Schmutzi Pipeline

##STEP 1: Sample preparation using BWA and SAMtools##

#1a: Align FASTQ sample to reference fasta and create a SAM file

bwa aln MT_reference_file input_file.fastq > input_file.sai

bwa samse MT_reference_file input_file.sai input_file.fastq > output_file_MTaligned.sam

#1b (optional): Realignment to mtDNA reference by CircularMapper (automatic conversion to BAM)

java -jar realign-1.93.5.jar -e 500 -i output_file_MTaligned.sam -r MT_reference_file.fa

#1c: Sort BAM file (use aligned BAM file derived in previous step)

samtools sort output_file_MTaligned _CM_realigned.bam -o output_file_MTaligned _CM_realigned_sort.bam

#1d: MD-Tagging (use aligned and sorted BAM file derived in previous step)

samtools calmd -b output_file_MTaligned _CM_realigned_sort.bam MT_reference_file.fa > output_file_MTaligned _CM_realigned_sort_MD.bam

#1e: Index the MD-tagged BAM file

samtools index output_file_MTaligned _CM_realigned_sort_MD.bam

##STEP 2: Estimate endogenous deamination using contDeam##

contDeam.pl --library double --out output_name output_file_MTaligned _CM_realigned_sort_MD.bam

##STEP 3: Estimate contamination and call the (unfiltered) consensus sequence using schmutzi##

#3a: First run the iterative procedure without the prediction of the contaminant

schmutzi.pl --notusepredC --uselength --ref MT_reference_file.fa --out output_name_npred freqs output_name output_file_MTaligned _CM_realigned_sort_MD.bam

#3b: Then repeat the above with the prediction of the contaminant

schmutzi.pl --uselength --ref MT_reference_file.fa --out output_name_npred freqs output_name output_file_MTaligned _CM_realigned_sort_MD.bam

##STEP 4: Apply filtering in the consensus sequence derived in the previous step using log2fasta##

#Use the log file created via running the schmutzi application to filter by base Q-score (in this case discards bases with Q-score below 10-repeat with different cut-offs)

log2fasta -q 10 output_name_npred_final_endo.log > output_name_filtered_schmu_q10.fasta

ANGSD Pipeline

##STEP 1: Sample preparation using SAMtools##

#1a: check whether the input BAM file is sorted

samtools stats input_file.bam | grep “is sorted:”

#1b: create a BAM index file (BAI)

samtools index input_file.bam input_file.bai

#1c: Isolate the MT chromosome as a separate BAM file (this step will not be needed if the mitocapture BAM file is available)

samtools view -b input_file.bam MT > input_file_MT.bam

##STEP 2: Call the consensus sequence using ANGSD (no filtering)##

#2a: Call the mitogenome consensus sequence from the MT BAM file (option 1: use the most common base)

angsd -out output_file -i input_file.bam -doFasta 2 -doCounts 1

#2b (optional): Call the mitogenome consensus sequence from the MT BAM file (option 2: use the base with the highest effective depth-EBD)

angsd -out output_file -i input_file.bam -doFasta 3

##STEP 3: Call the consensus sequence using ANGSD (with filtering)##

#3a: Call the mitogenome consensus sequence from the MT BAM file (option 1) and filter by trimming the first/last 1 base of each read (in this case trims first/last base of each read-repeat by setting different n of bases to be trimmed)

angsd -out output_file -i input_file.bam -doFasta 2 -doCounts 1 -trim 1

#3b: Call the mitogenome consensus sequence from the MT BAM file (option 1) and filter by trimming the first/last 1 base of each read and setting minimum base quality to 10 (in this case discards base quality below 10-repeat with different cut-offs)

angsd -out output_file -i input_file.bam -doFasta 2 -doCounts 1 -trim 1 -minQ 10

HaploGrep

#mtDNA Haplogroup prediction using Haplogrep 2, based on the fasta sequence files derived in the above pipelines

haplogrep classify --extend-report --format fasta --in output_file.fasta --out output_file.txt