Description
The Weizman ORFs (Open Reading Frames) track shows previously unannotated ORF
predictions based on Ribo-Seq and RNA-seq data. It is a collection of
tracks (super track)
that contains not only the predicted gene models, but also
data supporting them.
Display Conventions and Configuration
The Predicted ORFs track shows the predicted exons. All other tracks show the signal as
a x-y plot with bars.
Methods
Methods from Finkel et al:
To capture the full SARS-CoV-2 coding capacity, we applied a suite of ribosome
profiling approaches to Vero cells infected with SARS-CoV-2 for 5 and 24 hours,
and Calu3 cells infected for 7 hours. For each time point we prepared three
different ribosome-profiling libraries, each one in two biological replicates.
Two Ribo-seq libraries facilitate mapping of translation initiation sites, by
treating cells with lactimidomycin (LTM) or harringtonine (Harr), two drugs
with distinct mechanisms that prevent 80S ribosomes at translation initiation
sites from elongating. The third Ribo-seq library was prepared from cells
treated with the translation elongation inhibitor cycloheximide (CHX), and
gives a snap-shot of actively translating ribosomes across the body of the
translated ORF. In parallel, RNA-sequencing was applied to map viral
transcripts.
The ORF prediction was done by using two computational tools, PRICE and
ORF-RATER, that rely on different features of ribosome profiling data, and by
manual inspection of the data. The predictions are based on Ribo-seq libraries
from two time points (5 and 7 hpi) of two different cell lines (Vero E6 and
Calu3 cells), infected with separate virus isolates.
The Ribo-Seq data of the 24 hours samples do not show the expected profile of
read distribution on viral genes and therefore were not used for the procedure
of ORF predictions.
For more details see the paper in the References section below.
Data Access
The raw data can be explored interactively with the
Table Browser, or combined with other datasets in the
Data Integrator tool.
Please refer to our
mailing list archives
for questions, or our
Data Access FAQ
for more information.
References
Finkel Y, Mizrahi O, Nachshon A, Weingarten-Gabbay S, Morgenstern D, Yahalom-Ronen Y, Tamir H,
Achdout H, Stein D, Israeli O et al.
The coding capacity of SARS-CoV-2.
Nature. 2020 Sep 9;.
PMID: 32906143
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