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Bioinformatics Advances @bioinfoadv.bsky.social · 22h

📊 The study shows that OPALS enables fine-grained representation of compositional data that would otherwise be infeasible at scale, providing new opportunities for modeling in high-dimensional microbiome and molecular datasets.

Bioinformatics Advances @bioinfoadv.bsky.social · 22h

The method alleviates the computational burden, links estimates to pivot coordinates, and is demonstrated through regression and classification analyses of #molecularbiology datasets.

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Bioinformatics Advances @bioinfoadv.bsky.social · 22h

In this work, the OPALS algorithm is introduced to efficiently obtain all orthonormal pairwise logratios for high-dimensional compositional data using Latin squares theory.

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Bioinformatics Advances @bioinfoadv.bsky.social · 22h

🧮 Just out in Bioinformatics Advances: “Orthonormal pairwise logratio selection (OPALS) algorithm for compositional data analysis in high dimensions”

Explore the full study: https://doi.org/10.1093/bioadv/vbaf229

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Bioinformatics Advances @bioinfoadv.bsky.social · 23h

💻 optTMT is openly available as a Shiny app and #R package with code, documentation, and executables provided here:

GitHub - mgerault/optTMT: A Shiny application to find the optimal experimental design for your proteomics TMT experiment

A Shiny application to find the optimal experimental design for your proteomics TMT experiment - mgerault/optTMT

github.com

Bioinformatics Advances @bioinfoadv.bsky.social · 23h

By modeling isotopic impurities and cross-label interference, the tool identifies experimental layouts that minimize false positives, demonstrated using large-scale iPSC proteomics datasets.

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Bioinformatics Advances @bioinfoadv.bsky.social · 23h

The paper introduces optTMT, an optimization framework and Shiny application for designing TMT multiplexed #proteomics experiments.

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Bioinformatics Advances @bioinfoadv.bsky.social · 23h

🧪 Explore the latest from Bioinformatics Advances: “optTMT: optimizing any experimental design to minimize false positives caused by TMT reporter ion interference”

Full article available: https://doi.org/10.1093/bioadv/vbaf243

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Bioinformatics Advances @bioinfoadv.bsky.social · 1d

💻 Source code and sample data for EMAF SLiMs are openly available at:

GitHub - jdchhh/EMAF_SLiMs at master

SLiMs prediction. Contribute to jdchhh/EMAF_SLiMs development by creating an account on GitHub.

github.com

Bioinformatics Advances @bioinfoadv.bsky.social · 1d

On independent test sets, it achieved an AUC of 0.8217, outperforming MEME and GLAM2, with lower false positive rates and improved precision-recall balance.

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Bioinformatics Advances @bioinfoadv.bsky.social · 1d

The proposed EMAF SLiMs method integrates semantic embeddings, physicochemical characteristics, and evolutionary information with an enhanced attention model and multi-head attention fusion.

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Bioinformatics Advances @bioinfoadv.bsky.social · 1d

🧩 Now published in Bioinformatics Advances: “SLiMs prediction method based on Enhanced Attention Mechanism and Feature fusion”

Read the full paper here: https://doi.org/10.1093/bioadv/vbaf240

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Bioinformatics Advances @bioinfoadv.bsky.social · 4d

📊 Code and data used for figure generation are openly available here:

review_benchmark/figure at main · zlsys3/review_benchmark

Benchmark testing of cloning evolutionary inference tools on simulated data - zlsys3/review_benchmark

github.com

Bioinformatics Advances @bioinfoadv.bsky.social · 4d

Benchmarking of seven methods highlights strengths and weaknesses, and a novel spatiotemporal framework is proposed linking phylogenetic branch lengths with spatial transcriptomic gradients.

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Bioinformatics Advances @bioinfoadv.bsky.social · 4d

This review assesses over 20 tools for #tumor #phylogenetic inference across cross-sectional, regional bulk, single-cell, and lineage tracing designs.

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Bioinformatics Advances @bioinfoadv.bsky.social · 4d

🧪 Just out in Bioinformatics Advances: “Computational strategies in tumor phylogenetics: Evaluating multi-modal integration and methodological trade-offs across study designs”

Explore the full study: https://doi.org/10.1093/bioadv/vbaf242

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Bioinformatics Advances @bioinfoadv.bsky.social · 5d

💻 Resources including training, validation, and test datasets, along with representative GPT-2 models, are openly available via the Netrias Hugging Face organization (https://huggingface.co/netrias).

netrias (Netrias)

harmonization, standardization, curation, ontology alignment, data generation, large language models

huggingface.co

Bioinformatics Advances @bioinfoadv.bsky.social · 5d

Using data augmentation to mimic real-world term variations, the models achieved 96% in-dictionary accuracy and substantially reduced manual standardization effort compared to heuristics and zero-shot GPT-4o.

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Bioinformatics Advances @bioinfoadv.bsky.social · 5d

The study presents fine-tuned GPT-2 models for harmonizing inconsistent metadata across domains such as cancer, alcohol research, and infectious disease.

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Bioinformatics Advances @bioinfoadv.bsky.social · 5d

🗂️ Now published in Bioinformatics Advances: “Metadata harmonization from biological datasets with language models”

Read the full paper here: https://doi.org/10.1093/bioadv/vbaf241

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Bioinformatics Advances @bioinfoadv.bsky.social · 5d

🧭 The authors emphasize emerging directions including DNA language models, integration of comparative genomics and transcriptomic data, and improved benchmarking frameworks to advance accurate and robust gene prediction across diverse eukaryotic species.

Bioinformatics Advances @bioinfoadv.bsky.social · 5d

This review synthesizes eukaryotic gene prediction methods, proposing a taxonomy by gene-model reliance (gene-model-based, gene-model-free, hybrid). It covers classical and #deeplearning approaches, extrinsic evidence sources, and identifies key strengths, limitations, and challenges.

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Bioinformatics Advances @bioinfoadv.bsky.social · 5d

📚 Explore the latest from Bioinformatics Advances: “An overview of computational methods for gene prediction in eukaryotes: Strengths, limitations, and future directions”

Full article available: https://doi.org/10.1093/bioadv/vbaf222

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Bioinformatics Advances @bioinfoadv.bsky.social · 6d

💻 Code available at

GitHub - CMUSchwartzLab/TUSV-INT

Contribute to CMUSchwartzLab/TUSV-INT development by creating an account on GitHub.

github.com

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Bioinformatics Advances @bioinfoadv.bsky.social · 6d

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.

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