Web Technologies in Bioinformatics

T.J. Esposito

April 28, 2005

Advanced Bioinformatics Computing

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

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

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

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.

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

Reasons for JSP• JSP has well known advantages; it is:

- Efficient

- Convenient

- Powerful

- Inexpensive

- Portable

- Secure

- Java based

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

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

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.

Questions

Any questions?

Visualization of Frisina’s Research Data Using University

of Maryland’s Treemap 4.1John Boutell and Tom Maxon

Procedure

• Transform Frisina flat files into Treemap flat files or Excel files

• Determine relationships

• Determine organization / visualization preferences

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.

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.

Organization and Visualization Determination

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

ObtainingInformation Regarding Mouse

Array GenesChris Parkin

April 28, 2005

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

Overview:• Each gene in the expression data is given an accession number

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 14.71538938350851415673_at 10.6382802704383 10.8947849214261 9.7992056002344 10.04895619607921415675_at 12.6363495581221 12.310695824458 11.7665991587842 11.71928862807501415677_at 11.9224599733792 11.6230373622742 11.0882276072649 11.15845246207511415678_at 14.3403000148085 14.3258513901380 14.2753594390197 14.37584835520461415679_at 15.0959031716503 14.8066829033559 14.6876918364335 14.59118161580651415680_at 11.4203757035264 11.4120007012393 11.2384462748424 11.36847790232441415681_at 12.3004566771331 11.7383490484824 11.4995261583693 11.3078357750632

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

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

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…)

Questions?

Gene Group Correlation

• Presented by – Andrew Darling

Outline of Presentation

• Problem Statement

• Gene Group Correlation

• Methods

• Results

• Discussion

• Conclusion

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.

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

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

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

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

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

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

Conclusion

• If this works, it’s a freaking miracle

Gene Selection

What level

Of what gene

Does what?

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

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.

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)

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

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.

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

Where I’m going with this

• Analyze the weights– By distance between weights of opposite

categories

What does alarge differentiation mean

• Should point at – The gene of importance– The level of expression where the change

occurs

Data Set

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

Identify Classifying Genes of Presbycusis

Alex Haugh

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.

Step 1: Getting the Mean

1. Parse the files given to us by Tex.

2. Take those values and get a ‘Pre’ average.

3. Calculate the standard deviation

4. Remove any values are not contained within 95%

5. Calculate the ‘Post’ average with removed expression levels

6. Record them in a new condensed file format:

Gene Expression

at17186 10.56574

at17187 8.96768

Step 2: Calculating Classifying Genes

1. Read in each of the newly condensed files.

2. Place all of the values into a data structure.

3. 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.

4. Narrow down genes to much smaller set

5. Record the genes in a file for use later:

--------HIGHER --------- --------LOWER--------

at17186 10.56574 at15686 5.68869

at17187 8.96768 at17122 7.76859

Step 3: Testing Classifying Genes

1. Read in the classifying genes for each type2. Read in the unknown dataset3. Subtract the unknown expression value from

classifying gene and take the absolute value.4. If the gene less than the threshold value record a

plus one for that type.5. 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.

Step 4: Tracking Levels

1. After testing the classifying genes from each type empirically, record these (hopefully about 20)

2. Record the average value for the gene from all types.

3. Graph the values

4. Observe and record the trends in each gene.

5. Report any genes that don’t follow the given trends.

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.

ArrayView

Coherent visualization of clustered microarray data.

Madhu and Julia

Eisen Lab Software

• Cluster– Treeview– MapleTree

• FuzzyK– FuzzyExplorer– MapleTree

ArrayView Input

• Output from Cluster, FuzzyK– Convert to ArrayView datafile (XML)

• Attribute MySQL database– Gene title

– Gene symbol

– Public DB identifiers

– Protein families, domains

– Gene Ontology

– Metabolic pathways

ArrayView Output

• Hierarchical– Tree filter

• Possible layouts:– BalloonTree, RadialTree, SquarifiedTreeMapLayout,

TopDownTreeLayout, VerticalTreeLayout

• k-means– Graph filter

• Possible layouts:– ForceDirected, Random

Controls

• Change focus

• Rotate display

• Tool tips

• Zoom

• Filter

• Color code

Experimental data

• Cluster Frisina’s data– Cluster– FuzzyK

• View clustered Frisina data in ArrayView

Questions

Advanced Bioinformatics Computing Project

Kyle Shenk &

Laura Grell

Overview

• TIGR MultiExperiment Viewer (MeV) is a powerful analysis tool for microarray data.– Clustering– Classification – Visualization– Statistical Analysis

• We hope to use some of these tools to perform some analysis on the Frisina data

The Input File

• MeV requires a Affymetrix.txt file for input– Columns represent each individual sample –

so in this case each mouse/experiment– Rows represent the individual genes– Data points are the normalized expression

values– GeneName        Sample1     Sample2     Sample3        Sample4

> > MouseType   young       young       old_severe     old_mild> > 1415670_at  10.47015    13.195      9.620273       11.5090

Problem

• Dr. Frisina has provided us with four files –each representative of a different age group of mice

• The Affymetrix.txt file contains expression data from all samples

• We have to convert these four files into one large file the MeV can read and recognize

Solution

• Perl is an ideal language for editing/parsing text and generating files

• The program we developed reads in all four files and creates one large Affymetrix.txt file

• Basically the program consists of reading each file line by line and concatenating the line from one file onto the next

Kyle’s Solution

• page +page +page +page = BIG PAGE!!

+ ++ =

TIGR MeV

• The next step is to utilize the TIGR MeV tools and analyze the results.– Expression Viewer– Expression Graphs

• http://www.tm4.org/mev.html

PRINCIPAL COMPONENT ANALYSIS OF THE FRISINA MICROARRAY DATA

Presented by Lee Edsall

April 28, 2005

OUTLINE

• What is Principal Component Analysis?• Method• Goals

WHAT IS PRINCIPAL COMPONENT ANALYSIS?

• Also referred to as “PCA”

• Analysis of the variation in the data to find a new set of variables to describe the data

• Goal is to decrease the number of variables required

METHOD

• Library research and literature review to understand method and determine appropriate parameters

• Use Minitab to determine the new variables for:• Young data

• Middle age data

• Old with mild hearing loss data

• Old with severe hearing loss data

• Compare the four sets of variables to see if any of them are specific to a set of data

GOALS

• Determine if any genes uniquely identify a set of data

• Provide a much smaller number of genes to be used in future analysis

Comparying and analyzing the tools for the microarray data

Shruti Sharma/Jennifer D’Souza

GEPAS - "Gene Expression Pattern Analysis Suite"

• Normalization

• Preprocessing

• Viewers

• Clustering

• Differential Expression

• Supervised Classification

• Data Mining & Analysis

MIAME – “Minimum Information About a Microarray Experiment”

• Interpretes the results

• Reproduce the experiment.

EPCLUST- Expression Profile data CLUSTering and analysis

Tool for

• Clustering

• Visualization

• Analysis

for gene expression data as well as sequence data.

Cluster

• Performs cluster analysis

– Hierarchical clustering

– Self-organizing maps (SOMs)

– k-means clustering

– Principal component analysis

• Processes large microarray datasets

Links to the tools

• http://ep.ebi.ac.uk/EP/EPCLUST/

• http://www.mged.org/Workgroups/MIAME/miame.html

• http://gepas.bioinfo.cnio.es/tools.html

•