The COV module uses information from methylation calls in the form of a CGmap to calculate coverage of all methylation sites in three contexts. The inputs are a CGmap, and reference fasta. For each cytosine position, the coverage is determined, the first to identify all the cytosine sites from reference genome, and second to extract coverage information from CGmap file. The reverse cumulative plot for coverage distributions is reported.
The MET module uses information from methylation calls in the form of a CGmap and gene annotations to identify gene methylation levels and their respective promoter methylation levels. The inputs are a CGmap, gene annotation file, and reference fasta. For each chromosome in the reference genome, two dictionaries are generated, the first to mark the positions of methylation for each methylation contexts, and the second to note the methylation levels at each point of methylation. A list of genes and their bounds are generated from the gene annotation file in General Transfer Format (GTF). For each gene, all the methylation levels of 3 contexts within the gene bounds are averaged to produce its gene body methylation level. Promoters with a size specified by the user are then analyzed in the same manner to produce promoter methylation levels for each gene. Based on methylation contexts, results of gene methylation levels sorted by their gene ID are outputted in a text file.
The TXN module plots the methylation levels adjacent to the transcription factor binding sites (TFBSs) from methylation calls in the form of a CGmap and TFBS annotations. The inputs are a CGmap. For each TFBS, the methylation levels of sites within 1,500 bp are averaged over tiling windows (30 bp). The methylation levels distributions are reported in a scatter plot with smooth curve.
-t, –txnfiles <PATH>¶
Path to the preprocessing TXN files
-l, –txns <LABELS>¶
List of TXN labels
-c, –cgmap <FILE>
Path to CGmap file
The CNV calling module investigates the number of copies of genes in the genotype of an individual to find areas in the genome likely to have large-scale genome rearrangement. Inputs include a sorted BAM file and reference genome index. The PySAM pileups method is used in order to obtain the number of bases for reads at each position in the reference genome. Using the window size given by the user (default is 200,000), all the bases of the reads at the positions within each window are summed up. The standard Z score is calculated and converted to a P-value for each window. If there are 3 windows in a row (user can change this default setting) and all their P-values are smaller than a given threshold (default is 0.05), then this region is considered a CNV.
The PySAM pileups method is used in order to obtain the alleles for reads at each position in the reference genome. Allele counts are then determined for each position, and if the coverage, or number of reads present at that position, exceeds a given amount (default is 5), the alleles at that position are analyzed for the presence of a SNP. Homozygous SNPs are considered to have occurred at positions in which an allele exists with a frequency higher than the given major allele frequency (default is 0.9) in the reads that overlap at that position. Additionally, the major allele needs to differ from the allele in the reference genome at that position. Heterozygous SNPs are considered to have occurred when two alleles occur with frequencies in the reads within a range close to 0.5. A buffer is set (default is 0.1) around 0.5 for the frequencies of the two alleles to be within (so default frequencies are 0.4-0.6) to be considered a heterozygous SNP.
-c, –coverage <INT>¶
Coverage or minimum number of reads desired, default is 5
-m, –majorAlleleFreq <FLOAT>¶
Frequency to be considered homozygous allele, default is 0.9
-b, –buffer <FLOAT>¶
Buffer on either side of 0.5 to be considered heterozygous allele, default is 0.1