Matthew J. Lane

Genome shuffling enables quantitative trait locus mapping in Bacillus subtilis

This study uses genome shuffling in Bacillus subtilis to map quantitative trait loci for complex phenotypes, identifying high-confidence QTLs of moderate size.

Climate adaptation in P. trichocarpa: key adaptive loci identified for stomata and leaf traits

This study identifies key genetic loci associated with stomatal and leaf traits in Populus trichocarpa that contribute to climate adaptation, showing smaller stomata in genotypes from arid regions.

Population and adaptation history of 739 Thlaspi arvense natural accessions

The paper analyzes 739 Thlaspi arvense accessions to uncover genetic variation and adaptation mechanisms, identifying genomic regions linked to agronomic traits and climate resilience.

RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

RWRtoolkit enables multi-omic network analysis using random walks on multiplex networks across any species, providing tools for topological analysis and statistical evaluation.

MENTOR: Multiplex Embedding of Networks for Team-Based Omics Research

MENTOR is a network-based algorithm that partitions gene sets into functions and identifies biological functions in Populus trichocarpa associated with microbial abundance using supervised and unsupervised learning.

A guidance into the fungal metabolomic abyss: Network analysis for revealing relationships between exogenous compounds and their outputs

The paper introduces network analysis methods to identify exogenous compounds that trigger specialized metabolite production in fungi and distinguish unknown analytes.

Digging deeper into GWAS signal using GRIN implicates additional genes contributing to suicidal behavior

This study relaxes GWAS significance thresholds and uses GRIN to identify additional genes linked to suicidal behavior, which replicate across cohorts and show stronger biological connections.

Synchronized genetic activities in Alzheimer's brains revealed by heterogeneity-capturing network analysis

The paper introduces Maestro, a network analysis method that identifies synchronized genetic activities in Alzheimer's disease, finding a pattern associated with AD that is significantly more common in cases than controls.