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Antonis Rokas

Publications and source records attributed to Antonis Rokas.

5 recordsLinked to original sources
bioRxiv2016

Reconstructing the backbone of the Saccharomycotina yeast phylogeny using genome-scale data

Understanding the phylogenetic relationships among the yeasts of the subphylum Saccharomycotina is a prerequisite for understanding the evolution of their metabolisms and ecological lifestyles. In the last two decades, the use of rDNA and multi-locus data sets has greatly advanced our understanding of the yeast phylogeny, but many deep relationships remain unsupported. In contrast, phylogenomic analyses have involved relatively few taxa and lineages that were often selected with limited considerations for covering the breadth of yeast biodiversity. Here we used genome sequence data from 86 publicly available yeast genomes representing 9 of the 11 major lineages and 10 non-yeast fungal outgroups to generate a 1,233-gene, 96-taxon data matrix. Species phylogenies reconstructed using two different methods (concatenation and coalescence) and two data matrices (amino acids or the first two codon positions) yielded identical and highly supported relationships between the 9 major lineages. Aside from the lineage comprised by the family Pichiaceae, all other lineages were monophyletic. Most interrelationships among yeast species were robust across the two methods and data matrices. However, 8 of the 93 internodes conflicted between analyses or data sets, including the placements of: the clade defined by species that have reassigned the CUG codon to encode serine, instead of leucine; the clade defined by a whole genome duplication; and of Ascoidea rubescens. These phylogenomic analyses provide a robust roadmap for future comparative work across the yeast subphylum in the disciplines of taxonomy, molecular genetics, evolutionary biology, ecology, and biotechnology. To further this end, we have also provided a BLAST server to query the 86 Saccharomycotina genomes, which can be found at http://y1000plus.org/blast.

Evolutionary Biology↗
bioRxiv2016

A novel homology-based algorithm for the identification of physically linked clusters of paralogous genes

Highly diverse phenotypic traits are often encoded by clusters of gene paralogs that are physically linked on chromosomes. Examples include olfactory receptor gene clusters involved in the recognition of diverse odors, defensin and phospholipase gene clusters involved in snake venoms, and Hox gene clusters involved in morphological diversity. Historically, gene clusters have been identified subjectively as genomic neighborhoods containing several paralogs, however, their genomic arrangements are often highly variable with respect to gene number, intergenic distance, and synteny. For example, the prolactin gene cluster shows variation in paralogous gene number, order and intergenic distance across mammals, whereas animal Hox gene clusters are often broken into sub-clusters of different sizes. A lack of formal definition for clusters of gene paralogs does not only hamper the study of their evolutionary dynamics, but also the discovery of novel ones in the exponentially growing body of genomic data. To address this gap, we developed a novel homology-based algorithm, CGPFinder, which formalizes and automates the identification of clusters of gene paralogs (CGPs) by examining the physical distribution of individual gene members of families of paralogous genes across chromosomes. Application of CGPFinder to diverse mammalian genomes accurately identified CGPs for many well-known gene clusters in the human and mouse genomes (e.g., Hox, protocadherin, Siglec, and beta-globin gene clusters) as well as for 20 other mammalian genomes. Differences were due to the exclusion of non-homologous genes that have historically been considered parts of specific gene clusters, the inclusion or absence of one or more genes between the CGPs and their corresponding gene clusters, and the splitting of certain gene clusters into distinct CGPs. Finally, examination of human genes showing tissue-specific enhancement of their expression by CGPFinder identified members of several well-known gene clusters (e.g., cytochrome P450, aquaporins, and olfactory receptors) and revealed that they were unequally distributed across tissues. By formalizing and automating the identification of CGPs and of genes that are members of CGPs, CGPFinder will facilitate furthering our understanding of the evolutionary dynamics of genomic neighborhoods containing CGPs, their functional implications, and how they are associated with phenotypic diversity.

Genomics↗
bioRxiv2015

in silico Whole Genome Sequencer & Analyzer (iWGS): a computational pipeline to guide the design and analysis of de novo genome sequencing studies

The availability of genomes across the tree of life is highly biased toward vertebrates, pathogens, human disease models, and organisms with relatively small and simple genomes. Recent progress in genomics has enabled the de novo decoding of the genome of virtually any organism, greatly expanding its potential for understanding the biology and evolution of the full spectrum of biodiversity. The increasing diversity of sequencing technologies, assays, and de novo assembly algorithms have augmented the complexity of de novo genome sequencing projects in non-model organisms. To reduce the costs and challenges in de novo genome sequencing projects and streamline their experimental design and analysis, we developed iWGS (in silico Whole Genome Sequencer and Analyzer), an automated pipeline for guiding the choice of appropriate sequencing strategy and assembly protocols. iWGS seamlessly integrates the four key steps of a de novo genome sequencing project: data generation (through simulation), data quality control, de novo assembly, and assembly evaluation and validation. The last three steps can also be applied to the analysis of real data. iWGS is designed to enable the user to have great flexibility in testing the range of experimental designs available for genome sequencing projects, and supports all major sequencing technologies and popular assembly tools. Three case studies illustrate how iWGS can guide the design of de novo genome sequencing projects and evaluate the performance of a wide variety of user-specified sequencing strategies and assembly protocols on genomes of differing architectures. iWGS, along with a detailed documentation, is freely available at https://github.com/zhouxiaofan1983/iWGS.

Bioinformatics↗
bioRxiv2015

Computing the Internode Certainty and related measures from partial gene trees.

We present, implement, and evaluate an approach to calculate the internode certainty and tree certainty on a given reference tree from a collection of partial gene trees. Previously, the calculation of these values was only possible from a collection of gene trees with exactly the same taxon set as the reference tree. An application to sets of partial gene trees requires mathematical corrections in the internode certainty and tree certainty calculations. We implement our methods in RAxML and test them on empirical data sets. These tests imply that the inclusion of partial trees does matter. However, in order to provide meaningful measurements, any data set should also include trees containing the full species set.

Evolutionary Biology↗
bioRxiv2015

RNAseq in the mosquito maxillary palp: a little antennal RNA goes a long way

A comparative transcriptomic study of mosquito olfactory tissues recently published in BMC Genomics (Hodges et al, 2014) reported several novel findings that have broad implications for the field of insect olfaction. In this brief commentary, we outline why the conclusions of Hodges et al. are problematic under the current models of insect olfaction and then contrast their findings with those of other RNAseq based studies of mosquito olfactory tissues. We also generated a new RNAseq data set from the maxillary palp of Anopheles gambiae in an effort to replicate the novel results of Hodges et al. but were unable to reproduce their results. Instead, our new RNAseq data support the more straightforward explanation that the novel findings of Hodges et al. were a consequence of contamination by antennal RNA. In summary, we find strong evidence to suggest that the conclusions of Hodges et al were spurious, and that at least some of their RNAseq data sets were irrevocably compromised by cross-contamination between samples.

Genomics↗