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HTSeq—a Python framework to work with high-throughput sequencing data
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Abstract
Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq . Contact: sanders@fs.tum.de
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“Age-matched male mice were used in this study as these have been widely used for the aging studies and shown to correspond more closely to the phenotype of human aging55,56 and were ten to twenty seven months of age as described in the corresponding figure legends.”
From TERT activation targets DNA methylation and multiple aging hallmarks (2024) · cites this paper as [56]
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“These methods allocate multi-mapping reads among transcript and output within-sample normalized values corrected for sequencing biases [35, 41, 43].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [35]
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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 [30]
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Cites
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
Self-citationTrimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581
Self-citationBioconductor: 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-citationThe NumPy Array: A Structure for Efficient Numerical Computation
van der Walt, S., Colbert, S. C., & Varoquaux, G. (2011) · Computing in Science & Engineering · doi:10.1109/mcse.2011.37 · Cited by 9,381
Biopython: freely available python tools for computational molecular biology and bioinformatics
Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp163 · Cited by 6,080
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Items 401–500 of 1,250
Staged developmental mapping and X chromosome transcriptional dynamics during mouse spermatogenesis
Ernst, C., Eling, N., Martinez‐Jimenez, C. P., Marioni, J. C., & Odom, D. T. (2019) · Nature Communications · doi:10.1038/s41467-019-09182-1 · Cited by 303
Advanced Applications of RNA Sequencing and Challenges
Han, Y., Gao, S., Muegge, K., Zhang, W., & Zhou, B. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s28991 · Cited by 303
Mora‐Bermúdez, F., Badsha, F., Kanton, S., Camp, J. G., Vernot, B., Köhler, K., Voigt, B., Okita, K., Maričić, T., He, Z., et al. (2016) · eLife · doi:10.7554/elife.18683 · Cited by 301
Nm-seq maps 2′-O-methylation sites in human mRNA with base precision
Dai, Q., Moshitch-Moshkovitz, S., Han, D., Kol, N., Amariglio, N., Rechavi, G., Dominissini, D., & He, C. (2017) · Nature Methods · doi:10.1038/nmeth.4294 · Cited by 301
Establishment of mouse expanded potential stem cells
Yang, J. P., Ryan, D. J., Wang, W., Tsang, J. C.-H., Lan, G., Masaki, H., Gao, X., Antunes, L., Yu, Y., Zhu, Z., et al. (2017) · Nature · doi:10.1038/nature24052 · Cited by 301
5-Methylcytosine RNA Methylation in Arabidopsis Thaliana
Cui, X., Liang, Z., Shen, L., Zhang, Q., Bao, S., Geng, Y., Zhang, B., Leo, V. I., Vardy, L. A., Lu, T., et al. (2017) · Molecular Plant · doi:10.1016/j.molp.2017.09.013 · Cited by 299
Endothelial heterogeneity across distinct vascular beds during homeostasis and inflammation
Jambusaria, A., Hong, Z., Zhang, L., Srivastava, S., Jana, A., Tóth, P. T., Dai, Y., Malik, A. B., & Rehman, J. (2020) · eLife · doi:10.7554/elife.51413 · Cited by 299
Nakayama, R. T., Pulice, J. L., Valencia, A. M., McBride, M. J., McKenzie, Z. M., Gillespie, M. A. K., Ku, W. L., Teng, M., Cui, K., Williams, R. T., et al. (2017) · Nature Genetics · doi:10.1038/ng.3958 · Cited by 300
Dobnikar, L., Taylor, A. L., Chappell, J., Oldach, P., Harman, J. J., Oerton, E., Dzierzak, E. A., Bennett, M. R., Spivakov, M., & Jørgensen, H. F. (2018) · Nature Communications · doi:10.1038/s41467-018-06891-x · Cited by 298
Liu, Y., He, S., Wang, X., Peng, W., Chen, Q., Chi, D., Chen, J., Han, B., Lin, G., Li, Y., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21043-4 · Cited by 297
α-Ketoglutarate links p53 to cell fate during tumour suppression
Morris, J. P., Yashinskie, J. J., Koche, R., Chandwani, R., Tian, S., Chen, C.-C., Baslan, T., Marinkovic, Z. S., Sánchez-Rivera, F. J., Leach, S. D., et al. (2019) · Nature · doi:10.1038/s41586-019-1577-5 · Cited by 296
Gene expression in human brain implicates sexually dimorphic pathways in autism spectrum disorders
Werling, D. M., Parikshak, N. N., & Geschwind, D. H. (2016) · Nature Communications · doi:10.1038/ncomms10717 · Cited by 296
Genomic analyses identify recurrent MEF2D fusions in acute lymphoblastic leukaemia
Gu, Z., Churchman, M. L., Roberts, K. G., Li, Y., Liu, Y., Harvey, R. C., McCastlain, K., Reshmi, S. C., Payne-Turner, D., Iacobucci, I., et al. (2016) · Nature Communications · doi:10.1038/ncomms13331 · Cited by 296
Jadhav, U., Saxena, M., O’Neill, N. K., Saadatpour, A., Yuan, G., Herbert, Z. T., Murata, K., & Shivdasani, R. A. (2017) · Cell stem cell · doi:10.1016/j.stem.2017.05.001 · Cited by 296
Singh, N., Lee, Y. G., Shestova, O., Ravikumar, P., Hayer, K. E., Hong, S. J., Lu, X. M., Pajarillo, R., Agarwal, S., Kuramitsu, S., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0813 · Cited by 295
Wan, Y.-W., Al-Ouran, R., Mangleburg, C. G., Perumal, T. M., Lee, T. V., Allison, K., Swarup, V., Funk, C. C., Gaiteri, C., Allen, M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107908 · Cited by 295
“0 (Anders et al., 2015) or using the ‘quantMode’ option from the STAR aligner which utilizes HTSeq algorithm and produces similar results.”
MethodFredriksson, N. J., Ny, L., Nilsson, J. A., & Larsson, E. (2014) · Nature Genetics · doi:10.1038/ng.3141 · Cited by 295
Targeted DNA demethylation of the Arabidopsis genome using the human TET1 catalytic domain
Gallego‐Bartolomé, J., Gardiner, J., Liu, W., Papikian, A., Ghoshal, B., Kuo, H. Y., Zhao, J. M.-C., Segal, D. J., & Jacobsen, S. E. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1716945115 · Cited by 294
Pontis, J., Planet, E., Offner, S., Turelli, P., Duc, J., Coudray, A., Theunissen, T. W., Jaenisch, R., & Trono, D. (2019) · Cell stem cell · doi:10.1016/j.stem.2019.03.012 · Cited by 294
Rom, O., Liu, Y., Liu, Z., Zhao, Y., Wu, J., Ghrayeb, A., Villacorta, L., Fan, Y., Chang, L., Wang, L., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aaz2841 · Cited by 301
Resolving early mesoderm diversification through single-cell expression profiling
Scialdone, A., Tanaka, Y., Jawaid, W., Moignard, V., Wilson, N. K., Macaulay, I. C., Marioni, J. C., & Göttgens, B. (2016) · Nature · doi:10.1038/nature18633 · Cited by 293
Cui, H., Gobbato, E., Kracher, B., Qiu, J., Bautor, J., & Parker, J. E. (2016) · New Phytologist · doi:10.1111/nph.14302 · Cited by 293
Rabanus‐Wallace, M. T., Hackauf, B., Mascher, M., Lux, T. M., Wicker, T., Gundlach, H., Baez, M. A., Houben, A., Mayer, K., Guo, L., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00807-0 · Cited by 293
Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type
Boldog, E., Bakken, T. E., Hodge, R. D., Novotny, M., Aevermann, B. D., Baka, J., Bordé, S., Close, J. L., Diez-Fuertes, F., Ding, S.-L., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0205-2 · Cited by 291
Myocardial Gene Expression Signatures in Human Heart Failure With Preserved Ejection Fraction
Hahn, V. S., Knutsdottir, H., Luo, X., Bedi, K., Margulies, K. B., Haldar, S. M., Stolina, M., Yin, J., Khakoo, A. Y., Vaishnav, J., et al. (2021) · Circulation · doi:10.1161/circulationaha.120.050498 · Cited by 291
ENL links histone acetylation to oncogenic gene expression in acute myeloid leukaemia
Wan, L., Wen, H., Li, Y., Lyu, J., Xi, Y., Hoshii, T., Joseph, J. K., Wang, X., Loh, Y. E., Erb, M. A., et al. (2017) · Nature · doi:10.1038/nature21687 · Cited by 291
Therapeutic Targeting of RNA Splicing Catalysis through Inhibition of Protein Arginine Methylation
Fong, J. Y., Pignata, L., Goy, P.-A. V., Kawabata, K. C., Lee, S. C.-W., Koh, C. M., Musiani, D., Massignani, E., Kotini, A. G., Penson, A., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.07.003 · Cited by 294
Reyes, A., & Huber, W. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1165 · Cited by 290
Self-citationHolmqvist, E., Wright, P. R., Li, L., Bischler, T., Barquist, L., Reinhardt, R., Backofen, R., & Vogel, J. (2016) · The EMBO Journal · doi:10.15252/embj.201593360 · Cited by 290
The evening complex coordinates environmental and endogenous signals in Arabidopsis
Ezer, D., Jung, J., Lan, H., Biswas, S., Gregoire, L., Box, M. S., Charoensawan, V., Cortijo, S., Lai, X., Stӧckle, D., et al. (2017) · Nature Plants · doi:10.1038/nplants.2017.87 · Cited by 290
Promoter-proximal CTCF binding promotes distal enhancer-dependent gene activation
Kubo, N., Ishii, H., Xiong, X., Bianco, S., Meitinger, F., Hu, R., Hocker, J. D., Conte, M., Gorkin, D., Yu, M., et al. (2021) · Nature Structural & Molecular Biology · doi:10.1038/s41594-020-00539-5 · Cited by 289
Pisu, D., Huang, L., Grenier, J. K., & Russell, D. G. (2020) · Cell Reports · doi:10.1016/j.celrep.2019.12.033 · Cited by 289
Franzén, O., Ermel, R., Cohain, A., Akers, N. K., Di Narzo, A., Talukdar, H. A., Foroughi-Asl, H., Giambartolomei, C., Fullard, J. F., Sukhavasi, K., et al. (2016) · Science · doi:10.1126/science.aad6970 · Cited by 288
Rapid magnetic isolation of extracellular vesicles via lipid-based nanoprobes
Wan, Y., Cheng, G., Liu, X., Hao, S.-J., Nisic, M., Zhu, C.-D., Xia, Y.-Q., Li, W.-Q., Wang, Z., Zhang, W.-L., et al. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-017-0058 · Cited by 288
Patch-Seq Links Single-Cell Transcriptomes to Human Islet Dysfunction in Diabetes
Camuñas-Soler, J., Dai, X.-Q., Hang, Y., Bautista, A., Lyon, J. G., Suzuki, K., Kim, S. K., Quake, S. R., & MacDonald, P. E. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.04.005 · Cited by 286
Pirinen, E., Auranen, M., Khan, N. A., Brilhante, V., Urho, N., Pessia, A., Hakkarainen, A., Kuula, J., Heinonen, U., Schmidt, M. S., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.04.008 · Cited by 286
NeuroD1 reprograms chromatin and transcription factor landscapes to induce the neuronal program
Pataskar, A., Jung, J., Smialowski, P., Noack, F., Calegari, F., Straub, T., & Tiwari, V. K. (2015) · The EMBO Journal · doi:10.15252/embj.201591206 · Cited by 285
Dell’Acqua, M., Gatti, D. M., Pea, G., Cattonaro, F., Coppens, F., Magris, G., Hlaing, A., Aung, H., Nelissen, H., Baute, J., et al. (2015) · Genome biology · doi:10.1186/s13059-015-0716-z · Cited by 285
Unique phenotypes and clonal expansions of human CD4 effector memory T cells re-expressing CD45RA
Tian, Y., Babor, M., Lane, J., Schulten, V. M. J., Patil, V. S., Seumois, G., Rosales, S. L., Fu, Z., Picarda, G. G. A., Burel, J. G., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01728-5 · Cited by 284
Sarkar, M. K., Hile, G. A., Tsoi, L. C., Xing, X., Liu, J. J., Liang, Y., Berthier, C. C., Swindell, W. R., Patrick, M. T., Shao, S., et al. (2018) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2018-213197 · Cited by 284
Robust gene expression programs underlie recurrent cell states and phenotype switching in melanoma
Wouters, J., Atak, Z. K., Minnoye, L., Spanier, K. I., Waegeneer, M. D., González‐Blas, C. B., Mauduit, D., Davie, K., Hulselmans, G. J., Najem, A., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0547-3 · Cited by 284
Architecture and Dynamics of the Jasmonic Acid Gene Regulatory Network
Hickman, R. J., Verk, M. C. V., Dijken, A. J. H. V., Mendes, M. P., Vroegop-Vos, I. A., Caarls, L., Steenbergen, M., der Nagel, I. V., Wesselink, G. J., Jironkin, A., et al. (2017) · The Plant Cell · doi:10.1105/tpc.16.00958 · Cited by 283
Colbert, L. E., Alam, M. B. E., Wang, R., Karpinets, T. V., Lo, D., Lynn, E. J., Harris, T. A., Elnaggar, J. H., Yoshida-Court, K., Tomasic, K., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.09.012 · Cited by 286
Genome-wide DNA-methylation landscape defines specialization of regulatory T cells in tissues
Delacher, M., Imbusch, C. D., Weichenhan, D., Breiling, A., Hotz-Wagenblatt, A., Träger, U., Hofer, A.-C., Kägebein, D., Wang, Q., Frauhammer, F., et al. (2017) · Nature Immunology · doi:10.1038/ni.3799 · Cited by 282
SCnorm: robust normalization of single-cell RNA-seq data
Bacher, R., Chu, L.-F., Leng, N., Gasch, A. P., Thomson, J. A., Stewart, R. M., Newton, M., & Kendziorski, C. (2017) · Nature Methods · doi:10.1038/nmeth.4263 · Cited by 282
Akkermansia muciniphila mediates negative effects of IFNγ on glucose metabolism
Greer, R. L., Dong, X., de Moraes, A. C. F., Zielke, R. A., Fernandes, G. D. R., Peremyslova, E., Vasquez‐Perez, S., Schoenborn, A. A., Gomes, E. P., Pereira, A. C., et al. (2016) · Nature Communications · doi:10.1038/ncomms13329 · Cited by 282
McKenna, D. D., Scully, E. D., Pauchet, Y., Hoover, K., Kirsch, R., Geib, S. M., Mitchell, R. F., Waterhouse, R. M., Ahn, S., Arsala, D., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1088-8 · Cited by 282
Fisch, K. M., Gamini, R., Álvarez-García, Ó., Akagi, R., Saito, M., Muramatsu, Y., Sasho, T., Koziol, J. A., Su, A. I., & Lotz, M. (2018) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2018.07.012 · Cited by 282
Embryo model completes gastrulation to neurulation and organogenesis
Amadei, G., Handford, C. E., Qiu, C., Jonghe, J. D., Greenfeld, H., Tran, M., Martin, B., Chen, D.-Y., Aguilera-Castrejon, A., Hanna, J. H., et al. (2022) · Nature · doi:10.1038/s41586-022-05246-3 · Cited by 282
Birkenbihl, R. P., Kracher, B., Roccaro, M., & Somssich, I. E. (2016) · The Plant Cell · doi:10.1105/tpc.16.00681 · Cited by 281
Prolonged Mek1/2 suppression impairs the developmental potential of embryonic stem cells
Choi, J., Huebner, A. J., Clement, K., Walsh, R. M., Savol, A. J., Lin, K., Gu, H., Stefano, B. D., Brumbaugh, J., Kim, S. Y., et al. (2017) · Nature · doi:10.1038/nature23274 · Cited by 281
Mitochondrial translation requires folate-dependent tRNA methylation
Morscher, R. J., Ducker, G. S., Li, H., Mayer, J., Gitai, Z., Sperl, W., & Rabinowitz, J. D. (2018) · Nature · doi:10.1038/nature25460 · Cited by 281
Capturing human trophoblast development with naive pluripotent stem cells in vitro
Io, S., Kabata, M., Iemura, Y., Semi, K., Morone, N., Minagawa, A., Wang, B., Okamoto, I., Nakamura, T., Kojima, Y., et al. (2021) · Cell stem cell · doi:10.1016/j.stem.2021.03.013 · Cited by 281
Dong, L., Chen, C., Zhang, Y., Guo, P., Wang, Z., Li, J., Liu, Y., Liu, J., Chang, R., Li, Y., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.04.016 · Cited by 284
Initiation and amplification of SnRK2 activation in abscisic acid signaling
Lin, Z., Li, Y., Wang, Y., Liu, X., Ma, L., Zhang, Z., Mu, C., Zhang, Y., Li, P., Xie, S., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22812-x · Cited by 280
Distinct Classes of Chromatin Loops Revealed by Deletion of an RNA-Binding Region in CTCF
Hansen, A. S., Hsieh, T.-H. S., Cattoglio, C., Pustova, I., Saldaña-Meyer, R., Reinberg, D., Darzacq, X., & Tjian, R. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.07.039 · Cited by 279
B Cell Super-Enhancers and Regulatory Clusters Recruit AID Tumorigenic Activity
Qian, J., Wang, Q., Dose, M., Pruett, N. D., Kieffer-Kwon, K.-R., Resch, W., Liang, G., Tang, Z., Mathé, E. A., Benner, C. W., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.013 · Cited by 279
The mid-developmental transition and the evolution of animal body plans
Levin, M., Anavy, L., Cole, A. G., Winter, E. E., Mostov, N., Khair, S., Senderovich, N., Kovalev, E., Silver, D. H., Feder, M., et al. (2016) · Nature · doi:10.1038/nature16994 · Cited by 279
Hacquard, S., Kracher, B., Hiruma, K., Münch, P. C., Garrido-Oter, R., Thon, M. R., Weimann, A., Damm, U., Dallery, J.-F., Hainaut, M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11362 · Cited by 278
An interactive environment for agile analysis and visualization of ChIP-sequencing data
Lerdrup, M., Johansen, J. V., Singh, S. A., & Hansen, K. H. (2016) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3180 · Cited by 277
Single-cell analysis reveals the continuum of human lympho-myeloid progenitor cells
Karamitros, D., Stoilova, B., Aboukhalil, Z., Hamey, F., Reinisch, A., Samitsch, M., Quek, L. S., Otto, G., Repapi, E., Doondeea, J. B., et al. (2017) · Nature Immunology · doi:10.1038/s41590-017-0001-2 · Cited by 277
Defining the earliest step of cardiovascular lineage segregation by single-cell RNA-seq
Lescroart, F., Wang, X., Lin, X., Swedlund, B., Gargouri, S., Sánchez‐Danés, A., Moignard, V., Dubois, C., Paulissen, C., Kinston, S., et al. (2018) · Science · doi:10.1126/science.aao4174 · Cited by 277
An efficient KRAB domain for CRISPRi applications in human cells
Alerasool, N., Segal, D., Lee, H., & Taipale, M. (2020) · Nature Methods · doi:10.1038/s41592-020-0966-x · Cited by 277
The SS18-SSX Fusion Oncoprotein Hijacks BAF Complex Targeting and Function to Drive Synovial Sarcoma
McBride, M. J., Pulice, J. L., Beird, H. C., Ingram, D. R., D’Avino, A. R., Shern, J. F., Charville, G. W., Hornick, J. L., Nakayama, R. T., Garcia-Rivera, E., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.05.002 · Cited by 277
Yeung, M. L., Teng, J. L. L., Jia, L., Zhang, C., Huang, C., Cai, J., Zhou, R., Chan, K., Zhao, H., Zhu, L., et al. (2021) · Cell · doi:10.1016/j.cell.2021.02.053 · Cited by 276
The single-cell epigenomic and transcriptional landscape of immunity to influenza vaccination
Wimmers, F., Donato, M., Kuo, A. J., Ashuach, T., Gupta, S., Li, C., Dvorak, M., Foecke, M. H., Chang, S. E., Hagan, T., et al. (2021) · Cell · doi:10.1016/j.cell.2021.05.039 · Cited by 275
IL-10 constrains sphingolipid metabolism to limit inflammation
York, A. G., Skadow, M. H., Oh, J., Qu, R., Zhou, Q., Hsieh, W.-Y., Mowel, W. K., Brewer, J. R., Kaffe, E., Williams, K. J., et al. (2024) · Nature · doi:10.1038/s41586-024-07098-5 · Cited by 274
The Arabidopsis CERK1‐associated kinase PBL27 connects chitin perception to MAPK activation
Yamada, K., Yamaguchi, K., Shirakawa, T., Nakagami, H., Mine, A., Ishikawa, K., Fujiwara, M., Narusaka, M., Narusaka, Y., Ichimura, K., et al. (2016) · The EMBO Journal · doi:10.15252/embj.201694248 · Cited by 273
TERRA RNA Antagonizes ATRX and Protects Telomeres
Chu, H.-P. C., Cifuentes‐Rojas, C., Kesner, B., Aeby, E., Lee, H.-G., Wei, C., Oh, H. J., Boukhali, M., Haas, W., & Lee, J. T. (2017) · Cell · doi:10.1016/j.cell.2017.06.017 · Cited by 273
Xu, P., Zhang, X., Liu, Q., Xie, Y., Shi, X., Chen, J., Li, Y., Guo, H., Sun, R., Hong, Y., et al. (2019) · Cell Death and Disease · doi:10.1038/s41419-019-1777-9 · Cited by 273
de Klein, N., Tsai, E. A., Vochteloo, M., Baird, D., Huang, Y., Chen, C.-Y., van Dam, S., Oelen, R., Deelen, P., Bakker, O. B., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01300-6 · Cited by 272
pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components
Marini, F., & Binder, H. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2879-1 · Cited by 272
An early cell shape transition drives evolutionary expansion of the human forebrain
Benito-Kwiecinski, S. K., Giandomenico, S. L., Sutcliffe, M. A., Riis, E. S., Freire-Pritchett, P., Kelava, I., Wunderlich, S., Martin, U., Wray, G. A., McDole, K., et al. (2021) · Cell · doi:10.1016/j.cell.2021.02.050 · Cited by 272
Codon-specific translation reprogramming promotes resistance to targeted therapy
Rapino, F., Delaunay, S., Rambow, F., Zhou, Z., Tharun, L., De Tullio, P., Sin, O., Shostak, K., Schmitz, S., Piepers, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0243-7 · Cited by 271
Kim, D.-S., Camacho, C. V., Nagari, A., Malladi, V. S., Challa, S., & Kraus, W. L. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.06.020 · Cited by 270
A single gene underlies the dynamic evolution of poplar sex determination
Müller, N. A., Kersten, B., Montalvão, A. P. L., Mähler, N., Bernhardsson, C., Bräutigam, K., Lorenzo, Z. C., Hoenicka, H., Kumar, V., Mäder, M., et al. (2020) · Nature Plants · doi:10.1038/s41477-020-0672-9 · Cited by 270
Extraction and biomolecular analysis of dermal interstitial fluid collected with hollow microneedles
Miller, P. R., Taylor, R. M., Tran, B. Q., Boyd, G., Glaros, T., Chavez, V. H., Krishnakumar, R., Sinha, A., Poorey, K., Williams, K. P., et al. (2018) · Communications Biology · doi:10.1038/s42003-018-0170-z · Cited by 269
Haplotype-phased genome and evolution of phytonutrient pathways of tetraploid blueberry
Colle, M., Leisner, C. P., Wai, C. M., Ou, S., Bird, K. A., Wang, J., Wisecaver, J. H., Yocca, A. E., Alger, E. I., Tang, H., et al. (2019) · GigaScience · doi:10.1093/gigascience/giz012 · Cited by 269
Yoo, S., Song, Y. S., Lee, E. K., Hwang, J., Kim, H. H., Jung, G., Kim, Y. A., Kim, S., Cho, S. W., Won, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10680-5 · Cited by 269
Miguel-Escalada, I., Bonàs‐Guarch, S., Cebola, I., Ponsa-Cobas, J., Mendieta-Esteban, J., Atla, G., Javierre, B. M., Rolando, D., Farabella, I., Morgan, C. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0457-0 · Cited by 269
Design of synthetic bacterial communities for predictable plant phenotypes
Paredes, S. H., Gao, T., Law, T. F., Finkel, O. M., Mucyn, T. S., Teixeira, P. J. P. L., Salas-González, I., Feltcher, M. E., Powers, M. J., Shank, E. A., et al. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2003962 · Cited by 268
Wildfire-dependent changes in soil microbiome diversity and function
Nelson, A. R., Narrowe, A. B., Rhoades, C. C., Fegel, T. S., Daly, R. A., Roth, H. K., Chu, R., Amundson, K. K., Young, R. B., Steindorff, A. S., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01203-y · Cited by 268
Therapeutic targeting of preleukemia cells in a mouse model of NPM1 mutant acute myeloid leukemia
Uckelmann, H. J., Kim, S. M., Wong, E. M. C., Hatton, C., Giovinazzo, H., Gadrey, J. Y., Krivtsov, A. V., Rücker, F. G., Döhner, K., McGeehan, G. M., et al. (2020) · Science · doi:10.1126/science.aax5863 · Cited by 267
Chopra, N., Tovey, H., Pearson, A., Cutts, R., Toms, C., Proszek, P., Hubank, M. J. F., Dowsett, M., Dodson, A. R., Daley, F. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16142-7 · Cited by 267
Plouffe, S. W., Lin, K. C., Moore, J. L., Tan, F. E., Ma, S., Ye, Z., Qiu, Y., Ren, B., & Guan, K. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.ra118.002715 · Cited by 266
Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis
Corchete, L. A., Rojas, E. A., Alonso‐López, D., Rivas, J. D. L., Gutierrez, N. C., & Burguillo, F. J. (2020) · Scientific Reports · doi:10.1038/s41598-020-76881-x · Cited by 266
St. Jude Cloud: A Pediatric Cancer Genomic Data-Sharing Ecosystem
McLeod, C., Gout, A. M., Zhou, X., Thrasher, A., Rahbarinia, D., Brady, S. W., Macias, M., Birch, K., Finkelstein, D., Sunny, J., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1230 · Cited by 274
Polishchuk, E. V., Merolla, A., Lichtmannegger, J., Romano, A., Indrieri, A., Ilyechova, E. Y., Concilli, M., De Cegli, R., Crispino, R., Mariniello, M., et al. (2019) · Gastroenterology · doi:10.1053/j.gastro.2018.11.032 · Cited by 265
Sundell, D., Street, N. R., Kumar, M., Mellerowicz, E. J., Kücükoglu, M., Johnsson, C., Kumar, V., Mannapperuma, C., Delhomme, N., Nilsson, O. R., et al. (2017) · The Plant Cell · doi:10.1105/tpc.17.00153 · Cited by 265
N6-Methyladenosine Inhibits Local Ribonucleolytic Cleavage to Stabilize mRNAs in Arabidopsis
Anderson, S. J., Kramer, M. C., Gosai, S. J., Yu, X., Vandivier, L. E., Nelson, A. D. L., Anderson, Z. D., Beilstein, M. A., Fray, R. G., Lyons, E. H., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.10.020 · Cited by 265
Live-seq enables temporal transcriptomic recording of single cells
Chen, W., Guillaume-Gentil, O. J., Rainer, P. Y., Gäbelein, C. G., Saelens, W., Gardeux, V., Klaeger, A., Dainese, R., Zachara, M., Zambelli, T., et al. (2022) · Nature · doi:10.1038/s41586-022-05046-9 · Cited by 272
MYB orchestrates T cell exhaustion and response to checkpoint inhibition
Tsui, C., Kretschmer, L., Rapelius, S., Gabriel, S. S., Chisanga, D., Knöpper, K., Utzschneider, D. T., Nüssing, S., Liao, Y., Mason, T., et al. (2022) · Nature · doi:10.1038/s41586-022-05105-1 · Cited by 264
Ascl2-dependent cell dedifferentiation drives regeneration of ablated intestinal stem cells
Murata, K., Jadhav, U., Madha, S., van Es, J., Dean, J., Cavazza, A., Wucherpfennig, K., Michor, F., Clevers, H., & Shivdasani, R. A. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2019.12.011 · Cited by 264
Sour Sensing from the Tongue to the Brain
Zhang, J., Jin, H., Zhang, W., Ding, C. H., O’Keeffe, S., Ye, M., & Zuker, C. S. (2019) · Cell · doi:10.1016/j.cell.2019.08.031 · Cited by 263
Polycystic ovary syndrome is transmitted via a transgenerational epigenetic process
Mimouni, N. E. H., Paiva, I., Barbotin, A., Timzoura, F. E., Plassard, D., Gras, S. L., Ternier, G., Pigny, P., Catteau-Jonard, S., Simon, V., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.01.004 · Cited by 263
Vacca, M., Kamzolas, I., Harder, L. M., Oakley, F., Trautwein, C., Hatting, M., Ross, T., Bernardo, B., Oldenburger, A., Hjuler, S. T., et al. (2024) · Nature Metabolism · doi:10.1038/s42255-024-01043-6 · Cited by 262
Richard, A., Loïs, B., Herbach, U., Bonnafoux, A., Morin, V., Vallin, E., Guillemin, A., Gao, N. P., Gunawan, R., Cosette, J., et al. (2016) · PLoS Biology · doi:10.1371/journal.pbio.1002585 · Cited by 262
Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms
Walker, R. L., Ramaswami, G., Hartl, C., Mancuso, N., Gandal, M. J., de la Torre-Ubieta, L., Paşaniuc, B., Stein, J. L., & Geschwind, D. H. (2019) · Cell · doi:10.1016/j.cell.2019.09.021 · Cited by 262
Taftaf, R., Liu, X., Singh, S., Jia, Y., Dashzeveg, N. K., Hoffmann, A. D., El-Shennawy, L., Ramos, E. K., Adorno-Cruz, V., Schuster, E. J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25189-z · Cited by 261
Schiller, H. B., Fernandez, I. E., Burgstaller, G., Schaab, C., Scheltema, R. A., Schwarzmayr, T., Strom, T. M., Eickelberg, O., & Mann, M. (2015) · Molecular Systems Biology · doi:10.15252/msb.20156123 · Cited by 261
Metadata
- Authors
- Anders, S., Pyl, P. T., & Huber, W.
- Affiliations
- European Molecular Biology Laboratory (DE)
- Corresponding author
- Anders, Simon
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-09-25
- Volume
- 31
- Issue
- 2
- Pages
- 166-169
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC4287950
- OpenAlex
- W2134526812
- Cited by
- 21,464
- References
- 18
- Citations by year
- 2026: 1267 · 2025: 1823 · 2024: 2188 · 2023: 2215 · 2022: 2453 · 2021: 2749 · 2020: 2323 · 2019: 1943 · 2018: 1658 · 2017: 1272 · 2016: 901 · 2015: 516 · 2014: 134
- FWCI
- 383.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- European Commission (305626)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- HTSeq, high-throughput sequencing data, Python library, RNA-seq, read counting, differential expression analysis, variant calls, gene models
- MeSH terms
- Gene Expression Regulation, Humans, Software, Genome, Human, Genomics, High-Throughput Nucleotide Sequencing
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