Characterizing and Measuring Bias in Sequence Data
Bias and artifact papers are very uncool and we do not think press will be calling David Jaffe and his colleagues to know whether they changed biology textbooks. However, we like covering them, because their newly published Genome Biology paper will be very useful for those fighting in the trenches. To know how sequencing bias impacts de Bruijn graphs and next-gen assembly, also check C. Titus Brown’s paper covered in our prior commentary - Illumina Sequencing Artifacts Revealed by Connectivity Analysis of Metagenomic Datasets.
The abstract of paper from Broad Institute follows:
Background
DNA sequencing technologies deviate from the ideal uniform distribution of reads. These biases impair scientific and medical applications. Accordingly, we have developed computational methods for discovering, describing and measuring bias.
Results
We applied these methods to the Illumina, Ion Torrent, Pacific Biosciences and Complete Genomics sequencing platforms, using data from human and from a set of microbes with diverse base compositions. As in previous work, library construction conditions significantly influence sequencing bias. Pacific Biosciences coverage levels are the least biased, followed by Illumina, although all technologies exhibit error-rate biases in high- and low-GC regions and at long homopolymer runs. The GC-rich regions prone to low coverage include a number of human promoters, so we therefore catalog 1,000 that were exceptionally resistant to sequencing. Our results indicate that combining data from two technologies can reduce coverage bias if the biases in the component technologies are complementary and of similar magnitude. Analysis of Illumina data representing 120-fold coverage of a well-studied human sample reveals that 0.20% of the autosomal genome was covered at less than 10% of the genome-wide average. Excluding locations that were similar to known bias motifs or likely due to sample-reference variations left only 0.045% of the autosomal genome with unexplained poor coverage.
Conclusions
The assays presented in this paper provide a comprehensive view of sequencing bias, which can be used to drive laboratory improvements and to monitor production processes. Development guided by these assays should result in improved genome assemblies and better coverage of biologically important loci.
