Study of Arabidopsis’ Copper RegulationStudy of Arabidopsis’ Copper Regulationby High Throughput Sequence Data Analysisby High Throughput Sequence Data Analysis

Study of Arabidopsis’ Copper RegulationStudy of Arabidopsis’ Copper Regulationby High Throughput Sequence Data Analysisby High Throughput Sequence Data Analysis

Steven A. Cardenas, SoCal BSI

Dr. Pellegrini, PI, UCLA

Dr. Casero Diaz-Cano, Post Doc, UCLA

Steven A. Cardenas, SoCal BSI

Dr. Pellegrini, PI, UCLA

Dr. Casero Diaz-Cano, Post Doc, UCLA

Objective of ProjectObjective of ProjectObjective of ProjectObjective of Project

Analysis of Sets of Differentially Expressed Genes in

Plus and Minus Copper Conditions For Arabidopsis WT

Identify Spl7 Regulated Genes

Potential Upstream Motifs That Regulate the Genes

Project SignificanceProject SignificanceProject SignificanceProject Significance

To Further the Development of Techniques Used in High

Throughput Analysis.

The Study of Copper Regulation in Arabidopsis.

This Data Could Be Used to Help Increase Our

Understanding of Copper Regulation in the Human

Body.

Arabidopsis Thaliana

Tools Used

Solexa Sequencing

Low Level Data Analysis

Downstream Data Analysis

Future Work

Outline of PresentationOutline of Presentation

Arabidopsis ThalianaArabidopsis Thaliana

A Small Flowering Plant Related

to Cabbage and Mustard

Found in Europe, Asia, and

Northwestern Africa

First Plant Genome to be Sequenced

and it is Well Annotated

http://www.steve.gb.com/images/science/arabidopsis_thaliana.jpghttp://www.steve.gb.com/images/science/arabidopsis_thaliana.jpg

TAIR

Tools UsedTools Used

SOAP

www.arabidopsis.org

http://soap.genomics.org.cn

MATLAB

www.mathworks.com

Excelwww.microsoft.comwww.pythonwin.org

Solexa SequencingSolexa Sequencing1. Prepare Genomic DNA Sample

2. Attach DNA to Flow Cell Surface

3. Amplification

4. Determine First Base

5. Image First Base

6. Determine Second Base7. Sequence Reads Over Multiple

Chemistry Cycles

http://seqanswers.com/forums/showthread.php?t=21http://seqanswers.com/forums/showthread.php?t=21

AAAA

> 250 – 500 Mb 33 nt sequence

Random Hexamer Primed 1st Strand cDNA Synthesis

End Repair and Adaptor Ligation

PCR

AAAA

2nd Strand cDNA Synthesis

Metal Catalyzed Fragmentation

Sequence

60 – 200 nt

Size Selection200 bp

Illumina mRNA Sample Preparation by Illumina mRNA Sample Preparation by Whole Transcriptome Analysis (WTA)Whole Transcriptome Analysis (WTA)

Experimental Conditions of Analyzed Experimental Conditions of Analyzed DataData

Root Cell Shoot Cell

Arabidopsis

Wild Type Spl7 Mutant

+Cu and -Cu +Cu and -Cu

Root Cell Shoot Cell

Data AnalysisData AnalysisSolexa Data

Align Data

TAIR Refseq

Calculate Hits per Gene Normalize Regularize Check For Reproducibility Differentially Expressed Gene Statistical Analysis Spl7 Motif Statistical Analysis

Spreadsheet of Results

SOAP

MATLAB

Excel

Data ReproducibilityData Reproducibility

Replicate 1 (Alignment Hits per Million)Replicate 1 (Alignment Hits per Million)

Rep

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its

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Arabidopsis WT Root Cell Minus Copper ConditionArabidopsis WT Root Cell Minus Copper Condition

Statistical Analysis for Differential Statistical Analysis for Differential ExpressionExpression

Differential Expression of Genes in

Plus Copper vs. Minus Copper

Statistical Problems

Only Two Replicates

Large Dynamic Range of Data

Student’s T-test

Fails With Large Dynamic Range

Bayesian T-test

Makes Use of Genes With Similar Expression Levels

Currently Still Fails With Large Dynamic Range

Binomial Test

Combined Replicates

Fails When Reproducibility is Bad

Statistical Analysis for Differential Statistical Analysis for Differential ExpressionExpression

Top Differentially Expressed Genes with Top Differentially Expressed Genes with Binomial TestBinomial Test

Root Reference mRNA Sequence Hits per million (unique hits)

GeneWT +Cu

GAN1

WT +Cu

GAN5

WT -Cu

GAN2

WT -Cu

GAN6

WT log10 (P value) Bayesian

WT log10 (P value) Binomial

WT log10 (P value)

t-test

WT log2 (fold

change)

spl7 +Cu GAN4

spl7 +Cu

GAN8

spl7 -Cu

GAN3

spl7 -Cu

GAN7

Spl7 log10 (P value) Bayesian

spl7 log10 (P value) Binomial

Spl7 log10 (P value) t-

test

Spl7 log2(fold change)

Glycosyl Hydrolase Family 17 protein

(AT4G16260)591 336 934 1833 -2.93 -400.00 -0.73 -1.58 352 444 3133 3133 -8.53 -400.00 -3.55 -2.98

Copper Ion Transporter (COPT2) 14 26 478 531 -8.61 -400.00 -2.51 -4.66 11 20 33 38 -2.83 -7.17 -1.29 -1.22

Copper Chaperone (CCH) 218 155 1032 1055 -7.44 -400.00 -2.81 -2.49 141 130 94 116 -0.94 -4.19 -0.91 0.37

Ferric-Chelate Reductase

(ATFRO5/FRO5)0.50 0.50 1114 1141 -8.49 -400.00 -3.84 -11.14 0.50 1.45 0.50 0.50 -0.43 -0.38 -0.37 0.97

Zinc Ion Transporter (ZIP2) 21 32 425 486 -8.23 -308.36 -2.28 -4.10 2.87 4.32 6.34 4.97 -0.63 -0.91 -0.76 -0.65

Peroxidase, Putative (AT1G49570) 273 195 734 985 -5.43 -280.62 -1.38 -1.88 261 209 2144 2635 -6.94 -400.00 -1.89 -3.35

Pentatricopeptide (PPR) Repeat-

Containing Protein (AT1G07590)

5162 4478 6953 6625 -3.96 -279.75 -1.45 -0.49 3649 4474 3649 5982 -0.68 -55.87 -0.22 -0.25

Manganese Ion Binding (GLP5) 804 871 1734 1805 -4.71 -267.44 -2.56 -1.08 871 1006 1265 1306 -1.42 -46.38 -1.41 -0.45

Copper Ion Binding (UCC2) 2333 1888 996 1202 -2.36 -257.78 -1.27 0.94 1873 2111 1943 2434 -0.34 -9.03 -0.26 -0.14

Peroxidase, Putative (AT5G19890) 1209 1620 2077 2586 -2.24 -184.61 -0.97 -0.72 1309 1595 3998 4478 -6.88 -400.00 -2.00 -1.55

Min: Min: Bayesian -13.87Bayesian -13.87Binomial –infBinomial –infStudent T-test -5.63Student T-test -5.63

Motifs Analysis: The First ApproachMotifs Analysis: The First Approach

Select Potential Targets of transcription factor SPL7

Statistical Test Background Distribution Derived From

Word Counts In the Whole Genome

Retrieve Promoter Sequences From the Genome

Calculate Word Count For SPL7 Motif

Future WorkFuture Work

Research New Statistical Methods to Better Identify Differentially Expressed Genes

Use of Non Fixed Window For Bayesian T-test

Finish Analysis of Motifs That Regulate the Differentially Expressed Genes

Identify Transcribed Non Coding RNAs (e.g. microRNAs)

AcknowledgementsAcknowledgements UCLA and the Pellegrini Lab

Dr. Matteo Pellegrini Dr. David Casero Díaz-Cano Dr. Shawn Cokus

Collaborators Ute Krammer University of Heidelberg, Germany Sabeeha Merchant University of California Los Angeles

SoCalBSI Instructors and Fellow Researchers

Funding

www.ucla.edu

National Institutes of Health National Science Foundation Economic & Workforce Development The Department of Energy

http://instructional1.calstatela.edu/jmomand2/index.html