Article ; Online: ContigExtender: a new approach to improving de novo sequence assembly for viral metagenomics data.
2021 Volume 22, Issue 1, Page(s) 119
Abstract: Background: Metagenomics is the study of microbial genomes for pathogen detection and discovery in human clinical, animal, and environmental samples via Next-Generation Sequencing (NGS). Metagenome de novo sequence assembly is a crucial analytical step ... ...
| Abstract | Background: Metagenomics is the study of microbial genomes for pathogen detection and discovery in human clinical, animal, and environmental samples via Next-Generation Sequencing (NGS). Metagenome de novo sequence assembly is a crucial analytical step in which longer contigs, ideally whole chromosomes/genomes, are formed from shorter NGS reads. However, the contigs generated from the de novo assembly are often very fragmented and rarely longer than a few kilo base pairs (kb). Therefore, a time-consuming extension process is routinely performed on the de novo assembled contigs. Results: To facilitate this process, we propose a new tool for metagenome contig extension after de novo assembly. ContigExtender employs a novel recursive extending strategy that explores multiple extending paths to achieve highly accurate longer contigs. We demonstrate that ContigExtender outperforms existing tools in synthetic, animal, and human metagenomics datasets. Conclusions: A novel software tool ContigExtender has been developed to assist and enhance the performance of metagenome de novo assembly. ContigExtender effectively extends contigs from a variety of sources and can be incorporated in most viral metagenomics analysis pipelines for a wide variety of applications, including pathogen detection and viral discovery. |
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| MeSH term(s) | Algorithms ; Animals ; Genome, Viral ; High-Throughput Nucleotide Sequencing ; Humans ; Metagenome ; Metagenomics ; Sequence Analysis, DNA ; Software |
| Language | English |
| Publishing date | 2021-03-12 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 2041484-5 |
| ISSN | 1471-2105 ; 1471-2105 |
| ISSN (online) | 1471-2105 |
| ISSN | 1471-2105 |
| DOI | 10.1186/s12859-021-04038-2 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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