A Novel Application of GeneMark-RC to the Analysis ofProkaryotic Genomes and Human cDNAs: Sequence
Data with Statistical Deviations Are Rich in Important
Biological Information

Makoto Hirosawa 1

hirosawa@kazusa.or.jp
12Osamu Ohara 1

ohara@kazusa.or.jp

Katsumi Isono 2

isono@biol.kobe-u.ac.jp

Kazusa DNA Research Institute, 1532-3 Yana, Kisarazu-shi, Chiba 292 Japan
Department of Biology, Faculty of Science, Kobe University
1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan

1 De ning a self-consistent data-set for coding region assignment
Assignment of coding regions is the rst step in genome sequence analysis. Although the complete
genome sequences of sixteen organisms have already been published, the strategies adopted for coding
region assignment are di erent from one organism to another, and consequently the data are notreadily suited for direct comparative analysis. Evidently, a more uni ed method for de ning coding
regions must be established. For this purpose, we introduced the concept self-consistency in ourprevious study in which we performed coding region assignment within the Synechocystis genome [2].
A self-consistent set of ORFs can be de ned as a set of ORFs, if the statistical parameters derived
from it and used in conjunction with a gene- nding program result in the prediction of the same set as
a set of likely coding regions. We selected GeneMark [1] as the gene- nding program in our analysis
to obtain a self consistent set, and named the procedure GeneMark-RC. In the same framework,classi cation of coding regions was performed, which enabled us to distinguish between di erent setsof ORFs, exhibiting general, typical and atypical traits of the species. With the statistics for atypicalORFs, the coding regions of exogenous origins can also be included in the resultant set.
However, even if the procedure can be regarded as adequate for computational analyses, it might
not be acceptable if the de ned set thus obtained is very di erent from the published set of ORFs indatabases that have been established as a result of complex analyses. In the case of our analysis of
Synechocystis, the nal set of coding regions we de ned matched 98% of the annotated set. Similarlevels of matches were obtained with other organisms.

2 Mining valuable information out of statistically deviated data
A characteristic feature of our procedure is that it uses more than one homogeneous set of statistic
parameters to evaluate the score (up to 1.0) for a speci ed coding region. The re ned statistics makes
the score of a coding region extremely high. Deviation from it does not necessarily mean a problem
in our procedure, but rather it suggests the presence of valuable information for the coding region asdescribed below.
2.1

Unusual ORFs in the Synechocystis genome

Generally, the scores obtained for long coding regions were extremely high. There were some exceptions, however. Of the scores evaluated for 33 long annotated coding regions (more than 3000 bp) ofSynechocystis, the worst ve are listed in Table 1. As can be seen, the score was lowest for sll3600.
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Table 1: Annotated long coding regions with low evaluation.
ID Length Score
FromTo Direction
sll1360
3351 0.5418 1065660 1069010 complement
sll2005
3237 0.7427 1264328 1267564 complement
sll19515226 0.7661 1422426 1427651 complement
slr03233129 0.8169 2274868 2277996 direct
slr07443006 0.8809 134437 137442 direct

Homologues
DNA polymerase III subunit (dnaX)
DNA gyrase B subunit (gyrB)hemolysinalpha mannosidase (ams1)
initiation factor IF-2 (infB)

However, the value was not evenly low throughout the coding region: instead it was almost zero in aregion of longer than 1200 bp, which was found to correspond to an intein that was experimentally
determined [2, 4]. Similarly, within the second lowest, sll12005 [4], an intein has been predicted.
2.2

Analysis of human cDNAs

In principle, the concept of self-consistency can be applicable to the analysis of any protein coding
sequences. Thus, we recently developed a new version of GeneMark-RC for the analysis of humancDNAs. A typical cDNA derived from mature mRNA is generally expected to carry only a single
coding region with high evaluation score. In other words, coding regions with low evaluation scores
are likely to contain regions with unusual characteristics as observed with prokaryotic genes.
In addition, the introduction of GeneMark-RC to cDNA analysis has opened a way to detecting
spurious coding interruption caused by cloning artifacts. The well-known origins of cloning artifacts,
such as frame shifts and nonsense mutations in protein coding sequences, are caused by the low delityof reverse transcriptase. Although these cloning artifacts can be eliminated by analyzing multiple
cDNA clones, it is unpractical to do so in a large-scale cDNA sequencing project. GeneMark-RCdetects these artifacts by revealing the occurrence of multiple coding regions in a cDNA sequence.
Locations of possible cloning artifacts can be easily identi ed by viewing evaluation scores along thecDNA sequence or by simple statistic calculations.
We performed GeneMark-RC analysis with the 441 long cDNAs isolated from human brain (KIAA269to KIAA710) in our cDNA project at the Kazusa DNA Research Institute [3]. The detection sensitivity of cloning artifacts was found to be very high which was con rmed by comparing experimentallyrevised cDNA sequences with the predictions given by GeneMark-RC. For example, nonsense mutations were detected in 3 cDNAs with high certainty. In particular, nonsense mutations were detected
twice in the cDNA sequence of KIAA0443, which was experimentally supported.
Currently, all the cDNA sequences obtained in our project are routinely subjected to GeneMarkRC examination for possible cloning artifacts. If a possibility is detected, then the suspected cDNA
sequences are experimentally re-examined to avoid wrong assignment of protein coding regions.

References[1] Borodovsky, M. and McIninch, J.D., GENMARK:Parallel gene recognition for both DNA strands, Computer Chemistry, 17:123-133, 1993.
[2] Hirosawa, M. and Isono, K., GeneMark-RC, a recursive procedure with self-consistency evaluation forthe detection and classi cation of ORFs; its application to the analysis of prokaryotic genomes, Genome
Informatics 1997, Universal Academy Press, 197{206, 1997.
[3] Ishikawa, K. et al., Prediction of the coding sequences of unidenti ed human genes. X. The completesequences of 100 new cDNA clones from brain which can code for large proteins in vitro, DNA Res.,
5:169{176, 1998.

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[4] The New England Biolabs, Intein Database. http://www.neb.com/neb/frame NEB.html, 1997.

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