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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.
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“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
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
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Li, M., Jaffe, A. E., Straub, R. E., Tao, R., Shin, J. H., Wang, Y., Chen, Q., Li, C., Jia, Y., Ohi, K., et al. (2016) · Nature Medicine · doi:10.1038/nm.4096 · Cited by 163
Dietary Restriction Extends Lifespan through Metabolic Regulation of Innate Immunity
Wu, Z., Isik, M., Moroz, N., Steinbaugh, M. J., Zhang, P., & Blackwell, T. K. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.02.013 · Cited by 163
Kastenhuber, E. R., Lalazar, G., Houlihan, S. L., Tschaharganeh, D. F., Baslan, T., Chen, C.-C., Requena, D., Tian, S., Bosbach, B., Wilkinson, J. E., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1716483114 · Cited by 163
Jardé, T., Chan, W. H., Rossello, F. J., Kahlon, T. K., Theocharous, M., Arackal, T. K., Flores, T. J., Giraud, M., Richards, E., Chan, E., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.06.021 · Cited by 163
Targeting DNA Damage Response and Replication Stress in Pancreatic Cancer
Dreyer, S. B., Upstill‐Goddard, R., Paulus-Hock, V., Paris, C., Lampraki, E.-M., Dray, E. V., Serrels, B., Caligiuri, G., Rebus, S., Plenker, D., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.09.043 · Cited by 165
Single-cell multiomics sequencing reveals the functional regulatory landscape of early embryos
Wang, Y., Yuan, P., Yan, Z., Yang, M., Huo, Y., Nie, Y., Zhu, X., Qiao, J., & Yan, L. (2021) · Nature Communications · doi:10.1038/s41467-021-21409-8 · Cited by 163
Deng, S., Wang, C., Wang, Y., Xu, Y., Li, X., Johnson, N. A., Mukherji, A., Lo, U., Xu, L., Gonzalez, J., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00431-9 · Cited by 163
A disease-associated gene desert directs macrophage inflammation through ETS2
Stankey, C. T., Bourges, C., Haag, L.-M., Turner‐Stokes, T., Piedade, A. P., Palmer-Jones, C., Papa, I., dos Santos, M. S., Zhang, Q., Cameron, A. J., et al. (2024) · Nature · doi:10.1038/s41586-024-07501-1 · Cited by 163
Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging
Ma, S., Ji, Z., Zhang, B., Geng, L., Cai, Y., Nie, C., Li, J., Zuo, Y., Sun, Y., Xu, G., et al. (2024) · Cell · doi:10.1016/j.cell.2024.10.019 · Cited by 162
Brett, J. O., Arjona, M., Ikeda, M., Quarta, M., de Morrée, A., Egner, I. M., Perandini, L. A., Ishak, H. D., Goshayeshi, A., Benjamin, D. I., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-0190-0 · Cited by 162
Qin, Y., Roberts, J. D., Grimm, S. A., Lih, F. B., Deterding, L. J., Li, R., Chrysovergis, K., & Wade, P. A. (2018) · Genome biology · doi:10.1186/s13059-018-1389-1 · Cited by 162
Vara, C., Paytuví-Gallart, A., Cuartero, Y., Dily, F. L., Garcia, F., Salvà-Castro, J., Gómez-H, L., Julià, E., Moutinho, C., Cigliano, R. A., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.06.037 · Cited by 162
Liu, X., Bie, X. M., Lin, X., Li, M., Wang, H., Zhang, X., Yang, Y., Zhang, X. S., Zhang, X. S., & Xiao, J. Y. (2023) · Nature Plants · doi:10.1038/s41477-023-01406-z · Cited by 162
Distinct longevity mechanisms across and within species and their association with aging
Tyshkovskiy, A., Ma, S., Shindyapina, A. V., Tikhonov, S., Lee, S.-G., Bozaykut, P., Castro, J. P., Seluanov, A., Schork, N. J., Gorbunova, V., et al. (2023) · Cell · doi:10.1016/j.cell.2023.05.002 · Cited by 162
Litvinchuk, A., Suh, J. H., Guo, J. L., Lin, K., Davis, S. S., Bien-Ly, N., Tycksen, E., Tabor, G. T., Remolina Serrano, J., Manis, M., et al. (2024) · Neuron · doi:10.1016/j.neuron.2023.10.023 · Cited by 161
Comparison of rhesus and cynomolgus macaques as an infection model for COVID-19
Salguero, F. J., White, A., Slack, G. S., Fotheringham, S. A., Bewley, K. R., Gooch, K. E., Longet, S., Humphries, H. E., Watson, R. J., Hunter, L. E., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21389-9 · Cited by 163
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Maor-Nof, M., Shipony, Z., Lopez-Gonzalez, R., Nakayama, L., Zhang, Y., Couthouis, J., Blum, J. A., Castruita, P. A., Linares, G. R., Ruan, K., et al. (2021) · Cell · doi:10.1016/j.cell.2020.12.025 · Cited by 161
Yang, C. G., Zhang, H., Chen, M., Wang, S., Qian, R., Zhang, L., Huang, X., Wang, J., Liu, Z., Qin, W., et al. (2022) · eLife · doi:10.7554/elife.71880 · Cited by 161
Heyn, P. C., Logan, C. V., Fluteau, A., Challis, R. C., Auchynnikava, T., Martin, C.-A., Marsh, J. A., Taglini, F., Kilanowski, F. M., Parry, D. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0274-x · Cited by 160
Weedall, G. D., Mugenzi, L. M. J., Menze, B. D., Tchouakui, M., Ibrahim, S. S., Adjia, N. A., Irving, H., Wondji, M. J., Tchoupo, M., Djouaka, R., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aat7386 · Cited by 160
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Hancock, M., Meyer, R. C., Mistry, M., Khetani, R. S., Wagschal, A., Shin, T., Sui, S. J. H., Näär, A. M., & Flanagan, J. G. (2019) · Cell · doi:10.1016/j.cell.2019.02.030 · Cited by 160
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Raajendiran, A., Ooi, G., Bayliss, J., O’Brien, P. E., Schittenhelm, R. B., Clark, A. K., Taylor, R. A., Rodeheffer, M. S., Burton, P. R., & Watt, M. J. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.04.010 · Cited by 160
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Yang, M., Yu, H., Xiu, Y., Liang, S., Hu, Y., Luo, Y., Izsvák, Z., Sun, C., & Wang, J. (2022) · Cell stem cell · doi:10.1016/j.stem.2022.01.010 · Cited by 160
Wu, K. K.-L., Xu, X., Wu, M., Li, X., Hoque, M., Li, G. H., Lian, Q., Long, K., Zhou, T., Piao, H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53006-w · Cited by 160
Chiou, Y., Yang, Y., Rashid, N. U., Ye, R., Selby, C. P., & Sancar, A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1612917113 · Cited by 159
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Lasrado, R., Boesmans, W., Kleinjung, J., Pin, C., Bell, D. M., Bhaw, L., McCallum, S. E., Zong, H., Luo, L., Clevers, H. C., et al. (2017) · Science · doi:10.1126/science.aam7511 · Cited by 159
Hoffman, G. E., Hartley, B. J., Flaherty, E. K., Ladran, I., Gochman, P., Ruderfer, D. M., Stahl, E. A., Rapoport, J. L., Sklar, P. B., & Brennand, K. (2017) · Nature Communications · doi:10.1038/s41467-017-02330-5 · Cited by 162
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Campagne, A., Lee, M.-K., Zielinski, D., Michaud, A., Corre, S. L., Dingli, F., Chen, H., Shahidian, L. Z., Vassilev, I., Servant, N., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08255-x · Cited by 159
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Yue, X., Lio, C. J., Samaniego‐Castruita, D., Li, X., & Rao, A. (2019) · Nature Communications · doi:10.1038/s41467-019-09541-y · Cited by 159
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Jain, D., Puno, M. R., Meydan, C., Lailler, N., Mason, C. E., Lima, C. D., Anderson, K. V., & Keeney, S. (2018) · eLife · doi:10.7554/elife.30919 · Cited by 159
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Della‐Torre, E., Rigamonti, E., Perugino, C. A., Sain, S. B., Sun, N., Kaneko, N., Maehara, T., Rovati, L., Ponzoni, M., Milani, R., et al. (2019) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2019.07.004 · Cited by 159
Choi, J., Lyons, D. B., Kim, M. Y., Moore, J. D., & Zilberman, D. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.10.011 · Cited by 159
Fjæstad, K. Y., Rømer, A. M. A., Goitea, V. E., Johansen, A. Z., Thorseth, M., Carretta, M., Engelholm, L. H., Grøntved, L., Junker, N., & Madsen, D. H. (2022) · Oncogene · doi:10.1038/s41388-021-02170-0 · Cited by 163
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Gergues, M. M., Han, K. J., Choi, H. S., Brown, B., Clausing, K. J., Turner, V. S., Vainchtein, I. D., Molofsky, A. V., & Kheirbek, M. A. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0705-8 · Cited by 158
Girdhar, K., Hoffman, G. E., Jiang, Y., Brown, L., Kundakovic, M., Hauberg, M. E., Francoeur, N. J., Wang, Y.-C., Shah, H., Kavanagh, D. H., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0187-0 · Cited by 139
Vannozzi, A., Wong, D. C. J., ll, J. H. ⁄., Hmmam, I., Matus, J. T., Bogs, J., Ziegler, T., Dry, I. B., Barcaccia, G., & Lucchin, M. (2018) · Plant and Cell Physiology · doi:10.1093/pcp/pcy045 · Cited by 158
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Radke, J., Ishaque, N., Koll, R., Gu, Z., Schumann, E., Sieverling, L., Uhrig, S., Hübschmann, D., Toprak, U. H., López, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30050-y · Cited by 158
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Simats, A., Zhang, S., Messerer, D., Chong, F., Beşkardeş, S., Chivukula, A. S., Cao, J., Besson‐Girard, S., Montellano, F. A., Morbach, C., et al. (2024) · Cell · doi:10.1016/j.cell.2024.06.028 · Cited by 158
Arnoriaga-Rodríguez, M., Mayneris-Perxachs, J., Burokas, A., Contreras-Rodríguez, O., Blasco, G., Coll, C., Biarnés, C., Miranda-Olivos, R., Latorre, J., Moreno-Navarrete, J.-M., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.09.002 · Cited by 157
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Choi, Y. J., Lin, C., Risso, D., Chen, S., Kim, T. A., Tan, M. H., Li, J. B., Wu, Y., Chen, C., Xuan, Z., et al. (2017) · Science · doi:10.1126/science.aag1927 · Cited by 158
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Ahrens, S., Wu, M. V., Furlan, A., Hwang, G., Paik, R., Li, H., Penzo, M. A., Tollkühn, J., & Li, B. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0705-18.2018 · Cited by 157
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Mei, H., Kozuka, C., Hayashi, R., Kumon, M., Koseki, H., & Inoue, A. (2021) · Nature Genetics · doi:10.1038/s41588-021-00820-3 · Cited by 157
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Argaw-Denboba, A., Schmidt, T., Giacomo, M. D., Ranjan, B., Devendran, S., Mastrorilli, E., Lloyd, C. T., Pugliese, D., Paribeni, V., Dabin, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07336-w · Cited by 157
Messmer, T., von Meyenn, F., Savino, A., Santos, F., Mohammed, H., Lun, A. T. L., Marioni, J. C., & Reik, W. (2019) · Cell Reports · doi:10.1016/j.celrep.2018.12.099 · Cited by 156
Chung, H.-Y., Wickel, J., Hahn, N., Mein, N., Schwarzbrunn, M., Koch, P., Ceanga, M., Haselmann, H., Baade-Büttner, C., von Stackelberg, N., et al. (2023) · Science Advances · doi:10.1126/sciadv.abq7806 · Cited by 156
Sceneay, J., Goreczny, G. J., Wilson, K., Morrow, S., DeCristo, M. J., Ubellacker, J. M., Qin, Y., Laszewski, T., Stover, D. G., Barrera, V., et al. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-18-1454 · Cited by 156
Jaffe, A. E., Shin, J., Collado-Torres, L., Leek, J. T., Tao, R., Li, C., Gao, Y., Jia, Y., Maher, B. J., Hyde, T. M., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.3898 · Cited by 156
Lavy, M., Prigge, M. J., Tao, S., Shain, S., Kuo, A., Kirchsteiger, K., & Estelle, M. A. (2016) · eLife · doi:10.7554/elife.13325 · Cited by 156
Zhang, J., Yang, Y., Zheng, K., Xie, M., Feng, K., Jawdy, S. S., Gunter, L. E., Ranjan, P., Singan, V. R., Engle, N. L., et al. (2018) · New Phytologist · doi:10.1111/nph.15297 · Cited by 156
Fotakis, P., Kothari, V. M., Thomas, D. G., Westerterp, M., Molusky, M. M., Altin, E. S., Abramowicz, S., Wang, N., He, Y., Heinecke, J. W., et al. (2019) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.119.313253 · Cited by 156
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Gong, S., Gaccioli, F., Dopierala, J., Sovio, U., Cook, E., Volders, P., Martens, L., Kirk, P., Richardson, S. T., Smith, G. C. S., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22695-y · Cited by 157
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Walitt, B. T., Singh, K., LaMunion, S. R., Hallett, M., Jacobson, S., Chen, K. Y., Enose‐Akahata, Y., Apps, R., Barb, J. J., Bédard, P., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45107-3 · Cited by 162
Jacobs, J., Atkins, M., Davie, K., Imrichova, H., Romanelli, L., Christiaens, V., Hulselmans, G., Potier, D., Wouters, J., Taskiran, I. I., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0140-x · Cited by 155
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Locatelli, G., Theodorou, D., Kendirli, A., Jordão, M. J. C., Staszewski, O., Phulphagar, K., Cantuti-Castelvetri, L., Dagkalis, A., Bessis, A., Simons, M., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0212-3 · Cited by 155
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Hooks, K. B., Audoux, J., Fazli, H., Lesjean, S., Ernault, T., Dugot‐Senant, N., Lesté-Lasserre, T., Hagedorn, M., Rousseau, B., Danet, C., et al. (2017) · Hepatology · doi:10.1002/hep.29672 · Cited by 155
Legrand, J. M. D., Chan, A.-L., La, H. M., Rossello, F. J., Änkö, M., Fuller-Pace, F. V., & Hobbs, R. M. (2019) · Nature Communications · doi:10.1038/s41467-019-09972-7 · Cited by 155
Nagpal, A., Redvers, R. P., Ling, X., Ayton, S., Fuentes, M., Tavancheh, E., Diala, I., Lalani, A. S., Loi, S., David, S., et al. (2019) · Breast Cancer Research · doi:10.1186/s13058-019-1177-1 · Cited by 155
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Jayavelu, A. K., Schnoeder, T. M., Perner, F., Herzog, C., Meiler, A., Krishnamoorthy, G., Huber, N., Mohr, J., Edelmann-Stephan, B., Austin, R. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2968-3 · Cited by 155
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Bal, É., Kumar, R., Hadigol, M., Holmes, A. B., Hilton, L. K., Loh, J. W., Dreval, K., Wong, J., Vlasevska, S., Corinaldesi, C., et al. (2022) · Nature · doi:10.1038/s41586-022-04906-8 · Cited by 155
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Lankiewicz, T. S., Choudhary, H., Gao, Y., Amer, B., Lillington, S. P., Leggieri, P. A., Brown, J. L., Swift, C. L., Lipzen, A., Na, H., et al. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01336-8 · Cited by 165
Rege, J., Turcu, A., Kasa-Vubu, J. Z., Lerario, A. M., Auchus, G. C., Auchus, R. J., Smith, J. M., White, P. C., & Rainey, W. E. (2018) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2018-00736 · Cited by 154
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Zhang, X., Zhang, Y., Wang, P., Zhang, S., Dong, Y., Zeng, G., Yan, Y., Sun, L., Wu, Q., Liu, H., et al. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.09.016 · Cited by 154
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Chothani, S. P., Adami, E., Widjaja, A. A., Langley, S. R., Viswanathan, S., Pua, C. J., Tham, N., Harmston, N., D’Agostino, G. A., Whiffin, N., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.06.023 · Cited by 154
George, K. W., Thompson, M. G., Kim, J., Baidoo, E. E. K., Wang, G., Benites, V. T., Petzold, C. J., Chan, L. J. G., Yilmaz, S., Turhanen, P. A., et al. (2018) · Metabolic Engineering · doi:10.1016/j.ymben.2018.03.004 · Cited by 153
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Zhao, T., Fu, Y., Zhu, J., Liu, Y., Zhang, Q., Yi, Z., Chen, S., Jiao, Z., Xu, X., Xu, J., et al. (2018) · Cell stem cell · doi:10.1016/j.stem.2018.05.025 · Cited by 153
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Nugent, B. M., O’Donnell, C. M., Epperson, C. N., & Bale, T. L. (2018) · Nature Communications · doi:10.1038/s41467-018-04992-1 · Cited by 153
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis
den Berge, K. V., Hembach, K., Soneson, C., Tiberi, S., Clement, L., Love, M. I., Patro, R., & Robinson, M. D. (2019) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-072018-021255 · Cited by 153
Abe, K., Yamashita, A., Morioka, M., Horike, N., Takei, Y., Koyamatsu, S., Okita, K., Matsuda, S., & Tsumaki, N. (2023) · Nature Communications · doi:10.1038/s41467-023-36408-0 · Cited by 153
Transcription of MERVL retrotransposons is required for preimplantation embryo development
Sakashita, A., Kitano, T., Ishizu, H., Guo, Y., Masuda, H., Ariura, M., Murano, K., & Siomi, H. (2023) · Nature Genetics · doi:10.1038/s41588-023-01324-y · Cited by 153
Gene activation precedes DNA demethylation in response to infection in human dendritic cells
Pacis, A., Mailhot-Léonard, F., Tailleux, L., Randolph, H. E., Yotova, V., Dumaine, A., Grenier, J., & Barreiro, L. B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1814700116 · Cited by 152
Gothe, H. J., Bouwman, B. A. M., Gusmao, E. G., Piccinno, R., Petrosino, G., Sayols, S., Drechsel, O., Minneker, V., Josipović, N., Mizi, A., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.05.015 · Cited by 152
Turner, A. W., Hu, S. S., Mosquera, J. V., Feng, W., Hodonsky, C. J., Wong, D., Auguste, G. E., Song, Y., Sol‐Church, K., Farber, E., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01069-0 · Cited by 152
Ding, Y., Hao, Y., Wu, S., Fu, D., Li, M., Gong, Z., & Yang, S. (2022) · Science Advances · doi:10.1126/sciadv.abn7901 · Cited by 152
Exon junction complex shapes the m6A epitranscriptome
Yang, X., Triboulet, R., Liu, Q., Sendinc, E., & Gregory, R. I. (2022) · Nature Communications · doi:10.1038/s41467-022-35643-1 · Cited by 152
Harr, B., Karakoç, E., Neme, R., Teschke, M., Pfeifle, C., Pezer, Ž., Babiker, H. M. A., Linnenbrink, M., Montero, I., Scavetta, R. J., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.75 · Cited by 152
Tumor-associated B-cells induce tumor heterogeneity and therapy resistance
Somasundaram, R., Zhang, G., Fukunaga-Kalabis, M., Perego, M., Krepler, C., Xu, X., Wagner, C., Hristova, D., Zhang, J., Tian, T., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00452-4 · Cited by 151
The Polycomb-Dependent Epigenome Controls β Cell Dysfunction, Dedifferentiation, and Diabetes
Lu, T. T.-H., Heyne, S., Dror, E., Casas, E., Leonhardt, L., Boenke, T., Yang, C.-H., Sagar, Arrigoni, L., Dalgaard, K., et al. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.04.013 · Cited by 151
Branched-chain amino acid catabolism is a conserved regulator of physiological ageing
Mansfeld, J., Urban, N., Priebe, S., Groth, M., Frahm, C., Hartmann, N., Gebauer, J., Ravichandran, M., Dommaschk, A., Schmeisser, S., et al. (2015) · Nature Communications · doi:10.1038/ncomms10043 · Cited by 151
Kühl, I., Miranda, M., Posse, V., Milenkovic, D., Mourier, A., Siira, S. J., Bonekamp, N. A., Neumann, U., Filipovska, A., Polosa, P. L., et al. (2016) · Science Advances · doi:10.1126/sciadv.1600963 · Cited by 151
Super enhancers define regulatory subtypes and cell identity in neuroblastoma
Gartlgruber, M., Sharma, A. K., Quintero, A., Dreidax, D., Jansky, S., Park, Y.-G., Kreth, S., Meder, J., Doncevic, D., Saary, P., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-00145-w · Cited by 151
Wu, Y., Ma, L., Cai, S., Zhuang, Z., Zhao, Z., Jin, S., Xie, W., Zhou, L., Zhang, L., Zhao, J., et al. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00575-7 · Cited by 151
TCR signal strength defines distinct mechanisms of T cell dysfunction and cancer evasion
Shakiba, M., Zumbo, P., Espinosa-Carrasco, G., Menocal, L., Dündar, F., Carson, S. E., Bruno, E. M., Rivera, F. J. S., Lowe, S. W., Camara, S., et al. (2021) · The Journal of Experimental Medicine · doi:10.1084/jem.20201966 · Cited by 151
Dichotomous engagement of HDAC3 activity governs inflammatory responses
Nguyen, H. C. B., Adlanmerini, M., Hauck, A. K., & Lazar, M. A. (2020) · Nature · doi:10.1038/s41586-020-2576-2 · Cited by 150
Baumgart, M., Priebe, S., Groth, M., Hartmann, N., Menzel, U., Pandolfini, L., Koch, P., Felder, M., Ristow, M., Englert, C., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.01.014 · Cited by 150
An atlas of genetic scores to predict multi-omic traits
Xu, Y., Ritchie, S. C., Liang, Y., Timmers, P. R. H. J., Pietzner, M., Lannelongue, L., Lambert, S. A., Tahir, U. A., May-Wilson, S., Foguet, C., et al. (2023) · Nature · doi:10.1038/s41586-023-05844-9 · Cited by 150
OBOX regulates mouse zygotic genome activation and early development
Ji, S., Chen, F., Stein, P., Wang, J., Zhou, Z., Wang, L., Zhao, Q., Lin, Z., Liu, B., Xu, K., et al. (2023) · Nature · doi:10.1038/s41586-023-06428-3 · Cited by 150
Kidney-infiltrating T cells in murine lupus nephritis are metabolically and functionally exhausted
Tilstra, J. S., Avery, L., Menk, A. V., Gordon, R. A., Smita, S., Kane, L. P., Chikina, M., Delgoffe, G. M., & Shlomchik, M. J. (2018) · Journal of Clinical Investigation · doi:10.1172/jci120859 · Cited by 149
Cardiometabolic characteristics of people with metabolically healthy and unhealthy obesity
Petersen, M. C., Smith, G. I., Palacios, H. H., Farabi, S. S., Yoshino, M., Yoshino, J., Cho, K., Davila-Roman, V. G., Shankaran, M., Barve, R. A., et al. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.03.002 · Cited by 149
Deciphering the Innate Lymphoid Cell Transcriptional Program
Seillet, C., Mielke, L. A., Amann‐Zalcenstein, D., Su, S., Gao, J., Almeida, F. F., Shi, W., Ritchie, M. E., Naik, S. H., Huntington, N. D., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.09.025 · Cited by 149
Self-citationClonal polymorphism and high heterozygosity in the celibate genome of the Amazon molly
Warren, W. C., García-Pérez, R., Xu, S., Lampert, K. P., Chalopin, D., Stöck, M., Loewe, L., Lü, Y., Kuderna, L. F. K., Minx, P. J., et al. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0473-y · Cited by 149
Transcriptional recording by CRISPR spacer acquisition from RNA
Schmidt, F., Cherepkova, M. Y., & Platt, R. J. (2018) · Nature · doi:10.1038/s41586-018-0569-1 · Cited by 149
MdUGT88F1-Mediated Phloridzin Biosynthesis Regulates Apple Development and Valsa Canker Resistance
Zhou, K., Hu, L., Li, Y., Chen, X., Zhang, Z., Liu, B., Li, P., Gong, X., & Ma, F. (2019) · PLANT PHYSIOLOGY · doi:10.1104/pp.19.00494 · Cited by 149
Modeling Human Cancer-induced Cachexia
Talbert, E. E., Cuitiño, M. C., Ladner, K. J., Rajasekerea, P. V., Siebert, M., Shakya, R., Leone, G. W., Ostrowski, M. C., Paleo, B. J., Weisleder, N. L., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.07.016 · Cited by 151
Ji, Y., Huang, W., Wu, B., Fang, Z., & Wang, X. (2020) · Journal of Experimental Botany · doi:10.1093/jxb/eraa256 · Cited by 149
Non-redundant functions of group 2 innate lymphoid cells
Jarick, K. J., Forster, P. M., Jakob, M. O., Yano, H., Arifuzzaman, M., Gao, X., Boulekou, S., Stokic-Trtica, V., Leclère, P. S., Preußer, A., et al. (2022) · Nature · doi:10.1038/s41586-022-05395-5 · Cited by 149
Jessa, S., Mohammadnia, A., Harutyunyan, A. S., Hulswit, M., Varadharajan, S., Lakkis, H., Kabir, N., Bashardanesh, Z., Hébert, S., Faury, D., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01205-w · Cited by 150
Precardiac organoids form two heart fields via Bmp/Wnt signaling
Andersen, P., Tampakakis, E., Jimenez, D. V., Kannan, S., Miyamoto, M., Shin, H. K., Saberi, A., Murphy, S., Sulistio, E., Chelko, S. P., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05604-8 · Cited by 132
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
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