Kevin J. Liu

Coal-Miner

Coal-Miner is a statistical association mapping method which takes the form of a methodological pipeline. The initial stages of Coal-Miner seek to detect candidate loci, or loci which contain putatively causal markers. Subsequent stages of Coal-Miner perform test for association using a linear mixed model with multiple effects which account for sample relatedness locally within candidate loci and globally across the entire genome.
Coal-Map

Coal-Map is a coalescent-based method for detecting significant statistical associations between genomic loci and a quantitative trait, where both sets of characters have complex evolutionary origins.
PhyloNet-HMM

PhyloNet-HMM enables introgression detection from whole-genome sequences with greater accuracy than ever before, through joint inference to also account for dependence across sites, substitutions, recombination, and ancestral polymorphisms.
SATé-II

SATé-II efficiently and accurately estimates large-scale trees and alignments of comparable or often substantially improved accuracy compared to previous state-of-the-art methods, including SATé.
SATé

SATé efficiently and accurately estimates large-scale trees and alignments of comparable or often substantially improved accuracy compared to previous state-of-the-art methods.
DACTAL

DACTAL enables fast and accurate phylogenetic estimation on ultra-large sets of unaligned biomolecular sequences.

Coal-Miner