EPIC-CoGe: managing and analyzing genomic data.

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.

1 Introduction

Genome visualization is a useful means of inspecting content within the framework of a genome such as gene models, transcriptional information, or SNP data. JBrowse is a powerful browser-based genome viewer that handles large datasets with minimal resource requirements. It supports a wide variety of input files, allowing for fast and seamless data integration. The comparative genomics platform CoGe enables the rapid association of private and public experimental datasets with its large database of genomes (>33 000) to facilitate downstream analyses (Lyons and Freeling, 2008). Recognizing the need for an enhanced genome visualization experience when dealing with large and complex datasets, the CoGe Team developed an updated implementation of JBrowse (Buels ), which we call the EPIC-CoGe browser. Here we describe the new features associated with the EPIC-CoGe browser that will increase researcher’s ability to visualize genomic information and analyze data associated with those genomes.

2 Features

While genome browsers are incredibly helpful for the dynamic viewing of a genome and its associated annotation data (e.g. genes), most web-based genome browsers center on one or a handful of genomes, and restrict the ability to import data and perform analyses within the genome browser itself. In contrast, EPIC-CoGe allows users to take full advantage of the genomic resources and analysis tools available within the CoGe platform, including adding new data and annotation tracks, and performing comparative analyses across those tracks (Supplementary Fig. S1A and B). As described below, EPIC-CoGe includes several enhancements that allow the user to quickly integrate, visualize, search, compare, analyze and export/share their genomic data.

2.1 Integration with CoGe’s genome management systems

By integrating JBrowse within CoGe, we offer users the ability to rapidly view genomes and genome associated data for all species in the CoGe database. Users can load new genomes and perform genomic/transcriptomic analyses within CoGe using the LoadGenome and LoadExp+ suite of workflows (Grover ), and then easily visualize those data within EPIC-CoGe. Comparisons and analyses of data tracks (e.g. methylation overlapping SNPs) can be performed on a mixture of public and privately generated data, all while maintaining provenance and restricting access to specific users.

2.2 Search features

EPIC-CoGe offers a number of features for searching through datasets to quickly identify regions of interest.

Searching within the track selector: Tracks can be searched for by name or by type (i.e. show BAM alignment). On the right side of the display the user can filter by text that pertains to either a track name or the type of data associated with that track (Supplementary Fig. S2A). For instance, the user can search through their experiments for BAM files, or search for a specific BAM file based on its name. Only tracks associated with the genome being browsed are found in the track selector, minimizing confusion. (i.e. Homo sapiens RNA-seq data is only associated with the Homo sapiens genome).

Searching within the main display: Users can search features by name using the ‘Find Features’ button from the navigation bar at the top of the track viewer. Depending on the track types present, users can also search tracks within the viewer. For example, SNP tracks can be searched by type or by SNPs that overlap features (Supplementary Fig. S2B).

Searching a range of values: Users can quickly identify regions within their experimental track that correspond to a particular value, or range of values. For instance, when examining a track containing expression data, it is simple to identify the transcript with the most read depth by selecting ‘Search Experiment Data’ from the drop-down menu next to the track label (Supplementary Fig. S2C). A pop-up window asks the user if they want to search by maximum value, minimum value, or transcripts that fall within a range of user-selected values. Values can also be selected on a histogram of all values. The results of this search are displayed in a new track.

Searching data and feature tracks for overlap: The user can also identify data within a track that overlaps a particular feature based on feature type or feature name. For instance, data within a track that overlaps with a gene feature such as ‘miRNA’ can be quickly identified by clicking on the drop-down arrow next to the track label and selecting ‘Find Data that Overlaps Features’ (Supplementary Fig. S2D). A new track will be generated displaying the search results.

2.3 On-the-fly analyses

The EPIC-CoGe browser allows for multiple dynamic, on-the-fly comparison of datasets.

Comparisons: Users can analyze data and feature tracks for either intersection or complement of values with any other track. Such analyses are conducted by simply dragging one experimental track on top of another while holding the command/ctrl key. A dialog window then prompts the user about which type of data to identify—unique (non-overlapping) or common (overlapping). A new track displays the results (Supplementary Fig. S3A and B).

Merging tracks: Multiple tracks can be merged into a new, single track that contains all information from both. The ability to merge tracks can help alleviate ‘vertical bloat’ often encountered with genome browsers, where numerous tracks can become unwieldy. Merged track results are given a name that describes how they were generated (Supplementary Fig. S3C).

Merging markers: Datasets can be simplified by merging close markers into a single contiguous marker. By clicking on the drop-down arrow next to the track label, an option to merge markers is given. The user can specify how close two markers should be in order to be merged (i.e. 100 bp is the default). If the user is dissatisfied with the results of a merge, the data can easily be reversed using the revert command within the same window. Simplification can help clarify results from previous searches, especially when viewing a larger genome segment (Supplementary Fig. S3D).

Links to CoGe’s tools: When a feature such as a gene is clicked on in EPIC-CoGe, a popup will display information about that feature. This includes metadata about the feature such as its name, annotations, location, length and GC content. In addition, the popup contains links to other tools in CoGe for additional analyses such as CoGeBlast, SynFind and FeatView (Supplementary Fig. S4).

2.4 Data export

To facilitate data generation and analysis, the EPIC-CoGe browser includes enhanced save and export data features. Tracks, including search results and results from in-browser analyses can always be saved within CoGe, where they can be stored in notebooks and shared directly with collaborators, all while retaining metadata and provenance. Moreover, because users frequently use their results in downstream analyses, all data tracks can be exported to either the user’s local computer or CyVerse Datastore.

2.5 Portability

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