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MSU - MIC 433 - Class Notes - Week 1

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MSU - MIC 433 - Class Notes - Week 1

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background image 2/12/16 1 Microbial genomics MMG433 Prof. Yann Dufour Prof. Pat Venta Spring 2016 MSU Genetics 2 Phenotype Gene reverse forward random mutagenesis drugs with unknown targets targeted mutagenesis targeted drug
background image 2/12/16 2 Transposon mutagenesis 3 Ivics,  Zoltán, et  al.  Nature Protocols 6.10 (2011): 1521-1535. Signature tagged mutagenesis 4 Mazurkiewicz et al. (2006) Nature Reviews Genetics 7, 929–939
background image 2/12/16 3 Transposon sequencing (Tn-Seq) 5 Opijnen, T.  &  Camilli,  A. Nature Reviews Microbiology 11, 435–442 (2013). Identifying essential loci 6 Chao, M.,  Abel, S.,  Davis,  B. &  Waldor, M. Nature Reviews Microbiology 14, 119–128 (2016).
background image 2/12/16 4 Experimental bottlenecks 7 Chao, M.,  Abel, S.,  Davis,  B. &  Waldor, M. Nature Reviews Microbiology 14, 119–128 (2016). Identifying essential loci with high resolution 8 Chao, M.,  Abel, S.,  Davis,  B. &  Waldor, M. Nature Reviews Microbiology 14, 119–128 (2016).
background image 2/12/16 5 Insertion representation bias 9 Chao, M.,  Abel, S.,  Davis,  B. &  Waldor, M. Nature Reviews Microbiology 14, 119–128 (2016). Identifying conditionally essential loci 10 Chao, M.,  Abel, S.,  Davis,  B. &  Waldor, M. Nature Reviews Microbiology 14, 119–128 (2016).
background image 2/12/16 6 Mapping fitness onto the genome 11 Opijnen, T.  &  Camilli,  A. Nature Reviews Microbiology 11, 435–442 (2013). Disruption  fitness in the S. pneumoniae genome 12 Van Opijnen, T.,  Bodi, K. & Camilli,  A. Nat Methods 6, 767–772 (2009).
background image 2/12/16 7 Genetic interaction network 13 Van Opijnen, T.,  Bodi, K. & Camilli,  A. Nat Methods 6, 767–772 (2009).
background image 2/3/2016 1 Microbial genomics MMG433 Prof. Yann Dufour Prof. Pat Venta Spring 2016 MSU Machine learning 2 “[…] machine learning is concerned with the development and 
application of computer algorithms that improve with experience.” 
“[machine learning]has been used to annotate a wide variety of 
genomic sequence elements.”
“ […] if one can compile a list of sequence elements of a given type, 
then a machine learning method can probably be trained to 
recognize those elements.”
“Input of predictive algorithms can be any one or more of a wide 
variety of data types […].”
“[…] the design–learn–test process provides a principled way to test 
a hypothesis about machine learning […].”
“[…] the algorithm […] can be used to generate hypotheses […].”
background image 2/3/2016 2 Supervised learning 3 “Supervised methods are trained on 
labelled examples and then used to make 
predictions about unlabelled examples 
[…].”
“[…] use what is already known about 
the genome to help to build a predictive 
model.” 
“The model then uses this training data to 
learn the general properties of genes, such as 
the DNA sequence patterns that typically 
occur near a donor or an acceptor splice site; 
the fact that in-frame stop codons should 
not occur within coding exons; and the 
expected length distributions of 5
ʹ and 3ʹ UTRs and of initial, internal and final 
introns.”
Model Machine learning algorithm Training set Labels Data (‘features’) 1. 2. 3. 4. 5. 6. 7. 8. 1. Not TSS 2. TSS 3. TSS 4. Not TSS 5. Not TSS 6. TSS 7. Not TSS 8. TSS TSS TSS TSS TSS Not TSS Not TSS Not TSS Not TSS Testing set Predicted labels Prediction algorithm Libbrecht, M. W. & Noble, W. S. Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321–32 (2015). Sequence motif 4 D’haeseleer, P. What are DNA sequence  motifs? Nature Biotechnology 24, 423–425 (2006). “Sequence motifs are short, recurring 
patterns in DNA that are presumed to 
have a biological function.”
Conserved motifs are targets for sequence  specific DNA-binding proteins, such as  transcription factors, restriction enzymes, or  ribosome binding sites. The consensus sequence does not capture  the natural variability of each occurrence  throughout the genome.
The position frequency matrix (PFM) 
enumerates the frequency of each nucleotide  at each position.
A sequence logo is a graphical representation 
of the PFM.
The information contained in a motif model 
should be normalized by the background  frequency of each base in the genome.

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School: Michigan State University
Department: Biology
Course: Microbial Genomics
Professor: Pat Venta
Term: Fall 2016
Tags: genomics
Name: MMG 433 POWER POINT NOTES
Description: SPRING 2016
Uploaded: 09/19/2016
83 Pages 30 Views 24 Unlocks
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