Evidence map›Paper›PMID 35700225›Full record

ArticleMolecular biology and evolution2022

Accurate Identification of Transcription Regulatory Sequences and Genes in Coronaviruses.

Chuanyi Zhang, Palash Sashittal, Michael Xiang, Yichi Zhang, Ayesha Kazi, Mohammed El-Kebir

Open access · goldAbstract read
In one paragraph

Article in Molecular biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 39% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Chuanyi ZhangDepartment of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Palash SashittalDepartment of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Michael XiangDepartment of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Yichi ZhangDepartment of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Ayesha KaziDepartment of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Mohammed El-KebirDepartment of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.ORCID 0000-0002-1468-2407
University of Illinois Urbana-Champaign · US

Funding

The Role of the Microbiome in Cancer Suppression and Susceptibility Across SpeciesU54CA217376 · NCI · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI MALEY, CARLO, SHIBATA, DARRYL K · 2018 to 2022
$8.6M
Discover and Analyze Germline-Somatic Interactions in CancerR01LM013438 · NLM · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI LIU, LI, YANG, PING · 2021 to 2023
$1.0M
NCI NIH HHS U54 CA217376NLM NIH HHS R01 LM013438
6 · The paper itself

Abstract

Transcription regulatory sequences (TRSs), which occur upstream of structural and accessory genes as well as the 5' end of a coronavirus genome, play a critical role in discontinuous transcription in coronaviruses. We introduce two problems collectively aimed at identifying these regulatory sequences as well as their associated genes. First, we formulate the TRS Identification problem of identifying TRS sites in a coronavirus genome sequence with prescribed gene locations. We introduce CORSID-A, an algorithm that solves this problem to optimality in polynomial time. We demonstrate that CORSID-A outperforms existing motif-based methods in identifying TRS sites in coronaviruses. Second, we demonstrate for the first time how TRS sites can be leveraged to identify gene locations in the coronavirus genome. To that end, we formulate the TRS and Gene Identification problem of simultaneously identifying TRS sites and gene locations in unannotated coronavirus genomes. We introduce CORSID to solve this problem, which includes a web-based visualization tool to explore the space of near-optimal solutions. We show that CORSID outperforms state-of-the-art gene finding methods in coronavirus genomes. Furthermore, we demonstrate that CORSID enables de novo identification of TRS sites and genes in previously unannotated coronavirus genomes. CORSID is the first method to perform accurate and simultaneous identification of TRS sites and genes in coronavirus genomes without the use of any prior information.

Indexed as

CoronavirusCoronavirus InfectionsHumansRNA, MessengerRNA, ViralTranscription, GeneticRNA, MessengerRNA, Viralcore sequencescoronavirusgene identificationinterval graphlocal alignmentmaximum weight independent setmotif finding

Identifiers

PMID35700225
PMCPMC9214144
OpenAlexW4282913341

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.