Evidence map›Paper›PMID 40735494›Full record

ArticleWellcome open research2025

Decoding post-transcriptional gene expression controls in trypanosomatids using machine learning.

Michele Tinti, David Horn

Abstract read
In one paragraph

Article in Wellcome open research, 2025. 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
–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

2 citing papers in PubMed.

  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

2 authors.

Michele TintiThe Wellcome Centre for Anti-Infectives Research, Biological Chemistry & Drug Discovery, University of Dundee Division, Dundee, Scotland, UK.ORCID https://orcid.org/0000-0002-0051-017X
David HornThe Wellcome Centre for Anti-Infectives Research, Biological Chemistry & Drug Discovery, University of Dundee Division, Dundee, Scotland, UK.ORCID https://orcid.org/0000-0001-5173-9284

Funding

Wellcome Trust
6 · The paper itself

Abstract

Background: We recently described a pervasive cis-regulatory role for sequences in Methods: To address these questions, we applied machine learning to analyze existing transcriptome, TE, and proteomics data. Results: Our predictions indicate that both UTRs and codon usage bias impact gene expression in all three trypanosomatids, but with substantial differences. In Conclusions: Taken together, our findings indicate that gene expression control in trypanosomatids operates primarily at the point of translation, which is impacted by both UTRs and codon usage. We suggest a model whereby UTRs control the rate of translation initiation, while favoured codons increase the rate of translation elongation, thereby reducing mRNA turnover.

Indexed as

Codon BiasLeishmaniaMachine LearningTranslation EfficiencyTrypanosomaUTRs

Identifiers

PMID40735494
PMCPMC12304876

What Socratic holds

Textmetadata
LicenceCC BY
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.