Guidelines for the functional annotation of microRNAs using the Gene Ontology.

MicroRNA regulation of developmental and cellular processes is a relatively new field of study, and the available research data have not been organized to enable its inclusion in pathway and network analysis tools. The association of gene products with terms from the Gene Ontology is an effective method to analyze functional data, but until recently there has been no substantial effort dedicated to applying Gene Ontology terms to microRNAs. Consequently, when performing functional analysis of microRNA data sets, researchers have had to rely instead on the functional annotations associated with the genes encoding microRNA targets. In consultation with experts in the field of microRNA research, we have created comprehensive recommendations for the Gene Ontology curation of microRNAs. This curation manual will enable provision of a high-quality, reliable set of functional annotations for the advancement of microRNA research. Here we describe the key aspects of the work, including development of the Gene Ontology to represent this data, standards for describing the data, and guidelines to support curators making these annotations. The full microRNA curation guidelines are available on the GO Consortium wiki (http://wiki.geneontology.org/index.php/MicroRNA_GO_annotation_manual).R.P.H. and R.C.L are supported by funding from a British Heart Foundation grant (RG/13/5/30112) and the National Institute for Health Research University College London Hospitals Biomedical Research Centre. M.M. is a Senior Research Fellow of the British Heart Foundation (FS/13/2/29892). A.Z. is an Intermediate Fellow of the British Heart Foundation (FS/13/18/30207). D.S. is supported by a grant awarded to the Mouse Genome Database from the National Human Genome Research Institue at the US National Institutes of Health (HG-00330). P.D’E., M.G., M.O-M. are supported by grants from the US National Institutes of Health (P41 HG003751 and U54 GM114833), Ontario Research Fund, and the European Molecular Biology Laboratory. D.H. is supported by a grant awarded to the Zebrafish Information Network fromthe National Human Genome Research Institute at the US National Institutes of Health (HG002659). A.Z.K. is funded by a NIHR University College London Hospitals Biomedical Research Centre, Research Capability Funding award (RCF) (RCF123). L.M. is a Ragnar Söderberg fellow in Medicine (M-14/55), and received funding from Swedish Heart-Lung-Foundation (20120615, 20130664, 20140186). Huntley, RP 22 R.B. and D.O-S. are supported by R.B. and D.O-S. are supported by a grant awarded to The Gene Ontology Consortium (Principal Investigators: JA Blake, JM Cherry, S Lewis, PW Sternberg and P Thomas) by the National Human Genome Research Institute (NHGRI) (#U41 HG22073). V.P. and J.R.S. are supported by a grant from the National Heart, Lung, and Blood Institute on behalf of the National Institutes of Health (HL64541). K.V.A. is supported by a grant awarded to the Gene Ontology Consortium from the National Human Genome Research Institute at the US National Institutes of Health (HG002273). V.W. is supported by a Wellcome Trust grant (104967/Z/14/Z). We would like to thank Leonore Reiser and Tanya Berardini who provided guidance on the plant miRNA processing pathway. Also thanks to David Hill, Harold Drabkin, Judith Blake, Karen Christie, Donghui Li and Pascale Gaudet who contributed to discussions regarding GO curation procedures and to Lisa Matthews and Bruce May who provided helpful feedback on the manuscript. We are very grateful to Tony Sawford and Maria Martin from the European Bioinformatics Institute for access to the online GO curation tool, which is an essential component of this annotation project. Many thanks to members of the GO Editorial Office for useful discussions about the placement and definition of new GO terms. We also thank Alex Bateman and Anton Petrov for being responsive to our feedback regarding RNAcentral functionality. Author contributions: R.C.L. initiated discussions in the GO Consortium regarding miRNA curation guidelines and supervised the project, R.P.H. researched and constructed the guidelines and wrote the manuscript, R.P.H., R.C.L., D.S., R.B., P.D’E., M.G., M.O-M., D.H., V.P., J.R.S., K.V.A. and V.W. contributed to discussions regarding GO curation procedures and provided feedback on the manuscript. D.O-S. provided the expertise on definitions and placements of miRNA-related GO terms and performed the necessary updates and additions to both the GO and to the annotation extension relations used herein. M.M., A.Z., L.M. and A.Z.K. provided guidance with the scientific aspect of the guidelines and provided feedback on the manuscript.This is the final version of the article. It first appeared from Cold Spring Harbor Press via http://dx.doi.org/10.1261/rna.055301.11

Guidelines for the functional annotation of microRNAs using the Gene Ontology

ABSTRACT MicroRNA regulation of developmental and cellular processes is a relatively new field of study, and the available research data have not been organized to enable its inclusion in pathway and network analysis tools. The association of gene products with terms from the Gene Ontology is an effective method to analyze functional data, but until recently there has been no substantial effort dedicated to applying Gene Ontology terms to microRNAs. Consequently, when performing functional analysis of microRNA data sets, researchers have had to rely instead on the functional annotations associated with the genes encoding microRNA targets. In consultation with experts in the field of microRNA research, we have created comprehensive recommendations for the Gene Ontology curation of microRNAs. This curation manual will enable provision of a high-quality, reliable set of functional annotations for the advancement of microRNA research. Here we describe the key aspects of the work, including development of the Gene Ontology to represent this data, standards for describing the data, and guidelines to support curators making these annotations. The full microRNA curation guidelines are available on the GO Consortium wiki (http://wiki.geneontology.org/index.php/MicroRNA_GO_annotation_manual)

RNAcentral : a hub of information for non-coding RNA sequences

RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences, collating information on ncRNA sequences of all types from a broad range of organisms. We have recently added a new genome mapping pipeline that identifies genomic locations for ncRNA sequences in 296 species. We have also added several new types of functional annotations, such as tRNA secondary structures, Gene Ontology annotations, and miRNA-target interactions. A new quality control mechanism based on Rfam family assignments identifies potential contamination, incomplete sequences, and more. The RNAcentral database has become a vital component of many workflows in the RNA community, serving as both the primary source of sequence data for academic and commercial groups, as well as a source of stable accessions for the annotation of genomic and functional features. These examples are facilitated by an improved RNAcentral web interface, which features an updated genome browser, a new sequence feature viewer, and improved text search functionality. RNAcentral is freely available at https://rnacentral.org

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.Peer Reviewe

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Funder: Bundesministerium für Bildung und ForschungFunder: Bundesministerium für Bildung und Forschung (BMBF)We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies

Cyclin D associated events in G1

Reactome is open-source, open access, manually curated and peer-reviewed pathway database. Pathway annotations are authored by expert biologists, in collaboration with Reactome editorial staff and crossreferenced to many bioinformatics databases. A system of evidence tracking ensures that all assertions are backed up by the primary literature. Reactome is used by clinicians, geneticists, genomics researchers, and molecular biologists to interpret the results of high-throughput experimental studies, by bioinformaticians seeking to develop novel algorithms for mining knowledge from genomic studies, and by systems biologists building predictive models of normal and disease variant pathways.The development of Reactome is supported by grants from the US National Institutes of Health (P41HG003751), University of Toronto (CFREF Medicine by Design), European Union (EU STRP, EMI-CD), and the European Molecular Biology Laboratory (EBI Industry program).Contribution and expert reviewinginfo:eu-repo/semantics/publishe

The TAg-RB Murine Retinoblastoma Cell of Origin Has Immunohistochemical Features of Differentiated Müller Glia with Progenitor Properties

This work defines the cell of origin in the popular simian virus 40 large T antige–induced (TAg-RB) transgenic retinoblastoma mouse model as a Chx10-positive Müller glial cell. TAg-RB tumors escape apoptosis and share genomic and expression changes with human retinoblastoma

In SK-N-SH cells, MECP2_e1 induces expression of A) <i>FOXP2</i> and B) <i>DOCK8</i>.

<p>A weak signal is also visible when cells are infected with MECP2_e2 lentivirus vector. Frontal cortex was used as a positive control. Replicates represent biological replicates i.e. repeated infections. For <i>FOXP2</i>, real-time RT-PCR products were run on an agarose gel. For <i>DOCK8</i>, end point PCR was performed using KAPA2G Fast HotStartReadyMix (KapaBiosystems, KK5601. PCR consisted of 35 cycles, and the annealing temperature was 60°C. C) GAPDH was used as an endogenous control for RT-PCR.</p

Comparison of microarray results and qPCR validation data for neuronally differentiated SK-N-SH cells infected with either MECP2e1 or e2.

<p>F test was calculated for pairwise comparison of RT-PCR results for MECP2e1 or e2 versus eGFP infection. Comparison with published data from knockout (null) and transgenic mouse gene expression microarray data (Chahrour et al, 2008 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091742#pone.0091742-Chahrour1" target="_blank">[33]</a>) is shown. Overlap with several other gene expression/transcriptome analysis studies (Nectoux et al, 2010 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091742#pone.0091742-Nectoux1" target="_blank">[32]</a>; Yakabe et al, 2008 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091742#pone.0091742-Yakabe1" target="_blank">[31]</a>) is also commented on (see footnotes).</p>1<p>Chahrour et al, 2008.</p>%<p>no expression detectable by qRT-PCR in control cells (eGFP infected), thus no fold-change calculation possible.</p><p>*too low to measure.</p><p>NA = not applicable (i.e. unable to calculate p-value).</p><p>NC = no call (i.e. microarray didn’t show any fold change in expression).</p>&<p>from quantitative RT-PCR data.</p>#<p>Also identified as up-regulated in transcriptome analysis of Rett patients; Nectoux et al, 2010 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091742#pone.0091742-Nectoux1" target="_blank">[32]</a>.</p>$<p>Also identified as up-regulated after siRNA knockdown of <i>MECP2</i> in human derived cell-lines; Yakabe et al, 2008 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091742#pone.0091742-Yakabe1" target="_blank">[31]</a>.</p