We included tools applying the gapped-alignment (VarScan, FreeBayes) or split-read (Pindel) methods, respectively, and a combinatorial approach with local de-novo assembly (ScanIndel). 
VarScan
We included tools applying the gapped-alignment (VarScan, FreeBayes) or split-read (Pindel) methods, respectively, and a combinatorial approach with local de-novo assembly (ScanIndel). 
FreeBayes
We included tools applying the gapped-alignment (VarScan, FreeBayes) or split-read (Pindel) methods, respectively, and a combinatorial approach with local de-novo assembly (ScanIndel).  All tools tested were able to detect short indels (≤29 basepairs) at sensitivities close to 100%, albeit Pindel reported up to 20% false calls.  In contrast, gapped-alignment and split-read tools failed to recover large proportions of long indels (>29bp) even at 120-fold coverage, and again, Pindel produced significant numbers of false-positive calls. 
Pindel
We included tools applying the gapped-alignment (VarScan, FreeBayes) or split-read (Pindel) methods, respectively, and a combinatorial approach with local de-novo assembly (ScanIndel).  Outstandingly, ScanIndel detected and reconstructed 97% of long indels on average (95% confidence intervals, 88%-99%) and, at the same time, produced negligible amounts of false calls.  Hence, the combinatorial approach implemented in ScanIndel was able to recover the positions, types and sequences of indels with excellent sensitivity and false-discovery rate, by encompassing the full indel length spectrum present in the datasets.
ScanIndel