DEVELOPING WEB TOOLS FOR DATA MINING AND ANALYSIS OF SAGE

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1 DEVELOPING WEB TOOLS FOR DATA MINING AND ANALYSIS OF SAGE Kristin Wheeler BBSI, University of Pittsburgh Grambling State University Panayiotis Benos,Ph.d Center for Computational Biology & Bioinformatics University Of Pittsburgh

2 Abstract The novel techniques for studying the gene expression, like microarrays and SAGE, require new databases to store and exploit these data. Also, the need for tools to traverse and analyze the information has increased. UniGene is a collection of all currently known/predicted expressed sequences, including mrna of genes, ESTs and gene predictions. The focus of this project is to develop a web tool that will exploit this information and produce various SAGE tag sets according to user-specified criteria. This tool could be used for searching UniGene collection, or other gene expression datasets, for genes that match specific criteria. The search will return the name of the organism, the length of the sequence, and the position where the tag was found. Genomic information in this context gives a simplistic reference for gene expression patterns. We will test this tool on the human genome.

3 Introduction Gene expression is process that follows the synthesis of a protein from transcription through reverse transcriptase. Knowledge of this process has led to the development of several techniques for determining gene functionality. Most methods rely on either co-regulation or take a phylogenic approach to examine gene expression. An accurate analysis of data produced by these techniques is vital for molecular visualization, targeting binding sites, and defining biological pathways.

4 Serial Analysis of Gene Expression Technique that measures gene expression levels Produced a large quantity of transcripts from small biological samples Does not require prior transcript information. Resulting data represents absolute expression levels Has over 3,000,000 transcript tags in database SAGE data increases credibility when included in research Available via internet

5 Serial Analysis of Gene Expression Microdissection/cell purification Formation of defined position within each transcript by cleaveage with anchoring enzyme(nlaiii) Formation of ditags, followed bypcr amplification and concatermerization to facilitate sequencing Capture of poly-a RNA on oligo-dt beads and double-stranded cdna synthesis Release of SAGE tags after ligation to a linker with a IIS restriction enzyme site and cleavage with the tagging enzyme (usually BsnFl) Sequencing data analysis

6 My Project (SAGED) Delimiter Identifier # Set containing 1 members /gb=af /gi= /len=1113 >gnl UG Cre#S Chlamydomonas reinhardtii WD40-repeat-containing protein (Mut11) mrna, complete cds /cds=(1,1113) /gb=af /gi= /ug=cre.1 /len=1113 ATGGCGCGGGGCCCCGGTGACACGGACATGGACGAGGCCTCAGCCGACGCCGCCATCCCT TCCTCCACCCCCAACCCCACCGTTGCCTTCCGCTGCACGCACGCGCTCTCCGGCCACACC AAAGCGGTGGCCGCCGTCAAGTTCAGCCCCGACGGCTCGCTCCTCGCCTCCGGCTCCGCA GACCGCACCGTGGCCTTGTGGGATGCCGCCACGGGCGCCCGCGTTAACACACTCGCCGGG CACTCCTGCGGCGTGTCTGACGTGGCCTGGAACCCAAACGGTCGCTACCTCGCAACCGCC GCCGACGACCACAGCCTCAAGCTGTGGGATGCGGAGACCGGCGCCTGTCTGCGCACGCTG ACCGGCCACACCAACTACGTCTTCTGCTGCAACTTTGACGGCGCGGCTGGGCACCTGCTG GCCTCCGGCAGCTTCGACGAGACACTGCGGCTGTGGGATGTGCGCAGCGGGCGCTGCCTG CGGGAGGTGCCAGCGCACAGCGACCCAGTGACGTCGGCGGCCTTCAGCTACGACGGCAGC ATGGTCGTCACGAGCAGTCTGGACGGACTCATCCGGCTGTGGGACACTCAGACGGGCCAT TGCCTCAAGACGTTGTTCGACCGCGACTCACCGCCCGTGTCCTTCGCCGCCTTCACGCCC AACGCCAAGTACGTGCTGTGCAACACGCTGGACGGGCGCGCCAAGCTGTGGGACTACGCC GCCGGCCGCACCCGCCGCACCTACGCCGGCGGACACGTCAACACACAGTTCTGCATCAGC AGCGGCTTCCTCGGCGGCAGTAGCAGCGCCAGCTTCGATCTGGGCTGTAGCATGGTGGTG ACGGGCAGTGAGGACGGCAGCCTTGCGGCGTACGACATCTCGACGGGCCACGTGGTGGGT Cgcggggcggcggcggcggcagcggcggagggcggcggcgacgagggcagcgccgccgcc gcggcggcgggaggtgtggccggcgggcacacggcggcggtgctgtctgtgaatgtgcac CCCAGCGCGCCGCTGGTGGCCACGGGCGGGCACCACCCCGACAACAGCGTGCGGGTGTGG GCGGCGTCGCGCACAGAGCCAGCGGCCGCGTGA 10bp 3 most NlaIII

7 SAGED Species Sequence ID Tag position Sequence Length Cre#s

8 Conclusions The development of effective software/web tools to analyze SAGE data will aid in the progression of biological research. Perhaps with more time, a more effective algorithm, and efficient testing SAGED will become a common tool for the analysis of SAGE data.

9 References Velculescu VE, Vogelstein B, Kinzler KW. (2000) Analysing uncharted transcriptomes with SAGE. Trends Genet.vol.16,no. 10.p Flanagan, David. Java in a Nutshell. O Reilly & Associates, Inc. Cambridge, Qs,_Help,_and_Tutorials/Tutorials/

10 Acknowledgements Dr. Takis Benos Dr. Rajan Munshi BBSI NIH NSF Damione Moore Grambling State University