CHESS 3: an improved, comprehensive catalog of human genes and transcripts based on large-scale expression data, phylogenetic analysis, and protein structure

Abstract

The original CHESS database of human genes was assembled from nearly 10,000 RNA sequencing experiments in 53 human body sites produced by the Genotype-Tissue Expression (GTEx) project, and then augmented with genes from other databases to yield a comprehensive collection of protein-coding and noncoding transcripts. The construction of the new CHESS 3 database employed improved transcript assembly algorithms, a new machine learning classifier, and protein structure predictions to identify genes and transcripts likely to be functional and to eliminate those that appeared more likely to represent noise. The new catalog contains 41,356 genes on the GRCh38 reference human genome, of which 19,839 are protein-coding, and a total of 158,377 transcripts. These include 14,863 novel protein-coding transcripts. The total number of transcripts is substantially smaller than earlier versions due to improved transcriptome assembly methods and to a stricter protocol for filtering out noisy transcripts. Notably, CHESS 3 contains all of the transcripts in the MANE database, and at least one transcript corresponding to the vast majority of protein-coding genes in the RefSeq and GENCODE databases. CHESS 3 has also been mapped onto the complete CHM13 human genome, which gives a more-complete gene count of 43,773 genes and 19,968 protein-coding genes. The CHESS database is available at http://ccb.jhu.edu/chess.

Investigating Open Reading Frames in Known and Novel Transcripts using ORFanage

“…And because RNA-seq datasets often produce large numbers of novel transcripts, the efficiency and scalability of ORFanage make it suitable for datasets of any size. We have recently applied our method to annotate ORFs in novel transcripts for the revised CHESS 3 4 catalog, and to help identify novel structurally stable isoforms that were then confirmed using AlphaFold2 56 .…”

“…This optimization technique does not require sequence alignment or pre-computed genome indices, greatly reducing the computational burden of running the tool and making the analysis far more efficient than an alignment-based approach. We have tested ORFanage on datasets comprising tens of millions of transcripts assembled from thousands of RNA-seq experiments 3, 4 and found that it runs robustly on these data.…”

“…One of the main applications of ORFanage is to search for ORFs in datasets containing large numbers of transcripts that have not been assigned open reading frames. In particular, ORFanage can annotate transcriptome assemblies from RNA-seq experiments, which often contain many novel splice variants, even for well-annotated genomes 3, 4, 42 . In these cases, ORFanage can identify valid ORFs for protein-coding transcripts using known annotation as a guide.…”

“…We focused our experiments on 1,448 samples from brain tissue because these represented the most diverse collection of samples in the dataset. We ran ORFanage on the complete, unfiltered set of assemblies containing 6,674,316 isoforms that were assembled originally for the CHESS human annotation database 3, 4 .…”

“…Approximately 20,000 protein-coding genes have been annotated for the human genome [1][2][3][4][5] . While a single isoform is often the source of the dominant protein [6][7][8] , many human gene loci express isoforms that encode different protein sequences, some of which may be tissue-specific 9,10 .…”