Poran HLA I Track Settings
RECON HLA-I epitopes   (All Immunology tracks)

Display mode:      Duplicate track

Display data as a density graph:
Data schema/format description and download
Assembly: SARS-CoV-2 Jan. 2020 (NC_045512.2)
Data last updated at UCSC: 2020-04-17 16:25:19


This track shows putative epitopes for CD4+ and CD8+ T cells whose HLA binding properties cover over 99% for US, European, and Asian populations, for both HLA-I and HLA-II. The track includes 11,776 CD8 epitopes restricted to HLA-I as predicted by RECON. All the epitopes are scored using a combined coverage score reported for USA, EUR, and API. Specifically, score = (USA_coverage+EUR_coverage+API_coverage)*1000/3.

For more details, see here.

Display Conventions and Configuration

Genomic locations of epitopes are labeled with a unique ID. Mousing over an item shows the protein name and restrictions to HLA-I. A click on an item shows a standard feature detail page with the both the ID and the mouse over information.


For a full description of the methods used, refer here.

Data Access

The raw data can be explored interactively with the Table Browser, or combined with other datasets in the Data Integrator tool. For automated analysis, the genome annotation is stored in a bigBed file that can be downloaded from the download server.

Annotations can be converted from binary to ASCII text by our command-line tool bigBedToBed. Instructions for downloading this command can be found on our utilities page. The tool can also be used to obtain features within a given range without downloading the file, for example:

bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/wuhCor1/bbi/poranHla/CD8-hla1.bb -chrom=NC_045512v2 -start=0 -end=29902 stdout

Please refer to our mailing list archives for questions, or our Data Access FAQ for more information.


Asaf Poran, Dewi Harjanto, Matthew Malloy, Michael S. Rooney, Lakshmi Srinivasan, Richard B. Gaynor. Sequence-based prediction of vaccine targets for inducing T cell responses to SARS-CoV-2 utilizing the bioinformatics predictor RECON. bioRxiv 2020.04.06.027805