Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms

By Rob Patro, Stephen M. Mount, Carl Kingsford. Nature Biotechnology, 2013

632 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cd8d731b40b2304b61ac1ade1043fd39c5b4ab89

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Citations in the corpus

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, background): “Sailfish defines the transcript abundance likelihood in terms of the constituent kmers of the underlying transcriptome and their abundance in the read data [98] .”
Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference (2017, methodology): “Our novel quantification procedure, called Salmon, achieves greater accuracy than tools such as kallisto and eXpress, employs sample-specific bias models that account for sequencespecific, fragment-GC and positional biases, and simultaneously achieves the same order-ofmagnitude speed benefits as kal”
Alignment-free sequence comparison: benefits, applications, and tools (2017, methodology): “For example, alignment-free tools for transcript quantification (Kallisto [69], Sailfish [67], Salmon [70]) show that most of the information provided by aligners is not necessary for high-quality estimation of transcript levels.”
variancePartition: interpreting drivers of variation in complex gene expression studies (2016, neutral): “The software can easily be applied to RNA-seq quan-tifications from featureCounts [46], HTSeq [47], kallisto [48], sailfish [49], salmon [50], RSEM [51] and cufflinks [52] which have been processed in R with limma/voom [15], DESeq2 [16], tximport [53] and ballgown [54].”
Big Data: Astronomical or Genomical? (2015, background): “Already several such “ streaming ” algorithms have been published for this purpose, performing as well as or superior to their nonstreaming counterparts [31].”
Sensory Neurons Co-opt Classical Immune Signaling Pathways to Mediate Chronic Itch (2017, neutral): “6.3 Patro et al., 2014 N/A RSeQC v2.”
The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes (2014, methodology): “Exact alignment methods, often formulated as k-mer matching, can produce high precision results in a fraction of the time required for gapped alignment methods [60-62].”
TransRate: reference-free quality assessment of de novo transcriptome assemblies (2015, background): “4 (Patro et al., 2014) so that multi-mapping reads are assigned to a single contig based on the posterior probability that the reads come from that contig.”
A survey of best practices for RNA-seq data analysis (2016, methodology): “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].”