Evidence map›Paper›PMID 38841126›Full record

ArticleBioinformatics advances2024

Predmoter-cross-species prediction of plant promoter and enhancer regions.

Felicitas Kindel, Sebastian Triesch, Urte Schlüter, Laura Alexandra Randarevitch, Vanessa Reichel-Deland, Andreas P M Weber, Alisandra K Denton

Abstract read
In one paragraph

Article in Bioinformatics advances, 2024. 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. Review
  2. Review
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

7 authors.

Felicitas KindelInstitute of Plant Biochemistry, Math.-Nat. Faculty, Heinrich Heine University, Düsseldorf 40225, Germany.ORCID https://orcid.org/0009-0000-6149-0224
Sebastian TrieschInstitute of Plant Biochemistry, Math.-Nat. Faculty, Heinrich Heine University, Düsseldorf 40225, Germany.ORCID https://orcid.org/0000-0001-7796-4631
Urte SchlüterInstitute of Plant Biochemistry, Math.-Nat. Faculty, Heinrich Heine University, Düsseldorf 40225, Germany.ORCID https://orcid.org/0000-0002-9134-6511
Laura Alexandra RandarevitchCluster of Excellence on Plant Sciences (CEPLAS), Germany.
Vanessa Reichel-DelandInstitute of Plant Biochemistry, Math.-Nat. Faculty, Heinrich Heine University, Düsseldorf 40225, Germany.
Andreas P M WeberInstitute of Plant Biochemistry, Math.-Nat. Faculty, Heinrich Heine University, Düsseldorf 40225, Germany.
Alisandra K DentonInstitute of Plant Biochemistry, Math.-Nat. Faculty, Heinrich Heine University, Düsseldorf 40225, Germany.ORCID https://orcid.org/0000-0002-1132-0224

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: Identifying Results: We present Predmoter, a deep neural network that predicts base-wise Assay for Transposase Accessible Chromatin using sequencing (ATAC-seq) and histone Chromatin immunoprecipitation DNA-sequencing (ChIP-seq) read coverage for plant genomes. Predmoter uses only the DNA sequence as input. We trained our final model on 21 species for 13 of which ATAC-seq data and for 17 of which ChIP-seq data was publicly available. We evaluated our models on Availability and implementation: The source code for Predmoter is available at: https://github.com/weberlab-hhu/Predmoter. Predmoter takes a fasta file as input and outputs h5, and optionally bigWig and bedGraph files.

Identifiers

PMID38841126
PMCPMC11150885

What Socratic holds

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Registered trials

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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.