MOTIVATION: Genomic variant detection from next-generation sequencing has become established as an extremely important component of research and clinical diagnoses in both cancer and Mendelian disorders. Insertions and deletions (indels) are a common source of variation and can frequently impact functionality, thus making their detection vitally important. While substantial effort has gone into detecting indels from DNA, there is still opportunity for improvement. Further, detection of indels from RNA-Seq data has largely been an afterthought and offers another critical area for variant detection. RESULTS: We present here ABRA2, a redesign of the original ABRA implementation that offers support for realignment of both RNA and DNA short reads. The process results in improved accuracy and scalability including support for human whole genomes. Results demonstrate substantial improvement in indel detection for a variety of data types, including those that were not previously supported by ABRA. Further, ABRA2 results in broad improvements to variant calling accuracy across a wide range of post-processing workflows including whole genomes, targeted exomes and transcriptome sequencing. AVAILABILITY AND IMPLEMENTATION: ABRA2 is implemented in a combination of Java and C/C++ and is freely available to all from: https://github.com/mozack/abra2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: B-cell receptor (BCR) repertoire profiling is an important tool for understanding the biology of diverse immunologic processes. Current methods for analyzing adaptive immune receptor repertoires depend upon PCR amplification of VDJ rearrangements followed by long read amplicon sequencing spanning the VDJ junctions. While this approach has proven to be effective, it is frequently not feasible due to cost or limited sample material. Additionally, there are many existing datasets where short-read RNA sequencing data are available but PCR amplified BCR data are not. RESULTS: We present here V'DJer, an assembly-based method that reconstructs adaptive immune receptor repertoires from short-read RNA sequencing data. This method captures expressed BCR loci from a standard RNA-seq assay. We applied this method to 473 Melanoma samples from The Cancer Genome Atlas and demonstrate V'DJer's ability to accurately reconstruct BCR repertoires from short read mRNA-seq data. AVAILABILITY AND IMPLEMENTATION: V'DJer is implemented in C/C ++, freely available for academic use and can be downloaded from Github: https://github.com/mozack/vdjer CONTACT: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Variant detection from next-generation sequencing (NGS) data is an increasingly vital aspect of disease diagnosis, treatment and research. Commonly used NGS-variant analysis tools generally rely on accurately mapped short reads to identify somatic variants and germ-line genotypes. Existing NGS read mappers have difficulty accurately mapping short reads containing complex variation (i.e. more than a single base change), thus making identification of such variants difficult or impossible. Insertions and deletions (indels) in particular have been an area of great difficulty. Indels are frequent and can have substantial impact on function, which makes their detection all the more imperative. RESULTS: We present ABRA, an assembly-based realigner, which uses an efficient and flexible localized de novo assembly followed by global realignment to more accurately remap reads. This results in enhanced performance for indel detection as well as improved accuracy in variant allele frequency estimation. AVAILABILITY AND IMPLEMENTATION: ABRA is implemented in a combination of Java and C/C++ and is freely available for download at https://github.com/mozack/abra.