Abstract Motivation: In the previous works, we developed ATGpr, a computer program for predicting the fullness of a cDNA, i.e. whether it contains an initiation codon or not. Statistical information of short nucleotide fragments was fully exploited in the prediction algorithm. However, sequence similarities to known proteins, which are becoming increasingly available due to recent rapid growth of protein database, were not used in the prediction. In this work, we present a new prediction algorithm based on both statistical and similarity information, which provides better performance in sensitivity and specificity. Results: We evaluated the accuracy of ATGpr for predicting fullness of cDNA sequences from human clustered ESTs of UniGene, and we obtained specificity, sensitivity, and correlation coefficient of this prediction. Specificity and sensitivity crossed at 46% over the ATGpr score threshold of 0.33 and the maximum correlation coefficient of 0.34 was obtained at this threshold. Without ATGpr we found it effective to use alignments with known proteins for predicting the fullness of cDNA sequences. That is, specificity increased monotonously as similarity (identity of the alignments) increased. Specificity was achieved greater than 80% if identity was greater than 40%. For more effective prediction of fullness of cDNA sequences we combined the similarity (identity of query sequence) with known proteins and ATGpr score. As a result, specificity became greater than 80% if identity was greater than 20%. Availability: The prediction program, called ATGpr_ sim, is available at http://www.hri.co.jp/atgpr/ATGpr˙sim.html Contact: [email protected] To whom correspondence should be addressed. ** Present address: Biosystems Research Department, Central Research Laboratory, Hitachi Ltd, 1-280 Higashi-Koigakubo, Kokubunji-shi, Tokyo, 185-8601, Japan. *** Present address: Kyowa Hakko Kogyo Co. Ltd, Tokyo Research Laboratories, 3-6-6 Asahi-machi, Machida-shi, Tokyo, 194-8533, Japan.

MOTIVATION: In cDNA sequencing projects, it is vital to know whether the protein coding region of a sequence is complete, or whether errors have occurred during library construction. Here we present a linear discriminant approach that predicts this completeness by estimating the probability of each ATG being the initiation codon. RESULTS: Because of the current shortage of full-length cDNA data on which to base this work, tests were performed on a non-redundant set of 660 initiation codon-containing DNA sequences that had been conceptually spliced into mRNA/cDNA. We also used an edited set of the same sequences that only contained the region following the initiation codon as a negative control. Using the criterion that only a single prediction is allowed for each sequence, a cut-off was selected at which discrimination of both positive and negative sets was equal. At this cut-off, 67% of each set could be correctly distinguished, with the correct ATG codon also being identified in the positive set. Reliability could be increased further by raising the cut-off or including homologues, the relative merits of which are discussed. AVAILABILITY: The prediction program, called ATGpr, and other data are available at http://www.hri.co.jp/atgpr CONTACT: [email protected]