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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
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The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome

Xiaojia Tang1, Saurabh Baheti1, Khader Shameer1

  • 1Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA.

Nucleic Acids Research
|October 30, 2014
PubMed
Summary

A new computational system, eSNV-Detect, identifies, annotates, and prioritizes expressed single nucleotide variants (eSNVs) from RNA sequencing data. This tool achieves high precision and sensitivity, enabling robust variant detection in various cancer types.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) technologies facilitate genomic alteration identification from short reads.
  • Existing pipelines primarily focus on genomic DNA variants, lacking comprehensive solutions for expressed single nucleotide variants (eSNVs) from RNA sequencing (RNA-Seq) data.

Purpose of the Study:

  • To develop a novel computational system, eSNV-Detect, for identifying, annotating, and prioritizing eSNVs from non-directional paired-end RNA-Seq data.
  • To evaluate the performance of eSNV-Detect across multiple platforms and datasets.

Main Methods:

  • eSNV-Detect utilizes data from multiple aligners to call and rank variants from RNA-Seq, even at low read depths.
  • The system was validated using RNA-Seq data from a lymphoblastoid cell line, breast tumors (TCGA), and single-cell mRNA-Seq.
  • Sanger sequencing was employed for further validation of candidate eSNVs.

Main Results:

  • eSNV-Detect achieved 99.7% precision and 91.0% sensitivity for expressed SNPs in lymphoblastoid cell line data.
  • Comparisons with whole exome coding data from breast tumors showed 90.6-96.8% precision and 91.6-95.7% sensitivity.
  • Analysis of single-cell data revealed variant heterogeneity, and Sanger sequencing validated 29 out of 31 candidate eSNVs.

Conclusions:

  • eSNV-Detect is a comprehensive and accurate pipeline for identifying and prioritizing expressed single nucleotide variants from RNA-Seq data.
  • The system demonstrates high performance across diverse biological samples, including cancer.
  • eSNV-Detect provides valuable insights into variant heterogeneity at the single-cell level.