Bioinformatics Advances
@bioinfoadv.bsky.social
A fully open access, peer-reviewed journal published jointly by Oxford University Press and the International Society for Computational Biology.
💻 BacExplorer is open source and freely available:
GitHub - knowmics-lab/BacExplorer
Contribute to knowmics-lab/BacExplorer development by creating an account on GitHub.
github.com
November 28, 2025 at 10:02 AM
💻 BacExplorer is open source and freely available:
It supports FASTA and FASTQ inputs, automates quality control, assembly, MLST typing, and AMR/virulence #gene detection across multiple databases, and generates comprehensive HTML and Excel reports for streamlined #microbiological analysis.
November 28, 2025 at 10:02 AM
It supports FASTA and FASTQ inputs, automates quality control, assembly, MLST typing, and AMR/virulence #gene detection across multiple databases, and generates comprehensive HTML and Excel reports for streamlined #microbiological analysis.
🧠 All code is openly available: https://github.com/TheCOBRALab/metabolicRelationships
Dataset repository:
Dataset repository:
Network Inference of Simulated Arachadonic Acid Catabolism
Includes simulated sample concentrations, outputs of network inference algorithms, and visualizations of simulated sarcopenia networks. Data for: https://www.biorxiv.org/content/10.1101/2025.05.23.654112v1.full.pdf Also see: https://github.com/TheCOBRALab/metabolicRelationships
doi.org
November 27, 2025 at 10:02 AM
🧠 All code is openly available: https://github.com/TheCOBRALab/metabolicRelationships
Dataset repository:
Dataset repository:
Results show that even with large sample sizes, current methods fail to recover true metabolic interactions, though correlation-based approaches can still distinguish distinct metabolic states such as young versus aged networks in sarcopenia simulations.
November 27, 2025 at 10:01 AM
Results show that even with large sample sizes, current methods fail to recover true metabolic interactions, though correlation-based approaches can still distinguish distinct metabolic states such as young versus aged networks in sarcopenia simulations.
This study systematically benchmarks correlation-, regression-, and information theory-based network inference algorithms using a simulated arachidonic acid lipidomics model.
November 27, 2025 at 10:01 AM
This study systematically benchmarks correlation-, regression-, and information theory-based network inference algorithms using a simulated arachidonic acid lipidomics model.
Authors include: @daanspeth.bsky.social, @aroneys.bsky.social, @bcoltman.bsky.social, @benjwoodcroft.bsky.social, @michiwagner4.bsky.social
November 26, 2025 at 10:01 AM
🧬 Access the GlobDB database and resources here:
home | GlobDB
Welcome to the GlobDB genomes databaseThis website hosts the GlobDB, a dereplicated set of species representative microbial genomes. The genomic era offers great opportunities for microbial genome analyses, and individual (meta)genome studies can generate thousands of microbial genomes. Although the...
globdb.org
November 26, 2025 at 10:01 AM
🧬 Access the GlobDB database and resources here:
It integrates over 306,000 representative genomes—more than doubling the number included in the GTDB—and provides standardized #taxonomy, functional annotations, and resources for #metagenomic profiling and #comparativegenomics.
November 26, 2025 at 10:01 AM
It integrates over 306,000 representative genomes—more than doubling the number included in the GTDB—and provides standardized #taxonomy, functional annotations, and resources for #metagenomic profiling and #comparativegenomics.
💻 ntRoot is open source and available on GitHub:
GitHub - bcgsc/ntRoot: 🌳 Human ancestry inference from genomic data
🌳 Human ancestry inference from genomic data. Contribute to bcgsc/ntRoot development by creating an account on GitHub.
github.com
November 25, 2025 at 10:01 AM
💻 ntRoot is open source and available on GitHub:
Using Bloom filters and 1000 Genomes Project reference panels, ntRoot achieves 99–100% accuracy across 600+ samples while running up to 250x faster than ADMIXTURE, enabling efficient ancestry analysis at population scale.
November 25, 2025 at 10:01 AM
Using Bloom filters and 1000 Genomes Project reference panels, ntRoot achieves 99–100% accuracy across 600+ samples while running up to 250x faster than ADMIXTURE, enabling efficient ancestry analysis at population scale.
🧬 BEREN is open source and freely available: https://gitlab.com/benminch1/BEREN Associated data can be found at
Project - BEREN - Figshare
figshare.com
November 24, 2025 at 10:01 AM
🧬 BEREN is open source and freely available: https://gitlab.com/benminch1/BEREN Associated data can be found at
Benchmarking on mock #metagenomes showed that BEREN recovered 77% of NCLDV contigs and 100% of Preplasmiviricota genomes, outperforming other virus recovery tools in accuracy and completeness.
November 24, 2025 at 10:01 AM
Benchmarking on mock #metagenomes showed that BEREN recovered 77% of NCLDV contigs and 100% of Preplasmiviricota genomes, outperforming other virus recovery tools in accuracy and completeness.
BEREN introduces a modular workflow for identifying, annotating, and analyzing members of the Bamfordvirae kingdom, including NCLDVs and Preplasmiviricota.
November 24, 2025 at 10:01 AM
BEREN introduces a modular workflow for identifying, annotating, and analyzing members of the Bamfordvirae kingdom, including NCLDVs and Preplasmiviricota.
🧩 Source code and resources are open source:
GitHub - LorisNanni/Matrix-Representation-of-Vectors-in-Neural-Networks-for-Data-Classification: Matrix Representation of Vectors in Neural Networks for Data Classification
Matrix Representation of Vectors in Neural Networks for Data Classification - LorisNanni/Matrix-Representation-of-Vectors-in-Neural-Networks-for-Data-Classification
github.com
November 21, 2025 at 10:01 AM
🧩 Source code and resources are open source:
Across #peptide classification and DNA barcoding datasets, the ensemble of #neuralnetworks outperformed traditional SVM and other baseline models.
November 21, 2025 at 10:01 AM
Across #peptide classification and DNA barcoding datasets, the ensemble of #neuralnetworks outperformed traditional SVM and other baseline models.
This study introduces a Wigner transform-based approach for converting one-dimensional biological feature vectors into two-dimensional matrices, enabling the use of CNNs and transformer networks pre-trained on image data.
November 21, 2025 at 10:01 AM
This study introduces a Wigner transform-based approach for converting one-dimensional biological feature vectors into two-dimensional matrices, enabling the use of CNNs and transformer networks pre-trained on image data.