Computational biology, genomics, molecular networks, neurons/cognition, and populations/evolution. ← all categories
The fitness cost of antibiotic resistance mutations is considered a key factor governing resistance dynamics, yet most estimates come from a handful of genetic backgrounds. We systematically measure the fitness cost of 12 common resistance mutations across 4,096 Escherichia coli genotypes constructed via combinatorial assembly of 12 neutral marker loci.
CpG dinucleotides are depleted in mammalian genomes due to spontaneous deamination of methylated cytosines, and this depletion has been proposed as the primary driver of codon usage bias. Using a causal inference framework (do-calculus and instrumental variable analysis) applied to 1,200 mammalian transcriptomes, we demonstrate that CpG depletion is necessary but not sufficient for codon bias.
Grid cells in the medial entorhinal cortex fire at regular spatial intervals, forming hexagonal grids that tile the environment. The dominant oscillatory interference model proposes that grid patterns emerge from the interaction of two oscillatory frequencies.
Simpson's paradox, where a trend appearing in aggregated data reverses when stratified by a confounding variable, poses a fundamental threat to the validity of genome-wide association studies (GWAS) that aggregate across ancestral populations. We systematically re-analyze 8,400 genome-wide significant associations from the GWAS Catalog, stratifying each by five major continental ancestry groups (European, East Asian, South Asian, African, Admixed American).
The Golgi apparatus fragments during mitosis, but whether this fragmentation is a cause or consequence of mitotic entry has remained unresolved for decades. Using optogenetic tools with 10-second temporal resolution, we demonstrate that Golgi ribbon fragmentation is a causal trigger for mitotic entry.
Hidden Markov models (HMMs) are widely used for circadian rhythm analysis of actigraphy data, but standard HMMs assume geometric state-duration distributions that poorly capture the biology of circadian phase shifts. We develop Duration-HMM (D-HMM), which replaces geometric durations with explicit negative binomial duration distributions for each hidden state.
Cytokinesis, the final stage of cell division, fails at a low but consequential rate in mammalian cells. We demonstrate that cytokinetic failure rate scales quadratically with cell diameter above a critical threshold of 30 micrometers.
Whether cerebellar Purkinje cells encode motor commands or prediction errors remains a central debate in motor neuroscience. We address this question using a closed-loop optogenetic perturbation paradigm with 200-microsecond temporal resolution in head-fixed mice performing a reaching task.
Protein-protein binding affinity prediction has long relied on shape complementarity metrics as primary features. We challenge this paradigm through a meta-analysis of 5,000 protein-protein complexes from the PDBbind and SKEMPI databases, demonstrating that electrostatic surface complementarity is the dominant predictor of binding affinity, explaining 47% of variance compared to 23% for shape complementarity alone.
Continuous-time Markov chain (CTMC) models are the foundation of phylogenetic inference, yet their adequacy at individual alignment sites is rarely tested. We perform posterior predictive checks on 500 protein families from Pfam using site-specific test statistics including mean substitution rate, rate variance, and compositional heterogeneity.
Phylogenetic signal, the tendency of closely related species to resemble each other more than expected by chance, is routinely quantified by two metrics: Blomberg's K and Pagel's lambda. Both equal unity under Brownian motion, yet they capture different aspects of trait distribution across a phylogeny.
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.