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

2015 ◽  
Author(s):  
Xiaoyu Chen ◽  
Ole Schulz-Trieglaff ◽  
Richard Shaw ◽  
Bret Barnes ◽  
Felix Schlesinger ◽  
...  
Keyword(s):  
Clinical Analysis ◽  
Next Generation Sequencing Data ◽  
Normal Sample ◽  
Structural Variants ◽  
Sequencing Data ◽  
Community Resource ◽  
Split Read ◽  
Variant Analysis ◽  
Generation Sequencing ◽  
Better Than

Summary: We 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. Availability: Manta source code and Linux binaries are available from http://github.com/Illumina/manta. Contact: [email protected]

A Benchmark of Structural Variant Analysis Tools for Next Generation Sequencing Data

2013 ◽  
Vol 2 (4) ◽  
pp. 86-98
Author(s):  
Chatzinikolaou Panagiotis ◽  
Makris Christos ◽  
Dimitrios Vlachakis ◽  
Sophia Kossida
Keyword(s):  
Next Generation Sequencing ◽  
Level Structure ◽  
Atomic Level ◽  
Protein Sequencing ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Variant Analysis ◽  
Generation Sequencing

In language of genetics and biochemistry, sequencing is the determination of an unbranched biopolymer's primary structure. A sequence is a symbolic linear depiction, result of sequencing. This sequence is a succinct summary of the most of the sequenced molecule's atomic-level structure. (Most known is DNA-sequencing, RNA-sequencing, Protein-sequencing and Next-Generation-sequencing)

scholarly journals SMN1 copy‐number and sequence variant analysis from next‐generation sequencing data

2020 ◽  
Vol 41 (12) ◽  
pp. 2073-2077
Author(s):  
Daniel Lopez‐Lopez ◽  
Carlos Loucera ◽  
Rosario Carmona ◽  
Virginia Aquino ◽  
Josefa Salgado ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number ◽  
Next Generation Sequencing Data ◽  
Sequence Variant ◽  
Next Generation ◽  
Sequencing Data ◽  
Variant Analysis ◽  
Generation Sequencing

scholarly journals Clinical analysis of genome next-generation sequencing data using the Omicia platform

2013 ◽  
Vol 13 (6) ◽  
pp. 529-540 ◽  
Author(s):  
Emily M Coonrod ◽  
Rebecca L Margraf ◽  
Archie Russell ◽  
Karl V Voelkerding ◽  
Martin G Reese
Keyword(s):  
Next Generation Sequencing ◽  
Clinical Analysis ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing

scholarly journals Detection of somatic structural variants from short-read next-generation sequencing data

2019 ◽  
Author(s):  
Tingting Gong ◽  
Vanessa M Hayes ◽  
Eva KF Chan
Keyword(s):  
Next Generation Sequencing ◽  
Cancer Genomics ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Structural Variants ◽  
Sequencing Data ◽  
Short Read ◽  
Factors Affecting ◽  
Ngs Data ◽  
Generation Sequencing

AbstractSomatic structural variants (SVs) play a significant role in cancer development and evolution, but are notoriously more difficult to detect than small variants from short-read next-generation sequencing (NGS) data. This is due to a combination of challenges attributed to the purity of tumour samples, tumour heterogeneity, limitations of short-read information from NGS, and sequence alignment ambiguities. In spite of active development of SV detection tools (callers) over the past few years, each method has inherent advantages and limitations. In this review, we highlight some of the important factors affecting somatic SV detection and compared the performance of eight commonly used SV callers. In particular, we focus on the extent of change in sensitivity and precision for detecting different SV types and size ranges from samples with differing variant allele frequencies and sequencing depths of coverage. We highlight the reasons for why some SV callers perform well in some settings but not others, allowing our evaluation findings to be extended beyond the eight SV callers examined in this paper. As the importance of large structural variants become increasingly recognised in cancer genomics, this paper provides a timely review on some of the most impactful factors influencing somatic SV detection and guidance on selecting an appropriate SV caller.

scholarly journals Detection of somatic structural variants from short-read next-generation sequencing data

2020 ◽  
Author(s):  
Tingting Gong ◽  
Vanessa M Hayes ◽  
Eva K F Chan
Keyword(s):  
Next Generation Sequencing ◽  
Cancer Genomics ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Structural Variants ◽  
Sequencing Data ◽  
Short Read ◽  
Factors Affecting ◽  
Ngs Data ◽  
Generation Sequencing

Abstract Somatic structural variants (SVs), which are variants that typically impact >50 nucleotides, play a significant role in cancer development and evolution but are notoriously more difficult to detect than small variants from short-read next-generation sequencing (NGS) data. This is due to a combination of challenges attributed to the purity of tumour samples, tumour heterogeneity, limitations of short-read information from NGS and sequence alignment ambiguities. In spite of active development of SV detection tools (callers) over the past few years, each method has inherent advantages and limitations. In this review, we highlight some of the important factors affecting somatic SV detection and compared the performance of seven commonly used SV callers. In particular, we focus on the extent of change in sensitivity and precision for detecting different SV types and size ranges from samples with differing variant allele frequencies and sequencing depths of coverage. We highlight the reasons for why some SV callers perform well in some settings but not others, allowing our evaluation findings to be extended beyond the seven SV callers examined in this paper. As the importance of large SVs become increasingly recognized in cancer genomics, this paper provides a timely review on some of the most impactful factors influencing somatic SV detection that should be considered when choosing SV callers.

scholarly journals Correction: PeSV-Fisher: Identification of Somatic and Non-Somatic Structural Variants Using Next Generation Sequencing Data

PLoS ONE ◽  
2013 ◽  
Vol 8 (11) ◽  
Author(s):  
Geòrgia Escaramís ◽  
Cristian Tornador ◽  
Laia Bassaganyas ◽  
Raquel Rabionet ◽  
Jose M. C. Tubio ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Structural Variants ◽  
Sequencing Data ◽  
Generation Sequencing

scholarly journals SimFFPE and FilterFFPE: improving structural variant calling in FFPE samples

GigaScience ◽  
2021 ◽  
Vol 10 (9) ◽  
Author(s):  
Lanying Wei ◽  
Martin Dugas ◽  
Sarah Sandmann
Keyword(s):  
Next Generation Sequencing ◽  
Variant Calling ◽  
Real Data ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Structural Variants ◽  
Sequencing Data ◽  
Structural Variant ◽  
Ffpe Samples ◽  
Generation Sequencing

Abstract Background Artifact chimeric reads are enriched in next-generation sequencing data generated from formalin-fixed paraffin-embedded (FFPE) samples. Previous work indicated that these reads are characterized by erroneous split-read support that is interpreted as evidence of structural variants. Thus, a large number of false-positive structural variants are detected. To our knowledge, no tool is currently available to specifically call or filter structural variants in FFPE samples. To overcome this gap, we developed 2 R packages: SimFFPE and FilterFFPE. Results SimFFPE is a read simulator, specifically designed for next-generation sequencing data from FFPE samples. A mixture of characteristic artifact chimeric reads, as well as normal reads, is generated. FilterFFPE is a filtration algorithm, removing artifact chimeric reads from sequencing data while keeping real chimeric reads. To evaluate the performance of FilterFFPE, we performed structural variant calling with 3 common tools (Delly, Lumpy, and Manta) with and without prior filtration with FilterFFPE. After applying FilterFFPE, the mean positive predictive value improved from 0.27 to 0.48 in simulated samples and from 0.11 to 0.27 in real samples, while sensitivity remained basically unchanged or even slightly increased. Conclusions FilterFFPE improves the performance of SV calling in FFPE samples. It was validated by analysis of simulated and real data.

scholarly journals An integrative variant analysis suite for whole exome next-generation sequencing data

2012 ◽  
Vol 13 (1) ◽  
pp. 8 ◽  
Author(s):  
Danny Challis ◽  
Jin Yu ◽  
Uday S Evani ◽  
Andrew R Jackson ◽  
Sameer Paithankar ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Whole Exome ◽  
Variant Analysis ◽  
Generation Sequencing
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