Darren Patrick Martin

RhizAeon deconvolves reticulate evolution by analyzing sequence mosaics and detecting recombination breakpoints with high accuracy and efficiency.

SRARec detects recombination in sequencing reads, revealing distinct breakpoint patterns in SARS-CoV-2 and HIV-1.

This paper introduces B-STILL, a Bayesian framework to detect functional constraints in genomes by analyzing evolutionary stasis, identifying significant clusters of invariant sites in viral and mammalian genomes.

The paper analyzes over 3000 SARS-CoV-2 sequences from chronic infections to identify mutation patterns linked to viral persistence and long Covid.

This study uses a Bayesian mixed-effects molecular clock model to analyze HBV genomic data, revealing declining evolutionary rates and linking viral spread to human migration patterns.

scoup simulates codon sequences incorporating Darwinian selection as an Ornstein-Uhlenbeck process to bridge phylogenetics and population genetics.

PRIME models physicochemical selection in protein evolution by incorporating biophysical properties, improving model fit and revealing adaptive tuning mechanisms in protein structures.

The paper shows that mutations increasing ORF9b expression are common in SARS-CoV-2 variants and PCI sequences, suggesting ORF9b's role in viral evolution and immune evasion.

The paper identifies a mutation P812S in SARS-CoV-2 associated with immune evasion and upper airway adaptation, but reduced syncytia formation.

difFUBAR provides a scalable Bayesian method to compare adaptive evolution across different branch sets in phylogenetic trees, showing computational gains and moderate statistical benefits over existing methods.

The paper develops an Oxford Nanopore Technology-based protocol for HBV sequencing to improve genomic surveillance in clinical settings, generating full-genome sequences and detailed intra-patient diversity data.

This paper identifies biosignatures specific to different COVID-19 disease phases through integrative multi-omics network analysis and finds that disease severity correlates with increased biosignature interactions across omics data.

This study analyzes SARS-CoV-2 genomic data from Ethiopia to understand its introduction, spread, and evolution during the first four waves of the pandemic.

The paper links drug response in breast cancer cells to cell surface receptor (CSR) transcript levels and reveals off-target effects through analysis of transcriptional landscapes and clinical data.

This study analyzes SARS-CoV-2 genetic diversity and lineage dynamics in Egypt, showing that lineage C.36 evolved into sub-lineages to maintain dominance despite immunity.

The paper identifies conserved recombination patterns across six coronavirus subgenera and links them to transcriptional regulatory sequences and sequence features.

RASCL rapidly assesses SARS-CoV-2 clades using molecular sequence analysis to detect selection pressures in emerging variants.

This study uses genomic data from 8746 SARS-CoV-2 genomes across 33 African countries to show that most epidemics were initiated by European importations and later driven by local transmission and mobility, leading to the spread of variants of concern.

The paper presents a global, community-maintained platform for analyzing SARS-CoV-2 genomic data, demonstrating its utility in variant detection and reanalysis of existing datasets.

The paper identifies cancer-associated mutations in MAPK pathway genes across 40,848 tumors and links them to poor outcomes and drug responses.

MESSI infers coevolution rates of base-pairing interactions in nucleic acid sequences and identifies functionally important substructures.