Web Technologies in Bioinformatics

1. Web Technologies in Bioinformatics T.J. Esposito April 28, 2005 Advanced Bioinformatics Computing

2. Project Goal • To make the normalized Frisina data easy and convenient to work with • To avoid having to work with enormous text files of seemingly meaningless numbers

3. Project Goals • This will be accomplished by: • - Putting the data into a database • - Making the database easy to interact with as well • - Making the database available to whoever needs it • - Giving the data some sort of context

4. Methods • One of the most convenient ways of doing this is to: • - Use a relational database to store the data • - Give the database a web interface, which is convenient to use and readily available • - Link that data to other available data from Affymetrix and other sources

5. Methods • These goals will be reached using current database and web technology. • For the back end database, mySQL will be used. • For the web interface, JSP (Java Server Pages) will be used.

6. Reasons for mySQL • MySQL will be used due to its speed. • Competing systems, like Postgres, were considered; however, more fully featured (yet slower) systems were not necessary. • - the data will be manipulated using only SELECTS • - MySQL, having fewer features than other systems, makes it faster and thus better suited for use in web applications

7. JSP has well known advantages; it is: • - Efficient • - Convenient • - Powerful • - Inexpensive • - Portable • - Secure • - Java based Reasons for JSP

8. JSP • Perl and CGI were considered, but JSP was chosen due to: • - Its being a current web technology utilized by many major corporations • - It seems more convenient and full-featured compared to a Perl/CGI approach • - JSP fits current multi-tier database architectures better than CGI, due to the Java API and JSP being development so • - I will be working with JSP on co-op, so I wanted to brush up (or rather, learn it) before then

9. Data Expansion • One the data has been entered into a mySQL database, and given a moderately flexible web interface, it will also be linked to other sources • - Affymetrix data from their site • - Other sites like NCBI or GenBank? • - Linking data to new sources as needed should be fairly easy

10. Finally… • In the end, an expandable system will have been created that hopefully can be used in a real world application. • Even if it isn’t, at least I will have gotten the experience in developing such a system with a new technology (JSP), and continued in the Java nature of the course.

11. Questions Any questions?

12. Visualization of Frisina’s Research Data Using University of Maryland’s Treemap 4.1 John Boutell and Tom Maxon

13. Procedure • Transform Frisina flat files into Treemap flat files or Excel files • Determine relationships • Determine organization / visualization preferences

14. File Transformation • Treemap file considerations – Begins with a line consisting of a list of variables to be considered. The next line follows with definitions of variables. The subsequent consists of data, with relationships of each following list of data.

15. Determining Relationships • A maximum of four layers can be used, so we’ll need to determine what the four layers should be. Example: Middle-aged vs. Young vs. Old could be one layer.

16. Organization and Visualization Determination • This step will consist of ordering data and arranging coloration and spacing to insure that the visualization is easily understood.

17. ObtainingInformation Regarding Mouse Array Genes Chris Parkin April 28, 2005

18. Overview: Research involves expression data from Affymetrix mouse chip 430a Thousands of genes found on this gene chip, any of which could be of importance

19. Overview: Example Expression Data: X16_Frisina_S2_M430A.CEL X17_Frisina_S2_M430A.CEL X25_Frisina_S2_M430A.CEL X36_b_Frisina_S2_M430A.CEL 1415672_at 14.2636987581270 14.8166925938434 14.7202558244306 14.7153893835085 1415673_at 10.6382802704383 10.8947849214261 9.7992056002344 10.0489561960792 1415675_at 12.6363495581221 12.310695824458 11.7665991587842 11.7192886280750 1415677_at 11.9224599733792 11.6230373622742 11.0882276072649 11.1584524620751 1415678_at 14.3403000148085 14.3258513901380 14.2753594390197 14.3758483552046 1415679_at 15.0959031716503 14.8066829033559 14.6876918364335 14.5911816158065 1415680_at 11.4203757035264 11.4120007012393 11.2384462748424 11.3684779023244 1415681_at 12.3004566771331 11.7383490484824 11.4995261583693 11.3078357750632 Each gene in the expression data is given an accession number

20. Overview: Gene information based on accession # available at Affymetrix website, but is a tedious process Some of the information may not be that useful for this particular research

21. Project Goal: Develop a useful online tool for obtaining information about genes on the mouse chip Two powerful tools to be used in developing this: Perl & NCBI

22. Information to Include: Nucleotide sequence & amino acid translation NCBI Definition: What metabolic role does this sequence play a part in Any available links to PUBMED articles Homology groups (using NCBI’s “Homologene” Any available information in NCBI’s “Gene” database (descriptions, lineage, ontology…)

23. Questions?

24. Gene Group Correlation • Presented by • Andrew Darling

25. Outline of Presentation • Problem Statement • Gene Group Correlation • Methods • Results • Discussion • Conclusion

26. Problem Statement • Using ~20,000 expression levels taken from ~40 mice of various ages, find the genes responsible for progressive age related hearing loss in mice.

27. Gene Group Correlation • Search for genes with expression levels • Grouping similarly to the 4 mouse test groups • Corresponding to the severity of the hearing impairment • Exclude genes used for non hearing impairment genes

28. Methods • For each “gene” • Gather expression levels for each mouse • Segregate each expression level by mouse group • Apply mean and deviation calculations for each group • Calculate metric for quality of segregation • Do expression levels segregate by mouse group • Repeat for each gene • Sort for highly segregated (by group) expression values

29. Methods – examples 1 & 2 • Gene 1 • Young mice levels = 1, 1, 1, 1, 1, 1, 1, 1 • Middle mice levels = 3, 3, 3, 3, 3, 3, 3, 3 • Old mice levels = 6, 6, 6, 6, 6, 6, 6, 6 • Severe mice levels = 9, 9, 9, 9, 9, 9, 9, 9 • Conclusion – highly segregated by group in order of severity • Gene 2 • Young mice levels = 1, 1, 2, 2, 3, 3, 4, 4 • Middle mice levels = 3, 3, 4, 4, 5, 5, 6, 6 • Old mice levels = 5, 5, 6, 6, 7, 7, 8, 8 • Severe mice levels = 6, 6, 7, 7, 8, 8, 9, 9 • Conclusion – mostly segregated by group in order of severity

30. Methods – examples 3 & 4 • Gene 3 • Young mice levels = 1, 2, 3, 4, 5, 6, 7, 8 • Middle mice levels = 1, 2, 3, 4, 5, 6, 7, 8 • Old mice levels = 1, 2, 3, 4, 5, 6, 7, 8 • Severe mice levels = 1, 2, 3, 4, 5, 6, 7, 8 • Conclusion – not segregated by group • Gene 4 • Young mice levels = 1, 1, 1, 1, 2, 2, 2, 2 • Middle mice levels = 7, 7, 7, 7, 8, 8, 8, 8 • Old mice levels = 5, 5, 5, 5, 6, 6, 6, 6 • Severe mice levels = 3, 3, 3, 3, 4, 4, 4, 4 • Conclusion – mostly segregated by group not in order of severity

31. Results • Coding still in process • Working out a few parameters • Whether to sort by • Distance of group means from each other • Size of sigma for each group • Mutually exclusive grouping • Ordering of group means by severity

32. Discussion • Quality of prediction of related genes based on quality of correlation theory • Presumes related gene expression is progressive and consistent • Presumes a quality of gene expression level measurement • Further validation possible by sorting for redundant hits • Sequences referenced by several probes on the chip • Several similar probes each correlating highly

33. Conclusion • If this works, it’s a freaking miracle

34. Gene Selection What level Of what gene Does what?

35. Clustering • Radial Basis Neural Network • Develop clustering using 2 “old” data sets • Test with all 4 data sets to verify that it clusters correctly • Generates weights to form the clusters

36. Anfis • Tool to extract the neural network “rules” • Gives a formula based on all the inputs to show given any set of input what value it will generate • It is possible to extract the exact impact of each input from this formula.

37. Anfis Cont’ • However • Computationally very expensive • Training time for this type of network increases by a factor of 3 for each added line of input. • Time to train would be in the order of • 10 * 322680 seconds (324 secs = 10000 yrs)

38. Weights • Data values influence the weights • To eliminate those influences the values must be converted to binary values. • A set of threshold values is needed

39. Input • For each variable these threshold are used • Median Mean • 25/75 75/25 • 10/90 90/10 • 0/100 100/0 • Each of those data sets are combined into one large training set.

40. Where I’m going with this • What the network will learn is to classify the data by each of those sets • Does this already • except for the all or nothing case

41. Where I’m going with this • Analyze the weights • By distance between weights of opposite categories

42. What does alarge differentiation mean • Should point at • The gene of importance • The level of expression where the change occurs

43. Data Set • Each of those data sets are combined into one large training set.

44. Identify Classifying Genes of Presbycusis Alex Haugh

45. Project Outline • Step 1 – Calculate the mean of each of the datasets (Young, Midage, Mild, Severe). • Step 2 – Find a set of genes that have unique expressions for each type. • Step 3 – Test the ability of these genes to classify each type from training sets. • Step 4 – Plot the expression levels of these genes throughout the mouse life cycle.

46. Step 1: Getting the Mean • Parse the files given to us by Tex. • Take those values and get a ‘Pre’ average. • Calculate the standard deviation • Remove any values are not contained within 95% • Calculate the ‘Post’ average with removed expression levels • Record them in a new condensed file format: Gene Expression at17186 10.56574 at17187 8.96768

47. Step 2: Calculating Classifying Genes • Read in each of the newly condensed files. • Place all of the values into a data structure. • Compare all of the values of a gene against all other types and record those genes which are greater than or less than a given threshold value. • Narrow down genes to much smaller set • Record the genes in a file for use later: --------HIGHER --------- --------LOWER-------- at17186 10.56574 at15686 5.68869 at17187 8.96768 at17122 7.76859

48. Step 3: Testing Classifying Genes • Read in the classifying genes for each type • Read in the unknown dataset • Subtract the unknown expression value from classifying gene and take the absolute value. • If the gene less than the threshold value record a plus one for that type. • Report the type with the most genes within the threshold. Note: Given 100 Classifying genes per type and a threshold value of 0.35 there is a very high rate of accuracy.

49. Step 4: Tracking Levels • After testing the classifying genes from each type empirically, record these (hopefully about 20) • Record the average value for the gene from all types. • Graph the values • Observe and record the trends in each gene. • Report any genes that don’t follow the given trends.

50. Expectations • I expect to find about 20 genes per type that classify ‘unknown’ datasets very well. • I expect those genes to generally follow similar trends. • I expect to be able to a have a program that can read in datasets and produce reliable results that can assist research by quickly identifying those genes which are outliers and unique.