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ChimeraMiner: An Improved Chimeric Read Detection Pipeline and Its Application in Single Cell Sequencing.

Na Lu1, Junji Li2, Changwei Bi3

  • 1State Key Lab of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China. nlu@seu.edu.cn.

International Journal of Molecular Sciences
|April 24, 2019
PubMed
Summary

ChimeraMiner is a new pipeline that efficiently detects chimeric reads from multiple displacement amplification (MDA) single-cell sequencing data. It significantly improves structural variation detection by removing false positives caused by these artifacts.

Keywords:
MDAchimeric sequence readsingle cell sequencingstructural variation detection

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Multiple displacement amplification (MDA) is a widely used whole genome amplification technique for single-cell studies.
  • MDA, while effective, generates chimeric reads that disrupt downstream analyses.
  • Accurate detection of these chimeric sequences is crucial for reliable single-cell genomics.

Purpose of the Study:

  • To develop and evaluate ChimeraMiner, an improved pipeline for detecting and classifying chimeric reads from MDA sequencing data.
  • To compare the efficiency and performance of ChimeraMiner against existing methods.
  • To assess the impact of chimera removal on structural variation detection in single-cell datasets.

Main Methods:

  • Construction of the ChimeraMiner pipeline for analyzing MDA sequencing data.
  • Classification of chimeric sequences using ChimeraMiner.
  • Evaluation using two MDA datasets (MDA1 and MDA2) and comparison with a previous pipeline.
  • Application to single-cell datasets to assess structural variation detection.

Main Results:

  • ChimeraMiner demonstrated significantly improved processing efficiency, using only 43.4% of the time compared to the previous pipeline.
  • The pipeline accurately classified millions of chimeric read pairs, identifying a substantial number of novel chimeras missed by prior methods.
  • Application of ChimeraMiner effectively removed 83.8% of false positive structural variations in single-cell datasets.

Conclusions:

  • ChimeraMiner offers enhanced efficiency and accuracy in detecting and classifying chimeric reads from MDA-based single-cell sequencing.
  • The pipeline effectively mitigates false positives in structural variation detection, improving data reliability.
  • ChimeraMiner is a promising tool for widespread adoption in single-cell sequencing research.