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

How to gain valuable insight from scarce data with Machine Learning: a post-hoc explanation tool to identify biases in biological images classification

This study demonstrates that applying post-hoc explanation tools like SHAP to scarce biological image datasets can reveal hidden biases, such as models overfitting to individual animal characteristics rather than biological patterns, while simultaneously uncovering how to reframe tasks to extract valid biological insights from limited data.

Bolut, C., Pacary, A., Pieruccioni, L., Ousset, M., Paupert, J., Casteilla, L., Simoncini, D.2026-02-20

💻 bioinformatics

High-resolution population structure inference using genome-wide short tandem repeat variations

This study introduces a multi-modal framework incorporating a novel Directional Non-negative Matrix Factorization (dNMF) model to demonstrate that genome-wide short tandem repeat (STR) variations offer substantially finer and more biologically interpretable resolution for inferring human population structure compared to traditional single-nucleotide polymorphisms (SNPs).

Xia, F., Baudis, M., Anisimova, M.2026-02-20

💻 bioinformatics

ProteinConformers: large-scale and energetically profiled descriptions of protein conformational landscapes

ProteinConformers is a comprehensive resource that provides 2.7 million geometry-optimized protein conformations with extensive energetic and similarity annotations, addressing critical gaps in conformational coverage, energy profiling, and benchmarking standards for protein dynamics and drug discovery.

Zhou, Y., Wei, C., Sun, M., Wang, L., Song, J., Xu, F., Li, Y., Zheng, W., Zhang, Y.2026-02-20

💻 bioinformatics

Convergence of Angiotensin Signaling on Lung Pericyte and Stromal Behaviors

This study defines the cell-specific localization of angiotensin receptors in the human lung, identifying AGTR1 as a selective marker for pericytes that regulates their migration and proliferation, with its dysregulation linked to emphysema and aging, thereby offering a new framework for targeting airspace resilience in lung disorders.

Benjamin, K. J. M., Gonye, E., Sauler, M., Gidner, S., Malinina, A., Neptune, E. R.2026-02-19

💻 bioinformatics

Investigating the topological motifs of inversions in pangenome graphs

This study identifies two distinct topological motifs for inversion bubbles in pangenome graphs and develops a tool to annotate them, revealing that current state-of-the-art pipelines often misrepresent or fail to recover inversions, particularly in real human datasets.

Romain, S., Dubois, S., Legeai, F., Lemaitre, C.2026-02-19

💻 bioinformatics

Harnessing DNA Foundation Models for Cross-Species Transcription Factor Binding Site Prediction in Plant Genomes

This study demonstrates that DNA foundation models, particularly HyenaDNA, outperform specialized methods in accurately and efficiently predicting transcription factor binding sites across plant genomes using DAP-seq data from *Arabidopsis thaliana* and *Sisymbrium irio*.

Haghani, M., Dhulipalla, K. V., Li, S.2026-02-19

💻 bioinformatics

Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

This study demonstrates that the quality of both molecular and image data, particularly regarding sparsity, noise, and resolution, is a critical determinant of deep learning performance in predicting spatial transcriptomics from histology images, suggesting that improving data quality offers an essential, orthogonal strategy to model architecture optimization.

Hallinan, C., Lucas, C.-H. G., Fan, J.2026-02-19

💻 bioinformatics

The practical impact of numerical variability on structural MRI measures of Parkinson's disease

This study demonstrates that numerical variability in neuroimaging pipelines can significantly distort structural MRI findings in Parkinson's disease research, prompting the development of a practical framework to quantify and mitigate these errors to prevent false conclusions in existing literature.

Chatelain, Y. M. B., Sokołowski, A., Sharp, M., Poline, J.-B., Glatard, T.2026-02-19

💻 bioinformatics

jazzPanda: A hybrid approach to find spatial markergenes in imaging-based spatial transcriptomics data

The paper introduces jazzPanda, an R Bioconductor package that employs a hybrid binning approach to identify spatially specific marker genes in imaging-based transcriptomics data by integrating spatial coordinates with linear models, thereby outperforming existing methods in specificity and spatial correlation.

Jin, X., Putri, G. H., Cheng, J., Asselin-Labat, M.-L., Smyth, G. K., Phipson, B.2026-02-19

💻 bioinformatics

Pioneer and Altimeter: Fast Analysis of DIA Proteomics Data Optimized for Narrow Isolation Windows

The authors introduce Pioneer and Altimeter, open-source tools that enable fast, high-confidence DIA proteomics analysis by explicitly modeling narrow isolation window effects and deisotoped fragment intensities, achieving 2–6x speed improvements while maintaining rigorous false-discovery rate control.

Wamsley, N. T., Wilkerson, E. M., Major, M. B., Goldfarb, D.2026-02-19

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