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Frisch, M.* ; Frech, K.* ; Klingenhoff, A.* ; Cartharius, K.* ; Liebich, I.* ; Werner, T.

In Silico Prediction of Scaffold/Matrix Attachment Regions in Large Genomic Sequences.

Genome Res. 12, 349-354 (2002)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
Scaffold/matrix attachment regions (S/MARs) are essential regulatory DNA elements of eukaryotic cells. They are major determinants of locus control of gene expression and can shield gene expression from position effects. Experimental detection of S/MARs requires substantial effort and is not suitable for large-scale screening of genomic sequences. In silico prediction of S/MARs can provide a crucial first selection step to reduce the number of candidates. We used experimentally defined S/MAR sequences as the training set and generated a library of new S/MAR-associated, AT-rich patterns described as weight matrices. A new tool called SMARTest was developed that identifies potential S/MARs by performing a density analysis based on the S/MAR matrix library (http://www.genomatix.de/cgi-bin/smartest_pd/smartest.pl). S/MAR predictions were evaluated by using six genomic sequences from animal and plant for which S/MARs and non-S/MARs were experimentally mapped. SMARTest reached a sensitivity of 38% and a specificity of 68%. In contrast to previous algorithms, the SMARTest approach does not depend on the sequence context and is suitable to analyze long genomic sequences up to the size of whole chromosomes. To demonstrate the feasibility of large-scale S/MAR prediction, we analyzed the recently published chromosome 22 sequence and found 1198 S/MAR candidates
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
ISSN (print) / ISBN 1088-9051
e-ISSN 1549-5469
Journal Genome Research
Quellenangaben Volume: 12, Issue: 2, Pages: 349-354 Article Number: , Supplement: ,
Publisher Cold Spring Harbor Laboratory Press
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Experimental Genetics (IEG)