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Gene 467 (2010) 41–51

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Gene
j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / g e n e

Detecting novel genes with sparse arrays
Mikko Arvas a,⁎,1, Niina Haiminen b,1, Bart Smit c, Jari Rautio d, Marika Vitikainen a, Marilyn Wiebe a, Diego Martinez e, Christine Chee e, Joe Kunkel e, Charles Sanchez e, Mary Anne Nelsone, Tiina Pakula a, Markku Saloheimo a, Merja Penttilä a, Teemu Kivioja f
a

VTT Technical Research Centre of Finland, Tietotie 2, P.O. Box FI-1000, 02044 VTT, Espoo, Finland HIIT, Department of Computer Science, P.O. Box 68, FI-00014 University of Helsinki, Finland FrieslandCampina Innovation Europe, Nieuwe Kanaal 7C, 6709 PA Wageningen, The Netherlands d Plexpress, Helsinki, Viikinkaari 6,00790 Helsinki, Finland e Department of Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA f Department of Computer Science, PO Box 63, FI-00014 University of Helsinki, Finland
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a r t i c l e

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a b s t r a c t
Species-specific genes play an important role in defining the phenotype of an organism. However, current gene prediction methods can only efficiently findgenes that share features such as sequence similarity or general sequence characteristics with previously known genes. Novel sequencing methods and tiling arrays can be used to find genes without prior information and they have demonstrated that novel genes can still be found from extensively studied model organisms. Unfortunately, these methods are expensive and thus are not easily applicable, e.g.,to finding genes that are expressed only in very specific conditions. We demonstrate a method for finding novel genes with sparse arrays, applying it on the 33.9 Mb genome of the filamentous fungus Trichoderma reesei. Our computational method does not require normalisations between arrays and it takes into account the multiple-testing problem typical for analysis of microarray data. In contrast totiling arrays, that use overlapping probes, only one 25mer microarray oligonucleotide probe was used for every 100 b. Thus, only relatively little space on a microarray slide was required to cover the intergenic regions of a genome. The analysis was done as a by-product of a conventional microarray experiment with no additional costs. We found at least 23 good candidates for novel transcripts thatcould code for proteins and all of which were expressed at high levels. Candidate genes were found to neighbour ire1 and cre1 and many other regulatory genes. Our simple, low-cost method can easily be applied to finding novel species-specific genes without prior knowledge of their sequence properties. © 2010 Elsevier B.V. All rights reserved.

Article history: Accepted 15 July 2010 Available online 5August 2010 Received by I. King Jordan Keywords: Microarray Gene prediction Moulds Transcript

1. Introduction Recent progress in sequencing technology is increasing rapidly the number of available genomes. Consequently, efficient discovery of basic functional elements, such as all protein or RNA encoding genes, from newly sequenced genomes is becoming a crucial bottleneck in the use of genomicsto explore biological diversity. Automatic sequence-based

Abbreviations: RT, reverse transcriptase; RMA, Robust Multichip Average; UPR, Unfolded Protein Response; CAZyme, carbohydrate active enzyme; FDR, False Discovery Rate. ⁎ Corresponding author. E-mail addresses: mikko.arvas@vtt.fi (M. Arvas), niina.haiminen@cs.helsinki.fi (N. Haiminen), bart.smit@campina.com (B. Smit),jari.rautio@plexpress.fi (J. Rautio), marika.vitikainen@vtt.fi (M. Vitikainen), marilyn.wiebe@vtt.fi (M. Wiebe), admar@unm.edu (D. Martinez), cchee@unm.edu (C. Chee), jkunkel@unm.edu (J. Kunkel), csanche9@unm.edu (C. Sanchez), manelson@unm.edu (M.A. Nelson), tiina.pakula@vtt.fi (T. Pakula), markku.saloheimo@vtt.fi (M. Saloheimo), merja.penttila@vtt.fi (M. Penttilä), teemu.kivioja@helsinki.fi (T. Kivioja). 1 The first two...