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Annotated sample ChangeO code.txt
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Annotated sample ChangeO code.txt
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#Samples must have been named as follows for IMGT runs:
#Sample_atleast-2.fasta --> "Sample 2",
#
#This will result in the IMGT files being returned with the respective names:
# Sample_2.txz
#
#If your naming method varied, make sure to update the file names in the following lines
#
#Download updated germlines from IMGT for reference during processing
#
fetch_imgtdb.sh -o ~share/germlines/imgt
#
cd "DATE"_Seq
#
#Create subdirectory for ChangeO and R files (this will be set as the working directory when transitioning to R)
#
mkdir "DATE"_Seq_ChangeO_and_R
#
#Upload fasta files and IMGT data tables into directory with filezilla
#
cd "DATE"_Seq_ChangeO_and_R
#
#Sample WT_1_5N_LN
#
###For data set with > or = 2 sequences per UMI (with good coverage, this should be the primary data set)
#
#Creates a data table from the IMGT file and orinigal FASTA file
#
MakeDb.py imgt -i WT_1_5N_LN_2.txz -s WT_1_5N_LN_atleast-2.fasta --regions --scores
#
#Selects for functional sequences only
#
ParseDb.py select -d WT_1_5N_LN_2_db-pass.tab -f FUNCTIONAL -u T
#
#Adds full germline sequences to data table for comaprison and reference
#
CreateGermlines.py -d WT_1_5N_LN_2_db-pass_parse-select.tab -g full -r ~/share/germlines/imgt/mouse/vdj/*.fasta
#
#Repeat for remainder of samples
#
#Sample RAG_1_5N_LN
#
###For data set with > or = 2 sequences per UMI (with good coverage, this should be the primary data set)
#
#Creates a data table from the IMGT file and orinigal FASTA file
#
MakeDb.py imgt -i RAG_1_5N_LN_2.txz -s RAG_1_5N_LN_atleast-2.fasta --regions --scores
#
#Selects for functional sequences only
#
ParseDb.py select -d RAG_1_5N_LN_2_db-pass.tab -f FUNCTIONAL -u T
#
#Adds full germline sequences to data table for comaprison and reference
#
CreateGermlines.py -d RAG_1_5N_LN_2_db-pass_parse-select.tab -g full -r ~/share/germlines/imgt/mouse/vdj/*.fasta
#
#Repeat for remainder of samples
#
#Sample RAG_1_5P_LN
#
###For data set with > or = 2 sequences per UMI (with good coverage, this should be the primary data set)
#
#Creates a data table from the IMGT file and orinigal FASTA file
#
MakeDb.py imgt -i RAG_1_5P_LN_2.txz -s RAG_1_5P_LN_atleast-2.fasta --regions --scores
#
#Selects for functional sequences only
#
ParseDb.py select -d RAG_1_5P_LN_2_db-pass.tab -f FUNCTIONAL -u T
#
#Adds full germline sequences to data table for comaprison and reference
#
CreateGermlines.py -d RAG_1_5P_LN_2_db-pass_parse-select.tab -g full -r ~/share/germlines/imgt/mouse/vdj/*.fasta
#
#Repeat for remainder of samples
#
#Sample RAG_1_5P_LP
#
###For data set with > or = 2 sequences per UMI (with good coverage, this should be the primary data set)
#
#Creates a data table from the IMGT file and orinigal FASTA file
#
MakeDb.py imgt -i RAG_1_5P_LP_2.txz -s RAG_1_5P_LP_atleast-2.fasta --regions --scores
#
#Selects for functional sequences only
#
ParseDb.py select -d RAG_1_5P_LP_2_db-pass.tab -f FUNCTIONAL -u T
#
#Adds full germline sequences to data table for comaprison and reference
#
CreateGermlines.py -d RAG_1_5P_LP_2_db-pass_parse-select.tab -g full -r ~/share/germlines/imgt/mouse/vdj/*.fasta
#
#Repeat for remainder of samples
#
#Sample VH12_1_5N_LN
#
###For data set with > or = 2 sequences per UMI (with good coverage, this should be the primary data set)
#
#Creates a data table from the IMGT file and orinigal FASTA file
#
MakeDb.py imgt -i VH12_1_5N_LN_2.txz -s VH12_1_5N_LN_atleast-2.fasta --regions --scores
#
#Selects for functional sequences only
#
ParseDb.py select -d VH12_1_5N_LN_2_db-pass.tab -f FUNCTIONAL -u T
#
#Adds full germline sequences to data table for comaprison and reference
#
CreateGermlines.py -d VH12_1_5N_LN_2_db-pass_parse-select.tab -g full -r ~/share/germlines/imgt/mouse/vdj/*.fasta
#
#Repeat for remainder of samples
#
#Sample VH12_1_5P_LP
#
###For data set with > or = 2 sequences per UMI (with good coverage, this should be the primary data set)
#
#Creates a data table from the IMGT file and orinigal FASTA file
#
MakeDb.py imgt -i VH12_1_5P_LP_2.txz -s VH12_1_5P_LP_atleast-2.fasta --regions --scores
#
#Selects for functional sequences only
#
ParseDb.py select -d VH12_1_5P_LP_2_db-pass.tab -f FUNCTIONAL -u T
#
#Adds full germline sequences to data table for comaprison and reference
#
CreateGermlines.py -d VH12_1_5P_LP_2_db-pass_parse-select.tab -g full -r ~/share/germlines/imgt/mouse/vdj/*.fasta
#
#Repeat for remainder of samples
#
#Sample VH12RAG_1_5N_LN
#
###For data set with > or = 2 sequences per UMI (with good coverage, this should be the primary data set)
#
#Creates a data table from the IMGT file and orinigal FASTA file
#
MakeDb.py imgt -i VH12RAG_1_5N_LN_2.txz -s VH12RAG_1_5N_LN_atleast-2.fasta --regions --scores
#
#Selects for functional sequences only
#
ParseDb.py select -d VH12RAG_1_5N_LN_2_db-pass.tab -f FUNCTIONAL -u T
#
#Adds full germline sequences to data table for comaprison and reference
#
CreateGermlines.py -d VH12RAG_1_5N_LN_2_db-pass_parse-select.tab -g full -r ~/share/germlines/imgt/mouse/vdj/*.fasta
#
#Repeat for remainder of samples
#
###For data set with > or = 2 sequences per UMI (with good coverage, this should be the primary data set)
#
#Creates a data table from the IMGT file and orinigal FASTA file
#
MakeDb.py imgt -i VH12RAG_1_5P_LP_2.txz -s VH12RAG_1_5P_LP_atleast-2.fasta --regions --scores
#
#Selects for functional sequences only
#
ParseDb.py select -d VH12RAG_1_5P_LP_2_db-pass.tab -f FUNCTIONAL -u T
#
#Adds full germline sequences to data table for comaprison and reference
#
CreateGermlines.py -d VH12RAG_1_5P_LP_2_db-pass_parse-select.tab -g full -r ~/share/germlines/imgt/mouse/vdj/*.fasta
#
#Repeat for remainder of samples
#
#Data analysis will now continue in R using R Studio or R Studio Server (improves visualization, can be performed in an R shell on the same Linux terminal if desired but it will not be possible to preview graphs)