bioinformatics papers | Gist.Science

Bioinformatics sits at the exciting intersection where biology meets data science, using powerful computer tools to decode the vast complexity of living systems. From mapping the human genome to tracking how viruses evolve, this field transforms raw biological information into actionable insights that drive modern medicine and research forward without requiring a supercomputer to understand the basics.

On Gist.Science, we ensure you never miss a breakthrough by processing every new preprint in this category directly from bioRxiv. Our team provides both plain-language explanations and detailed technical summaries for each paper, making cutting-edge discoveries accessible to everyone regardless of their background.

Below are the latest bioinformatics papers added from bioRxiv, ready for you to explore with clarity and depth.

💻 bioinformatics

Carafe2 enables high quality in silico spectral library generation for timsTOF data-independent acquisition proteomics

The paper introduces Carafe2, a deep learning-based tool that generates high-quality, experiment-specific in silico spectral libraries directly from native timsTOF DIA raw data by fine-tuning retention time, fragment ion intensity, and ion mobility prediction models, thereby outperforming existing DDA-trained models and enabling superior peptide detection across diverse proteomic applications.

Wen, B., Paez, J. S., Hsu, C., Canzani, D., Chang, A. T., Shulman, N., MacLean, B. X., Berg, M. D., Villen, J., Fondrie (…)2026-03-31

💻 bioinformatics

Scalable Microbiome Network Inference: Mitigating Sparsity and Computational Bottlenecks in Random Effects Models

This paper introduces Parallel-REM, a scalable Python-based pipeline that utilizes batched parallelization to overcome the computational bottlenecks of traditional Random Effects Models, achieving a 26.1x speedup in inferring microbial interaction networks from large-scale metagenomic data while maintaining high statistical concordance with existing R implementations.

Roy, D., Ghosh, T. S.2026-03-31

💻 bioinformatics

BCAR: A fast and general barcode-sequence mapper for correcting sequencing errors

The paper introduces BCAR, a fast and general-purpose barcode-sequence mapper that leverages quality scores and comprehensive evidence for error correction to generate high-accuracy maps, outperforming existing homology-based aligners in both simulated and experimental datasets.

Andrews, B., Ranganathan, R.2026-03-31

💻 bioinformatics

scTGCL: A Transformer-Based Graph Contrastive Learning Approach for Efficiently Clustering Single-Cell RNA-seq Data

The paper proposes scTGCL, a novel Transformer-based graph contrastive learning framework that integrates multi-head self-attention with robust augmentation strategies to achieve superior accuracy, interpretability, and computational efficiency in clustering high-dimensional single-cell RNA-seq data compared to existing state-of-the-art methods.

Khan, M. S. A., Kabir, M. H., Faisal, M. M.2026-03-31

💻 bioinformatics

Scalable computation of ultrabubbles in pangenomes by orienting bidirected graphs

This paper introduces a linear-time algorithm that orients bidirected pangenome graphs to efficiently identify ultrabubbles, achieving speedups of up to 25x over existing tools and enabling scalable analysis of large-scale human pangenomes.

Harviainen, J., Sena, F., Moumard, C., Politov, A., Schmidt, S., Tomescu, A. I.2026-03-31

💻 bioinformatics

GraphBG: Fast Bayesian Domain Detection via Spectral Graph Convolutions for Multi-slice and Multi-modal Spatial Transcriptomics

GraphBG is a unified, scalable Bayesian framework that leverages spectral graph convolutions to accurately detect spatial domains in large-scale, multi-slice, and multi-modal spatial transcriptomics data, significantly outperforming existing methods in speed, coherence, and biological interpretability.

Do, V. H., Tran, T. P. L., Canzar, S.2026-03-31

💻 bioinformatics

KuafuPrimer: Machine learning empowers the design of 16S amplicon sequencing primers toward minimal bias for bacterial communities

KuafuPrimer is a machine learning-based tool that designs optimized 16S rRNA primers using few-shot learning to significantly reduce amplification bias and improve taxonomic accuracy across diverse environments, longitudinal studies, and clinical diagnostics compared to traditional universal primers.

Zhang, H., Jiang, X., Yu, X., Wang, H., Lu, P., Hou, J., Guo, Q., Xiao, T., Wu, S., Yin, H., Geng, P. X., Guo, J., Jouss (…)2026-03-31

💻 bioinformatics

MetaGEAR Explorer: Rapid interactive searches and cross-cohort analyses of microbiome gene associations in disease

MetaGEAR Explorer is a freely available web platform that enables rapid, interactive, and programmatic cross-cohort analysis of over 33 million microbial gene families across 9,053 metagenomic samples to facilitate the identification of disease-associated microbial genes in inflammatory bowel disease and colorectal cancer.

Rios, E., Jin, S., Zhang, C., Neuhaus, F., He, X., Weissenberger, S., Schirmer, M.2026-03-31

💻 bioinformatics

On the rise of AI technologies in virtual screening

The paper demonstrates that the AI foundational model Boltz-2 significantly outperforms existing rescoring strategies in virtual screening, achieving twice the success rate on challenging datasets and enabling accurate, efficient ranking of millions of compounds on commodity hardware.

Cecchini, M., Sinenka, H.2026-03-30

💻 bioinformatics

A shape-constrained regression and wild bootstrap framework for reproducible drug synergy testing

The paper introduces SIR, a nonparametric framework combining 2D isotonic regression with a wild bootstrap procedure to provide statistically rigorous, reproducible drug synergy testing that outperforms existing methods in concordance, robustness, and missing data prediction.

Asiaee, A., Long, J. P., Pal, S., Pua, H. H., Coombes, K. R.2026-03-30

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