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bioinformaticsProject_Submssion_Yu_Zhu_Sharma #12

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Oct 14, 2022
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script
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Merge pull request #1 from zyu3/main
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53 changes: 53 additions & 0 deletions Yu_Zhu_Sharma.sh
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#usage: this script could be run in the bioinformaticsproject folder, bash Yu_Zhu_Sharma.sh

#make a directory for output file, and if script needs to be run again, run rm -r output before running this script
mkdir output

#put all mcrA in one file
for file in ~/Private/Biocomputing2022/bioinformaticsProject/ref_sequences/m*.fasta
do
cat $file >> ~/Private/Biocomputing2022/bioinformaticsProject/output/mcrAall.txt
done


#put all hsp in one file
for file in ~/Private/Biocomputing2022/bioinformaticsProject/ref_sequences/h*.fasta
do
cat $file >> ~/Private/Biocomputing2022/bioinformaticsProject/output/hspall.txt
done

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@qtran4 qtran4 Oct 21, 2022

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+2

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@qtran4 qtran4 Oct 31, 2022

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You can make it without the for loop
cat mcrAgene_* > ref_mcrAgenes.fasta
cat hsp70gene_* > ref_hsp70genes.fasta

#align the mcrA and hsp genes using the muscle tool
~/Private/Biocomputing2022/tools/muscle -in ~/Private/Biocomputing2022/bioinformaticsProject/output/mcrAall.txt -out ~/Private/Biocomputing2022/bioinformaticsProject/output/alignedmcrA.txt
~/Private/Biocomputing2022/tools/muscle -in ~/Private/Biocomputing2022/bioinformaticsProject/output/hspall.txt -out ~/Private/Biocomputing2022/bioinformaticsProject/output/alignedhsp.txt

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+3

#use hmmbuild tool to build hmm profiles from a sequence alignment for both genes
~/Private/Biocomputing2022/tools/hmmbuild ~/Private/Biocomputing2022/bioinformaticsProject/output/mcrA.hmm ~/Private/Biocomputing2022/bioinformaticsProject/output/alignedmcrA.txt
~/Private/Biocomputing2022/tools/hmmbuild ~/Private/Biocomputing2022/bioinformaticsProject/output/hsp.hmm ~/Private/Biocomputing2022/bioinformaticsProject/output/alignedhsp.txt

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+3

#hmm search each proteom for mcrA genes and hsp genes, use 0.1 as a threshhold for E value
cd ~/Private/Biocomputing2022/bioinformaticsProject/proteomes
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You don't need the threshold here, the algorithm already checks it for you.

for file in *.fasta
do
~/Private/Biocomputing2022/tools/hmmsearch -E "0.1" --tblout ~/Private/Biocomputing2022/bioinformaticsProject/output/$file.mcrAout ~/Private/Biocomputing2022/bioinformaticsProject/output/mcrA.hmm $file
done

for file in *.fasta
do
~/Private/Biocomputing2022/tools/hmmsearch -E "0.1" --tblout ~/Private/Biocomputing2022/bioinformaticsProject/output/$file.hspout ~/Private/Biocomputing2022/bioinformaticsProject/output/hsp.hmm $file
done

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+6

#Write output csv with counts of matches for mcrA genes and hsp70 genes
echo "name,mcrA_match,hsp_match" > ~/Private/Biocomputing2022/bioinformaticsProject/Yu_Zhu_Sharma_final.csv
cd ~/Private/Biocomputing2022/bioinformaticsProject/proteomes
for file in *.fasta
do
name=$(echo $file)
mcrA_match=$(grep -v "#" ~/Private/Biocomputing2022/bioinformaticsProject/output/$file.mcrAout | wc -l)
hsp_match=$(grep -v "#" ~/Private/Biocomputing2022/bioinformaticsProject/output/$file.hspout | wc -l)
echo "$name,$mcrA_match,$hsp_match" >> ~/Private/Biocomputing2022/bioinformaticsProject/Yu_Zhu_Sharma_final.csv
done

sed -i 's/.fasta//g' ~/Private/Biocomputing2022/bioinformaticsProject/Yu_Zhu_Sharma_final.csv

#sort output csv to find out the most ideal proteomes
cat ~/Private/Biocomputing2022/bioinformaticsProject/Yu_Zhu_Sharma_final.csv | sed 1d | sort -t, -k1,2nr -k2,3nr
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@qtran4 qtran4 Oct 21, 2022 •
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-header comments, including usage line [1 point]
-general commenting of code throughout script [1 point]
    -code efficiency [1 points]

=> 1 point was taken off since you don't need the for loop for combining the files
You can also make a for loop for hmm search for both Hsp and mcrA, as well as outputting the table form in just one for loop.

51 changes: 51 additions & 0 deletions Yu_Zhu_Sharma_final.csv
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name,mcrA_match,hsp_match
proteome_01,0,4
proteome_02,0,2
proteome_03,1,3
proteome_04,0,4
proteome_05,1,2
proteome_06,0,0
proteome_07,1,2
proteome_08,0,5
proteome_09,0,1
proteome_10,0,3
proteome_11,0,6
proteome_12,0,6
proteome_13,0,3
proteome_14,0,1
proteome_15,1,1
proteome_16,1,1
proteome_17,0,1
proteome_18,0,8
proteome_19,2,1
proteome_20,0,3
proteome_21,0,4
proteome_22,0,8
proteome_23,2,2
proteome_24,1,2
proteome_25,0,5
proteome_26,0,1
proteome_27,0,1
proteome_28,0,0
proteome_29,1,0
proteome_30,0,1
proteome_31,0,7
proteome_32,0,4
proteome_33,0,0
proteome_34,0,1
proteome_35,0,1
proteome_36,0,3
proteome_37,0,1
proteome_38,1,1
proteome_39,1,1
proteome_40,0,2
proteome_41,0,1
proteome_42,1,3
proteome_43,0,3
proteome_44,1,1
proteome_45,1,3
proteome_46,0,2
proteome_47,0,1
proteome_48,1,1
proteome_49,0,3
proteome_50,1,3
65 changes: 65 additions & 0 deletions Yu_Zhu_Sharma_final_list.txt
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Group members: Zhuoran Yu (zyu3), Xianglu Zhu (xzhu7), Puja Sharma (psharma2)

We chose the E value threshold of 0.1 based on our research on E value for protein. E value less than 0.1 is considered significant.

Here are the most ideal proteins which have both mcrA genes and hsp70 genes, ranked by the number of mcrA gene first, and then hsp70 genes

proteome_23
proteome_19
proteome_03
proteome_42
proteome_45
proteome_50
proteome_05
proteome_07
proteome_24
proteome_15
proteome_16
proteome_38
proteome_39
proteome_44
proteome_48

This proteome has 1 mcrA gene, so it could make methane, however, there is no hsp gene found, therefore, it has low pH resistance.

proteome_29

Here is a list of proteomes which has no mcrA gene, but has one or more hsp70 genes, ranked by the pH resistance.

proteome_18
proteome_22
proteome_31
proteome_11
proteome_12
proteome_08
proteome_25
proteome_01
proteome_04
proteome_21
proteome_32
proteome_10
proteome_13
proteome_20
proteome_36
proteome_43
proteome_49
proteome_02
proteome_40
proteome_46
proteome_09
proteome_14
proteome_17
proteome_26
proteome_27
proteome_30
proteome_34
proteome_35
proteome_37
proteome_41
proteome_47

These 3 proteomes are not ideal because they have neither mcrA gene or hsp70 gene.

proteome_06
proteome_28
proteome_33