Assembly: Human Dec. 2013 (GRCh38/hg38) Data last updated at UCSC: 2026-05-19 11:39:36
Description
This track displays 229,251 non-canonical open reading frames (ORFs) from
nuORFdb v1.2
(novel unannotated ORF database), a database of ORFs with evidence of translation detected by
ribosome profiling (Ribo-seq). nuORFdb was developed at the Broad Institute of MIT and Harvard as a resource for
identifying non-canonical peptides in immunopeptidomic mass spectrometry datasets.
The ORFs were predicted using a hierarchical pipeline that aggregates ribosome profiling signal
across 29 primary healthy and cancer tissue samples and cell lines. The pipeline operates at
multiple levels—individual samples, tissues, and combined across all samples—to predict
lowly translated ORFs while maintaining sensitivity for tissue-specific variants.
All ORFs have a minimum length of 8 amino acids.
Display Conventions and Configuration
Items are displayed in bigGenePred format. Each item is labeled with the nuORFdb ORF
identifier, which encodes the source Ensembl transcript and ORF number (e.g.
ENST00000488147.1_1_1). Color reflects the categorical
Kozak consensus strength:
Strong – A/G at position −3 and G at position +4 Moderate – only one of those positions matches Weak – neither position matches non-ATG – near-cognate start codon; the Kozak rule does not apply no context – chromosome edge or context unavailable
Mouseover shows the ORF ID in its host gene, gene biotype, start codon, Kozak
strength and TE, predictor type, and the simplified plotType category.
Available filters: start codon, Kozak strength, Kozak TE, ORF category
(plotType: 8 broad classes; or type: 25 finer
categories).
The track includes the following ORF categories (by type):
Out-of-Frame – ORFs overlapping a CDS but in a different reading frame (57,713)
5' uORF – upstream ORFs in the 5' UTR (32,595)
3' dORF – downstream ORFs in the 3' UTR (30,656)
lincRNA – ORFs in long intergenic non-coding RNAs (20,399)
5' Overlap uORF – upstream ORFs overlapping the main CDS (20,119)
ncRNA Retained Intron – ORFs in retained-intron transcripts (19,259)
3' Overlap dORF – downstream ORFs overlapping the main CDS (18,028)
ncRNA Processed Transcript – ORFs in processed transcripts (14,173)
Pseudogene – ORFs in pseudogenes (7,727)
Antisense – ORFs in antisense transcripts (6,300)
and other minor categories
Each item also includes the predicted protein sequence and additional classification fields
(predictorType, plotType, geneType) from the nuORFdb annotations.
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
"nuorfdb".
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.
The original data files can be downloaded from the
nuORFdb website
at the Broad Institute.
Methods
The nuORFdb v1.2 data files (BED12 coordinates, Excel annotations, and protein FASTA sequences)
were downloaded from the Broad Institute. The BED12 file was combined with the annotation
spreadsheet (keyed on ORF_ID_hg38) and protein FASTA (keyed on sequence header ID) to
produce a bigGenePred+ format file with 23 fields (12 standard BED fields, 8 bigGenePred fields,
and 3 extended fields: predictorType, plotType, and proteinSequence).
A small number of entries (176 out of 229,251) used non-standard chromosome names
(e.g. chrGL000008.2, chrMT) which were mapped to UCSC standard names
(e.g. chr4_GL000008v2_random, chrM).
Credits
Thanks to Tamara Ouspenskaia, Travis Law, Karl Clauser, and colleagues at the Broad Institute
of MIT and Harvard for creating nuORFdb and making the data publicly available.
Thanks to Eric Malekos, UCSC, for suggesting this database.