1. Bioinformatics and Data Warehousing 1)Introduction to Bioinformatics 2)FASTA File Format 3)Searching Gene Sequences (BLAST) 4)Data Management in Biomedical Informatics Michael Kane, Ph.D. Computer & Information Technology Bindley Bioscience Center Purdue University

2. DNA is Information Storage

3. “Zipped Files” Decompression “Executable Files”

4. DNA is Double Stranded – One strand is the “coding strand” and the other strand is there to stabilize the DNA sequence when not in use. Double-stranded DNA is very durable in our environment.

5. CAGGACCATGGAACTCAGCGTCCTCCTCTTCCTTGCACTCCTCACAGGACTCTTGCTACT CCTGGTTCAGCGCCACCCTAACACCCATGACCGCCTCCCACCAGGGCCCCGCCCTCTG CCCCTTTTGGGAAACCTTCTGCAGATGGATAGAAGAGGCCTACTCAAATCCTTTCTGAG GTTCCGAGAGAAATATGGGGACGTCTTCACGGTACACCTGGGACCGAGGCCCGTGGTC ATGCTGTGTGGAGTAGAGGCCATACGGGAGGCCCTTGTGGACAAGGCTGAGGCCTTCT CTGGCCGGGGAAAAATCGCCATGGTCGACCCATTCTTCCGGGGATATGGTGTGATCTTT GCCAATGGAAACCGCTGGAAGGTGCTTCGGCGATTCTCTGTGACCACTATGAGGGACTT CGGGATGGGAAAGCGGAGTGTGGAGGAGCGGATTCAGGAGGAGGCTCAGTGTCTGAT AGAGGAGCTTCGGAAATCCAAGGGGGCCCTCATGGACCCCACCTTCCTCTTCCAGTCC ATTACCGCCAACATCATCTGCTCCATCGTCTTTGGAAAACGATTCCACTACCAAGATCAA GAGTTCCTGAAGATGCTGAACTTGTTCTACCAGACTTTTTCACTCATCAGCTCTGTATTCG GCCAGCTGTTTGAGCTCTTCTCTGGCTTCTTGAAATACTTTCCTGGGGCACACAGGCAA GTTTACAAAAACCTGCAGGAAATCAATGCTTACATTGGCCACAGTGTGGAGAAGCACCG TGAAACCCTGGACCCCAGCGCCCCCAAGGACCTCATCGACACCTACCTGCTCCACATG GAAAAAGAGAAATCCAACGCACACAGTGAATTCAGCCACCAGAACCTCAACCTCAACA CGCTCTCGCTCTTCTTTGCTGGCACTGAGACCACCAGCACCACTCTCCGCTACGGCTTC CTGCTCATGCTCAAATACCCTCATGTTGCAGAGAGAGTCTACAGGGAGATTGAACAGGT GATTGGCCCACATCGCCCTCCAGAGCTTCATGACCGAGCCAAAATGCCATACACAGAGG CAGTCATCTATGAGATTCAGAGATTTTCCGACCTTCTCCCCATGGGTGTGCCCCACATTG TCACCCAACACACCAGCTTCCGAGGGTACATCATCCCCAAGGACACAGAAGTATTTCTC ATCCTGAGCACTGCTCTCCATGACCCACACTA

6. THEREDCAT_HSDKLSD_WASNOTHOTBUT_WKKNASDN KSAOJ.ASDNALKS_WASWET_ASDFLKSDOFIJEIJKNAW DFN_ANDMAD_WERN.JSNDFJN_YETSAD_MNSFDGPOIJ D_BUTTHEFOX_SDKMFIDSJIR.JER_GOTWET_JSN.DFOI AMNJNER_ANDATEHIM.

7. Start with a thin 2 x 4 lego block… Add a 2 x 2 lego block… Add a 2 x 3 lego block… Add a 2 x 4 lego block…

8. What are the comparative genome sizes of humans and other organisms being studied? organismestimated size estimated gene number average gene density chromo -some number Homo sapiens (human) 2900 million bases~30,0001 gene per 100,000 bases46 Rattus norvegicus (rat) 2750 million bases~30,0001 gene per 100,000 bases42 Mus musculus (mouse) 2500 million bases~30,0001 gene per 100,000 bases40 Drosophila melanogaster (fruit fly) 180 million bases13,6001 gene per 9,000 bases8 Arabidopsis thaliana (plant) 125 million bases25,5001 gene per 4000 bases5 Caenorhabditis elegans (roundworm) 97 million bases19,1001 gene per 5000 bases6 Saccharomyces cerevisiae (yeast) 12 million bases63001 gene per 2000 bases16 Escherichia coli (bacteria) 4.7 million bases32001 gene per 1400 bases1 H. influenzae (bacteria) 1.8 million bases17001 gene per 1000 bases1 Genome size does not correlate with evolutionary status, nor is the number of genes proportionate with genome size.

10. >gi|1924940|emb|CAA67058.1| myosin-IF [Homo sapiens] QEKLTSRKMDSRWGGRSESINVTLNVEQAAYTRDALAKGLYARLFDFLVEAINRAMQKPQEEYSIGVLDI YGFEIFQKNGFEQFCINFVNEKLQQIFIELTLKAEQEEYVQEGIRWTPIQYFNNKVVCDLIENKLSPPGI MSVLDDVCATMHATGGGADQTLLQKLQAAVGTHEHFNSWSAGFVIHHYAGKVSYDVSGFCERNRDVLFSD LIELMQSSDQAFLRMLFPEKLDGDKKGRPSTAGSKIKKQANDLVATLMRCTPHYIRCIKPNETKHARDWE ENRVQHQVEYLGLKENIRVRRAGFAYRRQFAKFLQRYAILTPETWPRWRGDERQGVQHLLRAVNMEPDQY QMGSTKVFVKNPESLFLLEEVRERKFDGFARTIQKAWRRHVAVRKYEEMREEASNILLNKKERRRNSINR NFVGDYLGLEERPELRQFLGKKERVDFADSVTKYDRRFKPIKRDLILTPKCVYVIGREKMKKGPEKGPVC EILKKKLDIQALRGVSLSTRQDDFFILQEDAADSFLESVFKTEFVSLLCKRFEEATRRPLPLTFSDTLQF RVKKEGWGGGGTRSVTFSRGFGDLAVLKVGGRTLTVSVGDGLPKNSKPTGKGLAKGKPRRSSQAPTRAAP GAPQGMDRNGAPLCPQGGAPCPLEKFIWPRGHPQASPALRPHPWDASRRPRARPPSEHNTEFLNVPDQGM AGMQRKRSVGQRPVPVGRPKPQPRTHGPRCRALYQYVGQDVDELSFNVNEVIEILMEDPSGWWKGRLHGQ EGLFPGNYVEKI FASTA File Format TinySeq XML 1924939 X98411.1 9606 Homo sapiens Homo sapiens partial mRNA for myosin-IF 2711 CAGGAGAAGCTGACCAGCCGCAAGATGGACAGCCGCTGGGGCGGGCGCAGCGAGTCCATCAATGT……

11. >GENE NUMBER ONE AGCTGCTAGTAGAGTCGCTCGGATAGGACTGCTAGCTGC........ >GENE NUMBER TWO AGCTGCTAGTAGAGTCGCTCGGATAGGACTGCTAGCTGC........ >GENE NUMBER THREE AGCTGCTAGTAGAGTCGCTCGGATAGGACTGCTAGCTGC........ >GENE NUMBER FOUR AGCTGCTAGTAGAGTCGCTCGGATAGGACTGCTAGCTGC........ >GENE NUMBER FIVE AGCTGCTAGTAGAGTCGCTCGGATAGGACTGCTAGCTGC........ >GENE NUMBER SIX AGCTGCTAGTAGAGTCGCTCGGATAGGACTGCTAGCTGC........ >GENE NUMBER SEVEN AGCTGCTAGTAGAGTCGCTCGGATAGGACTGCTAGCTGC......... >GENE NUMBER TWENTY MILLION AGCTGCTAGTAGAGTCGCTCGGATAGGACTGCTAGCTGC........ FASTA File Format DATABASE (DATA WAREHOUSE)

12. DYNAMIC PROGRAMMING and SEQUENCE SEARCHES 'Dynamic programming' is an efficient programming technique for solving certain combinatorial problems. It is particularly important in bioinformatics as it is the basis of sequence alignment algorithms for comparing protein and DNA sequences. In the bioinformatics application Dynamic Programming gives a spectacular efficiency gain over a purely recursive algorithm. Don't expect much enlightenment from the etymology of the term 'dynamic programming,' though. Dynamic programming was formalized in the early 1950s by mathematician Richard Bellman, who was working at RAND Corporation on optimal decision processes. He wanted to concoct an impressive name that would shield his work from US Secretary of Defense Charles Wilson, a man known to be hostile to mathematics research. His work involved time series and planning—thus 'dynamic' and 'programming' (note, nothing particularly to do with computer programming). Bellman especially liked 'dynamic' because "it's impossible to use the word dynamic in a derogatory sense"; he figured dynamic programming was "something not even a Congressman could object to.”

13. The following is an example of global sequence alignment using Needleman/Wunsch techniques. For this example, the two sequences to be globally aligned are: G A A T T C A G T T A (sequence #1) G G A T C G A (sequence #2) Initialization Step Since this example assumes there is no gap opening or gap extension penalty, the first row and first column of the matrix can be initially filled with 0. DYNAMIC PROGRAMMING and SEQUENCE SEARCHES

14. Matrix Fill Step DYNAMIC PROGRAMMING and SEQUENCE SEARCHES

15. Traceback Step (Seq #1) A | (Seq #2) A (Seq #1) T A | (Seq #2) _ A (Seq #1)T T A | (Seq #2)_ _ A G A A T T C A G T T A | | | G G A _ T C _ G _ _ A _ G A A T T C A G T T A | | | | | | G G A _ _ T C _ G _ _ A There are multiple solutions to this alignment, and most dynamic programming algorithms print out only a single solution. DYNAMIC PROGRAMMING and SEQUENCE SEARCHES

16. BLAST (Basic Local Alignment Search Tool) Why is BLAST so fast? By preindexing all the possible 11-letter words into the database records (4 11 = 4,194,304).. GTCGTAGTCG ATCGTAGTCG CTCGTAGTCG. Steps: 1) Find all the 11-letter words in your query sequence, plus a few variations. 2) Look these up in the 11-letter-word index. 3) Retrieve all sequences containing those words. 4) Use a rigorous algorithm (e.g. Smith-Waterman) to extend the match in both directions

17. >UNKNOWN GENE SEQUENCE AGGACCATGGAACTCAGCGTCCTCCTCTTCCTTGCACTCCTCACAGGACTCTTGCTACTC CTGGTTCAGCGCCACCCTAACACCCATGACCGCCTCCCACCAGGGCCCCGCCCTCTGCC CCTTTTGGGAAACCTTCTGCAGATGGATAGAAGAGGCCTACTCAAATCCTTTCTGAGGTT CCGAGAGAAATATGGGGACGTCTTCACGGTACACCTGGGACCGAGGCCCGTGGTCATGC TGTGTGGAGTAGAGGCCATACGGGAGGCCCTTGTGGACAAGGCTGAGGCCTTCTCTGGC CGGGGAAAAATCGCCATGGTCGACCCATTCTTCCGGGGATATGGTGTGATCTTTGCCAAT GGAAACCGCTGGAAGGTGCTTCGGCGATTCTCTGTGACCACTATGAGGGACTTCGGGAT GGGAAAGCGGAGTGTGGAGGAGCGGATTCAGGAGGAGGCTCAGTGTCTGATAGAGGAG CTTCGGAAATCCAAGGGGGCCCTCATGGACCCCACCTTCCTCTTCCAGTCCATTACCGCC AACATCATCTGCTCCATCGTCTTTGGAAAACGATTCCACTACCAAGATCAAGAGTTCCTG AAGATGCTGAACTTGTTCTACCAGACTTTTTCACTCATCAGCTCTGTATTCGGCCAGCTGT TTGAGCTCTTCTCTGGCTTCTTGAAATA http://www.ncbi.nlm.nih.gov/

18. Genomic Database (DATA WAREHOUSE) >GENE Agtgctcgatagatcg ctcgcata… RESULTS New Gene Sequences (1 per second!) Results DB Gene Cloning DB