Explain how “read depth profile” is used to generate certain aspects of this fig
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Question
Explain how “read depth profile” is used to generate certain aspects of this figure. How can the read depth inform us as to whether a gene is likely to be an oncogene or tumor suppressor gene?
Mutations in key genes and pathways in pancreatic cancer
The upper panel shows non-silent single nucleotide variants and small insertions or deletions. The central matrix shows: non-silent mutations (blue), copy number changes (amplification (>5 copies) represented in red and loss represented in green) and genes affected by structural variants (SV, yellow). Pathogenic germline variants are highlighted with asterisk (*) symbols. The histogram on the left shows the number of each alteration in each gene.
Germline pathogenic Non-silent SNV or indel Loss (copy number 5) Non-silent SNV/indelamplification SV non-silent SNV/indel + loss SV+other mutation(s) 1,000 500 MAP2K4 ACVR18 RNF43 SF3B1 BRAF PREX2 II ARID1A 3 I II I ARID1B PBRM1 MLL2 BRCA ATM IISTK11 MLH1 MSH2 I IIROB01 9 III IROBO2 SLIT2 MYC 111 1 |GATA6 CDK6 NOV I IIMET ERBB2 PIK3CA PIK3R3 FGFR2 Subtype StableLocally RearrangedScatteredle PatientsExplanation / Answer
Read depth is the number of unique reads that contain a particular nucleotide in the reconstructed sequence. It refers to the number of times a single nucleotide at a particular position is covered during sequencing.
1. Single nucleotide variations - Since sequencing always contains some errors, it is difficult to understand whether the changes in the nucleotide sequence are real or artefacts of sequencing errors. For example a single nucleotide polymorphism (SNP) could be a true SNP or could be a result or error in sequencing.
To solve this problem, we can increase read depth. By reading the same nucleotide multiple times, we decrease the possibility of error. Hence, in deep sequencing or read depth profiling, single nucleotide variations correspond to true SNPs.
In this way single nucleotide variations can be analysed by comparing the test sequence with reference sequence by using read depth profile.
Whether the single nucleotide variations (SNV) are silent or non silent can be inferred by looking at the phenotype.
2. If a gene is present in multiple copies, each nucleotide from the sequence of the gene would show greater depth as conparcoto the sequences of single or double copy genes. For example, for a gene with three copies, the average read depth would be 1.5 times more as compared to a gene having two copies. (Provided, sufficient number of reads are taken).
Hence, from read depth profile, we can also know the amolificaampl status or simply, the number of copies of a particular gene. By this amplification and loss of genes can be analysed from the profile as shown in the figure.
3. Similarly, amplification or loss of non-silent SNV can be eatimated.
4. Mutations other than SNV can also be detected by comparing the test sequence with reference sequence (wt). These could be changes in multiple nucleotides causing structural changes in the gene product.
5. Pathogenic Germline variants are the genes which are selectively amplified in a tumor cells and are necessary for tumor progression. These are generally variants of tumor suppressing genes (eg, variants of BRCA1, BRCA2). These can be detected by comparing sequence of tumor cells with non-tumor cells from the same patient. The variants of wild type genes that are always amplified in tumor tissue can be marked as Germline Pathogenic Variants.
Comparison of read depth profiles of tumor tissue and non- tumor tissue of the same patient can tell us which genes are amplified in cancer. From this we can predict the oncogenes or genes that support metastasis. Similarly, downregulation or loss of certain genes could indicate that those genes are tumor suppressors. However for understanding this better, many sequences from patients need to be analysed and then the amplification or loss of a partcupart gene can be correlated with cancer progression. This would be a better way of identifying oncogenea and tumor suppressor genes.
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