As announced earlier, I wanted to follow up with instructions on how join the challenge introductions for developers interested in joining the sessions:
1. BioPlex challenge (Wed, Aug 4, 2:30-3:30 PM EST): - login to airmeet - take a seat at the "BioPlex" table in the "Lounge" area - details: https://kevinrue.github.io/BiocChallenges/articles/challenges/bioplex.html - participation / contribution: https://tinyurl.com/bioplex 2. BugSigDB challenge (Thu, Aug 5, 2:30-3:30 PM EST) - login to airmeet - take a seat at the "BugSigDB" table in the "Lounge" area - details: https://kevinrue.github.io/BiocChallenges/articles/challenges/bugsigdb.html - participation / contribution: https://tinyurl.com/bugsigdb Thanks, Ludwig ________________________________ From: Geistlinger, Ludwig Sent: Friday, July 30, 2021 4:16 AM To: bioc-devel@r-project.org <bioc-devel@r-project.org> Subject: BiocChallenges: BioPlex protein-protein interactions & BugSigDB microbiome signatures I'd like to announce two Bioc-community challenges: 1. BioPlex challenge (orga: Ludwig Geistlinger, Robert Gentleman): The BioPlex project (https://bioplex.hms.harvard.edu) project has created two proteome-scale, cell-line-specific protein-protein interaction (PPI) networks: the first in 293T cells, including 120k interactions among 15k proteins; and the second in HCT116 cells, including 70k interactions between 10k proteins. The BioPlex R package (https://github.com/ccb-hms/BioPlex, submitted to Bioconductor) implements access to the BioPlex PPI networks and related resources from within R. Besides PPI networks for 293T and HCT116 cells, this includes access to CORUM protein complex data, and transcriptome and proteome data for the two cell lines. The goal of this challenge is to introduce the BioPlex data and package to the community, and work together on several analysis and programming challenges around the data including: (a) transcriptomic and proteomic data integration on the BioPlex networks, (b) assessing the impact of alternative splicing on bait proteins of the networks, (c) integration with public databases for disease-associated genes and variants, (d) implementing a GraphFrames backend for efficient representation and analysis of the networks, and (e) designing an R/Shiny graph viewer that allows flexible inspection of experimental data and metadata for nodes and edges of the networks. We will introduce the challenge at Bioc2021, Wed, Aug 4, 2:30-3:30 PM EST, with all interested developers invited. Instructions on how to join the meeting on the virtual conference platform will follow. 2. BugSigDB challenge (orga: Ludwig Geistlinger, Levi Waldron): BugSigDB (https://bugsigdb.org) is a manually curated database of microbial signatures from published differential abundance studies, providing standardized data on geography, health outcomes, host body sites, and experimental, epidemiological, and statistical methods using controlled vocabulary. To date, BugSigDB provides more than 2,000 signatures from over 500 published studies, allowing systematic assessment of microbiome abundance changes within and across experimental conditions and body sites. The bugsigdbr package (https://github.com/waldronlab/bugsigdbr, submitted to Bioconductor) implements access to BugSigDB from within R/Bioconductor. This includes import of BugSigDB data into R/Bioconductor, utilities for extracting microbe signatures, and export of the extracted signatures to plain text files in standard file formats such as GMT. The goal of this challenge is to introduce the BugSigDB database and package to the community, and work together on several analysis and programming challenges around the data including: (a) identification of body site-specific signatures from healthy samples, (b) efficient calculation of similarity measures between signatures across the whole database or specific subsets of it, (c) automatic identification of candidate papers for curation based on recently proposed text mining approaches, (d) ontology-based queries to the database using controlled vocabulary for experimental factors and body sites, and (e) inference of abundance changes along the taxonomic hierarchy using phylogenetic approaches such as ancestral state reconstruction. We will introduce the challenge at Bioc2021, Thu, Aug 5, 2:30-3:30 PM EST, with all interested developers invited. Instructions on how to join the meeting on the virtual conference platform will follow. --- Dr. Ludwig Geistlinger Center for Computational Biomedicine Harvard Medical School [[alternative HTML version deleted]] _______________________________________________ Bioc-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel
