BackgroundA 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.ResultsWe 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.ConclusionsSystematic 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.

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 form high-coverage data. We show that such errors occur in approximately 1 in 1000 base pairs, and that quality scores at systematic error sites do not account for the extent of errors. 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). 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.Availability: Software for correcting systematic errors is freely available from

No abstract

The human capacity for anxiety is probably an evolutionary adaptation to deal with environmental threats. But this does not explain the high prevalence of anxiety disorders, which can be severely debilitating. We present a simple mathematical model to demonstrate that even if all individuals in a population behave optimally, a substantial subset of the population will become overly sensitive to threats, which we suggest may point towards an adaptive explanation for dysfunctional anxiety.

Costly signalling theory is commonly invoked as an explanation for how honest communication can be stable when interests conflict. However, the signal costs predicted by costly signalling models often turn out to be unrealistically high. These models generally assume that signal cost is determinate. Here, we consider the case where signal cost is instead stochastic. We examine both discrete and continuous signalling games and show that, under reasonable assumptions, stochasticity in signal costs can decrease the average cost at equilibrium for all individuals. This effect of stochasticity for decreasing signal costs is a fundamental mechanism that probably acts in a wide variety of circumstances.