Evidence map›Paper›PMID 23070853›Full record

ArticleGenome research2013

Comparative motif discovery combined with comparative transcriptomics yields accurate targetome and enhancer predictions.

Marina Naval-Sánchez, Delphine Potier, Lotte Haagen, Máximo Sánchez, Sebastian Munck, Bram Van de Sande, Fernando Casares, Valerie Christiaens, Stein Aerts

Abstract read
In one paragraph

Article in Genome research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Article
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  5. Systems Biology RevealsFrontiers in genetics · 2019
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  11. A Novel Method to Detect Bias in Short Read NGS Data.Journal of integrative bioinformatics · 2017
    Article
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  14. 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

9 authors.

Marina Naval-SánchezLaboratory of Computational Biology, Department of Human Genetics, University of Leuven, 3000 Leuven, Belgium.
Delphine Potier
Lotte Haagen
Máximo Sánchez
Sebastian Munck
Bram Van de Sande
Fernando Casares
Valerie Christiaens
Stein Aerts

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The identification of transcription factor binding sites, enhancers, and transcriptional target genes often relies on the integration of gene expression profiling and computational cis-regulatory sequence analysis. Methods for the prediction of cis-regulatory elements can take advantage of comparative genomics to increase signal-to-noise levels. However, gene expression data are usually derived from only one species. Here we investigate tissue-specific cross-species gene expression profiling by high-throughput sequencing, combined with cross-species motif discovery. First, we compared different methods for expression level quantification and cross-species integration using Tag-seq data. Using the optimal pipeline, we derived a set of genes with conserved expression during retinal determination across Drosophila melanogaster, Drosophila yakuba, and Drosophila virilis. These genes are enriched for binding sites of eye-related transcription factors including the zinc-finger Glass, a master regulator of photoreceptor differentiation. Validation of predicted Glass targets using RNA-seq in homozygous glass mutants confirms that the majority of our predictions are expressed downstream from Glass. Finally, we tested nine candidate enhancers by in vivo reporter assays and found eight of them to drive GFP in the eye disc, of which seven colocalize with the Glass protein, namely, scrt, chp, dpr10, CG6329, retn, Lim3, and dmrt99B. In conclusion, we show for the first time the combined use of cross-species expression profiling with cross-species motif discovery as a method to define a core developmental program, and we augment the candidate Glass targetome from a single known target gene, lozenge, to at least 62 conserved transcriptional targets.

Indexed as

Enhancer Elements, GeneticNucleotide MotifsTranscriptomeAnimalsBinding SitesDNA-Binding ProteinsDrosophilaDrosophila ProteinsGene Expression ProfilingGenomicsOrgan SpecificitySequence AlignmentSpecies SpecificityTranscription, GeneticDNA-Binding ProteinsDrosophila Proteinsgl protein, Drosophila

Identifiers

PMID23070853
PMCPMC3530685

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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.