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StringTie enables improved reconstruction of a transcriptome from RNA-seq reads
Open accessAbstract
Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.
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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
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
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-citationAlternative 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
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
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
Streaming fragment assignment for real-time analysis of sequencing experiments
Roberts, A., & Pachter, L. (2013) · Nature Methods · doi:10.1038/nmeth.2251 · Cited by 1,063
NCBI Reference Sequences: current status, policy and new initiatives
Pruitt, K. D., Tatusova, T., Klimke, W., & Maglott, D. R. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn721 · Cited by 736 · cited 1×
“The database of annotated protein coding and noncoding genes (41,409 genes and 171,904 transcripts in total) was produced by merging all annotated genes from the RefSeq database 29 , the UCSC Browser 24 and the Ensembl database 30 .”
MethodBiases in Illumina transcriptome sequencing caused by random hexamer priming
Hansen, K. D., Brenner, S. E., & Dudoit, S. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq224 · Cited by 713
Research works printed but not linked: 22
- Blencowe, B.J. Alternative splicing: new insights from global analyses. Cell 126, 37–47 (2006). · DOI · Paper page
- Ponting, C.P., Oliver, P.L. & Reik, W. Evolution and functions of long noncoding RNAs. Cell 136, 629–641 (2009). · DOI · Paper page
- Cabili, M.N. et al. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 25, 1915–1927 (2011). · DOI · Paper page
- Salzberg, S.L. Recent advances in RNA sequence analysis. F1000 Biol. Rep. 2, 64 (2010). · DOI
- Garber, M., Grabherr, M.G., Guttman, M. & Trapnell, C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat. Methods 8, 469–477 (2011). · DOI · Paper page
- Grabherr, M.G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011). · DOI · Paper page
- Schulz, M.H., Zerbino, D.R., Vingron, M. & Birney, E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics 28, 1086–1092 (2012). · DOI · Paper page
- Feng, J., Li, W. & Jiang, T. Inference of isoforms from short sequence reads. J. Comput. Biol. 18, 305–321 (2011). · DOI
- Li, J.J., Jiang, C.R., Brown, J.B., Huang, H. & Bickel, P.J. Sparse linear modeling of next-generation mRNA sequencing (RNA-Seq) data for isoform discovery and abundance estimation. Proc. Natl. Acad. Sci. USA 108, 19867–19872 (2011). · DOI
- Li, W., Feng, J. & Jiang, T. IsoLasso: a LASSO regression approach to RNA-Seq based transcriptome assembly. J. Comput. Biol. 18, 1693–1707 (2011). · DOI
- Mezlini, A.M. et al. iReckon: simultaneous isoform discovery and abundance estimation from RNA-seq data. Genome Res. 23, 519–529 (2013). · DOI
- Tomescu, A.I., Kuosmanen, A., Rizzi, R. & Makinen, V. A novel min-cost flow method for estimating transcript expression with RNA-Seq. BMC Bioinformatics 14 (suppl. 5), S15 (2013). · DOI
- Zhao, Q.Y. et al. Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study. BMC Bioinformatics 12 (suppl. 14), S2 (2011). · DOI · Paper page
- Behr, J. et al. MITIE: simultaneous RNA-Seq-based transcript identification and quantification in multiple samples. Bioinformatics 29, 2529–2538 (2013). · DOI
- Griebel, T. et al. Modelling and simulating generic RNA-Seq experiments with the flux simulator. Nucleic Acids Res. 40, 10073–10083 (2012). · DOI · Paper page
- Zimin, A.V. et al. The MaSuRCA genome assembler. Bioinformatics 29, 2669–2677 (2013). · DOI · Paper page
- Rehrauer, H., Opitz, L., Tan, G., Sieverling, L. & Schlapbach, R. Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching. BMC Bioinformatics 14, 370 (2013). · DOI
- Flicek, P. et al. Ensembl 2014. Nucleic Acids Res. 42, D749–D755 (2014). · DOI · Paper page
- Ford, L. & Fulkerson, D. Flows in Networks (Princeton University Press, Princeton, NJ, 1962).
- Goldberg, A. & Tarjan, R. A new approach to the maximum-flow problem. JACM 35, 921–940 (1988). · DOI · Paper page
- Dantzig, G. Linear Programming and Extensions (Princeton University Press, Princeton, NJ, 1962). · DOI · Paper page
- Goldberg, A., Plotkin, S. & Tardos, E. Combinatorial algorithms for the generalized circulation problem. Math. Oper. Res. 16, 351–381 (1991). · doi:10.1287/moor.16.2.351
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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
Patient-derived organoids model treatment response of metastatic gastrointestinal cancers
Vlachogiannis, G., Hedayat, S., Vatsiou, A., Jamin, Y., Fernández-Mateos, J., Khan, K., Lampis, A., Eason, K., Huntingford, I., Burke, R., et al. (2018) · Science · doi:10.1126/science.aao2774 · Cited by 2,096
The U6 snRNA m 6 A Methyltransferase METTL16 Regulates SAM Synthetase Intron Retention
Pendleton, K. E., Chen, B., Liu, K., Hunter, O. V., Xie, Y., Tu, B. P., & Conrad, N. K. (2017) · Cell · doi:10.1016/j.cell.2017.05.003 · Cited by 1,085
Regulatory evolution of innate immunity through co-option of endogenous retroviruses
Chuong, E. B., Elde, N. C., & Feschotte, C. (2016) · Science · doi:10.1126/science.aad5497 · Cited by 1,008
De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes
Hufford, M. B., Seetharam, A. S., Woodhouse, M. R., Chougule, K. M., Ou, S., Liu, J., Ricci, W. A., Guo, T., Olson, A., Qiu, Y., et al. (2021) · Science · doi:10.1126/science.abg5289 · Cited by 747
Metadata
- Authors
- Pertea, M., Pertea, G. M., Antonescu, C. M., Chang, T.-C., Mendell, J. T., & Salzberg, S. L.
- Venue
- Nature Biotechnology
- Published
- 2015-02-18
- Volume
- 33
- Issue
- 3
- Pages
- 290-295
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2104005232
- Cited by
- 14,298
- References
- 12
- FWCI
- 71.92
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
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