Evidence map›Paper›PMID 35664804›Full record

ArticleNAR genomics and bioinformatics2022

The Ramp Atlas: facilitating tissue and cell-specific ramp sequence analyses through an intuitive web interface.

Justin B Miller, Taylor E Meurs, Matthew W Hodgman, Benjamin Song, Kyle N Miller, Mark T W Ebbert, John S K Kauwe, Perry G Ridge

Abstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
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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

8 authors.

Justin B MillerSanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40504, USA.
Taylor E MeursDepartment of Biology, Brigham Young University, Provo, UT 84602, USA.
Matthew W HodgmanSanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40504, USA.
Benjamin SongDepartment of Biology, Brigham Young University, Provo, UT 84602, USA.
Kyle N MillerDepartment of Computer Science, Utah Valley University, Orem, UT 84058, USA.
Mark T W EbbertSanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40504, USA.
John S K KauweDepartment of Biology, Brigham Young University, Provo, UT 84602, USA.ORCID https://orcid.org/0000-0001-8641-2468
Perry G RidgeDepartment of Biology, Brigham Young University, Provo, UT 84602, USA.ORCID https://orcid.org/0000-0001-6944-2753

Funding

University of Kentucky Alzheimer's Disease Research CenterP30AG072946 · NIA · UNIVERSITY OF KENTUCKY · PI LINDA J VAN ELDIK · 2021 to 2026
$23.5M
Using long-range technologies as a multi-omic approach to understand Alzheimer’s disease in brain tissueR01AG068331 · NIA · UNIVERSITY OF KENTUCKY · PI EBBERT, MARK T W · 2020 to 2024
$3.0M
Understanding how structural mutations and individual RNA isoformsare involved in human health and diseaseR35GM138636 · NIGMS · UNIVERSITY OF KENTUCKY · PI Mark T W Ebbert · 2020 to 2026
$2.9M
NIA NIH HHS P30 AG072946NIA NIH HHS R01 AG068331NIGMS NIH HHS R35 GM138636
6 · The paper itself

Abstract

Ramp sequences occur when the average translational efficiency of codons near the 5' end of highly expressed genes is significantly lower than the rest of the gene sequence, which counterintuitively increases translational efficiency by decreasing downstream ribosomal collisions. Here, we show that the relative codon adaptiveness within different tissues changes the existence of a ramp sequence without altering the underlying genetic code. We present the first comprehensive analysis of tissue and cell type-specific ramp sequences and report 3108 genes with ramp sequences that change between tissues and cell types, which corresponds with increased gene expression within those tissues and cells. The Ramp Atlas (https://ramps.byu.edu/) allows researchers to query precomputed ramp sequences in 18 388 genes across 62 tissues and 66 cell types and calculate tissue-specific ramp sequences from user-uploaded FASTA files through an intuitive web interface. We used The Ramp Atlas to identify seven SARS-CoV-2 genes and seven human SARS-CoV-2 entry factor genes with tissue-specific ramp sequences that may help explain viral proliferation within those tissues. We anticipate that The Ramp Atlas will facilitate personalized and creative tissue-specific ramp sequence analyses for both human and viral genes that will increase our ability to utilize this often-overlooked regulatory region.

Identifiers

PMID35664804
PMCPMC9155233

What Socratic 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 Socratic graph.