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#bioinformatics

11 posts10 participants3 posts today
Public
Luke Pembleton

Cloud computing: where you're not just a bioinformatician, you’re also the finance manager, compliance officer, and IT support, all while carrying a hefty dose of mental fatigue. I miss those PhD days with a inhouse HPC when all you had to do was sbatch every parameter combo under the sun to test your research question… then complain about queue times and who was clogging the damn cluster with their BLAST jobs 😅
#bioinformatics

Public
Benjamin Carr, Ph.D. 👨🏻‍💻🧬

#Genetic data repo #OpenSNP to self-destruct before authoritarians weaponize it
The reason, according to #bioinformatics researcher Bastian Greshake Tzovaras, one of the founders of the project, is the dissolution of genetic testing biz #23andMe and the potential weaponization of genetic data by #farright #authoritarian regimes.
theregister.com/2025/04/01/ope

The Register · Genetic data repo OpenSNP to self-destruct before authoritarians weaponize itBy Thomas Claburn
Public
Thomas Sandmann

Big shoutout to the folks at NCBI GEO. They have been doing an incredible job managing enormous amounts of gene expression and related biomedical data for many, many years They do it efficiently and very, very well. I am so grateful: both when I download data published by others and when I submit my own data. You are true #bioinformatics heroes doing priceless and highly impactful work! 🦸 🧬 💻 #NCBI #nih

Public
Eli Roberson (he/him)

#Genomics
#Bioinformatics
#Academia

Beware US academic coders. If you have collaborators contributing code from a sanctioned region, your repos may be locked.

See mastodon.social/@organicmaps/1

The kicker is that the US is leaning authoritarian and all in on censorship. If Canada or Mexico become sanction targets and you have code pushes from there, Microsoft will lock you out of your GitHub accounts.

GitHub read-only
MastodonOrganic Maps (@organicmaps@mastodon.social)Attached: 1 image We have a temporary glitch with GitHub—probably some contributor was geolocated in a sanctioned region (no details yet). All required documents to unlock the account have been uploaded. Don't blame Microsoft/GitHub - it is just U.S. law. Please be patient. It should be unblocked soon.
Public
Luke Pembleton

Does anyone have any posts or past toots they could reshare about how they have setup or are working within a team in #bioinformatics, #genomics, #research etc. things that how worked or haven’t worked, organising and sharing projects and code, notes and documentation, collaboration and communication, software, network and local or cloud compute arrangement. Always find reading others experiences insightful and beneficial.

Public
Bioinfhotep

link.springer.com/article/10.1
#networksAnalysis #rstats #methods #bioinformatics

SpringerLinkCross-validation for training and testing co-occurrence network inference algorithms - BMC BioinformaticsBackground Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals and plants. While some microorganisms cause diseases, most of them help in biological processes such as decomposition, fermentation and nutrient cycling. Much research has been conducted on the study of microbial communities in various environments and how their interactions and relationships can provide insight into various diseases. Co-occurrence network inference algorithms help us understand the complex associations of micro-organisms, especially bacteria. Existing network inference algorithms employ techniques such as correlation, regularized linear regression, and conditional dependence, which have different hyper-parameters that determine the sparsity of the network. These complex microbial communities form intricate ecological networks that are fundamental to ecosystem functioning and host health. Understanding these networks is crucial for developing targeted interventions in both environmental and clinical settings. The emergence of high-throughput sequencing technologies has generated unprecedented amounts of microbiome data, necessitating robust computational methods for network inference and validation. Results Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples, both of which have several drawbacks that limit their applicability in real microbiome composition data sets. We propose a novel cross-validation method to evaluate co-occurrence network inference algorithms, and new methods for applying existing algorithms to predict on test data. Our method demonstrates superior performance in handling compositional data and addressing the challenges of high dimensionality and sparsity inherent in real microbiome datasets. The proposed framework also provides robust estimates of network stability. Conclusions Our empirical study shows that the proposed cross-validation method is useful for hyper-parameter selection (training) and comparing the quality of inferred networks between different algorithms (testing). This advancement represents a significant step forward in microbiome network analysis, providing researchers with a reliable tool for understanding complex microbial interactions. The method’s applicability extends beyond microbiome studies to other fields where network inference from high-dimensional compositional data is crucial, such as gene regulatory networks and ecological food webs. Our framework establishes a new standard for validation in network inference, potentially accelerating discoveries in microbial ecology and human health.
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