The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
TopHat: discovering splice junctions with RNA-Seq
Open accessAbstract
Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites. Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development. Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu Contact: cole@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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.
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
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Bartel (2004) · doi:10.1016/s0092-8674(04)00045-5 · Cited by 34,714
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
Self-citationBreiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Cited by
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
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
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
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Tissue-based map of the human proteome
Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A., et al. (2015) · Science · doi:10.1126/science.1260419 · Cited by 15,286
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
Self-citationKim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r36 · Cited by 12,668
Self-citationCircular RNAs are a large class of animal RNAs with regulatory potency
Memczak, S., Jens, M., Elefsinioti, A., Torti, F., Krueger, J., Rybak, A., Maier, L., Mackowiak, S. D., Gregersen, L. H., Munschauer, M., et al. (2013) · Nature · doi:10.1038/nature11928 · Cited by 7,990
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062
Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq
Dominissini, D., Moshitch-Moshkovitz, S., Schwartz, S., Salmon-Divon, M., Ungar, L., Osenberg, S., Cesarkas, K., Jacob-Hirsch, J., Amariglio, N., Kupiec, M., et al. (2012) · Nature · doi:10.1038/nature11112 · Cited by 4,884
N6-methyladenosine-dependent regulation of messenger RNA stability
Wang, X., Lu, Z., Gomez, A., Hon, G. C., Yue, Y., Han, D., Fu, Y., Parisien, M., Dai, Q., Jia, G., et al. (2014) · Nature · doi:10.1038/nature12730 · Cited by 4,558
Comprehensive Analysis of mRNA Methylation Reveals Enrichment in 3′ UTRs and near Stop Codons
Meyer, K. D., Saletore, Y., Zumbo, P., Elemento, O., Mason, C. E., & Jaffrey, S. R. (2012) · Cell · doi:10.1016/j.cell.2012.05.003 · Cited by 4,223
Super-enhancers in the control of cell identity and disease
Hnisz, D., Abraham, B. J., Lee, T. I., Lau, A., Saint-André, V., Sigova, A. A., Hoke, H. A., & Young, R. A. (2013) · Cell · doi:10.1016/j.cell.2013.09.053 · Cited by 3,662
Fagerberg, L., Hallström, B. M., Oksvold, P., Kampf, C., Djureinovic, D., Odeberg, J., Habuka, M., Tahmasebpoor, S., Danielsson, A., Edlund, K., et al. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.035600 · Cited by 3,665
ALKBH5 Is a Mammalian RNA Demethylase that Impacts RNA Metabolism and Mouse Fertility
Zheng, G., Dahl, J. A., Niu, Y., Fedorcsak, P., Huang, C.-M., Li, C. J., Vågbø, C. B., Shi, Y., Wang, W.-L., Song, S.-H., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.015 · Cited by 3,550
N6-methyladenosine Modulates Messenger RNA Translation Efficiency
Wang, X., Zhao, B. S., Roundtree, I. A., Lu, Z., Han, D., Ma, H., Weng, X., Chen, K., Shi, H., & He, C. (2015) · Cell · doi:10.1016/j.cell.2015.05.014 · Cited by 3,483
The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote
Liao, Y., Smyth, G. K., & Shi, W. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt214 · Cited by 3,064
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
Shen, S., Park, J. W., Lu, Z.-X., Lin, L., Henry, M. D., Wu, Y. N., Zhou, Q., & Xing, Y. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419161111 · Cited by 2,713
Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer
Öhlund, D., Handly-Santana, A., Biffi, G., Elyada, E., Almeida, A. S., Ponz-Sarvise, M., Corbo, V., Oni, T. E., Hearn, S. A., Lee, E. J., et al. (2017) · Journal of Experimental Medicine · doi:10.1084/jem.20162024 · Cited by 2,590
Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma
Ally, A., Balasundaram, M., Carlsen, R., Chuah, E., Clarke, A., Dhalla, N., Holt, R. A., Jones, S. J. M., Lee, D., Ma, Y., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.046 · Cited by 2,490
Coactivator condensation at super-enhancers links phase separation and gene control
Sabari, B. R., Dall’Agnese, A., Boija, A., Klein, I. A., Coffey, E. L., Shrinivas, K., Abraham, B. J., Hannett, N. M., Zamudio, A. V., Manteiga, J. C., et al. (2018) · Science · doi:10.1126/science.aar3958 · Cited by 2,595
An atlas of active enhancers across human cell types and tissues
The FANTOM Consortium, Andersson, R., Gebhard, C., Miguel-Escalada, I., Hoof, I., Bornholdt, J., Boyd, M., Chen, Y., Zhao, X., Schmidl, C., et al. (2014) · Nature · doi:10.1038/nature12787 · Cited by 2,550
Mammalian WTAP is a regulatory subunit of the RNA N6-methyladenosine methyltransferase
Ping, X.-L., Sun, B.-F., Wang, L., Xiao, W., Yang, X., Wang, W.-J., Adhikari, S., Shi, Y., Lv, Y., Chen, Y.-S., et al. (2014) · Cell Research · doi:10.1038/cr.2014.3 · Cited by 2,375
Mills, E. L., Kelly, B., Logan, A., Costa, A. S. H., Varma, M., Bryant, C. E., Tourlomousis, P., Däbritz, J. H. M., Gottlieb, E., Latorre, I., et al. (2016) · Cell · doi:10.1016/j.cell.2016.08.064 · Cited by 2,291
Tissue-Resident Macrophage Enhancer Landscapes Are Shaped by the Local Microenvironment
Lavin, Y., Winter, D., Blecher-Gonen, R., David, E., Keren-Shaul, H., Merad, M., Jung, S., & Amit, I. (2014) · Cell · doi:10.1016/j.cell.2014.11.018 · Cited by 2,167
Circ-ZNF609 is a circular RNA that can be translated and functions in myogenesis
Legnini, I., Di Timoteo, G., Rossi, F., Morlando, M., Briganti, F., Sthandier, O., Fatica, A., Santini, T., Andronache, A., Wade, M., et al. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.02.017 · Cited by 2,173
RNA sequencing: advances, challenges and opportunities
Ozsolak, F., & Milos, P. M. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2934 · Cited by 2,135
Dixon, S. J., Patel, D. N., Welsch, M., Skouta, R., Lee, E. D., Hayano, M., Thomas, A. G., Gleason, C. E., Tatonetti, N. P., Slusher, B. S., et al. (2014) · eLife · doi:10.7554/elife.02523 · Cited by 2,088
Holt, C., & Yandell, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-491 · Cited by 2,118
N6-methyladenosine-dependent RNA structural switches regulate RNA–protein interactions
Liu, N., Dai, Q., Zheng, G., He, C., Parisien, M., & Pan, T. (2015) · Nature · doi:10.1038/nature14234 · Cited by 1,988
Fast and SNP-tolerant detection of complex variants and splicing in short reads
Wu, T. D., & Nacu, S. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq057 · Cited by 1,894
Improving RNA-Seq expression estimates by correcting for fragment bias
Roberts, A., Trapnell, C., Donaghey, J., Rinn, J. L., & Pachter, L. (2011) · Genome Biology · doi:10.1186/gb-2011-12-3-r22 · Cited by 1,725
Self-citationYTHDF3 facilitates translation and decay of N6-methyladenosine-modified RNA
Shi, H., Wang, X., Lu, Z., Zhao, B. S., Ma, H., Hsu, P. J., Liu, C., & He, C. (2017) · Cell Research · doi:10.1038/cr.2017.15 · Cited by 1,815
Detecting differential usage of exons from RNA-seq data
Anders, S., Reyes, A., & Huber, W. (2012) · Genome Research · doi:10.1101/gr.133744.111 · Cited by 1,545
Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease
Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A. A., Zhang, C., Xie, T., Tran, L., Dobrin, R., et al. (2013) · Cell · doi:10.1016/j.cell.2013.03.030 · Cited by 1,657
Differential expression in RNA-seq: A matter of depth
Tarazona, S., García-Alcalde, F., Dopazo, J., Ferrer, A., & Conesa, A. (2011) · Genome Research · doi:10.1101/gr.124321.111 · Cited by 1,632
Repetitive DNA and next-generation sequencing: computational challenges and solutions
Treangen, T. J., & Salzberg, S. L. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3117 · Cited by 1,597
Self-citationBayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348
The origin and evolution of mutations in acute myeloid leukemia
Welch, J. S., Ley, T. J., Link, D. C., Miller, C. A., Larson, D. E., Koboldt, D. C., Wartman, L. D., Lamprecht, T. L., Liu, F., Xia, J., et al. (2012) · Cell · doi:10.1016/j.cell.2012.06.023 · Cited by 1,575
SC3: consensus clustering of single-cell RNA-seq data
Kiselev, V. Y., Kirschner, K., Schaub, M. T., Andrews, T., Yiu, A., Chandra, T., Natarajan, K. N., Reik, W., Barahona, M., Green, A. R., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4236 · Cited by 1,500
Analysis and design of RNA sequencing experiments for identifying isoform regulation
Katz, Y., Wang, E. T., Airoldi, E. M., & Burge, C. B. (2010) · Nature Methods · doi:10.1038/nmeth.1528 · Cited by 1,353
Integrated genomic and molecular characterization of cervical cancer
The Cancer Genome Atlas Research Network (2017) · Nature · doi:10.1038/nature21386 · Cited by 1,479
Transcriptional architecture and chromatin landscape of the core circadian clock in mammals
Koike, N., Yoo, S.-H., Huang, H.-C., Kumar, V., Lee, C., Kim, T.-K., & Takahashi, J. S. (2012) · Science · doi:10.1126/science.1226339 · Cited by 1,464
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
Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma
Johnson, B. E., Mazor, T., Hong, C., Barnes, M., Aihara, K., McLean, C. Y., Fouse, S. D., Yamamoto, S., Ueda, H., Tatsuno, K., et al. (2014) · Science · doi:10.1126/science.1239947 · Cited by 1,362
Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq
Treutlein, B., Brownfield, D. G., Wu, A. R., Neff, N. F., Mantalas, G. L., Espinoza, F. H., Desai, T. J., Krasnow, M. A., & Quake, S. R. (2014) · Nature · doi:10.1038/nature13173 · Cited by 1,342
Personal omics profiling reveals dynamic molecular and medical phenotypes
Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y. K., Chen, R., Miriami, E., Karczewski, K. J., Hariharan, M., Dewey, F. E., et al. (2012) · Cell · doi:10.1016/j.cell.2012.02.009 · Cited by 1,291
The human transcriptome across tissues and individuals
Melé, M., Ferreira, P. G., Reverter, F., DeLuca, D. S., Monlong, J., Sammeth, M., Young, T. R., Goldmann, J. M., Pervouchine, D. D., Sullivan, T. J., et al. (2015) · Science · doi:10.1126/science.aaa0355 · Cited by 1,304
Guttman, M., Garber, M., Levin, J. Z., Donaghey, J., Robinson, J., Adiconis, X., Fan, L., Koziol, M. J., Gnirke, A., Nusbaum, C., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt.1633 · Cited by 1,253
The evolution of gene expression levels in mammalian organs
Brawand, D., Soumillon, M., Necsulea, A., Julien, P., Csárdi, G., Harrigan, P., Weier, M., Liechti, A., Aximu-Petri, A., Kircher, M., et al. (2011) · Nature · doi:10.1038/nature10532 · Cited by 1,248
Microglia development follows a stepwise program to regulate brain homeostasis
Matcovitch-Natan, O., Winter, D. R., Giladi, A., Vargas Aguilar, S., Spinrad, A., Sarrazin, S., Ben-Yehuda, H., David, E., Zelada González, F., Perrin, P., et al. (2016) · Science · doi:10.1126/science.aad8670 · Cited by 1,180
Human cerebral organoids recapitulate gene expression programs of fetal neocortex development
Camp, J. G., Badsha, F., Florio, M., Kanton, S., Gerber, T., Wilsch-Bräuninger, M., Lewitus, E., Sykes, A., Hevers, W., Lancaster, M., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1520760112 · Cited by 1,130
MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery
Wang, K., Singh, D., Zeng, Z., Coleman, S. J., Huang, Y., Savich, G. L., He, X., Mieczkowski, P., Grimm, S. A., Perou, C. M., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq622 · Cited by 1,193
Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma
Gao, Q., Zhu, H., Dong, L., Shi, W., Chen, R., Song, Z., Huang, C., Li, J., Dong, X., Zhou, Y., et al. (2019) · Cell · doi:10.1016/j.cell.2019.08.052 · Cited by 1,096
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170
A Validated Regulatory Network for Th17 Cell Specification
Ciofani, M., Madar, A., Galan, C., Sellars, M., Mace, K., Pauli, F., Agarwal, A., Huang, W., Parkurst, C. N., Muratet, M., et al. (2012) · Cell · doi:10.1016/j.cell.2012.09.016 · Cited by 1,160
Genetic and functional drivers of diffuse large B cell lymphoma
Reddy, A., Zhang, J., Davis, N. S., Moffitt, A. B., Love, C. L., Waldrop, A., Leppa, S., Pasanen, A., Meriranta, L., Karjalainen-Lindsberg, M.-L., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.027 · Cited by 1,104
CRISPR Inversion of CTCF Sites Alters Genome Topology and Enhancer/Promoter Function
Guo, Y., Xu, Q., Canzio, D., Shou, J., Li, J., Gorkin, D. U., Jung, I., Wu, H., Zhai, Y., Tang, Y., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.038 · Cited by 958
Cellular senescence drives age-dependent hepatic steatosis
Ogrodnik, M., Miwa, S., Tchkonia, T., Tiniakos, D., Wilson, C. L., Lahat, A., Day, C. P., Burt, A., Palmer, A., Anstee, Q. M., et al. (2017) · Nature Communications · doi:10.1038/ncomms15691 · Cited by 1,044
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
The Evolutionary Landscape of Alternative Splicing in Vertebrate Species
Barbosa-Morais, N. L., Irimia, M., Pan, Q., Xiong, H. Y., Gueroussov, S., Lee, L. J., Slobodeniuc, V., Kutter, C., Watt, S., Çolak, R., et al. (2012) · Science · doi:10.1126/science.1230612 · Cited by 1,063
Computational methods for transcriptome annotation and quantification using RNA-seq
Garber, M., Grabherr, M. G., Guttman, M., & Trapnell, C. (2011) · Nature Methods · doi:10.1038/nmeth.1613 · Cited by 1,042
Self-citationBennett, B. J., Vallim, T. Q. D. A., Wang, Z., Shih, D. M., Meng, Y., Gregory, J., Allayee, H., Lee, R., Graham, M., Crooke, R., et al. (2013) · Cell Metabolism · doi:10.1016/j.cmet.2012.12.011 · Cited by 1,070
Comparative genomics reveals insights into avian genome evolution and adaptation
Zhang, G., Li, C., Li, Q., Li, B., Larkin, D. M., Lee, C., Storz, J. F., Antunes, A., Greenwold, M. J., Meredith, R. W., et al. (2014) · Science · doi:10.1126/science.1251385 · Cited by 1,055
O'Connell, R. J., Thon, M. R., Hacquard, S., Amyotte, S. G., Kleemann, J., Torres, M. F., Damm, U., Buiate, E. A., Epstein, L., Alkan, N., et al. (2012) · Nature Genetics · doi:10.1038/ng.2372 · Cited by 1,047
Activation of proto-oncogenes by disruption of chromosome neighborhoods
Hnisz, D., Weintraub, A. S., Day, D. S., Valton, A.-L., Bak, R. O., Li, C. H., Goldmann, J., Lajoie, B. R., Fan, Z. P., Sigova, A. A., et al. (2016) · Science · doi:10.1126/science.aad9024 · Cited by 1,042
iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections
Janky, R., Verfaillie, A., Imrichová, H., Van de Sande, B., Standaert, L., Christiaens, V., Hulselmans, G., Herten, K., Naval Sanchez, M., Potier, D., et al. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003731 · Cited by 900
Willsey, A. J., Sanders, S. J., Li, M., Dong, S., Tebbenkamp, A. T., Muhle, R. A., Reilly, S. K., Lin, L., Fertuzinhos, S., Miller, J. A., et al. (2013) · Cell · doi:10.1016/j.cell.2013.10.020 · Cited by 934
Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression
Schmiedel, B. J., Singh, D., Madrigal, A., Valdovino-Gonzalez, A. G., White, B. M., Zapardiel-Gonzalo, J., Ha, B., Altay, G., Greenbaum, J. A., McVicker, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.022 · Cited by 867
The evolution of lncRNA repertoires and expression patterns in tetrapods
Necsulea, A., Soumillon, M., Warnefors, M., Liechti, A., Daish, T., Zeller, U., Baker, J. C., Grützner, F., & Kaessmann, H. (2014) · Nature · doi:10.1038/nature12943 · Cited by 984
Modelling human development and disease in pluripotent stem-cell-derived gastric organoids
McCracken, K. W., Catá, E. M., Crawford, C. M., Sinagoga, K. L., Schumacher, M., Rockich, B. E., Tsai, Y.-H., Mayhew, C. N., Spence, J. R., Zavros, Y., et al. (2014) · Nature · doi:10.1038/nature13863 · Cited by 980
Deep proteome and transcriptome mapping of a human cancer cell line
Nagaraj, N., Wisniewski, J. R., Geiger, T., Cox, J., Kircher, M., Kelso, J., Pääbo, S., & Mann, M. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.81 · Cited by 979
CTCF-promoted RNA polymerase II pausing links DNA methylation to splicing
Shukla, S., Kavak, E., Gregory, M., Imashimizu, M., Shutinoski, B., Kashlev, M., Oberdoerffer, P., Sandberg, R., & Oberdoerffer, S. (2011) · Nature · doi:10.1038/nature10442 · Cited by 973
Evolutionary Dynamics of Gene and Isoform Regulation in Mammalian Tissues
Merkin, J., Russell, C., Chen, P., & Burge, C. B. (2012) · Science · doi:10.1126/science.1228186 · Cited by 972
The yak genome and adaptation to life at high altitude
Qiu, Q., Zhang, G., Ma, T., Qian, W., Wang, J., Ye, Z., Cao, C., Hu, Q., Kim, J., Larkin, D. M., et al. (2012) · Nature Genetics · doi:10.1038/ng.2343 · Cited by 983
Schurch, N. J., Schofield, P., Gierliński, M., Cole, C., Sherstnev, A., Singh, V., Wrobel, N., Gharbi, K., Simpson, G. G., Owen-Hughes, T., et al. (2016) · RNA · doi:10.1261/rna.053959.115 · Cited by 966
Kukurba, K. R., & Montgomery, S. B. (2015) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top084970 · Cited by 924
Hyperglycemia drives intestinal barrier dysfunction and risk for enteric infection
Thaiss, C. A., Levy, M., Grosheva, I., Zheng, D., Soffer, E., Blacher, E., Braverman, S., Tengeler, A. C., Barak, O., Elazar, M., et al. (2018) · Science · doi:10.1126/science.aar3318 · Cited by 942
Pollen, A. A., Nowakowski, T. J., Shuga, J., Wang, X., Leyrat, A. A., Lui, J. H., Li, N., Szpankowski, L., Fowler, B., Chen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2967 · Cited by 920
A survey of sequence alignment algorithms for next-generation sequencing
Li, H., & Homer, N. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbq015 · Cited by 875
The Transcription Factor NFAT Promotes Exhaustion of Activated CD8 + T Cells
Martinez, G. J., Pereira, R. M., Äijö, T., Kim, E. Y., Marangoni, F., Pipkin, M. E., Togher, S., Heissmeyer, V., Zhang, Y. C., Crotty, S., et al. (2015) · Immunity · doi:10.1016/j.immuni.2015.01.006 · Cited by 857
Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations
Thaiss, C. A., Levy, M., Korem, T., Dohnalová, L., Shapiro, H., Jaitin, D. A., David, E., Winter, D. R., Gury-BenAri, M., Tatirovsky, E., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.003 · Cited by 870
Antioxidants Accelerate Lung Cancer Progression in Mice
Sayin, V. I., Ibrahim, M. X., Larsson, E., Nilsson, J. A., Lindahl, P., & Bergo, M. O. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3007653 · Cited by 867
Latent Enhancers Activated by Stimulation in Differentiated Cells
Ostuni, R., Piccolo, V., Barozzi, I., Polletti, S., Termanini, A., Bonifacio, S., Curina, A., Prosperini, E., Ghisletti, S., & Natoli, G. (2013) · Cell · doi:10.1016/j.cell.2012.12.018 · Cited by 867
Structural variant calling: the long and the short of it
Mahmoud, M., Gobet, N., Cruz-Dávalos, D. I., Mounier, N., Dessimoz, C., & Sedlazeck, F. J. (2019) · Genome Biology · doi:10.1186/s13059-019-1828-7 · Cited by 725
Solovei, I., Wang, A. S., Thanisch, K., Schmidt, C. S., Krebs, S., Zwerger, M., Cohen, T. V., Devys, D., Foisner, R., Peichl, L., et al. (2013) · Cell · doi:10.1016/j.cell.2013.01.009 · Cited by 809
Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing
Cortés-Ciriano, I., Lee, J. J.-K., Xi, R., Jain, D., Jung, Y. L., Yang, L., Gordenin, D., Klimczak, L. J., Zhang, C.-Z., Pellman, D. S., et al. (2020) · Nature Genetics · doi:10.1038/s41588-019-0576-7 · Cited by 712
Guided self-organization and cortical plate formation in human brain organoids
Lancaster, M. A., Corsini, N. S., Wolfinger, S., Gustafson, E. H., Phillips, A. W., Burkard, T. R., Otani, T., Livesey, F. J., & Knoblich, J. A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3906 · Cited by 783
A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex
Zhong, S., Zhang, S., Fan, X., Wu, Q., Yan, L., Dong, J., Zhang, H., Li, L., Sun, L., Pan, N., et al. (2018) · Nature · doi:10.1038/nature25980 · Cited by 689
Neuron-Astrocyte Metabolic Coupling Protects against Activity-Induced Fatty Acid Toxicity
Ioannou, M. S., Jackson, J., Sheu, S.-H., Chang, C.-L., Weigel, A. V., Liu, H., Pasolli, H. A., Xu, C. S., Pang, S., Matthies, D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.04.001 · Cited by 760
KRAS and YAP1 Converge to Regulate EMT and Tumor Survival
Shao, D. D., Xue, W., Krall, E. B., Bhutkar, A., Piccioni, F., Wang, X., Schinzel, A. C., Sood, S., Rosenbluh, J., Kim, J. W., et al. (2014) · Cell · doi:10.1016/j.cell.2014.06.004 · Cited by 744
Distinct features of H3K4me3 and H3K27me3 chromatin domains in pre-implantation embryos
Liu, X., Wang, C., Liu, W., Li, J., Li, C., Kou, X., Chen, J., Zhao, Y., Gao, H., Wang, H., et al. (2016) · Nature · doi:10.1038/nature19362 · Cited by 746
Clonal evolution of preleukemic hematopoietic stem cells precedes human acute myeloid leukemia
Jan, M., Snyder, T. M., Corces-Zimmerman, M. R., Vyas, P., Weissman, I. L., Quake, S. R., & Majeti, R. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3004315 · Cited by 721
Transcriptome-wide Analysis of Regulatory Interactions of the RNA-Binding Protein HuR
Lebedeva, S., Jens, M., Theil, K., Schwanhäusser, B., Selbach, M., Landthaler, M., & Rajewsky, N. (2011) · Molecular Cell · doi:10.1016/j.molcel.2011.06.008 · Cited by 716
Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain
Guo, J. U., Su, Y., Shin, J. H., Shin, J., Li, H., Xie, B., Zhong, C., Hu, S., Le, T., Fan, G., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3607 · Cited by 714
Assessment of transcript reconstruction methods for RNA-seq
The RGASP Consortium, Steijger, T., Abril, J. F., Engström, P. G., Kokocinski, F., Hubbard, T. J., Guigó, R., Harrow, J., & Bertone, P. (2013) · Nature Methods · doi:10.1038/nmeth.2714 · Cited by 699
Quantitative assessment of single-cell RNA-sequencing methods
Wu, A. R., Neff, N. F., Kalisky, T., Dalerba, P., Treutlein, B., Rothenberg, M. E., Mburu, F. M., Mantalas, G. L., Sim, S., Clarke, M. F., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2694 · Cited by 696
Classification of low quality cells from single-cell RNA-seq data
Ilicic, T., Kim, J. K., Kolodziejczyk, A. A., Bagger, F. O., McCarthy, D. J., Marioni, J. C., & Teichmann, S. A. (2016) · Genome Biology · doi:10.1186/s13059-016-0888-1 · Cited by 693
Regulatory T Cells in Skin Facilitate Epithelial Stem Cell Differentiation
Ali, N., Zirak, B., Rodriguez, R. S., Pauli, M. L., Truong, H.-A., Lai, K., Ahn, R., Corbin, K., Lowe, M. M., Scharschmidt, T. C., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.002 · Cited by 680
Metadata
- Authors
- Trapnell, C., Pachter, L., & Salzberg, S. L.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-03-16
- Volume
- 25
- Issue
- 9
- Pages
- 1105-1111
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2097065948
- Cited by
- 11,617
- References
- 72
- FWCI
- 96.14
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
