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Quantification of sequence alignments may be performed at the gene, exon, or transcript level.<ref name="Thind"/><ref name="#24020486" /> Typical outputs include a table of read counts for each feature supplied to the software; for example, for genes in a [[general feature format]] file. Gene and exon read counts may be calculated quite easily using HTSeq, for example.<ref name="#25260700">{{cite journal | vauthors = Anders S, Pyl PT, Huber W | title = HTSeq—a Python framework to work with high-throughput sequencing data | journal = Bioinformatics | volume = 31 | issue = 2 | pages = 166–9 | date = January 2015 | pmid = 25260700 | pmc = 4287950 | doi = 10.1093/bioinformatics/btu638 }}</ref> Quantitation at the transcript level is more complicated and requires probabilistic methods to estimate transcript isoform abundance from short read information; for example, using cufflinks software.<ref name="#20436464" /> Reads that align equally well to multiple locations must be identified and either removed, aligned to one of the possible locations, or aligned to the most probable ___location.
Some quantification methods can circumvent the need for an exact alignment of a read to a reference sequence altogether. The kallisto software method combines pseudoalignment and quantification into a single step that runs 2 orders of magnitude faster than contemporary methods such as those used by tophat/cufflinks software, with less computational burden.<ref name="#27043002">{{cite journal | vauthors = Bray NL, Pimentel H, Melsted P, Pachter L | title = Near-optimal probabilistic RNA-seq quantification | journal = Nature Biotechnology | volume = 34 | issue = 5 | pages = 525–7 | date = May 2016 | pmid = 27043002 | doi = 10.1038/nbt.3519 | s2cid = 205282743 | url = https://resolver.caltech.edu/CaltechAUTHORS:20190506-110012992 }}</ref>
==== Differential expression ====
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