Microbiome sequencing yields compositional data: read counts for each taxon represent relative abundances constrained to sum to a constant. Applying standard statistical methods (Pearson correlation, linear regression, t-tests on proportions) to such data produces spurious associations because an increase in one component mechanically forces decreases in others.
Alpha diversity is the most frequently reported summary statistic in gut microbiome case-control studies, yet the choice among competing indices is rarely justified and the consequences of that choice for biological conclusions are seldom examined. We reanalyzed 16S rRNA amplicon data from 14 published gut microbiome datasets spanning seven disease categories (obesity, type 2 diabetes, inflammatory bowel disease, colorectal cancer, Clostridium difficile infection, cirrhosis, and rheumatoid arthritis), computing five standard alpha diversity indices (Shannon, Simpson, Chao1, observed OTUs, and Faith's phylogenetic diversity) for each.
Whole-genome GC content (GC_total) is the standard proxy for mutational bias in bacterial comparative genomics, but it conflates the effects of mutation and selection because most of the genome consists of coding regions under functional constraint. GC content at four-fold degenerate codon sites (GC4) should better approximate neutral mutation pressure, since substitutions at these positions do not alter the encoded amino acid.
The Kozak consensus sequence surrounding the AUG start codon governs translation initiation efficiency in eukaryotes, yet whether the standard genetic code itself is arranged to minimize spurious translation initiation near legitimate start sites has not been quantitatively addressed. We introduce the False Start Proximity (FSP) score, which measures how readily single-nucleotide mutations in the four positions flanking AUG (-3, -2, -1, +4) produce codon contexts that mimic strong Kozak motifs.
Microsatellite instability (MSI) is a critical biomarker for colorectal cancer (CRC) prognosis and immunotherapy response prediction. Approximately 15% of non-metastatic and 4–5% of metastatic CRCs exhibit MSI-high (MSI-H) status, defining a molecular subtype with distinct therapeutic implications.
Microsatellite instability (MSI) is a critical biomarker for colorectal cancer (CRC) prognosis and immunotherapy response prediction. While existing computational tools rely on read-count statistics or machine learning classifiers trained on fixed feature sets, they struggle with noisy sequencing data and cross-cohort generalization.
Substitution saturation—the erosion of phylogenetic signal due to repeated mutations at the same nucleotide position—imposes a fundamental limit on the temporal depth recoverable from molecular sequence data. Despite its importance, the precise threshold at which phylogenetic information becomes unrecoverable has never been systematically determined across realistic parameter regimes.
Computational prediction of protein stability changes upon mutation (ΔΔG) underpins rational protein engineering, yet the accuracy of these predictions has not been evaluated for systematic directional bias. We benchmarked six widely used ΔΔG predictors—FoldX, Rosetta ddg_monomer, DynaMut2, MAESTRO, PoPMuSiC, and ThermoNet—on a curated ProTherm-derived test set of 2,648 single-point mutations with experimentally measured stability changes.
Single-cell RNA sequencing has become the dominant technology for characterizing cellular heterogeneity, yet the stability of computational cell-type assignments remains poorly quantified. We systematically evaluated clustering reproducibility by running the standard Seurat pipeline (PCA dimensionality reduction, UMAP embedding, Louvain community detection) across 100 random seeds on each of 10 published scRNA-seq datasets spanning 847,000 cells total.
Mutation rates are typically reported as genome-wide averages, yet individual genes within a single bacterium experience vastly different mutational pressures. We analyzed mutation accumulation experiment data spanning five bacterial species—Escherichia coli, Staphylococcus aureus, Mycobacterium tuberculosis, Pseudomonas aeruginosa, and Bacillus subtilis—encompassing 14,287 protein-coding genes and 38,412 observed de novo mutations.
Epigenetic clocks have become the dominant molecular estimators of biological age, yet systematic comparisons across clocks and tissues within the same individuals remain sparse. We applied four established epigenetic age predictors—Horvath's multi-tissue clock, Hannum's blood-based clock, PhenoAge, and GrimAge—to 500 samples spanning blood, liver, lung, and brain tissue from the Genotype-Tissue Expression (GTEx) project, where multiple tissues were available per donor.
Whole-brain multivariate pattern analysis is widely assumed to outperform region-of-interest approaches by leveraging distributed neural representations. We tested this assumption by training linear support vector machine decoders on six fMRI task datasets—including the Human Connectome Project working memory and motor tasks, the Haxby face/object paradigm, and three additional cognitive paradigms—systematically varying the number of ANOVA-selected voxels from 10 to 5,000.
Molecular docking scoring functions remain central to computational drug discovery pipelines, yet their quantitative accuracy against experimental binding affinities is rarely audited at scale. We benchmarked four widely deployed scoring functions—AutoDock Vina, Glide SP, GOLD ChemScore, and RF-Score—against 5,316 protein-ligand complexes from the PDBbind v2020 refined set, computing Pearson correlations between predicted scores and experimental -log(Ki/Kd) values.
Gene trees frequently conflict with species trees, but the magnitude, predictors, and functional distribution of this disagreement remain poorly quantified for most clades. We reconstructed a species tree from 150 fungal genomes using ASTRAL-III and compared it against individual maximum-likelihood gene trees for 2,000 single-copy orthologs identified via OrthoFinder.
Normalization is a prerequisite for meaningful differential expression analysis of RNA-seq data, yet the choice among competing methods is typically made without quantifying its downstream impact on biological conclusions. We applied five normalization approaches—TMM, DESeq2 median-of-ratios, upper quartile, FPKM, and TPM—to 20 published RNA-seq datasets spanning cancer (n=10) and immunology (n=10) studies, then ran identical DESeq2 differential expression pipelines on each normalized dataset.
The Codon Adaptation Index (CAI) remains the dominant metric for predicting gene expression from sequence data in bacterial genomics, yet its dependence on an externally supplied reference set of highly expressed genes introduces an underappreciated source of variability. We computed CAI for all protein-coding genes across 500 complete bacterial genomes using four distinct reference sets: ribosomal protein genes, RNA-seq-validated highly expressed genes, the top 5% of genes ranked by codon usage frequency, and the original Sharp and Li reference set.
The fragility index for dichotomous outcomes quantifies how many event status changes reverse a trial's statistical significance, but no analogous metric exists for time-to-event endpoints. We define the Concordance Fragility Index (CFI) as the minimum number of patient exclusions required to reverse the conclusion of a survival analysis — either flipping the hazard ratio across 1.
LATAM-RX adjusts rheumatology clinical decision support for Latin American practice realities including TB burden, insurance formulary limitations (IMSS/ISSSTE), endemic infection screening, diagnostic delays, and access fragility. Four-domain composite with GLADEL/PANLAR/COPCORD references.
FLARE-BEFORE-FLARE models preclinical flare detection using wearable-derived digital biomarkers and patient-reported outcomes. Eight-domain personal z-score deviation with weighted composite scoring and pattern classification (inflammatory, musculoskeletal, fatigue-sleep).
RHEUM-POLYSHIELD aggregates retinal toxicity, glucocorticoid-induced osteoporosis, infection risk, and QT hazard flags into a unified safety profile for rheumatology patients under chronic immunomodulation. Four-domain weighted heuristic with text alerts.