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The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking, and dependency management. To manage these processes, a diverse set of tools is required, from command line utilities to powerful ontology engineering environments such as ROBOT. Particularly in the biomedical domain, which has developed a set of highly diverse yet inter-dependent ontologies, standardising release practices and metadata, and establishing shared quality standards, are crucial to enable interoperability. The Ontology Development Kit (ODK) provides a set of standardised, customisable, and automatically executable workflows, and packages all required tooling in a single Docker image. In this paper, we provide an overview of how the ODK works, show how it is used in practice, and describe how we envision it driving standardisation efforts in our community., 19 pages, 2 supplementary tables, 1 supplementary figure

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.

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 22nd annual Bioinformatics Open Source Conference (BOSC 2021, open-bio.org/events/bosc-2021/) was held online as a track of the 2021 Intelligent Systems for Molecular Biology / European Conference on Computational Biology (ISMB/ECCB) conference. Launched in 2000 and held every year since, BOSC is the premier meeting covering topics related to open source software and open science in bioinformatics. In 2020, BOSC partnered with the Galaxy Community Conference to form the Bioinformatics Community Conference (BCC2020); that was the first BOSC to be held online. This year, BOSC returned to its roots as part of ISMB/ECCB 2021. As in 2020, the Covid-19 pandemic made it impossible to hold the conference in person, so ISMB/ECCB 2021 took place as an online meeting attended by over 2000 people from 79 countries. Nearly 200 people participated in BOSC sessions, which included 27 talks reviewed and selected from submitted abstracts, and three invited keynote talks representing a range of global perspectives on the role of open science and open source in driving research and inclusivity in the biosciences, one of which was presented in French with English subtitles.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven’t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.

The Bioinformatics Open Source Conference is a volunteer-organized meeting that covers open source software development and open science in bioinformatics. Launched in 2000, BOSC has been held every year since. BOSC 2019, the 20th annual BOSC, took place as one of the Communities of Special Interest (COSIs) at the Intelligent Systems for Molecular Biology meeting (ISMB/ECCB 2019). The two-day meeting included a total of 46 talks and 55 posters, as well as eight Birds of a Feather interest groups. The keynote speaker was University of Cape Town professor Dr. Nicola Mulder, who spoke on “Building infrastructure for responsible open science in Africa”. Immediately after BOSC 2019, about 50 people participated in the two-day CollaborationFest (CoFest for short), an open and free community-driven event at which participants work together to contribute to bioinformatics software, documentation, training materials, and use cases.

Background Ontologies are invaluable in the life sciences, but building and maintaining ontologies often requires a challenging number of distinct tasks such as running automated reasoners and quality control checks, extracting dependencies and application-specific subsets, generating standard reports, and generating release files in multiple formats. Similar to more general software development, automation is the key to executing and managing these tasks effectively and to releasing more robust products in standard forms. For ontologies using the Web Ontology Language (OWL), the OWL API Java library is the foundation for a range of software tools, including the Protégé ontology editor. In the Open Biological and Biomedical Ontologies (OBO) community, we recognized the need to package a wide range of low-level OWL API functionality into a library of common higher-level operations and to make those operations available as a command-line tool. Results ROBOT (a recursive acronym for “ROBOT is an OBO Tool”) is an open source library and command-line tool for automating ontology development tasks. The library can be called from any programming language that runs on the Java Virtual Machine (JVM). Most usage is through the command-line tool, which runs on macOS, Linux, and Windows. ROBOT provides ontology processing commands for a variety of tasks, including commands for converting formats, running a reasoner, creating import modules, running reports, and various other tasks. These commands can be combined into larger workflows using a separate task execution system such as GNU Make, and workflows can be automatically executed within continuous integration systems. Conclusions ROBOT supports automation of a wide range of ontology development tasks, focusing on OBO conventions. It packages common high-level ontology development functionality into a convenient library, and makes it easy to configure, combine, and execute individual tasks in comprehensive, automated workflows. This helps ontology developers to efficiently create, maintain, and release high-quality ontologies, so that they can spend more time focusing on development tasks. It also helps guarantee that released ontologies are free of certain types of logical errors and conform to standard quality control checks, increasing the overall robustness and efficiency of the ontology development lifecycle.

Genome annotation is the process of identifying the location and function of a genome’s encoded features. Improving the biological accuracy of annotation is a complex and iterative process requiring researchers to review and incorporate multiple sources of information such as transcriptome alignments, predictive models based on sequence profiles, and comparisons to features found in related organisms. Because rapidly decreasing costs are enabling an ever-growing number of scientists to incorporate sequencing as a routine laboratory technique, there is widespread demand for tools that can assist in the deliberative analytical review of genomic information. To this end, Apollo is an open source software package that enables researchers to efficiently inspect and refine the precise structure and role of genomic features in a graphical browser-based platform.In this paper we first outline some of Apollo’s newer user interface features, which were driven by the needs of this expanding genomics community. These include support for real-time collaboration, allowing distributed users to simultaneously edit the same encoded features while also instantly seeing the updates made by other researchers on the same region in a manner similar to Google Docs. Its technical architecture enables Apollo to be integrated into multiple existing genomic analysis pipelines and heterogeneous laboratory workflow platforms. Finally, we consider the implications that Apollo and related applications may have on how the results of genome research are published and made accessible. Source: https://github.com/GMOD/ApolloLicense (BSD-3): https://github.com/GMOD/Apollo/blob/master/LICENSE.mdDocker: https://hub.docker.com/r/gmod/apollo/tags/, https://github.com/GMOD/docker-apolloRequirements: JDK 1.8, Node v6.0+User guide: http://genomearchitect.org; technical guide: http://genomearchitect.readthedocs.io/en/latest/Mailing list: apollo@lists.lbl.gov

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.

Computational biology comprises a wide range of different technologies and approaches. In order to combine different technologies to create more powerful workflows, multiple individuals and research groups generally need to collaborate. The format of data exchanged, the biology behind that data, and the individuals contributing the data or providing tools for its interpretation need to find mutual understanding and consensus. Much conversation and joint investigation are required in order to identify and implement the best approach to using and combining the latest technologies. Traditionally, scientific conferences feature talks that present novel technologies or insights, followed up by informal discussions during coffee breaks - not necessarily involving the presenting party. In multi-institution collaborations, in order to reach agreement on implementation details or to transfer deeper insights in a technology and practical skills, a representative of one collaborating research group typically visits the other. However, this does not scale well when the number of technologies or research groups is large. Conferences have responded to this issue by introducing Birds-of-a-Feather (BoF) sessions, which offer an opportunity for individuals sharing interests, commonly with prior acquaintance on mailing lists, to intensify their interaction. However, parallel BoF sessions often make it hard for participants to join multiple BoFs and find common ground between the different technologies. Also, industrial participants in these discussions are rare, since their focus is on selling a product, not in forming collaborations that may not be immediately profitable. We here summarise our experience with Codefests, Hackathons and Sprints, which are interactive developer meetings in our field that aim to reduce the limitations of traditional scientific meetings described above, by strengthening the interaction among peers and letting the participants determine the schedule and topics covered at the meeting. These meetings are commonly run as loosely scheduled “unconferences� (self-organized identification of participants and topics for meetings) over at least two days, with early introductory talks to welcome and schedule contributors, followed by intensive collaborative coding sessions. We describe some differences in the way these meetings are organised, the audience is addressed, and in their outreach to their respective communities. The most prominent achievements of those meetings are summarised. Hackathons, Codefests and Springs share a stimulating atmosphere that encourages participants to jointly brainstorm and tackle problems of shared interest in a selfdriven proactive environment.

Navigator is a molecular database visualization system, designed to support exploratory data analysis and informal structure-activity relationship studies. In addition to the operations commonly found in chemical database systems, it provides new tools that facilitate substituent analysis and help elucidate the relationships among similar molecules and between related assays. Navigator's capabilities include two ways of displaying the relationships between analogs, mouse-sensitive charts of sets of molecules, mouse-sensitive plots of assay relationships, and access to a system for three-dimensional quantitative structure-activity relationship discovery. Navigator's mouse-based user interface provides a one-object/one-window paradigm that makes data manipulation easy even for inexperienced users. Navigator runs on Silicon Graphics workstations.

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. Apollo's 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. Contact: elee@berkeleybop.org

This paper describes a program, GenInfer, which uses belief networks to calculate risks of inheriting genetic disorders. GenInfer is based on Pearl's (J. Pearl, Artif. Intell. 29 (1986) 241-288) algorithm for fusion and propagation in probabilistic belief networks. It is written in Common Lisp. GenInfer can calculate genotypes for any family affected with any single-gene inherited disorder. Besides considering both negative and positive information in the pedigree. GenInfer takes into account additional information about the specific disorder as well as supplementary information for family members. The output consists of genotype probabilities for all family members and estimated genetic risks for prospective children of the consultands. Belief networks provide a way to calculate probabilities for systems of conditionally dependent variables. The impacts of various pieces of information are propagated and fused in such a way that, when equilibrium is reached, each proposition can be assigned a degree of belief consistent with the axioms of probability theory. In Pearl's algorithm, information is communicated through the network by messages sent between nodes. Pearl's basic algorithm cannot directly handle multiple-connected networks, which arise in the genetic counseling domain whenever a family pedigree includes consanguinity or more than one child per couple. GenInfer makes use of two cycle breaking methods, clustering and conditioning, to handle these situations.

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.

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.

An intermediate stage in the process of eukaryotic RNA splicing is the formation of a lariat structure. It is anchored at an adenosine residue in intron between 10 and 50 nucleotides upstream of the 3' splice site. A short conserved sequence (the branch point sequence) functions as the recognition signal for the site of lariat formation. It has been generally assumed that the branch point is recognized mainly by the presence of its unique sequence where the lariat is formed. However, the known branch point consensus sequence is found to be distributed nearly randomly throughout the gene sequence with only a slightly higher frequency in the expected lariat region. Further, the known consensus sequence is found to be clearly inadequate to specify branch points. These observations have implications for understanding the mechanism of branch point recognition in the process of splicing, and the possible evolution of the branch point signal.