Doherty Chambers Submission

Antibodies to test

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+tsg101
+actin
+mhc1

Exercise7answers.py

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+# Exercise 7
+
+
+#Question 1
+
+# open fasta file
+InFile=open("Lecture11.fasta","r")
+
+#create lists for storing information about sequences
+sequenceID=[]
+sequenceLength=[]
+percentGC=[]
+
+#loop through each line of fasta file to process sequences
+for Line in InFile:
+    # remove newline character from file line
+    Line=Line.strip()
+    # if a sequence record
+    if '>' in Line:
+        # add the sequence ID (except the ">" character) to the sequenceID list
+        sequenceID.append(Line[1:])
+    # if a sequence line
+    else:
+        # get the number of characters in the sequence and convert to a float to avoid integer division
+        seqLen=float(len(Line))
+        # count the number of G's and C's
+        nG=Line.count("G")
+        nC=Line.count("C")
+
+        # append values to the lists
+        sequenceLength.append(seqLen)
+        percentGC.append((nG+nC)/seqLen*100)
+
+import pandas
+# combine lists into dataframe
+seqDF = pandas.DataFrame(list(zip(sequenceID,sequenceLength,percentGC)),columns=['sequenceID','sequenceLength','percentGC'])
+
+from plotnine import *
+# histogram of sequence length
+histogram1=ggplot(seqDF,aes(x="sequenceLength"))
+histogram1+geom_histogram(binwidth=20,fill='blue',color='black')+theme_classic()
+
+# histogram of percentGC
+histogram2=ggplot(seqDF,aes(x="percentGC"))
+histogram2+geom_histogram()+theme_classic()
+# changing colors and bins
+histogram2+geom_histogram(binwidth=15,fill='yellow',color='black')+theme_classic()
+
+#Question 2
+import pandas as pd
+import numpy as np
+data=pd.read_csv("dataset.csv")
+import matplotlib.pyplot as plt
+plt.title('Percent of Planted Corn in USA that is GMO')
+plt.ylabel('Percent')
+plt.xlabel('Year')
+plt.xlim([2000,2018])
+plt.scatter(data.Year, data.Percent)
+x=data.Year
+y=data.Percent
+z = np.polyfit(x,y, 1)
+p = np.poly1d(z)
+plt.plot(x,p(x),"r--")
+
+
+#Question 3
+
+#open file and import pandas
+import pandas
+q3=pandas.read_csv("data.txt", sep=",", header=0)
+
+#group by the region and find the mean of each region
+average=q3.groupby('region')['observations'].mean()
+#print to dataframe
+df=average.to_frame()
+#add the region rows - can find the order if you print the previous variable
+df['region']=["east", "north", "south", "west"]
+
+#making a bar graph with the avg of the corresponding regions
+from plotnine import *
+q3bp=ggplot(df)+theme_classic()+xlab("region")+ylab("observations")
+q3bp+geom_bar(aes(x="region",y="observations"),stat="summary",)
+
+#scatter plot with jitter applied
+from plotnine import *
+q3sp=ggplot(q3,aes(x="region",y="observations"))
+q3sp+geom_jitter()+coord_cartesian()
+
+

dataset.csv

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+Year,Percent
+2000,25
+2001,26
+2002,34
+2003,40
+2004,47
+2005,52
+2006,61
+2007,73
+2008,80
+2009,85
+2010,86
+2011,88
+2012,88
+2013,90
+2014,93
+2015,92
+2016,92
+2017,92