Generation and analysis of 280,000 human expressed sequence tags

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Expressed Sequence Tags (ESTs) are crucial for gene discovery and functional genomics.
• Publicly accessible databases facilitate large-scale biological data sharing and analysis.
• Normalization of cDNA libraries can reduce redundancy but may impact gene family representation.

Purpose of the Study:

• To generate a large dataset of human expressed sequence tags (ESTs).
• To deposit these ESTs into the public Data Base for Expressed Sequence Tags.
• To analyze the representation of known and novel sequences within the generated ESTs.

Main Methods:

• Generation of 319,311 single-pass sequencing reactions from 194,031 human cDNA clones.
• Construction of 26 oligo(dT) primed, directionally cloned cDNA libraries from 17 diverse human tissues across three developmental states.
• Application of automatic screening for efficient data deposition and Hidden Markov Models for protein family analysis.

Main Results:

• Successfully generated and deposited 319,311 human ESTs into a public database.
• Comparison with existing databases revealed that the ESTs represent numerous known sequences and a significant number of novel sequences.
• Analysis confirmed that while normalization reduces redundant clones, it does not eliminate all members of gene families.

Conclusions:

• The large-scale generation and public deposition of human ESTs significantly contribute to genomic resources.
• The dataset provides valuable insights into the human transcriptome, including the identification of novel sequences.
• Normalization is an effective strategy for library construction but requires careful consideration regarding gene family representation.