Asher Haug-Baltzell

Identification of dopamine receptors across the extant avian family tree and analysis with other clades uncovers a polyploid expansion among vertebrates.

Dopamine is an important central nervous system transmitter that functions through two classes of receptors (D1 and D2) to influence a diverse range of biological processes in vertebrates. With roles in regulating neural activity, behavior, and gene expression, there has been great interest in understanding the function and evolution dopamine and its receptors. In this study, we use a combination of sequence analyses, microsynteny analyses, and phylogenetic relationships to identify and characterize both the D1 (DRD1A, DRD1B, DRD1C, and DRD1E) and D2 (DRD2, DRD3, and DRD4) dopamine receptor gene families in 43 recently sequenced bird genomes representing the major ordinal lineages across the avian family tree. We show that the common ancestor of all birds possessed at least seven D1 and D2 receptors, followed by subsequent independent losses in some lineages of modern birds. Through comparisons with other vertebrate and invertebrate species we show that two of the D1 receptors, DRD1A and DRD1B, and two of the D2 receptors, DRD2 and DRD3, originated from a whole genome duplication event early in the vertebrate lineage, providing the first conclusive evidence of the origin of these highly conserved receptors. Our findings provide insight into the evolutionary development of an important modulatory component of the central nervous system in vertebrates, and will help further unravel the complex evolutionary and functional relationships among dopamine receptors.

Evolinc: A Tool for the Identification and Evolutionary Comparison of Long Intergenic Non-coding RNAs

Long intergenic non-coding RNAs (lincRNAs) are an abundant and functionally diverse class of eukaryotic transcripts. Reported lincRNA repertoires in mammals vary, but are commonly in the thousands to tens of thousands of transcripts, covering ~90% of the genome. In addition to elucidating function, there is particular interest in understanding the origin and evolution of lincRNAs. Aside from mammals, lincRNA populations have been sparsely sampled, precluding evolutionary analyses focused on their emergence and persistence. Here we present Evolinc, a two-module pipeline designed to facilitate lincRNA discovery and characterize aspects of lincRNA evolution. The first module (Evolinc-I) is a lincRNA identification workflow that also facilitates downstream differential expression analysis and genome browser visualization of identified lincRNAs. The second module (Evolinc-II) is a genomic and transcriptomic comparative analysis workflow that determines the phylogenetic depth to which a lincRNA locus is conserved within a user-defined group of related species. Here we validate lincRNA catalogs generated with Evolinc-I against previously annotated Arabidopsis and human lincRNA data. Evolinc-I recapitulated earlier findings and uncovered an additional 70 Arabidopsis and 43 human lincRNAs. We demonstrate the usefulness of Evolinc-II by examining the evolutionary histories of a public dataset of 5,361 Arabidopsis lincRNAs. We used Evolinc-II to winnow this dataset to 40 lincRNAs conserved across species in Brassicaceae. Finally, we show how Evolinc-II can be used to recover the evolutionary history of a known lincRNA, the human telomerase RNA (TERC). These latter analyses revealed unexpected duplication events as well as the loss and subsequent acquisition of a novel TERC locus in the lineage leading to mice and rats. The Evolinc pipeline is currently integrated in CyVerses Discovery Environment and is free for use by researchers.

SynMap2 and SynMap3D: web-based whole-genome synteny browsers

Summary Current synteny visualization tools either focus on small regions of sequence and do not illustrate genome-wide trends, or are complicated to use and create visualizations that are difficult to interpret. To address this challenge, The Comparative Genomics Platform (CoGe) has developed two web-based tools to visualize synteny across whole genomes. SynMap2 and SynMap3D allow researchers to explore whole genome synteny patterns (across two or three genomes, respectively) in responsive, web-based visualization and virtual reality environments. Both tools have access to the extensive CoGe genome database (containing over 30 000 genomes) as well as the option for users to upload their own data. By leveraging modern web technologies there is no installation required, making the tools widely accessible and easy to use. Availability and Implementation Both tools are open source (MIT license) and freely available for use online through CoGe ( https://genomevolution.org ). SynMap2 and SynMap3D can be accessed at http://genomevolution.org/coge/SynMap.pl and http://genomevolution.org/coge/SynMap3D.pl , respectively. Source code is available: https://github.com/LyonsLab/coge . Contact ericlyons@email.arizona.edu. Supplementary information Supplementary data are available at Bioinformatics online.

High-contrast imaging in the cloud with klipReduce and findr

Astronomical data sets are growing ever larger, and the area of high contrast imaging of exoplanets is no exception. With the advent of fast, low-noise detectors operating at 10 to 1000 Hz, huge numbers of images can be taken during a single hours-long observation. High frame rates offer several advantages, such as improved registration, frame selection, and improved speckle calibration. However, advanced image processing algorithms are computationally challenging to apply. Here we describe a parallelized, cloud-based data reduction system developed for the Magellan Adaptive Optics VisAO camera, which is capable of rapidly exploring tens of thousands of parameter sets affecting the Karhunen-Loève image processing (KLIP) algorithm to produce high-quality direct images of exoplanets. We demonstrate these capabilities with a visible wavelength high contrast data set of a hydrogen-accreting brown dwarf companion.

FractBias: a graphical tool for assessing fractionation bias following polyploidy

Summary: Following polyploidy events, genomes undergo massive reduction in gene content through a process known as fractionation. Importantly, the fractionation process is not always random, and a bias as to which homeologous chromosome retains or loses more genes can be observed in some species. The process of characterizing whole genome fractionation requires identifying syntenic regions across genomes followed by post‐processing of those syntenic datasets to identify and plot gene retention patterns. We have developed a tool, FractBias, to calculate and visualize gene retention and fractionation patterns across whole genomes. Through integration with SynMap and its parent platform CoGe, assembled genomes are pre‐loaded and available for analysis, as well as letting researchers integrate their own data with security options to keep them private or make them publicly available. Availability and Implementation: FractBias is freely available as a web application at https://genomevolution.org/CoGe/SynMap.pl. The software is open source (MIT license) and executable with Python 2.7 or iPython notebook, and available on GitHub (https://goo.gl/PaAtqy). Documentation for FractBias is available on CoGepedia (https://goo.gl/ou9dt6) Contact: ericlyons@email.arizona.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Leveraging cyverse resources for de novo comparative transcriptomics of underserved (Non-model) organisms

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (https://wiki.cyverse.org/wiki/x/dgGtAQ). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.

Previously reported placebo-response-associated variants do not predict patient outcomes in inflammatory disease Phase III trial placebo arms

In clinical trials, a placebo response refers to improvement in disease symptoms arising from the psychological effect of receiving a treatment rather than the actual treatment under investigation. Previous research has reported genomic variation associated with the likelihood of observing a placebo response, but these studies have been limited in scope and have not been validated. Here, we analyzed whole-genome sequencing data from 784 patients undergoing placebo treatment in Phase III Asthma or Rheumatoid Arthritis trials to assess the impact of previously reported variation on patient outcomes in the placebo arms and to identify novel variants associated with the placebo response. Contrary to expectations based on previous reports, we did not observe any statistically significant associations between genomic variants and placebo treatment outcome. Our findings suggest that the biological origin of the placebo response is complex and likely to be variable between disease areas.

A tutorial of diverse genome analysis tools found in the CoGe web-platform using Plasmodium spp. as a model

Abstract Integrated platforms for storage, management, analysis and sharing of large quantities of omics data have become fundamental to comparative genomics. CoGe (https://genomevolution.org/coge/) is an online platform designed to manage and study genomic data, enabling both data- and hypothesis-driven comparative genomics. CoGe’s tools and resources can be used to organize and analyse both publicly available and private genomic data from any species. Here, we demonstrate the capabilities of CoGe through three example workflows using 17 Plasmodium genomes as a model. Plasmodium genomes present unique challenges for comparative genomics due to their rapidly evolving and highly variable genomic AT/GC content. These example workflows are intended to serve as templates to help guide researchers who would like to use CoGe to examine diverse aspects of genome evolution. In the first workflow, trends in genome composition and amino acid usage are explored. In the second, changes in genome structure and the distribution of synonymous (Ks) and non-synonymous (Kn) substitution values are evaluated across species with different levels of evolutionary relatedness. In the third workflow, microsyntenic analyses of multigene families’ genomic organization are conducted using two Plasmodium-specific gene families—serine repeat antigen, and cytoadherence-linked asexual gene—as models. In general, these example workflows show how to achieve quick, reproducible and shareable results using the CoGe platform. We were able to replicate previously published results, as well as leverage CoGe’s tools and resources to gain additional insight into various aspects of Plasmodium genome evolution. Our results highlight the usefulness of the CoGe platform, particularly in understanding complex features of genome evolution. Database URL: https://genomevolution.org/coge/

EPIC-CoGe: managing and analyzing genomic data

Abstract Summary The EPIC-CoGe browser is a web-based genome visualization utility that integrates the GMOD JBrowse genome browser with the extensive CoGe genome database (currently containing over 30 000 genomes). In addition, the EPIC-CoGe browser boasts many additional features over basic JBrowse, including enhanced search capability and on-the-fly analyses for comparisons and analyses between all types of functional and diversity genomics data. There is no installation required and data (genome, annotation, functional genomic and diversity data) can be loaded by following a simple point and click wizard, or using a REST API, making the browser widely accessible and easy to use by researchers of all computational skill levels. In addition, EPIC-CoGe and data tracks are easily embedded in other websites and JBrowse instances. Availability and implementation EPIC-CoGe Browser is freely available for use online through CoGe (https://genomevolution.org). Source code (MIT open source) is available: https://github.com/LyonsLab/coge. Supplementary information Supplementary data are available at Bioinformatics online.

Evolinc: a comparative transcriptomics and genomics pipeline for quickly identifying sequence conserved lincRNAs for functional analysis.

Long intergenic non-coding RNAs (lincRNAs) are an abundant and functionally diverse class of eukaryotic transcripts. Reported lincRNA repertoires in mammals vary, but are commonly in the thousands to tens of thousands of transcripts, covering ~90% of the genome. In addition to elucidating function, there is particular interest in understanding the origin and evolution of lincRNAs. Aside from mammals, lincRNA populations have been sparsely sampled, precluding evolutionary analyses focused on lincRNA emergence and persistence. Here we present Evolinc, a two-module pipeline designed to facilitate lincRNA discovery and characterize aspects of lincRNA evolution. The first module (Evolinc-I) is a lincRNA identification workflow that also facilitates downstream differential expression analysis and genome browser visualization of identified lincRNAs. The second module (Evolinc-II) is a genomic and transcriptomic comparative analyses workflow that determines the phylogenetic depth to which a lincRNA locus is conserved within a user-defined group of related species. Evolinc-II builds families of homologous lincRNA loci, aligns constituent sequences, infers gene trees, and then uses gene tree / species tree reconciliation to reconstruct evolutionary processes such as gain, loss, or duplication of the locus. Here we demonstrate that Evolinc-I is agnostic to target organism by validating against previously annotated Arabidopsis and human lincRNA data. Using Evolinc-II, we examine ways in which conservation can rapidly be used to winnow down large lincRNA datasets to a small set of candidates for functional analysis. Finally, we show how Evolinc-II can be used to recover the evolutionary history of a known lincRNA, the human telomerase RNA (TERC). The analyses revealed unexpected duplication events as well as the loss and subsequent acquisition of a novel TERC locus in the lineage leading to mice and rats. The Evolinc pipeline is currently integrated in CyVerse’s Discovery Environment and is free to use by researchers.