Abstract Motivation: Sequencing-based assays such as ChIP-seq, DNase-seq and MNase-seq have become important tools for genome annotation. In these assays, short sequence reads enriched for loci of interest are mapped to a reference genome to determine their origin. Here, we consider whether false positive peak calls can be caused by particular type of error in the reference genome: multicopy sequences which have been incorrectly assembled and collapsed into a single copy. Results: Using sequencing data from the 1000 Genomes Project, we systematically scanned the human genome for regions of high sequencing depth. These regions are highly enriched for erroneously inferred transcription factor binding sites, positions of nucleosomes and regions of open chromatin. We suggest a simple masking procedure to remove these regions and reduce false positive calls. Availability: Files for masking out these regions are available at eqtl.uchicago.edu Contact: [email protected]; [email protected]; [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Next-generation sequencing has become an important tool for genome-wide quantification of DNA and RNA. However, a major technical hurdle lies in the need to map short sequence reads back to their correct locations in a reference genome. Here, we investigate the impact of SNP variation on the reliability of read-mapping in the context of detecting allele-specific expression (ASE). RESULTS: We generated 16 million 35 bp reads from mRNA of each of two HapMap Yoruba individuals. When we mapped these reads to the human genome we found that, at heterozygous SNPs, there was a significant bias toward higher mapping rates of the allele in the reference sequence, compared with the alternative allele. Masking known SNP positions in the genome sequence eliminated the reference bias but, surprisingly, did not lead to more reliable results overall. We find that even after masking, approximately 5-10% of SNPs still have an inherent bias toward more effective mapping of one allele. Filtering out inherently biased SNPs removes 40% of the top signals of ASE. The remaining SNPs showing ASE are enriched in genes previously known to harbor cis-regulatory variation or known to show uniparental imprinting. Our results have implications for a variety of applications involving detection of alternate alleles from short-read sequence data. AVAILABILITY: Scripts, written in Perl and R, for simulating short reads, masking SNP variation in a reference genome and analyzing the simulation output are available upon request from JFD. Raw short read data were deposited in GEO (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE18156. CONTACT: [email protected]; [email protected]; [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Comparisons of gene expression levels within and between species have become a central tool in the study of the genetic basis for phenotypic variation, as well as in the study of the evolution of gene regulation. DNA microarrays are a key technology that enables these studies. Currently, however, microarrays are only available for a small number of species. Thus, in order to study gene expression levels in species for which microarrays are not available, researchers face three sets of choices: (i) use a microarray designed for another species, but only compare gene expression levels within species, (ii) construct a new microarray for every species whose gene expression profiles will be compared or (iii) build a multi-species microarray with probes from each species of interest. Here, we use data collected using a multi-primate cDNA array to evaluate the reliability of each approach. RESULTS: We find that, for inter-species comparisons, estimates of expression differences based on multi-species microarrays are more accurate than those based on multiple species-specific arrays. We also demonstrate that within-species expression differences can be estimated using a microarray for a closely related species, without discernible loss of information. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.