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Novel Sequence Discovery by Subtractive Genomics
Published on: January 25, 2019
9.3K
Methods for the detection and assembly of novel sequence in high-throughput sequencing data
Manuel Holtgrewe1, Leon Kuchenbecker2, Knut Reinert1
1Department of Computer Science, Freie Universität Berlin and Max Planck Institute for Molecular Genetics, Berlin, Germany.
Bioinformatics (Oxford, England)
|February 5, 2015
Summary
We developed ANISE and BASIL, novel methods for assembling large insertions from high-throughput sequencing data. ANISE outperforms existing tools, accurately reconstructing novel sequences and improving structural variant detection.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Large insertions are significant structural variants impacting genomic studies.
- Previous methods for assembling these insertions from high-throughput sequencing (HTS) data were limited.
Purpose of the Study:
- To present novel approaches, BASIL and ANISE, for detecting insertion breakpoints and assembling large insertions from HTS paired data.
- To improve the accuracy and completeness of novel sequence assembly compared to existing methods.
Main Methods:
- Developed ANISE and BASIL for targeted assembly of large insertions using HTS paired data.
- ANISE incorporates a repeat resolution step to handle challenging near-identity repeats.
- Compared ANISE and BASIL against de novo assemblers (ABYSS, SGA) and other tools (MindTheGap).
Main Results:
- ANISE demonstrated superior reconstruction of inserted sequences, especially in repeat regions.
- The insert assembler was competitive with de novo assemblers on simulated data, providing anchored sequences.
- Novel sequences were detected and validated in real-world human data, with ANISE outperforming MindTheGap.
Conclusions:
- ANISE and BASIL offer effective solutions for assembling large insertions from HTS data.
- ANISE provides enhanced accuracy and anchored assembly, surpassing existing tools.
- These methods advance the detection and characterization of structural variants.
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