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      Towards comprehensive annotation of Drosophila melanogaster enzymes in FlyBase

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          Abstract

          The catalytic activities of enzymes can be described using Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. These annotations are available from numerous biological databases and are routinely accessed by researchers and bioinformaticians to direct their work. However, enzyme data may not be congruent between different resources, while the origin, quality and genomic coverage of these data within any one resource are often unclear. GO/EC annotations are assigned either manually by expert curators or inferred computationally, and there is potential for errors in both types of annotation. If such errors remain unchecked, false positive annotations may be propagated across multiple resources, significantly degrading the quality and usefulness of these data. Similarly, the absence of annotations (false negatives) from any one resource can lead to incorrect inferences or conclusions. We are systematically reviewing and enhancing the functional annotation of the enzymes of Drosophila melanogaster, focusing on improvements within the FlyBase ( www.flybase.org) database. We have reviewed four major enzyme groups to date: oxidoreductases, lyases, isomerases and ligases. Herein, we describe our review workflow, the improvement in the quality and coverage of enzyme annotations within FlyBase and the wider impact of our work on other related databases.

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          Most cited references18

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          The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology.

          The Gene Ontology Annotation (GOA) database (http://www.ebi.ac.uk/GOA) aims to provide high-quality electronic and manual annotations to the UniProt Knowledgebase (Swiss-Prot, TrEMBL and PIR-PSD) using the standardized vocabulary of the Gene Ontology (GO). As a supplementary archive of GO annotation, GOA promotes a high level of integration of the knowledge represented in UniProt with other databases. This is achieved by converting UniProt annotation into a recognized computational format. GOA provides annotated entries for nearly 60,000 species (GOA-SPTr) and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. By integrating GO annotations from other model organism groups, GOA consolidates specialized knowledge and expertise to ensure the data remain a key reference for up-to-date biological information. Furthermore, the GOA database fully endorses the Human Proteomics Initiative by prioritizing the annotation of proteins likely to benefit human health and disease. In addition to a non-redundant set of annotations to the human proteome (GOA-Human) and monthly releases of its GO annotation for all species (GOA-SPTr), a series of GO mapping files and specific cross-references in other databases are also regularly distributed. GOA can be queried through a simple user-friendly web interface or downloaded in a parsable format via the EBI and GO FTP websites. The GOA data set can be used to enhance the annotation of particular model organism or gene expression data sets, although increasingly it has been used to evaluate GO predictions generated from text mining or protein interaction experiments. In 2004, the GOA team will build on its success and will continue to supplement the functional annotation of UniProt and work towards enhancing the ability of scientists to access all available biological information. Researchers wishing to query or contribute to the GOA project are encouraged to email: goa@ebi.ac.uk.
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            Ras in cancer and developmental diseases.

            Somatic, gain-of-function mutations in ras genes were the first specific genetic alterations identified in human cancer about 3 decades ago. Studies during the last quarter century have characterized the Ras proteins as essential components of signaling networks controlling cellular proliferation, differentiation, or survival. The oncogenic mutations of the H-ras, N-ras, or K-ras genes frequently found in human tumors are known to throw off balance the normal outcome of those signaling pathways, thus leading to tumor development. Oncogenic mutations in a number of other upstream or downstream components of Ras signaling pathways (including membrane RTKs or cytosolic kinases) have been detected more recently in association with a variety of cancers. Interestingly, the oncogenic Ras mutations and the mutations in other components of Ras/MAPK signaling pathways appear to be mutually exclusive events in most tumors, indicating that deregulation of Ras-dependent signaling is the essential requirement for tumorigenesis. In contrast to sporadic tumors, separate studies have identified germline mutations in Ras and various other components of Ras signaling pathways that occur in specific association with a number of different familial, developmental syndromes frequently sharing common phenotypic cardiofaciocutaneous features. Finally, even without being a causative force, defective Ras signaling has been cited as a contributing factor to many other human illnesses, including diabetes and immunological and inflammatory disorders. We aim this review at summarizing and updating current knowledge on the contribution of Ras mutations and altered Ras signaling to development of various tumoral and nontumoral pathologies.
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              Evidence for 26 distinct acyl-coenzyme A synthetase genes in the human genome.

              Acyl-coenzyme A synthetases (ACSs) catalyze the fundamental, initial reaction in fatty acid metabolism. "Activation" of fatty acids by thioesterification to CoA allows their participation in both anabolic and catabolic pathways. The availability of the sequenced human genome has facilitated the investigation of the number of ACS genes present. Using two conserved amino acid sequence motifs to probe human DNA databases, 26 ACS family genes/proteins were identified. ACS activity in either humans or rodents was demonstrated previously for 20 proteins, but 6 remain candidate ACSs. For two candidates, cDNA was cloned, protein was expressed in COS-1 cells, and ACS activity was detected. Amino acid sequence similarities were used to assign enzymes into subfamilies, and subfamily assignments were consistent with acyl chain length preference. Four of the 26 proteins did not fit into a subfamily, and bootstrap analysis of phylograms was consistent with evolutionary divergence. Three additional conserved amino acid sequence motifs were identified that likely have functional or structural roles. The existence of many ACSs suggests that each plays a unique role, directing the acyl-CoA product to a specific metabolic fate. Knowing the full complement of ACS genes in the human genome will facilitate future studies to characterize their specific biological functions.
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                Author and article information

                Journal
                Database (Oxford)
                Database (Oxford)
                databa
                Database: The Journal of Biological Databases and Curation
                Oxford University Press
                1758-0463
                2019
                23 January 2019
                23 January 2019
                : 2019
                : bay144
                Affiliations
                [1]Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3DY, UK
                Author notes
                Corresponding author: Email: sjm41@ 123456cam.ac.uk
                Article
                bay144
                10.1093/database/bay144
                6343044
                30689844
                ccb3cbc0-ae70-45dd-9f3f-4c1c86ca5390
                © The Author(s) 2019. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 31 October 2018
                : 10 December 2018
                : 18 December 2018
                Page count
                Pages: 13
                Funding
                Funded by: National Human Genome Research Institute 10.13039/100000051
                Award ID: U41HG000739
                Categories
                Original Article

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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