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] Trade-offs, trade-ups, and high mutational parallelism underlie microbial adaptation during extreme cycles of feast and famine

• Authors: Megan G. Behringer, Wei-Chin Ho, Samuel F. Miller, Sarah B. Worthan, Zeer Cen et al.
• Year: 2024
• Venue: Current biology : CB
• URL: https://www.semanticscholar.org/paper/13c71a271ad81ea813516193454b0fed04b2cd2b
• DOI: 10.1016/j.cub.2024.02.040
• PMID: 38460514
• PMCID: 11066936
• Citations: 17
• Summary: It is found that evolution in response to extreme feast/famine is characterized by narrow adaptive trajectories with high mutational parallelism and notable mutational order, which demonstrate how microbes can navigate the adaptive landscapes of regularly fluctuating conditions and ultimately follow mutational trajectories that confer benefits across diverse environments.
• Evidence snippets:
• Snippet 1 (score: 0.775)
  > Genes overrepresented for nonsynonymous and structural mutations were classified into functional groups based on the functional annotation table constructed with DAVID v.2023q3 91,92 (Table S4). We established the following rules for generalizing genes into functional groups: Chaperone: genes that encode proteins that perform chaperone or chaperonin-like activities (Uniprot Keyword: KW-0143); Envelope: Genes that encode proteins involved in the maintenance of the cell-envelope such as phospholipid biosynthesis (GO-BP: 0008654) and peptidoglycan biosynthesis (GO-BP: 0000270); Metabolism: Genes that encode proteins involved in general metabolic functions, such as purine metabolism (KEGG: eco00230), amino acid metabolism (KEGG: eco00470, eco00330); or TCA Cycle (KEGG: eco00020); Regulators: Genes that encode proteins directly involved in transcription (GO-BP: 0031564; Uniprot Keyword: KW-0804), translation (GO-BP:0006412, GO-BP:0006413; Uniprot Keyword: KW-0689), or the regulation of transcription (GO-BP: 0006355, GO-BP:2000143, GO-BP:0006353, GO-BP: GO:0045893; Uniprot Keyword: KW-0805); and Transport: Genes that encode proteins involved in transport: such as ABC transporters (Interpro: PR017871), MFS transporters (Interpro: IPR011701); symporters (Interpro: IPR023954, IPR001204, IPR001734, IPR023025, IPR004840), or Antiporters (Interpro: IPR004771). A combination of resources were used to determine if a parallel mutation arose in a functional region of a protein including, EcoCyc, 96 UniProt, 97 and the primary literature (see supplemental bibliography).

[2] 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: 0.766)
  > 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.

[3] 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: 0.738)
  > 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.

[4] Comparative Transcriptomics Reveals Genes Commonly Induced by Distinct Stressors in Chlamydia

• Authors: Ronald Haines, Danny Wan, Guangming Zhong, Huizhou Fan
• Year: 2025
• Venue: bioRxiv
• URL: https://www.semanticscholar.org/paper/c51286c6ea8e33fd4c23666545f703bc3c2b4689
• DOI: 10.64898/2025.12.30.696969
• PMID: 41509351
• PMCID: 12776308
• Summary: It is demonstrated that adaptation to different biological stressors in C. trachomatis is driven by distinct transcriptomic reprogramming, while consistently involving a subset of functions that may represent points of vulnerability for disrupting chlamydial persistence.
• Evidence snippets:
• Snippet 1 (score: 0.727)
  > Genes annotated as "hypothetical protein" in the reference genomes were evaluated to refine functional descriptions and to support ontology assignments used in the functional-category analyses. For each hypothetical protein gene, we queried ChlamBase to retrieve curated locus information, alternative gene names, and any community-or literature-derived annotations for the corresponding gene product (74). We also reviewed the corresponding UniProtKB entry to extract the current protein name, description, predicted features, and evidence context for functional annotations (75).
  > To incorporate experimentally supported information not captured in database summaries, we performed targeted PubMed searches using locus tags and commonly used aliases (e.g., CT_694, CTL_0360), as well as gene and protein name synonyms. When peer-reviewed studies provided direct experimental evidence (e.g., secretion via the type III secretion system, inclusion membrane localization, interaction partners, or phenotypes associated with targeted mutagenesis or knockdown), we used those findings to refine the functional description applied in this study

[5] Potential role of multiple carbon fixation pathways during lipid accumulation in Phaeodactylum tricornutum

• Authors: Jacob J. Valenzuela, Aurélien Mazurie, R. Carlson, R. Gerlach, K. Cooksey et al.
• Year: 2012
• Venue: Biotechnology for Biofuels
• URL: https://www.semanticscholar.org/paper/5f205021fe6a9b10aba8237bdac8ceada97adde0
• DOI: 10.1186/1754-6834-5-40
• PMID: 22672912
• PMCID: 3457861
• Citations: 206
• Influential citations: 10
• Summary: The results indicate that P. tricornutum continued carbon dioxide reduction when population growth was arrested and different carbon-concentrating mechanisms were used dependent upon exogenous DIC levels, and suggest that the build-up of precursors to the acetyl-CoA carboxylases may play a more significant role in TAG synthesis rather than the actual enzyme levels of acetyl
• Evidence snippets:
• Snippet 1 (score: 0.712)
  > Cufflinks output files had transcripts identified by uniprot accessions. Using the DAVID (Database for Annotation, Visualization, and Integrated Discovery) [67] Gene ID conversion tool, uniprot accessions were converted to Locus Tag IDs and Protein IDs. Once all accessions were converted, the DAVID Functional Annotation tool was used to retrieve gene names as well as KEGG (Kyoto Encyclopedia of Genes and Genomes) Pathway information. For genes that were identified as hypothetical proteins, searches were performed on the JGI Phaeodactylum tricornutum v2.0 genome website (http://genome.jgi-psf. org/Phatr2/Phatr2.home.html) and based on best hits, % ID, score, and consistency of the top hits, genes were either identified or remained as hypothetical. Genes were also searched on NCBI and ENSEMBL genome browsers for cross-referencing. To assign genes into pathways, we used KEGG maps for P. tricornutum as a backbone. Genes for major pathways were searched manually to find genes not directly annotated in the P. tricornutum KEGG maps. Gene lists were compiled for the major pathways and developed into network maps.
  > Organelle targeting for transcript products was done based on annotations from the databases of JGI, NCBI, and ENSEMBL. If no localization was found, eukaryotic organelle localizations were predicted with TargetP 1.1. server [68] in both plant and non-plant mode. Amino acid sequences were also checked for a peroxisomal targeting sequence (SKL, serine-lysine-leucine). If potential targeting was not identified, we assumed that the gene product occurred in the cytoplasm. If the gene was an integral membrane protein we again checked JGI, NCBI, and ENSEMBL, and if targeting could not be determined we located the gene in the most biologically relevant membrane (e.g., light harvesting complex in the plastid).

[6] SHIELD: an integrative gene expression database for inner ear research

• Authors: Jun Shen, D. Scheffer, Kelvin Y. Kwan, D. Corey
• Year: 2015
• Venue: Database: The Journal of Biological Databases and Curation
• URL: https://www.semanticscholar.org/paper/fc7704155cefd9f1c767dc2a03a5025c77d19437
• DOI: 10.1093/database/bav071
• PMID: 26209310
• PMCID: 4513695
• Citations: 130
• Influential citations: 9
• Summary: The Shared Harvard Inner Ear Laboratory Database (SHIELD), an integrated resource that seeks to compile, organize and analyse the genomic, transcriptomic and proteomic knowledge of the inner ear, is developed.
• Evidence snippets:
• Snippet 1 (score: 0.707)
  > Many public databases of gene information are available (11)(12)(13)(14)(15)(16). However, different public databases often use different sets of unique identifiers (IDs) to describe the same genes or homologous genes in different species. One challenge of comparing large-scale biological datasets is the unification of gene names; otherwise, researchers spend a lot of effort in converting gene IDs when searching different databases. Another is the likelihood of missing some databases due to unfamiliarity; this is particularly true for clinicians and researchers who are specialized in inner ear research but are not necessarily familiar with genomics and bioinformatics. One goal of the SHIELD is to integrate relevant gene annotation information from various public databases in one centralized location.
  > For the SHIELD, annotations were derived from public databases and literature. Currently implemented annotations include official gene symbols, description of the gene name and synonyms, human and mouse chromosome cytogenetic banding, RefSeq RNA and protein (for protein coding genes) accession numbers, National Center for Biotechnology Information (NCBI) Entrez gene ID, genomic coordinates in both mouse reference genome assemblies mm9 and mm10, Ensembl, the Vertebrate Genome Annotation Database (VEGA) Mouse Genome Informatics, UniProt, Online Mendalian Inheritance in Man and gene ontology.
  > For each protein coding genes, we display all UniProt protein isoforms for that gene, the length in amino acid residues and the predicted number of transmembrane domains (TMs). We predicted TMs by running TMHMM2.0 run on all UniProt protein isoforms of each gene (17). The number of TMs is of special interest for research in sensory function, because many key proteins involved in signaling-such as the mechanotransduction ion channels-are integral membrane proteins. This information would help identify candidate genes for the components of the mechanotransduction apparatus of the inner ear.
  > We also performed manual curation of inner ear disorders including syndromic and non-syndromic hearing loss according to the Hereditary Hearing Loss Homepage (http://hereditaryhearingloss.org) and primary literature.

[7] 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: 0.700)
  > 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.

[8] 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: 0.698)
  > 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.

[9] Discovering and Summarizing Relationships Between Chemicals, Genes, Proteins, and Diseases in PubChem

• Authors: L. Zaslavsky, Tiejun Cheng, A. Gindulyte, Siqian He, Sunghwan Kim et al.
• Year: 2021
• Venue: Frontiers in Research Metrics and Analytics
• URL: https://www.semanticscholar.org/paper/57b86aef9aae576c2ae4199c0b74971f4c195211
• DOI: 10.3389/frma.2021.689059
• PMID: 34322655
• PMCID: 8311438
• Citations: 23
• Influential citations: 1
• Summary: The literature knowledge panels developed and implemented in PubChem help to uncover and summarize important relationships between chemicals, genes, proteins, and diseases by analyzing co-occurrences of terms in biomedical literature abstracts.
• Evidence snippets:
• Snippet 1 (score: 0.697)
  > We decided to prioritize human genes and proteins. The following strategy has been implemented to resolve gene and protein text entities to the most reasonable gene, protein, or enzyme symbol (corresponding to human, when possible):
  > -Try to find a match among Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC) names (Braschi et al., 2019;HUGO, 2021);
  > -Try to find a match among names in The IUPHAR/BPS Guide to Pharmacology (Armstrong et al., 2020; IUPHAR/BPS, 2021); -Try to find matches among names in UniProt (Bateman et al., 2017); -Otherwise, try to match to an enzyme name and resolve to an EC number (Bairoch, 2000;Expassy, 2021).
  > In general, it is very difficult and often impossible to distinguish the name of a gene from the name of the protein encoded by that gene. Therefore, gene and protein names are not strictly distinguished from each other but considered as one category. Therefore, the annotations considered in this study can be grouped into three categories: chemicals, genes/proteins, and diseases.

[10] A Combined Omics Approach to Generate the Surface Atlas of Human Naive CD4+ T Cells during Early T-Cell Receptor Activation*

• Authors: Anke Graessel, S. Hauck, C. von Toerne, Edda Kloppmann, T. Goldberg et al.
• Year: 2015
• Venue: Molecular & Cellular Proteomics
• URL: https://www.semanticscholar.org/paper/30ecc3a03a0172355ba77e397836c1adafae5ae9
• DOI: 10.1074/mcp.M114.045690
• PMID: 25991687
• Citations: 36
• Influential citations: 2
• Summary: An extensive surface atlas provides an overall naive CD4+ T cell surface resource and will enable future studies aiming at a deeper understanding of mechanisms of T-cell biology allowing the identification of novel immune targets usable for the development of therapeutic treatments.
• Evidence snippets:
• Snippet 1 (score: 0.696)
  > 30) accession numbers (AC; UniProt release 2013_10). In general, several human UniProt ACs can be assigned to the same gene name. To reduce redundancy, for each gene name we chose, if available, the reviewed UniProt ACs (Swiss-Prot) and otherwise the unreviewed (TrEMBL). Thereby, we assigned UniProt ACs to 12,263 gene names (supplemental Table S3). (Remaining redundancy is because of 29 gene names that are assigned to more than one reviewed UniProt AC. Additionally, we retained 36 unreviewed UniProt ACs that we predicted to be plasma membrane proteins (criteria see below)). For these UniProtKB ACs, we extracted the subcellular localization (Uni-Prot_SL) annotation from the UniProtKB/Swiss-Prot database (30), if available. Otherwise, we predicted subcellular localization using Loc-Tree3 (38) and transmembrane helices (TMHs) using PolyPhobius (39). We identified cell surface proteins as follows (supplemental Table S3): (1) From UniProt_SL, we accepted all experimentally verified and probable subcellular localizations. Here, we were in particular interested in cell surface and cell membrane (plasma membrane) annotations. For proteins localized in the plasma membrane, we additionally required that they are single-or multi-pass membrane proteins. If no further information is given, we additionally required at least one TMH predicted by PolyPhobius, otherwise these were classified as putative cell surface proteins. For peripheral or lipid-anchored membrane proteins, we required that they be localized on the extracellular side of the plasma membrane. Those located on the cytoplasmic side were excluded; otherwise, these proteins were classified as putative cell surface proteins. (2) If we could not annotate the protein using UniProt_SL, we used the subcellular localization predicted by LocTree3 and required at least one TMH predicted by PolyPhobius for plasma membrane proteins.
  > Genes passing these selection criteria were analyzed for differential gene expression with the GeneSpring software GX 12.5

[11] 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: 106
• 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: 0.695)
  > 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.

[12] 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: 0.693)
  > 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.

[13] Identifying orthologs with OMA: A primer

• Authors: Monique Zahn-Zabal, C. Dessimoz, Natasha M. Glover
• Year: 2020
• Venue: F1000Research
• URL: https://www.semanticscholar.org/paper/3b77eadcdd6979352c81d0876b0ed3a3ef4215d6
• DOI: 10.12688/f1000research.21508.1
• PMID: 32089838
• PMCID: 7014581
• Citations: 39
• Summary: This Primer is organized in two parts and provides all the necessary background information to understand the concepts of orthology, how to infer them and the different subtypes of Orthology in OMA, as well as what types of analyses they should be used for.
• Evidence snippets:
• Snippet 1 (score: 0.688)
  > Select 'Full-text search' in the drop down menu, and type your keyword to search for. This can be alternative identifiers; for example, a search with the Ensembl gene (ENSG00000163993), transcript (ENST00000296370) or protein (ENSP00000296370) identifiers, PubMed identifiers (PMID:15632002) all return our original gene, HUMAN22168. Other text that may be in the description of the gene can be used as well. Quotes (") can be used to search for an exact sequence of words. For example, a full-text search for "S100 calcium-binding protein P" also retrieves HUMAN22168.
  > 4. Get more information about your gene. After searching for your gene, you will be taken to the gene's page, which provides some external information. You can also find this by clicking on the Information tab. The information for our example gene, which corresponds to the human protein S100 calcium binding protein P, is shown in Figure 5. The information page includes the OMA ID, description, organism, locus, other IDs and cross-reference, domain architectures, and Gene Ontology annotations.

[14] 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: 1594
• Influential citations: 153
• 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: 0.687)
  > 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.

[15] Environment sensing and response mediated by ABC transporters

• Authors: Sarah E. Giuliani, A. Frank, Danielle M. Corgliano, Catherine Seifert, L. Hauser et al.
• Year: 2011
• Venue: BMC Genomics
• URL: https://www.semanticscholar.org/paper/cc4616ae2df0e74a413ae71b9560d956107ef0c5
• DOI: 10.1186/1471-2164-12-S1-S8
• PMID: 21810210
• PMCID: 3223731
• Citations: 64
• Influential citations: 2
• Summary: The functional screen identified specific ligands that bound to ABC transporter periplasmic binding subunits from R. palustris that provide unique insight for the metabolic capabilities of this organism and are consistent with the ecological niche of strain isolation.
• Evidence snippets:
• Snippet 1 (score: 0.686)
  > addition of these targets to the study efflux pump associated proteins resulted in a total of 108 candidate binding proteins (BP's) targeted for the protein production and ligand screening protocols.
  > Interestingly, of the 108 candidate BP's, 21 were not clustered with an integral membrane and ATPase subunits (Additional file 1 and TransportDB) based on either proximity in genome and/or functional annotation (predicted substrate) from sequence homology. There were 72 total gene clusters having at least one representative of each ABC transporter component; of these, 9 transporters were associated with 2 SBP's, 1 was associated with 3 SBP's, 61 had one associated SBP. Four additional gene clusters were each indicated by associating one SBP with either an integral membrane or an ATPase subunit. One transporter (RPA2039) was predicted to have a fused integral membrane and solute binding subunits in a single polypeptide but was not included in the final list.

[16] Gene and protein nomenclature in public databases

• Authors: Katrin Fundel, Ralf Zimmer
• Year: 2006
• Venue: BMC Bioinformatics
• URL: https://www.semanticscholar.org/paper/75e725b9aa79f1b6943b32164778e5b52f715852
• DOI: 10.1186/1471-2105-7-372
• PMID: 16899134
• PMCID: 1560172
• Citations: 58
• Influential citations: 2
• Summary: The combination of data contained in different databases allows the generation of gene and protein name dictionaries that contain significantly more used names than dictionaries obtained from individual data sources, and curation of combined dictionaries considerably increases size and decreases ambiguity.
• Evidence snippets:
• Snippet 1 (score: 0.686)
  > annotation is done manually and concerns, besides nomenclature, protein structure, function, associated diseases. The UniProt consortium is concerned with integrating information in the UniProt Knowledge-base. This provides a central, stable, comprehensive, fully classified, Overlap between different data sources Figure 3 Overlap between different data sources. The overlap between gene name dictionaries compiled from different data sources varies for different organisms and pairs of databases. Organism-specific databases and Entrez Gene show highest overlap for all organisms. For notation see Figure 1, for details see section 'Overlap between different data sources'. Overlap between data sources org-spec -SwissProt org-spec -Entrez SwissProt -Entrez richly and accurately annotated protein sequence database with extensive cross-references to other data sources. Currently, Swiss-Prot forms part of the UniProt Knowledgebase. With the advent of the UniProt project, the expectation will be that Swiss-Prot/UniProt and Entrez Gene will increasingly share nomenclature and that the mapping between databases will be increasingly complete and unambiguous. This will facilitate the generation of gene name dictionaries and text mining applications. Figure 4 shows the degree of ambiguity between the dictionaries and a lexicon of common English words, or domain-related non-gene and non-protein terms, respectively. This figure shows some important differences between gene names of different organisms. For the comparison of organisms we focus on the results for the combined and curated dictionaries. Yeast has the lowest ambiguity with common English words as well as with domain-related terms (0.01-0.3%, resp. 0.09-0.4%). The highest degree of ambiguity with common English words was found for fly (0.55%-2.4%). This is due to frequent phenotypic descriptions that are used as gene names and abbreviations thereof (e.g. in FlyBase, We is the abbrevia-tion and valid symbol for a gene named Washed eye, thus the abbreviation as well as the words of the long name are perfect English words). The gene nomenclature guideline for FlyBase is relatively unrestricted [29], it states that gene names must be concise, should allude to the genes function, mutant ph

[17] 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: 13
• 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: 0.681)
  > 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.

[18] A Scorecard for Information Synthesis in Multiple Experimental Conditions: Application to Bacterial Biofilm Matrix Transcriptomics

• Authors: Mauro Nascimben, Lia Rimondini
• Year: 2025
• Venue: Current Microbiology
• URL: https://www.semanticscholar.org/paper/ad5e7f52b482160ea1e8070e3861814b7826f13e
• DOI: 10.1007/s00284-025-04435-3
• PMID: 40924154
• PMCID: 12420718
• Summary: A Python-scripted software tool has been developed to help study the heterogeneity of gene changes, markedly or moderately expressed, when several experimental conditions are compared, and identified and tracked genes meaningful for bacterial metabolism and survival in response to antibiotics, adjuvants, and biocompatible materials.
• Evidence snippets:
• Snippet 1 (score: 0.672)
  > Finally, a comprehensive overview of all experimental conditions can be achieved by considering the descriptions of the genes located in the outer regions of the scorecards, which may help identify recurring biological processes from annotations. Frequent terms were "phenol-soluble modulin" (found 70 times in 1126 gene descriptions), "nitrate reductase" (60 times in 1126 gene descriptions), "PF07968:Leukocidin/Hemolysin toxin family" (appearing 62 on 1126 gene descriptions), "ABC transporter" (encountered 82 times), and "crwA (65 times); while "crwA" should be about the stress caused by antibiotics that target the cell wall, ABC are transporters of substances across cellular membranes also functioning as efflux pumps. A slightly less recurring keyword was "metallopeptidase" (36 times) and "complement convertase inhibitor" (23 occurrences), both as Efb (extracellular fibrinogen-binding protein) and Ecb (extracellular complement-binding protein).

[19] 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: 0.669)
  > In the mouse, rat and human genomes, there are approximately twice as many synonyms (aliases) as official symbols. This large number of obsolete gene symbols leads to the problem with unequivocal identification of genes in the literature data because some synonyms can be attributed to more than one current official symbol of protein-coding genes. The ambiguity of symbols may lead to misidentification of 10% of rodent genes and even 18% of human protein-coding genes. Such misidentifications are most likely in case of literature data retrieved from older studies using past versions of gene nomenclature with a large number of obsolete symbols. A simple solution for this problem is usage of stable gene IDs (Table 2) for the unequivocal identification of genes, provided that the genomic information associated with these IDs is retrieved directly from proprietary databases containing the most accurate data. The usage of stable identifiers is important not only for accurate interpretation of literature data but also for currently published datasets because neither the annotation of genomes nor the understanding of gene function are complete.
  > This incompleteness means that official gene symbols will change over time. Data based only on gene symbols should be used cautiously to avoid misidentification of genes. A solution for this problem is our R script that updates gene symbols and provides annotation about their unique or ambiguous character.

[20] A Metaproteomic Analysis of the Response of a Freshwater Microbial Community under Nutrient Enrichment

• Authors: D. A. Russo, Narciso Couto, A. Beckerman, J. Pandhal
• Year: 2016
• Venue: Frontiers in Microbiology
• URL: https://www.semanticscholar.org/paper/c4d605c6246481fb365055e26c9bca05adad70ea
• DOI: 10.3389/fmicb.2016.01172
• PMID: 27536273
• PMCID: 4971099
• Citations: 26
• Summary: In oligotrophic conditions, environmental adaptation proteins from cyanobacteria suggested better resilience compared to algae in a low carbon nutrient enriched environment, and how primary producers control bacterial resources in freshwater environments is highlighted.
• Evidence snippets:
• Snippet 1 (score: 0.666)
  > Proteins were semi-automatically attributed a functional classification. Briefly, a list of UniProt accession numbers was collated from each sample and queried utilizing the UniProt Retrieve/ID mapping tool 2 . Column options 'Keywords' and 'Gene ontology (biological process)' were selected. Incomplete or ambiguous annotations were then manually completed by searching for the individual UniProt accession numbers on Pfam 3 and EggNOG 4 .

Notes

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

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