Drought stress dynamics in maize using RNA-Seq

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1 Drought stress dynamics in maize using RNA-Seq Presented By: Akshay Kakumanu Easy chair journel club presentation Virginia Tech April 18, 2011

2 Objective To develope a systems biology view to study the drought response in vegitative and repoductive parts of maize Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

3 Objective To develope a systems biology view to study the drought response in vegitative and repoductive parts of maize To study the expression profiles of drought resistance genes like HSP and chaperones Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

4 Objective To develope a systems biology view to study the drought response in vegitative and repoductive parts of maize To study the expression profiles of drought resistance genes like HSP and chaperones To investigate and compare the constitutive gene expressions in both vegitative and reproductive tissues Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

5 Transcriptome Profiling RNA sources from leaf meristem and cob tissue (both well-watered and drought) were used for transcriptome profiling Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

6 Transcriptome Profiling RNA sources from leaf meristem and cob tissue (both well-watered and drought) were used for transcriptome profiling RNA-Seq which uses hight-throughtput sequencing technologies was used for the transcriptome analysis Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

7 RNA-Seq Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

8 RNA-Seq Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

9 Advantages over microarrays Less dependence on prior knowledge of the organism Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

10 Advantages over microarrays Less dependence on prior knowledge of the organism Unravel previously inaccessible complexities in the transciptome, such as allel specific expression and novel promoters and isoforms Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

11 Advantages over microarrays Less dependence on prior knowledge of the organism Unravel previously inaccessible complexities in the transciptome, such as allel specific expression and novel promoters and isoforms Very sensitive and high depth of coverage Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

12 Challenges Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

13 Challenges Accounting for the reads falling in the locus of the genome annoted with multiple isoforms Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

14 Challenges Accounting for the reads falling in the locus of the genome annoted with multiple isoforms High chances of the same read mapping to different locations Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

15 Challenges Accounting for the reads falling in the locus of the genome annoted with multiple isoforms High chances of the same read mapping to different locations Read count normalization Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

16 Read Libraries 8 Read libraries were used in total MCC-1 and MCC-2 :- Maize cob/kernel/reproductive-well watered (Biological replicates 1 and 2) MCD-1 and MCD-2 :- Maize cob/kernel/reproductive-drought (Biological replicates 1 and 2) MLC-1 and MLC-2 :- Maize leaf/meristem-well watered (Biologivcal replicates 1 and 2) MLD-1 and MLD-2 :- Maoze leaf/meristem-drought (Biological replicates 1 and 2) Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

17 Pipeline Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

18 Mapping Statistics Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

19 Mapping Statistics Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

20 Mapping Statistics Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

21 Mapping Statistics Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

22 Differentially expressed genes EdgeR R package was used for defining the differentially expressed genes Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

23 Differentially expressed genes EdgeR R package was used for defining the differentially expressed genes Models the table of count data as a negative binomial distribution Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

24 Differentially expressed genes EdgeR R package was used for defining the differentially expressed genes Models the table of count data as a negative binomial distribution An emperical Bayes procedure is used to shrink the dispersions towards a consensus value Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

25 Differentially expressed genes EdgeR R package was used for defining the differentially expressed genes Models the table of count data as a negative binomial distribution An emperical Bayes procedure is used to shrink the dispersions towards a consensus value DIfferential expression is assessed for each gene using an exact test analogous to Fisher s exact test A list of q-values were generated from the p-values using a Qvalue R package Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

26 Differentially expressed genes Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

27 Differentially expressed genes Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

28 Differentially expressed genes Presented By: Akshay Kakumanu (Virginia Tech) DSDRS April 18, / 13

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