Dataset Information

AVIA v2.0: annotation, visualization and impact analysis of genomic variants and genes.

ABSTRACT:

Unlabelled

As sequencing becomes cheaper and more widely available, there is a greater need to quickly and effectively analyze large-scale genomic data. While the functionality of AVIA v1.0, whose implementation was based on ANNOVAR, was comparable with other annotation web servers, AVIA v2.0 represents an enhanced web-based server that extends genomic annotations to cell-specific transcripts and protein-level functional annotations. With AVIA's improved interface, users can better visualize their data, perform comprehensive searches and categorize both coding and non-coding variants.

Availability and implementation

AVIA is freely available through the web at http://avia.abcc.ncifcrf.gov.

Contact

Hue.Vuong@fnlcr.nih.gov

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Vuong H

PROVIDER: S-EPMC4528632 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Publications

AVIA v2.0: annotation, visualization and impact analysis of genomic variants and genes.

Vuong Hue H Che Anney A Ravichandran Sarangan S Luke Brian T BT Collins Jack R JR Mudunuri Uma S US

Bioinformatics (Oxford, England) 20150409 16

<h4>Unlabelled</h4>As sequencing becomes cheaper and more widely available, there is a greater need to quickly and effectively analyze large-scale genomic data. While the functionality of AVIA v1.0, whose implementation was based on ANNOVAR, was comparable with other annotation web servers, AVIA v2.0 represents an enhanced web-based server that extends genomic annotations to cell-specific transcripts and protein-level functional annotations. With AVIA's improved interface, users can better visua ...[more]

PMID: 25861966

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Project description:BACKGROUND: Analysis of genomic sequence allows characterization of genome content and organization, and access beyond gene-coding regions for identification of functional elements. BAC libraries, where relatively large genomic regions are made readily available, are especially useful for species without a fully sequenced genome and can increase genomic coverage of phylogenetic and biological diversity. For example, no butterfly genome is yet available despite the unique genetic and biological properties of this group, such as diversified wing color patterns. The evolution and development of these patterns is being studied in a few target species, including Bicyclus anynana, where a whole-genome BAC library allows targeted access to large genomic regions. METHODOLOGY/PRINCIPAL FINDINGS: We characterize ?1.3 Mb of genomic sequence around 11 selected genes expressed in B. anynana developing wings. Extensive manual curation of in silico predictions, also making use of a large dataset of expressed genes for this species, identified repetitive elements and protein coding sequence, and highlighted an expansion of Alcohol dehydrogenase genes. Comparative analysis with orthologous regions of the lepidopteran reference genome allowed assessment of conservation of fine-scale synteny (with detection of new inversions and translocations) and of DNA sequence (with detection of high levels of conservation of non-coding regions around some, but not all, developmental genes). CONCLUSIONS: The general properties and organization of the available B. anynana genomic sequence are similar to the lepidopteran reference, despite the more than 140 MY divergence. Our results lay the groundwork for further studies of new interesting findings in relation to both coding and non-coding sequence: 1) the Alcohol dehydrogenase expansion with higher similarity between the five tandemly-repeated B. anynana paralogs than with the corresponding B. mori orthologs, and 2) the high conservation of non-coding sequence around the genes wingless and Ecdysone receptor, both involved in multiple developmental processes including wing pattern formation.

Project description:BackgroundWhole genome sequencing is effective at identification of small variants, but because it is based on short reads, assessment of structural variants (SVs) is limited. The advent of Optical Genome Mapping (OGM), which utilizes long fluorescently labeled DNA molecules for de novo genome assembly and SV calling, has allowed for increased sensitivity and specificity in SV detection. However, compared to small variant annotation tools, OGM-based SV annotation software has seen little development, and currently available SV annotation tools do not provide sufficient information for determination of variant pathogenicity.ResultsWe developed an R-based package, nanotatoR, which provides comprehensive annotation as a tool for SV classification. nanotatoR uses both external (DGV; DECIPHER; Bionano Genomics BNDB) and internal (user-defined) databases to estimate SV frequency. Human genome reference GRCh37/38-based BED files are used to annotate SVs with overlapping, upstream, and downstream genes. Overlap percentages and distances for nearest genes are calculated and can be used for filtration. A primary gene list is extracted from public databases based on the patient's phenotype and used to filter genes overlapping SVs, providing the analyst with an easy way to prioritize variants. If available, expression of overlapping or nearby genes of interest is extracted (e.g. from an RNA-Seq dataset, allowing the user to assess the effects of SVs on the transcriptome). Most quality-control filtration parameters are customizable by the user. The output is given in an Excel file format, subdivided into multiple sheets based on SV type and inheritance pattern (INDELs, inversions, translocations, de novo, etc.). nanotatoR passed all quality and run time criteria of Bioconductor, where it was accepted in the April 2019 release. We evaluated nanotatoR's annotation capabilities using publicly available reference datasets: the singleton sample NA12878, mapped with two types of enzyme labeling, and the NA24143 trio. nanotatoR was also able to accurately filter the known pathogenic variants in a cohort of patients with Duchenne Muscular Dystrophy for which we had previously demonstrated the diagnostic ability of OGM.ConclusionsThe extensive annotation enables users to rapidly identify potential pathogenic SVs, a critical step toward use of OGM in the clinical setting.

Dataset Information

AVIA v2.0: annotation, visualization and impact analysis of genomic variants and genes.

Unlabelled

Availability and implementation

Contact

Supplementary information

Publications

AVIA v2.0: annotation, visualization and impact analysis of genomic variants and genes.

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