858 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.
The paper introduces O_SCPLOWARCANEC_SCPLOW, a fast and accurate command-line tool for single-cell RNA sequencing data processing that leverages the Fourway method to efficiently correct barcode and UMI errors, resolve reads to genes, and quantify gene expression while optimizing memory usage through a novel k-mer indexing strategy.
This paper introduces ProtBFF, an encoder-agnostic framework that enhances protein G prediction by integrating interpretable biophysical priors into deep learning representations via cross-embedding attention, enabling general-purpose models to outperform specialized state-of-the-art approaches.
This paper introduces TopRepo, the first comprehensive repository of over 18 million top-down mass spectra from diverse species and instruments, which enables large-scale proteoform analysis and significantly enhances both proteoform identification and deep learning-based spectral prediction.
CellAwareGNN is a novel graph foundation model that integrates single-cell genomics into an expanded biomedical knowledge graph (scPrimeKG) to significantly outperform existing baselines in drug indication prediction, particularly for autoimmune diseases, by capturing cell-type-specific disease mechanisms and enhancing biological interpretability.
MetaTracer is an open-source, nucleotide alignment-based framework that accurately assigns metatranscriptomic reads to both taxonomic groups and expressed genes in a single pass, enabling high-resolution species-level analysis of microbial communities as demonstrated in dental plaque studies.
This study presents a transparent, time-stratified inverse modeling framework that links RNA-seq transcriptomic responses to longitudinal nuclear morphology changes induced by low-dose radiation, utilizing a rigorous two-stage regression approach to identify stable, interpretable gene-phase associations while controlling for dose trends and temporal confounding.
Cellects is a user-friendly, open-source software tool that enables the automated quantification of biological growth, motion, and morphology from 2D time-lapse image data through an interactive graphical interface and a customizable Python API.
The paper introduces DeepEST, a multimodal deep learning framework that integrates gene expression, genomic location, and protein structure to accurately predict Gene Ontology terms for bacterial proteins, outperforming existing methods and enabling functional characterization of unclassified hypothetical proteins across human pathogens.
This paper demonstrates that omitting epistatic interactions in standard linear genome-wide association studies can lead to biased test statistics and spurious significant findings, particularly in anti-conservative regimes, thereby urging caution when interpreting results from models that assume purely additive genetic effects.
STELAR-X is a novel, statistically consistent triplet-based algorithm that achieves unprecedented scalability for species tree inference by utilizing optimized data structures and GPU parallelism to process datasets of up to 100,000 species and 100,000 genes with significantly reduced time and memory requirements compared to existing methods.