1207 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.
This paper introduces OpticalFlow3D, an accessible Python and MATLAB tool that applies optical flow to 3D fluorescence microscopy images to quantitatively analyze complex, amorphous biological motion without requiring object segmentation, thereby facilitating new biological insights across various scales.
This paper introduces the Trivial Tangle Traverser (TTT), an automated algorithm that resolves complex assembly graph tangles in genome sequencing by combining depth of coverage and read alignment data to generate optimized, evidence-based sequence traversals, thereby eliminating the need for labor-intensive manual curation.
This study presents the first in silico analysis of human titin's Immunoglobulin-like, fibronectin type III, and Protein kinase domains, revealing their differential stability and proposing them as potential multi-domain biomarkers for postmortem interval estimation.
To address the difficulty of interrogating DIA-NN's compressed PARQUET output without programming skills, the authors developed DIA-NN EasyFilter (DEF), a fast and user-friendly KNIME-based workflow that enables comprehensive protein filtering, quality assessment, and interactive visualization for non-coders.
NanoVI is an open-source Nextflow pipeline that utilizes Bayesian variational inference to perform accurate, uncertainty-quantified species-level taxonomic classification of full-length 16S rRNA Nanopore reads, offering faster execution and fewer false positives compared to existing EM-based tools.
PhosSight is a unified deep learning framework that enhances phosphoproteome identification and accelerates search efficiency in both Data-Dependent and Data-Independent Acquisition modes by leveraging phosphorylation-specific features, ultimately enabling the discovery of novel prognosis-associated kinase targets in precision oncology.
FAMUS is a novel, modular contrastive learning framework that improves large-scale protein functional annotation by leveraging similarity scores against entire profile databases rather than single top hits, outperforming existing tools like KofamScan and InterProScan while offering accessible conda and web-based implementations.
This paper introduces SCL2205, a high-quality, rigorously curated protein sequence dataset derived from UniProtKB that addresses data leakage and quality issues in existing benchmarks, demonstrating significant performance improvements over state-of-the-art models for protein sub-cellular localization prediction while being openly available to foster reproducible deep learning research.
This study demonstrates that phosphorylation of a tumor-derived ASXL2 epitope enhances its binding affinity to HLA class I molecules by reconfiguring non-bonded interaction networks and altering the complex's conformational dynamics, thereby providing a structural basis for targeting such post-translationally modified epitopes in cancer immunotherapy.
The paper introduces STAR Suite, an AI-assisted modernization of the STAR aligner that integrates previously external functionalities directly into its C++ source code to eliminate intermediate file bottlenecks, thereby enabling scalable, dependency-free transcriptomics processing for the NIH MorPhiC consortium.