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Aperta - TÜBİTAK Açık Arşivi
Other literature type . 2017
License: CC BY
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Article . 2017 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Article . 2018
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Toolkit for automated and rapid discovery of structural variants

Authors: Soylev A.; Kockan C.; Hormozdiari F.; Alkan C.;

Toolkit for automated and rapid discovery of structural variants

Abstract

Structural variations (SV) are broadly defined as genomic alterations that affect >50bp of DNA, which are shown to have significant effect on evolution and disease. The advent of high throughput sequencing (HTS) technologies and the ability to perform whole genome sequencing (WGS), makes it feasible to study these variants in depth. However, discovery of all forms of SV using WGS has proven to be challenging as the short reads produced by the predominant HTS platforms (<200bp for current technologies) and the fact that most genomes include large amounts of repeats make it very difficult to unambiguously map and accurately characterize such variants. Furthermore, existing tools for SV discovery are primarily developed for only a few of the SV types, which may have conflicting sequence signatures (i.e. read pairs, read depth, split reads) with other, untargeted SV classes. Here we are introduce a new framework, Tardis, which combines multiple read signatures into a single package to characterize most SV types simultaneously, while preventing such conflicts. Tardis also has a modular structure that makes it easy to extend for the discovery of additional forms of SV.

Country
Turkey
Related Organizations
Keywords

Article, Combinatorial algorithms, Humans, base pairing, controlled study, High throughput sequencing, intermethod comparison, automation, whole genome sequencing, Whole Genome Sequencing, gene deletion, Genome, Human, High-Throughput Nucleotide Sequencing, DNA, gene mapping, prediction, Genomics, Sequence Analysis, DNA, gene structure, simulation, priority journal, genetic variation, Genomic Structural Variation, computer analysis, Structural variation, gene insertion, Algorithms, Software

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    23
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
23
Top 10%
Top 10%
Top 10%
Green
bronze