AutoGVP

/0 Comments/in , /by

AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification

Summary: With the increasing rates of exome and whole genome sequencing, the ability to classify large sets of germline sequencing variants using up-to-date American College of Medical Genetics-Association for Molecular Pathology (ACMG-AMP) criteria is crucial. Here, we present Automated Germline Variant Pathogenicity (AutoGVP), a tool that integrates germline variant pathogenicity annotations from ClinVar and sequence variant classifications from a modified version of InterVar (PVS1 strength adjustments, removal of PP5/BP6). This tool facilitates large-scale, clinically focused classification of germline sequence variants in a research setting.

Availability and implementation: AutoGVP is an open source dockerized workflow implemented in R and freely available on GitHub at https://github.com/diskin-lab-chop/AutoGVP.

 

Dr. Diskin is now Director of the Computational Biology Core for the CCCR

/0 Comments/in , /by

The Center for Childhood Cancer Research has now named the Computational Biology Core with Dr. Diskin as the Director.

The rest of the core consists of:

Dr. Alvin Farrel, Assistant Director

Rebecca Kaufman, Bioinformatics Scientist II
Dr. Evan Cresswell-Clay, Bioinformatics Scientist II
Kushbu Patel, Bioinformatics Scientist II
Dr. Rawan Shraim, Bioinformatics Scientist II
Pamela Mishra, Bioinformatics Scientist III

The core is a partnership between CCCR and DBHi to provide dedicated bioinformatics support for PIs within the CCCR. Staff will work with various and multiple teams to support ongoing research within the Cancer Center. We’re very excited to support other teams and expand the scope of research being done here at CHOP and the CCCR.

lab logo by apexa

Protocols.io

/1 Comment/in /by

Diskin Lab has joined protocols.io! We share our wet-bench methods on our group page there after publication.  You can find us by following this link or going to https://protocols.io and searching for Diskin Lab- CHOP
Happy Sciencing!
Visit Diskin Lab Protocols.io

GiaPronto: Graphical Interpretation and Analysis of Proteins and their Ontologies

/0 Comments/in /by

Analytical methods and tools for processing mass spectrometry-based proteomics data are still in early stages of development compared to other ‘omics fields. For this reason, we sought to develop an accessible online application to facilitate normalization, basic statistical testing, and biological interpretation of mass-spec based proteomics data. GiaPrOnto (Graphical Interpretation and Analysis of Proteins and their Ontologies) is an R-Shiny application written to fill this void (Weiner, et al, Mol. Cell. Proteom. 2018).  Input to GiaPrOnto can be the direct output of MaxQuant protein or PTM (commonly used protein quantification software), or any properly formatted text file. GiaPrOnto performs normalization, basic statistical tests (e.g. t-test), gene ontology (GO) evaluation, and provides visualization of sample features and results. Each figure can be exported in high resolution (right click) and includes a comprehensive legend to assist in its interpretation. Currently, supported Gene Ontology includes human, mouse, worm, fly, yeast and Arabidopsis. For questions, suggestions or bug reports, please contact Amber K. Weiner (amberkweiner@gmail.com).

Visit GiaPronto.DiskinLab.org.

CODEX: normalization and copy number variation (CNV) detection using high-throughput DNA sequencing

/0 Comments/in /by

High throughput DNA sequencing has revolutionized the study of both normal and disease tissues, including cancer.  However, batch effects and other potential artifacts can hinder the accurate identification of copy number variations (CNVs) from DNA sequencing data.  Issues that complicated CNV detection from single nucleotide polyporphism (SNP) array data can affect the analysis of DNA sequencing data (e.g. GC content).  We therefore developed a novel method for improving normalization and CNV detection from exome sequencing data (Jiang et al, Nucleic Acids Res. 2015). This work was spearheaded by Yuchao Jiang while he was a graduate student rotating in our lab and was completed in collaboration with Dr. Nancy Zhang at Wharton.  Version 2 has since been released by Dr. Jiang who is now a faculty member at the University of North Carolina.

Visit https://github.com/yuchaojiang/CODEX2

lab logo by apexa

Diskin Lab GitHub

/0 Comments/in /by

The Diskin Lab Github provides public repositories for our code and pipelines, for example:

  • Inferring ethnicity from genetic variant data
  • Analysis of  structural variations (SVs)

Visit Diskin Lab Github