Mapping’Bacterial’Transcriptomes’from’ ’’RNA6Seq ... ·...
Transcript of Mapping’Bacterial’Transcriptomes’from’ ’’RNA6Seq ... ·...
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Mapping Bacterial Transcriptomes from RNA-‐Seq Expression Data
Andrew Baxter
Broad Ins?tute of MIT and Harvard
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The importance of mapping bacterial transcriptomes
Defining transcribed regions and how they change in different environments gives us clues about bacterial physiology and evolu?on
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Using RNA-‐Seq to map bacterial transcriptomes
RNA-‐Seq • Convert RNA into cDNA libraries
• Generates sequencing “reads”
• Reads are aligned to a reference genome (when available) with nucleo?de resolu?on
• Number of reads in region correlates to expression of RNA from region
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The challenges of mapping bacterial transcripts by RNA-‐Seq
• Biases introduced during cDNA library construc?on
• Complexity of bacterial transcriptomes
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ORF1 ORF2 ORF3
ORF1 ORF2 ORF3
Many adjacent genes in bacteria are independently transcribed…
Many others are coordinately transcribed as part of a single mRNA (operons)
The complexity of bacterial transcriptomes
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The challenges of mapping bacterial transcripts by RNA-‐Seq: an example
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Developing an algorithm for using RNA-‐Seq data to define E. coli puta?ve transcrip?on units (PTUs)
Smooth RNA-‐Seq coverage data 1
Average coverage within “bricks”
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Group adjacent bricks into
PTUs 3
Refine PTU start and end sites
Compare PTUs to
transcripts, operons
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The algorithm in ac5on
Known transcrip?on units iden?fied, the algorithm worked
Raw data
Data smoothed, converted to
bricks
Bricks grouped, boundaries refined
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Operon missed, so close yet so far…
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How do we systema?cally measure the performance of our algorithm?
• Success of the algorithm is measured by sensi?vity and precision.
• Sensi5vity -‐ the total number of PTUs whose 5’ end is between 0 and 200bp upstream of the nearest annotated gene
• Precision – percent of total number of PTUs iden?fied whose 5’ end is between 0 and 200bp upstream of the nearest annotated gene
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Measuring the success of our algorithm
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Distance between 5’ end of PTU and 5’ end of nearest gene
Sensi5vity: 608 PTUs Precision: 74.0 %
Num
ber o
f PTU
s
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Candidates for previously unknown transcripts: an example
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Conclusions
• We have developed an algorithm for mapping bacterial transcriptomes with RNA-‐Seq data
• The algorithm successfully iden?fied many previously annotated transcrip?on units, failed to iden?fy others, and iden?fied numerous novel PTUs
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Going Forward
• Further fine tuning of the algorithm for higher precision and sensi?vity
• Tes?ng how decreasing the depth of RNA-‐Seq data effects the algorithm’s performance
• Tes?ng the algorithm on RNA-‐Seq data from different bacteria
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Acknowledgments Jonathan Livny Brian Haas Melissa Chin Moran Yassour Special Thanks to SRPG Bruce Birren Eboney Smith Brandon Ogbunugafor Francie Latour