The Gene Ontology Handbook

Dessimoz, Christophe; Škunca, Nives

doi:10.1007/978-1-4939-3743-1

Cited by 84 publications

(12 citation statements)

References 13 publications

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“…Another way to map between reaction type and similarity groupings is provided by the Gene Ontology (Holliday, Davidson, Akiva, & Babbitt, 2017) which is used in many large resources to describe similarities in enzyme function. Here, enzyme reactions can be defined using the Enzyme Nomenclature Commission classification (Tipton, 1994), which defines enzyme reactions by EC number.…”

Section: Resultsmentioning

confidence: 99%

“…For example, of the more than 5,000 proteins that can be assigned to the BioB-like Level 2 subgroup (of the BATs domain subgroup 6), only about 3,200 can be annotated with that reaction based on the presence of functionally important active site residues. The remainder are missing one or more of the residues considered critical for performing the canonical biotin synthase reaction (Betz et al, 2015; Holliday, Davidson, Akiva, & Babbitt, 2017), leading to the assertion that a large proportion of enzymes annotated with the BioB function in the Gene Ontology database (Ashburner et al, 2000) have been found to be misannotated (Holliday, Davidson, Akiva, & Babbitt, 2017). …”

Section: Resultsmentioning

confidence: 99%

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Atlas of the Radical SAM Superfamily: Divergent Evolution of Function Using a “Plug and Play” Domain

Holliday

Akiva

Meng

et al. 2018

Methods in Enzymology

109

113

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The radical SAM superfamily contains over 100,000 homologous enzymes that catalyze a remarkably broad range of reactions required for life, including metabolism, nucleic acid modification, and biogenesis of cofactors. While the highly conserved SAM-binding motif responsible for formation of the key 5'-deoxyadenosyl radical intermediate is a key structural feature that simplifies identification of superfamily members, our understanding of their structure-function relationships is complicated by the modular nature of their structures, which exhibit varied and complex domain architectures. To gain new insight about these relationships, we classified the entire set of sequences into similarity-based subgroups that could be visualized using sequence similarity networks. This superfamily-wide analysis reveals important features that had not previously been appreciated from studies focused on one or a few members. Functional information mapped to the networks indicates which members have been experimentally or structurally characterized, their known reaction types, and their phylogenetic distribution. Despite the biological importance of radical SAM chemistry, the vast majority of superfamily members have never been experimentally characterized in any way, suggesting that many new reactions remain to be discovered. In addition to 20 subgroups with at least one known function, we identified additional subgroups made up entirely of sequences of unknown function. Importantly, our results indicate that even general reaction types fail to track well with our sequence similarity-based subgroupings, raising major challenges for function prediction for currently identified and new members that continue to be discovered. Interactive similarity networks and other data from this analysis are available from the Structure-Function Linkage Database.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Atlas of the Radical SAM Superfamily: Divergent Evolution of Function Using a “Plug and Play” Domain

Holliday

Akiva

Meng

et al. 2018

Methods in Enzymology

109

113

View full text Add to dashboard Cite

show abstract

“…Consequently, it is possible to conduct a functional enrichment on a microRNA regulatory network and highlight the enriched GO terms associated with both the microRNA and the target genes [71]. For more information about how to browse, download and use bioontologies please see [110]. Identifiers: One of the major obstacles in merging RNA data is the choice of identifiers made by the different resources.…”

Section: Network Analysismentioning

confidence: 99%

Non-coding RNA regulatory networks

Panni

Lovering

Porras

et al. 2020

Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanism

311

202

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It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them are involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks.

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“…The nucleus regulates and coordinates vital cellular activities in order to minimize the deleterious effects of water stress within the cell (Fernández and Strand 2008). Gene ontology (GO) provides fundamental information on which particular mechanism or part of the cell the genes play a role GO basically groups the genes into three categories (Dessimoz andŠkunca 1984-2020). There are three fundamental processes describing gene ontology, namely, cellular component (CC), biological function (BF) and molecular process (MP) (Wood 2008).…”

Section: Identification Of the Candidate Genes Within The Major Qtls mentioning

confidence: 99%

Identification of QTLs and candidate genes for physiological traits associated with drought tolerance in cotton

Magwanga

Kirungu

et al. 2020

J Cotton Res

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Background: Cotton is mainly grown for its natural fiber and edible oil. The fiber obtained from cotton is the indispensable raw material for the textile industries. The ever changing climatic condition, threatens cotton production due to a lack of sufficient water for its cultivation. Effects of drought stress are estimated to affect more than 50% of the cotton growing regions. To elucidate the drought tolerance phenomenon in cotton, a backcross population was developed from G. tomentosum, a drought tolerant donor parent and G. hirsutum which is highly susceptible to drought stress. Results: A genetic map of 10 888 SNP markers was developed from 200 BC 2 F 2 populations. The map spanned 4 191.3 centi-Morgan (cM), with an average distance of 0.104 7 cM, covering 51% and 49% of At and Dt sub genomes, respectively. Thirty stable Quantitative trait loci (QTLs) were detected, in which more than a half were detected in the At subgenome. Eighty-nine candidate genes were mined within the QTL regions for three traits: cell membrane stability (CMS), saturated leaf weight (SLW) and chlorophyll content. The genes had varied physiochemical properties. A majority of the genes were interrupted by introns, and only 15 genes were intronless, accounting for 17% of the mined genes. The genes were found to be involved molecular function (MF), cellular component (CC) and biological process (BP), which are the main gene ontological (GO) functions. A number of miRNAs were detected, such as miR164, which is associated with NAC and MYB genes, with a profound role in enhancing drought tolerance in plants. Through RT-qPCR analysis, 5 genes were found to be the key genes involved in enhancing drought tolerance in cotton. Wild cotton harbors a number of favorable alleles, which can be exploited to aid in improving the narrow genetic base of the elite cotton cultivars. The detection of 30 stable QTLs and 89 candidate genes found to be contributed by the donor parent, G. tomentosum, showed the significant genes harbored by the wild progenitors which can be exploited in developing more robust cotton genotypes with diverse tolerance levels to various environmental stresses. Conclusion: This was the first study involving genome wide association mapping for drought tolerance traits in semi wild cotton genotypes. It offers an opportunity for future exploration of these genes in developing highly tolerant cotton cultivars to boost cotton production.

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The Gene Ontology Handbook

Cited by 84 publications

References 13 publications

Atlas of the Radical SAM Superfamily: Divergent Evolution of Function Using a “Plug and Play” Domain

Atlas of the Radical SAM Superfamily: Divergent Evolution of Function Using a “Plug and Play” Domain

Non-coding RNA regulatory networks

Identification of QTLs and candidate genes for physiological traits associated with drought tolerance in cotton

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