An Introduction to Bioinformatics (high-school version)

1. An Introduction to Bioinformatics(high-school version) Ying Xu Institute of Bioinformatics, and Biochemistry and Molecular Biology Department University of Georgia xyn@bmb.uga.edu

2. ccgtacgtacgtagagtgctagtctagtcgtagcgccgtagtcgatcgtgtgggtagtagctgatatgatgcgaggtaggggataggatagcaacagatgagcggatgctgagtgcagtggcatgcgatgtcgatgatagcggtaggtagacttcgcgcataaagctgcgcgagatgattgcaaagragttagatgagctgatgctagaggtcagtgactgatgatcgatgcatgcatggatgatgcagctgatcgatgtagatgcaataagtcgatgatcgatgatgatgctagatgatagctagatgtgatcgatggtaggtaggatggtaggtaaattgatagatgctagatcgtaggta…………………………………ccgtacgtacgtagagtgctagtctagtcgtagcgccgtagtcgatcgtgtgggtagtagctgatatgatgcgaggtaggggataggatagcaacagatgagcggatgctgagtgcagtggcatgcgatgtcgatgatagcggtaggtagacttcgcgcataaagctgcgcgagatgattgcaaagragttagatgagctgatgctagaggtcagtgactgatgatcgatgcatgcatggatgatgcagctgatcgatgtagatgcaataagtcgatgatcgatgatgatgctagatgatagctagatgtgatcgatggtaggtaggatggtaggtaaattgatagatgctagatcgtaggta………………………………… genome and sequencing chromosome metabolic pathway/network genes protein The Basics cell

3. Bioinformatics(or computational biology) ccgtacgtacgtagagtgctagtctagtcgtagcgccgtagtcgatcgtgtgggtagtagctgatatgatgcgaggtaggggataggatagcaacagatgagcggatgctgagtgcagtggcatgcgatgtcgatgatagcggtaggtagacttcgcgcataaagctgcgcgagatgattgcaaagragttagatgagctgatgctagaggtcagtgactgatgatcgatgcatgcatggatgatgcagctgatcgatgtagatgcaataagtcgatgatcgatgatgatgctagatgatagctagatgtgatcgatggtaggtaggatggtaggtaaattgatagatgctagatcgtaggta………………………………… • This interdisciplinary science … is aboutproviding computational support to studies on linking the behavior of cells, organisms and populations to the information encoded in the genomes • Temple Smith

4. Information Encoded in Genomes • What information? And how to find and interpret it? • Working molecules (proteins, RNAs) in our cells ccgtacgtacgtagagtgctagtctagtcgtagcgccgtagtcgatcgtgtgggtagtagctgatatgatgcgaggtaggggataggatagcaacagatgagcggatgctgagtgcagtggcatgcgatgtcgatgatagcggtaggtagacttcgcgcataaagctgcgcgagatgattgcaaagragttagatgagctgatgctagaggtcagtgactgatgatcgatgcatgcatggatgatgcagctgatcgatgtagatgcaataagtcgatgatcgatgatgatgctagatgatagctagatgtgatcgatggtaggtaggatggtaggtaaattgatagatgctagatcgtaggta………………………………… bacterial cell

5. Information Encoded in Genomes • How to find where protein-encoding genes are in a genome? • A genome is like a book written in “words” consisting of 4 letters (A, C, G, T), and each protein-encoding gene is like an instruction about how the protein is made • People have found that the six-letter words (e.g., AAGTGC) have different frequencies in genes from non-gene regions ccgtacgtacgtagagtgctagtctagtcgtagcgccgtagtcgatcgtgtgggtagtagctgatatgatgcgaggtaggggataggatagcaacagatgagcggatgctgagtgcagtggcatgcgatgtcgatgatagcggtaggtagacttcgcgcataaagctgcgcgagatgattgcaaagragttagatgagctgatgctagaggtcagtgactgatgatcgatgcatgcatggatgatgcagctgatcgatgtagatgcaataagtcgatgatcgatgatgatgctagatgatagctagatgtgatcgatggtaggtaggatggtaggtaaattgatagatgctagatcgtaggta…………………………

6. Information Encoded in Genomes Frequency in genes (AAA ATT) = 1.4%; Frequency in non-genes (AAA ATT) = 5.2% Frequency in genes (AAA GAC) = 1.9%; Frequency in non-genes (AAA GAC) = 4.8% Frequency in genes (AAA TAG) = 0.0%; Frequency in non-genes (AAA TAG) = 6.3% …. AAAATTAAAATTAAAGACAAAATTAAAGACAAACACAAAATTAAATAGAAATAGAAAATT ….. Is this a gene or non-gene region if you have to make a bet?

7. Information Encoded in Genomes • Preference model: • for each 6-letter word X (e.g., AAA AAA), calculate its frequencies in gene and non-gene regions, FC(X), FN(X) • calculate X’s preference value P(X) = log (FC(X)/FN(X)) • Properties: • P(X) is 0 if X has the same frequencies in gene and non-gene regions • P(X) has positive score if X has higher frequency in gene than in non- gene region; the larger the difference, the more positive the score is • P(X) has negative score if X has higher frequency in non-gene than in gene region; the larger the difference, the more negative the score is • Gene prediction: given a DNA region, calculate the sum of P(X) values for all 6-letter words X in the region; • if the sum is larger than zero, predict “gene” • otherwise predict non-gene

8. Information Encoded in Genomes • You just learned your first bioinformatics method for gene prediction –congratulations!

9. Information Encoded in Genomes • Ok, we now have learned how to find genes encoded in a genome • How do we find out what they do (their biological functions, e.g. sensors, transportors, regulators, enzymes)?

10. Information Encoded in Genomes • People have observed that similar protein sequences tend to have similar functions • Over the years, many genes have been thoroughly studied in different organisms,e.g.,human, mouse, fly, …., rice, … • their biological functions have been identified and documented • For a new protein, scientists can possibly predict its function by identifying well-studied proteins in other organisms, that have high sequence similarities to it • This works for ~60% of genes in a newly sequenced genome

11. Information Encoded in Genomes • Scientists have developed computational techniques for • identifying regulatory signals that controls gene transcription • predicting protein-protein interactions • elucidating biological networks for a particular function • …... and elucidating many other information

12. Information Encoded in Genomes E. Coli O157 and O111 are human pathogenic while E. Coli K12 is not; Can we tell why? Which genes or pathways in E. coli O157 and O111 are responsible for the pathogenicity?

13. human chromosome #1 B. pseudomallei E. coli K-12 E. coli O157 Random seq P. furiosus Information Encoded in Genomes

14. Information Encoded in Genomes Red: prokaryotes Blue: eukaryotes Green: plastids Orange: plasmids Black: mitochondria x-axis: average of variations of the K-mer frequencies, y-axis: average barcode similarity among fragments of a genome

15. Information Encoded in Genomes • Yes, biologists can derive a lot of information from genomes now • … but we are far from fully understanding any genome yet, even for the simplest living organisms, bacteria • We can clearly use new ideas from bright young minds – interested in doing bioinformatics?

16. ccgtacgtacgtagagtgctagtctagtcgtagcgccgtagtcgatcgtgtgggtagtagctgatatgatgcgaggtaggggataggatagcaacagatgagcggatgctgagtgcagtggcatgcgatgtcgatgatagcggtaggtagacttcgcgcataaag…………………………ccgtacgtacgtagagtgctagtctagtcgtagcgccgtagtcgatcgtgtgggtagtagctgatatgatgcgaggtaggggataggatagcaacagatgagcggatgctgagtgcagtggcatgcgatgtcgatgatagcggtaggtagacttcgcgcataaag………………………… gene protein Linking Genome Information to Biological Systems Behaviors • To fully understand cellular behaviors, we need to • elucidate information encoded in the genome, and • understand working molecules, encoded by the genome, behaves according to the physical laws on earth!

17. Key Drivers of Bioinformatics • Human genome project has fundamentally changed biological science • A key consequence of the genome project is scientists learned that they can produce biological data massively • genome sequences • microarray data for gene expression levels • yeast two hybrid systems for protein-protein interactions • …… and other “high-throughput” biological data These data reflect the cellular states, molecular structures and functions, in complex ways

18. Key Drivers of Bioinformatics • … and let bioinformaticians to (help to) decipher the meaning of these data, like in genome sequences • Together, high-throughput probing technologies and bioinformatics are transforming biological science into a new science more like physics

19. Key Drivers of Bioinformatics • Like physics, where general rules and laws are taught at the start, biology will surely be presented to future generations of students as a set of basic systems ....... duplicated and adapted to a very wide range of cellular and organismic functions, following basic evolutionary principles constrained by Earth’s geological history. • Temple Smith, Current Topics in Computational Molecular Biology

20. …… in a similar fashion to doing pregnancy test using a test kit, possibly at home Biomarker Identification • Our goal is to identify markers in blood that can tell if a person has a particular form of cancer

21. Biomarker Identification • Microarray gene expression data allow comparative analyses of gene expression patterns in cancer versus normal tissues Finding genes showing maximum difference in their expression levels between cancer and normal tissues on cancer tissues on normal tissues

22. Biomarker Identification proteins A, …, Z highly expressed in cancer

23. Biomarker Identification • Question: Can we predict which of these tissue marker proteins can get secreted into blood circulation so we can get markers in blood? • Through literature search, we found over proteins being secreted into blood circulation due to various physiological conditions • We then trained a “classifier” to identify “features” that distinguish between proteins that can be secreted into blood and proteins that cannot

24. Biomarker Identification • We have developed a classifier to distinguish blood-secretory proteins and other proteins • On a test set with 52 positive data and 3,629 negative data, our classifier achieves • 89.6% sensitivity, 98.5% specificity and 94% AUC

25. Biomarker Identification • The predicted marker proteins can be validated using mass spectrometry experiment

26. Biomarker Identification • If successful, it will be possible to test for cancer using a test-kit like pregnancy test-kits

27. Take-Home Message • Biological science is under rapid transformation because of high-throughput measurement technologies and bioinformatics • As an emerging field, bioinformatics is about using computational techniques to solve biological problems, and represents the future of biology

28. THANK YOU!