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- No explicit alignment to reference genome or transciptome
- Instead, uses “pseudoalignment” to transcriptome
- For each read, determine not where in each transcript it aligns, but rather which transcripts it is compatible with
- Simultaneously addresses 2 aspects of “multi-mapping” reads in traditional RNAseq pipelines
- Multiple possible genomic loci (addressed during alignment)
- Multiple possible transcripts of origin (addressed during quantification)
- Pseudoalignments are sufficient to quantify transcript abundances
- Expectation Maximization (EM) algorithm is applied to a “simple” RNAseq Likelihood function
- Report estimated abundances as Transcripts per Million (TPM) + counts
No P-value reported or differential expression (DE) support, but…
- kallisto re-runs EM on multiple bootstrap re-samples to estimate variance
- bootstraps are re-sampling with replacement from original sample, with same size then kallisto bootstraps are used by add-on sleuth DE package
Why Kallisto?
Speed and performance are greatly improved with Kallisto.
