MonoSeq Variant Caller Reveals Novel Mononucleotide Run Indel Mutations in Tumors with Defective DNA Mismatch Repair.

Next-generation sequencing has revolutionized cancer genetics, but accurately detecting mutations in repetitive DNA sequences, especially mononucleotide runs, remains a challenge. This is a particular concern for tumors with defective mismatch repair (MMR) that accumulate strand-slippage mutations. We developed MonoSeq to improve indel mutation detection in mononucleotide runs, and used MonoSeq to investigate strand-slippage mutations in endometrial cancers, a tumor type that has frequent loss of MMR. We performed extensive Sanger sequencing to validate both clonal and subclonal MonoSeq mutation calls. Eighty-one regions containing mononucleotide runs were sequenced in 540 primary endometrial cancers (223 with defective MMR). Our analyses revealed that the overall mutation rate in MMR-deficient tumors was 20-30-fold higher than in MMR-normal tumors. MonoSeq analysis identified several previously unreported mutations, including a novel hotspot in an A7 run in the terminal exon of ARID5B.The ARID5B indel mutations were seen in both MMR-deficient and MMR-normal tumors, suggesting biologic selection. The analysis of tumor mRNAs revealed the presence of mutant transcripts that could result in translation of neopeptides. Improved detection of mononucleotide run strand-slippage mutations has clear implications for comprehensive mutation detection in tumors with defective MMR. Indel frameshift mutations and the resultant antigenic peptides could help guide immunotherapy strategies.

MuCor: mutation aggregation and correlation.

MOTIVATION: There are many tools for variant calling and effect prediction, but little to tie together large sample groups. Aggregating, sorting and summarizing variants and effects across a cohort is often done with ad hoc scripts that must be re-written for every new project. In response, we have written MuCor, a tool to gather variants from a variety of input formats (including multiple files per sample), perform database lookups and frequency calculations, and write many types of reports. In addition to use in large studies with numerous samples, MuCor can also be employed to directly compare variant calls from the same sample across two or more platforms, parameters or pipelines. A companion utility, DepthGauge, measures coverage at regions of interest to increase confidence in calls. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/blachlylab/mucor and a Docker image is available at https://hub.docker.com/r/blachlylab/mucor/ CONTACT: james.blachly@osumc.eduSupplementary data: Supplementary data are available at Bioinformatics online.

Quality Control for RNA-Seq (QuaCRS): An Integrated Quality Control Pipeline.

QuaCRS (Quality Control for RNA-Seq) is an integrated, simplified quality control (QC) system for RNA-seq data that allows easy execution of several open-source QC tools, aggregation of their output, and the ability to quickly identify quality issues by performing meta-analyses on QC metrics across large numbers of samples in different studies. It comprises two main sections. First is the QC Pack wrapper, which executes three QC tools: FastQC, RNA-SeQC, and selected functions from RSeQC. Combining these three tools into one wrapper provides increased ease of use and provides a much more complete view of sample data quality than any individual tool. Second is the QC database, which displays the resulting metrics in a user-friendly web interface. It was designed to allow users with less computational experience to easily generate and view QC information for their data, to investigate individual samples and aggregate reports of sample groups, and to sort and search samples based on quality. The structure of the QuaCRS database is designed to enable expansion with additional tools and metrics in the future. The source code for not-for-profit use and a fully functional sample user interface with mock data are available at http://bioserv.mps.ohio-state.edu/QuaCRS/.