1197 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 a novel classification method using conserved motifs and gapped k-mers to detect randomly mutated 16S rRNA sequences that mimic natural biological data, achieving over 90% accuracy to safeguard public databases from potential pollution by AI-generated or modified sequences.
The paper introduces PRIORITI, a leakage-resistant framework that leverages domain-instructed large language models to synthesize drug-agnostic biological evidence for generating calibrated, interpretable forecasts of target-indication success, outperforming existing baselines in both historical and prospective evaluations.
This paper presents PanVasc Research, an executable framework for AI-assisted evidence analysis in panvascular interventions, which evaluates a local Qwen3-4B model's ability to maintain source fidelity and semantic accuracy through controlled experiments, ultimately highlighting the need for domain-specific annotation and validation rather than claiming autonomous scientific discovery.
This study reveals that canonical antifungal dsRNA targets used in spray-induced gene silencing frequently contain perfect 21-nucleotide matches to beneficial fungi, indicating a significant off-target hazard that current regulatory screening panels fail to detect.
The paper introduces ProcessNets, a framework utilizing a novel phylogeny-like data structure called nc-tree to compactly represent and efficiently compute network properties across ensembles of similar biological networks, achieving significant space and time savings compared to traditional independent analysis methods.
vep-rs is a high-throughput Rust reimplementation of Ensembl VEP that achieves near-perfect concordance with the original tool across millions of variants while delivering 78x to 284x faster performance and correcting several documented defects in the Perl implementation.
This study demonstrates that label-free cellular morphology can effectively substitute for targeted fluorescent measurements in specific contexts, as evidenced by its ability to recover the majority of strong gene knockout hits and prioritize compounds with significant metabolic losses across diverse screens, though its sufficiency ultimately depends on the specific target, predictor, and scientific task rather than predictive correlation alone.
This paper demonstrates that targeted finetuning of foundation models like Boltz-1 can overcome training data biases to accurately predict novel ligand-induced protein conformational states and allosteric binding sites, thereby enabling more effective drug discovery applications.
This study benchmarks ten general-purpose and three aging-specific single-cell foundation models across 2.5 million transcriptomes, revealing that while their performance varies by task, they offer distinct advantages for aging research, particularly in predicting chronological age, identifying rare cellular states, and recovering regulatory interactions.
RosaSeed is a configurable short read alignment algorithm that significantly accelerates the seeding process and overall alignment throughput compared to state-of-the-art tools like BWA-MEM2, Minimap2, and ERT, while maintaining comparable or superior accuracy and offering flexible memory-performance trade-offs.