Novel read density distribution score shows possible aligner artefacts, when mapping a single chromosome

Abstract

Background: The use of artificial data to evaluate the performance of aligners and peak callers not only improves its accuracy and reliability, but also makes it possible to reduce the computational time. One of the natural ways to achieve such time reduction is by mapping a single chromosome.

Results: We investigated whether a single chromosome mapping causes any artefacts in the alignments' performances. In this paper, we compared the accuracy of the performance of seven aligners on well-controlled simulated benchmark data which was sampled from a single chromosome and also from a whole genome. We found that commonly used statistical methods are insufficient to evaluate an aligner performance, and applied a novel measure of a read density distribution similarity, which allowed to reveal artefacts in aligners' performances. We also calculated some interesting mismatch statistics, and constructed mismatch frequency distributions along the read.

Conclusions: The generation of artificial data by mapping of reads generated from a single chromosome to a reference chromosome is justified from the point of view of reducing the benchmarking time. The proposed quality assessment method allows to identify the inherent shortcoming of aligners that are not detected by conventional statistical methods, and can affect the quality of alignment of real data.

Keywords: DNA alignment; Next-generation sequencing; Read density distribution.

Fig. 1

Coefficients of variation of the sample, case1. Panel labels: general (total) CVS are marked in green, head CVS - in mauve, tail CVS - in brown. SE are shown with light colour bars, PE – with dark colour bars

Fig. 2

Density profiles for mapping of reads in log-log coordinates, case1

Fig. 3

Fragment of coverage of SE alignments, case1. All the tracks have the same data range (vertical scale)

Fig. 4

Coefficients of variation of the sample, case2. Panel labels: general (total) CVS are marked in green, head CVS - in mauve, tail CVS - in brown. SE are shown with light colour bars, PE – with dark colour bars

Fig. 5

Fragment of coverage of PE alignments, case2

Fig. 6

Coefficients of variation of the sample, case3. Panel labels: general (total) CVS are marked in green, head CVS - in mauve, tail CVS - in brown. SE are shown with light colour bars, PE – with dark colour bars

Fig. 7

F1 scores. All reads – light colour bars, non-sero reads (‘reliable’) – dark colour bars

Fig. 8

Percentage of non-zero-scored and zero-scored reads with detected mismatches. SE – pale histogram bars, PE – bright histogram bars

Fig. 9

The frequency of mismatches depending on the position in the read, in log coordinate, case2

Fig. 10

Fragment of alignments in comparison with low mappability tracks, UCSC genome browser