BACKGROUND: Platform-specific error profiles necessitate confirmatory studies where predictions made on data generated using one technology are additionally verified by processing the same samples on an orthogonal technology. However, verifying all predictions can be costly and redundant, and testing a subset of findings is often used to estimate the true error profile. RESULTS: To determine how to create subsets of predictions for validation that maximize accuracy of global error profile inference, we developed Valection, a software program that implements multiple strategies for the selection of verification candidates. We evaluated these selection strategies on one simulated and two experimental datasets. CONCLUSIONS: Valection is implemented in multiple programming languages, available at: http://labs.oicr.on.ca/boutros-lab/software/valection.

MOTIVATION: Microarrays are widely used to quantify DNA methylation because they are economical, require only small quantities of input DNA and focus on well-characterized regions of the genome. However, pre-processing of methylation microarray data is challenging because of confounding factors that include background fluorescence, dye bias and the impact of germline polymorphisms. Therefore, we present valuable insights and a framework for those seeking the most optimal pre-processing method through a data-driven approach. RESULTS: Here, we show that Dasen is the optimal pre-processing methodology for the Infinium HumanMethylation450 BeadChip array in prostate cancer, a frequently employed platform for tumour methylome profiling in both the TCGA and ICGC consortia. We evaluated the impact of 11 pre-processing methods on batch effects, replicate variabilities, sensitivities and sample-to-sample correlations across 809 independent prostate cancer samples, including 150 reported for the first time in this study. Overall, Dasen is the most effective for removing artefacts and detecting biological differences associated with tumour aggressivity. Relative to the raw dataset, it shows a reduction in replicate variances of 67% and 76% for β- and M-values, respectively. Our study provides a unique pre-processing benchmark for the community with an emphasis on biological implications. AVAILABILITY AND IMPLEMENTATION: All software used in this study are publicly available as detailed in the article. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: It is extremely common to need to select a subset of reads from a BAM file based on their specific properties. Typically, a user unpacks the BAM file to a text stream using SAMtools, parses and filters the lines using AWK, then repacks them using SAMtools. This process is tedious and error-prone. In particular, when working with many columns of data, mix-ups are common and the bit field containing the flags is unintuitive. There are several libraries for reading BAM files, such as Bio-SamTools for Perl and pysam for Python. Both allow access to the BAM's read information and can filter reads, but require substantial boilerplate code; this is high overhead for mostly ad hoc filtering. RESULTS: We have created a query language that gathers reads using a collection of predicates and common logical connectives. Queries run faster than equivalents and can be compiled to native code for embedding in larger programs. CONCLUSIONS: BAMQL provides a user-friendly, powerful and performant way to extract subsets of BAM files for ad hoc analyses or integration into applications. The query language provides a collection of predicates beyond those in SAMtools, and more flexible connectives.

BACKGROUND: Chromothripsis, a newly discovered type of complex genomic rearrangement, has been implicated in the evolution of several types of cancers. To date, it has been described in bone cancer, SHH-medulloblastoma and acute myeloid leukemia, amongst others, however there are still no formal or automated methods for detecting or annotating it in high throughput sequencing data. As such, findings of chromothripsis are difficult to compare and many cases likely escape detection altogether. RESULTS: We introduce ShatterProof, a software tool for detecting and quantifying chromothriptic events. ShatterProof takes structural variation calls (translocations, copy-number variations, short insertions and loss of heterozygosity) produced by any algorithm and using an operational definition of chromothripsis performs robust statistical tests to accurately predict the presence and location of chromothriptic events. Validation of our tool was conducted using clinical data sets including matched normal, prostate cancer samples in addition to the colorectal cancer and SCLC data sets used in the original description of chromothripsis. CONCLUSIONS: ShatterProof is computationally efficient, having low memory requirements and near linear computation time. This allows it to become a standard component of sequencing analysis pipelines, enabling researchers to routinely and accurately assess samples for chromothripsis. Source code and documentation can be found at http://search.cpan.org/~sgovind/Shatterproof.