Tn-seq analysis pipeline (improved2)

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Tags: pipeline

Overview of Data Processing Procedure

1. Convert .fastq files to .fasta format (.reads).

"AGCTTCAGGGTTGAGATGTGTATAAGAGACAG", allowed a mismatch of 1 nt
2. Identify reads with the transposon prefix in R1. The sequence searched for is "AGCTTCAGGGTTGAGATGTGTATAAGAGACAG", allowed a mismatch of 1 nt, which must start between cycles 5 and 10 (inclusive). (Note that this ends in the canonical terminus of the Himar1 transposon, TGTTA.) The “staggered” position of this sequence is due to insertion a few nucleotides of variable length in the primers used in the Tn-Seq sample prep protocol (e.g. 4 variants of Sol_AP1_57, etc.). The number of mimatches allowed in searching reads for the transposon sequence pattern can be adjusted as an option in the interface; the default is 1.

#What are TACCACGACCA?
3. Extract genomic part of read 1. This is the suffix following the transposon sequence pattern above. However, for reads coming from fragments shorter than the read length, the adapter might appear at the other end of R1, TACCACGACCA. If so, the adapter suffix is stripped off. (These are referred to as “truncated” reads, but they can still be mapped into the genome just fine by BWA.) The length of the genomic part must be at least 20 bp.

3. Extract barcodes from read 2. Read 2 is searched for GATGGCCGGTGGATTTGTGnnnnnnnnnnTGGTCGTGGTAT”. The length of the barcode is typically 10 bp, but can be varaible, and must be between 5-15 bp.

4. Extract genomic portions of read 2. This is the part following TGGTCGTGGTAT…. It is often the whole suffix of the read. However, if the read comes from a short DNA fragment that is shorter than the read length, the adapter on the other end might appear, in which case it is stripped off and the nucleotides in the middle representing the genomic insert, TGGTCGTGGTATxxxxxxxTAACAGGTTGGCTGATAAG. The insert must be at least 20 bp long (inserts shorter than this are discarded, as they might map to spurious locations in the genome).

5. Map genomic parts of R1 and R2 into the genome using BWA. Mismatches are allowed, but indels are ignored. No trimming is performed. BWA is run in ‘sampe’ mode (treating reads as pairs). Both reads of a pair must map (on opposite strands) to be counted.

6. Count the reads mapping to each TA site in the reference genome (or all sites for Tn5).

7. Reduce raw read counts to unique template counts. Group reads by barcode AND mapping location of read 2 (aka fragment “endpoints”).

8. Output template counts at each TA site in a .wig file.

9. Calculate statistics like insertion_density and NZ_mean. Look for the site with the max template count. Look for reads matching the primer or vector sequences.

quality control

./240606_VH00358_96_AAFCFJGM5/kr11/initial_mutants_a_2_S6_R1_001.fastq.gz
./240606_VH00358_96_AAFCFJGM5/kr11/initial_mutants_a_2_S6_R2_001.fastq.gz
./240606_VH00358_96_AAFCFJGM5/kr13/LB_culture_a_2_S7_R1_001.fastq.gz
./240606_VH00358_96_AAFCFJGM5/kr13/LB_culture_a_2_S7_R2_001.fastq.gz
./240606_VH00358_96_AAFCFJGM5/kr15/growthout_control_24h_a_2_S8_R1_001.fastq.gz
./240606_VH00358_96_AAFCFJGM5/kr15/growthout_control_24h_a_2_S8_R2_001.fastq.gz
./240606_VH00358_96_AAFCFJGM5/kr17/extracellular_mutants_24h_a_2_S9_R1_001.fastq.gz
./240606_VH00358_96_AAFCFJGM5/kr17/extracellular_mutants_24h_a_2_S9_R2_001.fastq.gz
./240606_VH00358_96_AAFCFJGM5/kr19/intracellular_mutants_24h_a_2_S10_R1_001.fastq.gz
./240606_VH00358_96_AAFCFJGM5/kr19/intracellular_mutants_24h_a_2_S10_R2_001.fastq.gz

#from fastqc results of initial_mutants
49821406
35-161
49821406
35-161

https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=Info&id=1194086
https://www.ncbi.nlm.nih.gov/Traces/wgs/AKKR01?
https://www.ncbi.nlm.nih.gov/Traces/wgs/AKKR01?display=download
#Found 4,131 proteins

modify the tpp scripts

vim ~/.local/lib/python3.10/site-packages/pytpp/tpp_tools.py
#search for "DEBUG"
#-maxreads 10000 or not_given for take all!
#-primer AGATGTGTATAAGAGACAG     the default primer of Tn5 is TAAGAGACAG!
#-primer-start-window 0,159  set 0,159 as default!
#delete to import barcode-file, since we already demultipled the file!
#  pattern for read 2...
#    TAGTGGATGATGGCCGGTGGATTTGTG GTAATTACCA TGGTCGTGGTAT CCCAGCGCGACTTCTTCGGCGCACACACC TAACAGGTTGGCTGATAAGTCCCCG?AGAT AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGT
#    -----const1---------------- --barcode- ---const2--- ------genomic---------------- ------const3--------------------------------------------------------------

run Transposon Position Profiling (TPP) on multiple contigs

#https://transit.readthedocs.io/en/latest/transit_running.html;
#https://orca2.tamu.edu/tom/iLab.html

# Break-down of total reads (49821406):
#  29481783 reads (59.2%) lack the expected Tn prefix
# Break-down of trimmed reads with valid Tn prefix (20339623):

#primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG
#  primer_matches: 0 reads (0.0%) contain CTAGAGGGCCCAATTCGCCCTATAGTGAGT (Himar1)
#  vector_matches: 0 reads (0.0%) contain CTAGACCGTCCAGTCTGGCAGGCCGGAAAC (phiMycoMarT7)
#  adapter_matches: 0 reads (0.0%) contain GATCGGAAGAGCACACGTCTGAACTCCAGTCAC (Illumina/TruSeq index)
#  misprimed_reads: 0 reads (0.0%) contain Himar1 prefix but don't end in TGTTA

#kr11.trimmed1_failed_trim    22072406
#-rw-rw-r-- 1 jhuang jhuang  2,7G Jun 11 15:44 kr11.trimmed1    20339623
#-rw-rw-r-- 1 jhuang jhuang  2,9G Jun 11 15:46 kr11.trimmed2    20339623
#cat initial_mutants_a_2_S6_R1_001.fastq | echo $((`wc -l` / 4))  49821406=29481783 reads (59.2%) + 20339623 = 49821406

#vs.

# Break-down of total reads (49821406):
#  29356859 reads (58.9%) lack the expected Tn prefix
# Break-down of trimmed reads with valid Tn prefix (20464547):
#  primer_matches: 0 reads (0.0%) contain CTAGAGGGCCCAATTCGCCCTATAGTGAGT (Himar1)
#  vector_matches: 0 reads (0.0%) contain CTAGACCGTCCAGTCTGGCAGGCCGGAAAC (phiMycoMarT7)
#  adapter_matches: 0 reads (0.0%) contain GATCGGAAGAGCACACGTCTGAACTCCAGTCAC (Illumina/TruSeq index)
#  misprimed_reads: 0 reads (0.0%) contain Himar1 prefix but don't end in TGTTA
# read_length: 100 bp
# mean_R1_genomic_length: 73.4 bp
# mean_R2_genomic_length: 86.4 bp

#conda deactivate
##Test AGCTTCAGGGTTGAGATGTGTATAAGAGACAG --> TAAGAGACAG, the results are similar!
# Note that "-primer-start-window 0,161" is invalid and cannot replace the default value!
#python3 ~/.local/bin/tpp -bwa /usr/bin/bwa -protocol Tn5 -ref WA-314_m_.fasta -reads1 240606_VH00358_96_AAFCFJGM5/kr11/initial_mutants_a_2_S6_R1_001.fastq.gz -reads2 240606_VH00358_96_AAFCFJGM5/kr11/initial_mutants_a_2_S6_R2_001.fastq.gz -output kr11_10nt_primer -primer TAAGAGACAG -mismatches 1 -bwa-alg mem -replicon-ids contig_2_10,contig_2_9,contig_2_8,contig_2_7,contig_2_6,contig_2_5,contig_2_3,contig_2_2,contig_5_10,contig_5_11,contig_5_12,contig_5_13,contig_5_15,contig_5_16,contig_5_17,contig_5_18,contig_5_9,contig_5_8,contig_5_7,contig_5_6,contig_5_5,contig_5_4,contig_5_3,contig_5_2,contig_5_1,contig_4_2,contig_4_1,contig_3_59,contig_3_58,contig_3_57,contig_3_56,contig_3_55,contig_3_54,contig_3_53,contig_3_52,contig_3_51,contig_3_50,contig_3_49,contig_3_48,contig_3_47,contig_3_46,contig_3_44,contig_3_43,contig_3_42,contig_3_41,contig_3_40,contig_3_39,contig_3_38,contig_3_37,contig_3_36,contig_3_35,contig_3_34,contig_3_33,contig_3_32,contig_3_31,contig_3_30,contig_3_29,contig_3_28,contig_3_27,contig_3_26,contig_3_25,contig_3_24,contig_3_23,contig_3_22,contig_3_21,contig_3_20,contig_3_17,contig_3_16,contig_3_15,contig_3_14,contig_3_13,contig_3_12,contig_3_11,contig_3_9,contig_3_8,contig_3_7,contig_3_6,contig_3_5,contig_3_3,contig_3_2,contig_3_1,contig_1_48,contig_1_47,contig_1_46,contig_1_45,contig_1_44,contig_1_43,contig_1_42,contig_1_41,contig_1_40,contig_1_39,contig_1_38,contig_1_37,contig_1_34,contig_1_33,contig_1_32,contig_1_31,contig_1_28,contig_1_27,contig_1_26,contig_1_25,contig_1_24,contig_1_22,contig_1_20,contig_1_19,contig_1_18,contig_1_17,contig_1_16,contig_1_15,contig_1_14,contig_1_13,contig_1_12,contig_1_10,contig_1_8,contig_1_7,contig_1_6,contig_1_5,contig_1_4,contig_1_3,contig_1_2,contig_1_1,contig_C8715,contig_C8943,contig_C9371,contig_C8939,contig_C9357,contig_C8991,contig_C9445,contig_C8689
#mv tpp.cfg kr11_10nt_primer_tpp.cfg

#for initial_mutants
python3 ~/.local/bin/tpp -bwa /usr/bin/bwa -protocol Tn5 -ref WA-314_m_.fasta -reads1 240606_VH00358_96_AAFCFJGM5/kr11/initial_mutants_a_2_S6_R1_001.fastq.gz -reads2 240606_VH00358_96_AAFCFJGM5/kr11/initial_mutants_a_2_S6_R2_001.fastq.gz -output initial_mutants -primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG -mismatches 1 -bwa-alg mem -replicon-ids contig_2_10,contig_2_9,contig_2_8,contig_2_7,contig_2_6,contig_2_5,contig_2_3,contig_2_2,contig_5_10,contig_5_11,contig_5_12,contig_5_13,contig_5_15,contig_5_16,contig_5_17,contig_5_18,contig_5_9,contig_5_8,contig_5_7,contig_5_6,contig_5_5,contig_5_4,contig_5_3,contig_5_2,contig_5_1,contig_4_2,contig_4_1,contig_3_59,contig_3_58,contig_3_57,contig_3_56,contig_3_55,contig_3_54,contig_3_53,contig_3_52,contig_3_51,contig_3_50,contig_3_49,contig_3_48,contig_3_47,contig_3_46,contig_3_44,contig_3_43,contig_3_42,contig_3_41,contig_3_40,contig_3_39,contig_3_38,contig_3_37,contig_3_36,contig_3_35,contig_3_34,contig_3_33,contig_3_32,contig_3_31,contig_3_30,contig_3_29,contig_3_28,contig_3_27,contig_3_26,contig_3_25,contig_3_24,contig_3_23,contig_3_22,contig_3_21,contig_3_20,contig_3_17,contig_3_16,contig_3_15,contig_3_14,contig_3_13,contig_3_12,contig_3_11,contig_3_9,contig_3_8,contig_3_7,contig_3_6,contig_3_5,contig_3_3,contig_3_2,contig_3_1,contig_1_48,contig_1_47,contig_1_46,contig_1_45,contig_1_44,contig_1_43,contig_1_42,contig_1_41,contig_1_40,contig_1_39,contig_1_38,contig_1_37,contig_1_34,contig_1_33,contig_1_32,contig_1_31,contig_1_28,contig_1_27,contig_1_26,contig_1_25,contig_1_24,contig_1_22,contig_1_20,contig_1_19,contig_1_18,contig_1_17,contig_1_16,contig_1_15,contig_1_14,contig_1_13,contig_1_12,contig_1_10,contig_1_8,contig_1_7,contig_1_6,contig_1_5,contig_1_4,contig_1_3,contig_1_2,contig_1_1,contig_C8715,contig_C8943,contig_C9371,contig_C8939,contig_C9357,contig_C8991,contig_C9445,contig_C8689
mv tpp.cfg initial_mutants_tpp.cfg

#primer_start_window 0,159
# Break-down of total reads (49821406):
#  29481783 reads (59.2%) lack the expected Tn prefix
# Break-down of trimmed reads with valid Tn prefix (20339623):
#  primer_matches: 0 reads (0.0%) contain CTAGAGGGCCCAATTCGCCCTATAGTGAGT (Himar1)
#  vector_matches: 0 reads (0.0%) contain CTAGACCGTCCAGTCTGGCAGGCCGGAAAC (phiMycoMarT7)
#  adapter_matches: 0 reads (0.0%) contain GATCGGAAGAGCACACGTCTGAACTCCAGTCAC (Illumina/TruSeq index)
#  misprimed_reads: 0 reads (0.0%) contain Himar1 prefix but don't end in TGTTA

#for LB_culture
python3 ~/.local/bin/tpp -bwa /usr/bin/bwa -protocol Tn5 -ref WA-314_m_.fasta -reads1 ./240606_VH00358_96_AAFCFJGM5/kr13/LB_culture_a_2_S7_R1_001.fastq.gz -reads2 ./240606_VH00358_96_AAFCFJGM5/kr13/LB_culture_a_2_S7_R2_001.fastq.gz -output LB_culture -primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG -mismatches 1 -bwa-alg mem -replicon-ids contig_2_10,contig_2_9,contig_2_8,contig_2_7,contig_2_6,contig_2_5,contig_2_3,contig_2_2,contig_5_10,contig_5_11,contig_5_12,contig_5_13,contig_5_15,contig_5_16,contig_5_17,contig_5_18,contig_5_9,contig_5_8,contig_5_7,contig_5_6,contig_5_5,contig_5_4,contig_5_3,contig_5_2,contig_5_1,contig_4_2,contig_4_1,contig_3_59,contig_3_58,contig_3_57,contig_3_56,contig_3_55,contig_3_54,contig_3_53,contig_3_52,contig_3_51,contig_3_50,contig_3_49,contig_3_48,contig_3_47,contig_3_46,contig_3_44,contig_3_43,contig_3_42,contig_3_41,contig_3_40,contig_3_39,contig_3_38,contig_3_37,contig_3_36,contig_3_35,contig_3_34,contig_3_33,contig_3_32,contig_3_31,contig_3_30,contig_3_29,contig_3_28,contig_3_27,contig_3_26,contig_3_25,contig_3_24,contig_3_23,contig_3_22,contig_3_21,contig_3_20,contig_3_17,contig_3_16,contig_3_15,contig_3_14,contig_3_13,contig_3_12,contig_3_11,contig_3_9,contig_3_8,contig_3_7,contig_3_6,contig_3_5,contig_3_3,contig_3_2,contig_3_1,contig_1_48,contig_1_47,contig_1_46,contig_1_45,contig_1_44,contig_1_43,contig_1_42,contig_1_41,contig_1_40,contig_1_39,contig_1_38,contig_1_37,contig_1_34,contig_1_33,contig_1_32,contig_1_31,contig_1_28,contig_1_27,contig_1_26,contig_1_25,contig_1_24,contig_1_22,contig_1_20,contig_1_19,contig_1_18,contig_1_17,contig_1_16,contig_1_15,contig_1_14,contig_1_13,contig_1_12,contig_1_10,contig_1_8,contig_1_7,contig_1_6,contig_1_5,contig_1_4,contig_1_3,contig_1_2,contig_1_1,contig_C8715,contig_C8943,contig_C9371,contig_C8939,contig_C9357,contig_C8991,contig_C9445,contig_C8689
mv tpp.cfg LB_culture_tpp.cfg

#for growthout_control_24h
python3 ~/.local/bin/tpp -bwa /usr/bin/bwa -protocol Tn5 -ref WA-314_m_.fasta -reads1 ./240606_VH00358_96_AAFCFJGM5/kr15/growthout_control_24h_a_2_S8_R1_001.fastq.gz -reads2 ./240606_VH00358_96_AAFCFJGM5/kr15/growthout_control_24h_a_2_S8_R2_001.fastq.gz -output growthout_control_24h -primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG -mismatches 1 -bwa-alg mem -replicon-ids contig_2_10,contig_2_9,contig_2_8,contig_2_7,contig_2_6,contig_2_5,contig_2_3,contig_2_2,contig_5_10,contig_5_11,contig_5_12,contig_5_13,contig_5_15,contig_5_16,contig_5_17,contig_5_18,contig_5_9,contig_5_8,contig_5_7,contig_5_6,contig_5_5,contig_5_4,contig_5_3,contig_5_2,contig_5_1,contig_4_2,contig_4_1,contig_3_59,contig_3_58,contig_3_57,contig_3_56,contig_3_55,contig_3_54,contig_3_53,contig_3_52,contig_3_51,contig_3_50,contig_3_49,contig_3_48,contig_3_47,contig_3_46,contig_3_44,contig_3_43,contig_3_42,contig_3_41,contig_3_40,contig_3_39,contig_3_38,contig_3_37,contig_3_36,contig_3_35,contig_3_34,contig_3_33,contig_3_32,contig_3_31,contig_3_30,contig_3_29,contig_3_28,contig_3_27,contig_3_26,contig_3_25,contig_3_24,contig_3_23,contig_3_22,contig_3_21,contig_3_20,contig_3_17,contig_3_16,contig_3_15,contig_3_14,contig_3_13,contig_3_12,contig_3_11,contig_3_9,contig_3_8,contig_3_7,contig_3_6,contig_3_5,contig_3_3,contig_3_2,contig_3_1,contig_1_48,contig_1_47,contig_1_46,contig_1_45,contig_1_44,contig_1_43,contig_1_42,contig_1_41,contig_1_40,contig_1_39,contig_1_38,contig_1_37,contig_1_34,contig_1_33,contig_1_32,contig_1_31,contig_1_28,contig_1_27,contig_1_26,contig_1_25,contig_1_24,contig_1_22,contig_1_20,contig_1_19,contig_1_18,contig_1_17,contig_1_16,contig_1_15,contig_1_14,contig_1_13,contig_1_12,contig_1_10,contig_1_8,contig_1_7,contig_1_6,contig_1_5,contig_1_4,contig_1_3,contig_1_2,contig_1_1,contig_C8715,contig_C8943,contig_C9371,contig_C8939,contig_C9357,contig_C8991,contig_C9445,contig_C8689
mv tpp.cfg growthout_control_24h_tpp.cfg

#for extracellular_mutants_24h
python3 ~/.local/bin/tpp -bwa /usr/bin/bwa -protocol Tn5 -ref WA-314_m_.fasta -reads1 ./240606_VH00358_96_AAFCFJGM5/kr17/extracellular_mutants_24h_a_2_S9_R1_001.fastq.gz -reads2 ./240606_VH00358_96_AAFCFJGM5/kr17/extracellular_mutants_24h_a_2_S9_R2_001.fastq.gz -output extracellular_mutants_24h -primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG -mismatches 1 -bwa-alg mem -replicon-ids contig_2_10,contig_2_9,contig_2_8,contig_2_7,contig_2_6,contig_2_5,contig_2_3,contig_2_2,contig_5_10,contig_5_11,contig_5_12,contig_5_13,contig_5_15,contig_5_16,contig_5_17,contig_5_18,contig_5_9,contig_5_8,contig_5_7,contig_5_6,contig_5_5,contig_5_4,contig_5_3,contig_5_2,contig_5_1,contig_4_2,contig_4_1,contig_3_59,contig_3_58,contig_3_57,contig_3_56,contig_3_55,contig_3_54,contig_3_53,contig_3_52,contig_3_51,contig_3_50,contig_3_49,contig_3_48,contig_3_47,contig_3_46,contig_3_44,contig_3_43,contig_3_42,contig_3_41,contig_3_40,contig_3_39,contig_3_38,contig_3_37,contig_3_36,contig_3_35,contig_3_34,contig_3_33,contig_3_32,contig_3_31,contig_3_30,contig_3_29,contig_3_28,contig_3_27,contig_3_26,contig_3_25,contig_3_24,contig_3_23,contig_3_22,contig_3_21,contig_3_20,contig_3_17,contig_3_16,contig_3_15,contig_3_14,contig_3_13,contig_3_12,contig_3_11,contig_3_9,contig_3_8,contig_3_7,contig_3_6,contig_3_5,contig_3_3,contig_3_2,contig_3_1,contig_1_48,contig_1_47,contig_1_46,contig_1_45,contig_1_44,contig_1_43,contig_1_42,contig_1_41,contig_1_40,contig_1_39,contig_1_38,contig_1_37,contig_1_34,contig_1_33,contig_1_32,contig_1_31,contig_1_28,contig_1_27,contig_1_26,contig_1_25,contig_1_24,contig_1_22,contig_1_20,contig_1_19,contig_1_18,contig_1_17,contig_1_16,contig_1_15,contig_1_14,contig_1_13,contig_1_12,contig_1_10,contig_1_8,contig_1_7,contig_1_6,contig_1_5,contig_1_4,contig_1_3,contig_1_2,contig_1_1,contig_C8715,contig_C8943,contig_C9371,contig_C8939,contig_C9357,contig_C8991,contig_C9445,contig_C8689
mv tpp.cfg extracellular_mutants_24h_tpp.cfg

#for intracellular_mutants_24h
python3 ~/.local/bin/tpp -bwa /usr/bin/bwa -protocol Tn5 -ref WA-314_m_.fasta -reads1 ./240606_VH00358_96_AAFCFJGM5/kr19/intracellular_mutants_24h_a_2_S10_R1_001.fastq.gz -reads2 ./240606_VH00358_96_AAFCFJGM5/kr19/intracellular_mutants_24h_a_2_S10_R2_001.fastq.gz -output intracellular_mutants_24h -primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG -mismatches 1 -bwa-alg mem -replicon-ids contig_2_10,contig_2_9,contig_2_8,contig_2_7,contig_2_6,contig_2_5,contig_2_3,contig_2_2,contig_5_10,contig_5_11,contig_5_12,contig_5_13,contig_5_15,contig_5_16,contig_5_17,contig_5_18,contig_5_9,contig_5_8,contig_5_7,contig_5_6,contig_5_5,contig_5_4,contig_5_3,contig_5_2,contig_5_1,contig_4_2,contig_4_1,contig_3_59,contig_3_58,contig_3_57,contig_3_56,contig_3_55,contig_3_54,contig_3_53,contig_3_52,contig_3_51,contig_3_50,contig_3_49,contig_3_48,contig_3_47,contig_3_46,contig_3_44,contig_3_43,contig_3_42,contig_3_41,contig_3_40,contig_3_39,contig_3_38,contig_3_37,contig_3_36,contig_3_35,contig_3_34,contig_3_33,contig_3_32,contig_3_31,contig_3_30,contig_3_29,contig_3_28,contig_3_27,contig_3_26,contig_3_25,contig_3_24,contig_3_23,contig_3_22,contig_3_21,contig_3_20,contig_3_17,contig_3_16,contig_3_15,contig_3_14,contig_3_13,contig_3_12,contig_3_11,contig_3_9,contig_3_8,contig_3_7,contig_3_6,contig_3_5,contig_3_3,contig_3_2,contig_3_1,contig_1_48,contig_1_47,contig_1_46,contig_1_45,contig_1_44,contig_1_43,contig_1_42,contig_1_41,contig_1_40,contig_1_39,contig_1_38,contig_1_37,contig_1_34,contig_1_33,contig_1_32,contig_1_31,contig_1_28,contig_1_27,contig_1_26,contig_1_25,contig_1_24,contig_1_22,contig_1_20,contig_1_19,contig_1_18,contig_1_17,contig_1_16,contig_1_15,contig_1_14,contig_1_13,contig_1_12,contig_1_10,contig_1_8,contig_1_7,contig_1_6,contig_1_5,contig_1_4,contig_1_3,contig_1_2,contig_1_1,contig_C8715,contig_C8943,contig_C9371,contig_C8939,contig_C9357,contig_C8991,contig_C9445,contig_C8689
mv tpp.cfg intracellular_mutants_24h_tpp.cfg

generate statistics tables in Excel-format from the multiple contig running.

for sample in initial_mutants LB_culture growthout_control_24h extracellular_mutants_24h intracellular_mutants_24h; do
    echo "cd ${sample}"
    echo "cp ${sample}.tn_stats ${sample}.tn_stats_"
    echo "#Delete all general statistics before the table data in ${sample}.tn_stats_; delete the content after \"# FR_corr (Fwd templates vs. Rev templates):\""
    echo "sed -i 's/read_count (TA sites only, for Himar1)/read_counts/g' *.tn_stats_"
    echo "sed -i 's/NZ_mean (among templates)/NZ_mean (mean template count over non-zero TA sites)/g' *.tn_stats_"
    echo "python3 ~/Scripts/parse_tn_stats.py ${sample}.tn_stats_ ${sample}.tn_stats.xlsx"
    echo "#calculate the sum of the first and second columns by \"=SUM(B2:B130)\" and \"=SUM(C2:C130)\""
    echo "mkdir ${sample}_wig"
    echo "mv *.wig ${sample}_wig/"
    echo "zip -r ${sample}_wig.zip ${sample}_wig/"
done

cd initial_mutants
cp initial_mutants.tn_stats initial_mutants.tn_stats_
#Delete all general statistics before the table data in initial_mutants.tn_stats_; delete the content after "# FR_corr (Fwd templates vs. Rev templates):"
sed -i 's/read_count (TA sites only, for Himar1)/read_counts/g' *.tn_stats_
sed -i 's/NZ_mean (among templates)/NZ_mean (mean template count over non-zero TA sites)/g' *.tn_stats_
python3 ~/Scripts/parse_tn_stats.py initial_mutants.tn_stats_ initial_mutants.tn_stats.xlsx
#calculate the sum of the first and second columns by "=SUM(B2:B130)" and "=SUM(C2:C130)"
#16,228,513 and 2,454,346

cd LB_culture
cp LB_culture.tn_stats LB_culture.tn_stats_
#Delete all general statistics before the table data in LB_culture.tn_stats_; delete the content after "# FR_corr (Fwd templates vs. Rev templates):"
sed -i 's/read_count (TA sites only, for Himar1)/read_counts/g' *.tn_stats_
sed -i 's/NZ_mean (among templates)/NZ_mean (mean template count over non-zero TA sites)/g' *.tn_stats_
python3 ~/Scripts/parse_tn_stats.py LB_culture.tn_stats_ LB_culture.tn_stats.xlsx
#calculate the sum of the first and second columns by "=SUM(B2:B130)" and "=SUM(C2:C130)"
#19735541, 3266320

cd growthout_control_24h
cp growthout_control_24h.tn_stats growthout_control_24h.tn_stats_
#Delete all general statistics before the table data in growthout_control_24h.tn_stats_; delete the content after "# FR_corr (Fwd templates vs. Rev templates):"
sed -i 's/read_count (TA sites only, for Himar1)/read_counts/g' *.tn_stats_
sed -i 's/NZ_mean (among templates)/NZ_mean (mean template count over non-zero TA sites)/g' *.tn_stats_
python3 ~/Scripts/parse_tn_stats.py growthout_control_24h.tn_stats_ growthout_control_24h.tn_stats.xlsx
#calculate the sum of the first and second columns by "=SUM(B2:B130)" and "=SUM(C2:C130)"
#23812866, 3487969

cd extracellular_mutants_24h
cp extracellular_mutants_24h.tn_stats extracellular_mutants_24h.tn_stats_
#Delete all general statistics before the table data in extracellular_mutants_24h.tn_stats_; delete the content after "# FR_corr (Fwd templates vs. Rev templates):"
sed -i 's/read_count (TA sites only, for Himar1)/read_counts/g' *.tn_stats_
sed -i 's/NZ_mean (among templates)/NZ_mean (mean template count over non-zero TA sites)/g' *.tn_stats_
python3 ~/Scripts/parse_tn_stats.py extracellular_mutants_24h.tn_stats_ extracellular_mutants_24h.tn_stats.xlsx
#calculate the sum of the first and second columns by "=SUM(B2:B130)" and "=SUM(C2:C130)"
#6491071, 236041

cd intracellular_mutants_24h
cp intracellular_mutants_24h.tn_stats intracellular_mutants_24h.tn_stats_
#Delete all general statistics before the table data in intracellular_mutants_24h.tn_stats_; delete the content after "# FR_corr (Fwd templates vs. Rev templates):"
sed -i 's/read_count (TA sites only, for Himar1)/read_counts/g' *.tn_stats_
sed -i 's/NZ_mean (among templates)/NZ_mean (mean template count over non-zero TA sites)/g' *.tn_stats_
python3 ~/Scripts/parse_tn_stats.py intracellular_mutants_24h.tn_stats_ intracellular_mutants_24h.tn_stats.xlsx
#calculate the sum of the first and second columns by "=SUM(B2:B130)" and "=SUM(C2:C130)"
#20619934, 1402849

mkdir initial_mutants_wig
mv *.wig initial_mutants_wig/
cd initial_mutants_wig/
python3 ~/Scripts/update_wig_initial_mutants.py
cd ..
zip -r initial_mutants_wig.zip initial_mutants_wig/

mkdir LB_culture_wig
mv *.wig LB_culture_wig/
cd LB_culture_wig/
python3 ~/Scripts/update_wig_LB_culture.py
cd ..
zip -r LB_culture_wig.zip LB_culture_wig/

mkdir growthout_control_24h_wig
mv *.wig growthout_control_24h_wig/
cd growthout_control_24h_wig/
python3 ~/Scripts/update_wig_growthout_control_24h.py
cd ..
zip -r growthout_control_24h_wig.zip growthout_control_24h_wig/

mkdir extracellular_mutants_24h_wig
mv *.wig extracellular_mutants_24h_wig/
cd extracellular_mutants_24h_wig/
python3 ~/Scripts/update_wig_extracellular_mutants_24h.py
cd ..
zip -r extracellular_mutants_24h_wig.zip extracellular_mutants_24h_wig/

mkdir intracellular_mutants_24h_wig
mv *.wig intracellular_mutants_24h_wig/
cd intracellular_mutants_24h_wig/
python3 ~/Scripts/update_wig_intracellular_mutants_24h.py
cd ..
zip -r intracellular_mutants_24h_wig.zip intracellular_mutants_24h_wig/

zip -r genbank_files.zip genbank_files

run Transposon Position Profiling (TPP) on merged_genome

#for initial_mutants
python3 ~/.local/bin/tpp -bwa /usr/bin/bwa -protocol Tn5 -ref merged_genome.fasta -reads1 240606_VH00358_96_AAFCFJGM5/kr11/initial_mutants_a_2_S6_R1_001.fastq.gz -reads2 240606_VH00358_96_AAFCFJGM5/kr11/initial_mutants_a_2_S6_R2_001.fastq.gz -output initial_mutants_run2 -primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG -mismatches 1 -bwa-alg mem
mv tpp.cfg initial_mutants_tpp_run2.cfg

#for LB_culture
python3 ~/.local/bin/tpp -bwa /usr/bin/bwa -protocol Tn5 -ref merged_genome.fasta -reads1 ./240606_VH00358_96_AAFCFJGM5/kr13/LB_culture_a_2_S7_R1_001.fastq.gz -reads2 ./240606_VH00358_96_AAFCFJGM5/kr13/LB_culture_a_2_S7_R2_001.fastq.gz -output LB_culture_run2 -primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG -mismatches 1 -bwa-alg mem
mv tpp.cfg LB_culture_tpp_run2.cfg

#for growthout_control_24h
python3 ~/.local/bin/tpp -bwa /usr/bin/bwa -protocol Tn5 -ref merged_genome.fasta -reads1 ./240606_VH00358_96_AAFCFJGM5/kr15/growthout_control_24h_a_2_S8_R1_001.fastq.gz -reads2 ./240606_VH00358_96_AAFCFJGM5/kr15/growthout_control_24h_a_2_S8_R2_001.fastq.gz -output growthout_control_24h_run2 -primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG -mismatches 1 -bwa-alg mem
mv tpp.cfg growthout_control_24h_tpp_run2.cfg

#for extracellular_mutants_24h
python3 ~/.local/bin/tpp -bwa /usr/bin/bwa -protocol Tn5 -ref merged_genome.fasta -reads1 ./240606_VH00358_96_AAFCFJGM5/kr17/extracellular_mutants_24h_a_2_S9_R1_001.fastq.gz -reads2 ./240606_VH00358_96_AAFCFJGM5/kr17/extracellular_mutants_24h_a_2_S9_R2_001.fastq.gz -output extracellular_mutants_24h_run2 -primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG -mismatches 1 -bwa-alg mem
mv tpp.cfg extracellular_mutants_24h_tpp_run2.cfg

#for intracellular_mutants_24h
python3 ~/.local/bin/tpp -bwa /usr/bin/bwa -protocol Tn5 -ref merged_genome.fasta -reads1 ./240606_VH00358_96_AAFCFJGM5/kr19/intracellular_mutants_24h_a_2_S10_R1_001.fastq.gz -reads2 ./240606_VH00358_96_AAFCFJGM5/kr19/intracellular_mutants_24h_a_2_S10_R2_001.fastq.gz -output intracellular_mutants_24h_run2 -primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG -mismatches 1 -bwa-alg mem
mv tpp.cfg intracellular_mutants_24h_tpp_run2.cfg

# "=SUM(B2:B130)" 20619934; "=SUM(C2:C130)" 1402849
# command: python /home/jhuang/.local/bin/tpp -bwa /usr/bin/bwa -protocol Tn5 -ref merged_genome.fasta -reads1 ./240606_VH00358_96_AAFCFJGM5/kr19/intracellular_mutants_24h_a_2_S10_R1_001.fastq.gz -reads2 ./240606_VH00358_96_AAFCFJGM5/kr19/intracellular_mutants_24h_a_2_S10_R2_001.fastq.gz -output intracellular_mutants_24h_run2 -primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG -mismatches 1 -bwa-alg mem
# transposon type: Tn5
# protocol type: Tn5
# bwa flags:
# read1: ./240606_VH00358_96_AAFCFJGM5/kr19/intracellular_mutants_24h_a_2_S10_R1_001.fastq
# read2: ./240606_VH00358_96_AAFCFJGM5/kr19/intracellular_mutants_24h_a_2_S10_R2_001.fastq
# ref_genome: merged_genome.fasta
# replicon_ids:
# total_reads (or read pairs): 51244639
# truncated_reads 0 (genomic inserts shorter than the read length; ADAP2 appears in read1)
# trimmed_reads (reads with valid Tn prefix, and insert size>20bp): 23204461 *
# reads1_mapped: 20987078
# reads2_mapped: 20915836
# mapped_reads (both R1 and R2 map into genome, and R2 has a proper barcode): 20627374 * (since barcode is deleted, that means only this filters only the records in which the R2 not containing bacterial genome!)
# read_count (TA sites only, for Himar1): 20627374
# template_count: 1405508
# template_ratio (reads per template): 14.68
# TA_sites: 4537463
# TAs_hit: 93859
# density: 0.021
# max_count (among templates): 285
# max_site (coordinate): 31693
# NZ_mean (among templates): 15.0
# FR_corr (Fwd templates vs. Rev templates): 0.003
# BC_corr (reads vs. templates, summed over both strands): 0.919
# Break-down of total reads (51244639):
#  28040178 reads (54.7%) lack the expected Tn prefix
# Break-down of trimmed reads with valid Tn prefix (23204461):
#  primer_matches: 0 reads (0.0%) contain CTAGAGGGCCCAATTCGCCCTATAGTGAGT (Himar1)
#  vector_matches: 0 reads (0.0%) contain CTAGACCGTCCAGTCTGGCAGGCCGGAAAC (phiMycoMarT7)
#  adapter_matches: 0 reads (0.0%) contain GATCGGAAGAGCACACGTCTGAACTCCAGTCAC (Illumina/TruSeq index)
#  misprimed_reads: 0 reads (0.0%) contain Himar1 prefix but don't end in TGTTA
# read_length: 130 bp
# mean_R1_genomic_length: 75.6 bp
# mean_R2_genomic_length: 88.5 bp

./initial_mutants.tn_stats
# Break-down of total reads (49821406):
#  29481783 reads (59.2%) lack the expected Tn prefix
# Break-down of trimmed reads with valid Tn prefix (20339623): --> #16,228,513 and 2,454,346

./LB_culture.tn_stats
# Break-down of total reads (43486192):
#  20855173 reads (48.0%) lack the expected Tn prefix
# Break-down of trimmed reads with valid Tn prefix (22631019): --> #19,735,541 and 3,266,320

./growthout_control_24h.tn_stats
# Break-down of total reads (70663823):
#  43886543 reads (62.1%) lack the expected Tn prefix
# Break-down of trimmed reads with valid Tn prefix (26777280):

./extracellular_mutants_24h.tn_stats
# Break-down of total reads (47473664):
#  38115004 reads (80.3%) lack the expected Tn prefix
# Break-down of trimmed reads with valid Tn prefix (9358660):

./intracellular_mutants_24h.tn_stats
# Break-down of total reads (51244639):
#  28040178 reads (54.7%) lack the expected Tn prefix
# Break-down of trimmed reads with valid Tn prefix (23204461):

#grep "AGCTTCAGGGTTGAGATGTGTATAAGAGACAG" intracellular_mutants_24h_a_2_S10_R1_001.fastq | wc -l
#28404198
#grep "AGCTTCAGGGTTGAGATGTGTATAAGAGACAG" intracellular_mutants_24h_a_2_S10_R2_001.fastq | wc -l
#29

#AGCTTCAGGGTTGAGATGTGTATAAGAGACAG
#NOTE_IMPORTANT: explain that some multiple mapped reads have to been deleted for the down-stream analysis!

Prepare the wig files on merged_genome for transit running

#change all wigs title to WA314
#./initial_mutants_run2.wig
#./LB_culture_run2.wig
#./growthout_control_24h_run2.wig
#./intracellular_mutants_24h_run2.wig
#./extracellular_mutants_24h_run2.wig
sed -i 's/chrom=merged_genome/chrom=WA314/g' *_run2.wig

Prepare the sample-metadata file for transit running

#my
ID  Condition   Treatment   Filename
initial_mutants initial_mutants control initial_mutants_run2.wig
LB_culture  LB_culture  control LB_culture_run2.wig
growthout_control_24h   growthout_control_24h   control growthout_control_24h_run2.wig
intracellular_mutants_24h   intracellular_mutants_24h   treated intracellular_mutants_24h_run2.wig
extracellular_mutants_24h   extracellular_mutants_24h   treated extracellular_mutants_24h_run2.wig

#Doc
Id      Condition    Filename
glyc1   glycerol     /Users/example_data/glycerol_rep1.wig
glyc2   glycerol     /Users/example_data/glycerol_rep2.wig
chol1   cholesterol  /Users/example_data/cholesterol_rep1.wig
chol2   cholesterol  /Users/example_data/cholesterol_rep2.wig
chol2   cholesterol  /Users/example_data/cholesterol_rep3.wig

Run Transit on merged_genome

#https://training.galaxyproject.org/training-material/topics/genome-annotation/tutorials/tnseq/tutorial.html#compare-the-essential-genes-between-two-conditions

#File --> Export --> combined_wig, or IGV, or Mean_Gene_Counts.
#     --> Convert --> ...... [1]
#View --> Scatter_Plot (only two datasets are allowed)
#     --> Track_View
#     --> Quality_Control
#Analysis
--> Himar1_Methods

    * gumbel
    * resampling
    * hmm
    * example
    * binomial
    * griffin
    * randproduct
    * utest
    * gi
    * normalize
    * tnseq_stats
    * corrplot
    * heatmap
    * ttnfitness

--> Tn5_Methods

    * resampling: Resampling test of conditional essentiality between two conditions
    transit resampling -c combined.wig samples.metadata LB_culture intracellular_mutants_24h merged_genome.prot_table resampling_results_test.txt -s 10000 -n TTR -h -a -l -winz

        [resampling] site_restricted=False
        [resampling] Starting resampling Method
        [resampling] Winsorizing insertion counts
        [resampling] Getting Data
        Reading combined wig data...
        Filtering wigs by conditions...
        Checking condition: initial_mutants, included_conditions: ['lb_culture', 'intracellular_mutants_24h']
        Checking condition: LB_culture, included_conditions: ['lb_culture', 'intracellular_mutants_24h']
        Checking condition: growthout_control_24h, included_conditions: ['lb_culture', 'intracellular_mutants_24h']
        Checking condition: intracellular_mutants_24h, included_conditions: ['lb_culture', 'intracellular_mutants_24h']
        Checking condition: extracellular_mutants_24h, included_conditions: ['lb_culture', 'intracellular_mutants_24h']
        ['LB_culture' 'intracellular_mutants_24h']
        Creating data_ctrl and data_exp arrays...
        Shapes of data_ctrl and data_exp:
        data_ctrl.shape: (1, 4537463)
        data_exp.shape: (1, 4537463)
        [resampling] Preprocessing Ctrl data...
        [resampling] Normalizing using: TTR
        [resampling] Performing LOESS Correction
        /home/jhuang/.local/lib/python3.10/site-packages/pytransit/stat_tools.py:453: RuntimeWarning: invalid value encountered in divide
        normalized_Y[window*i:window*(i+1)] = Y[window*i:window*(i+1)] / (ysmooth[i]/mline)
        [resampling] Preprocessing Exp data...
        [resampling] Normalizing using: TTR
        [resampling] Performing LOESS Correction
        Creating Genes objects...
        Running resampling...
        [resampling] Running Resampling Method... 100.0%
        [resampling] Performing Benjamini-Hochberg Correction
        Writing output...
        [resampling] Number of significant conditionally essential genes (Padj<0.05): 37
        [resampling] Time: 748.52s
        [resampling] Finished resampling Method

    #Resampling
    #Console: python3 /home/jhuang/.local/bin/transit resampling -c combined.wig samples.metadata LB_culture intracellular_mutants_24h merged_genome.prot_table resampling_results.txt -s 10000 -n TTR -h -a -l -winz
    #Parameters: samples=10000, norm=TTR, histograms=True, adaptive=True, excludeZeros=False, pseudocounts=1.0, LOESS=True, trim_Nterm=0.0, trim_Cterm=0.0, site_restricted=False, winsorize=True
    #Control Data: b'lb_culture'
    #Experimental Data: b'intracellular_mutants_24h'
    #Annotation path: b'merged_genome.prot_table'
    #Number of significant conditionally essential genes (Padj<0.05): 37

* utest: Mann-Whitney U-test of conditional essentiality between two conditions. This is a method for comparing datasets from a TnSeq library evaluated in two different conditions, analogous to resampling.
transit utest LB_culture_run2.wig intracellular_mutants_24h_run2.wig merged_genome.prot_table utest_out -n TTR -l
        [utest] Starting Mann-Whitney U-test Method
        [utest] Getting Data
        [utest] Normalizing using: TTR
        [utest] Performing LOESS Correction
        /home/jhuang/.local/lib/python3.10/site-packages/pytransit/stat_tools.py:453: RuntimeWarning: invalid value encountered in divide
        normalized_Y[window*i:window*(i+1)] = Y[window*i:window*(i+1)] / (ysmooth[i]/mline)
        [utest] Running Mann-Whitney U-test Method... 100.0%
        [utest] Performing Benjamini-Hochberg Correction
        [utest] Adding File: utest_out_l
        [utest] Finished Mann-Whitney U-test Method
transit utest LB_culture_run2.wig intracellular_mutants_24h_run2.wig merged_genome.prot_table utest_out_without_l -n TTR
        [utest] Starting Mann-Whitney U-test Method
        [utest] Getting Data
        [utest] Normalizing using: TTR
        [utest] Running Mann-Whitney U-test Method... 100.0%
        [utest] Performing Benjamini-Hochberg Correction
        [utest] Adding File: utest_out
        [utest] Finished Mann-Whitney U-test Method

        #utest
        #Console: python3 /home/jhuang/.local/bin/transit utest LB_culture_run2.wig intracellular_mutants_24h_run2.wig merged_genome.prot_table utest_out -n TTR -l
        #Control Data: b'LB_culture_run2.wig'
        #Experimental Data: b'intracellular_mutants_24h_run2.wig'
        #Annotation path: b'merged_genome.prot_table'
        #Time: 56.52693510055542

        #-l              :=  Perform LOESS Correction; Helps remove possible genomic position bias. Default: Turned Off.

* ZINB (command line only, If you want to compare more than two conditions, see ZINB.): The ZINB (Zero-Inflated Negative Binomial) method is used to determine which genes exhibit statistically significant variability across multiple conditions, in either the magnitude of insertion counts or local saturation, agnostically (in any one condition compared to the others). Like ANOVA, the ZINB method takes a combined_wig file (which combines multiple datasets in one file) and a samples_metadata file (which describes which samples/replicates belong to which experimental conditions).
    transit zinb combined.wig samples.metadata merged_genome.prot_table zinb_out -n TTR --condition Condition --include-conditions LB_culture,intracellular_mutants_24h
    #grep "not analyzed" zinb_out | wc -l  #WARNING: Could run successful, but 4097 records are not analyzed!
#TODO: R is called by Transit for certain commands, such as ZINB, corrplot, and heatmap.
#install R (tested on v3.5.2)
#R packages: MASS, pscl, corrplot, gplots (run
    install.packages(c("MASS", "pscl", "corrplot", "gplots"))
    install.packages("remotes")
    remotes::install_version("MASS", version = "7.3-60")
#Python packages (for python3): rpy2 (v>=3.0) (run “pip3 install rpy2” on command line)

** ANOVA (command line only): The Anova (Analysis of variance) method is used to determine which genes exhibit statistically significant variability of insertion counts across multiple conditions. Unlike other methods which take a comma-separated list of wig files as input, the method takes a combined_wig file (which combined multiple datasets in one file) and a samples_metadata file (which describes which samples/replicates belong to which experimental conditions).
transit anova combined.wig samples.metadata merged_genome.prot_table anova_out -n TTR --include-conditions LB_culture,intracellular_mutants_24h --ref LB_culture -PC 5 -alpha 1000 -winz
    [anova] Starting Anova analysis
    [anova] Getting Data
    [anova] Normalizing using: TTR
    [anova] Winsorizing insertion counts
    [anova] Running Anova
    /home/jhuang/.local/lib/python3.10/site-packages/scipy/stats/_axis_nan_policy.py:531: ConstantInputWarning: Each of the input arrays is constant; the F statistic is not defined or infinite
    res = hypotest_fun_out(*samples, **kwds)
    [anova] Adding File: anova_out. 100.0%
    [anova] Finished Anova analysis
    [anova] Time: 105.4s
    #Console: python3 /home/jhuang/.local/bin/transit anova combined.wig samples.metadata merged_genome.prot_table anova_out -n TTR --include-conditions LB_culture,intracellular_mutants_24h --ref LB_culture -PC 5 -alpha 1000 -winz
    #parameters: normalization=TTR, trimming=0.0/0.0% (N/C), pseudocounts=5, alpha=1000.0

    #--ref <cond> := which condition(s) to use as a reference for calculating LFCs (comma-separated if multiple conditions)
    transit anova combined.wig samples.metadata merged_genome.prot_table anova_5samples_ref_LB_culture_out -n TTR --include-conditions initial_mutants,LB_culture,growthout_control_24h,intracellular_mutants_24h,extracellular_mutants_24h --ref LB_culture -PC 5 -alpha 1000 -winz

    #TODO_MERGE: merge combined.wig and combined_normalized.wig to Excel-file as the read_counts based on the gene!
    #Rv     Gene    TAs     Mean_initial_mutants    Mean_LB_culture Mean_growthout_control_24h      Mean_intracellular_mutants_24h  Mean_extracellular_mutants_24h  LFC_initial_mutants     LFC_LB_culture  LFC_growthout_control_24h       LFC_intracellular_mutants_24h   LFC_extracellular_mutants_24h     Fstat   Pval    Padj    status
            Orf    Gene ID.
    Name    Name of the gene.
    TAs    Number of TA sites in Gene
    Means…    Mean readcounts for each condition
    LFCs…    Log-fold-changes of counts in each condition vs mean across all conditions
    MSR    Mean-squared residual
    MSE+alpha    Mean-squared error, plus moderation value
    p-value    P-value calculated by the Anova test.
    p-adj    Adjusted p-value controlling for the FDR (Benjamini-Hochberg)
    status    Debug information (If any)
    transit example initial_mutants_run2.wig merged_genome.prot_table initial_mutants_mean_read-counts_per_gene.txt

    #TODO: MERGE anovo+example together, delete all headers of the results, save as the Excel-file!
    #Console: python3 /home/jhuang/.local/bin/transit anova combined.wig 5samples.metadata merged_genome.prot_table anova_out -n TTR --include-conditions LB_culture,intracellular_mutants_24h --ref LB_culture -PC 5 -alpha 1000 -winz
    #parameters: normalization=TTR, trimming=0.0/0.0% (N/C), pseudocounts=5, alpha=1000.0
    #Rv     Gene    TAs     Mean_LB_culture Mean_intracellular_mutants_24h  LFC_LB_culture  LFC_intracellular_mutants_24h   MSR     MSE+alpha       Fstat   Pval    Padj    status
    YWA314_00005            307     185.55  381.95  0.000   1.022   5434118.726573  4314598.914305  1.259473        0.262191        1.000000        -
    YWA314_00010            243     44.93   32.98   0.000   -0.395  7853.462976     476245.960914   0.016490        0.897874        1.000000        -
    YWA314_00015            103     0.00    0.00    0.000   0.000   0.000000        0.000000        -1.000000       1.000000        1.000000        No counts in all conditions
    YWA314_00020            190     1.67    0.00    0.000   -0.415  263.556577      1131.081049     0.233013        0.629578        1.000000        -

# ---- commands for one sample ----
** normalize: Normalization method: python transit.py norm glycerol_H37Rv_rep1.wig,glycerol_H37Rv_rep2.wig H37Rv.prot_table glycerol_TTR.txt -n TTR
    - TTR: Trimmed Total Reads (TTR), normalized by the total read-counts (like totreads), but trims top and bottom 5% of read-counts. This is the recommended normalization method for most cases, as it has the benefit of compensating for differences in saturation (which is especially important for resampling).
    - nzmean: Normalizes datasets to have the same mean over the non-zero sites.
    - totreads: Normalizes datasets by total read-counts, and scales them to have the same mean over all counts.
    - zinfnb: Fits a zero-inflated negative binomial model, and then divides read-counts by the mean. The zero-inflated negative binomial model will treat some empty sites as belonging to the “true” negative binomial distribution responsible for read-counts while treating the others as “essential” (and thus not influencing its parameters).
    - quantile: Normalizes datasets using the quantile normalization method described by Bolstad et al. (2003). In this normalization procedure, datasets are sorted, an empirical distribution is estimated as the mean across the sorted datasets at each site, and then the original (unsorted) datasets are assigned values from the empirical distribution based on their quantiles. This actually doesn’t work well on TnSeq data if a large fraction of TA sites have counts of 0 (ties).
    - betageom: Normalizes the datasets to fit an “ideal” Geometric distribution with a variable probability parameter p. Specially useful for datasets that contain a large skew.
    - nonorm: No normalization is performed.

** tnseq_stats: Statistical Metrics for TnSeq datasets
#Typically a skew < 50 is desired
#total_cts    Sum of total read-counts in the sample.    Indicates how much sequencing material was obtained. Typically >1M reads is desired for Himar1 datasets.
transit tnseq_stats -c combined.wig -o tnseq_stats
dataset                             density mean_ct NZmean  NZmedian  max_ct  total_cts  skewness kurtosis pickands_tail_index
initial_mutants_run2.wig            0.025   0.5     21.7    13      306.0   2458212      2.3     7.1     -0.078
LB_culture_run2.wig                 0.025   0.7     29.0    19      307.0   3270725      1.9     4.8     -0.160
growthout_control_24h_run2.wig      0.024   0.8     31.9    22      333.0   3492192      1.8     4.2     -0.082
intracellular_mutants_24h_run2.wig  0.021   0.3     15.0    6       285.0   1405508      3.0     11.3    0.080
extracellular_mutants_24h_run2.wig  0.011   0.1     4.7     3       159.0   236640       5.2     66.9    0.585

* example: Example method that calculates mean read-counts per gene. *
transit example initial_mutants_run2.wig,LB_culture_run2.wig growthout_control_24h_run2.wig intracellular_mutants_24h_run2.wig extracellular_mutants_24h_run2.wig merged_genome.prot_table mean_read-counts_per_gene.txt
#-->ERROR!
#Orf    Name    Desc    k       n       mean    nzmean
YWA314_00005            IS1329 transposase A    15      307     1.11    22.80
YWA314_00010            transposase B   7       243     0.30    10.57
YWA314_00015            hypothetical protein    0       103     0.00    0.00
YWA314_00020            phage protein   2       190     0.02    2.00
YWA314_00025            putative phage endopeptidase    1       418     0.05    20.00

* rankproduct: Rank product test for determining conditional essentiality.
transit rankproduct LB_culture_run2.wig intracellular_mutants_24h_run2.wig merged_genome.prot_table rankproduct_out #-s 100 -n TTR -h -a -l
#warnings.warn("\nOne or more of your .wig files does not include any empty sites (i.e. sites with zero read-counts). Proceeding as if data was Tn5 (all other sites assumed to be zero)!\n")
#ERROR!

#Tn5Gaps method: https://training.galaxyproject.org/training-material/topics/genome-annotation/tutorials/tnseq/tutorial.html#predict-the-essentiality-of-genes
** tn5gaps: It is based on a Gumbel analysis method Griffin et al. 2011 and adapted to Tn5 transposon specificity. The main difference comes from the fact that Tn5 transposon can insert everywhere, thus creating libraries with lower insertion rates.
#transit tn5gaps initial_mutants_run2.wig merged_genome.prot_table initial_mutants_tn5gaps_out #-m 2 -r Sum -iN 5 -iC 5;

for sample in initial_mutants LB_culture growthout_control_24h intracellular_mutants_24h extracellular_mutants_24h; do
    transit tn5gaps ${sample}_run2_normalized.wig merged_genome.prot_table ${sample}_tn5gaps_trimmed.dat -m 2 -r Sum -iN 5 -iC 5;
done
#grep "Essential" initial_mutants_tn5gaps_trimmed.dat | wc -l
#298
#grep "Non-essential" initial_mutants_tn5gaps_trimmed.dat | wc -l
#3834
~/Tools/csv2xls-0.4/csv_to_xls.py initial_mutants_tn5gaps_trimmed.dat LB_culture_tn5gaps_trimmed.dat \
growthout_control_24h_tn5gaps_trimmed.dat intracellular_mutants_24h_tn5gaps_trimmed.dat extracellular_mutants_24h_tn5gaps_trimmed.dat -d$'\t' -o Tn5Gaps.xls;

draw graphics to explain the r, ovr and lenovr based on the information below.
#Orf    Name    Desc    k       n(length)       r       ovr     lenovr  pval    padj    call
YWA314_00005            IS1329 transposase A    15      278     102     104     157     1.00000 1.00000 Non-essential
YWA314_00010            transposase B   7       220     54      55      70      1.00000 1.00000 Non-essential
YWA314_00015            hypothetical protein    0       94      94      96      502     0.29242 1.00000 Non-essential
YWA314_00020            phage protein   2       173     113     114     502     0.29242 1.00000 Non-essential
YWA314_00025            putative phage endopeptidase    1       379     315     317     439     0.81781 1.00000 Non-essential
YWA314_17634    dnaA    chromosomal replication initiation protein      1       1250    1137    1141    1287    0.00000 0.00000 Essential

k: Number of Transposon Insertions Observed within the ORF.
n: Total Number of TA dinucleotides within the ORF.
r: Length of the Maximum Run of Non-Insertions observed.
#TODO1_DEL: ovr: The number of nucleotides in the overlap with the longest run partially covering the gene.
lenovr: The length of the above run with the largest overlap with the gene.
pval: P-value calculated by the permutation test.
padj: Adjusted p-value controlling for the FDR (Benjamini-Hochberg).
call: Essentiality call for the gene. Depends on FDR corrected thresholds. Essential or Non-Essential.

r (Run of Non-Insertions):
    This value represents the length of the longest continuous region within an ORF (open reading frame) where no transposon insertions are observed.
    Graphically, this could be shown as a long unbroken line or bar within a longer gene representation, highlighting the absence of marks (insertions).

ovrovr (Overlap with Run):
    This is the number of nucleotides that overlap with the longest run, which might partially cover the gene. It is not the total length of the run, but how much of it overlaps with the gene.
    In a graphic, this could be illustrated by overlapping two segments: one for the gene and another for the run, with the overlapping part distinctly colored or shaded.

lenovrlenovr (Length of Overlap with Run):
    This measures the full length of the run that has the largest overlap with the gene.
    Visually, this could be depicted as a separate longer line or bar that extends beyond the gene boundaries but is highlighted where it overlaps with the gene.

* #WARNING: Since gumbel_out cannot be generated, the ttnfitness can be generated!
* TTN-Fitness (TTNFitness method that calculates mean read-counts per gene): Typically with individual TnSeq datasets, Gumbel and HMM are the methods used for evaluating essentiality.
    - Gumbel distinguishes between ES (essential) from NE (non-essential).
    - HMM adds the GD (growth-defect; suppressed counts; mutant has reduced fitness) and GA (growth advantage; inflated counts; mutant has selective advantage) categories.
    - Quantifying the magnitude of the fitness defect is risky because the counts at individual TA sites can be noisy. Sometimes the counts at a TA site in a gene can span a wide range of very low to very high counts. The TTN-Fitness gives a more fine-grained analysis of the degree of fitness effect by taking into account the insertion preferences of the Himar1 transposon.
    - These insertion preferences are influenced by the nucleotide context of each TA site. The TTN-Fitness method uses a statistical model based on surrounding nucleotides to estimate the insertion bias of each site. Then, it corrects for this to compute an overall fitness level as a Fitness Ratio, where the ratio is 0 for ES genes, 1 for typical NE genes, between 0 and 1 for GD genes and above 1 for GA genes.

transit ttnfitness <comma-separated .wig files> <annotation .prot_table> <genome .fna> <gumbel output file> <output1 file> <output2 file>
transit ttnfitness initial_mutants_run2_normalized.wig,LB_culture_run2_normalized.wig,growthout_control_24h_run2_normalized.wig,intracellular_mutants_24h_run2_normalized.wig,extracellular_mutants_24h_run2_normalized.wig merged_genome.prot_table merged_genome.fasta gumbel_out ttnfitness_out1 ttnfitness_out2

transit ttnfitness initial_mutants_run2_normalized.wig,LB_culture_run2_normalized.wig,growthout_control_24h_run2_normalized.wig,intracellular_mutants_24h_run2_normalized.wig,extracellular_mutants_24h_run2_normalized.wig merged_genome.prot_table <genome .fna> <gumbel output file> <gene-wise output file> <ta-site wise output file>
-  gumbel output file:* The Gumbel method must be run first on the dataset.The output of the Gumbel method is provided as an input to this method. ES (essential by Gumbel) and EB (essential by Binomial) is calculated in the TTN-Fitness method via this files

* Genetic Interactions: The genetic interactions (GI) method is a comparative analysis used used to determine genetic interactions. It is a Bayesian method that estimates the distribution of log fold-changes (logFC) in two strain backgrounds under different conditions, and identifies significantly large changes in enrichment (delta_logFC) to identify those genes that imply a genetic interaction.

* Pathway Enrichment Analysis: Pathway Enrichment Analysis provides a method to identify enrichment of functionally-related genes among those that are conditionally essential (i.e. significantly more or less essential between two conditions).
    transit pathway_enrichment <resampling_file> <associations> <pathways> <output_file> [-M <FET|GSEA|GO>] [- ...

** corrplot (Correlation among TnSeq datasets, command line only): A useful tool when evaluating the quality of a collection of TnSeq datasets is to make a correlation plot of the mean insertion counts (averaged at the gene-level) among samples.
#INCOMPLETE
cut -f1-6 combined.wig > combined_.wig
transit corrplot combined_.wig corrplot.png
#INCOMPLETE
cut -f1-6 combined_normalized.wig > combined_normalized_.wig
transit corrplot combined_normalized_.wig corrplot_normalized.png
transit corrplot anova_5samples_ref_LB_culture_out corrplot_anova.png -anova
#[corrplot] Starting Corrplot
correlations based on 74 genes
[corrplot] Finished Corrplot

** heatmap (Heatmap among Conditions, command line only): The output of ANOVA or ZINB can be used to generate a heatmap that simultaneously clusters the significant genes and clusters the conditions, which is especially useful for shedding light on the relationships among the conditions apparent in the data.
transit heatmap anova_5samples_ref_LB_culture_out heatmap.png -anova -qval 0.05 -low_mean_filter 3
#heatmap based on 74 genes
transit heatmap anova_5samples_ref_LB_culture_out heatmap.png -anova -qval 0.1 -low_mean_filter 3

#-- convert gbk to prot_table --
python3 ~/Scripts/gbk_to_prottable.py merged_genome.gbk merged_genome.prot_table

#transit export
#https://transit.readthedocs.io/en/latest/transit_normalization_tutorial.html
#-1- transit export combined_wig <comma-separated .wig files> <annotation .prot_table> <output file> -n TTR
#RUN
    transit export combined_wig  initial_mutants_run2.wig,LB_culture_run2.wig,growthout_control_24h_run2.wig,intracellular_mutants_24h_run2.wig,extracellular_mutants_24h_run2.wig merged_genome.prot_table combined.wig -n nonorm
    transit export combined_wig initial_mutants_run2.wig,LB_culture_run2.wig,growthout_control_24h_run2.wig,intracellular_mutants_24h_run2.wig,extracellular_mutants_24h_run2.wig merged_genome.prot_table combined_normalized.wig -n TTR

#-2-
    transit export igv initial_mutants_run2.wig,LB_culture_run2.wig,growthout_control_24h_run2.wig,intracellular_mutants_24h_run2.wig,extracellular_mutants_24h_run2.wig merged_genome.prot_table combined_normalized.igv -n TTR
    #TODO: replace merged_genome to WA314!
    #DEBUG: how to run it?
#-3- transit export mean_counts -c combined.wig combined.mean_counts
    # note: append -c if inputing a combined_wig file

# -- detect essential genes (DnaA is essential, which is why there are no insertion counts in the first few TA sites)
#https://transit.readthedocs.io/en/v3.2.5/method_ttnfitness.html
#对于 transit gumbel 分析，通常期望输入的是已经规范化（normalized）的 .wig 文件。规范化的数据可以减少由于不同实验条件或测序深度导致的偏差，使得后续的统计分析更加可靠
#-- Normalization --
#https://transit.readthedocs.io/en/latest/method_normalization.html
for sample in initial_mutants LB_culture growthout_control_24h intracellular_mutants_24h extracellular_mutants_24h; do
    transit normalize ${sample}_run2.wig ${sample}_run2_normalized.wig -n TTR    #betageom
done
#https://transit.readthedocs.io/en/latest/method_tnseq_stats.html
#pre-normalization
for sample in initial_mutants LB_culture growthout_control_24h intracellular_mutants_24h extracellular_mutants_24h; do
    transit tnseq_stats ${sample}_run2.wig
done

#post-normalization
for sample in initial_mutants LB_culture growthout_control_24h intracellular_mutants_24h extracellular_mutants_24h; do
    transit tnseq_stats ${sample}_run2_normalized.wig
done

Reports

#1. For gene_based reports: example (mean_read-counts_per_gene.txt) + ANOVA (anova_5samples_ref_LB_culture_out)
        #TAs: Number of TA sites in Gene

        #mean: Average read-count, including empty sites.
        #nzmean: Average read-count, excluding empty sites.

        Orf: Gene ID
        Name: Name of the gene.
        Desc    Gene description.
        k: Number of Transposon Insertions Observed within the ORF.
        n: Total Number of TA sites within the ORF
        Normalized_initial_mutants: Mean read counts for the condition initial_mutants (normalized with TTR)
        Normalized_LB_culture: Mean read counts for the condition LB_culture (normalized with TTR)
        Normalized_growthout_control_24h: Mean read counts for the condition growthout_control_24h (normalized with TTR)
        Normalized_intracellular_mutants_24h: Mean read counts for the condition intracellular_mutants_24h (normalized with TTR)
        Normalized_extracellular_mutants_24h: Mean read counts for the condition extracellular_mutants_24h (normalized with TTR)

Orf    Name    Desc    k       n       mean    nzmean
Rv     Gene    TAs     Normalized_initial_mutants    Normalized_LB_culture Normalized_growthout_control_24h      Normalized_intracellular_mutants_24h  Normalized_extracellular_mutants_24h

#check both f1 are the same;
cut -f4-8 anova_5samples_ref_LB_culture_out > f4_8;
paste mean_read-counts_per_gene.txt f4_8 > overview_gene_based.txt
#delete the columns mean and nzmean.

#2. For essentiall gene report: transit tn5gaps
TODO: delete the headers; DEL ovr; merge tn5gaps also the gene-based tables, add columns r, ovr, lenovr, pval, padj, call to the gene-based table.

#Tn5Gaps method: https://training.galaxyproject.org/training-material/topics/genome-annotation/tutorials/tnseq/tutorial.html#predict-the-essentiality-of-genes
#dinucleotides
ORF     Gene ID.
Name    Name of the gene.
Desc    Gene description.
k   Number of Transposon Insertions Observed within the ORF.
n   Total Number of TA sites within the ORF.
r   Length of the Maximum Run of Non-Insertions observed.
pval    P-value calculated by the permutation test.
padj    Adjusted p-value controlling for the FDR (Benjamini-Hochberg).
call    Essentiality call for the gene. Depends on FDR corrected thresholds. Essential or Non-Essential.

#3. Only choose ANOVA for DEG reports, since it can be drawn + are consistent with the gene-based view!
# delete MSR and MSE+alpha.

Orf: Gene ID
Name: Name of the gene
TAs: Number of TA sites in the gene
Means…: Mean read counts for each condition
LFCs…: Log-fold changes of counts in each condition vs. the mean across all conditions
p-value: P-value calculated by the ANOVA test
p-adj: Adjusted p-value controlling for the FDR (False Discovery Rate, Benjamini-Hochberg method)
status: Debug information

#4. 2 plots (Temporarily do not send, since the based on 74 genes, we have only 30+ significant genes between LB_culture and intracellular_mutants_24h!)
corrplot: A useful tool when evaluating the quality of a collection of TnSeq datasets is to make a correlation plot of the mean insertion counts (averaged at the gene-level) among samples.
heatmap: The output of ANOVA can be used to generate a heatmap that simultaneously clusters the significant genes and clusters the conditions, which is especially useful for shedding light on the relationships among the conditions apparent in the data.

#5. (Temporarily do not send, since for previous wig files can also beed used!) five wig-files and merged_genome.gb and merged_genome.fa
#resampling
#Orf    Name    Desc    Sites   Mean Ctrl   Mean Exp    log2FC  Sum Ctrl    Sum Exp Delta Mean  p-value Adj. p-value
YWA314_00463        chaperone protein DnaJ  1129    38.5    1.3 -4.13   43427.8 1413.92 -37.2   0   0

#utest
#Orf    Name    Desc    Sites   Mean Ctrl   Mean Exp    log2FC  U-Statistic p-value Adj. p-value
YWA314_00463        chaperone protein DnaJ  1129    1696.7  339.9   -2.32   635 0   0

#ANOVA
#Rv Gene    TAs Mean_LB_culture Mean_intracellular_mutants_24h  LFC_LB_culture  LFC_intracellular_mutants_24h   MSR MSE+alpha   Fstat   Pval    Padj    status

YWA314_05189        2506    106.58  0   0   -4.48   14054884.5521   338643.330313   41.503503   0   0.000001
YWA314_00518    carB    3232    194.02  31.97   0   -2.428  42162662.154189 1121416.346908  37.597688   0   0.000002
YWA314_16630        2146    34.88   0   0   -2.996  1261390.068193  36940.341092    34.146682   0   0.000008
YWA314_00463        1129    49.05   1.87    0   -2.976  1051922.250591  110643.16916    9.50734 0.002071    0.213913

YWA314_00463            1129    58.38   49.05   19.61   1.87    22.56   0.230   0.000   -1.135  -2.976  -0.972  489073.319278   169569.370262   2.884208        0.021237        0.669899

plot Figure 1. Overview of the Yersinia enterocolitica subsp. enterocolitica WA-314 Transposon Mutant Library This figure illustrates the distribution of transposon insertion sites across the genome. The outermost black circle represents the WA-314 genome in kilobase pairs (Kbp). The middle circles, which feature scatter points, indicate the normalized number of sequencing reads at each unique transposon insertion site, with each axis line representing an increment of 50,000 in data values. The circles are color-coded to represent different conditions: green for extracellular mutants, blue for intracellular mutants, red for growthout control, purple for LB culture, and yellow for initial mutants. The innermost orange circle highlights the locations of genes identified as essential.

#circos -conf circos.conf

<<include /etc/circos/colors_fonts_patterns.conf>>

karyotype = circos_data/karyotype.microbe.txt

chromosomes_units           = 10000
chromosomes_display_default = yes

<ideogram>
show = yes
<spacing>
default = 0.01r
break   = 0.5r  #8r  # Adds a gap between the first and last position of the single chromosome
</spacing>
radius    = 0.9r
thickness = 25p
fill      = no
stroke_thickness = 2
stroke_color     = black
show_bands = yes
fill_bands = yes
band_transparency = 0
show_label       = yes
label_font       = default
label_radius = 1.05r
label_size       = 75
label_parallel   = yes
orientation      = 100 # Rotate the plot by 90 degrees
</ideogram>

#<<include ticks.conf>>
show_ticks          = yes
show_tick_labels    = yes

show_grid          = no
grid_start         = dims(ideogram,radius_inner)-0.5r
grid_end           = dims(ideogram,radius_inner)

<ticks>
skip_first_label     = yes
skip_last_label      = no
radius               = dims(ideogram,radius_outer)
tick_separation      = 2p
min_label_distance_to_edge = 0p
label_separation = 5p
label_offset     = 5p
label_size = 8p
multiplier = 0.001
color = black

thickness = 3p
size      = 20p

<tick>
size           = 10p
spacing        = 1u
color          = black
show_label     = no
label_size     = 12p
format         = %.2f
grid           = no
grid_color     = lblue
grid_thickness = 1p
</tick>
<tick>
size           = 15p
spacing        = 5u
color          = black
show_label     = yes
label_size     = 16p
format         = %s
grid           = yes
grid_color     = lgrey
grid_thickness = 1p

</tick>
<tick>
size           = 18p
spacing        = 10u
color          = black
show_label     = yes
label_size     = 16p
format         = %s
grid           = yes
grid_color     = grey
grid_thickness = 1p
</tick>
<tick>
spacing        = 100u
color          = black
show_label     = yes
suffix = " kb"
label_size     = 36p
format         = %s
grid           = yes
grid_color     = dgrey
grid_thickness = 1p
</tick>
</ticks>

<image>
<<include /etc/circos/image.conf>>
</image>

<colors>
<<include /etc/circos/colors.conf>>
</colors>

<fonts>
<<include /etc/circos/fonts.conf>>
</fonts>

# -- Scatter plot 1 --
<plots>

<plot>
type       = scatter
file       = circos_data/extracellular_mutants.txt
r1         = 0.99r
r0         = 0.79r
#python3 identify_min_max.py circos_data/initial_mutants.txt
min        = 0
max        = 400000
glyph      = circle
glyph_size   = 5
color      = dgreen

<axes>
    <axis>
        spacing   = 50000
        color     = lgrey
    </axis>
</axes>
#<axes>
#<axis>
#spacing   = 0.1r
#color     = lgrey
#<ticks>
#<tick>
#spacing        = 0.1
#size           = 10p
#thickness      = 2p
#color          = lgrey
#show_label     = yes
#label_size     = 20p
#label_offset   = 5p
#format         = %0.1f
#</tick>
#</ticks>
#</axis>
#</axes>

#<rules>
#<rule>
#condition    = var(value) > 10000
#stroke_color = dred
#fill_color   = red
#glyph        = rectangle
#glyph_size   = 2
#</rule>
#</rules>
</plot>

# -- Scatter plot 2 --
<plot>
type       = scatter
file       = circos_data/intracellular_mutants.txt
r1         = 0.79r
r0         = 0.69r
min        = 0
max        = 200000
glyph      = circle
glyph_size   = 5
color      = dblue
<axes>
    <axis>
        spacing   = 50000
        color     = lgrey
    </axis>
</axes>
</plot>

# -- Scatter plot 3 --
<plot>
type       = scatter
file       = circos_data/growthout_control.txt
r1         = 0.69r
r0         = 0.59r
min        = 0
max        = 200000
glyph      = circle
glyph_size   = 5
color      = dred
<axes>
    <axis>
        spacing   = 50000
        color     = lgrey
    </axis>
</axes>
</plot>

# -- Scatter plot 4 --
<plot>
type       = scatter
file       = circos_data/LB_culture.txt
r1         = 0.59r
r0         = 0.49r
min        = 0
max        = 200000
glyph      = circle
glyph_size   = 5
color      = dpurple
<axes>
    <axis>
        spacing   = 50000
        color     = lgrey
    </axis>
</axes>
</plot>

# -- Scatter plot 5 --
<plot>
type       = scatter
file       = circos_data/initial_mutants.txt
r1         = 0.49r
r0         = 0.39r
min        = 0
max        = 200000
glyph      = circle
glyph_size   = 5
color      = dyellow
<axes>
    <axis>
        spacing   = 50000
        color     = lgrey
    </axis>
</axes>
</plot>

# Gene Locations
<plot>
type       = heatmap
file       = circos_data/merged_genome.txt
r1         = 0.35r
r0         = 0.32r
color      = orange
</plot>

#grep "Essential" tn5_gap_inituial_mutant.csv > essential_genes.txt
#cut -f1 -d$'\t' essential_genes.txt > f1
#vim merged_genome.prot_table
#" merged_genome.prot_table >> merged_genome.prot_table_essential \ngrep "
#python3 generate_gene_locations.py
#replace "\n\t" with "\nchr\t"

</plots>

<<include /etc/circos/housekeeping.conf>>

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