A k-mer grammar analysis to uncover maize regulatory architecture
Identifieur interne : 000748 ( Main/Exploration ); précédent : 000747; suivant : 000749A k-mer grammar analysis to uncover maize regulatory architecture
Auteurs : María Katherine Mejía-Guerra [États-Unis] ; Edward S. Buckler [États-Unis]Source :
- BMC Plant Biology [ 1471-2229 ] ; 2019.
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Abstract
Only a small percentage of the genome sequence is involved in regulation of gene expression, but to biochemically identify this portion is expensive and laborious. In species like maize, with diverse intergenic regions and lots of repetitive elements, this is an especially challenging problem that limits the use of the data from one line to the other. While regulatory regions are rare, they do have characteristic chromatin contexts and sequence organization (the grammar) with which they can be identified.
We developed a computational framework to exploit this sequence arrangement. The models learn to classify regulatory regions based on sequence features -
These models now provide powerful tools to annotate regulatory regions in other maize lines beyond the reference, at low cost and with high accuracy.
The online version of this article (10.1186/s12870-019-1693-2) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12870-019-1693-2
PubMed: 30876396
PubMed Central: 6419808
Affiliations:
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Le document en format XML
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-mer grammar analysis to uncover maize regulatory architecture</title>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Only a small percentage of the genome sequence is involved in regulation of gene expression, but to biochemically identify this portion is expensive and laborious. In species like maize, with diverse intergenic regions and lots of repetitive elements, this is an especially challenging problem that limits the use of the data from one line to the other. While regulatory regions are rare, they do have characteristic chromatin contexts and sequence organization (the grammar) with which they can be identified.</p>
</sec>
<sec><title>Results</title>
<p>We developed a computational framework to exploit this sequence arrangement. The models learn to classify regulatory regions based on sequence features - <italic>k</italic>
-mers. To do this, we borrowed two approaches from the field of natural language processing: (1) “bag-of-words” which is commonly used for differentially weighting key words in tasks like sentiment analyses, and (2) a vector-space model using word2vec (vector-<italic>k</italic>
-mers), that captures semantic and linguistic relationships between words. We built “bag-of-<italic>k</italic>
-mers” and “vector-<italic>k</italic>
-mers” models that distinguish between regulatory and non-regulatory regions with an average accuracy above 90%. Our “bag-of-<italic>k</italic>
-mers” achieved higher overall accuracy, while the “vector-<italic>k</italic>
-mers” models were more useful in highlighting key groups of sequences within the regulatory regions.</p>
</sec>
<sec><title>Conclusions</title>
<p>These models now provide powerful tools to annotate regulatory regions in other maize lines beyond the reference, at low cost and with high accuracy.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (10.1186/s12870-019-1693-2) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
</front>
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<author><name sortKey="Yevshin, Is" uniqKey="Yevshin I">IS Yevshin</name>
</author>
<author><name sortKey="Soboleva, Av" uniqKey="Soboleva A">AV Soboleva</name>
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<author><name sortKey="Kasianov, As" uniqKey="Kasianov A">AS Kasianov</name>
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<author><name sortKey="Ashoor, H" uniqKey="Ashoor H">H Ashoor</name>
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<author><name sortKey="Ba Alawi, W" uniqKey="Ba Alawi W">W Ba-Alawi</name>
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<author><name sortKey="Bajic, Vb" uniqKey="Bajic V">VB Bajic</name>
</author>
<author><name sortKey="Medvedeva, Ya" uniqKey="Medvedeva Y">YA Medvedeva</name>
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<author><name sortKey="Bailey, Tl" uniqKey="Bailey T">TL Bailey</name>
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<affiliations><list><country><li>États-Unis</li>
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<tree><country name="États-Unis"><region name="État de New York"><name sortKey="Mejia Guerra, Maria Katherine" sort="Mejia Guerra, Maria Katherine" uniqKey="Mejia Guerra M" first="María Katherine" last="Mejía-Guerra">María Katherine Mejía-Guerra</name>
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