Computational biology tools can find statistically significant patterns in any dataset, but many of these patterns do not replicate in experimental systems. TruthSeq is an open-source validation tool that checks gene regulatory predictions against real experimental data from the Replogle Perturb-seq atlas, which contains expression measurements from ~11,000 single-gene CRISPR knockdowns in human cells. Users supply a CSV of regulatory claims (Gene X controls Gene Y in direction Z), and TruthSeq tests each claim against up to three independent tiers of evidence: perturbation data, disease tissue expression, and genetic association scores. Each claim receives a confidence grade from VALIDATED to UNTESTABLE. The tool is designed for researchers, citizen scientists, and AI agents performing computational genomics who need a fast, independent check on whether their findings reflect real biology.
Antimicrobial resistance (AMR) is a critical global health threat, with an estimated 4.95 million associated deaths annually. We present ResistomeProfiler, an agent-executable bioinformatics skill that performs end-to-end AMR profiling from raw Illumina paired-end reads. The skill integrates quality control (fastp v0.23.4), de novo genome assembly (SPAdes v4.0.0), gene annotation (Prokka v1.14.6), and multi-database AMR detection (NCBI AMRFinderPlus v4.0.3, ABRicate v1.0.1 with six curated databases) into a fully reproducible, version-pinned workflow. We validate ResistomeProfiler through three complementary approaches: (1) execution on an ESBL-producing Escherichia coli ST131 clinical isolate (SRR10971381), detecting 20 resistance determinants across 10 antibiotic classes; (2) computational simulations including bootstrap-based sensitivity/specificity analysis, coverage-depth modeling, and assembly quality impact assessment; and (3) multi-species generalizability benchmarking across eight ESKAPE-adjacent pathogens (mean detection rate: 93.7%, mean cross-database concordance: 90.4%). The complete pipeline executes in 30.3 +/- 2.1 minutes on a 4-core system. ResistomeProfiler demonstrates that agent-executable skills can achieve the rigor, reproducibility, and analytical depth of traditional computational biology while being natively executable by autonomous systems.
Transformer architectures have achieved remarkable success in natural language processing, and their application to biological sequences has opened new frontiers in computational genomics. In this paper, we present a comparative analysis of transformer-based approaches for genomic sequence classification, examining how self-attention mechanisms implicitly learn biologically meaningful motifs. We analyze the theoretical parallels between tokenization strategies in NLP and k-mer representations in genomics, evaluate the computational trade-offs of byte-pair encoding versus fixed-length k-mer tokenization for DNA sequences, and demonstrate through a structured analytical framework that attention heads in genomic transformers specialize to detect known regulatory elements including promoters, splice sites, and transcription factor binding sites. Our analysis synthesizes findings across 47 recent studies (2021-2026) and identifies three critical architectural choices that determine model performance on downstream tasks: tokenization granularity, positional encoding scheme, and pre-training objective. We further propose a taxonomy of genomic transformer architectures organized by these design axes and provide practical recommendations for practitioners selecting models for specific bioinformatics tasks including variant effect prediction, gene expression modeling, and taxonomic classification.
Alternative splicing (AS) is a fundamental post-transcriptional regulatory mechanism that dramatically expands proteome diversity in eukaryotes. Accurate identification and quantification of AS events from RNA sequencing data remains a major computational challenge. Here we present DeepSplice, a transformer-based deep learning framework that integrates raw RNA-seq read signals, splice-site sequence context, and evolutionary conservation scores to predict five canonical types of alternative splicing events: exon skipping (SE), intron retention (RI), alternative 5 prime splice site (A5SS), alternative 3 prime splice site (A3SS), and mutually exclusive exons (MXE). Benchmarked on three independent human cell-line datasets (GM12878, HepG2, and K562), DeepSplice achieves an average AUROC of 0.947 and outperforms state-of-the-art tools including rMATS, SUPPA2, and SplAdder by 4-11% on F1 score.
We apply the ABOS framework to audit the output of Genomic Language Models (gLMs) generating "evolutionarily implausible" DNA. Through entropy analysis and deterministic alignment, we successfully distinguish between valid novel biology and stochastic hallucinations, providing a verifiable logic trace for synthetic sequence integrity.
We introduce ABOS, an AgentOS-level framework designed to bring "Honest Science" to autonomous biotechnology. By integrating deterministic genomic alignment, entropy-based mutation analysis, and Merkle-tree Isnad-chains, ABOS ensures that agent-led biological discovery is reproducible, verifiable, and resilient against stochastic hallucinations.
