Asta Literature Retrieval: Gene Research for Functional Annotation ⚠️ CRITICAL: Gene/Protein Identification Context BEFORE YOU BEGIN RESEARCH: Y...

This report is retrieval-only and is generated directly from Asta results.

• Papers retrieved: 20
• Snippets retrieved: 20

Relevant Papers

[1] Avian Immunome DB: an example of a user-friendly interface for extracting genetic information

• Authors: Ralf C. Mueller, Nicolai Mallig, Jacqueline Smith, Lél Eöry, Richard I. Kuo et al.
• Year: 2020
• Venue: BMC Bioinformatics
• URL: https://www.semanticscholar.org/paper/b894d9ca8ea2d653bf1711a0c67dab71d054487c
• DOI: 10.1186/s12859-020-03764-3
• PMID: 33176685
• PMCID: 7661159
• Citations: 6
• Summary: The Avian Immunome DB (Avimm) for easy gene property extraction as exemplified by avian immune genes is presented and described, which contains 1170 distinct avian immune genes with canonical gene symbols and 612 synonyms across 363 bird species.
• Evidence snippets:
• Snippet 1 (score: 0.770)
  > Ever since the advent of commercial next-generation sequencing platforms in the early 2000s with its associated decrease in sequencing costs [1], the number of DNA sequences increased considerably [2]. Generally, these data become publicly accessible in databases provided by projects focussing on different aspects of biological sequence information [3,4]. Ensembl [5] and NCBI [6] for instance, have a strong focus on genome annotation with the help of RNA transcript information while UniProt has a pronounced emphasis on protein-coding genes and biological function of proteins. UniProt's records are either based on manually annotated, non-redundant protein sequences (SwissProt) or on highquality computationally analysed records, which are enriched with automatic annotation (TrEMBL) [7]. Relying on accurate genome annotations and protein descriptions, Gene Ontology (GO) [8,9] categorises gene products and fits them into a computational model of biological systems. Their assignment deploys a controlled vocabulary, so-called GO terms, to link genes and gene products to biological processes, cellular components, or molecular functions.
  > However, genome annotation is not standardised, and each service provider uses their own custom-built annotation pipelines. As a consequence, this often leads to ambiguity in gene names during genome annotation with different gene symbols being given to the same gene or the same gene symbol being given to different, but similar genes. Additionally, since the pre-existing wealth of sequencing information relies on model organisms like human and mouse, there is a strong bias in gene symbols towards those chosen for these species. Particularly for model species, this issue has been partially addressed, for example by the Human Genome Organisation (HUGO) Gene Nomenclature Committee (HGNC) [10], the Vertebrate Gene Nomenclature Committee (VGNC) [11], or the Chicken Gene Nomenclature Consortium [12]. However, this neither guarantees that gene names are harmonised among these consortia, nor does it keep researchers from assigning alternative gene symbols in their annotations, especially when working with non-model species.

[2] Construction of an Ortholog Database Using the Semantic Web Technology for Integrative Analysis of Genomic Data

• Authors: H. Chiba, Hiroyo Nishide, I. Uchiyama
• Year: 2015
• Venue: PLoS ONE
• URL: https://www.semanticscholar.org/paper/7cc805575c642aa8efdc1204383a7662965fbb60
• DOI: 10.1371/journal.pone.0122802
• PMID: 25875762
• PMCID: 4395280
• Citations: 14
• Summary: The ortholog database using the Semantic Web technology can contribute to biological knowledge discovery through integrative data analysis and examples demonstrate that the ortholog information described in RDF can be used to link various biological data such as taxonomy information and Gene Ontology.
• Evidence snippets:
• Snippet 1 (score: 0.744)
  > A typical use of an ortholog database is transferring functional annotations from known genes in model organisms to genes of unknown function in other organisms, on the basis of the conjecture that orthologs are usually functionally conserved. To demonstrate such an application in our database, we showed a query to retrieve ortholog information of a specified protein.
  > Here, we specified a UniProt ID to obtain ortholog information. For describing functional categories of genes, we used Gene Ontology (GO) [24]. The UniProt GO Annotation (UniProt-GOA) database [25] (http://www.ebi.ac.uk/GOA) provides GO term assignment to proteins with evidence codes (http://www.geneontology.org/GO.evidence.shtml). We created an ontology for GO annotation (GOA-O, Table 1, http://purl.jp/bio/11/goa) and described UniProt-GOA data in RDF using it (Table 2). If some model organisms have experimentally verified GO annotations, we can transfer such a validated annotation to orthologs of other organisms.

[3] Mitotic Spindle Proteomics in Chinese Hamster Ovary Cells

• Authors: Mary Kate Bonner, D. Poole, Tao Xu, Ali Sarkeshik, J. Yates et al.
• Year: 2011
• Venue: PLoS ONE
• URL: https://www.semanticscholar.org/paper/8a46e242e657489c1933c76e06a37618f7d1901f
• DOI: 10.1371/journal.pone.0020489
• PMID: 21647379
• PMCID: 3103581
• Citations: 51
• Influential citations: 3
• Summary: This work reports the first proteomic study of the mitotic spindle from Chinese Hamster Ovary (CHO) cells and identifies proteins that are unique to the CHO spindle.
• Evidence snippets:
• Snippet 1 (score: 0.714)
  > The lists of proteins used for the comparison contain more items than listed previously due to expansion out of gene clusters, for example, to allow the updating and comparison of current HGNC gene symbols. These lists of proteins were compared in Microsoft Excel 2011 using PivotTable.
  > The protein set for the CHO midbody was derived from the accession numbers in Table S1 and Table S2 from Skop et al. [9]. Original accession numbers were updated to more recent UniProt accessions (2/2010), and duplicates from different species or different protein isoforms were removed. The unique UniProt accessions were mapped to gene names using UniProt KB Unimart, UniProt dataset [82], and these gene names were confirmed manually as HGNC symbols using HGNC, with ambiguities checked using BLASTP of the sequence corresponding to the original accession number. Accessions that didn't map successfully in Unimart were manually analyzed using BLASTP against the human RefSeq protein set using sequences from the original accessions, combined with TreeFam.org data for the non-human UniProt accessions. HGNC symbols were updated again on 12/14/2010 before comparison with this paper's protein set.
  > The protein set for the HeLa spindle proteome is derived from the 1121 accession numbers in Sauer et al. supplementary table 1 column 2 [17]. Updating the 1116 UniProt accession numbers and 5 IPI accession numbers from 795 rows required several steps. Most proteins were updated to current UniProt accessions using UniProt retrieve. Duplicates were removed. Sequences for the IPI accession numbers and 16 defunct UniProt accession numbers were recovered from other sources on the web, and BLASTP against the human RefSeq protein set with a cutoff of at least 90% identity was used to update some of these accessions. The unique current UniProt accessions were mapped to HGNC symbols using UniProt ID mapping to HGNC IDs. Biomart, database Ensembl Genes 60, dataset GRCh37.p2 [http://uswest.ensembl.org/biomart/martview/]

[4] LMPD: LIPID MAPS proteome database

• Authors: Dawn Cotter, A. Maer, C. Guda, Brian Saunders, S. Subramaniam
• Year: 2005
• Venue: Nucleic Acids Research
• URL: https://www.semanticscholar.org/paper/265c37b45326b7927e396484751e84e4aeff92d5
• DOI: 10.1093/nar/gkj122
• PMID: 16381922
• PMCID: 1347484
• Citations: 92
• Influential citations: 2
• Summary: The initial release of the LIPID MAPS Proteome Database contains 2959 records, representing human and mouse proteins involved in lipid metabolism, and this LMPD protein list was enhanced with annotations from UniProt, EntrezGene, ENZYME, GO, KEGG and other public resources.
• Evidence snippets:
• Snippet 1 (score: 0.714)
  > For each record selected from the results summary, all LMPD data relevant to that protein are displayed, with external database IDs linked to their respective resources.
  > Annotations are organized by category: Record Overview, Gene/GO/KEGG Information, UniProt Annotations, and Related Proteins. The record overview contains LMPD_ID, species, description, gene symbols, lipid categories, EC number, molecular weight, sequence length and protein sequence. Gene information includes Entrez Gene ID, chromosome, map location, primary name, primary symbol and alternate names and symbols; Gene Ontology (GO) IDs and descriptions, and KEGG pathway IDs and descriptions. UniProt annotations include primary accession number, entry name and comments such as catalytic activity, enzyme regulation, function and similarity. For related proteins and splice variants, we display source database, database ID, sequence length, and title.

[5] CRONOS: the cross-reference navigation server

• Authors: Brigitte Waegele, I. Dunger, G. Fobo, Corinna Montrone, H. Mewes et al.
• Year: 2008
• Venue: Bioinformatics
• URL: https://www.semanticscholar.org/paper/8c05b3aa0ba01c41ee97c2dc98ea7b5b14ce0e9c
• DOI: 10.1093/bioinformatics/btn590
• PMID: 19010804
• PMCID: 2638938
• Citations: 20
• Summary: CRONOS, a cross-reference server that contains entries from five mammalian organisms presented by major gene and protein information resources, is developed, which shows that the cross-references are highly accurate.
• Evidence snippets:
• Snippet 1 (score: 0.696)
  > In order to detect gene and protein names which are assigned to products of different genes and thus result in erroneous cross-references, dedicated lists are created for each organism separately. Organism-specific lists are necessary, since terms that are ambiguous in one organism might be explicit in another. For example, ADORA2 is an ambiguous gene name in Homo sapiens but not in mouse, and GALT in mouse but not in H.sapiens.
  > In a first step, ambiguous names within the databases were extracted. If a name occurs in at least two entries describing different genes or proteins (splice variants count as one gene/protein), this particular name is marked as ambiguous and is excluded from the mapping process. In a second step, corresponding gene names occurring in the manually annotated sections of RefSeq as well as in UniProt were analyzed. Entries containing the same gene product name and having a one-to-many or many-to-many relation (e.g. one Swiss-Prot entry maps to many RefSeq entries) were scrutinized for misleading annotation. This process is done manually by inspecting additional information like sequence similarity or functional information about the involved entries. In most of the cases, the exclusion of the ambiguous gene names resulted in correct one-to-one relations.
  > As statistical analysis revealed (Supplementary Material S2) that gene names with less than four letters are exceptionally error-prone, only gene names with at least four letters are kept for mapping purposes. However, gene names with less than four letters can be queried, e.g. a search for the tumor suppressor 'p53' reveals the respective entries with the official gene name 'TP53'. Organism-specific lists of ambiguous gene and protein names are available for download on the CRONOS home page.

[6] Virulence and pathogenicity determinants in whole genome sequence of Fusarium udum causing wilt of pigeon pea

• Authors: A. Srivastava, R. Srivastava, Jagriti Yadav, Ashutosh Kumar Singh, P. Tiwari et al.
• Year: 2023
• Venue: Frontiers in Microbiology
• URL: https://www.semanticscholar.org/paper/ac4c8e1cd07dbd943c544dab0dff140617956e3a
• DOI: 10.3389/fmicb.2023.1066096
• PMID: 36876067
• PMCID: 9981795
• Citations: 2
• Summary: The present study concludes that deciphering the whole genome of F. udum would be instrumental in understanding evolution, virulence determinants, host-pathogen interaction, possible control strategies, ecological behavior, and many other complexities of the pathogen.
• Evidence snippets:
• Snippet 1 (score: 0.693)
  > The BLASTx homology search tool, a component of the standalone NCBI-blast-2.3.0+, was used to perform functional annotation of the F. udum genes (Altschul et al., 1990). With a cut-off E value of ≤1e−06 and a similarity of 34%, BLASTx identified the homologous sequences of the genes in the NCBI non-redundant protein database. Gene ontology (GO) analysis was carried out using Blast2GO PRO 4.1.5 (Conesa and Gotz, 2008). In three different mappings, B2G performed as follows: (1) Using two NCBI-provided mapping files, blast result accessions are used to get gene names (symbols; gene info, gene 2 accessions). (2) Blast result GI identifiers were used to retrieve UniProt IDs using a mapping file from PIR (non-redundant reference protein database), which includes PSD, Swiss-Prot, UniProt, TrEMBL, GenPept, RefSeq, and PDB. The names of the identified genes were searched in the species-specific entries of the gene product table of the GO database. With the aid of the KAAS-KEGG Automatic Annotation Server, pathway analyses were carried out. This database provides functional annotation of genes using other data servers (Moriya et al., 2007). Accessions from the blast results were looked for in the DBXRef table of the GO database.

[7] PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse

• Authors: P. Hornbeck, J. Kornhauser, S. Tkachev, Bin Zhang, E. Skrzypek et al.
• Year: 2011
• Venue: Nucleic Acids Research
• URL: https://www.semanticscholar.org/paper/93e4acf4ba3bca1b379ae8292e73dddb344abd90
• DOI: 10.1093/nar/gkr1122
• PMID: 22135298
• PMCID: 3245126
• Citations: 1605
• Influential citations: 155
• Summary: PhosphoSitePlus (http://www.phosphosite.org) is an open, comprehensive, manually curated and interactive resource for studying experimentally observed post-translational modifications, primarily of human and mouse proteins. It encompasses 1 30 000 non-redundant modification sites, primarily phosphorylation, ubiquitinylation and acetylation. The interface is designed for clarity and ease of navigation. From the home page, users can launch simple or complex searches and browse high-throughput d...
• Evidence snippets:
• Snippet 1 (score: 0.690)
  > Accession numbers from UniPROT KB, NCBI and Ensembl (24)(25)(26), as well as gene symbols from HGNC (27), are curated for all proteins when possible. Basic protein descriptions include information parsed from UniPROT KB (24), and may include additional information from the literature. Descriptions are updated in bulk occasionally. Gene Ontology (28) annotations are parsed from NCBI (25). Editors assign protein types.

[8] Structure-Aware Mycobacterium tuberculosis Functional Annotation Uncloaks Resistance, Metabolic, and Virulence Genes

• Authors: Samuel J. Modlin, A. Elghraoui, Deepika Gunasekaran, Alyssa M Zlotnicki, N. Dillon et al.
• Year: 2021
• Venue: mSystems
• URL: https://www.semanticscholar.org/paper/76ff9a62b36b32cc10e46e71ffd4dd90344e4706
• DOI: 10.1128/mSystems.00673-21
• PMID: 34726489
• PMCID: 8562490
• Citations: 15
• Summary: This work systematically updates the functional genome annotation of Mycobacterium tuberculosis virulent type strain H37Rv and identifies hundreds of high-confidence candidates for mechanisms of antibiotic resistance, virulence factors, and basic metabolism and other functions key in clinical and basic tuberculosis research.
• Evidence snippets:
• Snippet 1 (score: 0.687)
  > 3. Fig. S2B -match/mismatch colours mixed up? (I think match should be teal and mismatch -red?) 4. Line 162-163: Rv1430 is in UniProt (EC 3.1.1.-) and has been present in Uniprot since version 45 of the gene record: https://www.uniprot.org/uniprot/L7N697. I presume you had conducted your literature analysis before the UniProt entry was updated to include the EC code, so maybe you can add the dates when the data was retrieved from UniProt and other databases you used in the Materials and Methods section? 5. Supplementary text, p. 9, first paragraph. I believe that an unrelated fragment of text was copy-pasted into the second sentence of the paragraph ("Many mutations that altered bacterial clearance...") 6. Supplementary text, p. 12, final paragraph. It should be Rv1191, not Rv1191c. Could you also add a short explanation why you believe it should be classified as a cathepsin (what protein did you transfer this annotation from)?
  > Reviewer #3 (Comments for the Author):
  > In this manuscript, Modlin et al., attempt to tackle the problem of assigning functions to ~1700 hypothetical and/or underannotated genes in the Mycobacterium tuberculosis H37Rv (Mtb) genome. Rapid and accurate annotation of microbial genomes is indeed a very critical and under appreciated part of microbial ecophysiology. This step is especially crucial for pathogenic organisms such as Mtb where accurate functional annotation of these hypothetical proteins could unravel mechanisms which could act as drug targets. The authors employed a two-pronged strategy to define a set of these unannotated or under-annotated genes and to then provide possible functions for many of these genes. First, they undertook a large-scale manual curation of literature to assign functions (including EC numbers for enzymatic functions) to ~575 genes.

[9] The Surface Proteome of Bovine Unsexed and Sexed Spermatozoa

• Authors: P. Pinto-Pinho, Joana Quelhas, Francis Impens, Sara Dufour, Delphi Van Haver et al.
• Year: 2025
• Venue: Animals : an Open Access Journal from MDPI
• URL: https://www.semanticscholar.org/paper/66496158140e8f55a2c2ca8965bd298300ce9ab0
• DOI: 10.3390/ani15040484
• PMID: 40002966
• PMCID: 11852025
• Citations: 2
• Summary: Differences in surface proteins between X- and Y-chromosome-bearing bovine spermatozoa are explored to identify potential targets for sperm sexing by LC-MS/MS analysis, with 5 transmembrane proteins showing promise as markers for X-sperm.
• Evidence snippets:
• Snippet 1 (score: 0.684)
  > The protein sequences were functionally annotated by combining information retrieved from the UniProt database ( [25], accessed on 9 June 2022) and one-to-one fast orthology assignments using the eggNOG-mapper v.2.1.7 tool ( [26], accessed on 9 June 2022), as described in [27]. Briefly, the gene name, protein name, length, Gene Ontology (GO) IDs, and chromosome associated with each protein entry were obtained from UniProt using the retrieve/ID mapping tool. Additionally, gene names, descriptions, and experimentally validated GO IDs were obtained from eggNOG, with consideration given to a taxonomic scope auto-adjusted per query, a minimum hit bit-score of 60, and thresholds of 80% for identity, minimum query coverage, and minimum subject coverage.
  > Out of the 130 detected proteins, 71 (54.6%) had information manually verified by UniProt curators (Supplementary Spreadsheet S1.5). Utilizing the eggNOG-mapper v2.1.7 tool, a total of 122 entries were scanned (Supplementary Spreadsheet S1.6). By combining data from both tools, a total of 123 proteins were characterized with a gene name, and 127 had GO information. However, 2 proteins still lacked information on a descrip-tion, protein, gene, and preferred names. Figure 1 provides a summary of the functional annotation results.
  > tool, a total of 122 entries were scanned (Supplementary Spreadsheet S1.6). By combining data from both tools, a total of 123 proteins were characterized with a gene name, and 127 had GO information. However, 2 proteins still lacked information on a description, protein, gene, and preferred names. Figure 1 provides a summary of the functional annotation results.

[10] Molecular Mechanisms Underlying Response to Influenza in Grey Seals (Halichoerus grypus), a Potential Wild Reservoir

• Authors: Christina M McCosker, E. Unal, Alayna K Gigliotti, Wendy B Puryear, Jonathan A. Runstadler et al.
• Year: 2025
• Venue: Molecular Ecology
• URL: https://www.semanticscholar.org/paper/bebb135aae1c1182d098fce839c9a3df0cfb2b21
• DOI: 10.1111/mec.70012
• PMID: 40613337
• PMCID: 12288799
• Citations: 3
• Summary: It is hypothesized that the combination of down‐ and up‐regulated immune gene expression may prevent overstimulation of the immune response, acting as an adaptation in grey seals to resist IAV‐associated mortality.
• Evidence snippets:
• Snippet 1 (score: 0.683)
  > Top hits were required to have a percent query coverage (QC) ≥ 80 to be used for annotating transcripts. A subsequent blastx search against the Swiss-Prot database (downloaded from NCBI 07/02/2021) for transcripts without a sufficient hit was conducted using the parameters max_target_seqs 2, max_hsps 1, e-value 0.001 and qcov_hsp_perc 80. Genes without a published gene symbol (named 'LOC' + Gene ID in NCBI's database) were assigned a UniProt gene symbol based on the protein annotation listed in the RefSeq entries, when possible. Ultimately, a list of transcript identifiers and gene symbols were compiled into a transcript-to-gene map for subsequent analyses.
  > To further facilitate analyses of gene functions, additional steps were taken to identify the putative function of genes annotated with a symbol that began with 'LOC'. First 'LOC' genes with a protein-coding gene description in NCBI's database were manually assigned the appropriate gene symbol. Transcript sequences of remaining 'LOC' genes were queried through a blastn search against NCBI's nucleotide database (parameters: max target seq = 2, max hsps = 1, evalue = 0.01, perc identity = 90). Results from the blastn search were filtered to exclude hits with query coverage < 90 and hits that included vague terms (e.g., 'uncharacterized', 'genome assembly' and 'chromosome'). Gene symbols were extracted from the first hit for each transcript and assigned as the identity of that gene for genes with only a single transcript with hits or with consistent hits across transcripts. For genes with multiple transcripts that matched different gene symbols, the annotation was manually determined based on the number of transcripts for each gene symbol hit and query coverage/% identity values. For any 'LOC' genes that were identified as a gene already present in the dataset, gene counts were concatenated for further analysis.

[11] Protein-coding genes in humans and model mammals (mouse, rat and pig): gene identifiers and disambiguation of gene nomenclature retrieved from the Ensembl genome browser

• Authors: Grzegorz R. Juszczak, C. Pareek, U. Czarnik, M. Pierzchała
• Year: 2025
• Venue: BMC Genomics
• URL: https://www.semanticscholar.org/paper/d9089dfc889d790deb49cbc5b4617bda55bcc4da
• DOI: 10.1186/s12864-025-12329-8
• PMID: 41408139
• PMCID: 12822150
• Citations: 2
• Summary: An R script is developed that performs a gene symbol update to current official versions combined with identification of ambiguous symbols and retrieval of other IDs from the Ensembl database and provides a single list of updated symbols with annotation about their ambiguity.
• Evidence snippets:
• Snippet 1 (score: 0.679)
  > Gene nomenclature contains current official symbols and various numbers of synonyms, which pose a challenge to integrating genomic data and increase the probability that different genes share the same symbol. Therefore, we retrieved identifiers assigned to all protein-coding genes in human, mouse, rat and pig genomes that are available in the Ensembl genome browser (release 113) to assess the number of genes, compare species and identify ambiguous symbols. Results: Our analysis revealed that the total number of symbols, both official symbols and synonyms, used to identify protein-coding genes ranges from 16,600 in pigs to 64,580 in mice. Furthermore, the gene nomenclature is not complete because there are also genes without an assigned symbol, which indicates gaps in understanding protein-coding genes, especially in pigs. We also found a large number of gene symbols that map to more than one gene. These symbols might complicate the identification of about 10% of rat and mouse genes and 18% of human protein-coding genes. A simple solution for this problem is the usage of stable gene IDs assigned by scientific institutions and committees (Ensembl, NCBI, RGD, HGNC and VGNC) provided that the genomic information associated with these IDs is retrieved directly from proprietary databases containing the most accurate data. Finally, although gene symbols may pose a problem with unequivocal identification of genes, there are instances when no other identifiers are available in the literature. Therefore, we have developed an R script performing search of the Ensembl database and integrating data to provide a single list of updated symbols with annotation about their ambiguity. Conclusions: Gene symbols are not always reliable and should be reported together with stable IDs to enable unequivocal identification of genes. Therefore, data containing only gene symbols should be used cautiously to avoid misidentification of genes. A solution for this problem is our R script REgeness that performs a gene symbol update to current official versions combined with identification of ambiguous symbols and retrieval of other IDs from the Ensembl database.

[12] Synthesis, characterization, and computational evaluation of some synthesized xanthone derivatives: focus on kinase target network and biomedical properties

• Authors: Wisam Taher Muslim, L. J. Mohammad, Munaf M. Naji, Isaac Karimi, Matheel D. Al-Sabti et al.
• Year: 2025
• Venue: Frontiers in Pharmacology
• URL: https://www.semanticscholar.org/paper/659ab502877a1d6b5ab7ce45fa51f0f9a13dcf24
• DOI: 10.3389/fphar.2024.1511627
• PMID: 39830340
• PMCID: 11738930
• Summary: Acute leukemic T-cells were one of the top predicted tumor cell lines for these ligands and the possible antileukemic effects of synthesized xanthone derivatives are potentially very interesting and warrant further studies.
• Evidence snippets:
• Snippet 1 (score: 0.672)
  > The UniProt accession identification of target kinases was converted to gene symbols for humans using the SynGO gene set analysis tool (Koopmans et al., 2019), and pooled together, and submitted to GeneMANIA to construct target kinase network. GeneMANIA is a handy web interface for acquiring gene ontology, scrutinizing gene lists, and highlighting genes for functional assays (Warde-Farley et al., 2010). After choosing Homo sapiens from the list of optional organisms, the genes of interest in the previous step were entered into the search bar and the results were collated and high-scored genes were culled for further discussion. Moreover, the protein-protein network was also constructed in STRING ver. 12 launched at https://string-db.org, and submitted to Cytoscape ver. 3.10.2 for network analysis using a novel Cytoscape plugin cytoHubba and visualization (Shannon et al. , 2003).

[13] Network Pharmacology-Based Strategy to Investigate the Pharmacological Mechanisms of Ginkgo biloba Extract for Aging

• Authors: Yanfei Liu, Yue Liu, Wantong Zhang, Mingyue Sun, Weiliang Weng et al.
• Year: 2020
• Venue: Evidence-based Complementary and Alternative Medicine : eCAM
• URL: https://www.semanticscholar.org/paper/fadeff691eb41dff82e019cc3d38b846fdc605c4
• DOI: 10.1155/2020/8508491
• PMID: 32802136
• PMCID: 7403930
• Citations: 8
• Summary: The study found that flavonoids (quercetin, luteolin, and kaempferol) and beta-sitosterol and the top eight candidate targets, namely, PTGS2, PPARG, DPP4, GSK3B, CCNA2, AR, MAPK14, and ESR1, were selected as the main therapeutic targets of EGb.
• Evidence snippets:
• Snippet 1 (score: 0.668)
  > e validated target proteins of the active components were obtained from the TCMSP database. e target protein name of the active ingredient was converted to the standard target gene name through the UniProt Knowledge Base (UniProtKB, http://www. uniprot.org/). e UniProtKB is the central hub for the collection of functional information on proteins, with accurate, consistent, and rich annotation. e target protein names were inputted into UniProtKB, with the organism restricted to "Homo sapiens," eventually gaining the official symbol.

[14] Discovery of Simple Sequence Repeat Markers through Transcriptome Analysis of Baccaurea motleyana

• Authors: K. Nasir, Muhammad Fairuz Mohd Yusof, M. S. F. A. Razak, Siti Norsaidah Ibrahim, Mira Farzana Mohamad Moktar et al.
• Year: 2020
• Venue: Journal of Food Science and Engineering
• URL: https://www.semanticscholar.org/paper/f99fe2940881ec45ecbd8ba3da7f10b4fb22fc3b
• DOI: 10.17265/2159-5828/2020.02.001
• Summary: Baccaurea motleyana (rambai) is underutilized fruits that are native to Malaysia, Indonesia and Thailand and used for simple sequence repeat (SSR) analysis by MIcroSAtellite (MISA).
• Evidence snippets:
• Snippet 1 (score: 0.668)
  > To get comprehensive gene function of rambai genes, gene annotation to seven databases, namely National Center for Biotechnology Information (NCBI) non-redundant protein sequences (NR), NCBI nucleotide sequences (NT), Kyoto Encyclopedia of Genes and Genome Ortholog (KO), SwissProt, Protein family (Pfam), Gene Ontology (GO) and Cluster of Orthologous Groups (KOG), was used as reference.
  > The NCBI non-redundant protein sequences (NR), include protein sequence information from GenBank, Protein Data Bank (PDB), SwissProt, Protein Information Resource (PIR) and Protein Research Foundation (PRF). The NCBI nucleotide sequences (NT) are the nucleotide sequence database that includes nucleotide sequence from GenBank of the European Bioinformatics Institute (EMBL) and DNA Data Bank of Japan (DDBJ). KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as cell, organism and ecosystem, from molecular-level information, especially for large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. KEGG is an established Cluster of Orthologous (KO) annotation system that can accomplish the function annotation of the genome/transcriptome of a newly sequenced species. SwissProt is a manual annotated and reviewed protein sequence database that has a high-quality protein sequence database from experimental results, computed features and scientific conclusions. Pfam is comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile of hidden Markov models. Many proteins are composed of structural domains, and the protein sequence of a specific structural domain possesses a certain degree of conservative property. GO is the established standard for the functional annotation of gene products and controlled vocabulary used to classify the functional attributes of gene products of a biological process, a molecular function and a cellular component.

[15] WormBase 2024: status and transitioning to Alliance infrastructure

• Authors: Paul W. Sternberg, K. V. Van Auken, Qinghua Wang, Adam J. Wright, K. Yook et al.
• Year: 2024
• Venue: Genetics
• URL: https://www.semanticscholar.org/paper/dcfce02a58655b0872a8265f5c85dbbd94957294
• DOI: 10.1093/genetics/iyae050
• PMID: 38573366
• PMCID: 11075546
• Citations: 196
• Influential citations: 9
• Summary: The current state of WormBase as well as progress and plans for moving core WormBase infrastructure to the Alliance of Genome Resources (the Alliance) are discussed.
• Evidence snippets:
• Snippet 1 (score: 0.665)
  > Of the 19,984 protein-coding genes in C. elegans, 10,838 have a name/gene symbol (e.g. lin-12). Gene naming is largely investigator-initiated, with a request to WormBase through genenames@wormbase.org ; this will continue with C. elegans content in the Alliance. The gene symbol, which is formatted in lowercase and italics, communicates information about the gene based on mutant phenotype, functional criteria, orthology, or homology. To make genes easily recognizable to non-elegans researchers, the current preference is to name genes with human orthologs after the human gene if characterized. Gene name stability and formatting are important to avoid confusion in the literature and to facilitate searches of other databases that use the C. elegans gene symbols (e.g. UniProt) and text mining. However, occasionally names have been changed because of incorrect orthology or not supported by functional studies from the community. Names have also been changed based on requests from multiple community members (e.g. daf-21 renamed as hsp-90). To avoid name changes and to have the published name used in databases, researchers should contact WormBase prior to manuscript submission; the most common issue is that the requested name is already in use in C. elegans or another model organism for a nonhomologous gene product. Over the past 10 years, the community has averaged 180 new gene names per year, often with a corresponding publication, increasing knowledge about our favorite organism. More information about C. elegans-specific nomenclature can be found at https://wormbase.org/about/ userguide/nomenclature.

[16] Protein Localization Analysis of Essential Genes in Prokaryotes

• Authors: Chong Peng, Feng Gao
• Year: 2014
• Venue: Scientific Reports
• URL: https://www.semanticscholar.org/paper/69181762648fd77a085b2f93618a71b43b62cf76
• DOI: 10.1038/srep06001
• PMID: 25105358
• PMCID: 4126397
• Citations: 27
• Summary: A comprehensive protein localization analysis of essential genes in 27 prokaryotes including 24 bacteria, 2 mycoplasmas and 1 archaeon has been performed and shows that proteins encoded by essential genes are enriched in internal location sites, while exist in cell envelope with a lower proportion compared with non-essential ones.
• Evidence snippets:
• Snippet 1 (score: 0.665)
  > Bioinformatics Databases. DEG is a database of essential genes (http://www. essentialgene.org/). The newly released DEG 10 has been developed to accommodate the quantitative and qualitative advancements brought by the progressive identification methods. Currently available records of both essential and nonessential genes among a wide range of organisms can be downloaded from DEG 10, making it possible to compare the two different types of genes in many aspects 21 .
  > 27 prokaryotic organisms including 24 bacteria, 2 mycoplasmas and Methanococcus maripaludis S2, the only record of the Archaea domain were selected to analyze the protein localization and GO distribution of the essential and nonessential genes. There are 31 bacterial records corresponding to 27 organisms in the database in total and 26 sets of data were selected in the current study. Streptococcus pneumonia was not chosen for the lack of non-essential genes. Since the essential genes were not genome-widely identified, it's not reasonable to regard the complementary set of essential genes as non-essential genes in Streptococcus pneumonia 29,30 . In the case of multiple records for one organism, the one with the most convincing experimental methods was chosen. The non-essential genes in Methanococcus maripaludis S2 and 13 bacteria such as Escherichia coli MG1655 are obtained based on the original literatures, while non-essential genes in other 12 organisms such as Bacillus subtilis 168 are the complementary set of essential genes. The information of the organisms used in the current study are displayed in Table 1.
  > The three model genomes' subcellular location information and the Gene Ontology (GO) terms used for the analysis in the current study were downloaded from the Universal Protein Resource (UniProt; http://www.uniprot.org). Maintained by the UniProt Consortium, UniProt is committed to providing biologists with a comprehensive, high-quality and freely accessible resource of protein sequences and functional annotation 27 . Among the wealth of annotation data, detailed GO annotation statements are included.

[17] GeneTools – application for functional annotation and statistical hypothesis testing

• Authors: V. Beisvåg, Frode K. R. Jünge, Hallgeir Bergum, Lars Jølsum, S. Lydersen et al.
• Year: 2006
• Venue: BMC Bioinformatics
• URL: https://www.semanticscholar.org/paper/1d9e0c2f67acd5bf64c659f1f3f8624325b6be8a
• DOI: 10.1186/1471-2105-7-470
• PMID: 17062145
• PMCID: 1630634
• Citations: 105
• Influential citations: 11
• Summary: GeneTools is the first "all in one" annotation tool, providing users with a rapid extraction of highly relevant gene annotation data for e.g. thousands of genes or clones at once.
• Evidence snippets:
• Snippet 1 (score: 0.661)
  > The database enables searching by gene symbols/names, GenBank accession numbers, UniGene cluster IDs, Swiss-Prot entry names and several unique clone IDs (IMAGE clone IDs, University of Iowa clone IDs, Operon oligo IDs, TAIR IDs and a subset of selected Affymetrix and Agilent IDs).
  > The names and symbols of genes/proteins may be highly ambiguous [20]. We therefore recommend using primary gene IDs, like GeneBank accession numbers or specific probe IDs when querying the database. However, if gene names or symbols are used, caution is advised because only official names/symbols associated with UniProt knowledgebase will be recognized. The underlying database is updated on a weekly basis with annotation information from several external databases including UniGene, Swiss-Prot, Entrez Gene and GO. User data are submitted to the database as text files of gene reporters and analysis of the annotation data can be performed through three user interfaces: the NMC Annotation Tool, the GO Annotator Tool and eGOn. Analysis results and annotation data can be exported in various formats.

[18] Network pharmacology-based strategic prediction and target identification of apocarotenoids and carotenoids from standardized Kashmir saffron (Crocus sativus L.) extract against polycystic ovary syndrome

• Authors: Anshul Tiwari, Siddharth J Modi, A. Girme, L. Hingorani
• Year: 2023
• Venue: Medicine
• URL: https://www.semanticscholar.org/paper/3e3253804574634d1968a0fd5b65dd1674bff6c6
• DOI: 10.1097/MD.0000000000034514
• PMID: 37565925
• PMCID: 10419424
• Citations: 2
• Summary: A network pharmacology-based method to determine the potential therapeutic pathways of phytoconstituents of UHPLC-PDA standardized stigma-based Crocus sativus extract for the management of PCOS revealed that the apocarotenoids and carotenoidal could act on various targets to regulate multiple pathways related to PCOS.
• Evidence snippets:
• Snippet 1 (score: 0.661)
  > The target protein name of the active ingredient was converted to the standard target gene name using the UniProt Knowledge Base (UniProtKB). UniProt KB is the central hub for the collection of functional information on proteins, with accurate, consistent, and rich annotation. The target protein names were uploaded into UniProtKB, with the organism restricted to "Homo sapiens," eventually gaining the official symbol. The potential targets obtained from the UniproKB are depicted in Figures 3 and 4.

[19] The quality of metabolic pathway resources depends on initial enzymatic function assignments: a case for maize

• Authors: Jesse R. Walsh, M. Schaeffer, Peifen Zhang, S. Rhee, J. Dickerson et al.
• Year: 2016
• Venue: BMC Systems Biology
• URL: https://www.semanticscholar.org/paper/c41be7766c80fddb3f81c57ced799b8562370cc8
• DOI: 10.1186/s12918-016-0369-x
• PMID: 27899149
• PMCID: 5129634
• Citations: 11
• Influential citations: 1
• Summary: CornCyc’s computational predictions are more accurate than those in MaizeCyc when compared to experimentally determined function assignments, demonstrating the relative strength of the enzymatic function assignment pipeline used to generate CornCyc.
• Evidence snippets:
• Snippet 1 (score: 0.657)
  > A gold standard set of protein functional annotations was generated by extracting data from UniProt [16] and BRENDA [17]. We extracted all protein sequence and annotation data from UniProt (release 2016_05) for the organism Zea mays, keeping the EC annotations only from the manually reviewed component of UniProt, while removing those annotations that had not undergone manual review. We also extracted experimentally verified protein annotations for Zea mays from BRENDA (release 2016.1). The UniProt and BRENDA annotations were then merged by matching proteins based on the database crosslinks provided by BRENDA, resulting in the union of the reviewed annotations from UniProt and the experimentally verified annotations of BRENDA with duplicates removed. The merged protein annotations were then matched to the B73 RefGen_v2 translated gene models using BLASTP based on a sequence identity cutoff of 96% and an e-value cutoff of 1e-20. We selected the top scoring hit for each protein which resulted in matches to 1,815 unique maize proteins. EC annotations for alternate isoforms were consolidated at the gene level, resulting in 1,475 experimentally verified or manually reviewed protein functional annotations across 1,450 maize genes.

[20] A novel neural response algorithm for protein function prediction

• Authors: H. Yalamanchili, Quan-Wu Xiao, Junwen Wang
• Year: 2012
• Venue: BMC Systems Biology
• URL: https://www.semanticscholar.org/paper/0ae3a515fb360b8a4f225d623b23f86a63b5659c
• DOI: 10.1186/1752-0509-6-S1-S19
• PMID: 23046521
• PMCID: 3403322
• Citations: 7
• Summary: This work designed a novel automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain and gives it an edge over other available methods on annotation accuracy.
• Evidence snippets:
• Snippet 1 (score: 0.654)
  > Recent advances in high-throughput sequencing technologies have enabled the scientific community to sequence a large number of genomes. Currently there are 1,390 complete genomes [1] annotated in the KEGG genome repository and many more are in progress. However, experimental functional characterization of these genes cannot match the data production rate. Adding to this, more than 50% of functional annotations are enigmatic [2]. Even the well studied genomes, such as E. coli and C. elegans, have 51.17% and 87.92% ambiguous annotations (putative, probable and unknown) respectively [2]. To fill the gap between the number of sequences and their (quality) annotations, we need fast, yet accurate automated functional annotation methods. Such computational annotation methods are also critical in analyzing, interpreting and characterizing large complex data sets from high-throughput experimental methods, such as protein-protein interactions (PPI) [3] and gene expression data by clustering similar genes and proteins.
  > The definition of biological function itself is enigmatic in biology and highly context dependent [4][5][6]. This is part of the reason why more than 50% of functional annotations are ambiguous. The functional scope of a protein in an organism differs depending on the aspects under consideration. Proteins can be annotated based on their mode of action, i.e. Enzyme Commission (EC) number [7] (physiological aspect) or their association with a disease (phenotypic aspect). The lack of functional coherence increases the complexity of automated functional annotation. Another major barrier is the use of different vocabulary by different annotations. A function can be described differently in different organisms [8]. This problem can be solved by using ontologies, which serve as universal functional definitions. Enzyme Commission (E.C) [9], MIPS Functional Catalogue (FunCat) [10] and Gene Ontology (GO) [11] are such ontologies. With GO being the most recently and widely used, many automated annotation methods use GO for functional annotation.
  > Protein function assignment methods can be divided into two main categories -structure-based methods and sequence-based methods. A protein's function is highly related to its structure. Protein

Notes

• This provider combines search_papers_by_relevance with snippet_search.
• No synthesis or second-stage model call is performed.

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17. V. Beisvåg, Frode K. R. Jünge, Hallgeir Bergum, Lars Jølsum, S. Lydersen et al. (2006). GeneTools – application for functional annotation and statistical hypothesis testing. BMC Bioinformatics. https://www.semanticscholar.org/paper/1d9e0c2f67acd5bf64c659f1f3f8624325b6be8a
18. Anshul Tiwari, Siddharth J Modi, A. Girme, L. Hingorani (2023). Network pharmacology-based strategic prediction and target identification of apocarotenoids and carotenoids from standardized Kashmir saffron (Crocus sativus L.) extract against polycystic ovary syndrome. Medicine. https://www.semanticscholar.org/paper/3e3253804574634d1968a0fd5b65dd1674bff6c6
19. Jesse R. Walsh, M. Schaeffer, Peifen Zhang, S. Rhee, J. Dickerson et al. (2016). The quality of metabolic pathway resources depends on initial enzymatic function assignments: a case for maize. BMC Systems Biology. https://www.semanticscholar.org/paper/c41be7766c80fddb3f81c57ced799b8562370cc8
20. H. Yalamanchili, Quan-Wu Xiao, Junwen Wang (2012). A novel neural response algorithm for protein function prediction. BMC Systems Biology. https://www.semanticscholar.org/paper/0ae3a515fb360b8a4f225d623b23f86a63b5659c