Bioinformatics (2010) 26(12), i318–i324 doi: 10.1093/bioinformatics/btq214 Bioinformatics, (2011) 27(15), 2047–2053 doi: 10.1093/bioinformatics/btr344 Vikas Bansal, author and co-author of the above articles, wishes to alert readers to the following availability links, as the links given in the published papers are no longer active: For A statistical method for the detection of variants from next-generation resequencing of DNA pools implementation of this method is available at: https://sites.google.com/site/vibansal/software/crisp For A probabilistic method for the detection and genotyping of small indels from population-scale sequence data a program implementing this method is available at: https://sites.google.com/site/vibansal/software/picall The author also wishes to notify readers that the email address listed in these articles ([email protected]) is no longer working. Vikas Bansal can now be contacted at the following email address: [email protected]. The author apologizes for any inconvenience.

BACKGROUND: Estimation of individual ancestry from genetic data is useful for the analysis of disease association studies, understanding human population history and interpreting personal genomic variation. New, computationally efficient methods are needed for ancestry inference that can effectively utilize existing information about allele frequencies associated with different human populations and can work directly with DNA sequence reads. RESULTS: We describe a fast method for estimating the relative contribution of known reference populations to an individual's genetic ancestry. Our method utilizes allele frequencies from the reference populations and individual genotype or sequence data to obtain a maximum likelihood estimate of the global admixture proportions using the BFGS optimization algorithm. It accounts for the uncertainty in genotypes present in sequence data by using genotype likelihoods and does not require individual genotype data from external reference panels. Simulation studies and application of the method to real datasets demonstrate that our method is significantly times faster than previous methods and has comparable accuracy. Using data from the 1000 Genomes project, we show that estimates of the genome-wide average ancestry for admixed individuals are consistent between exome sequence data and whole-genome low-coverage sequence data. Finally, we demonstrate that our method can be used to estimate admixture proportions using pooled sequence data making it a valuable tool for controlling for population stratification in sequencing based association studies that utilize DNA pooling. CONCLUSIONS: Our method is an efficient and versatile tool for estimating ancestry from DNA sequence data and is available from https://sites.google.com/site/vibansal/software/iAdmix .

MOTIVATION: High-throughput sequencing technologies have made population-scale studies of human genetic variation possible. Accurate and comprehensive detection of DNA sequence variants is crucial for the success of these studies. Small insertions and deletions represent the second most frequent class of variation in the human genome after single nucleotide polymorphisms (SNPs). Although several alignment tools for the gapped alignment of sequence reads to a reference genome are available, computational methods for discriminating indels from sequencing errors and genotyping indels directly from sequence reads are needed. RESULTS: We describe a probabilistic method for the accurate detection and genotyping of short indels from population-scale sequence data. In this approach, aligned sequence reads from a population of individuals are used to automatically account for context-specific sequencing errors associated with indels. We applied this approach to population sequence datasets from the 1000 Genomes exon pilot project generated using the Roche 454 and Illumina sequencing platforms, and were able to detect a significantly greater number of indels than reported previously. Comparison to indels identified in the 1000 Genomes pilot project demonstrated the sensitivity of our method. The consistency in the number of indels and the fraction of indels whose length is a multiple of three across different human populations and two different sequencing platforms indicated that our method has a low false discovery rate. Finally, the method represents a general approach for the detection and genotyping of small-scale DNA sequence variants for population-scale sequencing projects. AVAILABILITY: A program implementing this method is available at http://polymorphism.scripps.edu/~vbansal/software/piCALL/