A guide to phylotranscriptomic analysis for phycologists

Phylotranscriptomics is the study of phylogenetic relationships among taxa based on their DNA sequences derived from transcriptomes. Because of the relatively low cost of transcriptome sequencing compared with genome sequencing and the fact that phylotranscriptomics is almost as reliable as phylogenomics, the phylotranscriptomic analysis has recently emerged as the preferred method for studying evolutionary biology. However, it is challenging to perform transcriptomic and phylogenetic analyses together without programming expertise. This study presents a protocol for phylotranscriptomic analysis to aid marine biologists unfamiliar with UNIX command-line interface and bioinformatics tools. Here, we used transcriptomes to reconstruct a molecular phylogeny of dinoflagellate protists, a diverse and globally abundant group of marine plankton organisms whose large and complex genomic sequences have impeded conventional phylogenic analysis based on genomic data. We hope that our proposed protocol may serve as practical and helpful information for the training and education of novice phycologists.

Galactic Circos: User-friendly Circos plots within the Galaxy platform

Background Circos is a popular, highly flexible software package for the circular visualization of complex datasets. While especially popular in the field of genomic analysis, Circos enables interactive graphing of any analytical data, including alternative scientific domain data and non-scientific data. This high degree of flexibility also comes with a high degree of complexity, which may present an obstacle for researchers not trained in programming or the UNIX command line. The Galaxy platform provides a user-friendly browser-based graphical interface incorporating a broad range of “wrapped” command line tools to facilitate accessibility. Findings We have developed a Galaxy wrapper for Circos, thus combining the power of Circos with the accessibility and ease of use of the Galaxy platform. The combination substantially simplifies the specification and configuration of Circos plots for end users while retaining the power to produce publication-quality visualizations of complex multidimensional datasets. Conclusions Galactic Circos enables the creation of publication-ready Circos plots using only a web browser, via the Galaxy platform. Users may download the full set of Circos configuration files of their plots for further manual customization. This version of Circos is available as an open-source installable application from the Galaxy ToolShed, with its use clarified in a training manual hosted by the Galaxy Training Network.

Phylonium: fast estimation of evolutionary distances from large samples of similar genomes

Motivation Tracking disease outbreaks by whole-genome sequencing leads to the collection of large samples of closely related sequences. Five years ago, we published a method to accurately compute all pairwise distances for such samples by indexing each sequence. Since indexing is slow, we now ask whether it is possible to achieve similar accuracy when indexing only a single sequence. Results We have implemented this idea in the program phylonium and show that it is as accurate as its predecessor and roughly 100 times faster when applied to all 2678 Escherichia coli genomes contained in ENSEMBL. One of the best published programs for rapidly computing pairwise distances, mash, analyzes the same dataset four times faster but, with default settings, it is less accurate than phylonium. Availability and implementation Phylonium runs under the UNIX command line; its C++ sources and documentation are available from github.com/evolbioinf/phylonium. Supplementary information Supplementary data are available at Bioinformatics online.

Gene Annotation Easy Viewer (GAEV): Integrating KEGG’s Gene Function Annotations and Associated Molecular Pathways

We developed a Gene Annotation Easy Viewer (GAEV) that integrates the gene annotation data from the KEGG (Kyoto Encyclopedia of Genes and Genomes) Automatic Annotation Server. GAEV generates an easy-to-read table that summarizes the query gene name, the KO (KEGG Orthology) number, name of gene orthologs, functional definition of the ortholog, and the functional pathways that query gene has been mapped to. Via links to KEGG pathway maps, users can directly examine the interaction between gene products involved in the same molecular pathway. We provide a usage example by annotating the newly published freshwater microcrustacean Daphnia pulex genome. This gene-centered view of gene function and pathways will greatly facilitate the genome annotation of non-model species and metagenomics data. GAEV runs on a Windows or Linux system equipped with Python 3 and provides easy accessibility to users with no prior Unix command line experience.

The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads

The first steps in the analysis of RNA sequencing (RNA-seq) data are usually to map the reads to a reference genome and then to count reads by gene, by exon or by exon-exon junction. These two steps are at once the most common and also typically the most expensive computational steps in an RNA-seq analysis. These steps are typically undertaken using Unix command-line or Python software tools, even when downstream analysis is to be undertaken using R.We present Rsubread, a Bioconductor software package that provides high-performance alignment and counting functions for RNA-seq reads. Rsubread provides the ease-of-use of the R programming environment, creating a matrix of read counts directly as an R object ready for downstream analysis. It has no software dependencies other than R itself. Using SEQC data and simulations, we compare Rsubread to the popular non-R tools TopHat2, STAR and HTSeq. We also compare to counting functions provided in the Bioconductor infrastructure packages. We show that Rsubread is faster, uses less memory and produces read count summaries that more accurately correlate with true values. The results show that users can adopt the R environment for alignment and quantification without suffering any loss of performance.

Gene Annotation Easy Viewer (GAEV): Integrating KEGG’s Gene Function Annotations and Associated Molecular Pathways

We developed a Gene Annotation Easy Viewer (GAEV) that integrates the gene annotation data from the KEGG (Kyoto Encyclopedia of Genes and Genomes) Automatic Annotation Server. GAEV generates an easy-to-read table that summarizes the query gene name, the KO (KEGG Orthology) number, name of gene orthologs, functional definition of the ortholog, and the functional pathways that query gene has been mapped to. Via links to KEGG pathway maps, users can directly examine the interaction between gene products involved in the same molecular pathway. We provide a usage example by annotating the newly published freshwater microcrustacean Daphnia pulex genome. This gene-centered view of gene function and pathways will greatly facilitate the genome annotation of non-model species and metagenomics data. GAEV runs on a Windows or Linux system equipped with Python 3 and provides easy accessibility to users with no prior Unix command line experience.

Gene Annotation Easy Viewer (GAEV): Integrating KEGG’s Gene Function Annotations and Associated Molecular Pathways

We developed a Gene Annotation Easy Viewer (GAEV) that integrates the gene annotation data from the KEGG (Kyoto Encyclopedia of Genes and Genomes) Automatic Annotation Server. GAEV generates an easy-to-read table that summarizes the query gene name, the KO (KEGG Orthology) number, name of gene orthologs, functional definition of the ortholog, and the functional pathways that query gene has been mapped to. Via links to KEGG pathway maps, users can directly examine the interaction between gene products involved in the same molecular pathway. We provide a usage example by annotating the newly published freshwater microcrustacean Daphnia pulex genome. This gene-centered view of gene function and pathways will greatly facilitate the genome annotation of non-model species and metagenomics data. GAEV runs on a Windows or Linux system equipped with Python 3 and provides easy accessibility to users with no prior Unix command line experience.