dictyNews
Electronic Edition
Volume 30, number 10
March 21, 2008

Please submit abstracts of your papers as soon as they have been
accepted for publication by sending them to dicty@northwestern.edu
or by using the form at
http://dictybase.org/db/cgi-bin/dictyBase/abstract_submit.

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useful information is available at dictyBase - http://dictybase.org.


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Abstracts
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De novo search for non-coding RNA genes in the AT-rich genome of 
Dictyostelium discoideum: performance of Markov-dependent genome 
feature scoring

Pontus Larsson1, Andrea Hinas2,4, David H Ardell3,5*, Leif A Kirsebom1, 
Anders Virtanen1, and Fredrik Soderbom2,*

1Department of Cell and Molecular Biology, Biomedical Center, 
Uppsala University, Sweden. 
2Department of Molecular Biology, Biomedical Center, Swedish 
University of Agricultural Sciences, Uppsala, Sweden. 
3Linnaeus Centre for Bioinformatics, Biomedical Center, Uppsala, Sweden. 
4Present address: Department of Molecular and Cellular Biology, 
Harvard University, USA. 
5Present address: School of Natural Sciences, University of California, 
Merced, CA, 95344, USA.

*Corresponding authors


Genome Research, accepted

Genome data are increasingly important in the computational identification 
of novel regulatory non-coding RNAs (ncRNAs). However, most ncRNA 
gene-finders are either specialized to well-characterized ncRNA gene 
families or require comparisons of closely related genomes. We developed 
a method for de novo screening for ncRNA genes with a nucleotide 
composition that stands out against the background genome based on a 
partial sum process. We compared the performance when assuming 
independent and first-order Markov dependent nucleotides, respectively 
and used Karlin-Altschul and Karlin-Dembo statistics to evaluate 
significance of hits. We hypothesized that a first-order Markov-dependent 
process might have better power to detect ncRNA genes since 
nearest-neighbor models have shown to be successful in predicting RNA 
structures. A model based on a first-order partial sum process 
(analyzing overlapping dinucleotides) had better sensitivity and 
specificity than a zeroth-order model when applied to the AT-rich genome 
of the amoeba Dictyostelium discoideum. In this genome we detected 94 
percent of previously known ncRNA genes (at this sensitivity, the false 
positive rate was estimated to 25% in a simulated background). The 
predictions were further refined by clustering candidate genes according 
to sequence similarity and/or searching for an ncRNA-associated upstream 
element. We experimentally verified six out of ten tested ncRNA gene 
predictions. We conclude that higher-order models, in combination with 
other information, are useful for identification of novel ncRNA gene 
families in single genome analysis of D. discoideum. Our generalizable 
approach extends the range of genomic data that can be searched for 
novel ncRNA genes using well-grounded statistical methods.


Submitted by: Fredrik Soderbom [fredde@xray.bmc.uu.se]
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[End dictyNews, volume 30, number 10]