The DOI matches its Crossref record; 46 of the 47 references it cites were found in a registry or an index.
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
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
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
Self-citationBEDTools: 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
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
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
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
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
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
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
Self-citation
Flagged first, then by citations
1 of the 47 references couldn’t be matched to a known paper.
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
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
Self-citationTwo 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
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
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
Self-citationMetabolic 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
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
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
Liao, Y., Smyth, G. K., & Shi, W. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz114 · Cited by 2,878
Self-citationA 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
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
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
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
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
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
McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw777 · Cited by 1,852
The Landscape of Circular RNA in Cancer
Vo, J. N., Cieslik, M. P., Zhang, Y., Shukla, S. K., Xiao, L., Zhang, Y., Wu, Y., Dhanasekaran, S. M., Engelke, C. G., Cao, X., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.021 · Cited by 1,745
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
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
A step-by-step workflow for low-level analysis of single-cell RNA-seq data
Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.1 · Cited by 1,551
Differential analysis of RNA-seq incorporating quantification uncertainty
Pimentel, H., Bray, N. L., Puente, S., Melsted, P., & Pachter, L. (2017) · Nature Methods · doi:10.1038/nmeth.4324 · Cited by 1,534
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
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
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
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
Functional genomic landscape of acute myeloid leukaemia
Tyner, J. W., Tognon, C. E., Bottomly, D., Wilmot, B., Kurtz, S. E., Savage, S. L., Long, N., Schultz, A. R., Traer, E. A., Abel, M. L., et al. (2018) · Nature · doi:10.1038/s41586-018-0623-z · Cited by 1,359
Araport11: a complete reannotation of the Arabidopsis thaliana reference genome
Cheng, C., Krishnakumar, V., Chan, A. P.-Y., Thibaud‐Nissen, F., Schobel, S., & Town, C. D. (2016) · The Plant Journal · doi:10.1111/tpj.13415 · Cited by 1,329
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
Chen, Y., Lun, A. T. L., & Smyth, G. K. (2016) · F1000Research · doi:10.12688/f1000research.8987.2 · Cited by 1,272
Self-citationL1 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
“Counts mapping to the genome were determined using featureCounts 43 .”
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
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
Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome
Carrión, V. J., Pérez‐Jaramillo, J. E., Cordovez, V., Tracanna, V., de Hollander, M., Ruiz-Buck, D., Mendes, L. W., van IJcken, W. F. J., Expósito, R. G., Elsayed, S. S., et al. (2019) · Science · doi:10.1126/science.aaw9285 · Cited by 1,193
BCG Educates Hematopoietic Stem Cells to Generate Protective Innate Immunity against Tuberculosis
Kaufmann, E., Sanz, J., Dunn, J. L., Khan, N., Mendonça, L. E., Pacis, A., Tzelepis, F., Pernet, E., Dumaine, A., Grenier, J.-C., et al. (2018) · Cell · doi:10.1016/j.cell.2017.12.031 · Cited by 1,191
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
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
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
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
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
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
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
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
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
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
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
Genetic compensation triggered by mutant mRNA degradation
El-Brolosy, M. A., Kontarakis, Z., Rossi, A., Kuenne, C., Günther, S., Fukuda, N., Kikhi, K., Boezio, G. L. M., Takacs, C. M., Lai, S.-L., et al. (2019) · Nature · doi:10.1038/s41586-019-1064-z · Cited by 1,028
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
Self-citationHigh-resolution TADs reveal DNA sequences underlying genome organization in flies
Ramírez, F., Bhardwaj, V., Arrigoni, L., Lam, K. C., Grüning, B. A., Villaveces, J. M., Habermann, B. H., Akhtar, A., & Manke, T. (2018) · Nature Communications · doi:10.1038/s41467-017-02525-w · Cited by 993
SEQC/MAQC-III Consortium (2014) · Nature Biotechnology · doi:10.1038/nbt.2957 · Cited by 983
Self-citationHeightened Innate Immune Responses in the Respiratory Tract of COVID-19 Patients
Zhou, Z. L., Ren, L., Zhang, L., Zhong, J., Xiao, Y., Jia, Z., Guo, L., Yang, J., Wang, C., Jiang, S., et al. (2020) · Cell Host & Microbe · doi:10.1016/j.chom.2020.04.017 · Cited by 980
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
Self-citationThe 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
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
Sala 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
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
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
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
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
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
Sahm, F., Schrimpf, D., Stichel, D., Jones, D., Hielscher, T., Schefzyk, S., Okonechnikov, K., Koelsche, C., Reuß, D., Capper, D., et al. (2017) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30155-9 · Cited by 874
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
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
Circulating Tumor Cell Clustering Shapes DNA Methylation to Enable Metastasis Seeding
Gkountela, S., Castro-Giner, F., Szczerba, B. M., Vetter, M. H. F., Landin, J., Scherrer, R., Krol, I., Scheidmann, M. C., Beisel, C., Stirnimann, C. U., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.046 · Cited by 822
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
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
P-Body Purification Reveals the Condensation of Repressed mRNA Regulons
Hubstenberger, A., Courel, M., Bénard, M., Souquère, S., Ernoult‐Lange, M., Chouaib, R., Zhou, Y., Morlot, J.-B., Munier, A., Fradet, M., et al. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.09.003 · Cited by 808
RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
Law, C. W., Alhamdoosh, M., Su, S., Dong, X., Tian, L., Smyth, G. K., & Ritchie, M. E. (2016) · F1000Research · doi:10.12688/f1000research.9005.3 · Cited by 795
Self-citationDiurnal transcriptome atlas of a primate across major neural and peripheral tissues
Mure, L. S., Le, H. D., Benegiamo, G., Chang, M. W., Ríos, L., Jillani, N., Ngotho, M., Kariuki, T., Dkhissi‐Benyahya, O., Cooper, H. M., et al. (2018) · Science · doi:10.1126/science.aao0318 · Cited by 791
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
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
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
CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells
Liu, S. J., Horlbeck, M. A., Cho, S. W., Birk, H. S., Malatesta, M., He, D. X., Attenello, F. J., Villalta, J. E., Cho, M. Y., Chen, Y., et al. (2016) · Science · doi:10.1126/science.aah7111 · Cited by 756
Ferroptosis surveillance independent of GPX4 and differentially regulated by sex hormones
Liang, D., Feng, Y., Zandkarimi, F., Wang, H., Zhang, Z., Kim, J., Cai, Y., Gu, W. J., Stockwell, B. R., & Jiang, X. (2023) · Cell · doi:10.1016/j.cell.2023.05.003 · Cited by 769
Li, J., Byrne, K. T., Yan, F., Yamazoe, T., Chen, Z., Baslan, T., Richman, L. P., Lin, J., Sun, Y. H., Rech, A. J., et al. (2018) · Immunity · doi:10.1016/j.immuni.2018.06.006 · Cited by 746
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
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
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
Bachmayr-Heyda, A., Reiner, A. T., Auer, K., Sukhbaatar, N., Aust, S., Bachleitner‐Hofmann, T., Mesteri, I., Grunt, T. W., Zeillinger, R., & Pils, D. (2015) · Scientific Reports · doi:10.1038/srep08057 · Cited by 736
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
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
Distinct Regulation of Th17 and Th1 Cell Differentiation by Glutaminase-Dependent Metabolism
Johnson, M. O., Wolf, M. M., Madden, M. Z., Andrejeva, G., Sugiura, A., Contreras, D. C., Maseda, D., Liberti, M. V., Paz, K. G., Kishton, R. J., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.001 · Cited by 729
Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia
Grant, R. A., Morales‐Nebreda, L., Markov, N. S., Swaminathan, S., Querrey, M., Rios-Guzman, E., Abbott, D. A., Donnelly, H. K., Donayre, A., Goldberg, I. A., et al. (2021) · Nature · doi:10.1038/s41586-020-03148-w · Cited by 718
HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine
Fang, S., Dong, L., Liu, L., Guo, J., Zhao, L., Zhang, J.-Y., Bu, D., Liu, X., Huo, P., Cao, W., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1063 · Cited by 710
Liu, J., Dou, X., Chen, C., Chen, C., Liu, C., Xu, M. M., Zhao, S., Shen, B., Gao, Y., Han, D., et al. (2020) · Science · doi:10.1126/science.aay6018 · Cited by 709
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
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
Time-Restricted Feeding Prevents Obesity and Metabolic Syndrome in Mice Lacking a Circadian Clock
Chaix, A., Lin, T. C., Le, H. D., Chang, M. W., & Panda, S. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.08.004 · Cited by 687
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
Müller, S., Kohanbash, G., Liu, S. J., Alvarado, B., Carrera, D. A., Bhaduri, A., Watchmaker, P. B., Yagnik, G., Lullo, E. D., Malatesta, M., et al. (2017) · Genome biology · doi:10.1186/s13059-017-1362-4 · Cited by 676
Muhl, L., Genové, G., Leptidis, S., Liu, J., He, L., Mocci, G., Sun, Y., Gustafsson, S., Buyandelger, B., Chivukula, I. V., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17740-1 · Cited by 684
RNA splicing is a primary link between genetic variation and disease
Li, Y. I., van de Geijn, B., Raj, A., Knowles, D. A., Petti, A. A., Golan, D., Gilad, Y., & Pritchard, J. K. (2016) · Science · doi:10.1126/science.aad9417 · Cited by 660
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
Hudson, W. H., Gensheimer, J., Hashimoto, M., Wieland, A., Valanparambil, R. M., Li, P., Lin, J.-X., Konieczny, B. T., Im, S. J., Freeman, G. J., et al. (2019) · Immunity · doi:10.1016/j.immuni.2019.11.002 · Cited by 656
Locally renewing resident synovial macrophages provide a protective barrier for the joint
Culemann, S., Grüneboom, A., Nicolás-Ávila, J. Á., Weidner, D., Lämmle, K. F., Rothe, T., Quintana, J. A., Kirchner, P., Krljanac, B., Eberhardt, M., et al. (2019) · Nature · doi:10.1038/s41586-019-1471-1 · Cited by 656
Propionic Acid Shapes the Multiple Sclerosis Disease Course by an Immunomodulatory Mechanism
Duscha, A., Gisevius, B., Hirschberg, S., Yissachar, N., Stangl, G. I., Dawin, E., Bader, V., Haase, S., Kaisler, J., David, C., et al. (2020) · Cell · doi:10.1016/j.cell.2020.02.035 · Cited by 654
Nava, M. M., Miroshnikova, Y. A., Biggs, L. C., Whitefield, D. B., Metge, F., Bouças, J., Vihinen, H., Jokitalo, E., Li, X., ARCOS, J. M. G., et al. (2020) · Cell · doi:10.1016/j.cell.2020.03.052 · Cited by 653
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
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
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
Metadata
- 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
- 47
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review