Consensus coding sequence (CCDS) database: a standardized set of human and mouse protein-coding regions supported by expert curation

Abstract

The Consensus Coding Sequence (CCDS) project provides a dataset of protein-coding regions that are identically annotated on the human and mouse reference genome assembly in genome annotations produced independently by NCBI and the Ensembl group at EMBL-EBI. This dataset is the product of an international collaboration that includes NCBI, Ensembl, HUGO Gene Nomenclature Committee, Mouse Genome Informatics and University of California, Santa Cruz. Identically annotated coding regions, which are generated using an automated pipeline and pass multiple quality assurance checks, are assigned a stable and tracked identifier (CCDS ID). Additionally, coordinated manual review by expert curators from the CCDS collaboration helps in maintaining the integrity and high quality of the dataset. The CCDS data are available through an interactive web page (https://www.ncbi.nlm.nih.gov/CCDS/CcdsBrowse.cgi) and an FTP site (ftp://ftp.ncbi.nlm.nih.gov/pub/CCDS/). In this paper, we outline the ongoing work, growth and stability of the CCDS dataset and provide updates on new collaboration members and new features added to the CCDS user interface. We also present expert curation scenarios, with specific examples highlighting the importance of an accurate reference genome assembly and the crucial role played by input from the research community.

Genetic discovery and translational decision support from exome sequencing of 20,791 type 2 diabetes cases and 24,440 controls from five ancestries

“…principal), deleteriousness of annotation (i.e. prefer transcripts with higher impact annotations), CCDS 95 status of transcript (i.e. a high-quality transcript set), canonical status of transcript, and transcript length (i.e.…”

Stop codon context influences genome-wide stimulation of termination codon readthrough by aminoglycosides

“…All transcripts with the 3′ most stop codon were initially selected, ensuring that all 3′UTRs did not overlap with the CDS of alternatively spliced transcripts when quantifying SCR. Genes with multiple transcripts sharing this stop codon were then filtered, first selecting genes with primary APPRIS transcripts (Rodriguez et al, 2013), and then alternative APPRIS transcripts, followed by inclusion in the consensus CDS gene set (CCDS) (Pujar et al, 2018), and lastly selecting transcripts with the longest coding sequence for genes without APPRIS or CCDS transcripts for this 3′ most stop codon. For genes still containing multiple isoforms, transcripts were finally filtered by choosing transcripts with the shortest 3′UTRs, and lastly selecting those with the shortest 5′UTRs.…”

Comprehensive analysis of lncRNAs reveals candidate prognostic biomarkers in multiple cancer types

“…Counts-per-million (CPM) values were calculated for all genes to normalize read counts resulting from per-replicate differences of sequencing depths. We focused on transcript abundance values of consensus coding sequence genes (CCDS) database V22 [72] and filtered other classes of genes from our dataset. We also filtered lowly expressed genes and only included genes with above-baseline transcript abundance (CPM > 0.5) in at least two replicates.…”