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BackgroundAs costs of high throughput sequencing have fallen, we are seeing vast quantities of short read genomic data being generated. Often, the data is exchanged and stored as aligned reads, which provides high compression and convenient access for many analyses. However, aligned data becomes outdated as new reference genomes and alignment methods become available. Moreover, some applications cannot utilise pre-aligned reads at all, necessitating conversion back to raw format (FASTQ) before they can be used. In both cases, the process of extraction and realignment is expensive and time consuming.FindingsWe describe Bazam, a tool that efficiently extracts the original paired FASTQ from reads stored in aligned form (BAM or CRAM format). Bazam extracts reads in a format that directly allows realignment with popular aligners with high concurrency. Through eliminating steps and increasing the accessible concurrency, Bazam facilitates up to a 90% reduction in the time required for realignment compared to standard methods. Bazam can support selective extraction of read pairs from focused genomic regions, further increasing efficiency for targeted analyses. Bazam is additionally suitable as a base for other applications that require efficient paired read information, such as quality control, structural variant calling and alignment comparison.ConclusionsBazam offers significant improvements for users needing to realign genomic data.

Detection of copy number variation (CNVs) is a challenging but highly valuable application of exome and targeted high throughput sequencing (HTS) data. While there are dozens of CNV detection methods available, using these methods remains challenging due to variable accuracy both across different data sets and within the same data set with different methods. We propose that extracting good results from CNV detection on HTS data requires a systematic approach involving rigorous quality control, adjustment of method parameters and calibration of confidence measures for filtering results. We present Ximmer, a tool which supports an end to end process for applying these procedures including a simulation framework, CNV detection analysis pipeline, and a visualisation and curation tool which enables interactive exploration of CNV results. We apply Ximmer to perform a comprehensive evaluation of CNV detection on four data sets using four different detection methods, representing one of the most comprehensive evaluations to date. Ximmer is open source and freely available at http://ximmer.org (example results are viewable at http://example.ximmer.org).

Short tandem repeat (STR) expansions have been identified as the causal DNA mutation in dozens of Mendelian diseases. Historically, pathogenic STR expansions could only be detected by single locus techniques, such as PCR and electrophoresis. The ability to use short read sequencing data to screen for STR expansions has the potential to reduce both the time and cost to reaching diagnosis and enable the discovery of new causal STR loci. Most existing tools detect STR variation within the read length, and so are unable to detect the majority of pathogenic expansions. Those tools that can detect large expansions are limited to a set of known disease loci and as yet no new disease causing STR expansions have been identified with high-throughput sequencing technologies.Here we address this by presenting STRetch, a new genome-wide method to detect STR expansions at all loci across the human genome. We demonstrate the use of STRetch for detecting pathogenic STR expansions in short-read whole genome sequencing data with a very low false discovery rate. We further demonstrate the application of STRetch to solve cases of patients with undiagnosed disease and apply STRetch to the analysis of 97 whole genomes to reveal variation at STR loci. STRetch assesses expansions at all STR loci in the genome and allows screening for novel disease-causing STRs.STRetch is open source software, available fromgithub.com/Oshlack/STRetch.