Evidence map›Paper›PMID 35340296›Full record

ArticleBio-protocol2022

Normalized Ribo-Seq for Quantifying Absolute Global and Specific Changes in Translation.

Katharina Hoerth, Sonja Reitter, Johanna Schott

Open access · greenAbstract read
In one paragraph

Article in Bio-protocol, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 40% 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

6 citing papers in PubMed, 7 citations in OpenAlex.

  1. Advances in ribosome profiling technologies.Biochemical Society transactions · 2025
    Review
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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

3 authors at 2 institutions in 1 country.

Katharina HoerthDivision of Biochemistry, Mannheim Institute for Innate Immunoscience, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Sonja ReitterDivision of Biochemistry, Mannheim Institute for Innate Immunoscience, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Johanna SchottDivision of Biochemistry, Mannheim Institute for Innate Immunoscience, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Heidelberg University · DEDKFZ-ZMBH Alliance · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ribosome profiling (Ribo-Seq) is a highly sensitive method to quantify ribosome occupancies along individual mRNAs on a genome-wide scale. Hereby, ribosome-protected fragments (= footprints) are generated by nuclease digestion, isolated, and sequenced together with the corresponding randomly fragmented input samples, to determine ribosome densities (RD). For library preparation, equal amounts of total RNA are used. Subsequently, all transcript fragments are subjected to linker ligation, cDNA synthesis, and PCR amplification. Importantly, the number of reads obtained for every transcript in input and footprint samples during sequencing depends on sequencing depth and library size, as well as the relative abundance of the transcript in the sample. However, the information pertaining to the absolute amount of input and footprint sequences is lost during sample preparation, hence ruling out any conclusion whether translation is generally suppressed or activated in one condition over the other. Therefore, the RD fold-changes determined for individual genes do not reflect absolute regulation, but have to be interpreted as relative to bulk mRNA translation. Here, we modified the original ribosome profiling protocol that was first established by Ingolia

Indexed as

Normalized ribosome densityRegulation of translationRibo-seqRibosome profilingSpike-in

Identifiers

PMID35340296
PMCPMC8899559
OpenAlexW4212930846

What OpenQuestion holds

Textmetadata
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.