1012 papers
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.
OMIO is a lightweight, open-source Python library that ensures reproducible microscopy image analysis by separating low-level file reading from a centralized, policy-driven layer that enforces canonical axis conventions and robust metadata normalization across heterogeneous file formats.
The paper proposes and validates a unique normalization method called PFlogPF, which combines additive and multiplicative proportional fitting with a logarithmic transform to effectively stabilize variance, account for sequencing depth, and preserve feature abundance monotonicity in single-cell genomics data.
The paper introduces BootDA, a non-parametric bootstrap method that simultaneously corrects for four major sources of bias in microbiome data without relying on transformations or pseudocounts, thereby achieving superior sensitivity and specificity in identifying true differential abundance and refining the microbial signature of coronary artery disease.
This study employs pseudoperplexity to demonstrate that the protein language model ProtT5 exhibits detectable but limited memorization of its training data, suggesting it primarily generalizes the statistical grammar of proteins rather than simply rote-learning sequences.
This study presents ECMME, a comprehensive web atlas analyzing per-residue selection pressures across 272 human extracellular matrix proteins using 228 mammal species, revealing pervasive purifying selection alongside distinct patterns of episodic positive selection in collagens to provide an open-access resource for investigating ECM evolutionary dynamics.
The authors present FLAG-X, a Python package that bridges the gap between manual and automated flow cytometry analysis by integrating state-of-the-art machine learning methods with expert annotations into hybrid workflows, thereby addressing the lack of standardization and efficiency in routine clinical gating.
The paper introduces cryoPARES, a supervised deep learning method that accelerates Cryo-EM image alignment and enables real-time, automated structural determination for drug discovery by leveraging prior pose information and eliminating manual intervention.
This study addresses the reproducibility challenges of colorimetric Loop-Mediated Isothermal Amplification (LAMP) by developing a machine learning classification model that utilizes thermodynamic and sequence features, particularly from F1c and B1c primers, to predict assay success and improve primer design fidelity.
This study demonstrates that intra-slide calibration technology, when combined with computational analysis, significantly improves the harmonization and reproducibility of p53 immunohistochemical assays across different anatomic pathology laboratories, thereby supporting more objective diagnostic decision-making in neuro-oncology.
DDI_single is a novel single-sequence-based algorithm that leverages the ESM-1b protein language model and a gated cross-attention module to accurately predict inter-domain residue interactions, thereby achieving superior accuracy in assembling multi-domain protein structures compared to existing methods like trRosettaX_single.