I have been looking at some of my human RNA-Seq data and started to notice some strange things, which brings up some potentially difficult questions. For example do aligners (e.g. tophat) take into account the probability of incorrectly mapping a given 35-bp single end given all the possible derivatives of (0,1,2) mismatches in a given genome? For example, AGG...GCT may be in the genome 100 times more than AAA...AAA? More importantly, given the random placement of reads mapping identically or pseudo-indentically, should we also consider for a given sequence the probability of incorrectly mapping the sequence because there may be 4 places in the genome with ACTG..., but 400 with ATTG... (1bp mismatch) and 4000 with ATTT... (2bp mismatch)?
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Researchers using sequencing and genomics tools often have to make trade-offs. They can choose between speed or scale, short reads or long-range information, or targeted panels or a view of the whole transcriptome. New technologies that have been released this year are built to address those tough choices.
We asked six companies the same four questions to learn about their latest products. The new technologies bring a lot to the table, including rethinking sequencing...
