CaltechAUTHORS
  A Caltech Library Service

Near-optimal probabilistic RNA-seq quantification

Bray, Nicolas L. and Pimentel, Harold and Melsted, Páll and Pachter, Lior (2016) Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology, 34 (5). pp. 525-527. ISSN 1087-0156. https://resolver.caltech.edu/CaltechAUTHORS:20190506-110012992

[img] Image (JPEG) (Supplementary Figure 1 : Median relative difference for abundance estimates using varying values of k) - Supplemental Material
See Usage Policy.

35kB
[img] Image (JPEG) (Supplementary Figure 2 : Accuracy of kallisto, Cufflinks, Sailfish, eXpress and RSEM) - Supplemental Material
See Usage Policy.

28kB
[img] Image (JPEG) (Supplementary Figure 3 : Performance of different quantification programs on the set of paralogs in the human genome) - Supplemental Material
See Usage Policy.

15kB
[img] Image (JPEG) (Supplementary Figure 4 : Count distribution of one simulation) - Supplemental Material
See Usage Policy.

16kB
[img] Image (JPEG) (Supplementary Figure 5 : Comparison of technical variance in abundances) - Supplemental Material
See Usage Policy.

52kB
[img] Image (JPEG) (Supplementary Figure 6 : Median relative error (with respect to 1,000 bootstraps) of inferred transcript variances) - Supplemental Material
See Usage Policy.

28kB
[img] Image (JPEG) (Supplementary Figure 7 : Relationship between the mean and variance of estimated counts from subsamples) - Supplemental Material
See Usage Policy.

53kB
[img] Image (JPEG) (Supplementary Figure 8 : Relationship between the mean and variance of estimated counts from bootstraps) - Supplemental Material
See Usage Policy.

43kB
[img] Image (JPEG) (Supplementary Figure 9 : Median relative difference from 30 million 75-bp PE reads simulated with error for different values of k) - Supplemental Material
See Usage Policy.

36kB
[img] Image (JPEG) (Supplementary Figure 10 : Run time for index building and quantification) - Supplemental Material
See Usage Policy.

42kB
[img] Image (JPEG) (Supplementary Figure 11 : The distribution of the number of k-mers hashed per read) - Supplemental Material
See Usage Policy.

37kB
[img] PDF (Supplementary Figures 1–11) - Supplemental Material
See Usage Policy.

899kB
[img] MS Excel (Supplementary Table 1a) - Supplemental Material
See Usage Policy.

10kB
[img] MS Excel (Supplementary Table 1b) - Supplemental Material
See Usage Policy.

10kB
[img] MS Excel (Supplementary Table 2) - Supplemental Material
See Usage Policy.

11kB
[img] Archive (ZIP) (Supplementary Software) - Supplemental Material
See Usage Policy.

1MB
[img] Archive (ZIP) (Supplementary Code) - Supplemental Material
See Usage Policy.

26MB

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20190506-110012992

Abstract

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1038/nbt.3519DOIArticle
https://rdcu.be/bAy59PublisherFree ReadCube access
https://doi.org/10.1038/nbt0816-888dDOIErratum
https://rdcu.be/bAy6iPublisherFree ReadCube access - Erratum
ORCID:
AuthorORCID
Melsted, Páll0000-0002-8418-6724
Pachter, Lior0000-0002-9164-6231
Additional Information:© 2016 Springer Nature Publishing AG. Received 15 October 2015; Accepted 25 February 2016; Published 04 April 2016. N.L.B., H.P. and L.P. were partially funded by NIH R01 HG006129. P.M. was partially funded by a Fulbright fellowship. Author Contributions: N.L.B. and L.P. developed the concept of pseudoalignment and conceived the idea for applying it to RNA-seq quantification. P.M. conceived the implementation using De Bruijn graphs. N.L.B., H.P., P.M. and L.P. designed the kallisto software and N.L.B. implemented a prototype. H.P. and P.M. wrote the current kallisto implementation. N.B. and H.P. automated production of the results. N.L.B., H.P., P.M. and L.P. analyzed results and wrote the paper. The authors declare no competing financial interests.
Errata:In the version of this article initially published, in the HTML version only, the equation “α_tN > 0.01” was written as “α_(tN) > 0.01.” In addition, in the Figure 1 legend, the formatting of the nodes was incorrect (v_1, etc., rather than v1). The errors have been corrected in the HTML and PDF versions of the article.
Funders:
Funding AgencyGrant Number
NIHR01 HG006129
Fulbright FoundationUNSPECIFIED
Issue or Number:5
Record Number:CaltechAUTHORS:20190506-110012992
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190506-110012992
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:95246
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:06 May 2019 18:20
Last Modified:03 Oct 2019 21:11

Repository Staff Only: item control page