limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.

GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses.

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.

/pdf/the-pride-database-and-related-tools-and-resources-in-2019-54q3h1hfre.pdf

The PRIDE database and related tools and resources in 2019: improving support for quantification data.

The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

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The Ensembl Variant Effect Predictor.

The EMBL-EBI provides free access to popular bioinformatics sequence analysis applications as well as to a full-featured text search engine with powerful cross-referencing and data retrieval capabilities. Access to these services is provided via user-friendly web interfaces and via established RESTful and SOAP Web Services APIs (https://www.ebi.ac.uk/seqdb/confluence/display/JDSAT/EMBL-EBI+Web+Services+APIs+-+Data+Retrieval). Both systems have been developed with the same core principles that allow them to integrate an ever-increasing volume of biological data, making them an integral part of many popular data resources provided at the EMBL-EBI. Here, we describe the latest improvements made to the frameworks which enhance the interconnectivity between public EMBL-EBI resources and ultimately enhance biological data discoverability, accessibility, interoperability and reusability.

/pdf/the-embl-ebi-search-and-sequence-analysis-tools-apis-in-2019-447au9jccz.pdf

The EMBL-EBI search and sequence analysis tools APIs in 2019

Report on the scalability of semantic web integration in BioMedBridges

Knowledge discovery through pattern finding in data is central to modern molecular biology, which now has thousands of databases and similar numbers of tools for processing those data. Any data analysis in molecular biology involves gathering and processing data from many sources, even before the analysis for the central biological question takes place. Taverna is a workflow workbench that allows bioinformaticians to create data pipelines involving distributed Web services and other forms of tool; these workflows gather and manage data in order to perform analyses that answer biological questions. RapidMiner brings a large suite of data processing, visualisation and data mining tools to bear upon tables of data, but there is a disconnect between these operators and the services available to users of Taverna. Through a RapidMiner extension to Taverna we have combined the ability to gather and process data from many molecular biological sources with RapidMiner's data mining capabilities to provide a powerful tool for scientific analysis. In this article we describe this RapidMiner extension to Taverna and some preliminary analyses we have performed using RapidMiner on biological data.

/pdf/combining-rapidminer-operators-with-bioinformatics-services-lgqvdksy32.pdf

Combining RapidMiner Operators with Bioinformatics Services - A Powerful Combination

We present Populous, an open source application for gathering content for an ontology and populating that ontology en masse. Populous presents authors with a table-based form where columns are tied to take values from particular ontologies; the user can select a concept from an ontology via its meaningful label to give a value for a given entity. Populated tables are fed into templates that can then be used to generate the ontology’s axioms. Populous separates knowledge gathering from the conceptualisation; it also removes users from the usual ontology authoring tools. Availability: Download, source and video via http://www.e-lico.eu/ populous. Ontology building environments such as Protege and OBOEdit offer facilities for the manual authoring of axioms. Such tools are vital for capturing an ontology’s form. Many ontologies are, however, large with considerable portions formed of repetitions of the same pattern of axioms, varying only in the fillers within that pattern. To avoid the tedium and potential errors of doing this manually, templates can be filled and the axioms for the pattern generated, avoiding the manual authoring of many axioms. Populous [1] does this by presenting a familiar form-filling table-based user interface for any ontology authors to populate ontology patterns or templates. Rows are tied to the entities being described; columns are tied to properties and the cells constrained to take values from particular ontologies or fragments of an ontology. As an author fills out the template, he or she is guided to place appropriate values within the template. The content of this table can then be transformed into the axioms of the target ontology with an OWL scripting language. Populous is an extension of RightField,1 which is used for creating Excel documents that contain ontology based restrictions on a spreadsheet’s content. RightField is primarily designed for generating spreadsheet templates for data annotation; Populous extends RightField to support knowledge gathering and ontology generation. Populous and RightField are both open source, cross platform Java applications released under the BSD licence. They use the Apache-POI 2 for interacting with Microsoft documents and manipulating Excel spreadsheets. 1 http://www.rightfield.org.uk 2 http://poi.apache.org Both OWL and OBO ontologies can be uploaded into Populous. Users can also browse and load ontologies directly from BioPortal. Once the ontologies are loaded they are classified by a reasoner and the basic class hierarchy can be inspected. Terms can be selected from the ontology to create validation sets for values that are permitted for a particular selection of cells in the table. Labels from an ontology’s entities can be used within a cell, not just URI or URI fragments. Populous allows the addition of free text, even if the cell has an associated validation range; these values are highlighted in red and can act as placeholders for new or suggested terms when no suitable candidate can be found in the validation set. Populous supports the use of the Ontology Pre-Processor Language 3 (OPPL) patterns in order to generate new OWL axioms from the populated template. OPPL is an extension of Manchester OWL Syntax to select, add and remove axioms and it has an interpreter for scripts that manipulate the ontology. Variables from the OPPL pattern are mapped to columns from the table using the column name through the Populous pattern Wizard. We have used Populous with biologists to populate large portions of a kidney and urinary pathway ontology [2]. Populous is another piece in the ‘jigsaw’ of tools that support the ontology authoring process. It starts to fill the gap between the term request system and the manual axiom authoring systems by providing a mechanism for ‘filling out’ templates in such a way that they can be validated against the ontologies with which the ontology is being composed. We see Populous as a means for engaging domain experts who are not ontology experts in the authoring process and any ontology author to more effectively populate their ontology’s content. Acknowledgements: We acknowledge Mikel Egana Aranguren for his advice, requirements and testing of Populous. This work was funded by the e-LICO project— EU/FP7/ICT-2007.4.4 and by SysMO-DB BBSRC grant BBG0102181.

/pdf/populous-a-tool-for-populating-an-ontology-57oeo071qy.pdf

Populous: A Tool for Populating an Ontology

The increase in volume and complexity of biological data has led to increased requirements to reuse that data. Consistent and accurate metadata is essential for this task, creating new challenges in semantic data annotation and in the constriction of terminologies and ontologies used for annotation. The BioSharing community are developing standards and terminologies for annotation, which have been adopted across bioinformatics, but the real challenge is to make these standards accessible to laboratory scientists. Widespread adoption requires the provision of tools to assist scientists whilst reducing the complexities of working with semantics. This paper describes unobtrusive `stealthy' methods for collecting standards compliant, semantically annotated data and for contributing to ontologies used for those annotations. Spreadsheets are ubiquitous in laboratory data management. Our spreadsheet-based RightField tool enables scientists to structure information and select ontology terms for annotation within spreadsheets, producing high quality, consistent data without changing common working practices. Furthermore, our Populous spreadsheet tool proves effective for gathering domain knowledge in the form of Web Ontology Language (OWL) ontologies. Such a corpus of structured and semantically enriched knowledge can be extracted in Resource Description Framework (RDF), providing further means for searching across the content and contributing to Open Linked Data (http://linkeddata.org/).

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.2941

Stealthy Annotation of Experimental Biology by Spreadsheets

The Experimental Factor Ontology (EFO) is an application ontology driven by experimental variables including cell lines to organize and describe the diverse experimental variables and data resided in the EMBL-EBI resources. The Cell Line Ontology (CLO) is an OBO community-based ontology that contains information of immortalized cell lines and relevant experimental components. EFO integrates and extends ontologies from the bio-ontology community to drive a number of practical applications. It is desirable that the community shares design patterns and therefore that EFO reuses the cell line representation from the Cell Line Ontology (CLO). There are, however, challenges to be addressed when developing a common ontology design pattern for representing cell lines in both EFO and CLO. In this study, we developed a strategy to compare and map cell line terms between EFO and CLO. We examined Cellosaurus resources for EFO-CLO cross-references. Text labels of cell lines from both ontologies were verified by biological information axiomatized in each source. The study resulted in the identification 873 EFO-CLO aligned and 344 EFO unique immortalized permanent cell lines. All of these cell lines were updated to CLO and the cell line related information was merged. A design pattern that integrates EFO and CLO was also developed. Our study compared, aligned, and synchronized the cell line information between CLO and EFO. The final updated CLO will be examined as the candidate ontology to import and replace eligible EFO cell line classes thereby supporting the interoperability in the bio-ontology domain. Our mapping pipeline illustrates the use of ontology in aiding biological data standardization and integration through the biological and semantics content of cell lines.

/pdf/comparison-alignment-and-synchronization-of-cell-line-4adkmoodxt.pdf

Simon Jupp

Papers

Report on the scalability of semantic web integration in BioMedBridges

Combining RapidMiner Operators with Bioinformatics Services - A Powerful Combination

Populous: A Tool for Populating an Ontology

Stealthy Annotation of Experimental Biology by Spreadsheets

Comparison, alignment, and synchronization of cell line information between CLO and EFO.