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Several efforts are under way to partition single-read expressed sequence tag (EST), as well as full-length transcript data, into large-scale gene indices, where transcripts are in common index classes if and only if they share a common progenitor gene. Accurate gene indexing facilitates gene expression studies, as well as inexpensive and early gene sequence discovery through assembly of ESTs that are derived from genes that have not been sequenced by classical methods. We extend, correct, and enhance the information obtained from index groups by splitting index classes into subclasses based on sequence dissimilarity (diversity). Two applications of this are highlighted in this report. First it is shown that our method can ameliorate the damage that artifacts, such as chimerism, inflict on index integrity. Additionally, we demonstrate how the organization imposed by an effective subpartition can greatly increase the sensitivity of gene expression studies by accounting for the existence and tissue- or pathology-specific regulation of novel gene isoforms and polymorphisms. We apply our subpartitioning treatment to the UniGene gene indexing project to measure a marked increase in information quality and abundance (in terms of assembly length and insertion/deletion error) after treatment and demonstrate cases where new levels of information concerning differential expression of alternate gene forms, such as regulated alternative splicing, are discovered. [Tables 2 and 3 can be viewed in their entirety as Online Supplements at http://www.genome.org.]

Alternative Gene Form Discovery and Candidate Gene Selection from Gene Indexing Projects