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We are analyzing https://liorpachter.wordpress.com/2015/05/10/near-optimal-rna-seq-quantification-with-kallisto/.

Title:
Near-optimal RNA-Seq quantification with kallisto | Bits of DNA
Description:
Today IĀ posted the preprintĀ N. Bray, H. Pimentel, P. Melsted and L. Pachter, Near-optimal RNA-Seq quantification with kallistoĀ toĀ the arXiv. It describes the RNA-Seq quantification programĀ kallistoā€¦
Website Age:
25 years and 4 months (reg. 2000-03-03).

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šŸš„ Respectable Traffic: 10k - 20k visitors per month


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Keywords {šŸ”}

reply, kallisto, reads, counts, lior, analysis, read, gene, rnaseq, kmers, pachter, data, transcriptome, pseudoalignment, kmer, reference, transcript, expression, results, variance, isoform, quantification, program, alignment, work, october, level, number, error, august, paper, june, match, bootstrap, april, time, errors, accuracy, transcripts, abundances, million, bootstraps, false, differential, genes, bowtie, point, build, human, based,

Topics {āœ’ļø}

/nbt/journal/v31/n1/full/nbt 6Ā millionĀ geuvadisĀ human rna-seq reads constituentĀ k-mer ā€œmini-readsā€ org/content/early/2015/03/26/017087 conceivably gov/pmc/articles/pmc3546801/ rna-seq quantificationĀ called sailfish rna-seq experiment suffices rna-seq quantification programĀ kallisto strain-level metagenomics quantification optimal rna-seq quantification underlying rna-seq analysis allele-specific expression calls removing redundant k-mers efficient k-mer counting rna-seq analysis program de bruijn graph january error-free kmer match improve rna-seq analysis matching k-mers required de analysis tool/package initial full pseudo-alignment k-mer simply results kallisto ignores k-mers short single-end reads rna-seq quantification rna-seq processing email printmore loading studentĀ nicolas bray realized error k-mer doesn obtain consistent estimates things amanzi statistics rna-seq analysis july 7 gene-level de ribosomal rna depletion anti-sense rna single k-mer obtain gene counts bioinformatics core facility bioinformatics brownian notions single cpu core redundant k-mers weekly journal club journal club discussion increasingly complex models innovative genomics initiative sexual assault allegations scientist genomes unzipped gen seek rrresearch

Questions {ā“}

  • (1) Whatā€™s the best way to estimate transcript proportions (relative abundance) from Kallisto outputs?
  • (2) Can we use the estimated counts from Kallisto for differential analysis, say, with DESeq2?
  • > Do the estimated counts from Kallisto allow us to get around these issues?
  • > Presumably the summing up of transcript counts to gene level counts (which the DEseq authors recommend as part of a pipleline to use sailfish with DEseq2) would still be somewhat iffy for doing gene-level DE?
  • Are real genetic polymorphisms treated equivalently to sequencing errors in pseudoalignment?
  • Can you elaborate a little more on this?
  • Can you elaborate on the commercial license?
  • Can you please elaborate on how the arithmetic goes awry when determining counts of reads per gene based on the counts of reads per transcript?
  • Can you talk more about that?
  • Could one tell after the analysis if you ran too few bootstraps, or would the variance in sampling be indistinguishable from variability caused by the sequencer?
  • Could one then increase pseudoalignment sensitivity, perhaps by epsilon, by representing common variants in the T-DBG?
  • Could this approach of ā€˜not depending on exact alignment but rather which transcript the read originated fromā€™ be used for quantifying transcripts from closely related co-growing/co-cultured species ?
  • Do the estimated counts from Kallisto allow us to get around these issues?
  • Do the estimated read counts produced by Kallisto (and similar tools such as sailfish/salmon) work well with count-based DE tools tools such as DEseq2?
  • Do you feel we are at a point now (finally) with kallisto and maybe RSEM or eXpress that we can make pretty reliable inference of differential splicing (via isoform abundances) and, if so, do you or your labbies have any preference in differential testing approaches for evaluating changes in a gene locus?
  • Has that been tested?
  • How exactly does Kallisto estimate the empirical fragment length distribution?
  • I gather that overall accuracy is good, but did you observe any gene-specific deviations?
  • I have just one question: Why do you write in your FAQ that kallisto canā€™t be used for finding differentialy expressed genes?
  • I was wondering if the test data provided with the software is real data or just artificially constructed data?
  • In terms of reporting magnitudes of fold changes (Analogous to DEseq2 and Edger), am I correct in saying that we would report the ā€˜bā€™ (beta) values from the Sleuth results tables, which indicate effect size?
  • In the future, will it possible to run additional bootstraps, without redoing the initial full pseudo-alignment?
  • Is it possible that a future iteration of Kallisto will provide an estimate of the time the analysis will take?
  • Is the github of kallisto repository up?
  • Is this a fair assessment?
  • Just how fast is pseudoalignment?
  • Nick had the insight to ask: what can be gained if we let go of that paradigm?
  • Out of curiosity, what makes you think that no further gains in accuracy can be made?
  • Perhaps this is a naive question, but arenā€™t you using a single library here?
  • Presumably if you simply sum the transcript counts, you will be double-counting reads that hit shared exons between transcripts, and therefore overestimating the gene counts?
  • The most embarrassing citation ever?
  • What are you defining as technical variance?
  • What do you mean by "heterogeneity"?
  • What is optimality in reference to?
  • What is the minimal number of matching k-mers required for a read to be pseudo-aligned?
  • What should be considered when deciding how many bootstraps to run?
  • What would you recommend as a sufficient level of bootstrapping with Kallisto to then run Sleuth?
  • Where is my logic going awry?
  • Will this be a problem for me if my reference transcribtome does not contain the (previously identified) mutations?
  • ā€ Is this for k=31?

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