Description

This track displays 664,558 unique small open reading frames (sORFs) in the human genome from MetamORF, a meta-database that consolidates sORF data identified by both experimental and computational approaches. sORFs are defined as ORFs encoding fewer than 100 amino acids (excluding stop codons and introns).

MetamORF was built by gathering publicly available sORF data from multiple sources, normalizing it, and removing redundancy. From 2,594,154 source ORFs across human and mouse, MetamORF identified 1,162,675 unique ORFs (664,771 human, 497,904 mouse) associated with 153,553 unique transcripts. The database enables comparison of sORFs across distinct original data sources at the ORF, transcript, and gene levels. For full documentation, see the MetamORF documentation page.

Data Sources

The human sORFs in MetamORF were compiled from seven primary data sources and 46 individual ribosome profiling datasets from sORFs.org. The primary sources are:

Source	Description	Reference
Erhard et al. 2018	Union of ORFs detected by PRICE, RP-BP, ORF-RATER, or annotated in Ensembl v75	Nat Methods 2018
Johnstone et al. 2016	Location and translation data for analyzed transcripts and ORFs	EMBO J 2016
Laumont et al. 2016	Cryptic MAPs (minor ORF-encoded peptides) with genomic and proteomic features	Nat Commun 2016
Mackowiak et al. 2015	Systematic identification of sORFs across vertebrate genomes	Genome Biol 2015
Samandi et al. 2017	Alternative protein predictions based on RefSeq GRCh38	eLife 2017
sORFs.org	Repository of sORFs from 46 individual ribosome profiling experiments	Olexiouk et al., Nucleic Acids Res 2018

ORFs were identified using three main approaches: bioinformatic predictions, ribosome profiling experiments, and mass spectrometry (proteomics, peptidomics, and proteogenomics).

ORF Classification

MetamORF classifies ORFs by their position relative to annotated coding sequences:

Upstream – located in the 5' UTR, upstream of the main CDS
Downstream – located in the 3' UTR, downstream of the main CDS
Overlapping – overlapping with the annotated CDS
Intronic – located within an intron
InCDS / CDS / NewCDS – within or coinciding with a coding sequence
Alternative – in a different reading frame than the annotated CDS start
Opposite – on the opposite strand from the transcript

ORFs are also classified by the biotype of their host RNA: intergenic, ncRNA, pseudogene, NMD (nonsense-mediated decay), or readthrough transcripts.

Display Conventions and Configuration

Items are displayed in BED 12 format showing the exon/intron structure of each sORF.

Data Access

The raw data can be explored interactively with the Table Browser or the Data Integrator. The data can be accessed from scripts through our API; the track name is "metamorf".

For automated download and analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed, which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g.

bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/ncOrfs/metamorf/MetamORF.bb -chrom=chr21 -start=0 -end=100000000 stdout

The original data and additional downloads are available from the MetamORF website. Source code is available on GitHub.

Methods

The MetamORF BED 12 data was obtained from the MetamORF track hub and converted to bigBed format at UCSC. Coordinates are on the GRCh38/hg38 assembly (based on Ensembl release 90).

Credits

Thanks to the MetamORF team at the TAGC (Theories and Approaches of Genomic Complexity) laboratory, Aix-Marseille University, for creating this resource and making it publicly available.

References

Erhard F, Halenius A, Zimmermann C, L'Hernault A, Kowalewski DJ, Weekes MP, Stevanovic S, Zimmer R, Dölken L. Improved Ribo-seq enables identification of cryptic translation events. Nat Methods. 2018 May;15(5):363-366. PMID: 29529017; PMC: PMC6152898

Johnstone TG, Bazzini AA, Giraldez AJ. Upstream ORFs are prevalent translational repressors in vertebrates. EMBO J. 2016 Apr 1;35(7):706-23. PMID: 26896445; PMC: PMC4818764

Laumont CM, Daouda T, Laverdure JP, Bonneil É, Caron-Lizotte O, Hardy MP, Granados DP, Durette C, Lemieux S, Thibault P et al. Global proteogenomic analysis of human MHC class I-associated peptides derived from non-canonical reading frames. Nat Commun. 2016 Jan 5;7:10238. PMID: 26728094; PMC: PMC4728431

Mackowiak SD, Zauber H, Bielow C, Thiel D, Kutz K, Calviello L, Mastrobuoni G, Rajewsky N, Kempa S, Selbach M et al. Extensive identification and analysis of conserved small ORFs in animals. Genome Biol. 2015 Sep 14;16:179. PMID: 26364619; PMC: PMC4568590

Olexiouk V, Van Criekinge W, Menschaert G. An update on sORFs.org: a repository of small ORFs identified by ribosome profiling. Nucleic Acids Res. 2018 Jan 4;46(D1):D497-D502. PMID: 29140531; PMC: PMC5753181

Samandi S, Roy AV, Delcourt V, Lucier JF, Gagnon J, Beaudoin MC, Vanderperre B, Breton MA, Motard J, Jacques JF et al. Deep transcriptome annotation enables the discovery and functional characterization of cryptic small proteins. Elife. 2017 Oct 30;6. PMID: 29083303; PMC: PMC5703645