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Mastodon: AboutGet the appKeyboard shortcutsView source codev4.4.0-nightly.2025-03-25

#bioinformatics

35 posts25 participants11 posts today
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.
Public
Pierre Lindenbaum

github.com/yangao07/longcallD LongcallD is a local-haplotagging-based variant caller designed for detecting small variants and structural variants (SVs) using long-read sequencing data. It supports both PacBio HiFi and Oxford Nanopore reads. #sv #structuralvariant #calling #mapping #bioinformatics

(via Heng Li on twitter)

A local-haplotagging-based small and structural variant caller - yangao07/longcallD
GitHubGitHub - yangao07/longcallD: A local-haplotagging-based small and structural variant callerA local-haplotagging-based small and structural variant caller - yangao07/longcallD
Public
naturepoker

Yikes modkit likes to eat up ram when making summaries of methylation called bams.

Subsampled bam with 100k reads still requires close to 50gb of ram, which for me is cutting it way too close. I'm beginning to envy those real lab people with monster rigs.

#bioinformatics
Public
CSBJ

This study introduces plmCP, an innovative method for detecting circular permutations (CPs) in proteins using Protein Language Models (PLMs)—paving the way for deeper insights into protein evolution, engineering, and synthetic biology.

🔗 Detection of circular permutations by Protein Language Models. Computational and Structural Biotech Journal, DOI: doi.org/10.1016/j.csbj.2024.12

📚 CSBJ: csbj.org/

Detection of circular permutations by Protein Language Models. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2024.12.029
#ProteinScience#AIinBiotech#SyntheticBiology
Public
naturepoker

Dorado polisher supporting 'any HTS file type' alongside the assembly input is a bit misleading. From what I can tell it only runs on reads containing model information under @RG tag. So it essentially limits your option to Dorado generated BAM file and nothing else.

#bioinformatics
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