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Near-optimal probabilistic RNA-seq quantification

Authors: Nicolas L Bray; Harold Pimentel; Páll Melsted; Lior Pachter;

Near-optimal probabilistic RNA-seq quantification

Abstract

(Uploaded by Plazi for the Bat Literature Project) We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.

Country
United States
Keywords

570, Models, Statistical, Sequence Analysis, RNA, bats, 610, High-Throughput Nucleotide Sequencing, Reproducibility of Results, bat, Biodiversity, Sensitivity and Specificity, Pattern Recognition, Automated, Data Interpretation, Statistical, Chiroptera, Mammalia, RNA, Animalia, Computer Simulation, Chordata, Sequence Alignment, Algorithms, Software

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8K
Top 0.01%
Top 0.01%
Top 0.01%
Green