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We explore connections between metagenomic read assignment and the quantification of transcripts from RNA-Seq data. In particular, we show that the recent idea of pseudoalignment introduced in the RNA-Seq context is suitable in the metagenomics setting. When coupled with the Expectation-Maximization (EM) algorithm, reads can be assigned far more accurately and quickly than is currently possible with state of the art software., Replaced accidentally duplicated figure with correct version; fixed some issues with figure generation and labeling; fixed problem with some missing genomes from database; added link to GitHub repo containing analysis code; included assessment of aggregate sensitivity and precision; clarified assessment metrics used

Summary: Advances in sequencing capacity have led to the generation of unprecedented amounts of genomic data. The processing of this data frequently leads to I/O bottlenecks, e. g. when analyzing a small genomic region across a large number of samples. The largest I/O burden is, however, often not imposed by the amount of data needed for the analysis but rather by index files that help retrieving this data. We have developed chopBAI, a program that can chop a BAM index (BAI) file into small pieces. The program outputs a list of BAI files each indexing a specified genomic interval. The output files are much smaller in size but maintain compatibility with existing software tools. We show how preprocessing BAI files with chopBAI can lead to a reduction of I/O by more than 95% during the analysis of 10 kb genomic regions, eventually enabling the joint analysis of more than 10 000 individuals. Availability and Implementation: The software is implemented in C ++, GPL licensed and available at http://github.com/DecodeGenetics/chopBAI Contact: birte.kehr@decode.is

Motivation: Several applications in bioinformatics, such as genome assemblers and error corrections methods, rely on counting and keeping track of k -mers (substrings of length k ). Histograms of k -mer frequencies can give valuable insight into the underlying distribution and indicate the error rate and genome size sampled in the sequencing experiment. Results: We present KmerStream, a streaming algorithm for estimating the number of distinct k -mers present in high-throughput sequencing data. The algorithm runs in time linear in the size of the input and the space requirement are logarithmic in the size of the input. We derive a simple model that allows us to estimate the error rate of the sequencing experiment, as well as the genome size, using only the aggregate statistics reported by KmerStream. As an application we show how KmerStream can be used to compute the error rate of a DNA sequencing experiment. We run KmerStream on a set of 2656 whole genome sequenced individuals and compare the error rate to quality values reported by the sequencing equipment. We discover that while the quality values alone are largely reliable as a predictor of error rate, there is considerable variability in the error rates between sequencing runs, even when accounting for reported quality values. Availability and implementation: The tool KmerStream is written in C++ and is released under a GPL license. It is freely available at https://github.com/pmelsted/KmerStream Supplementary information: Supplementary data are available at Bioinformatics online. Contact: pmelsted@hi.is or Bjarni.Halldorsson@decode.is .