In-depth annotation of SNPs arising from resequencing projects using NGS-SNP

Grant, Jason R.; Arantes, Adriano S.; Liao, Xiaoping; Stothard, Paul

doi:10.1093/bioinformatics/btr372

Cited by 102 publications

(101 citation statements)

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“…The variant calls generated by LifeScope or Bioscope software packages were annotated using a local copy of the Ensembl database, 14 dbSNP and in-house variant databases. Variants were selected for further investigation, if the global minor allele frequency of the sequence change was o5% based on dbSNP (http:// www.ncbi.nlm.nih.gov/projects/SNP/) and the Exome Variant Server (http://evs.gs.washington.edu/EVS/).…”

Section: Patientsmentioning

confidence: 99%

Panel-based next generation sequencing as a reliable and efficient technique to detect mutations in unselected patients with retinal dystrophies

et al. 2013

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Hereditary retinal dystrophies (RD) constitute a group of blinding diseases that are characterized by clinical variability and pronounced genetic heterogeneity. The different forms of RD can be caused by mutations in 4100 genes, including 41600 exons. Consequently, next generation sequencing (NGS) technologies are among the most promising approaches to identify mutations in RD. So far, NGS is not routinely used in gene diagnostics. We developed a diagnostic NGS pipeline to identify mutations in 170 genetically and clinically unselected RD patients. NGS was applied to 105 RD-associated genes. Underrepresented regions were examined by Sanger sequencing. The NGS approach was successfully established using cases with known sequence alterations. Depending on the initial clinical diagnosis, we identified likely causative mutations in 55% of retinitis pigmentosa and 80% of Bardet-Biedl or Usher syndrome cases. Seventy-one novel mutations in 40 genes were newly associated with RD. The genes USH2A, EYS, ABCA4, and RHO were more frequently affected than others. Occasionally, cases carried mutations in more than one RD-associated gene. In addition, we found possible dominant de-novo mutations in cases with sporadic RD, which implies consequences for counseling of patients and families. NGS-based mutation analyses are reliable and cost-efficient approaches in gene diagnostics of genetically heterogeneous diseases like RD.

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Section: Patientsmentioning

confidence: 99%

Panel-based next generation sequencing as a reliable and efficient technique to detect mutations in unselected patients with retinal dystrophies

et al. 2013

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“…Default settings were used in all cases, except that the -N option was used with the "assemble" command to specify the number of haplotypes present in each sample. The shorter, filtered SNP list was annotated using NGS-SNP 45 and release 57 of Ensembl, 44 which includes SNPs from dbSNP build 130. 46 Total RNA-Seq-library construction and sequencing using SOLiD platform.…”

Section: Methodsmentioning

confidence: 99%

“…The lists were subjected to additional filtering, to remove SNPs with particularly high read depth (higher than 95% of the other SNPs from the same animal), and to remove SNPs that could not be unambiguously placed on bovine genome assembly UMD3.1 47 using the megablast algorithm 48 in BLAST+, 49 100 bp of flanking sequence, and an e-value threshold of 1e-35. SNPs were annotated using NGS-SNP 45 and release 57 of Ensembl, 44 which includes SNPs from dbSNP build 130. 46 Genotyping and data quality control.…”

Section: Supplemental Materialsmentioning

confidence: 99%

The identification of candidate genes and SNP markers for classical bovine spongiform encephalopathy susceptibility

Thomson

Bowles

Choi

et al. 2012

Prion

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“…NGS platforms produce large data and analyzing the functional impact of variants is very crucial. Various tools such as ANNOVAR [36], NGS-SNP [37], SVA [38], VARIANT [39], etc. are used for variant annotation which determines the significance of the detected variants in the sequence.…”

Section: Annotation Of Variantmentioning

confidence: 99%

Computational Approaches for Variant Identification

Garg¹,

Jiwan²,

Singh³

2017

IJCA

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Variant identification is a fundamental part in the analysis of genetic diseases. Variants are the alterations which occur in the arrangement of nucleotide in the DNA sequence. Genetic diseases are caused by variations occurring in genes which may cause change in protein, affecting the survival and adaptation of an individual. A number of computational techniques are applied to identify these variant. Precise diagnosis of genetic diseases is important for proper treatment of patients and to determine explicit prevention strategies. Introduction of next generation sequencing (NGS) techniques in the past have made large number of DNA sequences easily available. This has made variant identification using NGS data a area of interest. This paper briefly discussed the analysis steps followed for NGS data analysis. This paper later explains in detail a few approaches that are used for identifying variants such as Support vector machine based approach, Machine learning based approach, MOSAIK: hashbase approach, Bayesian statistical based approach, JointSLM based approach.

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In-depth annotation of SNPs arising from resequencing projects using NGS-SNP

Cited by 102 publications

References 11 publications

Panel-based next generation sequencing as a reliable and efficient technique to detect mutations in unselected patients with retinal dystrophies

Panel-based next generation sequencing as a reliable and efficient technique to detect mutations in unselected patients with retinal dystrophies

The identification of candidate genes and SNP markers for classical bovine spongiform encephalopathy susceptibility

Computational Approaches for Variant Identification

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