![]() Of note is that our method can be easily extended to incorporate other sources of information such as genetic maps or even a reference genome. Moreover, our method can also exploit the information derived from Hi-C data to obtain chromosome-scale scaffolding groups in studies even with a complicated genome structure or those with low sequencing quality. 1) and separates two steps: firstly, it clusters raw reads and contigs from preliminary assemblies into multiple scaffolding groups, each representing a single chromosome (sometimes a chromosome arm) secondly, it assembles each scaffolding group from raw reads. GALA is implemented through a multi-layer computer graph (Fig. In this study, we report on GALA ( Gap-free long-read Assembly tool), a computational framework for chromosome-based sequencing data separation and assembly. However, chromosome sorting techniques required highly complicated protocols to resolve the optical properties, e.g., light scatters and fluorescence of the target chromosomes 18, making it highly expensive, time-consuming and labour-intensive, thus limiting its applications. It has been successfully applied to human, wheat 15 and some other genome studies 16, 17. To allow single-chromosome sequencing, the chromosome flow-sorting technique has been proposed. The second drawback is that the sequencing data, especially those from repetitive regions or mobile elements, may interfere with each other. For example, computational loads and storage may increase exponentially with data size for some de novo assembly algorithms, and alignment algorithms can be ten times faster if reads are only aligned to a specific chromosome rather than to the whole genome. The first is that extra computational resources are required for data analyses. There are two main drawbacks for this pooled sequencing. Most past and current genome studies sequence all chromosomes together. Plant and animal genomes usually contain multiple chromosomes. Moreover, gaps and mis-assemblies have been reported to account for a large number of gene model errors in existing genome assembly studies 13, 14. In intraspecific genome comparisons, large gaps not only significantly increase the possibility of failure to detect long structure variants, but also produce inaccurate results of gene annotation 11, 12. For example, a lot of sequence alignment tools have much lower performances when query sequences contain gaps 9, 10. Gaps and mis-assemblies in a genome assembly can seriously undermine genomic studies. As a consequence, the final genome assembly usually contains numerous gaps, and sometimes, is also plagued with mis-assemblies, as reported in ref. To produce chromosome-scale assembly, various information sources, such as Hi-C, genetic maps, or a reference genome, have been increasingly used to anchor contigs into big scaffolds 6, 7. ![]() In most studies, however, assemblies by these tools comprise hundreds or even thousands of contigs. To date, numerous de novo assembly tools have been developed to obtain longer and more accurate representative sequences from raw sequencing data 3, 4, 5. ![]() However, it is still very challenging for long-read platforms, such as Nanopore and PacBio (Pacific Bioscience), to provide chromosome-scale assemblies 1, 2. Thus, our method enables straightforward assembly of genomes with multiple data sources and overcomes barriers that at present restrict the application of de novo genome assembly technology.ĭe novo genome assembly has wide applications in plant, animal, and human genetics. We also demonstrate the proposed method’s applicability with a gap-free assembly of the human genome using PacBio high-fidelity (HiFi) long reads. elegans genome using combined PacBio and Nanopore sequencing data and a rice cultivar genome using Nanopore sequencing data from publicly available datasets. As a proof of principle we de novo assemble the C. This flexible framework also allows us to integrate data from various technologies, such as Hi-C, genetic maps, and even motif analyses to generate gap-free chromosome-scale assemblies. The subsequent independent assembly of each scaffolding group generates a gap-free assembly likely free from the mis-assembly errors which usually hamper existing workflows. Here we report on GALA ( Gap-free long-read Assembly tool), a computational framework for chromosome-based sequencing data separation and de novo assembly implemented through a multi-layer graph that identifies discordances within preliminary assemblies and partitions the data into chromosome-scale scaffolding groups. However, it is still very challenging to achieve gap-free chromosome-scale assemblies using current workflows for long-read platforms. High-quality genome assembly has wide applications in genetics and medical studies.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |