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Morales, Joannella; Pujar, Shashikant; Loveland, Jane E; Astashyn, Alex; Bennett, Ruth; Berry, Andrew; Cox, Eric; Davidson, Claire; Ermolaeva, Olga; Farrell, Catherine M; Fatima, Reham; Gil, Laurent; Goldfarb, Tamara; Gonzalez, Jose M; Haddad, Diana; Hardy, Matthew; Hunt, Toby; Jackson, John; Joardar, Vinita S; Kay, Michael; Kodali, Vamsi K; McGarvey, Kelly M; McMahon, Aoife; Mudge, Jonathan M; Murphy, Daniel N; Murphy, Michael R; Rajput, Bhanu; Rangwala, Sanjida H; Riddick, Lillian D; Thibaud-Nissen, Françoise; Threadgold, Glen; Vatsan, Anjana R; Wallin, Craig; Webb, David; Flicek, Paul; Birney, Ewan; Pruitt, Kim D; Frankish, Adam; Cunningham, Fiona; Murphy, Terence D
Nature, 04/2022, Volume: 604, Issue: 7905Journal Article
Comprehensive genome annotation is essential to understand the impact of clinically relevant variants. However, the absence of a standard for clinical reporting and browser display complicates the process of consistent interpretation and reporting. To address these challenges, Ensembl/GENCODE and RefSeq launched a joint initiative, the Matched Annotation from NCBI and EMBL-EBI (MANE) collaboration, to converge on human gene and transcript annotation and to jointly define a high-value set of transcripts and corresponding proteins. Here, we describe the MANE transcript sets for use as universal standards for variant reporting and browser display. The MANE Select set identifies a representative transcript for each human protein-coding gene, whereas the MANE Plus Clinical set provides additional transcripts at loci where the Select transcripts alone are not sufficient to report all currently known clinical variants. Each MANE transcript represents an exact match between the exonic sequences of an Ensembl/GENCODE transcript and its counterpart in RefSeq such that the identifiers can be used synonymously. We have now released MANE Select transcripts for 97% of human protein-coding genes, including all American College of Medical Genetics and Genomics Secondary Findings list v3.0 (ref. ) genes. MANE transcripts are accessible from major genome browsers and key resources. Widespread adoption of these transcript sets will increase the consistency of reporting, facilitate the exchange of data regardless of the annotation source and help to streamline clinical interpretation.
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