About this Research Topic
Genome assembly methods usually overlook the variations among homologous chromosomes, producing the assembly of a pseudo-haploid sequence. As genome research developing, it is found that a single genome sequence for one chromosome is difficult to fully demonstrate the comprehensive information of the species. The variant between homologous chromosomes significantly influences biological phenotypes and play an important role in gene expression and function. Haplotype refers to the combination of correlated variants (Single Nucleotide Polymorphism, INDEL, Structural Variants) located on one chromosome. For one chromosome of diploid individuals, it should have two haplotypes. Over the last few years, short read and long-read sequencing technologies have greatly improved haplotype assembly because of the tremendous advantages in terms of read accuracy or read length. Many methods for haplotype assembly have been developed based on different sequencing technologies. These methods can be commonly divided into reference-based haplotype assembly and de-novo haplotype assembly. Although existing haplotype methods have great improved the development of genome research, researchers need to further explore more efficient and accurate haplotype methods.
The goal of haplotype assembly is to generate high-quality haplotype assembly results. Despite many haplotype assembly methods have obtained good results, new sequencing technologies bring new challenges, e.g. high error rates in third-generation sequencing. In addition, there are other specific technical issues in the process of haplotype assembly. This Special Issue, titled "The Algorithm Developments and Applications of Haplotype assembly" aims to bring together researchers and practitioners from diverse backgrounds to explore the latest advancements and challenges in the fields. We invite submissions that have new breaks in the process of haplotype assembly.
Topics of interest include, but are not limited to:
• Haplotype assembly method based on reference
• De-novo haplotype assembly
• Deep learning methods for haplotype assembly
• Development and application of haplotype assembly based on third-generation sequencing data
• Algorithms and statistical models for SNP and structural variation (SV) detection, alignment and error correction in long reads.
The goal of haplotype assembly is to generate high-quality haplotype assembly results. Despite many haplotype assembly methods have obtained good results, new sequencing technologies bring new challenges, e.g. high error rates in third-generation sequencing. In addition, there are other specific technical issues in the process of haplotype assembly. This Special Issue, titled "The Algorithm Developments and Applications of Haplotype assembly" aims to bring together researchers and practitioners from diverse backgrounds to explore the latest advancements and challenges in the fields. We invite submissions that have new breaks in the process of haplotype assembly.
Topics of interest include, but are not limited to:
• Haplotype assembly method based on reference
• De-novo haplotype assembly
• Deep learning methods for haplotype assembly
• Development and application of haplotype assembly based on third-generation sequencing data
• Algorithms and statistical models for SNP and structural variation (SV) detection, alignment and error correction in long reads.
Keywords: haplotype assembly; third-generation sequencing; de-novo assembly; deep learning
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