Biology subjects

Ole Schulz-Trieglaff

Publications and source records attributed to Ole Schulz-Trieglaff.

4 recordsLinked to original sources

bioRxiv2016

AKT: Ancestry and Kinship Toolkit

Ancestry and Kinship Toolkit (AKT) is a statistical genetics tool for analysing large cohorts of whole-genome sequenced samples. It can rapidly detect related samples, characterise sample ancestry, calculate correlation between variants, check Mendel consistency and perform data clustering. AKT brings together the functionality of many state-of-the-art methods, with a focus on speed and a unified interface. We believe it will be an invaluable tool for the curation of large WGS data-sets.\n\nAvailabilityThe source code is available at https://illumina.github.io/akt\n\nContactjoconnell@illumina.com, rudy.d.arthur@gmail.com

Bioinformatics↗

bioRxiv2015

Rapid Genotype Refinement for Whole-Genome Sequencing Data using Multi-Variate Normal Distributions

Whole-genome low-coverage sequencing has been combined with linkage-disequilibrium (LD) based genotype refinement to accurately and cost-effectively infer genotypes in large cohorts of individuals. Most genotype refinement methods are based on hidden Markov models, which are accurate but computationally expensive. We introduce an algorithm that models LD using a simple multivariate Gaussian distribution. The key feature of our algorithm is its speed, it is hundreds of times faster than other methods on the same data set and its scaling behaviour is linear in the number of samples. We demonstrate the performance of the method on both low-coverage and high-coverage samples.\n\nAvailability: The source code is available at https://github.com/sequencing/marvin\n\nContact: rarthur@illumina.com

Bioinformatics↗

bioRxiv2015

Manta: Rapid detection of structural variants and indels for clinical sequencing applications

SummaryWe describe Manta, a method to discover structural variants and indels from next generation sequencing data. Manta is optimized for rapid clinical analysis, calling structural variants, medium-sized indels and large insertions on standard compute hardware in less than a tenth of the time that comparable methods require to identify only subsets of these variant types: for example NA12878 at 50x genomic coverage is analyzed in less than 20 minutes. Manta can discover and score variants based on supporting paired and split-read evidence, with scoring models optimized for germline analysis of diploid individuals and somatic analysis of tumor-normal sample pairs. Call quality is similar to or better than comparable methods, as determined by pedigree consistency of germline calls and comparison of somatic calls to COSMIC database variants. Manta consistently assembles a higher fraction of its calls to basepair resolution, allowing for improved downstream annotation and analysis of clinical significance. We provide Manta as a community resource to facilitate practical and routine structural variant analysis in clinical and research sequencing scenarios.\n\nAvailabilityManta source code and Linux binaries are available from http://github.com/sequencing/manta.\n\nContactcsaunders@illumina.com\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics↗

bioRxiv2014

NxTrim: optimized trimming of Illumina mate pair reads

Motivation: Mate pair protocols add to the utility of paired-end sequencing by boosting the genomic distance spanned by each pair of reads, potentially allowing larger repeats to be bridged and resolved. The Illumina Nextera Mate Pair (NMP) protocol employs a circularisation-based strategy that leaves behind 38bp adapter sequences which must be computationally removed from the data. While "adapter trimming" is a well-studied area of bioinformatics, existing tools do not fully exploit the particular properties of NMP data and discard more data than is necessary. Results: We present NxTrim, a tool that strives to discard as little sequence as possible from NMP reads. The sequence either side of the adapter site is triaged into "virtual libraries" of mate pairs, paired-end reads and single-ended reads. When combined, these data boost coverage and can substantially improve the de novo assembly of bacterial genomes.

Bioinformatics↗