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#40 · Genomics

Scalable inference of phylogenetic networks and repetitive sequence evolution

6 published
Items per day, last 8 weekstoday: 6

The dominant thread across these items is scalable inference from phylogenetic and sequence data that conventional methods handle poorly. CAMUS estimates level-1 phylogenetic networks from rooted constraint trees and quartets, reported faster than PhyloNet-MPL and scaling to 201 species. Phlag applies an HMM over quartet statistics from gene trees along the genome to detect regions inconsistent with the species tree. Three repeat-robust k-mer estimators quantify mutation rates between related sequences and are evaluated on highly repetitive alpha satellite sequences. The seed-chain-extend analysis extends average-case guarantees to indel channels, proving expected recoverability of at least 1-O(1/m) and bounded runtime. GBSC provides linear-time clustering of protein short tandem repeats. What is new is explicit handling of repetitive and graph-structured data at scale, although the items differ in data type and evaluation, and reported performance figures come from individual papers rather than a common benchmark.

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