Biology subjects

Kehr, B.

Publications and source records attributed to Kehr, B..

6 recordsLinked to original sources

bioRxiv2024

GGTyper: genotyping complex structural variants using short-read sequencing data

MotivationComplex structural variants are genomic rearrangements that involve multiple segments of DNA. They contribute to human diversity and have been shown to cause Mendelian disease. Nevertheless, our abilities to analyse complex structural variants are very limited. As opposed to deletions and other canonical types of structural variants (SVs), there are no established tools that have explicitly been designed for analysing complex SVs. ResultsHere, we describe a new computational approach that we specifically designed for genotyping complex SVs in short-read sequenced genomes. Given a variant description, our approach computes genotype-specific probability distributions for observing aligned read pairs with a wide range of properties. Subsequently, these distributions can be used to efficiently determine the most likely genotype for any set of aligned read pairs observed in a sequenced genome. In addition, we use these distributions to compute a genotyping difficulty for a given variant, which predicts the amount of data needed to achieve a reliable call. Careful evaluation confirms that our approach outperforms other genotypers by making reliable genotype predictions across both simulated and real data. On up to 7829 human genomes, we achieve high concordance with population-genetic assumptions and expected inheritance patterns. On simulated data, we show that precision correlates well with our prediction of genotyping difficulty. This together with low memory and time requirements makes our approach well-suited for application in biomedical studies involving small to very large numbers of short-read sequenced genomes. AvailabilitySource code is available at https://github.com/kehrlab/Complex-SV-Genotyping. Contactbirte.kehr@klinik.uni-regensburg.de

bioinformatics↗

bioRxiv2022

Bcmap: fast alignment-free barcode mapping for linked-read sequencing data

MotivationThe bottleneck for genome analysis will soon shift from sequencing cost to computationally expensive read alignment. When only portions of the genome are of interest, read alignment on whole-genome sequencing (WGS) data can be replaced by a fast mapping step. ResultsWe propose an approach that can map both long reads or linked-read molecules, as a pre-processing step for downstream targeted analyses. Our "molecule mapping" approach, implemented in the tool MoleMap, uses a minimized open-addressing k-mer index of the reference genome and a fast k-mer clustering procedure. We demonstrate that MoleMaps accuracy is competitive with standard alignment and mapping tools and its running time outperforms other mapping tools on 32 threads by a factor of 3 to 8 and read alignment tools by a factor of 10 to 60. Its low memory footprint allows us to analyze whole genomes on a standard laptop computer. As proofs of concept, we use MoleMap to filter reads for local assembly of a known variant region that involves non-reference sequence and showcase its use in diagnosing a patient with a rare disease. Our work contributes to more scalable genome analysis and promotes WGS for targeted analyses. Availability and ImplementationSource code of MoleMap is available at https://github.com/kehrlab/molemap.

bioinformatics↗

bioRxiv2021

Population-scale detection of non-reference sequence variants using colored de Bruijn Graphs

MotivationWith the increasing throughput of sequencing technologies, structural variant (SV) detection has become possible across tens of thousands of genomes. Non-reference sequence (NRS) variants have drawn less attention compared to other types of SVs due to the computational complexity of detecting them. When using short-read data, the detection of NRS variants inevitably involves a de novo assembly which requires high-quality sequence data at high coverage. Previous studies have demonstrated how sequence data of multiple genomes can be combined for the reliable detection of NRS variants. However, the algorithms proposed in these studies have limited scalability to larger sets of genomes. ResultsWe introduce PopIns2, a tool to discover and characterize NRS variants in many genomes, which scales to considerably larger numbers of genomes than its predecessor PopIns. In this article, we briefly outline the PopIns2 workflow and highlight our novel algorithmic contributions. We developed an entirely new approach for merging contig assemblies of unaligned reads from many genomes into a single set of NRS using a colored de Bruijn graph. Our tests on simulated data indicate that the new merging algorithm ranks among the best approaches in terms of quality and reliability and that PopIns2 shows the best precision for a growing number of genomes processed. Results on the Polaris Diversity Cohort and a set of 1000 Icelandic human genomes demonstrate unmatched scalability for the application on population-scale datasets. AvailabilityThe source code of PopIns2 is available from https://github.com/kehrlab/PopIns2. Contactthomas.krannich@bihealth.de or birte.kehr@klinik.uni-regensburg.de

bioinformatics↗

bioRxiv2019

PopDel identifies medium-size deletions jointly in tens of thousands of genomes

Thousands of genomic structural variants segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. We describe a novel approach, PopDel, which jointly identifies deletions of about 500 to at least 10,000 bp in length in many genomes together. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6,794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDels running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies.

bioinformatics↗

bioRxiv2017

Recurrence of de novo mutations in families

De novo mutations (DNMs) cause a large fraction of severe rare diseases of childhood. DNMs that occur in early embryos may result in mosaicism of both somatic and germ cells. Such early mutations may be transmitted to more than one offspring and cause recurrence of serious disease. We scanned 1,007 sibling pairs from 251 families and identified 885 DNMs shared by siblings (ssDNMs) at 451 genomic sites. We estimated the probability of DNM recurrence based on presence in the blood of the parent, sharing by other siblings, parent-of-origin, mutation type, and genomic position. We detected 52.1% of ssDNMs in the parental blood. The probability of a DNM being shared goes down by 2.28% per year for paternal DNMs and 1.82% for maternal DNMs. Shared paternal DNMs are more likely to be T>C mutations than maternal ones, but less likely to be C>T mutations. Depending on DNM properties, the probability of recurrence in a younger sibling ranges from 0.013% to 29.6%. We have launched an online DNM recurrence probability calculator, to use in genetic counselling in cases of rare genetic diseases.

genomics↗

bioRxiv2017

Graphtyper: Population-scale genotyping using pangenome graphs

A fundamental requisite for genetic studies is an accurate determination of sequence variation. While human genome sequence diversity is increasingly well characterized, there is a need for efficient ways to utilize this knowledge in sequence analysis. Here we present Graphtyper, a publicly available novel algorithm and software for discovering and genotyping sequence variants. Graphtyper realigns short-read sequence data to a pangenome, a variation-aware graph structure that encodes sequence variation within a population by representing possible haplotypes as graph paths. Our results show that Graphtyper is fast, highly scalable, and provides sensitive and accurate genotype calls. Graphtyper genotyped 89.4 million sequence variants in whole-genomes of 28,075 Icelanders using less than 100,000 CPU days, including detailed genotyping of six human leukocyte antigen (HLA) genes. We show that Graphtyper is a valuable tool in characterizing sequence variation in population-scale sequencing studies.

bioinformatics↗