MOTIVATION: Recombined T- and B-cell receptor repertoires are increasingly being studied using next generation sequencing (NGS) in order to interrogate the repertoire composition as well as changes in the distribution of receptor clones under different physiological and disease states. This type of analysis requires efficient and unambiguous clonotype assignment to a large number of NGS read sequences, including the identification of the incorporated V and J gene segments and the CDR3 sequence. Current tools have deficits with respect to performance, accuracy and documentation of their underlying algorithms and usage. RESULTS: We present IMSEQ, a method to derive clonotype repertoires from NGS data with sophisticated routines for handling errors stemming from PCR and sequencing artefacts. The application can handle different kinds of input data originating from single- or paired-end sequencing in different configurations and is generic regarding the species and gene of interest. We have carefully evaluated our method with simulated and real world data and show that IMSEQ is superior to other tools with respect to its clonotyping as well as standalone error correction and runtime performance. AVAILABILITY AND IMPLEMENTATION: IMSEQ was implemented in C++ using the SeqAn library for efficient sequence analysis. It is freely available under the GPLv2 open source license and can be downloaded at www.imtools.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: [email protected] or [email protected].

MOTIVATION: When analyzing a case group of patients with ultra-rare disorders the ethnicities are often diverse and the data quality might vary. The population substructure in the case group as well as the heterogeneous data quality can cause substantial inflation of test statistics and result in spurious associations in case-control studies if not properly adjusted for. Existing techniques to correct for confounding effects were especially developed for common variants and are not applicable to rare variants. RESULTS: We analyzed strategies to select suitable controls for cases that are based on similarity metrics that vary in their weighting schemes. We simulated different disease entities on real exome data and show that a similarity-based selection scheme can help to reduce false positive associations and to optimize the performance of the statistical tests. Especially when data quality as well as ethnicities vary a lot in the case group, a matching approach that puts more weight on rare variants shows the best performance. We reanalyzed collections of unrelated patients with Kabuki make-up syndrome, Hyperphosphatasia with Mental Retardation syndrome and Catel-Manzke syndrome for which the disease genes were recently described. We show that rare variant association tests are more sensitive and specific in identifying the disease gene than intersection filters and should thus be considered as a favorable approach in analyzing even small patient cohorts. AVAILABILITY AND IMPLEMENTATION: Datasets used in our analysis are available at ftp://ftp.1000genomes.ebi.ac.uk./vol1/ftp/ CONTACT: : [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Next-generation sequencing and exome-capture technologies are currently revolutionizing the way geneticists screen for disease-causing mutations in rare Mendelian disorders. However, the identification of causal mutations is challenging due to the sheer number of variants that are identified in individual exomes. Although databases such as dbSNP or HapMap can be used to reduce the plethora of candidate genes by filtering out common variants, the remaining set of genes still remains on the order of dozens. RESULTS: Our algorithm uses a non-homogeneous hidden Markov model that employs local recombination rates to identify chromosomal regions that are identical by descent (IBD = 2) in children of consanguineous or non-consanguineous parents solely based on genotype data of siblings derived from high-throughput sequencing platforms. Using simulated and real exome sequence data, we show that our algorithm is able to reduce the search space for the causative disease gene to a fifth or a tenth of the entire exome. AVAILABILITY: An R script and an accompanying tutorial are available at http://compbio.charite.de/index.php/ibd2.html.