Estimating the total genome length of a metagenomic sample using k-mers
Abstract Background Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community. This task is challenging due to the complex...
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| Format: | Article |
| Language: | English |
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BMC
2019-04-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12864-019-5467-x |
| _version_ | 1819063478400319488 |
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| author | Kui Hua Xuegong Zhang |
| author_facet | Kui Hua Xuegong Zhang |
| author_sort | Kui Hua |
| collection | DOAJ |
| description | Abstract Background Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community. This task is challenging due to the complexity of microbiome composition, limited availability of known reference genomes, and usually insufficient sequencing coverage. Results As an initial step toward understanding the complete composition of a metagenomic sample, we studied the problem of estimating the total length of all distinct component genomes in a metagenomic sample. We showed that this problem can be solved by estimating the total number of distinct k-mers in all the metagenomic sequencing data. We proposed a method for this estimation based on the sequencing coverage distribution of observed k-mers, and introduced a k-mer redundancy index (KRI) to fill in the gap between the count of distinct k-mers and the total genome length. We showed the effectiveness of the proposed method on a set of carefully designed simulation data corresponding to multiple situations of true metagenomic data. Results on real data indicate that the uncaptured genomic information can vary dramatically across metagenomic samples, with the potential to mislead downstream analyses. Conclusions We proposed the question of how long the total genome length of all different species in a microbial community is and introduced a method to answer it. |
| first_indexed | 2024-12-21T15:15:19Z |
| format | Article |
| id | doaj.art-3093d06f52d441bb894a300023ae07a1 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-21T15:15:19Z |
| publishDate | 2019-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-3093d06f52d441bb894a300023ae07a12022-12-21T18:59:11ZengBMCBMC Genomics1471-21642019-04-0120S29310110.1186/s12864-019-5467-xEstimating the total genome length of a metagenomic sample using k-mersKui Hua0Xuegong Zhang1MOE Key Laboratory of Bioinformatics Division and Center for Synthetic & System Biology, BNRISTMOE Key Laboratory of Bioinformatics Division and Center for Synthetic & System Biology, BNRISTAbstract Background Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community. This task is challenging due to the complexity of microbiome composition, limited availability of known reference genomes, and usually insufficient sequencing coverage. Results As an initial step toward understanding the complete composition of a metagenomic sample, we studied the problem of estimating the total length of all distinct component genomes in a metagenomic sample. We showed that this problem can be solved by estimating the total number of distinct k-mers in all the metagenomic sequencing data. We proposed a method for this estimation based on the sequencing coverage distribution of observed k-mers, and introduced a k-mer redundancy index (KRI) to fill in the gap between the count of distinct k-mers and the total genome length. We showed the effectiveness of the proposed method on a set of carefully designed simulation data corresponding to multiple situations of true metagenomic data. Results on real data indicate that the uncaptured genomic information can vary dramatically across metagenomic samples, with the potential to mislead downstream analyses. Conclusions We proposed the question of how long the total genome length of all different species in a microbial community is and introduced a method to answer it.http://link.springer.com/article/10.1186/s12864-019-5467-xMetagenomicsSequencing coverageDistinct k-mersGenome length |
| spellingShingle | Kui Hua Xuegong Zhang Estimating the total genome length of a metagenomic sample using k-mers BMC Genomics Metagenomics Sequencing coverage Distinct k-mers Genome length |
| title | Estimating the total genome length of a metagenomic sample using k-mers |
| title_full | Estimating the total genome length of a metagenomic sample using k-mers |
| title_fullStr | Estimating the total genome length of a metagenomic sample using k-mers |
| title_full_unstemmed | Estimating the total genome length of a metagenomic sample using k-mers |
| title_short | Estimating the total genome length of a metagenomic sample using k-mers |
| title_sort | estimating the total genome length of a metagenomic sample using k mers |
| topic | Metagenomics Sequencing coverage Distinct k-mers Genome length |
| url | http://link.springer.com/article/10.1186/s12864-019-5467-x |
| work_keys_str_mv | AT kuihua estimatingthetotalgenomelengthofametagenomicsampleusingkmers AT xuegongzhang estimatingthetotalgenomelengthofametagenomicsampleusingkmers |