This post is inspired by the Bluesky Network Analyzer made by @theo.io. I’m encouraging everyone I know online to join the scientific community on Bluesky. In that post I link to several starter packs — lists of accounts posting about a topic that you can follow individually or all at once to start filling out your network. I started following accounts of people I knew from X and from a few starter packs I came across.
I joined Twitter1 way back in 2009. For nearly 10 years “scitwitter” was an amazing place for discussion, discovery, and engagement with the scientific community. The #Rstats and #pydata hashtags were great places to learn about something new in programming, #icanhazpdf was great for getting papers you didn’t have access to, and conference live-tweeting was common and useful for those of us with FOMO not able to make it in person.
This week’s recap highlights an AI agent for automated multi-omic analysis (AutoBA), rapid species-level metagenome profiling and containment (sylph), a review on genome-wide association analysis beyond SNPs, private information leakage from scRNA-seq count matrices, and a method to “unlearn” viral knowledge in protein language models as a means to develop safe PLM-based variant effect analysis (PROEDIT). Others that caught my attention include
In the spirit of Learning in Public, I wanted an excuse to explore (1) click for creating command line interfaces, (2) Cookiecutter project templates, and (3) modern tools in the Python packaging ecosystem. If you’re primarily an R developer like me, I recently wrote about resources for getting better at Python for R users.
This week's recap highlights a new pipeline for metagenome quality assessment and taxonomic annotation (MAGFlow &
I just returned from a week in Barcelona where I attended the Nextflow Summit and nf-core hackathon, and I can hardly contain my excitement for the near term future of bioinformatics, computational biology, and open science in general.
This week’s recap highlights protein design with RoseTTAFold, surveillance with wastewater sequencing, T2T human genomes, Vitessce for visualization of multimodal spatial single-cell data, and Taxometer for taxonomic classification of metagenomics contigs.
A Google search for “R vs Python” returns thousands of hits across sites like Reddit, IBM, Datacamp, Coursera, Kaggle, and many others.
This week’s recap highlights a new Nextflow workflow for calculating polygenic scores with adjustments for genetic ancestry, a paper demonstrating that whole exome plus imputation on more samples is more powerful than whole genome sequencing for finding more trait associated variants, a new deep-learning-based splice site predictor that improves spliced alignments, a new method for accurate community profiling of large metagenomic datasets, and
I am in the middle of writing a review / perspectives paper. One that I’m confident will be exciting once we get it published. Some sections of the review cover subject matter at the outer periphery of my expertise.
This week’s recap highlights a new method for gene-level alignment of single-cell trajectories, an R package for integrating gene and protein identifiers across biological sequence databases, characterization of SVs across humans and apes, universal prediction of cellular phenotypes, a method to quantify cell state heritability versus plasticity and infer cell state transition with single cell data, and a new AI-driven, natural language-oriented