A Novel Approach Towards a Comprehensive Consensus Representation of the Expressed Human Genome

In order to provided a novel maximised approach to the generation of accurate, comprehensive, consensus sequences of the expressed human genome, we have developed and produced a system for a novel-representation, broad gene coverage, consensus database of expressed human gene fragments (ESTs). To perform clustering of ESTs, we have developed and employed D2-cluster, an algorithm based on the d2-search algorithm (Hide et al. 1994) speci cally for EST clustering. D2-cluster does not require alignment in order to perform clustering (Burke, Davison and Hide, in prep). We have incorporated d2-cluster into a portable and novel system to perform clustering, alignment and automated error analysis of publicly available expressed sequence tags (STACK PACK). The system includes a statistically robust algorithm that can detect and compensate for error within an aligned cluster of ESTs. We have manufactured a database of partial human consensus sequences from 552 013 ESTs from dbEST 040896 and TIGR. The database is termed Sequence Tag Alignment and Consensus Knowledgebase (STACK). STACK 1.0 contains 18 divisions based on tissue annotation identifying 204 431 unique sequences and generating 76 131 consensi which represent 321 134 ESTs. The consensus sequences have an average length of 497 bases, a 39% increase over the 357 base average length of the input data set. Clone Ids are used to join 92 759 unique sequences and 48 858 consensi into 61 632 linked sequences, averaging 900 bases each. The distribution of clusters compares favourably with UniGene, re ecting the di erence in methodology of clustering and the higher input number of sequences into STACK. SANIGENE high accuracy database is also generated, consisting of sequences which agree in at least two ESTs. STACK is a distributable, core information resource upon which a comprehensive knowledgebase can be built.