Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms
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We introduce Sailfish, a computational method for quantifying the abundance of previously annotated RNA isoforms from RNA-seq data. Because Sailfish entirely avoids mapping reads, a time-consuming step in all current methods, it provides quantification estimates much faster than do existing approaches (typically 20 times faster) without loss of accuracy. By facilitating frequent reanalysis of data and reducing the need to optimize parameters, Sailfish exemplifies the potential of lightweight algorithms for efficiently processing sequencing reads.
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RNA-seq quantification is primarily based on the number of reads that map to each transcript sequence, although algorithms such as Sailfish rely on k-mer counting in reads without the need for mapping.
“This application is primarily based on the number of reads that map to each transcript sequence, although there are algorithms such as Sailfish that rely on k-mer counting in reads without the need for mapping [34].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [34]
Salmon's two-phase inference lets it construct a probabilistic model of the sequencing experiment that includes information not used by Sailfish and kallisto.
“This two-phase inference procedure allows Salmon to build a probabilistic model of the sequencing experiment that incorporates information — like terms contributing to the conditional probability of drawing a fragment given a transcript — not considered by Sailfish [9] and kallisto [10].”
From Salmon provides fast and bias-aware quantification of transcript expression (2017) · cites this paper as [9]
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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 · see citing works
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 · see citing works
Full-length transcriptome assembly from RNA-Seq data without a reference genome
Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D. A., Amit, I., Adiconis, X., Fan, L., Raychowdhury, R., Zeng, Q., et al. (2011) · Nature Biotechnology · doi:10.1038/nbt.1883 · Cited by 20,776 · see citing works
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 · 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
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,477 · see citing works
A fast, lock-free approach for efficient parallel counting of occurrences of <i>k</i> -mers
Marçais, G., & Kingsford, C. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr011 · Cited by 4,661 · see citing works
Self-citationMAQC Consortium, Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1239 · Cited by 2,126 · see citing works
Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples
Wagner, G. P., Kin, K., & Lynch, V. J. (2012) · Theory in Biosciences · doi:10.1007/s12064-012-0162-3 · Cited by 2,085 · see citing works
Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680 · see citing works
NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy
Pruitt, K. D., Tatusova, T., Brown, G. R., & Maglott, D. R. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1079 · Cited by 1,149 · cited 1× · see citing works
“The set of transcripts used in this experiment were the curated RefSeq [9] genes (accession prefix NM) from hg18.”
MethodStreaming 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 · see citing works
A comparison of methods for differential expression analysis of RNA-seq data
Soneson, C., & Delorenzi, M. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-91 · Cited by 887 · see citing works
Flicek, P., Ahmed, I., Amode, M. R., Barrell, D. G., Beal, K. M., Brent, S., Carvalho‐Silva, D., Clapham, P., Coates, G. M. P., Fairley, S., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1236 · Cited by 869 · see citing works
Personalized Oncology Through Integrative High-Throughput Sequencing: A Pilot Study
Roychowdhury, S., Iyer, M. K., Robinson, D. R., Lonigro, R. J., Wu, Y.-M., Cao, X., Sundaram, S. K., Sam, L. T., Balbin, O. A., Quist, M. J., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3003161 · Cited by 629 · see citing works
Intel Threading Building Blocks
Kukanov, A. (2015) · The MIT Press eBooks · doi:10.7551/mitpress/9486.003.0016 · Cited by 525 · see citing works
Simple and Globally Convergent Methods for Accelerating the Convergence of Any EM Algorithm
Varadhan, R., & ROLAND, C. (2008) · Scandinavian Journal of Statistics · doi:10.1111/j.1467-9469.2007.00585.x · Cited by 353 · see citing works
Modelling and simulating generic RNA-Seq experiments with the flux simulator
Griebel, T., Zacher, B., Ribeca, P., Raineri, E., Lacroix, V., Guigó, R., & Sammeth, M. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks666 · Cited by 278 · 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
Bias detection and correction in RNA-Sequencing data
Zheng, W., Chung, L. M., & Zhao, H. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-290 · Cited by 161 · see citing works
KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data
Sacomoto, G., Kielbassa, J., Chikhi, R., et al. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s6-s5 · Cited by 128 · see citing works
Intel® threading building blocks
Pheatt, C. (2008) · Journal of computing sciences in colleges · Cited by 127 · see citing works
Simple and Space-Efficient Minimal Perfect Hash Functions
Botelho, F. C., Pagh, R., & Ziviani, N. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73951-7_13 · Cited by 100 · see citing works
CRAC: an integrated approach to the analysis of RNA-seq reads
Philippe, N., Salson, M., Commes, T., et al. (2013) · Genome biology · doi:10.1186/gb-2013-14-3-r30 · Cited by 77 · see citing works
Statistical Modeling of RNA-Seq Data
Salzman, J., Jiang, H., & Wong, W. H. (2011) · Statistical Science · doi:10.1214/10-sts343 · Cited by 68 · see citing works
Statistical modeling of RNA-Seq data
Salzman, J., Jiang, H., & Wong, W. H. (2011) · Cited by 62 · see citing works
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Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723 · see citing works
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Flygare, S., Simmon, K., Miller, C., Qiao, Y., Kennedy, B., Di Sera, T., Graf, E. H., Tardif, K. D., Kapusta, A., Rynearson, S., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0969-1 · Cited by 164 · see citing works
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Vu, T. N., Wills, Q. F., Kalari, K. R., Niu, N., Wang, L., Rantalainen, M., & Pawitan, Y. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw202 · Cited by 157 · see citing works
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Munger, S. C., Raghupathy, N., Choi, K., Simons, A. K., Gatti, D. M., Hinerfeld, D. A., Svenson, K. L., Keller, M. P., Attie, A. D., Hibbs, M. A., et al. (2014) · Genetics · doi:10.1534/genetics.114.165886 · Cited by 85 · see citing works
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- Authors
- Patro, R., Mount, S. M., & Kingsford, C.
- Affiliations
- Carnegie Mellon University (US)
- University of Maryland, College Park (US)
- Corresponding author
- Kingsford, Carl
- Venue
- Nature Biotechnology
- Published
- 2014-04-20
- Volume
- 32
- Issue
- 5
- Pages
- 462-464
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4077321
- OpenAlex
- W2065128082
- Cited by
- 586
- References
- 26
- Citations by year
- 2026: 12 · 2025: 17 · 2024: 28 · 2023: 35 · 2022: 38 · 2021: 61 · 2020: 62 · 2019: 60 · 2018: 80 · 2017: 69 · 2016: 71 · 2015: 41 · 2014: 12
- FWCI
- 26.40
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- KiltHub Repository (submittedVersion)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- KiltHub Repository (cc-by)
- Funders
- National Human Genome Research Institute (R21HG006913, R01HG007104)
- National Institute of Allergy and Infectious Diseases (R21AI085376)
- 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 › RNA Research and Splicing
- Keywords
- sailfish, RNA-seq, transcript abundance estimation, lightweight algorithms, computational efficiency
- MeSH terms
- Algorithms, Brain Chemistry, Humans, Models, Biological, Software, Sequence Analysis, RNA, Computational Biology, RNA Isoforms
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