Multiobjective Formulation of Multiple Sequence Alignment for Phylogeny Inference.

Multiple sequence alignment (MSA) is a preliminary task for estimating phylogenies. It is used for homology inference among the sequences of a set of species. Generally, the MSA task is handled as a single-objective optimization process. The alignments computed under one criterion may be different from the alignments generated by other criteria, inferring discordant homologies and thus leading to different hypothesized evolutionary histories relating the sequences. The multiobjective (MO) formulation of MSA has recently been advocated by several researchers, to address this issue. An MO approach independently optimizes multiple (often conflicting) objective functions at the same time and outputs a set of competitive alignments. However, no conceptual or experimental rational from a real-world application perspective has been reported so far for any MO formulation of MSA. This article work investigates the impact of MO formulation in the context of an important scientific problem, namely, phylogeny estimation. Employing popular evolutionary MO algorithms, we show that: 1) trees inferred based on alignments produced by the existing MSA methods used in practice are substantially worse in quality than the trees inferred based on the alignment's output by an MO algorithm and 2) even high-quality alignments (according to popular measures available in the literature) may fail to achieve acceptable accuracy in generating phylogenetic trees. Thus, we essentially ask the following natural question: "can a phylogeny-aware (i.e., application-aware) metric guide in selecting appropriate MO formulations to ensure better phylogeny estimation?" Here, we report a carefully designed extensive experimental study that positively answers this question.

Species Tree Estimation from Gene Trees by Minimizing Deep Coalescence and Maximizing Quartet Consistency: A Comparative Study and the Presence of Pseudo Species Tree Terraces.

Species tree estimation from multilocus data sets is extremely challenging, especially in the presence of gene tree heterogeneity across the genome due to incomplete lineage sorting (ILS). Summary methods have been developed which estimate gene trees and then combine the gene trees to estimate a species tree by optimizing various optimization scores. In this study, we have extended and adapted the concept of phylogenetic terraces to species tree estimation by "summarizing" a set of gene trees, where multiple species trees with distinct topologies may have exactly the same optimality score (i.e., quartet score, extra lineage score, etc.). We particularly investigated the presence and impacts of equally optimal trees in species tree estimation from multilocus data using summary methods by taking ILS into account. We analyzed two of the most popular ILS-aware optimization criteria: maximize quartet consistency (MQC) and minimize deep coalescence (MDC). Methods based on MQC are provably statistically consistent, whereas MDC is not a consistent criterion for species tree estimation. We present a comprehensive comparative study of these two optimality criteria. Our experiments, on a collection of data sets simulated under ILS, indicate that MDC may result in competitive or identical quartet consistency score as MQC, but could be significantly worse than MQC in terms of tree accuracy-demonstrating the presence and impacts of equally optimal species trees. This is the first known study that provides the conditions for the data sets to have equally optimal trees in the context of phylogenomic inference using summary methods. [Gene tree; incomplete lineage sorting; phylogenomic analysis, species tree; summary method.].

The genome of the Black Bengal goat (Capra hircus).

OBJECTIVES: Black Bengal goat (Capra hircus), a member of the Bovidae family with the unique traits of high prolificacy, skin quality and low demand for food is the most socioeconomically significant goat breed in Bangladesh. Furthermore, the aptitude of adaptation and disease resistance capacity of it is highly notable which makes its whole genome information an area of research interest. DATA DESCRIPTION: The genomic DNA of a local (Chattogram, Bangladesh) healthy male Black Bengal goat (Capra hircus) was extracted and then sequenced. Sequencing was completed using the Illumina HiSeq 2500 sequencing platform and the draft assembly was generated using the "ARS1" genome as the reference. MAKER gene annotation pipeline was utilized to annotate 26,458 gene models. Genome completeness was assessed using BUSCO (Benchmarking Universal Single-Copy Orthologs) which showed 82.5% completeness of the assembled genome.