Genenames.org: the HGNC and VGNC resources in 2019

Braschi, Bryony; Denny, Paul; Gray, Kristian; Jones, Tamsin E. M.; Seal, Ruth L.; Tweedie, Susan; Yates, Bethan; Bruford, Elspeth A.

doi:10.1093/nar/gky930

Cited by 322 publications

(275 citation statements)

References 30 publications

Supporting

Mentioning

272

Contrasting

Unclassified

Order By: Relevance

“…These sequences were initially divided into 9 phylogenetic groups as per the Uniprot nomenclature: AGC, CAMK, CK1, CMGC, NEK, RGC, TKL, TYR, and STE, and a tenth group of diverse kinases designated OTHER. Gene names were retrieved from the Human Gene Nomenclature Committee website (http://genenames.org) 34 . Each kinase sequence was labeled by group name underscore HGNC gene name, for example AGC_PRKACA for KAPCA_HUMAN.…”

Section: Identification Of Human Protein Kinasesmentioning

confidence: 99%

A Structurally-Validated Multiple Sequence Alignment of 497 Human Protein Kinase Domains

Modi

Dunbrack

2019

Preprint

View full text Add to dashboard Cite

Protein kinases are important in a large number of signaling pathways. Their dysregulation is involved in a number of human diseases, especially cancer. Studies on the structures of individual kinases have been used to understand the functions and phenotypes of mutations in other kinases that do not yet have experimental structures. The key factor in accurate inference by homology is an accurate sequence alignment. We present a parsimonious structure-based sequence alignment of 497 human protein kinase domains excluding atypical kinases, even those with related but somewhat different folds. Starting with a computed multiple sequence alignment, the alignment was manually refined in Jalview based on pairwise structural superposition onto a single kinase (Aurora A), followed by sequence alignment of the remaining kinases to their closest relatives with known structures. The alignment is arranged in 17 blocks of conserved regions and unaligned blocks in between that contain insertions of varying lengths present in only a subset of kinases. The aligned blocks contain well-conserved elements of secondary structure and well-known functional motifs, such as the DFG and HRD motifs. We validated the multiple sequence alignment by a pairwise, all-against-all alignment of 272 human kinases with known crystal structures. Our alignment has true-positive rate (TPR) and positive predictive value (PPV) accuracies of 97%. The remaining inaccuracy in our alignment comes from a few structures with shifted elements of secondary structure, and from the boundaries of aligned and unaligned regions, where compromises need to be made to encompass the majority of kinases. A new phylogeny of the protein kinase domains in the human genome based on our alignment indicates that 14 kinases previously labeled as "OTHER" can be confidently placed into the CAMK group. These kinases comprise the Aurora kinases, Polo kinases, ULK kinases, Calcium/calmodulin-dependent kinase kinases, and STK36.

show abstract

Section: Identification Of Human Protein Kinasesmentioning

confidence: 99%

A Structurally-Validated Multiple Sequence Alignment of 497 Human Protein Kinase Domains

Modi

Dunbrack

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…• Gene name: HUGO Gene Nomenclature Committee (HGNC) gene symbols [31] • Associated disease: Monarch Disease Ontology (MONDO) terms [32] •…”

Section: Text Box 1: Text Of Original Acmg/amp Recommendation For Funmentioning

confidence: 99%

Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework

Brnich

Tayoun

Couch³

et al. 2019

Preprint

193

View full text Add to dashboard Cite

Background:The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) clinical variant interpretation guidelines established criteria (PS3/BS3) for functional assays that specified a "strong" level of evidence. However, they did not provide detailed guidance on how functional evidence should be evaluated, and differences in the application of the PS3/BS3 codes is a contributor to variant interpretation discordance between laboratories. This recommendation seeks to provide a more structured approach to the assessment of functional assays for variant interpretation and guidance on the use of various levels of strength based on assay validation. Methods: The Clinical Genome Resource (ClinGen) Sequence Variant Interpretation (SVI)Working Group used curated functional evidence from ClinGen Variant Curation Expert Paneldeveloped rule specifications and expert opinions to refine the PS3/BS3 criteria over multiple inperson and virtual meetings. We estimated odds of pathogenicity for assays using various numbers of variant controls to determine the minimum controls required to reach moderate level evidence. Feedback from the ClinGen Steering Committee and outside experts were incorporated into the recommendations at multiple stages of development. Results:The SVI Working Group developed recommendations for evaluators regarding the assessment of the clinical validity of functional data and a four-step provisional framework to determine the appropriate strength of evidence that can be applied in clinical variant interpretation. These steps are: 1. Define the disease mechanism; 2. Evaluate applicability of general classes of assays used in the field; 3. Evaluate validity of specific instances of assays; 4.Apply evidence to individual variant interpretation. We found that a minimum of eleven total 4 pathogenic and benign variant controls are required to reach moderate-level evidence in the absence of rigorous statistical analysis. Conclusions:The recommendations and approach to functional evidence evaluation described here should help clarify the clinical variant interpretation process for functional assays. Further, we hope that these recommendations will help develop productive partnerships with basic scientists who have developed functional assays that are useful for interrogating the function of a variety of genes.

show abstract

“…Human protein symbols of both the PIN and protein sets (see below) adopt the HUGO gene nomenclature (Braschi et al 2019). The InWeb_IM network (v 2016_09_12) (Li et al 2017) was taken for the human PPI network.…”

Section: Protein-protein Interaction Network (Pin)mentioning

confidence: 99%

Association Study based on Topological Constraints of Protein-Protein Interaction Networks

Guo

Qin

2020

Preprint

View full text Add to dashboard Cite

The non-random interaction pattern of a protein-protein interaction network (PIN) is biologically informative but its potentials have not been fully utilized in omics studies. Here, we propose a network-permutation-based association study (NetPAS) method that gauges the observed interactions between two sets of genes based on the comparison between permutation null models and the empirical networks. This enables NetPAS to evaluate relationships, constrained by network topology, between gene sets related to different phenotypes. We demonstrated the utility of NetPAS in 50 well-curated gene sets and comparison of association studies using Z-scores, p-values or overrepresentations.Using NetPAS, a weighted human disease network was generated from the association scores of 19 gene sets from OMIM. We also applied NetPAS in gene sets derived from gene ontology and pathway annotations and showed that NetPAS uncovered functional terms missed by DAVID and other network-based enrichment tools. Overall, we show that NetPAS can take topological constraints of molecular networks into account and offer new perspectives than existing methods.

show abstract

Genenames.org: the HGNC and VGNC resources in 2019

Cited by 322 publications

References 30 publications

A Structurally-Validated Multiple Sequence Alignment of 497 Human Protein Kinase Domains

A Structurally-Validated Multiple Sequence Alignment of 497 Human Protein Kinase Domains

Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework

Association Study based on Topological Constraints of Protein-Protein Interaction Networks

Contact Info

Product

Resources

About