Tool:Genetalk - A Platform To Analyse Your Genetic Variant Data And Talk About It

Question

[alexej.knaus]

www.Gene-Talk.de is a web-based platform, tool, and database, for filtering, reduction and prioritization of human sequence variants from next-generation sequencing (NGS) data. Users can creat a free account and upload VCF files. All entries of the file are preprocessed and shown in the integrated VCF viewer. Filtering tools are set by the user to reduce the number of clinically non-relevant variants. After filtering and prioritization users can interpret relevant variants by retrieving information (annotations) from the GeneTalk knowledge-base. The communication platform allow users to contact experts about specific variants, genes, or genetic disorders, to exchange knowledge and expertise. Users can exchange VCF files, custom made GenePanelFilter, annotations and filter settings on the platform and colaborate with colleages.

Analysis procedure - Five steps for for variant analysis:

1. Upload VCF file to www.Gene-Talk.com
2. Edit pedigree and phenotype information for segregation filtering
3. Filter VCF file by editing the filtering options
4. View results and annotations
5. Add annotations

Filtering tools

The following filtering options may be used to reduce the non-relevant sequence variants in VCF files.

1. Functional – filter out variants that have effects on protein level
2. Linkage – filter out variants that are on specified chromosomes
3. Gene panel – filter variants by genes or gene panels, subscribe to publicly available gene panels or create own ones
4. Frequency – show only variants with a genotype frequency lower than specified
5. Inheritance – filter out variants by presumed mode of inheritance
6. Annotation – show only variants with a score for medical relevance and scientific evidence

Communication platform & expert network

Users can share VCF files with colleagues and coworkers. The integrated mailing systems allows users to contact experts easily. Users can create annotations and comments and rate annotations regarding medical relevance and scientific evidence, that is helpful for the community of users for diagnosis of genetic disorders. Registered users provide information about their field of knowledge in their profile and can be contacted by other users.

What do you think about thy platform? What do you miss?

Answer 1

[alexej.knaus]

A new paper is out discribing how to estimate genotyping accuracy by comparing exome or genome data against larg scale sequencing project (such as 1kg or 6500 exomes project) (read more here).

Quality metrics are now available at GeneTalk for all uploaded VCF files. V. Heinrich developed a metrics algorithm that compares variant data of the uploaded VCF file against a matching population group from the 1000 Genomes Project data.

In the file manager in GeneTalk one can click the quality button to see the plotted genotyping accuracy in two graphics:

The plots display how well a sample matches into a population group of the 1000 Genomes Project data (Non-Metric MDS) and estimates the genotyping accuracy in percent (Genotyping Accuracy).

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Abstract:

With exome sequencing becoming a tool for mutation detection in routine diagnostics there is increasing need for platform-independent methods of quality control. We present a genotype-weighted metric that allows comparing all the variant calls of an exome to a high-quality reference dataset of an ethnically matched population. The exome-wide genotyping accuracy is estimated from the distance to this reference set, and does not require any further knowledge about data generation or the bioinformatics involved. The distances of our metric are visualized by non-metric multidimensional scaling and serve as a standardizable score for the quality assessment of exome data.

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Any comments about the paper and the quality plots in GeneTalk are appreciated.

Visit Gene-Talk.de, register for free and upload our VCF file to estimate the genotyping accuracy.

Answer 2

[alexej.knaus]

In case you are wondering how an exome with a poor genotyping accuracy would look like.

It would be either due to few common variants or too many rare variants that were detected in this exome compared to the FIN population group (that would still be the best matching population to the sample_s exome data), that the control data would group desnly and fra appart from the sample (as one can see in this pic)

the orange plotted line depicts the genotyping accuracy of about 0.9997...