Identification and correction of systematic error in high-throughput sequence data
1 Department of Mathematics, University of California, Berkeley, 970 Evans Hall #3840, Berkeley, CA 94720 USA
2 Children's Hospital Oakland Research Institute, 5700 Martin Luther King Jr Way, Oakland, CA 94609 USA
3 Computer Science Division, University of California, Berkeley, 387 Soda Hall, Berkeley, CA 94720 USA
4 Department of Molecular & Cell Biology, University of California, Berkeley, 142 LSA #3200, Berkeley, CA 94720
BMC Bioinformatics 2011, 12:451 doi:10.1186/1471-2105-12-451Published: 21 November 2011
A feature common to all DNA sequencing technologies is the presence of base-call errors in the sequenced reads. The implications of such errors are application specific, ranging from minor informatics nuisances to major problems affecting biological inferences. Recently developed "next-gen" sequencing technologies have greatly reduced the cost of sequencing, but have been shown to be more error prone than previous technologies. Both position specific (depending on the location in the read) and sequence specific (depending on the sequence in the read) errors have been identified in Illumina and Life Technology sequencing platforms. We describe a new type of systematic error that manifests as statistically unlikely accumulations of errors at specific genome (or transcriptome) locations.
We characterize and describe systematic errors using overlapping paired reads from high-coverage data. We show that such errors occur in approximately 1 in 1000 base pairs, and that they are highly replicable across experiments. We identify motifs that are frequent at systematic error sites, and describe a classifier that distinguishes heterozygous sites from systematic error. Our classifier is designed to accommodate data from experiments in which the allele frequencies at heterozygous sites are not necessarily 0.5 (such as in the case of RNA-Seq), and can be used with single-end datasets.
Systematic errors can easily be mistaken for heterozygous sites in individuals, or for SNPs in population analyses. Systematic errors are particularly problematic in low coverage experiments, or in estimates of allele-specific expression from RNA-Seq data. Our characterization of systematic error has allowed us to develop a program, called SysCall, for identifying and correcting such errors. We conclude that correction of systematic errors is important to consider in the design and interpretation of high-throughput sequencing experiments.