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Using Next Generation Sequencing to Identify Mutations Associated with Repair of a CAS9-induced Double Strand Break Near the CD4 Promoter
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Identifying micro-inversions using high-throughput sequencing reads.

Feifei He1, Yang Li2, Yu-Hang Tang3

  • 1State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, and Center for Quantitative Biology, Peking University, Beijing, 100871, China. ffhe@pku.edu.cn.

BMC Genomics
|January 29, 2016
PubMed
Summary
This summary is machine-generated.

Micro-inversions (MIs) are challenging to detect, but the new Micro-Inversion Detector (MID) tool reliably identifies these small DNA variations from sequencing data. MID advances the study of genetic disease and human genome variation.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Micro-inversions (MIs), DNA segments shorter than 100 bp, are difficult to detect with current next-generation sequencing methods.
  • MIs represent important genomic variation and are implicated in genetic diseases.
  • Existing alignment techniques lack sensitivity for identifying MIs.

Purpose of the Study:

  • To develop a novel computational tool, Micro-Inversion Detector (MID), for identifying MIs in human genomes.
  • To improve the detection of small genomic inversions using next-generation sequencing reads.
  • To enhance the study of MIs as a significant type of genetic variant.

Main Methods:

  • Developed MID, a tool utilizing a dynamic programming path-finding algorithm.
  • MID is designed to handle small MIs and multiple breakpoints within unmapped reads.
  • Integrated multiple samples to improve reliability in low-coverage sequencing data.

Main Results:

  • MID demonstrated superior performance compared to existing tools like Gustaf for inversion detection.
  • MID successfully identified previously unknown MIs in the 1000 Genomes Project (1KGP) data.
  • Identified MIs overlapping with genes and regulatory elements, as well as in cancer cell lines (CCLE).

Conclusions:

  • MID is the first method capable of efficiently and reliably identifying MIs from unmapped short sequencing reads.
  • MID's reliability with low-coverage data makes it suitable for large-scale projects like 1KGP.
  • MID is expected to advance research into micro-inversions and their role in human genetics and disease.