Nomi L. Harris

Apollo: a sequence annotation editor

The well-established inaccuracy of purely computational methods for annotating genome sequences necessitates an interactive tool to allow biological experts to refine these approximations by viewing and independently evaluating the data supporting each annotation. Apollo was developed to meet this need, enabling curators to inspect genome annotations closely and edit them. FlyBase biologists successfully used Apollo to annotate the Drosophila melanogaster genome and it is increasingly being used as a starting point for the development of customized annotation editing tools for other genome projects.

Annotation of the Drosophila melanogaster euchromatic genome: a systematic review

BackgroundThe recent completion of the Drosophila melanogaster genomic sequence to high quality and the availability of a greatly expanded set of Drosophila cDNA sequences, aligning to 78% of the predicted euchromatic genes, afforded FlyBase the opportunity to significantly improve genomic annotations. We made the annotation process more rigorous by inspecting each gene visually, utilizing a comprehensive set of curation rules, requiring traceable evidence for each gene model, and comparing each predicted peptide to SWISS-PROT and TrEMBL sequences.ResultsAlthough the number of predicted protein-coding genes in Drosophila remains essentially unchanged, the revised annotation significantly improves gene models, resulting in structural changes to 85% of the transcripts and 45% of the predicted proteins. We annotated transposable elements and non-protein-coding RNAs as new features, and extended the annotation of untranslated (UTR) sequences and alternative transcripts to include more than 70% and 20% of genes, respectively. Finally, cDNA sequence provided evidence for dicistronic transcripts, neighboring genes with overlapping UTRs on the same DNA sequence strand, alternatively spliced genes that encode distinct, non-overlapping peptides, and numerous nested genes.ConclusionsIdentification of so many unusual gene models not only suggests that some mechanisms for gene regulation are more prevalent than previously believed, but also underscores the complex challenges of eukaryotic gene prediction. At present, experimental data and human curation remain essential to generate high-quality genome annotations.

Viral genome organizer: a system for analyzing complete viral genomes.

The viral genome organizer (VGO) is designed to simplify the characterization and annotation of complete viral genomes (particularly those of large poxviruses) and to help researchers discover new genes and detect gene fragmentation. VGO is based on Genotator [Harris, N.L., 1997. Genome Res. 7, 754-762], an annotation workbench designed for the analysis of eukaryotic genomic sequences. VGO automates a number of database search routines (FASTA, BLASTP, PSI-BLAST and TBLASTN), processes the results through a multiple-alignment viewer (MView; [Brown, N.P., Leroy, C., Sander, C. , 1998. Bioinformatics 14, 380-381]) and serves to manage the hundreds of DNA, protein and database search results files that must be organized when dealing with large complete poxviral genomes. It also directs the generation a self-dotplot of the genome by Dotter [Sonnhammer, E.L.L., Durbin, R., 1995. A dot-matrix program with dynamic threshold control suited for genomic DNA and protein sequence analysis. Gene 167: GC1-10. http://www.sanger.ac. uk/Software/Dotter/] to uncover repeated genes and sequences and provides Internet links to programs for generation of restriction maps and analysis of potential PCR primers. The user-friendly graphical interface displays DNA and protein sequences, links to search results, ORFs, stop-start codons, restriction sites and flags of database searches. Currently, VGO and associated programs run in an X-windows environment on commonly available UNIX machines.

Apollo: a community resource for genome annotation editing

SUMMARY Apollo is a genome annotation-editing tool with an easy to use graphical interface. It is a component of the GMOD project, with ongoing development driven by the community. Recent additions to the software include support for the generic feature format version 3 (GFF3), continuous transcriptome data, a full Chado database interface, integration with remote services for on-the-fly BLAST and Primer BLAST analyses, graphical interfaces for configuring user preferences and full undo of all edit operations. Apollos user community continues to grow, including its use as an educational tool for college and high-school students. AVAILABILITY Apollo is a Java application distributed under a free and open source license. Installers for Windows, Linux, Unix, Solaris and Mac OS X are available at http://apollo.berkeleybop.org, and the source code is available from the SourceForge CVS repository at http://gmod.cvs.sourceforge.net/gmod/apollo.

The DOE Systems Biology Knowledgebase (KBase)

The U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology — predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computing infrastructure; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.

Using Apollo to Browse and Edit Genome Annotations

An annotation is any feature that can be tied to genomic sequence, such as an exon, transcript, promoter, or transposable element. As biological knowledge increases, annotations of different types need to be added and modified, and links to other sources of information need to be incorporated, to allow biologists to easily access all of the available sequence analysis data and design appropriate experiments. The Apollo genome browser and editor offers biologists these capabilities. Apollo can display many different types of computational evidence, such as alignments and similarities based on BLAST searches (UNITS 3.3 & 3.4), and enables biologists to utilize computational evidence to create and edit gene models and other genomic features, e.g., using experimental evidence to refine exon-intron structures predicted by gene prediction algorithms. This protocol describes simple ways to browse genome annotation data, as well as techniques for editing annotations and loading data from different sources.

Annotation of the Drosophila melanogaster

BackgroundThe recent completion of the Drosophila melanogaster genomic sequence to high quality and the availability of a greatly expanded set of Drosophila cDNA sequences, aligning to 78% of the predicted euchromatic genes, afforded FlyBase the opportunity to significantly improve genomic annotations. We made the annotation process more rigorous by inspecting each gene visually, utilizing a comprehensive set of curation rules, requiring traceable evidence for each gene model, and comparing each predicted peptide to SWISS-PROT and TrEMBL sequences.ResultsAlthough the number of predicted protein-coding genes in Drosophila remains essentially unchanged, the revised annotation significantly improves gene models, resulting in structural changes to 85% of the transcripts and 45% of the predicted proteins. We annotated transposable elements and non-protein-coding RNAs as new features, and extended the annotation of untranslated (UTR) sequences and alternative transcripts to include more than 70% and 20% of genes, respectively. Finally, cDNA sequence provided evidence for dicistronic transcripts, neighboring genes with overlapping UTRs on the same DNA sequence strand, alternatively spliced genes that encode distinct, non-overlapping peptides, and numerous nested genes.ConclusionsIdentification of so many unusual gene models not only suggests that some mechanisms for gene regulation are more prevalent than previously believed, but also underscores the complex challenges of eukaryotic gene prediction. At present, experimental data and human curation remain essential to generate high-quality genome annotations.

KBase: The United States Department of Energy Systems Biology Knowledgebase

Author(s): Arkin, Adam P; Cottingham, Robert W; Henry, Christopher S; Harris, Nomi L; Stevens, Rick L; Maslov, Sergei; Dehal, Paramvir; Ware, Doreen; Perez, Fernando; Canon, Shane; Sneddon, Michael W; Henderson, Matthew L; Riehl, William J; Murphy-Olson, Dan; Chan, Stephen Y; Kamimura, Roy T; Kumari, Sunita; Drake, Meghan M; Brettin, Thomas S; Glass, Elizabeth M; Chivian, Dylan; Gunter, Dan; Weston, David J; Allen, Benjamin H; Baumohl, Jason; Best, Aaron A; Bowen, Ben; Brenner, Steven E; Bun, Christopher C; Chandonia, John-Marc; Chia, Jer-Ming; Colasanti, Ric; Conrad, Neal; Davis, James J; Davison, Brian H; DeJongh, Matthew; Devoid, Scott; Dietrich, Emily; Dubchak, Inna; Edirisinghe, Janaka N; Fang, Gang; Faria, Jose P; Frybarger, Paul M; Gerlach, Wolfgang; Gerstein, Mark; Greiner, Annette; Gurtowski, James; Haun, Holly L; He, Fei; Jain, Rashmi; Joachimiak, Marcin P; Keegan, Kevin P; Kondo, Shinnosuke; Kumar, Vivek; Land, Miriam L; Meyer, Folker; Mills, Marissa; Novichkov, Pavel S; Oh, Taeyun; Olsen, Gary J; Olson, Robert; Parrello, Bruce; Pasternak, Shiran; Pearson, Erik; Poon, Sarah S; Price, Gavin A; Ramakrishnan, Srividya; Ranjan, Priya; Ronald, Pamela C; Schatz, Michael C; Seaver, Samuel MD; Shukla, Maulik; Sutormin, Roman A; Syed, Mustafa H; Thomason, James; Tintle, Nathan L; Wang, Daifeng; Xia, Fangfang; Yoo, Hyunseung; Yoo, Shinjae; Yu, Dantong

The Bioinformatics Open Source Conference (BOSC) 2013

In July 2013, more than 100 bioinformatics researchers, developers and users of Open Source Software gathered in Berlin, Germany, to attend the 14th Annual Bioinformatics Open Source Conference (BOSC, http://www.open-bio.org/wiki/BOSC_2013, Harris et al., 2013). Since its inception in 2000, BOSC has provided bioinformatics developers with a forum for communicating the results of their latest efforts to the wider research community, and a focused environment for developers and users to interact and share ideas about standards, software development practices and practical techniques for solving bioinformatics problems. BOSC includes a 2 day ‘CodeFest’ preceding the formal conference, which provides a venue for developers to meet in person to work on or plan joint projects (Moller et al., 2013). The session topics at BOSC 2013 included Cloud and Parallel Computing, Visualization, Software Interoperability, Genome-scale Data Management, a session for updates on ongoing Open Source projects and two new sessions: Open Science and Reproducible Research and Translational Bioinformatics, recognizing the growing use of computational biology in medical applications. Slides from all of the presentations are available on the BOSC Web site, along with some of the posters and video recordings for selected talks, including the keynotes and panel discussion. Open Science is a movement dedicated to making all aspects of scientific knowledge production freely available for reuse and extension, including scientific data, methods and analyses. In response to the increasing traction that this movement has gained, BOSC 2013 featured a new session devoted explicitly to Open Science. One of the objectives of Open Science is the wider issue of making published research reproducible. Aside from openness in software licensing, this also includes openness of data and unhindered access to scientific papers themselves (Open Access). When researchers can freely access publications and the source code and data that support them, it becomes possible for them to recreate the steps that the authors went through to reach their conclusions and to then go beyond them. In this way, Open Science not only stands to provide the value of validating published results by recreating them but also to accelerate the pace of scientific discovery, by enabling researchers to more effectively build on the results of previous work, rather than having to reinvent tools and reassemble datasets. Each day of BOSC traditionally starts off with a keynote talk by a person of influence in Open Source bioinformatics. BOSC 2013’s first keynote speaker was Cameron Neylon, the Advocacy Director for the Public Library of Science, who is a prominent advocate for open science. Neylon discussed the cultural issues that are hindering open science, and spoke about the potential of openness in scientific collaborations for generating impact. Our second keynote speaker was Sean Eddy, a group leader at the Howard Hughes Medical Institutes Janelia Farm, who is perhaps best known as the author of the HMMER software suite (Eddy, 2011). He discussed how his own experience and practices show that dedicating effort to thorough engineering in tool development—which is often shunned as uninteresting and unpublishable—can be a key to creating a lasting impact. To stimulate discussion on controversial or multifaceted topics, BOSC includes a panel, in which panelists representing a range of viewpoints, answer questions from the audience. This year’s panel was on Strategies for Funding and Maintaining Open Source Software. To secure continued funding for a software project, researchers must be able to demonstrate its impact. The panelists explored ways to quantify usage of one’s software as a measure of impact. They agreed that traditional publications, and tracking their citations, still play an important role in publicizing and demonstrating the use of one’s software, but they are not the only metric. With the increasing reliance of more and more fields of biology on computational tools to manage and analyze their data, BOSC seems assured to stay relevant to life science, and thus to life scientists. Additional Information Plan to join us for BOSC: 15th Annual Bioinformatics Open Source Conference in Boston, USA, July 11–12, 2014. For more information visit http://www.open-bio.org/wiki/BOSC_2014.

Annotating Sequence Data Using Genotator

In this postgenomic era, it is no longer necessary to argue the need for automated methods for sequence annotation. Many researchers have designed tools for analyzing DNA sequences, but running multiple tools and interpreting the results can be tedious and confusing. In the last few years, many analysis workbenches have been developed to help streamline the process of sequence annotation. Genotator, developed in 1996, is still a popular choice owing to its ease of use and its configurability. This article will review annotating sequence data using the Genotator.