Chicken genomics resource: sequencing and annotation of 35,407 ESTs from single and multiple tissue cDNA libraries and CAP3 assembly of a chicken gene index.

Its accessibility, unique evolutionary position, and recently assembled genome sequence have advanced the chicken to the forefront of comparative genomics and developmental biology research as a model organism. Several chicken expressed sequence tag (EST) projects have placed the chicken in 10th place for accrued ESTs among all organisms in GenBank. We have completed the single-pass 5'-end sequencing of 37,557 chicken cDNA clones from several single and multiple tissue cDNA libraries and have entered 35,407 EST sequences into GenBank. Our chicken EST sequences and those found in public databases (on July 1, 2004) provided a total of 517,727 public chicken ESTs and mRNAs. These sequences were used in the CAP3 assembly of a chicken gene index composed of 40,850 contigs and 79,192 unassembled singlets. The CAP3 contigs show a 96.7% match to the chicken genome sequence. The University of Delaware (UD) EST collection (43,928 clones) was assembled into 19,237 nonredundant sequences (13,495 contigs and 5,742 unassembled singlets). The UD collection contains 6,223 unique sequences that are not found in other public EST collections but show a 76% match to the chicken genome sequence. Our chicken contig and singlet sequences were annotated according to the highest BlastX and/or BlastN hits. The UD CAP3 contig assemblies and singlets are searchable by nucleotide sequence or key word (http://cogburn.dbi.udel.edu), and the cDNA clones are readily available for distribution from the chick EST website and clone repository (http://www.chickest.udel.edu). The present paper describes the construction and normalization of single and multiple tissue chicken cDNA libraries, high-throughput EST sequencing from these libraries, the CAP3 assembly of a chicken gene index from all public ESTs, and the identification of several nonredundant chicken gene sets for production of custom DNA microarrays.

Development of a cDNA array for chicken gene expression analysis.

BACKGROUND: The application of microarray technology to functional genomic analysis in the chicken has been limited by the lack of arrays containing large numbers of genes. RESULTS: We have produced cDNA arrays using chicken EST collections generated by BBSRC, University of Delaware and the Fred Hutchinson Cancer Research Center. From a total of 363,838 chicken ESTs representing 24 different adult or embryonic tissues, a set of 11,447 non-redundant ESTs were selected and added to an existing collection of clones (4,162) from immune tissues and a chicken bursal cell line (DT40). Quality control analysis indicates there are 13,007 useable features on the array, including 160 control spots. The array provides broad coverage of mRNAs expressed in many tissues; in addition, clones with expression unique to various tissues can be detected. CONCLUSIONS: A chicken multi-tissue cDNA microarray with 13,007 features is now available to academic researchers from http://genomics@fhcrc.org. Sequence information for all features on the array is in GenBank, and clones can be readily obtained. Targeted users include researchers in comparative and developmental biology, immunology, vaccine and agricultural technology. These arrays will be an important resource for the entire research community using the chicken as a model.

An adaptive meta-clustering approach: combining the information from different clustering results.

With the development of microarray techniques, there is an increasing need of information processing methods to analyze the high throughput data. Clustering is one of the most promising candidates because of its simplicity, flexibility and robustness. However, there is no "perfect" clustering approach outperforming its counterparts, and it is hard to evaluate and combine the results from different techniques, especially in a field without much prior knowledge, such as bioinformatics. This paper proposes a meta-clustering approach to extract the information from results of different clustering techniques, so that a better interpretation of the data distribution can be obtained. A special distance measure is defined to represent the statistical "signal" of each cluster produced by various clustering techniques. The algorithm is applied on both artificial and real data Simulations show that the proposed approach is able to extract the information efficiently and accurately from the input clustering structure.