bioRxiv Subject Collection: Bioinformatics
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This feed contains articles for bioRxiv Subject Collection "Bioinformatics"
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Calibration of in-frame indel variant effect predictors for clinical variant classification
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https://www.biorxiv.org/content/10.64898/2026.04.15.718599v1?rss=1
2026-04-18doi:10.64898/2026.04.15.718599Cold Spring Harbor Laboratory2026-04-18
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LagCI Enables Inference of Temporal Causal Relationships from Dense Multi-Omic Time Series
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https://www.biorxiv.org/content/10.64898/2026.04.15.718654v1?rss=1
2026-04-18doi:10.64898/2026.04.15.718654Cold Spring Harbor Laboratory2026-04-18
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Unsupervised Machine Learning for Adaptive Immune Receptors with immuneML
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https://www.biorxiv.org/content/10.64898/2026.04.15.718648v1?rss=1
2026-04-18doi:10.64898/2026.04.15.718648Cold Spring Harbor Laboratory2026-04-18
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Pan-cancer survival modeling reveals structural limits of genomic feature integration in immunotherapy outcomes
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https://www.biorxiv.org/content/10.64898/2026.04.15.718634v1?rss=1
2026-04-18doi:10.64898/2026.04.15.718634Cold Spring Harbor Laboratory2026-04-18
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GANGE: Achieving Sequencing Without Sequencing With Diffusion Guided Generative Genomic Transformer
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https://www.biorxiv.org/content/10.64898/2026.04.15.718133v1?rss=1
92% accuracy consistently. This all makes it possible to drastically pull down sequencing project cost. GANGE was tested across A. thaliana, O. sativa genomes and Human chromosome 1 where it delivered outstanding assembly performance. Besides this, it was also used to accurately generate 2kb upstream promoters of all the genes from 12 different species, demonstrating that one can now also take up regulomics research just using RNA data alone when genome sequence is not available. With this all, GANGE brings a democratic turning point in the area of genomics and sequencing research.
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cellNexus: Quality control, annotation, aggregation and analytical layers for the Human Cell Atlas data
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https://www.biorxiv.org/content/10.64898/2026.04.14.718336v1?rss=1
2026-04-17doi:10.64898/2026.04.14.718336Cold Spring Harbor Laboratory2026-04-17
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Benchmarking Tools for Identification of rRNA Modifications in Escherichia coli using Oxford Nanopore Direct RNA Sequencing
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https://www.biorxiv.org/content/10.64898/2026.04.15.718756v1?rss=1
0.9 on both 16S and 23S rRNA), with DiffErr achieving the highest F1 score on 16S and JACUSA2 showing the most consistent precision-recall balance across both rRNAs. Both tools achieved full transcript-wide scoring and, along with DRUMMER, exact positional localisation. Several other tools produced no output at many rRNA positions, and restricting evaluation to reported positions inflated apparent performance. Signal-based tools showed a systematic 1-4 nucleotide 5'; offset from known modified positions, consistent with the ~5-mer nucleotide stretch present in the read head of the nanopore; applying tool-specific offset corrections substantially improved per-site recovery and reduced false positives, substantially improving the performance of tools such as EpiNano and nanoDoc. At single-site resolution, no known modified site was recovered by all tools, and several m5C, m5U, and m6A sites were missed by the majority of tools. Tool combination analysis showed that pairing error-rate-based tools with offset-corrected signal-based tools improved site recovery beyond any individual tool, with the best three-tool combination recovering 30 of the 36 known sites while maintaining low false positive rates. These results establish that discrimination metrics (e.g. AUROC) alone are insufficient to evaluate modification detection tools: output completeness, positional precision, and per-modification-type sensitivity should be reported alongside standard benchmarking metrics.
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Using machine learning to overcome mosquito collections missing data for malaria modeling
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https://www.biorxiv.org/content/10.64898/2026.04.15.718796v1?rss=1
2026-04-17doi:10.64898/2026.04.15.718796Cold Spring Harbor Laboratory2026-04-17
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Hybrid Gated Fusion: A Multimodal Deep Learning Framework for Protein Function Annotation
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https://www.biorxiv.org/content/10.64898/2026.04.14.718564v1?rss=1
2026-04-17doi:10.64898/2026.04.14.718564Cold Spring Harbor Laboratory2026-04-17
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Recursive Repeat Extender (RRE): A recursive approach to automatically extend repeat element models
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https://www.biorxiv.org/content/10.64898/2026.04.14.718546v1?rss=1
2026-04-17doi:10.64898/2026.04.14.718546Cold Spring Harbor Laboratory2026-04-17
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Virtual multiplex staining of the pancreatic islets across type 1 diabetes progression using a Schroedinger bridge
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https://www.biorxiv.org/content/10.64898/2026.04.14.718559v1?rss=1
2026-04-17doi:10.64898/2026.04.14.718559Cold Spring Harbor Laboratory2026-04-17
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PathwaySeeker: Evidence-Grounded AI Reasoning over Organism-Specific Metabolic Networks
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https://www.biorxiv.org/content/10.64898/2026.04.14.718256v1?rss=1
2026-04-17doi:10.64898/2026.04.14.718256Cold Spring Harbor Laboratory2026-04-17
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Active Learning for Budget-Constrained TCR--pMHC Wet-Lab Validation
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https://www.biorxiv.org/content/10.64898/2026.04.14.718510v1?rss=1
2026-04-17doi:10.64898/2026.04.14.718510Cold Spring Harbor Laboratory2026-04-17
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FairTCR: Equity-Aware TCR--pMHC Binding Prediction\\Across HLA Alleles and Cohort Strata
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https://www.biorxiv.org/content/10.64898/2026.04.14.718511v1?rss=1
2026-04-17doi:10.64898/2026.04.14.718511Cold Spring Harbor Laboratory2026-04-17
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Uncertainty-aware benchmarking reveals ambiguous transcripts in mRNA-lncRNA classification
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https://www.biorxiv.org/content/10.64898/2026.04.14.718168v1?rss=1
2026-04-17doi:10.64898/2026.04.14.718168Cold Spring Harbor Laboratory2026-04-17
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Agent-Guided De Novo Design of Nanobody Binders Against a Novel Cancer Target
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https://www.biorxiv.org/content/10.64898/2026.04.13.717816v1?rss=1
2026-04-17doi:10.64898/2026.04.13.717816Cold Spring Harbor Laboratory2026-04-17
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Integrating glycosylation in de novo protein design with ReGlyco Binder Design Filter
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https://www.biorxiv.org/content/10.64898/2026.04.16.718906v1?rss=1
2026-04-17doi:10.64898/2026.04.16.718906Cold Spring Harbor Laboratory2026-04-17
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Canonical self-supervised pretraining paradigm constrains the capacity of genomic language models on regulatory decoding
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https://www.biorxiv.org/content/10.64898/2026.04.13.715198v1?rss=1
2026-04-16doi:10.64898/2026.04.13.715198Cold Spring Harbor Laboratory2026-04-16
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Inferring division-associated stochasticity from time-series single-cell transcriptomes
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https://www.biorxiv.org/content/10.64898/2026.04.14.718485v1?rss=1
2026-04-16doi:10.64898/2026.04.14.718485Cold Spring Harbor Laboratory2026-04-16
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ProteomeScan: A Toolkit For Target Validation By Proteome-Wide Docking And Analysis
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https://www.biorxiv.org/content/10.64898/2026.04.14.718479v1?rss=1
2026-04-16doi:10.64898/2026.04.14.718479Cold Spring Harbor Laboratory2026-04-16
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MICRON learns outcome-associated representations of spatial immune microenvironments
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https://www.biorxiv.org/content/10.64898/2026.04.14.718488v1?rss=1
2026-04-16doi:10.64898/2026.04.14.718488Cold Spring Harbor Laboratory2026-04-16
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DIOPT: the DRSC Integrative Ortholog Prediction Tool, 2026 update
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https://www.biorxiv.org/content/10.64898/2026.04.15.718708v1?rss=1
2026-04-16doi:10.64898/2026.04.15.718708Cold Spring Harbor Laboratory2026-04-16
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MISSTE: a multiscale integrative spatial simulator for understanding the mechanisms underlying tissue ecosystems
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https://www.biorxiv.org/content/10.64898/2026.04.14.718434v1?rss=1
2026-04-16doi:10.64898/2026.04.14.718434Cold Spring Harbor Laboratory2026-04-16
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vcfilt: A Zero-Allocation Streaming Filter for High-Throughput VCF Processing
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https://www.biorxiv.org/content/10.64898/2026.04.14.718370v1?rss=1
2026-04-16doi:10.64898/2026.04.14.718370Cold Spring Harbor Laboratory2026-04-16
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Sampling antibody conformational ensembles withABodyBuilder4-STEROIDS
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https://www.biorxiv.org/content/10.64898/2026.04.14.718378v1?rss=1
2026-04-16doi:10.64898/2026.04.14.718378Cold Spring Harbor Laboratory2026-04-16
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Multiscale transcriptomic organization of the human brain with DigitalBrain
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https://www.biorxiv.org/content/10.64898/2026.04.14.718492v1?rss=1
2026-04-16doi:10.64898/2026.04.14.718492Cold Spring Harbor Laboratory2026-04-16
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scDisent: disentangled representation learning with causal structure for multi-omic single-cell analysis
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https://www.biorxiv.org/content/10.64898/2026.04.12.717909v1?rss=1
2026-04-16doi:10.64898/2026.04.12.717909Cold Spring Harbor Laboratory2026-04-16
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Three-dimensional Virtual Adult Cardiomyocyte Transcriptomics
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https://www.biorxiv.org/content/10.64898/2026.04.14.718375v1?rss=1
2026-04-16doi:10.64898/2026.04.14.718375Cold Spring Harbor Laboratory2026-04-16
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Thermoadaptation of EndoG proteins in the Xenopus frog genus
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https://www.biorxiv.org/content/10.64898/2026.04.14.718403v1?rss=1
2026-04-16doi:10.64898/2026.04.14.718403Cold Spring Harbor Laboratory2026-04-16
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Generative design of intrinsically disordered proteins based on conditioned protein language models: Data is the limit
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https://www.biorxiv.org/content/10.64898/2026.04.14.718363v1?rss=1
2026-04-16doi:10.64898/2026.04.14.718363Cold Spring Harbor Laboratory2026-04-16