Bioinformatics Pipeline for Somatic Variant and Mutation Signature Analysis from Tissue Biopsies Using RNA-Seq
Cancer Genetics and Therapeutics
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Primary Categories:
- Genomic Medicine
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Secondary Categories:
- Genomic Medicine
Introduction:
Introduction: Persistent cutaneous infections (PCI) pose significant challenges, particularly in patients with inborn errors of immunity (IEI) or underlying genetic susceptibilities. Advances in bioinformatics now enable deeper insights into the molecular underpinnings of PCI through integrated genomic and transcriptomic analyses.
Methods:
Methods: Here, we introduce HumMicroScan, a bioinformatics pipeline that exploits whole-transcriptome RNA sequencing (RNA-Seq) for somatic variant detection, mutation signature analysis, and microbial profiling directly from tissue biopsies. HumMicroScan is designed to analyze RNA-Seq data from lesional and non-lesional skin biopsies, facilitating the detection of somatic mutations and mutational signatures associated with cancer and other diseases. Reads aligned to the human genome enable precise identification of somatic variants, while unaligned reads are used to characterize microbial content. Our pipeline also incorporates gene expression profiling and splicing analysis to elucidate the pathogenic impact of detected mutations.
Results:
Results: In a case study of PCI patients, HumMicroScan identified critical somatic mutations, including those in cancer-associated genes such as RTEL1, and XPC along with their mutational signatures, highlighting potential links to tumorigenesis and DNA damage repair processes, respectively. The identified germline variant for a case with RTEL1 mutation was RTEL1: NM_001283010: exon17:c.C925T: p.R309W with downregulation of RTEL1 in three skin biopsies from the patient along with the enrichment of mutation signatures 5 and 12. In the other case with XPC gene mutation, signatures 5, 12, and 20 were observed. All the mentioned signatures are reported to be observed in cancers. This integrated analysis uncovered pathogenic mechanisms driving chronic infections and provided a comprehensive view of the tissue microenvironment, combining human genomic variants with microbial factors.
Conclusion:
Conclusions: HumMicroScan represents a significant advancement in bioinformatics, providing clinicians and researchers with a robust tool for somatic mutation detection and signature analysis from tissue biopsies. Its application enhances the clinical utility of RNA-Seq, supporting precision medicine approaches for infection management and cancer risk assessment. Availability: https://github.com/FPalizban/HumMicroScan
Introduction: Persistent cutaneous infections (PCI) pose significant challenges, particularly in patients with inborn errors of immunity (IEI) or underlying genetic susceptibilities. Advances in bioinformatics now enable deeper insights into the molecular underpinnings of PCI through integrated genomic and transcriptomic analyses.
Methods:
Methods: Here, we introduce HumMicroScan, a bioinformatics pipeline that exploits whole-transcriptome RNA sequencing (RNA-Seq) for somatic variant detection, mutation signature analysis, and microbial profiling directly from tissue biopsies. HumMicroScan is designed to analyze RNA-Seq data from lesional and non-lesional skin biopsies, facilitating the detection of somatic mutations and mutational signatures associated with cancer and other diseases. Reads aligned to the human genome enable precise identification of somatic variants, while unaligned reads are used to characterize microbial content. Our pipeline also incorporates gene expression profiling and splicing analysis to elucidate the pathogenic impact of detected mutations.
Results:
Results: In a case study of PCI patients, HumMicroScan identified critical somatic mutations, including those in cancer-associated genes such as RTEL1, and XPC along with their mutational signatures, highlighting potential links to tumorigenesis and DNA damage repair processes, respectively. The identified germline variant for a case with RTEL1 mutation was RTEL1: NM_001283010: exon17:c.C925T: p.R309W with downregulation of RTEL1 in three skin biopsies from the patient along with the enrichment of mutation signatures 5 and 12. In the other case with XPC gene mutation, signatures 5, 12, and 20 were observed. All the mentioned signatures are reported to be observed in cancers. This integrated analysis uncovered pathogenic mechanisms driving chronic infections and provided a comprehensive view of the tissue microenvironment, combining human genomic variants with microbial factors.
Conclusion:
Conclusions: HumMicroScan represents a significant advancement in bioinformatics, providing clinicians and researchers with a robust tool for somatic mutation detection and signature analysis from tissue biopsies. Its application enhances the clinical utility of RNA-Seq, supporting precision medicine approaches for infection management and cancer risk assessment. Availability: https://github.com/FPalizban/HumMicroScan
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