Evidence map›Paper›PMID 19814816›Full record

ArticleBMC genomics2009

The word landscape of the non-coding segments of the Arabidopsis thaliana genome.

Jens Lichtenberg, Alper Yilmaz, Joshua D Welch, Kyle Kurz, Xiaoyu Liang, Frank Drews, Klaus Ecker, Stephen S Lee, Matt Geisler, Erich Grotewold and 1 more

Abstract read
In one paragraph

Article in BMC genomics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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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

11 citing papers in PubMed.

  1. Article
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  3. K-mer-Based Motif Analysis in Insect Species acrossComputational and mathematical methods in medicine · 2019
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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

11 authors.

Jens LichtenbergBioinformatics Laboratory, School of Electrical Engineering and Computer Science, Ohio University, Athens, OH, USA. lichtenj@ohio.edu
Alper Yilmaz
Joshua D Welch
Kyle Kurz
Xiaoyu Liang
Frank Drews
Klaus Ecker
Stephen S Lee
Matt Geisler
Erich Grotewold
Lonnie R Welch

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenome sequences can be conceptualized as arrangements of motifs or words. The frequencies and positional distributions of these words within particular non-coding genomic segments provide important insights into how the words function in processes such as mRNA stability and regulation of gene expression.

resultsUsing an enumerative word discovery approach, we investigated the frequencies and positional distributions of all 65,536 different 8-letter words in the genome of Arabidopsis thaliana. Focusing on promoter regions, introns, and 3' and 5' untranslated regions (3'UTRs and 5'UTRs), we compared word frequencies in these segments to genome-wide frequencies. The statistically interesting words in each segment were clustered with similar words to generate motif logos. We investigated whether words were clustered at particular locations or were distributed randomly within each genomic segment, and we classified the words using gene expression information from public repositories. Finally, we investigated whether particular sets of words appeared together more frequently than others.

conclusionOur studies provide a detailed view of the word composition of several segments of the non-coding portion of the Arabidopsis genome. Each segment contains a unique word-based signature. The respective signatures consist of the sets of enriched words, 'unwords', and word pairs within a segment, as well as the preferential locations and functional classifications for the signature words. Additionally, the positional distributions of enriched words within the segments highlight possible functional elements, and the co-associations of words in promoter regions likely represent the formation of higher order regulatory modules. This work is an important step toward fully cataloguing the functional elements of the Arabidopsis genome.

Indexed as

Genome, PlantModels, Statistical3' Untranslated Regions5' Untranslated RegionsArabidopsisComputational BiologyDNA, PlantGene Expression Regulation, PlantIntronsMarkov ChainsPromoter Regions, GeneticSequence Analysis, DNA3' Untranslated Regions5' Untranslated RegionsDNA, Plant

Identifiers

PMID19814816
PMCPMC2770528

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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.