1. Computational methods to quantify transcriptome changes in bacteria Rebecca Pankow Mentor: Dr. Jeff Chang Botany and Plant Pathology Oregon State University

2. What makes a pathogen? Infections caused by Pseudomonas syringae Overcome host defenses Manipulate host cell Survive in host environment

3. Hypothesis Genes that are expressed in conditions that mimic the plant are candidates for host- associated genes.

4. Experimental Setup Grow P. syringae in KB (rich media) No virulence gene expression Grow P. syringae in minimal media: simulates environment of plant host Virulence gene expression Identify differential expression of genes

5. How to identify expressed genes? Transcriptome: all mRNAs in a cell at a given time DNA mRNA protein sequenced transcriptome completely sequenced genome aligning back AGAGCAATAGCA TAATTCTCGTTATCGTCCGG ATTAAGAGCAATAGCAGGCC AGAGCAATAGCA

6. How to quantify transcriptome changes? Next-Generation Illumina IIG Genome Sequencer ACATAGGAGCTAGATAGCTATGCATCGATCGACATG GATCGACATGAGAGTTACGAGTAGACTGAGAGATAT CTGAGAGATATGTTTACCCAGATTACTCTCCGATGC GATCGACATGAGAGTTACGAGTAGACTGAGAGATAT mRNAs in transcriptome 36 base-long reads (36-mers)

7. Computational Pipeline TGTTTACCCAGATTACTCTCCGATGCCAGGGAGAAT GATCGACAGATGCATGTTTACCCAGATTACTCTCCG ACATAGGAGCTAGATAGCTATGCATCGATCGACAGA GATCGACAGATGCATGTTTACCCAGATTACTCTCCG Processed 36-mers Align to ref. genome

8. Signal Processing genome coordinates of a potential transcription unit # reads that map to coordinates Graph signal Not very informative! … 0010100234201231201001022410301022040102020 …

9. Signal Processing Using sliding window approach to minimize noise Set old signal processed signal Sum of reads in sliding window = ____________ __________________________… 19 ___________ 19 _________________________… 19 20 __________ 19 20 _______________________… 1920 “sliding window” = 15 22 19 20 22 ________ 19 20 22 _____________________…

10. Resulting signal old signal scaled and processed signal More informative, but signal is jagged

11. Smoothing the Signal Iteration of the sliding window

12. Deconvoluting Signal Changes in the signal found by using the sliding window on the first and second derivatives of the signal.

13. Deconvoluting Signal Refine signal divisions by looking in-between previous divisions Categorize signal divisions as increasing, decreasing, or flat

14. Processing Empirical Data Next-Generation Illumina IIG Genome Sequencer ACATAGGAGCTAGATAGCTATGCATCGATCGACATG GATCGACATGAGAGTTACGAGTAGACTGAGAGATAT CTGAGAGATATGTTTACCCAGATTACTCTCCGATGC GATCGACATGAGAGTTACGAGTAGACTGAGAGATAT 36 base-long reads (36-mers)

15. Problems Mistakes in sequencing can be made! ACATAGGAGCTAGATAGCTATGCATCGAT C GACATG GATCGACATGAGAGTTACGAGTAGAC T GAGAGATAT CTGAGAGATATGTTTACCCAGATTACTCTCCGATGC GATCGACATGAGAGTTACGAGTAGACTGAG A GATAT 30% of reads match P.syringae genome

16. Solution Account for mismatches by treating each base in a 36-mer as a wildcard ACATAGGAGCTAGATAGCTATGCATCGAT C GACATG _CATAGGAGCTAGATAGCTATGCATCGAT C GACATG A_ATAGGAGCTAGATAGCTATGCATCGAT C GACATG AC_TAGGAGCTAGATAGCTATGCATCGAT C GACATG 36-mers containing wildcards are mapped back to the original genome

17. Conclusions Computational pipeline developed to – Generate and smooth signal – Divide signal into sections that are going up, down, or are flat 30% of reads from transcriptome map back to original genome

18. Future Work Quantify changes in bacterial transcriptome under different treatments

19. Acknowledgements Jeff Chang Jason Cumbie Jeff Kimbrel Bill Thomas Cait Thireault Allison Smith Ryan Lilley Phillip Hillenbrand Jayme Stout HHMI/USDA Kevin Ahern