[A bioinformatic pipeline for NGS data analysis and mutation calling in human solid tumors].

Area of Science:

• Bioinformatics
• Genomics
• Cancer Research

Background:

• Next-generation sequencing (NGS) generates vast amounts of data requiring robust analysis pipelines.
• Accurate detection and interpretation of somatic mutations are crucial for understanding cancer development and for targeted therapies.

Purpose of the Study:

• To develop and validate a comprehensive bioinformatics pipeline for analyzing NGS data.
• To detect and functionally annotate single-nucleotide somatic mutations in tumor DNA.
• To assess the pipeline's performance on breast cancer samples from the Russian population.

Main Methods:

• NGS data processing using Cutadapt, BWA, and SAMtools for quality control and alignment.
• Somatic single-nucleotide variant (SNV) calling with MuTect.
• Functional annotation and pathogenicity prediction using SnpEff, COSMIC, dbNSFP, Clinvar, OMIM, SIFT, and PolyPhen2.
• Targeted enrichment using the MYbaits Onconome Panel and sequencing on Illumina HiSeq 2500.

Main Results:

• The pipeline successfully identified SNVs in key cancer-associated genes (BRCA1, BRCA2, ATM, CDH1, CHEK2, TP53).
• Identified mutations affected protein sequences, indicating potential functional impact.
• Demonstrated the pipeline's applicability to NGS data from breast cancer patients, including a Russian cohort.

Conclusions:

• The developed pipeline provides an effective method for the detection and annotation of tumor SNVs.
• This study represents the first application of this pipeline to NGS data from the Russian population.
• Further validation on larger datasets and diverse tumor types is recommended to confirm pipeline accuracy and efficiency.