identifying mutational significance in cancer genomes
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Updated
Nov 16, 2022 - Perl
identifying mutational significance in cancer genomes
Framework to process and call somatic variation from NGS dataset generated using MSK-IMPACT assay
Open-source software pipeline for cancer classification from high-throughput data using machine learning.
A software to detect virome-wide integrations
Explore and filter structural variant calls from Lumpy and Delly VCF files
ICDC data model representations and build tools
Bpipe-based pipeline for processing cancer genomics data in fastq format through to annotated variants in a simple report.
Various utility scripts and ICGC DCC data specification templates / conversion tools written during early development work on the current ICGC DCC data portal (2.0)
Open-source command-line pipeline for cancer type classification of high-throughput data using machine learning.
My lab book for current project: Identifying structural variation in WGS data
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