• Seifert, M., Banaei, A., Keilwagen, J., Mette, M.F., Houben, A., Roudier, F., Colot, V., Grosse, I., and Strickert, M. (2009): Array-based Genome Comparison of Arabidopsis Ecotypes Using Hidden Markov Models, Biosignals 2009, Porto, Portugal.

• Talk (Biosignals 2009, Porto, Portugal, January 2009)
• Paper (Biosignals 2009, Porto, Portugal, January 2009)
• Biosignals 2009
• Talk (Department Seminar 2009)
• Talk (Bioinformatics Progress Seminar 2009)

• Motivation

  • Why is it interesting to compare genomes of different Arabidopsis thaliana ecotypes

• Downloads

  • TilingArrayAnalyzer: Executable JAR-file for HMM-based analysis of tiling array data including

    • HMM training

    • Detection of copy number changes

    • GFF and Tab-delimited output

    • Array-CGH data set of ecotypes C24 and Columbia

• Case Study

  • Shows how to use the TilingArrayAnalyzer in combination with the Array-CGH data set.

• General Usage

  • Shows how a data set must be structured for the application of the TilingArrayAnalyzer.

• Arabidopsis thaliana ecotypes have a broad geographic distribution around the world

• Natural variation of different ecotypes is expected to be reflected in the DNA of the ecotypes including changes in

  • Circadian Clock

  • Flowering Time

  • Pathogen Resistance

  • Drought Response

  • Freezing Tolerance

  • Transposon Distribution

  • Epigenetic Variation

• Copy Number Changes between different ecotypes can be measured by Array Comparative Genomic Hybridization (Array-CGH) using whole genome tiling arrays

• The detection of copy number changes in Array-CGH data by convenient bioinformatics approaches is one essential point for studying natural variation

• The TilingArrayAnalyzer is an HMM-based method that allows you to detect these copy number changes

Exemplary comparison of segmentation results for DNA regions on chromosome 4 for ecotype C24 compared to Columbia. From left to right separated by gray dashed lines: Region 1 [654,108 bp - 697,518 bp], Region 2 [1,305,320 bp - 1,324,132 bp], Region 3 [3,731,013 bp - 3,761,229 bp], and Region 4 [5,411,025 bp - 5,433,126 bp]. The two top plots represent segmentation results of the HMM approach for two interleaved arrays Array 1 and Array 2. Green dots label tiles predicted by the HMM to have a decreased copy number. Red dots label tiles predicted by the HMM to have an increased copy number. Blue dashed lines highlight DNA segments significantly different from permuted data at a Score-value threshold of 0.01. Black dots label tiles predicted by the HMM to have unchanged copy numbers. The two bottom plots represent segmentation results of the segMNTalgorithm for both arrays. Red dashed lines show that no segmentation was obtained. Both approaches provide a quite different segmentation of the DNA regions. Here, the segMNT algorithm failed to identify segments with decreased or increased copy numbers. The HMM approach clearly identifies segments with significantly decreased or increased copy numbers, and in addition, these biologically interesting results are reproducible for both arrays.

• TilingArrayAnalyzer 64-Bit (5.2 MB) and TilingArrayAnalyzer 32-Bit (5.2 MB)

  • Requires Java 1.6

  • Usage information

    • Unpack TilingArrayAnalyzer.tar.gz

    • Type: tar xvzf TilingArrayAnalyzer.tar.gz

    • Go into folder TilingArrayAnalyzer

    • Type: java -jar TilingArrayAnalyzer.jar

  • Ensure to have enough heap space for the 32-Bit version using the java flag -Xmx500m

1. Download the TilingArrayAnalyzer

2. Unpack the file TilingArrayAnalyzer.tar.gz

3. You obtain the folder TilingArrayAnalyzer containing

   • Folder: Analysis

     • Structured storage of analysis results

   • Folder: RawData

     • Storage of data sets including the data set of ecotype C24 and Columbia

   • JAR-File: TilingArrayAnalyzer.jar

     • TilingArrayAnalyzer

4. Here we analyze the data set 6486702.txt of ecotype C24 compared to Columbia stored in the directory TilingArrayAnalyzer/RawData

Training

• Go to the directory TilingArrayAnalyzer

• Start the TilingArrayAnalyzer

java -jar TilingArrayAnalyzer.jar -TRAINING -startDistribution 0.2 0.75 0.05 -stateDurationScalingFactor 0.025 -means -2.5 0.0 1.5 -stds 1 1 0.5 -ess 10 -scaleOfAprioriMeans 10000 1000 7500 -shapeOfStandardDeviations 20000 1 1000 -scaleOfStandardDeviations 1E-4 1E-4 1E-4 -output true -dataSetNumber 6486702

• You obtain the HMM file in the directory TilingArrayAnalyzer/Analysis/HMM

  • HMM_6486702.txt

• You obtain the GFF file of Viterbi annotations in the directory TilingArrayAnalyzer/Analysis/Score/ViterbiAnnotation

  • VibAn_6486702.gff

  • Use a GFF-viewer like SignalMap or the Integrative Genome Browser to look if the results satisfy your expectations (red is state '+', black is state '=', and green is state '-')

  • If yes than go to scoring, otherwise test other parameter settings restarting the training.

Scoring

• Go to the directory TilingArrayAnalyzer

• Start the TilingArrayAnalyzer

java -jar TilingArrayAnalyzer.jar -SCORING -output true -dataSetNumber 6486702 -numberOfPermutations 10 -scoreValueLevel 0.01

• You obtain scores of '-' and '+' segments in the original data in the directory TilingArrayAnalyzer/Analysis/Score/OrgScores

  • Loss_6486702.txt

  • Gain_6486702.txt

• You obtain tab-delimited files including the genome-wide segmentation into '-', '=', and '+' segments for the original data in the directory TilingArrayAnalyzer/Analysis/Score/Segments

  • Seg_6486702.txt

  • Seg_Extended_6486702.txt

• You obtain scores of '-' and '+' segments for permutated data in the directory TilingArrayAnalyzer/Analysis/Score/H0

  • Score_H0_Loss_6486702.txt

  • Score_H0_Gain_6486702.txt

• You obtain tab-delimited files for significantly changed '-' and '+' segments in the original data stored in the directory TilingArrayAnalyzer/Analysis/Score/PValueLists

  • LossSeg_6486702.txt

  • GainSeg_6486702.txt

• You obtain the GFF file of significant '-' and '+' Viterbi segments in the directory TilingArrayAnalyzer/Analysis/Score/ViterbiAnnotation

  • SigSeg_6486702.txt

• The TilingArrayAnalyzer can be applied to data sets structured in the following manner:

1. Headline with columns ID, Chr, Chr, End, Pos, and Log-Ratio

   • Chr: Chromosome where the tile is located

   • Pos: Position of the tile on the chromosome

   • LogRatio: Log-Ratio of Test vs. Control

2. The column delimiter must be the tabulator

3. No missing values

4. All rows in the data set file must be sorted first by increasing values for Chr and second by increasing values for Pos

5. Data set must be stored in the directory TilingArrayAnalyzer/RawData and its file name extension must be .txt