Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments
Open accessAbstract
Alternative splicing, polyadenylation of pre-messenger RNA molecules and differential promoter usage can produce a variety of transcript isoforms whose respective expression levels are regulated in time and space, thus contributing specific biological functions. However, the repertoire of mammalian alternative transcripts and their regulation are still poorly understood. Second-generation sequencing is now opening unprecedented routes to address the analysis of entire transcriptomes. Here, we developed methods that allow the prediction and quantification of alternative isoforms derived solely from exon expression levels in RNA-Seq data. These are based on an explicit statistical model and enable the prediction of alternative isoforms within or between conditions using any known gene annotation, as well as the relative quantification of known transcript structures. Applying these methods to a human RNA-Seq dataset, we validated a significant fraction of the predictions by RT-PCR. Data further showed that these predictions correlated well with information originating from junction reads. A direct comparison with exon arrays indicated improved performances of RNA-Seq over microarrays in the prediction of skipped exons. Altogether, the set of methods presented here comprehensively addresses multiple aspects of alternative isoform analysis. The software is available as an open-source R-package called Solas at http://cmb.molgen.mpg.de/2ndGenerationSequencing/Solas/.
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
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?
RNA-seq can distinguish between the expression of different splice variants.
“RNA-seq can also distinguish between the expression of different splice variants [46, 47], which can have distinct interaction partners [48] and biological functions [49].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [46]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696 · see citing works
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R., Carey, V., Bates, D., 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 13,133 · see citing works
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
Irizarry, R. A. (2003) · Biostatistics · doi:10.1093/biostatistics/4.2.249 · Cited by 10,603 · see citing works
Regression Analysis of Count Data
Cameron, A. C., & Trivedi, P. K. (2013) · doi:10.1017/cbo9781139013567 · Cited by 6,640 · see citing works
Regression Analysis of Count Data: Author Index
Cameron, A., & Trivedi, P. (1998) · Cambridge University Press eBooks · doi:10.1017/cbo9780511814365 · Cited by 6,406 · see citing works
Alternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181 · see citing works
Pan, Q., Shai, O., Lee, L. J., Frey, B. J., & Blencowe, B. J. (2008) · Nature Genetics · doi:10.1038/ng.259 · Cited by 3,951 · see citing works
The Significance of Digital Gene Expression ProfilesPreprint
Audic, S., & Claverie, J. (1997) · Genome Research · doi:10.1101/gr.7.10.986 · Cited by 2,793 · see citing works
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008) · Genome Research · doi:10.1101/gr.079558.108 · Cited by 2,771 · see citing works
The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing
Nagalakshmi, U., Wang, Z., Waern, K., Shou, C., Raha, D., Gerstein, M., & Snyder, M. (2008) · Science · doi:10.1126/science.1158441 · Cited by 2,479 · see citing works
Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis
Lister, R., O’Malley, R. C., Tonti‐Filippini, J., Gregory, B. D., Berry, C. C., Millar, A. H., & Ecker, J. R. (2008) · Cell · doi:10.1016/j.cell.2008.03.029 · Cited by 2,424 · see citing works
Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data
Dai, M. (2005) · Nucleic Acids Research · doi:10.1093/nar/gni179 · Cited by 1,797 · see citing works
Genome-Wide Survey of Human Alternative Pre-mRNA Splicing with Exon Junction Microarrays
Johnson, J. M., Castle, J. C., Garrett-Engele, P. W., Kan, Z., Loerch, P., Armour, C. D., Santos, R., Schadt, E. E., Stoughton, R. B., & Shoemaker, D. D. (2003) · Science · doi:10.1126/science.1090100 · Cited by 1,418 · see citing works
Sandberg, R., Neilson, J. R., Sarma, A., Sharp, P. A., & Burge, C. B. (2008) · Science · doi:10.1126/science.1155390 · Cited by 1,349 · see citing works
Sultan, M., Schulz, M. H., Richard, H., Magen, A., Klingenhoff, A., Scherf, M., Seifert, M., Borodina, T., Soldatov, A., Parkhomchuk, D., et al. (2008) · Science · doi:10.1126/science.1160342 · Cited by 1,293 · see citing works
Self-citationAlternative Splicing: New Insights from Global Analyses
Blencowe, B. J. (2006) · Cell · doi:10.1016/j.cell.2006.06.023 · Cited by 1,122 · see citing works
Substantial biases in ultra-short read data sets from high-throughput DNA sequencing
Dohm, J. C., Lottaz, C., Borodina, T., & Himmelbauer, H. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn425 · Cited by 1,088 · see citing works
Stem cell transcriptome profiling via massive-scale mRNA sequencing
Cloonan, N., Forrest, A. R. R., Kolle, G., Gardiner, B. B. A., Faulkner, G. J., Brown, M. K., Taylor, D. F., Steptoe, A. L., Wani, S., Bethel, G., et al. (2008) · Nature Methods · doi:10.1038/nmeth.1223 · Cited by 1,035 · cited 1× · see citing works
“Transcriptome analysis by RNA-Seq allows the direct detection of both canonical exons as well as alternative isoforms from sequence reads overlapping splice junctions (25,27,29).”
BackgroundDynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution
Wilhelm, B. T., Marguerat, S., Watt, S. B., Schubert, F., Wood, V., Goodhead, I., Penkett, C. J., Rogers, J., & Bähler, J. (2008) · Nature · doi:10.1038/nature07002 · Cited by 974 · see citing works
Transcriptome analysis by strand-specific sequencing of complementary DNA
Parkhomchuk, D., Borodina, T. A., Amstislavskiy, V. S., Banaru, M., Hallen, L., Krobitsch, S., Lehrach, H. R., & Солдатов, А. В. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp596 · Cited by 934 · see citing works
Self-citationProliferation Marker Ki-67 in Early Breast Cancer
Urruticoechea, A., Smith, I. E., & Dowsett, M. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2005.07.501 · Cited by 837 · see citing works
Alternative pre-mRNA splicing and proteome expansion in metazoans
Maniatis, T., & Tasic, B. (2002) · Nature · doi:10.1038/418236a · Cited by 766 · see citing works
Alternative splicing and genome complexity
Brett, D. J., Pospisil, H., Valcárcel, J., Jg, R., & Peer Bork, B. (2001) · Nature Genetics · doi:10.1038/ng803 · Cited by 537 · see citing works
Casadio, R., & Myers, G. (2005) · Lecture notes in computer science · doi:10.1007/11557067 · Cited by 515 · see citing works
Statistical inferences for isoform expression in RNA-Seq
Jiang, H., & Wong, W. H. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp113 · Cited by 453 · see citing works
Model-based analysis of tiling-arrays for ChIP-chip
Johnson, W. E., Li, W., Meyer, C. A., Gottardo, R., Carroll, J. S., Brown, M. A., & Liu, X. S. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601180103 · Cited by 428 · see citing works
Genomewide Analysis of mRNA Processing in Yeast Using Splicing-Specific Microarrays
Clark, T. A., Sugnet, C. W., & Ares, M. (2002) · Science · doi:10.1126/science.1069415 · Cited by 411 · see citing works
The functional consequences of alternative promoter use in mammalian genomes
Davuluri, R. V., Suzuki, Y., Sugano, S., Plass, C., & Huang, T. H.-M. (2008) · Trends in Genetics · doi:10.1016/j.tig.2008.01.008 · Cited by 408 · see citing works
Gardina, P. J., Clark, T. A., Shimada, B., Staples, M. K., Yang, Q., Veitch, J. M., Schweitzer, A. C., Awad, T. A., Sugnet, C. W., Dee, S., et al. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-325 · Cited by 352 · see citing works
Pan, Q., Shai, O., Misquitta, C., Zhang, W., Saltzman, A. L., Mohammad, N., Babak, T., Siu, H., Hughes, T. R., Morris, Q. D., et al. (2004) · Molecular Cell · doi:10.1016/j.molcel.2004.12.004 · Cited by 323 · see citing works
Biased alternative polyadenylation in human tissues
Zhang, H., Lee, J. Y., & Tian, B. (2005) · Genome biology · doi:10.1186/gb-2005-6-12-r100 · Cited by 292 · see citing works
Discovery of tissue-specific exons using comprehensive human exon microarrays
Clark, T. A., Schweitzer, A. C., Chen, T. X., Staples, M. K., Lu, G., Wang, H., Williams, A., & Blume, J. E. (2007) · Genome biology · doi:10.1186/gb-2007-8-4-r64 · Cited by 280 · see citing works
Unusual Intron Conservation near Tissue-Regulated Exons Found by Splicing Microarrays
Sugnet, C. W., Srinivasan, K., Clark, T. A., O’Brien, G. S., Cline, M., Wang, H., Williams, A., Kulp, D., Blume, J. E., Haussler, D. H., et al. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020004 · Cited by 194 · see citing works
Xing, Y. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl396 · Cited by 160 · see citing works
Multiple Alternative Splicing Markers for Ovarian Cancer
Klinck, R., Bramard, A., Inkel, L., Dufresne‐Martin, G., Gervais-Bird, J., Madden, R., Paquet, É. R., Koh, C., Venables, J. P., Prinos, P., et al. (2008) · Cancer Research · doi:10.1158/0008-5472.can-07-2580 · Cited by 147 · see citing works
FIRMA: a method for detection of alternative splicing from exon array data
Purdom, E., Simpson, K. M., Robinson, M. D., et al. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn284 · Cited by 132 · see citing works
Exon arrays provide accurate assessments of gene expression
Kapur, K., Xing, Y., Ouyang, Z., & Wong, W. H. (2007) · Genome biology · doi:10.1186/gb-2007-8-5-r82 · Cited by 112 · see citing works
Statistical modeling of sequencing errors in SAGE libraries
Beißbarth, T., Hyde, L., Smyth, G. K., Job, C., Boon, W., Tan, S., Scott, H. S., & Speed, T. P. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth924 · Cited by 112 · see citing works
Le, K., Mitsouras, K., Roy, M., Wang, Q., Xu, Q., Nelson, S. F., & Lee, C. J. (2004) · Nucleic Acids Research · doi:10.1093/nar/gnh173 · Cited by 100 · see citing works
Gene structure-based splice variant deconvolution using a microarry platform
Wang, H., Hubbell, E. A., Hu, J., Mei, G., Cline, M., Lu, G., Clark, T. A., Siani-Rose, M. A., Ares, M., Kulp, D., et al. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg1044 · Cited by 100 · see citing works
Li, H., Lovci, M., Kwon, Y., Rosenfeld, M. G., Fu, X., & Yeo, G. W.-M. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0807121105 · Cited by 94 · see citing works
Xing, Y., Stoilov, P., Kapur, K., Han, A., Jiang, H., Shen, S., Black, D. L., & Wong, W. H. (2008) · RNA · doi:10.1261/rna.1070208 · Cited by 94 · see citing works
Genome wide identification and classification of alternative splicing based on EST data
Gupta, S., Zink, D., Korn, B., Vingron, M., & Haas, S. A. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth288 · Cited by 91 · see citing works
Self-citationDas, D., Clark, T. A., Schweitzer, A. C., Yamamoto, M. L., Marr, H., Arribere, J., Minovitsky, S., Poliakov, A., Dubchak, I. L., Blume, J. E., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm485 · Cited by 89 · see citing works
Bioinformatics analysis of alternative splicing
Lee, C. J. (2005) · Briefings in Bioinformatics · doi:10.1093/bib/6.1.23 · Cited by 86 · see citing works
French, P. J., Peeters, J. K., Horsman, S., Duijm, E. J., Siccama, I., van den Bent, M. J., Luider, T. M., Kros, J. M., van der Spek, P. J., & Smitt, P. A. E. S. (2007) · Cancer Research · doi:10.1158/0008-5472.can-06-2869 · Cited by 85 · see citing works
Swanson, B. J., Jäck, H., & Lyons, G. E. (1998) · Molecular Immunology · doi:10.1016/s0161-5890(98)00058-3 · Cited by 82 · see citing works
Analysis of alternative splicing with microarrays: successes and challenges.
Lee, C., & Roy, M. (2004) · Genome biology · doi:10.1186/gb-2004-5-7-231 · Cited by 76 · see citing works
Alternative splicing and the progesterone receptor in breast cancer
Cork, D. M. W., Lennard, T. W. J., & Tyson-Capper, A. J. (2008) · Breast Cancer Research · doi:10.1186/bcr2097 · Cited by 60 · see citing works
Exact Transcriptome Reconstruction from Short Sequence Reads
Lacroix, V., Sammeth, M., Guigó, R., & Bergeron, A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87361-7_5 · Cited by 52 · see citing works
GeneNest: automated generation and visualization of gene indices
Haas, S. A., Beißbarth, T., Rivals, É., et al. (2000) · Trends in Genetics · doi:10.1016/s0168-9525(00)02116-8 · Cited by 47 · see citing works
Self-citationGenome-scale design of PCR primers and long oligomers for DNA microarrays
Haas, S. A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg752 · Cited by 36 · see citing works
Self-citationSPACE: an algorithm to predict and quantify alternatively spliced isoforms using microarrays
Anton, M. A., Gorostiaga, D., Guruceaga, E., Segura, V., Carmona‐Sáez, P., Pascual-Montano, A., Pı́o, R., Montuenga, L. M., & Rubio, Á. (2008) · Genome biology · doi:10.1186/gb-2008-9-2-r46 · Cited by 32 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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 · see citing works
RNA sequencing: advances, challenges and opportunities
Ozsolak, F., & Milos, P. M. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2934 · Cited by 2,135 · see citing works
“By counting the number of reads mapping to each exon and spanning each splice junction, these approaches also allowed the splice efficiency of each junction to be determined and the levels of distinct isoforms to be quantifie”
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 · see citing works
Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works
SMN2 splice modulators enhance U1–pre-mRNA association and rescue SMA mice
Palacino, J., Swalley, S. E., Song, C., Cheung, A. K., Shu, L., Zhang, X., Van Hoosear, M., Shin, Y., Chin, D. N., Keller, C. G., et al. (2015) · Nature Chemical Biology · doi:10.1038/nchembio.1837 · Cited by 432 · see citing works
Aanes, H., Winata, C. L., Lin, C. H., Chen, J. P., Srinivasan, K. G., Lee, S. G. P., Lim, A. Y. M., Hajan, H. S., Collas, P., Bourque, G., et al. (2011) · Genome Research · doi:10.1101/gr.116012.110 · Cited by 285 · see citing works
Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads
Turro, E., Su, S.-Y., Gonçalves, Â., Coin, L. J., Richardson, S., & Lewin, A. (2011) · Genome Biology · doi:10.1186/gb-2011-12-2-r13 · Cited by 264 · see citing works
Estimation of alternative splicing isoform frequencies from RNA-Seq data
Nicolae, M., Mangul, S., Măndoiu, I. I., & Zelikovsky, A. (2011) · Algorithms for Molecular Biology · doi:10.1186/1748-7188-6-9 · Cited by 178 · see citing works
Giorgi, F. M., Del Fabbro, C., & Licausi, F. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt053 · Cited by 103 · see citing works
Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads
Li, W., & Jiang, T. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts559 · Cited by 97 · see citing works
RNA‐seq Data: Challenges in and Recommendations for Experimental Design and Analysis
Williams, A. G., Thomas, S., Wyman, S. K., & Holloway, A. K. (2014) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg1113s83 · Cited by 93 · see citing works
Structural basis for spumavirus GAG tethering to chromatin
Lesbats, P., Serrao, E., Maskell, D. P., Pye, V. E., O’Reilly, N., Lindemann, D., Engelman, A. N., & Cherepanov, P. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1621159114 · Cited by 51 · see citing works
- Authors
- Richard, H., Schulz, M. H., Sultan, M., Nürnberger, A., Schrinner, S., Balzereit, D., Dagand, E., Rasche, A., Lehrach, H., Vingron, M., Haas, S. A., & Yaspo, M.-L.
- Affiliations
- Max Planck Society (DE)
- Corresponding author
- Richard, Hugues
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-02-11
- Volume
- 38
- Issue
- 10
- Pages
- e112-e112
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC2879520
- OpenAlex
- W2086323500
- Cited by
- 160
- References
- 54
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 2 · 2020: 6 · 2019: 5 · 2018: 7 · 2017: 8 · 2016: 5 · 2015: 12 · 2014: 24 · 2013: 22 · 2012: 26
- FWCI
- 10.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Max Planck Institute for Plasma Physics (submittedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- European Commission
- Max-Planck-Gesellschaft
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- 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
- Keywords
- alternative splicing, RNA-seq, skipped exons, polyadenylation, transcript quantification, human transcriptome
- MeSH terms
- Cell Line, Computer Simulation, Exons, Humans, Models, Statistical, Alternative Splicing, Sequence Analysis, RNA, Protein Isoforms, Expressed Sequence Tags, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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
