[ SOFTWARE ] Statistical genomics software:

HyperClust by David Mas-Ponte. https://github.com/davidmasp/hyperclust

• • A statistical framework to detect clustered mutations in genomes, while accounting for mutation rate heterogenety and for estimated timing of the mutations.

  • associated with the publication Mas-Ponte & Supek (2020) Nature Genetics "DNA mismatch repair promotes APOBEC3-mediated diffuse hypermutation in human cancers"
    

CellLineMutSigs by Jurica Levatić. https://github.com/jlevatic/CellLineMutSigs

• • extracting mutational signatures in cancer cell line genomes, and association of mutational signatures with drug activity.

  • associated with the publication Levatić, Salvadores, Fuster & Supek (2022) Nature Communications [in press] "Mutational signatures are markers of drug sensitivity of cancer cells"

[ SOFTWARE ] Bioinformatics and machine learning tools:

to be released soon: "Pipeline6" (working name) by Daniel Naro

• A modern, extensible and scalable pipeline for cancer genomic data processing based on the NextFlow enviroment

• Includes the gamut of bioinformatics tools from the Hartwig Medical Foundation "Platinum pipeline" (Sage, GRIDSS, PURPLE...) and several additional tools:

  • Strelka2 (SNV, indel calling), Manta (SV calling), Paragraph (germline SV genotyping), Sequenza (CNA calling) GangSTR (repeat indel calling) etc.

BioPanPipe by Daniel Ortiz-Martinez. https://daormar.github.io/bio-panpipe/

• • A cancer genomics pipeline implementing several tools for variant calling and download from genomics databases.

FastRandomForest2 (beta) by Jordi Piqué Sellés. https://github.com/GenomeDataScience/FastRandomForest

• • A re-implementation of the Random Forest classifier (RF) for the Weka machine learning environment, bringing massive speed and memory use improvements.

[ DATASETS ] CRISPR genetic screening experiment data:

APOBEC3A conditional essentiality screens by Josep Biayna (CRISPR screening experiment) and Miguel Álvarez (data analysis).

• • Genetic screens on lung adenocarcinoma cell lines A549 (both the TP53 wild-type and the TP53-/- isogenic pair), and LXF289.

    • APOBEC3A overexpression or not, multiple time points

    • Contains gRNA counts & enrichment analyses via MAGECK

    • associated with the publication Biayna, [...] Supek and Stracker (2021) PLOS Biology. Data is in supplementary material of the open-access PLOS publication >> LINK

  • [ TO BE RELEASED SOON ] Genetic screens on H358 lung adenocarcinoma cell line by Josep Biayna (CRISPR screening experiment) and Miguel Álvarez (data analysis).

    • APOBEC3A overexpression or not, multiple time points

    • Contains gRNA counts & enrichment analyses via MAGECK

    • associated with upcoming manuscript by Álvarez et al.

[ DATASETS ] genomic analysis data:

Cancer cell line ranking by their similarity to TCGA tumors, via transcriptome and DNA methylome analysis. Lists a "golden set" of preferred cell lines, as well as the cell lines putatively misanotated to the incorrect tissue. by Marina Salvadores

• Associated to publication by Salvadores et al. Science Advances. "Matching cell lines with cancer type and subtype of origin via mutational, epigenomic, and transcriptomic patterns".

• Dataset can be downloaded via supplementary material of the open-access Science publication >> LINK.