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Bioinformatics Advances @bioinfoadv.bsky.social · 16h

TUSV-int integrates bulk DNA-seq and scRNA-seq within an integer linear programming framework to jointly model SNVs, CNAs, and SVs. Benchmarks on simulated and real #breastcancer data show improved clonal deconvolution and #phylogeny inference over existing methods.

Bioinformatics Advances @bioinfoadv.bsky.social · 16h

🧬 Explore the latest from Bioinformatics Advances: "Deconvolution and phylogeny inference of diverse variant types integrating bulk DNA-seq with single-cell RNA-seq"

Full article available: https://www.doi.org/10.1093/bioadv/vbaf234

Cytoscape App Store - SocialViruses

Bioinformatics Advances @bioinfoadv.bsky.social · 1d

💻 Open source Cytoscape app available here:

apps.cytoscape.org

Bioinformatics Advances @bioinfoadv.bsky.social · 1d

SocialViruses is a Cytoscape application for rational phage cocktail design. It incorporates quantitative phage–bacteria and phage–phage interaction networks, supports up to 12 phages, minimizes antagonism and redundancy, and provides detailed performance metrics across diverse datasets.

Bioinformatics Advances @bioinfoadv.bsky.social · 1d

🦠 Now published in Bioinformatics Advances: "SocialViruses: Integrating quantitative phage–bacteria and phage–phage interaction networks for rational cocktail design"

Read the full paper here: https://doi.org/10.1093/bioadv/vbaf239

GitHub - yuyiwen-yiyuwen/Disc_Hub: a software package for evaluating machine learning performance

Bioinformatics Advances @bioinfoadv.bsky.social · 1d

📦 Open source code available here:

a software package for evaluating machine learning performance - yuyiwen-yiyuwen/Disc_Hub

Bioinformatics Advances @bioinfoadv.bsky.social · 1d

Disc-Hub benchmarks 3 training strategies and 4 classifiers on DIA-MS datasets, showing that K-fold training with multilayer perceptrons best balances identification depth and FDR control. The package enables rapid, reproducible evaluation of #machinelearning configurations for DIA identification.

Bioinformatics Advances @bioinfoadv.bsky.social · 1d

🧪 Just out in Bioinformatics Advances: "Disc-Hub: a python package for benchmarking machine learning strategies in DIA-MS identification"

Explore the full study: https://www.doi.org/10.1093/bioadv/vbaf232

GitHub - Mordziarz/TAILcaller: an R package for analyzing polyA tails after dorado basecalling.

Bioinformatics Advances @bioinfoadv.bsky.social · 2d

🛠️ Software available open source:

an R package for analyzing polyA tails after dorado basecalling. - Mordziarz/TAILcaller

Bioinformatics Advances @bioinfoadv.bsky.social · 2d

TAILcaller is an R package designed to analyze poly(A) tail length differences directly from dorado-generated BAM files. It supports both direct RNA and cDNA nanopore sequencing data, enabling global, gene-level, and transcript-level analyses with flexible statistical testing and visualization.

GitHub - MaayanLab/sc2DAT

Bioinformatics Advances @bioinfoadv.bsky.social · 2d

🧬 Just out in Bioinformatics Advances: "TAILcaller: An R package for analyzing differences in poly(A) tail length for Oxford Nanopore RNA sequencing”

Full article available: https://doi.org/10.1093/bioadv/vbaf235

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Bioinformatics Advances @bioinfoadv.bsky.social · 5d

🛠️ Try sc2DAT here: https://sc2dat.maayanlab.cloud/
Source code available:

Contribute to MaayanLab/sc2DAT development by creating an account on GitHub.

Bioinformatics Advances @bioinfoadv.bsky.social · 5d

sc2DAT is a web-based workflow that integrates single-cell and bulk RNA-seq data to automatically identify cell subpopulations, rank cell-surface targets, and predict therapeutic compounds. It leverages resources like LINCS L1000 and TargetRanger for drug and target prioritization.

Bioinformatics Advances @bioinfoadv.bsky.social · 5d

🧬 Now published in Bioinformatics Advances: “sc2DAT: Workflow for targeting tumor subpopulations of single cells”

Full article available: https://doi.org/10.1093/bioadv/vbaf237

GitHub - epifluidlab/FinaleToolkit: FinaleToolkit is a package and standalone program to extract fragmentation features of cell-free DNA from paired-end sequencing data.

Bioinformatics Advances @bioinfoadv.bsky.social · 6d

🧰 Open-source toolkit with CLI, Python API, and documentation available:

FinaleToolkit is a package and standalone program to extract fragmentation features of cell-free DNA from paired-end sequencing data. - epifluidlab/FinaleToolkit

Bioinformatics Advances @bioinfoadv.bsky.social · 6d

Benchmarks on >1B fragments confirmed accuracy, scalability, and memory efficiency.

Bioinformatics Advances @bioinfoadv.bsky.social · 6d

FinaleToolkit is a Python package for efficient extraction of cfDNA fragmentation features. It replicates >10 published fragmentation metrics, supports parallel processing, and achieves up to 50-fold faster performance than original implementations.

Bioinformatics Advances @bioinfoadv.bsky.social · 6d

🧪 Now published in Bioinformatics Advances: "FinaleToolkit: Accelerating cell-free DNA fragmentation analysis with a high-speed computational toolkit"

Explore the full study: https://doi.org/10.1093/bioadv/vbaf236

GitHub - NiklasBrunn/Sparse-dimension-reduction

Bioinformatics Advances @bioinfoadv.bsky.social · 7d

🧰 Implementation with Jupyter notebooks and example data available:

Contribute to NiklasBrunn/Sparse-dimension-reduction development by creating an account on GitHub.

Bioinformatics Advances @bioinfoadv.bsky.social · 7d

By integrating a soft clustering step, the approach identifies ligand–receptor interactions linked to specific clusters of interacting cell pairs, enabling intuitive visualization and interpretation.

Bioinformatics Advances @bioinfoadv.bsky.social · 7d

The study adapts the Boosting Autoencoder for sparse, interpretable analysis of cell–cell interaction matrices derived from scRNA-seq or spatial transcriptomics.

Bioinformatics Advances @bioinfoadv.bsky.social · 7d

📊 Explore the latest from Bioinformatics Advances: "Sparse dimensionality reduction for analyzing single-cell-resolved interactions"

Read the full paper here: https://doi.org/10.1093/bioadv/vbaf230

GitHub - LLNL/DeepAL: This project is an implementation of the Deep Active Learning (DeepAL) framework from the paper Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics.

Bioinformatics Advances @bioinfoadv.bsky.social · 8d

⚙️ Open-source code and documentation are available:

This project is an implementation of the Deep Active Learning (DeepAL) framework from the paper Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host T...

Bioinformatics Advances @bioinfoadv.bsky.social · 8d

On a 356 x 356 HIV dataset, DeepAL uncovered 92% of the top 400 synergistic gene pairs after observing less than 6.3% of the space, outperforming baseline strategies.