At that time, the change in how we communicate sciences had already begun [2]: social media was blooming, the information deluge was ongoing, and the fear of missing information was anchored in each of us.The Ten Simple Rules collection filled a space in scientific publications where researchers eager to share their experiences, wisdom, and doubts could quickly do it using a colloquial narrative.The topics covered were broad themes in scientific practice, such as soft skills and career development, captured in a concise and quick-to-read format, something between a blog post and a scientific article.This type of article attracted much interest from readers.In 2018, the collection reached the milestone of 1,000 Rules [3], and today this figure is above 250 papers (2,500 Rules!) [4].With the increase of technical and scientific topics, in 2013, PLOS Computational Biology tried a new experience with a similar format-we introduced "Quick Tips" (QT) articles-with the attempt to make a clear distinction between the more specific and focused scientific activities and skills presented with resources, databases, and other tools in Quick Tips versus the broader themes presented in a Ten Simple Rules article."Ten Simple Rules for Writing a PLOS Ten Simple Rules Article" [5] explains the Ten Simple Rules concept, format, and reasoning very well and is still relevant today.Inspired by that article, we wrote this Ten Simple Rules paper intending to accomplish a similar task: explaining to our community what a Quick Tips article is about and how a Quick Tips article differs from a Ten Simple Rules article.The authors are the Section Editors of the Education Collection (PP and BFFO), which encompasses the Quick Tips, and the Section Editors of the Ten Simple Rules Collection (RS and SM).In our work at PLOS CB, we are routinely deliberating the merits of a submission being a Ten Simple Rules or a Quick Tips, and for this reason, we decided to put these Ten Simple Rules about Quick Tips together.Historically, several papers were submitted as Ten Simple Rules that should have been Quick Tips.Still, we will refrain from commiserating about things not done but rather present what we hope will be a clear distinction that will make it obvious in the future why articles are best considered as Quick Tips versus Ten Simple Rules (or vice-versa).We hope and plan for this present article to be helpful for future writers willing to contribute to this collection and to help clarify the differences between Ten Simple Rules and Quick Tips.Why is this not a Quick Tips article but a Ten Simple Rules article, and why are we not as endearing as Dashnow, Lonsdale, and Bourne were in doing a "Ten Quick Tips for Writing a PLOS Quick Tips Article"?The reason is simple.

Bioinformatics.ca has been hosting continuing education programs in introductory and advanced bioinformatics topics in Canada since 1999 and has trained more than 2,000 participants to date. These workshops have been adapted over the years to keep pace with advances in both science and technology as well as the changing landscape in available learning modalities and the bioinformatics training needs of our audience. Post-workshop surveys have been a mandatory component of each workshop and are used to ensure appropriate adjustments are made to workshops to maximize learning. However, neither bioinformatics.ca nor others offering similar training programs have explored the long-term impact of bioinformatics continuing education training. Bioinformatics.ca recently initiated a look back on the impact its workshops have had on the career trajectories, research outcomes, publications, and collaborations of its participants. Using an anonymous online survey, bioinformatics.ca analyzed responses from those surveyed and discovered its workshops have had a positive impact on collaborations, research, publications, and career progression.

"Scientific community" refers to a group of people collaborating together on scientific-research-related activities who also share common goals, interests, and values. Such communities play a key role in many bioinformatics activities. Communities may be linked to a specific location or institute, or involve people working at many different institutions and locations. Education and training is typically an important component of these communities, providing a valuable context in which to develop skills and expertise, while also strengthening links and relationships within the community. Scientific communities facilitate: (i) the exchange and development of ideas and expertise; (ii) career development; (iii) coordinated funding activities; (iv) interactions and engagement with professionals from other fields; and (v) other activities beneficial to individual participants, communities, and the scientific field as a whole. It is thus beneficial at many different levels to understand the general features of successful, high-impact bioinformatics communities; how individual participants can contribute to the success of these communities; and the role of education and training within these communities. We present here a quick guide to building and maintaining a successful, high-impact bioinformatics community, along with an overview of the general benefits of participating in such communities. This article grew out of contributions made by organizers, presenters, panelists, and other participants of the ISMB/ECCB 2013 workshop "The 'How To Guide' for Establishing a Successful Bioinformatics Network" at the 21st Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and the 12th European Conference on Computational Biology (ECCB).

With the advent of YouTube channels in bioinformatics, open platforms for problem solving in bioinformatics, active web forums in computing analyses and online resources for learning to code or use a bioinformatics tool, the more traditional continuing education bioinformatics training programs have had to adapt. Bioinformatics training programs that solely rely on traditional didactic methods are being superseded by these newer resources. Yet such face-to-face instruction is still invaluable in the learning continuum. Bioinformatics.ca, which hosts the Canadian Bioinformatics Workshops, has blended more traditional learning styles with current online and social learning styles. Here we share our growing experiences over the past 12 years and look toward what the future holds for bioinformatics training programs.

The etymology of the word “education” in Wikipedia is enlightening: “a rearing” and “I lead forth” (http://en.wikipedia.org/wiki/Education#Etymology). Computational biology educators are leading and raising the next generation of scientists and, in doing so, are in need of new tools, methods, and approaches. The need for education in science, and in computational biology in particular, is greater than ever. Large datasets, -omics technologies, and overlapping domains permeate many of the big research questions of our day. PLOS Computational Biology originally created the Education section to highlight the importance of education in the field [1]. Thus, it was a great honor when Fran Lewitter, Education Editor for the past eight years, along with Philip E. Bourne and Ruth Nussinov, contacted us to work as editors of the PLOS Computational Biology Education section. In our minds, educational initiatives in computational biology and bioinformatics serve two important goals: to communicate digital biology with each other, and to educate others on how best to do this. These are themes we practice as educators in our university teaching, in our involvement with the bioinfomatics.ca workshops series, and in our outreach efforts. We are very excited to continue Fran's great vision as we continue her work with the PLOS Computational Biology staff. Examples of tutorials, specialized workshops, and outreach programs that bridge the knowledge gap created by this fast pace of research have been previously highlighted in this collection. There have been several types of articles, but two stand out. Firstly, there are tutorials about a specific biological problem requiring a specific approach, tools, and databases. For example, ”Practical Strategies for Discovering Regulatory DNA Sequence Motifs„ by MacIsaak and Fraenkel [2]. Tutorial articles provide theoretical context, as well as the type of questions and how to answer them. The other type of article we frequently find in the Education collection are “primers” or “quick guides.” For example, Eglen's “A Quick Guide to Teaching R Programming to Computational Biology Students” [3] or Bassi's “A Primer on Python for Life Science Researchers” [4]. Both of these examples from the Education collection address an important niche within the community. The “Quick Guide” series provides a more generic introduction to an approach in computational biology that can be applied across multiple domains. All of these types of articles will continue to be well-supported and encouraged in the Education collection. Many other articles have also been well-received, and seem to address gaps in the education material. We want to revisit older collection papers and identify where methods and technologies have evolved to a point where new methods are now in use, and invite previous or new authors to contribute. These initiatives help to extend computational biology beyond the domain of specialized laboratories. Researchers, at all levels, need to keep themselves up-to-date with the quickly changing world of computational biology, and trainees need programs where bioinformatics skills are embedded so they can have comprehensive training. New bioinformatics workflows can be adopted more widely if education efforts keep pace. As previously pointed out [5], starting early is also very important. There is still room for programs that capture the excitement and enthusiasm of secondary school students and convey the potential of computational biology to the public. We welcome additions to the PLOS Computational Biology “Bioinformatics: Starting Early” collection (www.ploscollections.org/cbstartingearly). We would like to involve the community in this endeavor. With this editorial, we are calling out to educators and researchers who have experience in teaching, specifically, those keen to raise the expectations and the inquisitiveness of the next generation of biologists. The Education collection will continue to publish leading edge education materials in the form of tutorials that can be used in a “classroom” setting (whatever that may mean nowadays: stated more generically, “the places where people learn”). We will continue to encourage articles set in the context of addressing a particular biological question and, as mentioned above, we welcome new “primers” and “quick guides.” We will also be inviting tutorials from the various computational meetings. A new category of papers that is in the pipeline for the Education collection is the “Quick Tips” format, the first of which was just published [6]. The “Quick Tips” articles address specific tools or databases that are in wide use in the community. We also hope, and plan, to incorporate new thinking and perspectives in the greater field of education of computational biology and bioinformatics. For example, articles that highlight the use of new tools such as those used in cloud computing or methods for using third and fourth generation sequencing technologies are encouraged. We would also like to see articles that incorporate best practices in teaching, including the use of new media, flexible online teaching tools, and the use and re-use of large well-defined data sets that are computed on in classes, courses, and programs. We encourage articles that highlight new types of training initiatives, the use of workflows to help students in the path to reproducibility in science, and open course materials (open lecture notes and open course notes and datasets for exercises) that reach more learners. In the end, the Education section belongs to the community and thus comes with responsibilities. We need to identify the gaps and the material with which we want to educate ourselves; we need to recognize and encourage great teachers and writers to communicate openly about what works best with the specific methods. We invite you to contact us via gro.solp@loibpmocsolp with your ideas for the kind of articles you would like to see in the PLOS Computational Biology Education section. We hope to see you in the classroom soon, where we learn together. About The Authors Joanne A. Fox (xofnosilaennaoj@ on Twitter) has a PhD in Genetics from the University of British Columbia (UBC). As a faculty member at the Michael Smith Laboratories and in the Department of Microbiology and Immunology at UBC, she is involved in a range of education and outreach initiatives at the undergraduate and secondary school levels, and teaches a variety of courses. She is a former instructor and current review committee member of the Canadian Bioinformatics.ca Workshops. B.F. Francis Ouellette (offb@ on Twitter) did his graduate studies in Developmental Biology and is now an Associate Professor in Cell and Systems Biology at the University of Toronto, as well as a senior scientist and Associate Director of Informatics and Biocomputing at the Ontario Institute for Cancer Research. He was one of the founders and is still the scientific director and an instructor for the Canadian Bioinformatics.ca Workshops. The authors have worked together in the past, and have known each other for more than 15 years.

BACKGROUND: MEDLINE®/PubMed® indexes over 20 million biomedical articles, providing curated annotation of its contents using a controlled vocabulary known as Medical Subject Headings (MeSH). The MeSH vocabulary, developed over 50+ years, provides a broad coverage of topics across biomedical research. Distilling the essential biomedical themes for a topic of interest from the relevant literature is important to both understand the importance of related concepts and discover new relationships. RESULTS: We introduce a novel method for determining enriched curator-assigned MeSH annotations in a set of papers associated to a topic, such as a gene, an author or a disease. We generate MeSH Over-representation Profiles (MeSHOPs) to quantitatively summarize the annotations in a form convenient for further computational analysis and visualization. Based on a hypergeometric distribution of assigned terms, MeSHOPs statistically account for the prevalence of the associated biomedical annotation while highlighting unusually prevalent terms based on a specified background. MeSHOPs can be visualized using word clouds, providing a succinct quantitative graphical representation of the relative importance of terms. Using the publication dates of articles, MeSHOPs track changing patterns of annotation over time. Since MeSHOPs are quantitative vectors, MeSHOPs can be compared using standard techniques such as hierarchical clustering. The reliability of MeSHOP annotations is assessed based on the capacity to re-derive the subset of the Gene Ontology annotations with equivalent MeSH terms. CONCLUSIONS: MeSHOPs allows quantitative measurement of the degree of association between any entity and the annotated medical concepts, based directly on relevant primary literature. Comparison of MeSHOPs allows entities to be related based on shared medical themes in their literature. A web interface is provided for generating and visualizing MeSHOPs.

Carcinogenesis is a complex process with multiple genetic and environmental factors contributing to the development of one or more tumors. Understanding the underlying mechanism of this process and identifying related markers to assess the outcome of this process would lead to more directed treatment and thus significantly reduce the mortality rate of cancers. Recently, molecular diagnostics and prognostics based on the identification of patterns within gene expression profiles in the context of protein interaction networks were reported. However, the predictive performances of these approaches were limited. In this study we propose a novel integrated approach, named CAERUS, for the identification of gene signatures to predict cancer outcomes based on the domain interaction network in human proteome. We first developed a model to score each protein by quantifying the domain connections to its interacting partners and the somatic mutations present in the domain. We then defined proteins as gene signatures if their scores were above a preset threshold. Next, for each gene signature, we quantified the correlation of the expression levels between this gene signature and its neighboring proteins. The results of the quantification in each patient were then used to predict cancer outcome by a modified naïve Bayes classifier. In this study we achieved a favorable accuracy of 88.3%, sensitivity of 87.2%, and specificity of 88.9% on a set of well-documented gene expression profiles of 253 consecutive breast cancer patients with different outcomes. We also compiled a list of cancer-associated gene signatures and domains, which provided testable hypotheses for further experimental investigation. Our approach proved successful on different independent breast cancer data sets as well as an ovarian cancer data set. This study constitutes the first predictive method to classify cancer outcomes based on the relationship between the domain organization and protein network.

Abstract Motivation: Many biological phenomena involve extensive interactions between many of the biological pathways present in cells. However, extraction of all the inherent biological pathways remains a major challenge in systems biology. With the advent of high-throughput functional genomic techniques, it is now possible to infer biological pathways and pathway organization in a systematic way by integrating disparate biological information. Results: Here, we propose a novel integrated approach that uses network topology to predict biological pathways. We integrated four types of biological evidence (protein–protein interaction, genetic interaction, domain–domain interaction and semantic similarity of Gene Ontology terms) to generate a functionally associated network. This network was then used to develop a new pathway finding algorithm to predict biological pathways in yeast. Our approach discovered 195 biological pathways and 31 functionally redundant pathway pairs in yeast. By comparing our identified pathways to three public pathway databases (KEGG, BioCyc and Reactome), we observed that our approach achieves a maximum positive predictive value of 12.8% and improves on other predictive approaches. This study allows us to reconstruct biological pathways and delineates cellular machinery in a systematic view. Availability: The method has been implemented in Perl and is available for downloading from http://www.oicr.on.ca/research/ouellette/pandora. It is distributed under the terms of GPL (http://opensource.org/licenses/gpl-2.0.php) Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Protein-Protein Interactions (PPIs) play important roles in many biological functions. Protein domains, which are defined as independently folding structural blocks of proteins, physically interact with each other to perform these biological functions. Therefore, the identification of Domain-Domain Interactions (DDIs) is of great biological interests because it is generally accepted that PPIs are mediated by DDIs. As a result, much effort has been put on the prediction of domain pair interactions based on computational methods. Many DDI prediction tools using PPIs network and domain evolution information have been reported. However, tools that combine the primary sequences, domain annotations, and structural annotations of proteins have not been evaluated before. RESULTS: In this study, we report a novel approach called Gram-bAsed Interaction Analysis (GAIA). GAIA extracts peptide segments that are composed of fixed length of continuous amino acids, called n-grams (where n is the number of amino acids), from the annotated domain and DDI data set in Saccharomyces cerevisiae (budding yeast) and identifies a list of n-grams that may contribute to DDIs and PPIs based on the frequencies of their appearance. GAIA also reports the coordinate position of gram pairs on each interacting domain pair. We demonstrate that our approach improves on other DDI prediction approaches when tested against a gold-standard data set and achieves a true positive rate of 82% and a false positive rate of 21%. We also identify a list of 4-gram pairs that are significantly over-represented in the DDI data set and may mediate PPIs. CONCLUSION: GAIA represents a novel and reliable way to predict DDIs that mediate PPIs. Our results, which show the localizations of interacting grams/hotspots, provide testable hypotheses for experimental validation. Complemented with other prediction methods, this study will allow us to elucidate the interactome of cells.

Protein-protein interactions (PPIs) play an extremely important role in performing a variety of biological functions. The interactomes of several model organisms including budding yeast Saccharomyces cerevisiae have recently been studied using experimental techniques such as the yeast two-hybrid assay. However, these techniques are generally biased against integral membrane proteins due to their intrinsic limitations. Given the fact that the interactions between integral membrane proteins cover a large fraction of the whole interactome, we report a study of predicting interactions between integral membrane proteins in yeast by a quantitative model We integrate protein-protein interaction and domain-domain interaction (DDI) data from disparate sources and apply a log likelihood scoring method on all putative integral membrane proteins in yeast to predict their interactions based on a cut-off threshold. We show that our approach improves on other predictive approaches when tested on a ldquogold-standardrdquo data set and achieves 74.6% true positive rate at the expense of 0.43% false positive rate. Furthermore, we find that two integral membrane proteins are more likely to interact with each other if they share more common interaction partners. This study allows us to reach a more extensive understanding of the yeast integral membrane proteins from a network view, which also complements the previous prediction approaches based on the genomic context.

BACKGROUND: To date, 35 human diseases, some of which also exhibit anticipation, have been associated with unstable repeats. Anticipation has been reported in a number of diseases in which repeat expansion may have a role in etiology. Despite the growing importance of unstable repeats in disease, currently no resource exists for the prioritization of repeats. Here we present Satellog, a database that catalogs all pure 1-16 repeat unit satellite repeats in the human genome along with supplementary data. Satellog analyzes each pure repeat in UniGene clusters for evidence of repeat polymorphism. RESULTS: A total of 5,546 such repeats were identified, providing the first indication of many novel polymorphic sites in the genome. Overall, polymorphic repeats were over-represented within 3'-UTR sequence relative to 5'-UTR and coding sequence. Interestingly, we observed that repeat polymorphism within coding sequence is restricted to trinucleotide repeats whereas UTR sequence tolerated a wider range of repeat period polymorphisms. For each pure repeat we also calculate its repeat length percentile rank, its location either within or adjacent to EnsEMBL genes, and its expression profile in normal tissues according to the GeneNote database. CONCLUSION: Satellog provides the ability to dynamically prioritize repeats based on any of their characteristics (i.e. repeat unit, class, period, length, repeat length percentile rank, genomic co-ordinates), polymorphism profile within UniGene, proximity to or presence within gene regions (i.e. cds, UTR, 15 kb upstream etc.), metadata of the genes they are detected within and gene expression profiles within normal human tissues. Unstable repeats associated with 31 diseases were analyzed in Satellog to evaluate their common repeat properties. The utility of Satellog was highlighted by prioritizing repeats for Huntington's disease and schizophrenia. Satellog is available online at http://satellog.bcgsc.ca.

BACKGROUND: We present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development. DESCRIPTION: The Atlas system is based on relational data models that we developed for each of the source data types. Data stored within these relational models are managed through Structured Query Language (SQL) calls that are implemented in a set of Application Programming Interfaces (APIs). The APIs include three languages: C++, Java, and Perl. The methods in these API libraries are used to construct a set of loader applications, which parse and load the source datasets into the Atlas database, and a set of toolbox applications which facilitate data retrieval. Atlas stores and integrates local instances of GenBank, RefSeq, UniProt, Human Protein Reference Database (HPRD), Biomolecular Interaction Network Database (BIND), Database of Interacting Proteins (DIP), Molecular Interactions Database (MINT), IntAct, NCBI Taxonomy, Gene Ontology (GO), Online Mendelian Inheritance in Man (OMIM), LocusLink, Entrez Gene and HomoloGene. The retrieval APIs and toolbox applications are critical components that offer end-users flexible, easy, integrated access to this data. We present use cases that use Atlas to integrate these sources for genome annotation, inference of molecular interactions across species, and gene-disease associations. CONCLUSION: The Atlas biological data warehouse serves as data infrastructure for bioinformatics research and development. It forms the backbone of the research activities in our laboratory and facilitates the integration of disparate, heterogeneous biological sources of data enabling new scientific inferences. Atlas achieves integration of diverse data sets at two levels. First, Atlas stores data of similar types using common data models, enforcing the relationships between data types. Second, integration is achieved through a combination of APIs, ontology, and tools. The Atlas software is freely available under the GNU General Public License at: http://bioinformatics.ubc.ca/atlas/

BACKGROUND: We present Pegasys--a flexible, modular and customizable software system that facilitates the execution and data integration from heterogeneous biological sequence analysis tools. RESULTS: The Pegasys system includes numerous tools for pair-wise and multiple sequence alignment, ab initio gene prediction, RNA gene detection, masking repetitive sequences in genomic DNA as well as filters for database formatting and processing raw output from various analysis tools. We introduce a novel data structure for creating workflows of sequence analyses and a unified data model to store its results. The software allows users to dynamically create analysis workflows at run-time by manipulating a graphical user interface. All non-serial dependent analyses are executed in parallel on a compute cluster for efficiency of data generation. The uniform data model and backend relational database management system of Pegasys allow for results of heterogeneous programs included in the workflow to be integrated and exported into General Feature Format for further analyses in GFF-dependent tools, or GAME XML for import into the Apollo genome editor. The modularity of the design allows for new tools to be added to the system with little programmer overhead. The database application programming interface allows programmatic access to the data stored in the backend through SQL queries. CONCLUSIONS: The Pegasys system enables biologists and bioinformaticians to create and manage sequence analysis workflows. The software is released under the Open Source GNU General Public License. All source code and documentation is available for download at http://bioinformatics.ubc.ca/pegasys/.

UNLABELLED: We recently demonstrated that combining the output from Genscan and HMMgene can provide increased accuracy of gene predictions. We have created a robust software system that runs algorithms previously described on DNA sequences and provides a public web interface to the system for use by the biological community worldwide. The GeneComber system performs ab initio gene prediction by first taking a user inputted DNA sequence and running Genscan and HMMgene. The outputs of Genscan and HMMgene are then integrated using the EUI, GI and EUI_frame algorithms. All results are then stored into a relational database management system (RDBMS) and can then be retrieved through a web interface. The web interface provides a unified view of the GeneComber predictions by graphically overlaying outputs from Genscan, HMMgene, EUI, GI and EUI_frame. Outputs can also be retrieved in general feature format (GFF) or FASTA format. The software is written in the Perl programming language and is both dependent on and interoperable with the Bioperl toolkit. It includes high-level application programming interfaces (APIs) to run Genscan, HMMgene and a database API to insert prediction results into an RDBMS. The APIs are assembled into the genecomber script which is executed by the web interface or can be run directly from the Unix command line. The web interface is written in PHP and is structured so as to be easily modified for viewing data from any database that stores gene structures. AVAILABILITY: The GeneComber public web interface and supplementary information is located at http://bioinformatics.ubc.ca/genecomber The source code is released under the GNU General Public License and is available at ftp://ftp.bioinformatics.ubc.ca/pub/genecomber/software.

MOTIVATION: Despite constant improvements in prediction accuracy, gene-finding programs are still unable to provide automatic gene discovery with desired correctness. The current programs can identify up to 75% of exons correctly and less than 50% of predicted gene structures correspond to actual genes. New approaches to computational gene-finding are clearly needed. RESULTS: In this paper we have explored the benefits of combining predictions from already existing gene prediction programs. We have introduced three novel methods for combining predictions from programs Genscan and HMMgene. The methods primarily aim to improve exon level accuracy of gene-finding by identifying more probable exon boundaries and by eliminating false positive exon predictions. This approach results in improved accuracy at both the nucleotide and exon level, especially the latter, where the average improvement on the newly assembled dataset is 7.9% compared to the best result obtained by Genscan and HMMgene. When tested on a long genomic multi-gene sequence, our method that maintains reading frame consistency improved nucleotide level specificity by 21.0% and exon level specificity by 32.5% compared to the best result obtained by either of the two programs individually. AVAILABILITY: The scripts implementing our methods are available from http://www.cs.ubc.ca/labs/beta/genefinding/