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(3) Identification of homoplasmic and heteroplasmic variants;

(4) Functional mappinganalysis;

(5)  Get Get familiarized with Plink and R environmentsenvironment.


## All materials are located in the following links: haplocheck_results, haplogroups_workshopsamples.txt, HG0096c_test.bam, HG0097c_test.bam, HG0099c_test.bam, HG0100c_test.bam, HG0101c_test.bam, HG0102c_test.bam, HG0103c_test.bam, HG0105c_test.bam, HG0106c_test.bam, HG0107c_test.bam, Merged.txt, Merged.vcf.gz, Workshop_samples_05-17-23_nocont_homo_common.bim, AnnotatedVariants.txt.

## ***Powerpoint slides for this workshop: Workshop_mtDNA_QC_analysis.pptx

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  1. Reference files: Human genome 
    1. Download the reference files using this link https://console.cloud.google.com/storage/browser/genomics-public-data/references/hg38/v0;tab=objects?pageState=(%22StorageObjectListTable%22:(%22f%22:%22%255B%255D%22))&prefix=&forceOnObjectsSortingFiltering=false
  2.  Nextflow (https://www.nextflow.io/)
    1. Need to install nextflow in your work directory by using the command: curl -s https://get.nextflow.io | bash
  3. Singularity
    1. Need to load singularity module if using CAMH Specialized Computing Cluster: e.g., module load tools/Singularity/3.8.5
  4. Mutserve (https://github.com/seppinho/mutserve) 
    1. Need to install mutserve in your work directory by using the command: curl -sL mutserve.vercel.app | bash
  5. Our pipeline was adapted from https://github.com/gatk-workflows/gatk4-mitochondria-pipeline.
  6. PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/)
    1. PLINK 1.9 (https://www.cog-genomics.org/plink/)
    2. PLINK 2.0 (PLINK 2.0 (cog-genomics.org))
    3. Need to load plink module if using CAMH Specialized Computing Cluster: e.g., module load bio/PLINK/1.9b_6.15-x86_64
    R (http://cran.r-project.org/)
    2. Need to load R module if using CAMH Specialized Computing Cluster: e.g., module load lang/R/4.0.3-Python-3.8.5-Anaconda3-2020.11
  7. Unix-based command terminal
  8. A text editor

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  • Download reference files.
  • Module load Java (e.g. module load lang/Java/11.0.6), Singularity, and install nextflow and mutserve. 
  • Clone your pipeline into your work directory: e.g. git clone pipeline_link_depository

NOTE #1: all written in red is editable.

1. Aligment and variants calling

  • To run the alignment and variants calling use the command line below.

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module load bio/BCFtools/1.11-GCC-10.2.0
for i in $(ls *.vcf.gz); do bcftools index --tbi $i; done
bcftools merge -Oz /work_directory/*.vcf.gz -o Merged.vcf.gz

Haplocheck

2. Haplocheck

Haplocheck detects in-sample contamination in mtDNA or WGS sequencing studies by analyzing the mitchondrial content. To run haplocheck, you can either use their cloud web service or install it locally.Contamination

wget https://github.com/genepi/haplocheck/releases/download/v1.3.3/haplocheck.zip
unzip haplocheck.zip
./haplocheck --out haplocheck_results Merged.vcf.gz

Haplogrep

3. Haplogrep

The haplogroup classifications in Haplogrep are based on the revised tree by Dür et al, 2021, which is an update of the latest PhyloTree version 17 by van Oven, 2016 based on the work of van Oven & Kayser, 2009.Haplogroup

curl -sL haplogrep.now.sh | bash
./haplogrep classify --in Merged.vcf.gz --format vcf --out haplogroups_workshopsamples.txt
  • Before go to the Quality Control steps, please download the all_samples.csv and filename.txt files to your computer.

Quality Control analysis 


4. Quality Control analysis 

The following files are going to be used for QC analysis: Merged.txt (raw variants data)Use the excel to open the files Merged.txt, haplocheck_results (contains the contamination status), andhaplogroups_workshopsamples.txt (contains the haplogroup information for each one of the samples).

Filtering out variants from samples identified as contaminated

  1.  By  First, to identify the samples that are contaminated we will be using the haplocheck_results file. For that, you will check which samples are contaminated (check the column B : Contamination (Contaminated Status) . If and verify if there is any sample indicating YES in the contamination status column. If , you will need to copy the Sample IDs (column A: Sample) and paste in a new excel file. Name your file as samples_to_remove and save it as txt format (see the image Figure 1 below as an example). 

Figure 1 - samples_to_remove.txt file example.

#NOTE: The samples_to_remove.txt file should have two columns and no header. Both columns have the same information (Sample IDs). This file is going to be used in further steps. If you want, you can already upload this file into your work directory in the SCC cluster.

Quality Control Steps

  1. Continue the next steps using the

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  1. Merged.txt file.
  2. Create the first Excel tab named "Merged_raw_data" using the Merged.txt file.
  3. Create a 2nd tab named "Merged_nocont". Copy the entire data from the raw results (1st tab - Merged_raw_data) and paste it into the 2nd tab. Using the Sample IDs from the samples_to_remove.txt file you will identify the variants from the each one of the contaminated samples and manually remove their respective rows on the Merged_nocont tab.
  4. Create a 3rd tab named "coverage>200_both_strand". Copy the entire data from 2nd sheet (Merge_nocont tab) and paste it into the 3rd tab. After that, remove all the rows containing variants with coverage lower than 200x in both strands (verify for both CoverageFWD and CoverageREV columns).
  5. Create a 4th tab named "Fwd-rev_ratio". Copy the entire data from the 3rd tab (overage>200_both_strand tab) and paste it into the 4th. Next, create a new column called FWD-Rev_ratio and calculate the ratio between the CoverageFWD and CoverageREV values (columns L and M). After that, filter out all rows with variants showing Fwd/Rev ratio below 0.5 or higher than 1.5.
  6. Create a 5th tab and call it as remove_del. Copy the entire data from the 4th tab (Fwd-rev_ratio tab) and paste it into the 5th . Check the Ref column and exclude all the rows containing the letter N (which means deletion) on this column.
  7. Create a 6th tab named "remove_primer_phantom". Copy the entire data from the 5th tab (remove_del tab) and paste it into the 6th . After that, remove all the rows containing variants in the primer regions (e.g. 0-500 bp and 16000-16655 bp). Also check if there is any variant at the known phantom mutation sites (72':['G','T'], 257':['A','C'], '414':['G','T'], 3492':['A','C'], 3511':['A','C'], 4774':['T','A'], 5290':['A','T'], '9801':['G','T'], 10306':['A','C'], '10792':['A','C'], '11090':['A','C']). If yes, you should remove the rows containing these variants as well.
  8. Create a 7th tab named "homoplasmy_only". Copy the entire data from the 6th tab (remove_primer_phantom tab) and paste it into the 7th . Check the VariantLevel column and remove all the rows containing values lower than 95%. 

    #NOTE: You have the flexibility to adjust the threshold values based on the quality of your sequencing data and the specific requirements of your analysis.

  9. Create a 8th tab named "heteroplasmy_only". Copy the entire data fromthe 6th tab (remove_primer_phantom tab) and paste it into the 8th tab. Check the VariantLevel column and remove all the rows containing values lower than 3% and higher than 95%.

#NOTE: Check it out an example of how the Merged_QC excel file should be organized in different tabs based on the QC steps described above (Figure 2).

Image Added

Figure 2 - Merged_QC.csv file example

IMPORTANT! After following the QC steps, create a new excel file. Copy and paste the entire data from homoplasmic_only tab into this new excel. Leave only the column containing the variant position (column C: Pos) in the document and exclude the duplicate values. Add MT- ahead to the position of each variant(example MT-73) in Pos column. Remove the header and save as text file called homoplasmic_variants (Figure 3).

Image Added

Figure 3 - homoplasmic_variants.txt file

#NOTE: The homoplasmic_variants.txt file has only one column with variants names (e.g MT-73) and no header. This file will be further used in the Homoplasmic variants calling step described below and also for statistical analysis.

#NOTE: The homoplasmic_variants.txt file should be uploaded into your folder (directory) in the SCC cluster.

#NOTE: Save the excel file as Merged_QC.csv. 

Homoplasmy – transition and transversion ratio.

VERY IMPORTANT STEP! It is highly recommended to have at least two people doing together the steps described in this section.

  1. Copy the entire data from the homoplasmy_only tab (from Merged_QC.csv file) and paste it into a new excel file. Next, follow the steps described below.
  2. First, name the 1st tab as homoplasmic_variants. The first tab contains the total number of variants (number of rows). Take a side note of the total number of variants you have.
  3. Create a 2nd tab and call it as unique_homoplasmic_variants. Copy and paste the entire data from the 1st tab into the 2nd and exclude all the duplicate variants, this way you are going to keep only unique/bi-allelic variants. Take a side note of the number of unique variants you have at this step. 
  4. Create a new column in the unique_homoplasmic_variants tab called Type_of_mutation. The Type_of_mutation column will show a number that indicates the type of substitution mutation which 1 will refer to a transition type and 2 to a transversion type. In transition, one purine is substituted for another purine or one pyrimidine is substituted for another pyrimidine (Transitions are: A>G, G>A, C>T or T>C). In transversion, a purine base is substituted for a pyrimidine base or vice versa (Transversions are: A>C, C>A, G>T, T>G, G>C or C>G). In figure 4, you can double-check the different types of substitution mutations.

Image Added

Figure 4 - Type of mutation 
  1. Check out the columns Ref and Variant (columns D and E) and add the value 1 (when you identify a transition) or add the value 2 (when you identify a transversion) type to the column Type_of_mutation.
  2.   Next, take a side note of the total number of transitions and transversions you have into the column Type_of_mutation.
  3. Calculate the percentage (%) of both transitions and transversions in comparison to the total of unique variants.
  4. Calculate transition/transversion ratio by dividing the percentage (%) of transitions by the percentage (%) of transversions.

Example:

Total number of homoplasmic variants: 1211

Total number of unique/bi-allelic variants: 335

Number of transitions in comparison to the total number of unique/bi-allelic variants: 325 (97,01%)

Number of transversions in comparison to the total number of unique/bi-allelic variants: 10 (2,99%)

Transitions/transversions ratio Ti/Tv: 32.44:1.

NOTE: The reference ratio of Ti/Tv for homoplasmic variants goes from 20:1 to 38:1.

Heteroplasmy - transition and transversion ratio.

  1. Copy the entire data from the heteroplasmy _only tab (from Merged_QC.csv file) and paste it into a new excel file. Next, follow all the steps described above for the "Homoplasmy - transition and tranversion ratio" analysis.

#NOTE: There is no established reference ratio of Ti/Tv for heteroplasmic variants.

5. Homoplasmic and common variants filtering using PLINK in the SCC cluster

Converting vcf to plink files

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Filtering out the samples that were contaminated

  • To filter out the contaminated samples we will use the file samples_to_remove.txt created in previous steps on the QC section. Verify if you have already uploaded this file into the work directory and run the commands described below.
module load bio/PLINK/1.9b_6.22-x86_64
plink --bfile Workshop_samples_05-17-23_nv --remove samples_to_remove.txt --make-bed --out Workshop_samples_05-17-23_nocont

#NOTE: The samples_to_remove.txt file was created at on the QC section.

Homoplasmic variants calling

  • For this part of the analysis, you are going to call only the homoplasmic variants by using the homoplasmic_variants.txtfile. This file was created in the section Quality Control (QC) steps, step 5. Before continuing, you must certify that the homoplasmic_variants.txtfile was uploaded into your work directory in the SCC cluster. Next, run the analysis by using the commands described below.

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Common variants (MAF ≥ 5%) calling

  • Minor allele frequency (MAF) is the frequency at which the second most common allele occurs in a given population. MAF is widely used in population genetics studies because it provides information to differentiate between common, low-frequency and rare variants in the population.

#NOTE: Common (MAF ≥ 5%), low-frequency (0.5 ≤ MAF ≤ 5%) and rare (MAF ˂ 0.5%) variants.

plink --bfile Workshop_samples_05-17-23_nocont_homo --maf 0.05 --make-bed --out Workshop_samples_05-17-23_nocont_homo_common

#NOTE: Download the Workshop_samples_05-17-23_nocont_homo_common.bim , open it in excel and save it as common_variants.

Example: Phenotypes and correlation analysis

plink --bfile Workshop_samples_05-17-23_nocont_homo_common --allow-no-sex --linear --pheno phenotype.txt --pheno-name phenotype_interested_measure --out res_phenotype_interested_measure

NOTE: do the command described previously for all phenotype variables you have interest in your data. The type of test used (e.g. linear model, logistic, etc) will depend on the type of data you have.

6. Functional analysis

The effect of mutations-caused amino acid changes on protein function was predicted by a combination of tools that use sequence homology, evolutionary conservation, and protein structural information.

  1. To run the functional analysis, first you need to create the variantsfile.txt. For that, you will utilize the information from the Workshop_samples_05-17-23_nocont_homo_common.bim file obtained in the previous step. The variantsfile.txt file should be structured with two columns, namely Pos and Variant. For further reference, please consult Figure 5.

Image Added

Figure 5 - variantsfile.txt example

2. Upload the variantsfile.txt into your work directory in the SCC cluster and run the command below.

./mutserve annotate --input variantsfile.txt --annotation rCRS_annotation_2020-08-20.txt --output AnnotatedVariants.txt

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https://github.com/seppinho/mutserve/blob/master/files/rCRS_annotation_descriptions.md

 Interpretation of the results

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