74 research outputs found
Sensitive Long-Indel-Aware Alignment of Sequencing Reads
The tremdendous advances in high-throughput sequencing technologies have made
population-scale sequencing as performed in the 1000 Genomes project and the
Genome of the Netherlands project possible. Next-generation sequencing has
allowed genom-wide discovery of variations beyond single-nucleotide
polymorphisms (SNPs), in particular of structural variations (SVs) like
deletions, insertions, duplications, translocations, inversions, and even more
complex rearrangements. Here, we design a read aligner with special emphasis on
the following properties: (1) high sensitivity, i.e. find all (reasonable)
alignments; (2) ability to find (long) indels; (3) statistically sound
alignment scores; and (4) runtime fast enough to be applied to whole genome
data. We compare performance to BWA, bowtie2, stampy and find that our methods
is especially advantageous on reads containing larger indels
Next Generation Cluster Editing
This work aims at improving the quality of structural variant prediction from
the mapped reads of a sequenced genome. We suggest a new model based on cluster
editing in weighted graphs and introduce a new heuristic algorithm that allows
to solve this problem quickly and with a good approximation on the huge graphs
that arise from biological datasets
CLEVER: Clique-Enumerating Variant Finder
Next-generation sequencing techniques have facilitated a large scale analysis
of human genetic variation. Despite the advances in sequencing speeds, the
computational discovery of structural variants is not yet standard. It is
likely that many variants have remained undiscovered in most sequenced
individuals. Here we present a novel internal segment size based approach,
which organizes all, including also concordant reads into a read alignment
graph where max-cliques represent maximal contradiction-free groups of
alignments. A specifically engineered algorithm then enumerates all max-cliques
and statistically evaluates them for their potential to reflect insertions or
deletions (indels). For the first time in the literature, we compare a large
range of state-of-the-art approaches using simulated Illumina reads from a
fully annotated genome and present various relevant performance statistics. We
achieve superior performance rates in particular on indels of sizes 20--100,
which have been exposed as a current major challenge in the SV discovery
literature and where prior insert size based approaches have limitations. In
that size range, we outperform even split read aligners. We achieve good
results also on real data where we make a substantial amount of correct
predictions as the only tool, which complement the predictions of split-read
aligners. CLEVER is open source (GPL) and available from
http://clever-sv.googlecode.com.Comment: 30 pages, 8 figure
Repeat- and Error-Aware Comparison of Deletions
Motivation: The number of reported genetic variants is rapidly growing, empowered by ever faster accumulation of next-generation sequencing data. A major issue is comparability. Standards that address the combined problem of inaccurately predicted breakpoints and repeat-induced ambiguities are missing. This decisively lowers the quality of ‘consensus’ callsets and hampers the removal of duplicate entries in variant databases, which can have deleterious effects in downstream analyses.
Results: We introduce a sound framework for comparison of deletions that captures both tool-induced inaccuracies and repeat-induced ambiguities. We present a maximum matching algorithm that outputs virtual duplicates among two sets of predictions/annotations. We demonstrate that our approach is clearly superior over ad hoc criteria, like overlap, and that it can reduce the redundancy among callsets substantially. We also identify large amounts of duplicate entries in the Database of Genomic Variants, which points out the immediate relevance of our approach.
Availability and implementation: Implementation is open source and available from https://bitbucket.org/readdi/readd
Repeat- and error-aware comparison of deletions
Wittler R, Marschall T, Schönhuth A, Makinen V. Repeat- and error-aware comparison of deletions. Bioinformatics. 2015;31(18):2947-2954
SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines
Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included: a) Creating an automated, standardized pipeline for SV prediction. b) Identifying the best tool(s) for SV prediction through benchmarking. c) Providing a statistically sound method for merging SV calls
Characteristics of de novo structural changes in the human genome
Small insertions and deletions (indels) and large structural variations (SVs) are major contributors to human genetic diversity and disease. However, mutation rates and characteristics of de novo indels and SVs in the general population have remained largely unexplored. We report 332 validated de novo structural changes identified in whole genomes of 250 families, including complex indels, retrotransposon insertions, and interchromosomal events. These data indicate a mutation rate of 2.94 indels (1–20 bp) and 0.16 SVs (>20 bp) per generation. De novo structural changes affect on average 4.1 kbp of genomic sequence and 29 coding bases per generation, which is 91 and 52 times more nucleotides than de novo substitutions, respectively. This contrasts with the equal genomic footprint of inherited SVs and substitutions. An excess of structural changes originated on paternal haplotypes. Additionally, we observed a nonuniform distribution of de novo SVs across offspring. These results reveal the importance of different mutational mechanisms to changes in human genome structure across generations
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