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Input format

VEXOR accepts as input : 1/ a comma-delimited text file (.csv), 2/ a tabulation-delimited text file (.tsv) or 3/ a single-sheet Excel file (.xls or .xlsx) with one header. The input file must contain at least the following columns (case sensitive) : Chr, Pos, End_pos, RsID, Ref, Alt . Uploaded file size is currently limited to 150 megaoctets (approximately 1 000 000 lines). For larger files, users will need to contact us to carry out data file entry, and private access to their data can be provided.
Please note, the input file should not contain the following column names : "seqnames", "ranges", "strand", "seqlevels", "seqlengths", "isCircular", "start", "end", "width", "element".
Download sample file (hg19, 1-based)

Annotations

Select the annotations you want to apply on your matrix.


These annotations are coming from UCSC Table Browser.

You can download the wanted annotations through the UCSC Table Browser HERE (select 'bed format' for positional annotations or 'data points' for quantitative annotations) or use your own annotation files. (200Mo max.)






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Selection


FeatMap

Filters


Selection

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FeatMap

Allele frequencies

By default, the analysis will be made for the 100 first variants. Please use 'Filter' and 'Selection' panels for more accurate results.

These frequencies have been calculated by VEP (see 'Effect Prediction panel for more information') from 1000 Genomes Project Phase 3



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Linkage Disequilibrium

By default, the analysis will be made for the 100 first variants. Please use 'Filter' and 'Selection' panels for more accurate results.

Compute LD score with 1000Genomes data (phase 3). It may take several minutes depending on the number of variants in your query and their distance.




VEP

By default, the analysis will be made for the 100 first variants. Please use 'Filter' and 'Selection' panels for more accurate results.

The VEP determines the effect of your variants on genes, transcripts, and protein sequence, as well as regulatory regions.



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RegulomeDB

By default, the analysis will be made for the 100 first variants. Please use 'Filter' and 'Selection' panels for more accurate results.

RegulomeDB is a database that annotates SNPs with known and redicted regulatory elements in the intergenic regions of the H. sapiens genome. Known and predicted regulatory DNA elements include regions of DNAase hypersensitivity, binding sites of factors, and promoter regions that have been biochemically characterized to regulation transcription. Source of these data include public datasets from GEO, the ENCODE project, and published literature.




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HaploReg

By default, the analysis will be made for the 100 first variants. Please use 'Filter' and 'Selection' panels for more accurate results.

HaploReg is a tool for exploring annotations of the noncoding genome at variants on haplotype blocks, such as candidate regulatory SNPs.



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Download

CADD

By default, the analysis will be made for the 100 first variants. Please use 'Filter' and 'Selection' panels for more accurate results.

CADD is a tool for scoring the deleteriousness of single nucleotide variants as well as insertion/deletions variants in the human genome.Combined Annotation Dependent Depletion (CADD) is a framework that integrates multiple annotations into one metric by contrasting variants that survived natural selection with simulated mutations.



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Manual scoring

By default, the analysis will be made for the 100 first variants. Please use 'Filter' and 'Selection' panels for more accurate results.

This section enables to compute a local scoring for each variant of your region. The weights can be chosen arbitrary or can be based on enrichissement results coming from prediction algorithms like PAINTOR (See below). You can manually weigthed the features and change the thresholds.

Please note that the heatmap contains only the 1000 first variants







fastPAINTOR

By default, the analysis will be made for the 100 first variants. Please use 'Filter' and 'Selection' panels for more accurate results.

fastPAINTOR (Probabilistic Annotation INtegraTOR), a probabilistic framework that integrates association strength with genomic functional annotation data to improve accuracy in selecting plausible causal variants for functional validation.The main output of PAINTOR are probabilities for every variant to be causal that can be used for prioritization in functional assays to establisb biological causality.

More information here .


If you have already identified a set of regions of interest, select 'Multi loci' analysis. In this case, each region should have a unique identification number. For technical reasons, if a selected locus contains more than 2000 variants, it will be arbitrary split into subloci.


Temporarily unavailable, please compute LD through VEXOR.



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fGWAS

By default, the analysis will be made for the 100 first variants. Please use 'Filter' and 'Selection' panels for more accurate results.

fGWAS is a command line tool for integrating functional genomic information into a genome-wide association study (GWAS).

More information here .


If you have already identified a set of regions of interest, select 'Multi loci' analysis. In this case, each region should have a unique identification number.





A lot of other arguments enable to modify gwas behavior. You can add your tuning parameters as described fgwas user manual. For example, write '-k 1000' in the following box to change the number of SNPs in a region (default 5000)

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CAVIAR-BF

By default, the analysis will be made for the 100 first variants. Please use 'Filter' and 'Selection' panels for more accurate results.

CAVIAR-BF, a bayesian framework that incorporates genomic functional annotations dfor fine-mapping of causal variants.

In the present interface, we decided to use CAVIAR-BF with for the most part default settings. The details can be found in our User Manual.

More information here .


If you have already identified a set of regions of interest, select 'Multi loci' analysis. In this case, each region should have a unique identification number.


Temporarily unavailable, please compute LD through VEXOR.




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Annotations

By default, the analysis will be made for the 100 first variants. Please use 'Filter' and 'Selection' panels for more accurate results.

The annotation data have been extracted from the following sources : 4DGenome , Hnisz D research , Roadmap Epigenomics Project , MiTranscriptome catalog and FANTOM5.

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GTEx Project

By default, the analysis will be made for the 100 first variants. Please use 'Filter' and 'Selection' panels for more accurate results.

Correlations between genotype and tissue-specific gene expression levels will help identify regions of the genome that influence whether and how much a gene is expressed. GTEx will help researchers to understand inherited susceptibility to disease and will be a resource database and tissue bank for many studies in the future.



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VEXOR

VEXOR is a platform-independent browser-based integrative environment for functional annotation in R, based on the Shiny package. This interface provides a comprehensive analytical framework to characterize the role of variants driving susceptibility signals in regions defined by GWAS.

The matrix is connected to 1) publicly available functional annotations through UCSC and Ensembl Browsers, 2) genetic diversity information (1000 Genomes third phase MAF, Linkage Disequilibrium) 3) effect prediction tools (VEP, HaploReg), 4) functional scoring tools (CADD, RegulomeDB) 5) prioritization algorithm (PAINTOR), 6) pathway information (GeneMANIA) 7) regulation data (Enhancers, Super-enhancers, IM-PET, Chromatine states) and finally 8) relevant experimental data such as chromosomal interactions (Hi-C, 3C, 5C, ChIA-PET) and expression data (GTex).

Thus, the user is provided with a proximal and distal context for each variant to assist in the prediction of putative functional effects. VEXOR is a versatile and scalable tool designed to help the understanding of the functional context in fine-mapping analyses of complex traits.

The site is kindly hosted on Compute Canada infrastructure and benefits from its high performance computing environment.

F.A.Q

Coming soon...

How to cite

To cite the tool, please refer to : Audrey Lemaçon, Charles Joly Beauparlant, Penny Soucy, Jamie Allen, Douglas Easton, Peter Kraft, Jacques Simard, Arnaud Droit; VEXOR: an integrative environment for prioritization of functional variants in fine-mapping analysis. Bioinformatics 2016 btw826. doi: 10.1093/bioinformatics/btw826

Questions, suggestions and bug reports

For any issues please contact audrey.lemacon.1 at ulaval dot ca or file an issue in VEXOR github issue page .