<p>I have sought previous posts (<a href="https://www.biostars.org/p/137613/#137955">post1</a>, <a href="https://www.biostars.org/p/19104/">post2</a>) on how to call somatic variants and it seems the general practice is to intersect multiple callers to insure that low VAF mutations are being called.</p>

<p>My approach was to samtools, MuTect2, SomaticSniper, VarScan2 but I found an interesting post saying that as long as read placements are perfect, any caller suffices (even samtool mpileup). I should mention that I am working with RNA seq of cancer samples (matched with normal).</p>

<pre>
In general, my view is as long as read placement is perfect, even naive methods work sufficiently well... To me, the simplest yet most effective strategy is to use two distinct alignment algorithms, such as <a href="http://bio-bwa.sourceforge.net/">bwa</a> and <a href="http://bio-bwa.sourceforge.net/">bwa</a>-sw, which have distinct error modes. You only consider mutations shared between the two alignments... Another complication is structural variations, in which I am less experienced. In some sense, false mutations caused by structural variations are still indication of something different between normal and tumor... In all, I think you do not need to worry about which software to use for detecting somatic mutations - anything reasonable is fine. You should pay more attention to mismapping and structural variations.</pre>

<p>First, does read placement mean how well aligners align our samples to the reference? Does working with RNA-Seq introduce higher error rates in read placements? Has the consensus changed or is intersecting multiple callers still recommended? Thank you very much for your help.</p>

If someone tells you that somatic mutation calling is easy or a solved problem, they have never really tried to do somatic mutation calling.

There are a host of issue to contend with - sequencing artifacts, problems with the reference, differential coverage, (and yes, mismappings are common!), etc. Your approach of using mutliple callers seems sensible. The tricky part is figuring out how to combine them. Straight intersections will give you high specificity, but low sensitivity. Unioning the three will result in the opposite. A more nuanced approach has been been explored by recent tools like [somaticseq](http://www.genomebiology.com/2015/16/1/197). I haven't used that one in particular, but I am convinced that an approach of that nature is most likely to succeed.