title: RNA-Skim: a rapid method for RNA-Seq quantification at transcript level.
abstract: BACKGROUND
  RNA-Seq technique has been demonstrated as a revolutionary means for exploring transcriptome because it provides deep coverage and base pair-level resolution. RNA-Seq quantification is proven to be an efficient alternative to Microarray technique in gene expression study, and it is a critical component in RNA-Seq differential expression analysis. Most existing RNA-Seq quantification tools require the alignments of fragments to either a genome or a transcriptome, entailing a time-consuming and intricate alignment step. To improve the performance of RNA-Seq quantification, an alignment-free method, Sailfish, has been recently proposed to quantify transcript abundances using all k-mers in the transcriptome, demonstrating the feasibility of designing an efficient alignment-free method for transcriptome quantification. Even though Sailfish is substantially faster than alternative alignment-dependent methods such as Cufflinks, using all k-mers in the transcriptome quantification impedes the scalability of the method.
  
  
  RESULTS
  We propose a novel RNA-Seq quantification method, RNA-Skim, which partitions the transcriptome into disjoint transcript clusters based on sequence similarity, and introduces the notion of sig-mers, which are a special type of k-mers uniquely associated with each cluster. We demonstrate that the sig-mer counts within a cluster are sufficient for estimating transcript abundances with accuracy comparable with any state-of-the-art method. This enables RNA-Skim to perform transcript quantification on each cluster independently, reducing a complex optimization problem into smaller optimization tasks that can be run in parallel. As a result, RNA-Skim uses <4% of the k-mers and <10% of the CPU time required by Sailfish. It is able to finish transcriptome quantification in <10 min per sample by using just a single thread on a commodity computer, which represents >100 speedup over the state-of-the-art alignment-based methods, while delivering comparable or higher accuracy.
  
  
  METHODS
  The software is available at http://www.csbio.unc.edu/rs.
authors.affiliation: Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA and Department of Computer Science, University of California, Los Angeles, CA, USA.
authors.forename: Zhaojun
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA and Department of Computer Science, University of California, Los Angeles, CA, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000818:Animals; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D016678:Genome; D059014:High-Throughput Nucleotide Sequencing; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software