General Feature Format Research Articles

DivBrowse-interactive visualization and exploratory data analysis of variant call matrices.

The sequencing of whole genomes is becoming increasingly affordable. In this context, large-scale sequencing projects are generating ever larger datasets of species-specific genomic diversity. As a consequence, more and more genomic data need to be made easily accessible and analyzable to the scientific community. We present DivBrowse, a web application for interactive visualization and exploratory analysis of genomic diversity data stored in Variant Call Format (VCF) files of any size. By seamlessly combining BLAST as an entry point together with interactive data analysis features such as principal component analysis in one graphical user interface, DivBrowse provides a novel and unique set of exploratory data analysis capabilities for genomic biodiversity datasets. The capability to integrate DivBrowse into existing web applications supports interoperability between different web applications. Built-in interactive computation of principal component analysis allows users to perform ad hoc analysis of the population structure based on specific genetic elements such as genes and exons. Data interoperability is supported by the ability to export genomic diversity data in VCF and General Feature Format 3 files. DivBrowse offers a novel approach for interactive visualization and analysis of genomic diversity data and optionally also gene annotation data by including features like interactive calculation of variant frequencies and principal component analysis. The use of established standard file formats for data input supports interoperability and seamless deployment of application instances based on the data output of established bioinformatics pipelines.

VisProDom: an interactive Shiny/R application for displaying protein domains with transcriptional features

BackgroundBoth the protein domains and transcript structures influence protein functional variation. The genomic location of both protein domains and transcript structural features can be described using the genomic coordinates of their encoded sequences. However, the coordinates of protein domains and transcriptional features often differ greatly, and it is difficult to view them in combination at the genome-wide level. In this paper, we describe the development of a new tool that allows users to visualize domains and transcript features together, using either built-in or uploaded genome datasets, and export publication-ready figures. ResultsWe developed a user-friendly, independent R package and Shiny web application named “VisProDom”. VisProDom consists of a genome-wide database containing entire annotated transcripts merged with annotated protein domains from the Pfam database. The built-in dataset includes 82 files, which merge genome general feature format (GFF) annotations with rpsblast tabular outputs from protein sequence searches in the Pfam database. Multiple genomes can be simultaneously screened for protein domains or transcript names. VisProDom includes step-by-step introductions and clickable elements for ease of use.ConclusionVisProDom can display hundreds of transcripts alongside protein domains and export figures in a publication-ready format. This makes it a valuable tool for molecular evolution and comparative genomics.

GFF Utilities: GffRead and GffCompare.

GTF (Gene Transfer Format) and GFF (General Feature Format) are popular file formats used by bioinformatics programs to represent and exchange information about various genomic features, such as gene and transcript locations and structure. GffRead and GffCompare are open source programs that provide extensive and efficient solutions to manipulate files in a GTF or GFF format. While GffRead can convert, sort, filter, transform, or cluster genomic features, GffCompare can be used to compare and merge different gene annotations. Availability and implementation: GFF utilities are implemented in C++ for Linux and OS X and released as open source under an MIT license ( https://github.com/gpertea/gffread, https://github.com/gpertea/gffcompare).

GPress: a framework for querying general feature format (GFF) files and expression files in a compressed form.

Sequencing data are often summarized at different annotation levels for further analysis, generally using the general feature format (GFF) or its descendants, gene transfer format (GTF) and GFF3. Existing utilities for accessing these files, like gffutils and gffread, do not focus on reducing the storage space, significantly increasing it in some cases. We propose GPress, a framework for querying GFF files in a compressed form. GPress can also incorporate and compress expression files from both bulk and single-cell RNA-Seq experiments, supporting simultaneous queries on both the GFF and expression files. In brief, GPress applies transformations to the data which are then compressed with the general lossless compressor BSC. To support queries, GPress compresses the data in blocks and creates several index tables for fast retrieval. We tested GPress on several GFF files of different organisms, and showed that it achieves on average a 61% reduction in size with respect to gzip (the current de facto compressor for GFF files) while being able to retrieve all annotations for a given identifier or a range of coordinates in a few seconds (when run in a common laptop). In contrast, gffutils provides faster retrieval but doubles the size of the GFF files. When additionally linking an expression file, we show that GPress can reduce its size by more than 68% when compared to gzip (for both bulk and single-cell RNA-Seq experiments), while still retrieving the information within seconds. Finally, applying BSC to the data streams generated by GPress instead of to the original file shows a size reduction of more than 44% on average. GPress is freely available at https://github.com/qm2/gpress. Supplementary data are available at Bioinformatics online.

Bitacora: A comprehensive tool for the identification and annotation of gene families in genome assemblies.

Gene annotation is a critical bottleneck in genomic research, especially for the comprehensive study of very large gene families in the genomes of nonmodel organisms. Despite the recent progress in automatic methods, state-of-the-art tools used for this task often produce inaccurate annotations, such as fused, chimeric, partial or even completely absent gene models for many family copies, errors that require considerable extra efforts to be corrected. Here we present bitacora, a bioinformatics solution that integrates popular sequence similarity-based search tools and Perl scripts to facilitate both the curation of these inaccurate annotations and the identification of previously undetected gene family copies directly in genomic DNA sequences. We tested the performance of bitacora in annotating the members of two chemosensory gene families with different repertoire size in seven available genome sequences, and compared its performance with that of augustus-ppx, a tool also designed to improve automatic annotations using a sequence similarity-based approach. Despite the relatively high fragmentation of some of these drafts, bitacora was able to improve the annotation of many members of these families and detected thousands of new chemoreceptors encoded in genome sequences. The program creates general feature format (GFF) files, with both curated and newly identified gene models, and FASTA files with the predicted proteins. These outputs can be easily integrated in genomic annotation editors, greatly facilitating subsequent manual annotation and downstream evolutionary analyses.

GFF Utilities: GffRead and GffCompare

Summary: GTF (Gene Transfer Format) and GFF (General Feature Format) are popular file formats used by bioinformatics programs to represent and exchange information about various genomic features, such as gene and transcript locations and structure. GffRead and GffCompare are open source programs that provide extensive and efficient solutions to manipulate files in a GTF or GFF format. While GffRead can convert, sort, filter, transform, or cluster genomic features, GffCompare can be used to compare and merge different gene annotations. Availability and implementation: GFF utilities are implemented in C++ for Linux and OS X and released as open source under an MIT license ( https://github.com/gpertea/gffread, https://github.com/gpertea/gffcompare).

GFF Utilities: GffRead and GffCompare.

GTF (Gene Transfer Format) and GFF (General Feature Format) are popular file formats used by bioinformatics programs to represent and exchange information about various genomic features, such as gene and transcript locations and structure. GffRead and GffCompare are open source programs that provide extensive and efficient solutions to manipulate files in a GTF or GFF format. While GffRead can convert, sort, filter, transform, or cluster genomic features, GffCompare can be used to compare and merge different gene annotations. Availability and implementation: GFF utilities are implemented in C++ for Linux and OS X and released as open source under an MIT license ( https://github.com/gpertea/gffread, https://github.com/gpertea/gffcompare).

GFFview: A Web Server for Parsing and Visualizing Annotation Information of Eukaryotic Genome.

Owing to wide application of RNA sequencing (RNA-seq) technology, more and more eukaryotic genomes have been extensively annotated, such as the gene structure, alternative splicing, and noncoding loci. Annotation information of genome is prevalently stored as plain text in General Feature Format (GFF), which could be hundreds or thousands Mb in size. Therefore, it is a challenge for manipulating GFF file for biologists who have no bioinformatic skill. In this study, we provide a web server (GFFview) for parsing the annotation information of eukaryotic genome and then generating statistical description of six indices for visualization. GFFview is very useful for investigating quality and difference of the de novo assembled transcriptome in RNA-seq studies.

GenePlots: Large-Scale Gene Structures Visualization and Comparison Based on GenBank CDS Annotations

Drawing and comparing structures of homologous genes is an important way to study the gene evolution. In recent years, several programs and web tools have been proposed to draw gene structures according to genome sequences or GFF (General Feature Format) gene data. It is unfitted for them to draw a large number of gene structures due to time-consuming preparation of input data. The visualization tools for comparing the structures of hundreds or even thousands of genes are particularly lacking. We developed GenePlots, a comparative and interactive viewer for large scale gene structures based on CDS annotations in GenBank. With CDS annotations formatted like FASTA, GenePlots can draw all exon-intron structures for numerous genes in a unified scale. The most important assets of GenePlots are: (i) using GenBank CDS annotations as input data; (ii) the capability for analyzing thousands of genes simultaneously; (iii) the exhibition of intron phase; (iv) the flexibility allowing users to change the resulting diagram. GenePlots provides a simple and high efficient way to produce a large number of gene structures for large-scale genes comparison. Keywords: Exon and intron, intron phase, GenBank CDS annotation, GenePlots, gene structure visualization, software.

NextSearch: A Search Engine for Mass Spectrometry Data against a Compact Nucleotide Exon Graph.

Proteogenomics research has been using six-frame translation of the whole genome or amino acid exon graphs to overcome the limitations of reference protein sequence database; however, six-frame translation is not suitable for annotating genes that span over multiple exons, and amino acid exon graphs are not convenient to represent novel splice variants and exon skipping events between exons of incompatible reading frames. We propose a proteogenomic pipeline NextSearch (Nucleotide EXon-graph Transcriptome Search) that is based on a nucleotide exon graph. This pipeline consists of constructing a compact nucleotide exon graph that systematically incorporates novel splice variations and a search tool that identifies peptides by directly searching the nucleotide exon graph against tandem mass spectra. Because our exon graph stores nucleotide sequences, it can easily represent novel splice variations and exon skipping events between incompatible reading frame exons. Searching for peptide identification is performed against this nucleotide exon graph, without converting it into a protein sequence in FASTA format, achieving an order of magnitude reduction in the size of the sequence database storage. NextSearch outputs the proteome-genome/transcriptome mapping results in a general feature format (GFF) file, which can be visualized by public tools such as the UCSC Genome Browser.

Introgression browser: high-throughput whole-genome SNP visualization.

Breeding by introgressive hybridization is a pivotal strategy to broaden the genetic basis of crops. Usually, the desired traits are monitored in consecutive crossing generations by marker-assisted selection, but their analyses fail in chromosome regions where crossover recombinants are rare or not viable. Here, we present the Introgression Browser (iBrowser), a bioinformatics tool aimed at visualizing introgressions at nucleotide or SNP (Single Nucleotide Polymorphisms) accuracy. The software selects homozygous SNPs from Variant Call Format (VCF) information and filters out heterozygous SNPs, multi-nucleotide polymorphisms (MNPs) and insertion-deletions (InDels). For data analysis iBrowser makes use of sliding windows, but if needed it can generate any desired fragmentation pattern through General Feature Format (GFF) information. In an example of tomato (Solanum lycopersicum) accessions we visualize SNP patterns and elucidate both position and boundaries of the introgressions. We also show that our tool is capable of identifying alien DNA in a panel of the closely related S. pimpinellifolium by examining phylogenetic relationships of the introgressed segments in tomato. In a third example, we demonstrate the power of the iBrowser in a panel of 597 Arabidopsis accessions, detecting the boundaries of a SNP-free region around a polymorphic 1.17 Mbp inverted segment on the short arm of chromosome 4. The architecture and functionality of iBrowser makes the software appropriate for a broad set of analyses including SNP mining, genome structure analysis, and pedigree analysis. Its functionality, together with the capability to process large data sets and efficient visualization of sequence variation, makes iBrowser a valuable breeding tool.

CrossMap: a versatile tool for coordinate conversion between genome assemblies

Reference genome assemblies are subject to change and refinement from time to time. Generally, researchers need to convert the results that have been analyzed according to old assemblies to newer versions, or vice versa, to facilitate meta-analysis, direct comparison, data integration and visualization. Several useful conversion tools can convert genome interval files in browser extensible data or general feature format, but none have the functionality to convert files in sequence alignment map or BigWig format. This is a significant gap in computational genomics tools, as these formats are the ones most widely used for representing high-throughput sequencing data, such as RNA-seq, chromatin immunoprecipitation sequencing, DNA-seq, etc. Here we developed CrossMap, a versatile and efficient tool for converting genome coordinates between assemblies. CrossMap supports most of the commonly used file formats, including BAM, sequence alignment map, Wiggle, BigWig, browser extensible data, general feature format, gene transfer format and variant call format. CrossMap is written in Python and C. Source code and a comprehensive user's manual are freely available at: http://crossmap.sourceforge.net/.

Gff2sequence, a new user friendly tool for the generation of genomic sequences

BackgroundGeneral Feature Format (GFF) files are used to store genome features such as genes, exons, introns, primary transcripts etc. Although many software packages (i.e. ab initio gene prediction programs) can annotate features by using such a standard, a small number of tools have been developed to extract the corresponding sequence information from the original genome. However the present tools do not execute either a quality control or a customizable filter of the annotated features is available.Findingsgff2sequence is a program that extracts nucleotide/protein sequences from a genomic multifasta by using the information provided by a general feature format file. While a graphical user interface makes this software very easy to use, a C++ algorithm allows high performance together with low hardware demand. The software also allows the extraction of the genic portions such as the untranslated and the coding sequences. Moreover a highly customizable quality control pipeline can be used to deal with anomalous splicing sites, incorrect open reading frames and not canonical characters within the retrieved sequences.Conclusionsgff2sequence is a user friendly program that allows the generation of highly customizable sequence datasets by processing a general feature format file. The presence of a wide range of quality filters makes this tool also suitable for refining the ab initio gene predictions.

MetaPathways: a modular pipeline for constructing pathway/genome databases from environmental sequence information

BackgroundA central challenge to understanding the ecological and biogeochemical roles of microorganisms in natural and human engineered ecosystems is the reconstruction of metabolic interaction networks from environmental sequence information. The dominant paradigm in metabolic reconstruction is to assign functional annotations using BLAST. Functional annotations are then projected onto symbolic representations of metabolism in the form of KEGG pathways or SEED subsystems.ResultsHere we present MetaPathways, an open source pipeline for pathway inference that uses the PathoLogic algorithm to map functional annotations onto the MetaCyc collection of reactions and pathways, and construct environmental Pathway/Genome Databases (ePGDBs) compatible with the editing and navigation features of Pathway Tools. The pipeline accepts assembled or unassembled nucleotide sequences, performs quality assessment and control, predicts and annotates noncoding genes and open reading frames, and produces inputs to PathoLogic. In addition to constructing ePGDBs, MetaPathways uses MLTreeMap to build phylogenetic trees for selected taxonomic anchor and functional gene markers, converts General Feature Format (GFF) files into concatenated GenBank files for ePGDB construction based on third-party annotations, and generates useful file formats including Sequin files for direct GenBank submission and gene feature tables summarizing annotations, MLTreeMap trees, and ePGDB pathway coverage summaries for statistical comparisons.ConclusionsMetaPathways provides users with a modular annotation and analysis pipeline for predicting metabolic interaction networks from environmental sequence information using an alternative to KEGG pathways and SEED subsystems mapping. It is extensible to genomic and transcriptomic datasets from a wide range of sequencing platforms, and generates useful data products for microbial community structure and function analysis. The MetaPathways software package, installation instructions, and example data can be obtained from http://hallam.microbiology.ubc.ca/MetaPathways.

BEDTools: a flexible suite of utilities for comparing genomic features

Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner.Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets.Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtoolsContact: aaronquinlan@gmail.com; imh4y@virginia.eduSupplementary information: Supplementary data are available at Bioinformatics online.

The CGView Server: a comparative genomics tool for circular genomes

The CGView Server generates graphical maps of circular genomes that show sequence features, base composition plots, analysis results and sequence similarity plots. Sequences can be supplied in raw, FASTA, GenBank or EMBL format. Additional feature or analysis information can be submitted in the form of GFF (General Feature Format) files. The server uses BLAST to compare the primary sequence to up to three comparison genomes or sequence sets. The BLAST results and feature information are converted to a graphical map showing the entire sequence, or an expanded and more detailed view of a region of interest. Several options are included to control which types of features are displayed and how the features are drawn. The CGView Server can be used to visualize features associated with any bacterial, plasmid, chloroplast or mitochondrial genome, and can aid in the identification of conserved genome segments, instances of horizontal gene transfer, and differences in gene copy number. Because a collection of sequences can be used in place of a comparison genome, maps can also be used to visualize regions of a known genome covered by newly obtained sequence reads. The CGView Server can be accessed at http://stothard.afns.ualberta.ca/cgview_server/

Gene models from ESTs (GeneModelEST): an application on the Solanum lycopersicum genome

BackgroundThe structure annotation of a genome is based either on ab initio methodologies or on similaritiy searches versus molecules that have been already annotated. Ab initio gene predictions in a genome are based on a priori knowledge of species-specific features of genes. The training of ab initio gene finders is based on the definition of a data-set of gene models. To accomplish this task the common approach is to align species-specific full length cDNA and EST sequences along the genomic sequences in order to define exon/intron structure of mRNA coding genes.ResultsGeneModelEST is the software here proposed for defining a data-set of candidate gene models using exclusively evidence derived from cDNA/EST sequences.GeneModelEST requires the genome coordinates of the spliced-alignments of ESTs and of contigs (tentative consensus sequences) generated by an EST clustering/assembling procedure to be formatted in a General Feature Format (GFF) standard file. Moreover, the alignments of the contigs versus a protein database are required as an NCBI BLAST formatted report file.The GeneModelEST analysis aims to i) evaluate each exon as defined from contig spliced alignments onto the genome sequence; ii) classify the contigs according to quality levels in order to select candidate gene models; iii) assign to the candidate gene models preliminary functional annotations.We discuss the application of the proposed methodology to build a data-set of gene models of Solanum lycopersicum, whose genome sequencing is an ongoing effort by the International Tomato Genome Sequencing Consortium.ConclusionThe contig classification procedure used by GeneModelEST supports the detection of candidate gene models, the identification of potential alternative transcripts and it is useful to filter out ambiguous information. An automated procedure, such as the one proposed here, is fundamental to support large scale analysis in order to provide species-specific gene models, that could be useful as a training data-set for ab initio gene finders and/or as a reference gene list for a human curated annotation.

Integrated functional visualization of eukaryotic genomes

BackgroundIncreasing amounts of data from large scale whole genome analysis efforts demands convenient tools for manipulation, visualization and investigation. Whole genome plots offer an intuitive window to the analysis. We describe two applications that enable users to easily plot and explore whole genome data from their own or other researchers' experiments.ResultsSTRIPE and GFFtool (General Feature Format Tool) are softwares designed to support integration, visualization and exploration of whole genome data from eukaryotic genomes. STRIPE, in addition to providing a highly customizable and interactive data plot, provides access to numerous well-selected databases with updated information on all genes of a genome. GFFtool provides a user-friendly solution to integrating experimental data with the genomic information available in public databases. They also obviate the need for users to maintain large annotation resources, as they link to well-known resources using standard gene and protein identifiers.ConclusionThe programs provide the user with broad genomic overviews of data distribution, fast access to data of interest, and the ability to navigate speedily from one resource to another, and gain a better understanding of result of whole genome analysis experiments.

ASGS: an alternative splicing graph web service.

Alternative transcript diversity manifests itself a prime cause of complexity in higher eukaryotes. The Alternative Splicing Graph Server (ASGS) is a web service facilitating the systematic study of alternatively spliced genes of higher eukaryotes by generating splicing graphs for the compact visual representation of transcript diversity from a single gene. Taking a set of transcripts in General Feature Format as input, ASGS identifies distinct reference and variable exons, generates a transcript splicing graph, an exon summary, splicing events classification and a single line graph to facilitate experimental analysis. This freely available web service can be accessed at .

GATA: a graphic alignment tool for comparative sequence analysis

BackgroundSeveral problems exist with current methods used to align DNA sequences for comparative sequence analysis. Most dynamic programming algorithms assume that conserved sequence elements are collinear. This assumption appears valid when comparing orthologous protein coding sequences. Functional constraints on proteins provide strong selective pressure against sequence inversions, and minimize sequence duplications and feature shuffling. For non-coding sequences this collinearity assumption is often invalid. For example, enhancers contain clusters of transcription factor binding sites that change in number, orientation, and spacing during evolution yet the enhancer retains its activity. Dot plot analysis is often used to estimate non-coding sequence relatedness. Yet dot plots do not actually align sequences and thus cannot account well for base insertions or deletions. Moreover, they lack an adequate statistical framework for comparing sequence relatedness and are limited to pairwise comparisons. Lastly, dot plots and dynamic programming text outputs fail to provide an intuitive means for visualizing DNA alignments.ResultsTo address some of these issues, we created a stand alone, platform independent, graphic alignment tool for comparative sequence analysis (GATA ). GATA uses the NCBI-BLASTN program and extensive post-processing to identify all small sub-alignments above a low cut-off score. These are graphed as two shaded boxes, one for each sequence, connected by a line using the coordinate system of their parent sequence. Shading and colour are used to indicate score and orientation. A variety of options exist for querying, modifying and retrieving conserved sequence elements. Extensive gene annotation can be added to both sequences using a standardized General Feature Format (GFF) file.ConclusionsGATA uses the NCBI-BLASTN program in conjunction with post-processing to exhaustively align two DNA sequences. It provides researchers with a fine-grained alignment and visualization tool aptly suited for non-coding, 0–200 kb, pairwise, sequence analysis. It functions independent of sequence feature ordering or orientation, and readily visualizes both large and small sequence inversions, duplications, and segment shuffling. Since the alignment is visual and does not contain gaps, gene annotation can be added to both sequences to create a thoroughly descriptive picture of DNA conservation that is well suited for comparative sequence analysis.