Staszewski, O., Frosch, M., Villa, M., Fuchs, V., Mayer, A., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.10.010 · Cited by 478 · see citing works
c-MAF-dependent regulatory T cells mediate immunological tolerance to a gut pathobiont
Xu, M., Pokrovskii, M., Ding, Y., Yi, R., Au, C., Harrison, O. J., Galan, C., Belkaid, Y., Bonneau, R., & Littman, D. R. (2018) · Nature · doi:10.1038/nature25500 · Cited by 498 · see citing works
Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex
Hrvatin, S., Hochbaum, D. R., Nagy, M. A., Cicconet, M., Robertson, K., Cheadle, L., Zilionis, R., Ratner, A., Borges-Monroy, R., Klein, A. M., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0029-5 · Cited by 497 · see citing works
Brain Cell Type Specific Gene Expression and Co-expression Network Architectures
McKenzie, A. T., Wang, M., Hauberg, M. E., Fullard, J. F., Kozlenkov, A., Keenan, A., Hurd, Y. L., Dracheva, S., Casaccia, P., Roussos, P., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-27293-5 · Cited by 481 · see citing works
METTL1 Promotes let-7 MicroRNA Processing via m7G Methylation
Pandolfini, L., Barbieri, I., Bannister, A. J., Hendrick, A., Andrews, B., Webster, N., Murat, P., Mach, P., Brandi, R., Robson, S. C., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.03.040 · Cited by 491 · see citing works
Loss of p53 drives neuron reprogramming in head and neck cancer
Amit, M., Takahashi, H., Dragomir, M. P., Lindemann, A., Gleber-Netto, F. O., Pickering, C. R., Anfossi, S., Osman, A. A., Cai, Y., Wang, R., et al. (2020) · Nature · doi:10.1038/s41586-020-1996-3 · Cited by 483 · see citing works
Transcriptional and Translational Differences of Microglia from Male and Female Brains
Guneykaya, D., Ivanov, A., Hernandez, D. P., Haage, V., Wojtas, B., Meyer, N., Maricos, M., Jordan, P., Buonfiglioli, A., Gielniewski, B., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.08.001 · Cited by 477 · see citing works
Myelin dysfunction drives amyloid-β deposition in models of Alzheimer’s disease
Depp, C., Sun, T., Sasmita, A. O., Spieth, L., Berghoff, S. A., Nazarenko, T., Overhoff, K., Steixner-Kumar, A. A., Subramanian, S., Arinrad, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06120-6 · Cited by 476 · see citing works
Acceleration of β Cell Aging Determines Diabetes and Senolysis Improves Disease Outcomes
Aguayo-Mazzucato, C., Andle, J., Lee, T. B., Midha, A., Talemal, L., Chipashvili, V., Hollister-Lock, J., van Deursen, J., Weir, G., & Bonner-Weir, S. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.05.006 · Cited by 469 · see citing works
Transcription Elongation Can Affect Genome 3D Structure
Heinz, S., Texari, L., Hayes, M. G. B., Urbanowski, M., Chang, M. W., Givarkes, N., Rialdi, A., White, K. M., Albrecht, R. A., Pache, L., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.047 · Cited by 453 · see citing works
Epigenetic regulation of brain region-specific microglia clearance activity
Ayata, P., Badimon, A., Strasburger, H. J., Duff, M. K., Montgomery, S. E., Loh, Y.-H. E., Ebert, A., Pimenova, A. A., Ramirez, B. R., Chan, A. T., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0192-3 · Cited by 460 · see citing works
Transgenerational transmission of environmental information in <i>C. elegans</i>
Klosin, A., Casas, E., Hidalgo-Carcedo, C., Vavouri, T., & Lehner, B. (2017) · Science · doi:10.1126/science.aah6412 · Cited by 456 · see citing works
Regulation of life span by the gut microbiota in the short-lived African turquoise killifish
Smith, P., Willemsen, D., Popkes, M., Metge, F., Gandiwa, E., Reichard, M., & Valenzano, D. R. (2017) · eLife · doi:10.7554/elife.27014 · Cited by 423 · see citing works
An Efficient Platform for Astrocyte Differentiation from Human Induced Pluripotent Stem Cells
TCW, J., Wang, M., Pimenova, A. A., Bowles, K. R., Hartley, B. J., Lacin, E., Machlovi, S. I., Abdelaal, R., Karch, C. M., Phatnani, H., et al. (2017) · Stem Cell Reports · doi:10.1016/j.stemcr.2017.06.018 · Cited by 435 · see citing works
Effects of microbiota-directed foods in gnotobiotic animals and undernourished children
Gehrig, J. L., Venkatesh, S., Chang, H.-W., Hibberd, M. C., Kung, V. L., Cheng, J., Chen, R. Y., Subramanian, S., Cowardin, C. A., Meier, M. F., et al. (2019) · Science · doi:10.1126/science.aau4732 · Cited by 431 · see citing works
Katsura, H., Sontake, V., Tata, A., Kobayashi, Y., Edwards, C. E., Heaton, B. E., Konkimalla, A., Asakura, T., Mikami, Y., Fritch, E. J., et al. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2020.10.005 · Cited by 416 · see citing works
Senescence atlas reveals an aged-like inflamed niche that blunts muscle regeneration
Moiseeva, V., Cisneros, A., Sica, V., Deryagin, O., Lai, Y., Jung, S., Andrés, E., An, J., Segalés, J., Ortet, L., et al. (2023) · Nature · doi:10.1038/s41586-022-05535-x · Cited by 423 · see citing works
The BrainSeq Consortium, Jaffe, A. E., Straub, R. E., Shin, J. H., Tao, R., Gao, Y., Collado-Torres, L., Kam-Thong, T., Xi, H. S., Quan, J., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0197-y · Cited by 364 · see citing works
Intracellular Staphylococcus aureus persisters upon antibiotic exposure
Peyrusson, F., Varet, H., Nguyen, T. K., Legendre, R., Sismeiro, O., Coppée, J.-Y., Wolz, C., Tenson, T., & Van Bambeke, F. (2020) · Nature Communications · doi:10.1038/s41467-020-15966-7 · Cited by 392 · see citing works
Cholesterol and matrisome pathways dysregulated in astrocytes and microglia
TCW, J., Qian, L., Pipalia, N. H., Chao, M. J., Liang, S. A., Shi, Y., Jain, B. R., Bertelsen, S. E., Kapoor, M., Marcora, E., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.017 · Cited by 411 · see citing works
the Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Consortium, Der, E., Suryawanshi, H., Morozov, P., Kustagi, M., Goilav, B., Ranabothu, S., Izmirly, P., Clancy, R., Belmont, H. M., et al. (2019) · Nature Immunology · doi:10.1038/s41590-019-0386-1 · Cited by 403 · see citing works
Hsieh, T.-H. S., Cattoglio, C., Slobodyanyuk, E., Hansen, A. S., Darzacq, X., & Tjian, R. (2022) · Nature Genetics · doi:10.1038/s41588-022-01223-8 · Cited by 406 · see citing works
Winkler, E. S., Gilchuk, P., Yu, J., Bailey, A. L., Chen, R. E., Chong, Z., Zost, S. J., Jang, H., Huang, Y., Allen, J. D., et al. (2021) · Cell · doi:10.1016/j.cell.2021.02.026 · Cited by 366 · see citing works
Genome editing reveals a role for OCT4 in human embryogenesis
Fogarty, N. M. E., McCarthy, A., Snijders, K. E., Powell, B. E., Kubikova, N., Blakeley, P., Lea, R., Elder, K., Wamaitha, S. E., Kim, D., et al. (2017) · Nature · doi:10.1038/nature24033 · Cited by 392 · see citing works
Resurrection of endogenous retroviruses during aging reinforces senescence
Liu, X., Liu, Z., Wu, Z., Ren, J., Fan, Y., Sun, L., Cao, G., Niu, Y., Zhang, B., Ji, Q., et al. (2023) · Cell · doi:10.1016/j.cell.2022.12.017 · Cited by 390 · see citing works
Benayoun, B. A., Pollina, E. A., Singh, P. P., Mahmoudi, S., Harel, I., Casey, K. M., Dulken, B. W., Kundaje, A., & Brunet, A. (2019) · Genome Research · doi:10.1101/gr.240093.118 · Cited by 360 · see citing works
Daemen, S., Gainullina, A., Kalugotla, G., He, L., Chan, M. M., Beals, J. W., Liss, K. H., Klein, S., Feldstein, A. E., Finck, B. N., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2020.108626 · Cited by 378 · see citing works
“Gene counts were derived from the number of uniquely aligned unambiguous reads by Subread:featureCount version 1.4.5 (Liao et al., 2014).”
InfluentialHypoxia induces rapid changes to histone methylation and reprograms chromatin
Batie, M., Frost, J., Frost, M., Wilson, J. W., Schofield, P., & Rocha, S. (2019) · Science · doi:10.1126/science.aau5870 · Cited by 385 · see citing works
Batch effects and the effective design of single-cell gene expression studies
Tung, P.-Y., Blischak, J. D., Hsiao, C. J., Knowles, D. A., Burnett, J. E., Pritchard, J. K., & Gilad, Y. (2017) · Scientific Reports · doi:10.1038/srep39921 · Cited by 356 · see citing works
Sahraeian, S. M. E., Mohiyuddin, M., Sebra, R., Tilgner, H., Afshar, P. T., Au, K. F., Bani Asadi, N., Gerstein, M. B., Wong, W. H., Snyder, M. P., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00050-4 · Cited by 376 · see citing works
Multiplexed Engineering and Analysis of Combinatorial Enhancer Activity in Single Cells
Xie, S., Duan, J., Li, B., Zhou, P., & Hon, G. C. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.03.007 · Cited by 334 · see citing works
Silvin, A., Uderhardt, S., Piot, C., Da Mesquita, S., Yang, K., Geirsdottir, L., Mulder, K., Eyal, D., Liu, Z., Bridlance, C., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.07.004 · Cited by 368 · see citing works
- Authors
- Liao, Y., Smyth, G. K., & Shi, W.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Shi, Wei
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-11-13
- Volume
- 30
- Issue
- 7
- Pages
- 923-930
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2138207763
- Cited by
- 27,219
- References
- 11
- Citations by year
- 2026: 3661 · 2025: 4344 · 2024: 4323 · 2023: 3554 · 2022: 3316 · 2021: 2761 · 2020: 2083 · 2019: 1326 · 2018: 894 · 2017: 561 · 2016: 250 · 2015: 99 · 2014: 31 · 2013: 6 · 2012: 1
- FWCI
- 58.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Australian Government
- Medical Research Council
- National Health and Medical Research Council (1023454)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- featureCounts, read counting, genomic features, paired-end reads, RNA-seq
- MeSH terms
- Algorithms, Histones, Software, Genome, Sequence Analysis, RNA, Genomics, High-Throughput Nucleotide Sequencing
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