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This effectively eliminates an artifact typically found in UniGene. If 5 and 3 sequences from the same gene are non-overlapping in UniGene, then UniGene will put these 5 and 3 sequences into two different clusters, overestimating the number of human genes. Because we only cluster 3 end sequences bearing poly-A tails we do not see this 5/3 splitting artifact (Table 1). Moreover, because the sequences that we have clustered to date are contiguous with the poly-A tails, we are less likely to be confused by splice variants, which occur less frequently in the 3 untranslated regions of transcripts (Table 1). Utilizing the 3 sequences as a unique gene-identification tag has been demonstrated as an effective gene-specific marker. Because the 3UTRs are not as conserved as the coding sequences, this makes it easier to distinguish between individual genes and paralogous gene family members that may have sequence homology in their coding sequences1.

Fig.2

Our clustering algorithm is rigorous. We first identify commonly repeated sequence elements in the data set. We then require that for any two sequences to cluster, they must match at 96% identity over 100 or more base pairs and the percentage of alignable sequence must be greater than 90%. Figure 2 shows how the percentage of alignable sequence is determined. Two sequences are compared and aligned to maximize the number of matching base pairs. At each end of the local alignment, the shorte
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