Evidence map›Paper›PMID 34965853›Full record

ArticleBMC genomics2021

Epigenetic features improve TALE target prediction.

Annett Erkes, Stefanie Mücke, Maik Reschke, Jens Boch, Jan Grau

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 24% 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

3 citing papers in PubMed, 2 citations in OpenAlex.

  1. 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

5 authors at 2 institutions in 1 country.

Annett ErkesInstitute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany. annett.erkes@informatik.uni-halle.de.
Stefanie MückeInstitute of Plant Genetics, Leibniz Universität Hannover, Hannover, Germany.
Maik ReschkeInstitute of Plant Genetics, Leibniz Universität Hannover, Hannover, Germany.
Jens BochInstitute of Plant Genetics, Leibniz Universität Hannover, Hannover, Germany.
Jan GrauInstitute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany. grau@informatik.uni-halle.de.
Leibniz University Hannover · DEMartin Luther University Halle-Wittenberg · DE

Funding

Deutsche Forschungsgemeinschaft BO 1496/8-1Deutsche Forschungsgemeinschaft GR 4587/1-1European Cooperation in Science and Technology CA16107
6 · The paper itself

Abstract

backgroundThe yield of many crop plants can be substantially reduced by plant-pathogenic Xanthomonas bacteria. The infection strategy of many Xanthomonas strains is based on transcription activator-like effectors (TALEs), which are secreted into the host cells and act as transcriptional activators of plant genes that are beneficial for the bacteria.The modular DNA binding domain of TALEs contains tandem repeats, each comprising two hyper-variable amino acids. These repeat-variable diresidues (RVDs) bind to their target box and determine the specificity of a TALE.All available tools for the prediction of TALE targets within the host plant suffer from many false positives. In this paper we propose a strategy to improve prediction accuracy by considering the epigenetic state of the host plant genome in the region of the target box.

resultsTo this end, we extend our previously published tool PrediTALE by considering two epigenetic features: (i) chromatin accessibility of potentially bound regions and (ii) DNA methylation of cytosines within target boxes. Here, we determine the epigenetic features from publicly available DNase-seq, ATAC-seq, and WGBS data in rice.We benchmark the utility of both epigenetic features separately and in combination, deriving ground-truth from RNA-seq data of infections studies in rice. We find an improvement for each individual epigenetic feature, but especially the combination of both.Having established an advantage in TALE target predicting considering epigenetic features, we use these data for promoterome and genome-wide scans by our new tool EpiTALE, leading to several novel putative virulence targets.

conclusionsOur results suggest that it would be worthwhile to collect condition-specific chromatin accessibility data and methylation information when studying putative virulence targets of Xanthomonas TALEs.

Indexed as

Plant DiseasesXanthomonasBacterial ProteinsEpigenesis, GeneticTranscription Activator-Like EffectorsBacterial ProteinsTranscription Activator-Like EffectorsChromatinComputational predictionDNA methylationTranscription activator-like effector

Identifiers

PMID34965853
PMCPMC8717664
OpenAlexW4206486340

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LicenceCC BY
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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 OpenQuestion graph.