scholarly journals Nautilus: A Bioinformatics Package for the Analysis of HIV Type 1 Targeted Deep Sequencing Data

2013 ◽  
Vol 29 (10) ◽  
pp. 1361-1364 ◽  
Author(s):  
Gustavo H. Kijak ◽  
Phuc Pham ◽  
Eric Sanders-Buell ◽  
Elizabeth A. Harbolick ◽  
Leigh Anne Eller ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
Sequencing Data ◽  
Deep Sequencing Data ◽  
Targeted Deep Sequencing ◽  
Hiv Type 1

scholarly journals Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

2018 ◽  
Author(s):  
Dimitrios Kleftogiannis ◽  
Marco Punta ◽  
Anuradha Jayaram ◽  
Shahneen Sandhu ◽  
Stephen Q. Wong ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
Low Frequency ◽  
Poisson Model ◽  
Real Data ◽  
Analytical Sensitivity ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Deep Sequencing Data ◽  
Targeted Deep Sequencing

AbstractBackgroundTargeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs).MethodsTo address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection.ResultsOur tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments.ConclusionsAmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve.

scholarly journals Evaluation of Nine Somatic Variant Callers for Detection of Somatic Mutations in Exome and Targeted Deep Sequencing Data

PLoS ONE ◽  
2016 ◽  
Vol 11 (3) ◽  
pp. e0151664 ◽  
Author(s):  
Anne Bruun Krøigård ◽  
Mads Thomassen ◽  
Anne-Vibeke Lænkholm ◽  
Torben A. Kruse ◽  
Martin Jakob Larsen
Keyword(s):  
Deep Sequencing ◽  
Somatic Mutations ◽  
Sequencing Data ◽  
Somatic Variant ◽  
Deep Sequencing Data ◽  
Targeted Deep Sequencing

scholarly journals Comparison of somatic variant detection algorithms using Ion Torrent targeted deep sequencing data

2019 ◽  
Vol 12 (S9) ◽  
Author(s):  
Qing Wang ◽  
Vassiliki Kotoula ◽  
Pei-Chen Hsu ◽  
Kyriaki Papadopoulou ◽  
Joshua W. K. Ho ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
Ion Torrent ◽  
Sequencing Data ◽  
Somatic Variation ◽  
Somatic Variant ◽  
Detection Algorithms ◽  
Deep Sequencing Data ◽  
Targeted Deep Sequencing ◽  
Variant Detection ◽  
Paired Tumour

Abstract Background The application of next-generation sequencing in cancer has revealed the genomic landscape of many tumour types and is nowadays routinely used in research and clinical settings. Multiple algorithms have been developed to detect somatic variation from sequencing data using either paired tumour-blood or tumour-only samples. Most of these methods have been developed and evaluated for the identification of somatic variation using Illumina sequencing datasets of moderate coverage. However, a comprehensive evaluation of somatic variant detection algorithms on Ion Torrent targeted deep sequencing data has not been performed. Methods We have applied three somatic detection algorithms, Torrent Variant Caller, MuTect2 and VarScan2, on a large cohort of ovarian cancer patients comprising of 208 paired tumour-blood samples and 253 tumour-only samples sequenced deeply on Ion Torrent Proton platform across 330 amplicons. Subsequently, the concordance and performance of the three somatic variant callers were assessed. Results We have observed low concordance across the algorithms with only 0.5% of SNV and 0.02% of INDEL calls in common across all three methods. The intersection of all methods showed better performance when assessed using correlation with known mutational signatures, overlap with COSMIC variation and by examining the variant characteristics. The Torrent Variant Caller also performed well with the advantage of not eliminating a high number of variants that could lead to high type II error. Conclusions Our results suggest that caution should be taken when applying state-of-the-art somatic variant algorithms to Ion Torrent targeted deep sequencing data. Better quality control procedures and strategies that combine results from multiple methods should ensure that higher accuracy is achieved. This is essential to ensure that results from bioinformatics pipelines using Ion Torrent deep sequencing can be robustly applied in cancer research and in the clinic.

scholarly journals GenomeGems: evaluation of genetic variability from deep sequencing data

2012 ◽  
Vol 5 (1) ◽  
pp. 338
Author(s):  
Sharon Ben-Zvi ◽  
Adi Givati ◽  
Noam Shomron
Keyword(s):  
Genetic Variability ◽  
Deep Sequencing ◽  
Sequencing Data ◽  
Deep Sequencing Data

Considering DNA damage when interpreting mtDNA heteroplasmy in deep sequencing data

2017 ◽  
Vol 26 ◽  
pp. 1-11 ◽  
Author(s):  
Molly M. Rathbun ◽  
Jennifer A. McElhoe ◽  
Walther Parson ◽  
Mitchell M. Holland
Keyword(s):  
Dna Damage ◽  
Deep Sequencing ◽  
Sequencing Data ◽  
Deep Sequencing Data

scholarly journals Analyzing the microRNA Transcriptome in Plants Using Deep Sequencing Data

Biology ◽  
2012 ◽  
Vol 1 (2) ◽  
pp. 297-310 ◽  
Author(s):  
Xiaozeng Yang ◽  
Lei Li
Keyword(s):  
Deep Sequencing ◽  
Sequencing Data ◽  
Deep Sequencing Data

scholarly journals Discovery of Novel Human Breast Cancer MicroRNAs from Deep Sequencing Data by Analysis of Pri-MicroRNA Secondary Structures

PLoS ONE ◽  
2011 ◽  
Vol 6 (2) ◽  
pp. e16403 ◽  
Author(s):  
Seongho Ryu ◽  
Natasha Joshi ◽  
Kevin McDonnell ◽  
Jongchan Woo ◽  
Hyejin Choi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Sequencing ◽  
Human Breast Cancer ◽  
Secondary Structures ◽  
Human Breast ◽  
Sequencing Data ◽  
Deep Sequencing Data
Sign in / Sign up

Export Citation Format

Share Document