An 'eFP-Seq Browser' for visualizing and exploring RNA sequencing data.

Improvements in next-generation sequencing technologies have resulted in dramatically reduced sequencing costs. This has led to an explosion of '-seq'-based methods, of which RNA sequencing (RNA-seq) for generating transcriptomic data is the most popular. By analysing global patterns of gene expression in organs/tissues/cells of interest or in response to chemical or environmental perturbations, researchers can better understand an organism's biology. Tools designed to work with large RNA-seq data sets enable analyses and visualizations to help generate hypotheses about a gene's function. We present here a user-friendly RNA-seq data exploration tool, called the 'eFP-Seq Browser', that shows the read map coverage of a gene of interest in each of the samples along with 'electronic fluorescent pictographic' (eFP) images that serve as visual representations of expression levels. The tool also summarizes the details of each RNA-seq experiment, providing links to archival databases and publications. It automatically computes the reads per kilobase per million reads mapped expression-level summaries and point biserial correlation scores to sort the samples based on a gene's expression level or by how dissimilar the read map profile is from a gene splice variant, to quickly identify samples with the strongest expression level or where alternative splicing might be occurring. Links to the Integrated Genome Browser desktop visualization tool allow researchers to visualize and explore the details of RNA-seq alignments summarized in eFP-Seq Browser as coverage graphs. We present four cases of use of the eFP-Seq Browser for ABI3, SR34, SR45a and U2AF65B, where we examine expression levels and identify alternative splicing. The URL for the browser is https://bar.utoronto.ca/eFP-Seq_Browser/. OPEN RESEARCH BADGES: This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. Tool is at https://bar.utoronto.ca/eFP-Seq_Browser/; RNA-seq data at https://s3.amazonaws.com/iplant-cdn/iplant/home/araport/rnaseq_bam/ and https://s3.amazonaws.com/iplant-cdn/iplant/home/araport/rnaseq_bam/Klepikova/. Code is available at https://github.com/BioAnalyticResource/eFP-Seq-Browser.

ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology.

A big challenge in current systems biology research arises when different types of data must be accessed from separate sources and visualized using separate tools. The high cognitive load required to navigate such a workflow is detrimental to hypothesis generation. Accordingly, there is a need for a robust research platform that incorporates all data and provides integrated search, analysis, and visualization features through a single portal. Here, we present ePlant (http://bar.utoronto.ca/eplant), a visual analytic tool for exploring multiple levels of Arabidopsis thaliana data through a zoomable user interface. ePlant connects to several publicly available web services to download genome, proteome, interactome, transcriptome, and 3D molecular structure data for one or more genes or gene products of interest. Data are displayed with a set of visualization tools that are presented using a conceptual hierarchy from big to small, and many of the tools combine information from more than one data type. We describe the development of ePlant in this article and present several examples illustrating its integrative features for hypothesis generation. We also describe the process of deploying ePlant as an "app" on Araport. Building on readily available web services, the code for ePlant is freely available for any other biological species research.

Transcriptome and methylome profiling reveals relics of genome dominance in the mesopolyploid Brassica oleracea.

BACKGROUND: Brassica oleracea is a valuable vegetable species that has contributed to human health and nutrition for hundreds of years and comprises multiple distinct cultivar groups with diverse morphological and phytochemical attributes. In addition to this phenotypic wealth, B. oleracea offers unique insights into polyploid evolution, as it results from multiple ancestral polyploidy events and a final Brassiceae-specific triplication event. Further, B. oleracea represents one of the diploid genomes that formed the economically important allopolyploid oilseed, Brassica napus. A deeper understanding of B. oleracea genome architecture provides a foundation for crop improvement strategies throughout the Brassica genus. RESULTS: We generate an assembly representing 75% of the predicted B. oleracea genome using a hybrid Illumina/Roche 454 approach. Two dense genetic maps are generated to anchor almost 92% of the assembled scaffolds to nine pseudo-chromosomes. Over 50,000 genes are annotated and 40% of the genome predicted to be repetitive, thus contributing to the increased genome size of B. oleracea compared to its close relative B. rapa. A snapshot of both the leaf transcriptome and methylome allows comparisons to be made across the triplicated sub-genomes, which resulted from the most recent Brassiceae-specific polyploidy event. CONCLUSIONS: Differential expression of the triplicated syntelogs and cytosine methylation levels across the sub-genomes suggest residual marks of the genome dominance that led to the current genome architecture. Although cytosine methylation does not correlate with individual gene dominance, the independent methylation patterns of triplicated copies suggest epigenetic mechanisms play a role in the functional diversification of duplicate genes.

Defining the transcriptome assembly and its use for genome dynamics and transcriptome profiling studies in pigeonpea (Cajanus cajan L.).

This study reports generation of large-scale genomic resources for pigeonpea, a so-called 'orphan crop species' of the semi-arid tropic regions. FLX/454 sequencing carried out on a normalized cDNA pool prepared from 31 tissues produced 494 353 short transcript reads (STRs). Cluster analysis of these STRs, together with 10 817 Sanger ESTs, resulted in a pigeonpea trancriptome assembly (CcTA) comprising of 127 754 tentative unique sequences (TUSs). Functional analysis of these TUSs highlights several active pathways and processes in the sampled tissues. Comparison of the CcTA with the soybean genome showed similarity to 10 857 and 16 367 soybean gene models (depending on alignment methods). Additionally, Illumina 1G sequencing was performed on Fusarium wilt (FW)- and sterility mosaic disease (SMD)-challenged root tissues of 10 resistant and susceptible genotypes. More than 160 million sequence tags were used to identify FW- and SMD-responsive genes. Sequence analysis of CcTA and the Illumina tags identified a large new set of markers for use in genetics and breeding, including 8137 simple sequence repeats, 12 141 single-nucleotide polymorphisms and 5845 intron-spanning regions. Genomic resources developed in this study should be useful for basic and applied research, not only for pigeonpea improvement but also for other related, agronomically important legumes.

Comparative analysis between homoeologous genome segments of Brassica napus and its progenitor species reveals extensive sequence-level divergence.

Homoeologous regions of Brassica genomes were analyzed at the sequence level. These represent segments of the Brassica A genome as found in Brassica rapa and Brassica napus and the corresponding segments of the Brassica C genome as found in Brassica oleracea and B. napus. Analysis of synonymous base substitution rates within modeled genes revealed a relatively broad range of times (0.12 to 1.37 million years ago) since the divergence of orthologous genome segments as represented in B. napus and the diploid species. Similar, and consistent, ranges were also identified for single nucleotide polymorphism and insertion-deletion variation. Genes conserved across the Brassica genomes and the homoeologous segments of the genome of Arabidopsis thaliana showed almost perfect collinearity. Numerous examples of apparent transduplication of gene fragments, as previously reported in B. oleracea, were observed in B. rapa and B. napus, indicating that this phenomenon is widespread in Brassica species. In the majority of the regions studied, the C genome segments were expanded in size relative to their A genome counterparts. The considerable variation that we observed, even between the different versions of the same Brassica genome, for gene fragments and annotated putative genes suggest that the concept of the pan-genome might be particularly appropriate when considering Brassica genomes.

The TIGR Plant Transcript Assemblies database.

The TIGR Plant Transcript Assemblies (TA) database (http://plantta.tigr.org) uses expressed sequences collected from the NCBI GenBank Nucleotide database for the construction of transcript assemblies. The sequences collected include expressed sequence tags (ESTs) and full-length and partial cDNAs, but exclude computationally predicted gene sequences. The TA database includes all plant species for which more than 1000 EST or cDNA sequences are publicly available. The EST and cDNA sequences are first clustered based on an all-versus-all pairwise sequence comparison, followed by the generation of consensus sequences (TAs) from individual clusters. The clustering and assembly procedures use the TGICL tool, Megablast and the CAP3 assembler. The UniProt Reference Clusters (UniRef100) protein database is used as the reference database for the functional annotation of the assemblies. The transcription orientation of each TA is determined based on the orientation of the alignment with the best protein hit. The TA sequences and annotation are available via web interfaces and FTP downloads. Assemblies can be retrieved by a text-based keyword search or a sequence-based BLAST search. The current version of the TA database is Release 2 (July 17, 2006) and includes a total of 215 plant species.

Plant database resources at The Institute for Genomic Research.

With the completion of the genome sequences of the model plants Arabidopsis and rice, and the continuing sequencing efforts of other economically important crop plants, an unprecedented amount of genome sequence data is now available for large-scale genomics studies and analyses, such as the identification and discovery of novel genes, comparative genomics, and functional genomics. Efficient utilization of these large data sets is critically dependent on the ease of access and organization of the data. The plant databases at The Institute for Genomic Research (TIGR) have been set up to maintain various data types including genomic sequence, annotation and analyses, expressed transcript assemblies and analyses, and gene expression profiles from microarray studies. We present here an overview of the TIGR database resources for plant genomics and describe methods to access the data.

Databases and information integration for the Medicago truncatula genome and transcriptome.

An international consortium is sequencing the euchromatic genespace of Medicago truncatula. Extensive bioinformatic and database resources support the marker-anchored bacterial artificial chromosome (BAC) sequencing strategy. Existing physical and genetic maps and deep BAC-end sequencing help to guide the sequencing effort, while EST databases provide essential resources for genome annotation as well as transcriptome characterization and microarray design. Finished BAC sequences are joined into overlapping sequence assemblies and undergo an automated annotation process that integrates ab initio predictions with EST, protein, and other recognizable features. Because of the sequencing project's international and collaborative nature, data production, storage, and visualization tools are broadly distributed. This paper describes databases and Web resources for the project, which provide support for physical and genetic maps, genome sequence assembly, gene prediction, and integration of EST data. A central project Web site at medicago.org/genome provides access to genome viewers and other resources project-wide, including an Ensembl implementation at medicago.org, physical map and marker resources at mtgenome.ucdavis.edu, and genome viewers at the University of Oklahoma (www.genome.ou.edu), the Institute for Genomic Research (www.tigr.org), and Munich Information for Protein Sequences Center (mips.gsf.de).