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1、Computational methods to quantify transcriptome changes in bacteria,Rebecca PankowMentor: Dr. Jeff ChangBotany and Plant PathologyOregon State University,What makes a pathogen?,Infections caused by Pseudomonas syringae,Overcome host defenses Manipulate host cell Survive in host environment,Hypothesi
2、s,Genes that are expressed in conditions that mimic the plant are candidates for host-associated genes.,Experimental Setup,Grow P. syringae inKB (rich media),No virulence geneexpression,Grow P. syringae in minimal media:simulates environment of plant host,Virulence geneexpression,Identify differenti
3、al expression of genes,How to identify expressed genes?,Transcriptome: all mRNAs in a cell at a given time,sequencedtranscriptome,completely sequenced genome,aligning back,AGAGCAATAGCA,TAATTCTCGTTATCGTCCGGATTAAGAGCAATAGCAGGCC,AGAGCAATAGCA,How to quantify transcriptome changes?,Next-Generation Illumi
4、na IIG Genome Sequencer,ACATAGGAGCTAGATAGCTATGCATCGATCGACATGGATCGACATGAGAGTTACGAGTAGACTGAGAGATATCTGAGAGATATGTTTACCCAGATTACTCTCCGATGCGATCGACATGAGAGTTACGAGTAGACTGAGAGATAT,mRNAs in transcriptome,36 base-long reads (36-mers),Computational Pipeline,TGTTTACCCAGATTACTCTCCGATGCCAGGGAGAAT GATCGACAGATGCATGTTT
5、ACCCAGATTACTCTCCG ACATAGGAGCTAGATAGCTATGCATCGATCGACAGAGATCGACAGATGCATGTTTACCCAGATTACTCTCCG,Processed 36-mers,Align to ref. genome,Signal Processing,genome coordinates of a potential transcription unit,# reads thatmap to coordinates,Graph signal,Not very informative!,001010023420123120100102241030102
6、2040102020,Signal Processing,Using sliding window approach to minimize noise,Set,old signal,processed signal,Sum of reads in sliding window =,_,19 _,19 20 _,19,20,“sliding window” = 15,22,19 20 22 _,Resulting signal,old signal,scaled and processed signal,More informative, but signal is jagged,Smooth
7、ing the Signal,Iteration of the sliding window,Deconvoluting Signal,Changes in the signal found by using the sliding window on the first and second derivatives of the signal.,Deconvoluting Signal,Refine signal divisions by looking in-between previous divisions Categorize signal divisions as increasi
8、ng, decreasing, or flat,Processing Empirical Data,Next-Generation Illumina IIG Genome Sequencer,ACATAGGAGCTAGATAGCTATGCATCGATCGACATGGATCGACATGAGAGTTACGAGTAGACTGAGAGATATCTGAGAGATATGTTTACCCAGATTACTCTCCGATGCGATCGACATGAGAGTTACGAGTAGACTGAGAGATAT,36 base-long reads (36-mers),Problems,Mistakes in sequencin
9、g can be made!,ACATAGGAGCTAGATAGCTATGCATCGATCGACATGGATCGACATGAGAGTTACGAGTAGACTGAGAGATATCTGAGAGATATGTTTACCCAGATTACTCTCCGATGCGATCGACATGAGAGTTACGAGTAGACTGAGAGATAT,30% of reads match P.syringae genome,Solution,Account for mismatches by treating each base in a 36-mer as a wildcard,ACATAGGAGCTAGATAGCTATGC
10、ATCGATCGACATG,_CATAGGAGCTAGATAGCTATGCATCGATCGACATG,A_ATAGGAGCTAGATAGCTATGCATCGATCGACATG,AC_TAGGAGCTAGATAGCTATGCATCGATCGACATG,36-mers containing wildcards are mapped back to the original genome,Conclusions,Computational pipeline developed toGenerate and smooth signalDivide signal into sections that a
11、re going up, down, or are flat30% of reads from transcriptome map back to original genome,Future Work,Quantify changes in bacterial transcriptome under different treatments,Acknowledgements,Jeff ChangJason CumbieJeff KimbrelBill ThomasCait ThireaultAllison SmithRyan LilleyPhillip HillenbrandJayme StoutHHMI/USDAKevin Ahern,
