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Survey of gene splicing algorithms based on reads
Xiuhua Si1, Qian Wang2, Lei Zhang1
1a Department of Computer Science & Technology , Heilongjiang University , Harbin , China.
Bioengineered
|September 6, 2017
Summary
Gene splicing assembles short DNA fragments into a complete genome sequence. This review compares popular reference genome and de novo splicing algorithms, including graph-based and MapReduce methods, offering insights for future research.
Area of Science:
- Genomics
- Bioinformatics
Background:
- Gene splicing is crucial for reconstructing complete genome sequences from short DNA fragments.
- Accurate assembly is essential for genomic research and applications.
Purpose of the Study:
- To review and compare popular gene splicing algorithms.
- To introduce a novel MapReduce-based splicing strategy.
- To provide recommendations for future gene splicing research.
Main Methods:
- Review of reference genome-based splicing algorithms.
- Analysis of de novo splicing algorithms: Greedy-extension, Overlap-Layout-Consensus (OLC) graph, and De Bruijn graph.
- Discussion of a new MapReduce strategy and Hadoop for gene splicing.
Main Results:
- Comparison of the strengths and weaknesses of different splicing algorithms.
- Evaluation of the efficiency and scalability of graph-based and MapReduce approaches.
- Identification of key factors influencing assembly accuracy and performance.
Conclusions:
- Different splicing algorithms are suited for various genomic contexts and data types.
- MapReduce offers a scalable solution for large-scale gene splicing.
- Further research should focus on hybrid approaches and improving algorithm accuracy.
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