biosynthCourseNew.html

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		<title>Bioinformatics tutorial</title>

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		<meta name="author" content="Sebastian Theobald">

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				<section>
					<h3>Bioinformatics tutorial</h3>
					<p>Sebastian Theobald</p>
					<p>New Bioactive Compounds</p>
				<small><p>Novo Nordisk Foundation Center for Biosustainability</p></small>
				</section>

<section>
	<h3 align = "left" >Learning outcome</h3>
	<p align = "left" > At the end of this session you should be able to:</p>
<ul>
	<li>Know how to download and further process data from antiSMASH</li>
	<li>Load data into R from a tsv format</li>
	<li>Join datasets</li>
	<li>Create plots using ggplot</li>
</ul>
</section>

<section>
	<h1>Why work with R?</h1>
</section>

<section data-background-image = "figures/bac.png" data-background-size = "1200px">
	<div class="fragment">

	<h3 style="color : white; background-color: rgba(0,0,0,0.5);">
		GUIs provide easy data access
	</h3>
</div>
</section>

<section data-background-image = "figures/fung.png" data-background-size = "1200px">
<div class="fragment">

	<h3 style="color : white; background-color: rgba(0,0,0,0.5);">
		How will we process multiple organisms in an efficient manner?
	</h3>
</div>
</section>

<section data-background-image = "figures/library.png">

</section>

<section data-background = "figures/ggplot2.gif" data-background-size = "900px">

</section>

<section>
	<h3>Tutorial</h3>
</section>

<section>
	<h3>Adding another analysis</h3>
	<p> We can download antismash data to make further analysis on the protein sequences of clusters.</p>
</section>

<section>
	<h3>Extracting sequences from antismash</h3>
	<pre><code data-trim data-noescape>
from Bio import SeqIO

for cluster in clusterFiles:

# print " Processing file :" + cluster

try:
	record = SeqIO.read(cluster, 'genbank')
except Exception:
	print "Error reading file %s" % cluster

clusterProts = [str(f.extract(record).seq.translate()) for f in record.features if f.type == "CDS"]

name = cluster.split("cluster")[1].rstrip('.gbk').lstrip('0')

clusters[str(name)] = clusterProts
orgs[orgDir] = clusters

</code></pre>
</section>


<section>
	<h3>Extracting sequences from antismash</h3>
	<pre><code data-trim data-noescape>
>nidulans_42_2
MVATPDDPRAQTIVDLFNGQGSAPAPFDVLTSALSFPTRDQEQWWRKTGPMFGQMLASSGYTLDQQYRHLTFYYNQLVPR
LGPHPATFHSSLTVSGLPMEFSINYQQKGAHPMVRIGAEPIDSFSGTERDPFNQIPPAEMVNHFSRAGVKGFDPELYAYF
EPKHSLTREQQARLPKEVPGGDKLKTQYAFGFDFKGDEVSLKGYSYPGLKATMAGQEVAKLVGDGVKDLKNQGKLDCTEA
WAAVEAYMTELNGWGYHNLWAWDYVSPAKSRLKFYSFVMDVVDKTKLEELWTLNGRATSPAHQEGLRHLKELWDIIDLKN
VGKRDLPADAPQIPEDAAPMVWNYEMTAGNPLPFGKGYFPLQGLNDAGCIQKLVKFFELMGWKDLAAKYPETIQSFYPGL
DLSKTSHLLMTSFELWAKNTRRHVKSAEVASRKSSPVFSAPFQDHKRMRADAARPRRTGAPDYDDPSFWDTKFATGQDVG
EWLNPGEVLLDALLSFLESEPAPTAAQPNTPKVLHLGPGISKLGSRARDAFVARGWKANGIVVGSFASIGTANADFSSEA
VRLGQEAEKMQDPSHAMHWIQTDLRSWTDVSRLLPAGPFEAIIDKSTSDAIATSSPVTFTSAAAHTSAVCPVIKDILKNA
KEAGEGITLTAIELLGLHLVPLTKEGSKWFVLSYSAFRFEDVPRLSSFWEIVGQQIRSGITSPLQLIVATTNEPSRIDVA
X
>nidulans_42_3
MSAPQVVQSLSSASGPAGGWFRTQLATLRNRFINTEKRRQRAALIGTSFAAHRPVWITAGGGAYTTAVAVYLTMKLMRRI
PVX
>nidulans_43_1
MPPSATDPTPSAATGKGTTNSQESSASSAKVKMQFPKPPVFEDKLQEREYLKGRLAAAFRIFGKNGYDEGVAGHITVRDP
VDPTTFWVNPFGVSFSQIKASDLIQVDHSGNVIDGGPCRLLNAAAFMIHSAIHAARPDVLCAAHSHSLYGRAFCSLGREL
DIITQDACAFYNVETPFNIFADHVVYRQFNGVVLAEEEGRNIASALGSKKAALLQNHGLLTVGKTIEETVFWFVSLEKCC
HAQLLADAAAAGRGGATVKINDADAAFTYKTVGTPMAGWFSAKPLFDVIHQETGVSRNPVSX
</code></pre>
</section>

<section data-background = "figures/interpro_screenshot.png" data-background-size = "1000px">

</section>

<section>
	<h3>Adding another analysis</h3>
	<pre><code data-trim data-noescape>
	/home/seth/tools/interproscan.sh -appl 'Pfam' -f tsv -i clusters.fasta
</code></pre>

<pre><code data-trim data-noescape>
niger_76_7      e9d53b96667beb9d3f18e0a8a045fc50        2443    Pfam    PF16197 Ketoacyl-synthetase C-terminal extension        378     487     6.5E-11 T       19-04-2017
niger_76_7      e9d53b96667beb9d3f18e0a8a045fc50        2443    Pfam    PF00550 Phosphopantetheine attachment site      2287    2337    8.9E-6  T       19-04-2017
niger_43_9      4fdf082b0b0c7ff1aa93512abe38e333        526     Pfam    PF00083 Sugar (and other) transporter   22      476     1.6E-95 T       19-04-2017
niger_52_15     c93c5c6fc5a448ae4780e7fa52ffa49a        2433    Pfam    PF08242 Methyltransferase domain        1343    1454    1.7E-17 T       19-04-2017
niger_52_15     c93c5c6fc5a448ae4780e7fa52ffa49a        2433    Pfam    PF02801 Beta-ketoacyl synthase, C-terminal domain       252     364     2.5E-33 T       19-04-2017
niger_52_15     c93c5c6fc5a448ae4780e7fa52ffa49a        2433    Pfam    PF00107 Zinc-binding dehydrogenase      1862    1955    5.5E-15 T       19-04-2017
niger_52_15     c93c5c6fc5a448ae4780e7fa52ffa49a        2433    Pfam    PF08659 KR domain       2065    2242    6.8E-57 T       19-04-2017
niger_52_15     c93c5c6fc5a448ae4780e7fa52ffa49a        2433    Pfam    PF16197 Ketoacyl-synthetase C-terminal extension        367     483     2.0E-24 T       19-04-2017
</code></pre>

</section>

<section>
	<h3>Remember to write a script and save occasionally!</h3>
</section>

<section>
	<h3 >Loading data</h3>
	<pre><code data-trim data-noescape>
setwd("your/directory") # must be the directory of the file
smurf <- read.table("yourFile.tsv", header = TRUE)
	</code></pre>
	<div class="fragment">

<ul>
	<li>Download the files under Aspergillus tutorial 2 on campusnet into the folder you want to work in</li>
	<li> Import your data like in the example above</li>
	<li>Tip: You can check the location of the current working directory using getwd()</li>
</ul>
</div>

</section>

<section>
<h3>Joining dataframes</h3>
<pre><code data-trim data-noescape>
# Standard usage
merge(df1, df2, by = c("column1", "column2"))

# Specify data frames (df) and columns
comb <- merge(smurf, ipr, by = c("org_id","sm_protein_id"))
</code></pre>
<div class="fragment">
<ul>
	<li>Inspect the data frames in Rstudio by using the head() function. On which columns can you perform a join?</li>
	<li>Join the clusters and pfam datasets and assign them to a variable called smashMerged</li>
</ul>
</div>

</section>

<section>
	<h3>ggplot 2</h3>
	<div>
		<img src="figures/smOverview.svg" alt="">
	</div>
</section>

<section>
	<h3>ggplot2</h3>
	<div>
		<pre><code data-trim data-noescape>
ggplot(subset(smurf, sm_short!= 'none') )+

geom_histogram(aes(x = factor(org_id), fill = sm_short),
colour = 'black', stat = 'count', position = 'stack')
		</code></pre>
	</div>

<div class="fragment">
	<li>Generate a barplot of secondary metabolite clusters per organism</li>
	<li>You need to change the variables according to your smashMerged data frame!</li>
	<li> You data input should be this: <pre><code data-trim data-noescape>
	unique(smashMerged[,c('org', 'cluster_id', 'sm_type')] )
	</code></pre></li>
	<li>Why do we have call the unique function on part of the dataframe?</li>
</div>

</section>


<section>
	<h3>result</h3>
	<img src="figures/smSum.svg" alt="">
</section>

<section>
	<h3>Subsetting</h3>
	<pre><code data-trim data-noescape>
meth <- comb[
(comb$sm_short == 'PKS' &
comb$ipr_desc == 'Methyltransferase type 12'),
c('org_id', 'sm_protein_id', 'clust_backbone')]
	</code></pre>
</section>

<section>
<p>What if you do not know the exact term?</p>
</section>

<section>
	<h3>Searching substrings</h3>
	<pre><code data-trim data-noescape>
# Select first 5 rows
mtcars[c(1:5) , ]

# Show rownames of a data frame
rownames(mtcars)

# Find all Mercedes cars
grepl('Merc', rownames(mtcars))

# Get all rows with Mercedes cars
mtcars[grepl('Merc', rownames(mtcars)) , ]

	</code></pre>
</section>

<section>
	<h3>Exercise</h3>
	<ul>
		<li>Search for the assigned natural products using subsetting</li>
	</ul>
</section>
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