Human Whole Genome Sequencing

The sequencing error rate is the major confounding factor of precise detection of low-frequency variations by deep sequencing. It determines the quality of the sequencing data. The sequencing error rate is highly associated with the sequencing cycle, escalating towards the end of each read because of the consumption of chemical reagents, which is a common feature of the Illumina high throughput sequencing platform.

Note: The x-axis represents position in reads, and the y-axis represents the average error rate of bases of all reads at a position.

GC content distribution aims to check the potential of AT/GC separation. Sample contamination, sequencing bias, and errors during library preparation can impact on the sequencing results.

Note: The x-axis is position in reads, and the y-axis is percentage of each type of bases (A, T, G, C); different bases are distinguishable by different colors.

Sequencing Depth & Coverage Distribution

Sequencing depth and coverage illustrate the average number of pair-end clean reads which are aligned to the known reference nucleotides. The sequencing coverage distribution determines whether the identification of variations can be done with a certain degree of confidence at specific base

Average sequencing depth (bar plot) and coverage (dot-line plot) in each chromosome.

Note: The x-axis represents chromosome;the left y-axis is the average depth; the right y-axis is the coverage (proportion of covered bases).

Single nucleotide polymorphisms (SNPs), also known as single nucleotide variants (SNVs), constitute the largest class of genetic variants in the genome. Another class of genetic variations includes small insertions and deletions (InDels) which are <50 bp in length. The InDels present in the coding region or splicing sites may cause changes in mRNA transcripts and proteins.

The number of SNPs/InDels in various genomic regions

The number of different types of SNPs/InDels in the coding regionStructural variants (SVs) are genetic variants with relatively larger sizes (>50 bp), and they include deletions, duplications, insertions, inversions, and translocations. SVs form the underlying genetic basis of individual differences and have potential effects on the disease and cancer susceptibility.

number of relatively larger fragments (longer than 50 bp) among individuals. There are two types of CNVs, i.e. gain and loss of copies. CNVs may form the underlying genetic basis of individual differences and cancers.

The size of genomic regions affected by CNVs in each sample

Note: The x-axis represents samples, and the y-axis indicates the number of each type of SV.

Only a few mutations related to cancer can drive tumorigenesis by affecting genes. Significantly mutated genes (SMGs) refer to those mutations that show a significantly higher mutation rate than the background mutation rate (BMR), thus indicating a positive selection during tumorigenesis. Analysis of SMGs helps us pinpoint the key genes that are critical for cancer initiation and progression.

Heatmap of significantly mutated genes (SMGs) across samples

 Note:
The bar plot at the top shows the mutation rate of each sample (Mutations/Mb). The heatmap in the center shows the types of mutations of each SMG across samples. The horizontal axis represents samples, and the vertical axis represents SMGs. Different types of mutations are distinguished by different colors. The bar plot on the left side of the heatmap shows the percentage of samples affected by mutations in each SMG, and the plot on the right side shows p values of SMGs.

Intra-tumor heterogeneity refers to the heterogeneous composition of tumor cells. Deciphering the intra-tumor heterogeneity and clonal architecture may contribute towards the understanding of therapeutic resistance.
The number and content (subclonal somatic mutations) of tumor subclones were identified by analyzing the variant allele frequencies of somatic mutations.

Inferred clonal architecture
Inferred clonal architecture

Note:
The horizontal axis of each panel represents variant allele frequency (VAF). A cluster of mutations with relatively low VAF represents a subclonal population. The top panel shows kernel density of VAFs across regions with copy number one, two, or three, posterior predictive densities summed over all clusters for copy number neutral variants, and posterior densities for each cluster/component. The panels below the top panel show read depth versus VAFs for each class of copy number regions.

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