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Inferring an Origin of Replication With Computational Methods Brian Smith
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Inferring an Origin of Replication With Computational Methods Brian Smith.

Dec 29, 2015

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Page 1: Inferring an Origin of Replication With Computational Methods Brian Smith.

Inferring an Origin of Replication With Computational MethodsBrian Smith

Page 2: Inferring an Origin of Replication With Computational Methods Brian Smith.

OverviewIntroduction

Methods

Results

Future work

Page 3: Inferring an Origin of Replication With Computational Methods Brian Smith.

Pseudomonas as a pathogen

A cryptic megaplasmid found in Psuedomonas syringae

Phenotypic costs associated with large scale HGT

Introduction

P. aeruginosa

P. syringae pv. aesculi

Page 4: Inferring an Origin of Replication With Computational Methods Brian Smith.

The Problem

Conjugation of pMP into P. aeruginosa has failed.

In other Pseudomonads the pMP is transferred successfully at high numbers.

Several reasons why this might be including host range, or genes on the pMP that may illicit P. aeruginosa resistance

Page 5: Inferring an Origin of Replication With Computational Methods Brian Smith.

The pMP is sequenced and the annotations are mostly ‘hypothetical protein’

Various methods for predicating bacterial origins of replication for chromosomes

I wanted to see if similar methods would work on this plasmid

GC skew

Repetitive Motifs

Searching for the Origin

Page 6: Inferring an Origin of Replication With Computational Methods Brian Smith.

Previously shown that a dramatic shift in GC content is associated with the chromosome origin and terminus (Lobry 1996).

GC Skew

E. coli

Page 7: Inferring an Origin of Replication With Computational Methods Brian Smith.

I Used seqinR and an R script to calculate GC across the pMP

GC Skew

myseq <- read.fasta(file = "Desktop/pMP.fasta", as.string = FALSE, forceDNAtolower = TRUE, set.attributes = FALSE, seqonly = TRUE, strip.desc = TRUE)

Page 8: Inferring an Origin of Replication With Computational Methods Brian Smith.

I Used seqinR and an R script to calculate GC across the pMP

GC Skew

myseq <- read.fasta(file = "Desktop/pMP.fasta", as.string = FALSE, forceDNAtolower = TRUE, set.attributes = FALSE, seqonly = TRUE, strip.desc = TRUE)

Origin of Replication

Page 9: Inferring an Origin of Replication With Computational Methods Brian Smith.

oriFinder

DnaA boxes vary in size

E.coli’s is (TTATCCACA)

Programs like this require that you know your motif

Built a custom python script from scratch

Repetitive Motifs

Page 10: Inferring an Origin of Replication With Computational Methods Brian Smith.

Input: fasta file Output to Terminal:

Sequence selection Min count # of Motifs found Top 10 common Motifs found

Output to file: Dictionary of Dictionaries

containing motif, count, and sequence position

Repetitive Motifs

Page 11: Inferring an Origin of Replication With Computational Methods Brian Smith.

Repetitive Motifs

Page 12: Inferring an Origin of Replication With Computational Methods Brian Smith.

Repetitive Motifs

Page 13: Inferring an Origin of Replication With Computational Methods Brian Smith.

Blast unknown protein sequence in this region involved in replication

Engineer smaller plasmids containing min. tools for replication using restriction enzymes

Attempt to conjugate new minimalist plasmids into P. aeruginosa

Current/Future Goals

Page 14: Inferring an Origin of Replication With Computational Methods Brian Smith.

print “Thank you & Questions?”