[Bioc-devel] BiocChallenges: BioPlex protein-protein interactions & BugSigDB microbiome signatures

[Geistlinger, Ludwig]

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 using r-project.org <bioc-devel using 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

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