Tandy J. Warnow

CAMUS estimates phylogenetic networks using a constraint tree and quartet trees, achieving high accuracy and speed for large datasets.

TIPP-SD improves species detection accuracy in microbiomes, especially for low-abundance species and with sequencing errors, while maintaining reasonable speed.

The paper introduces Min-Frame Transformation (MFT) to improve viral genome alignment by enhancing MUM-based seeding, resulting in longer and more contiguous matches with better alignment coverage and SNP recall.

This study reconstructs a time-calibrated hummingbird phylogeny, finding stepwise diversification in the Andes rather than a monophyletic Andean clade.

TIPP3 and TIPP3-fast improve metagenomic abundance profiling accuracy, especially for novel genomes and error-containing reads, with TIPP3-fast offering faster runtime while maintaining high accuracy.

This paper introduces a polynomial time quartet-based method for reconstructing unrooted level-1 phylogenetic networks from SNPs, proving its correctness and statistical consistency.

EMMA adds unaligned sequences to a constraint alignment with high accuracy and scalability for large datasets.

The paper improves the scalability of phylogenetic placement by combining SCAMPP with FastTree, achieving scalability comparable to APPLES-2 and better accuracy than other methods.

DISCO+QR roots species trees by combining gene duplication loss and incomplete lineage sorting, showing varying accuracy depending on dataset properties.

The paper introduces WITCH-NG, an improved version of WITCH that aligns datasets with sequence length heterogeneity more efficiently while maintaining accuracy.

The paper introduces QR-STAR, a method to statistically consistently root species trees under the multispecies coalescent model, improving on previous methods.

BATCH-SCAMPP improves phylogenetic placement scalability by enabling efficient batch processing of query sequences on large trees.

HMMerge improves multiple sequence alignment by merging HMMs for short sequences into a backbone alignment, achieving better accuracy than existing methods.

SALMA improves multiple sequence alignment accuracy and scalability for large datasets with high evolutionary rates and sequence length variation.

UPP2 improves alignment accuracy for large datasets with sequence length heterogeneity by reducing runtime while maintaining accuracy comparable to UPP.

This paper introduces Exact-RFS-2, a polynomial-time algorithm for optimal supertree construction under the Robinson-Foulds criterion, and GreedyRFS, a heuristic that improves accuracy over existing methods.

The paper shows that using the alignment/tree pair with the best maximum likelihood score in PASTA improves accuracy compared to the default three-iteration approach.

This study benchmarks statistical multiple sequence alignment methods, finding BAli-Phy more accurate on simulated data but less accurate on biological benchmarks.

ASTRID is a new coalescent-based method for species tree estimation that is accurate and faster than existing methods like ASTRAL-2.

This paper compares SVDquartets with ASTRAL-2, NJst, and concatenation for species tree estimation under varying ILS, finding ASTRAL-2 most accurate under high ILS, concatenation under low ILS, and SVDquartets competitive with short gene sequences.

This study evaluates quartet-based species tree methods for datasets with both incomplete lineage sorting and horizontal gene transfer, finding they are highly accurate even with high HGT rates.