DNA Classifications with Self-Organizing Maps (SOMs)

1. DNA Classifications with Self-Organizing Maps (SOMs) Thanakorn Naenna Mark J. Embrechts Robert A. Bress May 2003 IEEE International Workshop on Soft Computing in Industrial Application

2. Presentation Outline • Introduction to DNA Splice Junctions • Data Collection • Introduction to SOMs • SOM for DNA Splice Junction Classification • Results • Conclusions

4. Human genome in a nutshell • Human : 23 chromosomes • Chromosomes  thousands of genes • Gene  info : exons , comments : introns Splice junction are like /* comment flags */ in C-code • Exons and introns  codons • Codon  bases

5. Exon Splice Junction Splice Junction Intron Intron ….GTGAAGGTTAA AGATGTAGATGT ATTG… DNA Splice Junctions • DNA  billions of nucleotides ( A, C, G, T) • Genes  sequences of amino acids (exons) that are often interrupted by non-coding nucleotides (introns) • <.1% of human DNA is made up of exons • 99% of splice junctions have the same motif, for • Exon to intron it is GT • Intron to exon it is AG

6. Data Collection: HTML Browser + Perl scripts

8. Splice Junction Exon Intron …TGTAAGG AGACGAGTT… DNA Splice Junction (Cont.) • A complete gene is made up of different exons • Splice junction identification aids in the discovery of new genes • The dataset used for this study is made up of 1,424 sequences • Data were created ab initio from GENBANK • Each sequence is 32 nucleotides long with regions comprising -15 to +15 nucleotides from the splice-junction

9. i w i 1 Component 1 w Component 2 i 2 w c c 1 w Component 3 c 2 w i 3 w c 3 Component 4 w c 4 w i 4 Component 5 w 5 c w i 5 Output layer Input layer Self-Organizing Maps (SOM) Network • Unsupervised learning neural network • Projects high-dimensional input data onto two-dimensional output map • Preserves the topology of the input data • Visualizes structures and clusters of the data

10. B C A Use of SOM for DNA Splice Junction Classification Model Neuron identification methods - Highest frequency class - Closest neuron DNA test set SOM Classification Map U-Matrix Map SOM DNA training set Classification Class A: intron to exon Class B: exon to intron Class C: no transition

11. The U-matrix of the DNA Training Set

12. B C Confusion matrix of 424-DNA test set A SOM Results for DNA Splice Junction Data The U-matrix of the DNA training set

13. Conclusions • SOM is effective in DNA splice junction classification • SOM is powerful visualization for high dimensional data

14. Demo with Analyze Code • 800 training data, 324 test data (160 features) • 96% correct overall classification on test data Confusion Matrix

15. THE END GATCAATGAGGTGGACACCAGAGGCGGGGACTTGTAAATAACACTGGGCTGTAGGAGTGA TGGGGTTCACCTCTAATTCTAAGATGGCTAGATAATGCATCTTTCAGGGTTGTGCTTCTA TCTAGAAGGTAGAGCTGTGGTCGTTCAATAAAAGTCCTCAAGAGGTTGGTTAATACGCAT GTTTAATAGTACAGTATGGTGACTATAGTCAACAATAATTTATTGTACATTTTTAAATAG CTAGAAGAAAAGCATTGGGAAGTTTCCAACATGAAGAAAAGATAAATGGTCAAGGGAATG GATATCCTAATTACCCTGATTTGATCATTATGCATTATATACATGAATCAAAATATCACA CATACCTTCAAACTATGTACAAATATTATATACCAATAAAAAATCATCATCATCATCTCC ATCATCACCACCCTCCTCCTCATCACCACCAGCATCACCACCATCATCACCACCACCATC ATCACCACCACCACTGCCATCATCATCACCACCACTGTGCCATCATCATCACCACCACTG TCATTATCACCACCACCATCATCACCAACACCACTGCCATCGTCATCACCACCACTGTCA TTATCACCACCACCATCACCAACATCACCACCACCATTATCACCACCATCAACACCACCA CCCCCATCATCATCATCACTACTACCATCATTACCAGCACCACCACCACTATCACCACCA CCACCACAATCACCATCACCACTATCATCAACATCATCACTACCACCATCACCAACACCA CCATCATTATCACCACCACCACCATCACCAACATCACCACCATCATCATCACCACCATCA CCAAGACCATCATCATCACCATCACCACCAACATCACCACCATCACCAACACCACCATCA CCACCACCACCACCATCATCACCACCACCACCATCATCATCACCACCACCGCCATCATCA TCGCCACCACCATGACCACCACCATCACAACCATCACCACCATCACAACCACCATCATCA CTATCGCTATCACCACCATCACCATTACCACCACCATTACTACAACCATGACCATCACCA CCATCACCACCACCATCACAACGATCACCATCACAGCCACCATCATCACCACCACCACCA CCACCATCACCATCAAACCATCGGCATTATTATTTTTTTAGAATTTTGTTGGGATTCAGT ATCTGCCAAGATACCCATTCTTAAAACATGAAAAAGCAGCTGACCCTCCTGTGGCCCCCT TTTTGGGCAGTCATTGCAGGACCTCATCCCCAAGCAGCAGCTCTGGTGGCATACAGGCAA CCCACCACCAAGGTAGAGGGTAATTGAGCAGAAAAGCCACTTCCTCCAGCAGTTCCCTGT GATCAATGAGGTGGACACCAGAGGCGGGGACTTGTAAATAACACTGGGCTGTAGGAGTGA TGGGGTTCACCTCTAATTCTAAGATGGCTAGATAATGCATCTTTCAGGGTTGTGCTTCTA TCTAGAAGGTAGAGCTGTGGTCGTTCAATAAAAGTCCTCAAGAGGTTGGTTAATACGCAT GTTTAATAGTACAGTATGGTGACTATAGTCAACAATAATTTATTGTACATTTTTAAATAG CTAGAAGAAAAGCATTGGGAAGTTTCCAACATGAAGAAAAGATAAATGGTCAAGGGAATG GATATCCTAATTACCCTGATTTGATCATTATGCATTATATACATGAATCAAAATATCACA CATACCTTCAAACTATGTACAAATATTATATACCAATAAAAAATCATCATCATCATCTCC ATCATCACCACCCTCCTCCTCATCACCACCAGCATCACCACCATCATCACCACCACCATC ATCACCACCACCACTGCCATCATCATCACCACCACTGTGCCATCATCATCACCACCACTG TCATTATCACCACCACCATCATCACCAACACCACTGCCATCGTCATCACCACCACTGTCA TTATCACCACCACCATCACCAACATCACCACCACCATTATCACCACCATCAACACCACCA CCCCCATCATCATCATCACTACTACCATCATTACCAGCACCACCACCACTATCACCACCA CCACCACAATCACCATCACCACTATCATCAACATCATCACTACCACCATCACCAACACCA CCATCATTATCACCACCACCACCATCACCAACATCACCACCATCATCATCACCACCATCA CCAAGACCATCATCATCACCATCACCACCAACATCACCACCATCACCAACACCACCATCA CCACCACCACCACCATCATCACCACCACCACCATCATCATCACCACCACCGCCATCATCA TCGCCACCACCATGACCACCACCATCACAACCATCACCACCATCACAACCACCATCATCA CTATCGCTATCACCACCATCACCATTACCACCACCATTACTACAACCATGACCATCACCA CCATCACCACCACCATCACAACGATCACCATCACAGCCACCATCATCACCACCACCACCA CCACCATCACCATCAAACCATCGGCATTATTATTTTTTTAGAATTTTGTTGGGATTCAGT ATCTGCCAAGATACCCATTCTTAAAACATGAAAAAGCAGCTGACCCTCCTGTGGCCCCCT TTTTGGGCAGTCATTGCAGGACCTCATCCCCAAGCAGCAGCTCTGGTGGCATACAGGCAA CCCACCACCAAGGTAGAGGGTAATTGAGCAGAAAAGCCACTTCCTCCAGCAGTTCCCTGT DDASSL Drug Design and Semi-Supervised Learning