Deep Whole-Genome Sequencing to Detect Mixed Infection of Mycobacterium tuberculosis.
Mixed infection by multiple Mycobacterium tuberculosis (MTB) strains is associated with poor treatment outcome of tuberculosis (TB). Traditional genotyping methods have been used to detect mixed infections of MTB, however, their sensitivity and resolution are limited. Deep whole-genome sequencing (W...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4938208?pdf=render |
| _version_ | 1828481075653902336 |
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| author | Mingyu Gan Qingyun Liu Chongguang Yang Qian Gao Tao Luo |
| author_facet | Mingyu Gan Qingyun Liu Chongguang Yang Qian Gao Tao Luo |
| author_sort | Mingyu Gan |
| collection | DOAJ |
| description | Mixed infection by multiple Mycobacterium tuberculosis (MTB) strains is associated with poor treatment outcome of tuberculosis (TB). Traditional genotyping methods have been used to detect mixed infections of MTB, however, their sensitivity and resolution are limited. Deep whole-genome sequencing (WGS) has been proved highly sensitive and discriminative for studying population heterogeneity of MTB. Here, we developed a phylogenetic-based method to detect MTB mixed infections using WGS data. We collected published WGS data of 782 global MTB strains from public database. We called homogeneous and heterogeneous single nucleotide variations (SNVs) of individual strains by mapping short reads to the ancestral MTB reference genome. We constructed a phylogenomic database based on 68,639 homogeneous SNVs of 652 MTB strains. Mixed infections were determined if multiple evolutionary paths were identified by mapping the SNVs of individual samples to the phylogenomic database. By simulation, our method could specifically detect mixed infections when the sequencing depth of minor strains was as low as 1× coverage, and when the genomic distance of two mixed strains was as small as 16 SNVs. By applying our methods to all 782 samples, we detected 47 mixed infections and 45 of them were caused by locally endemic strains. The results indicate that our method is highly sensitive and discriminative for identifying mixed infections from deep WGS data of MTB isolates. |
| first_indexed | 2024-12-11T07:50:23Z |
| format | Article |
| id | doaj.art-445e161a4b85466bb86338760b34b9cb |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-11T07:50:23Z |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-445e161a4b85466bb86338760b34b9cb2022-12-22T01:15:22ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01117e015902910.1371/journal.pone.0159029Deep Whole-Genome Sequencing to Detect Mixed Infection of Mycobacterium tuberculosis.Mingyu GanQingyun LiuChongguang YangQian GaoTao LuoMixed infection by multiple Mycobacterium tuberculosis (MTB) strains is associated with poor treatment outcome of tuberculosis (TB). Traditional genotyping methods have been used to detect mixed infections of MTB, however, their sensitivity and resolution are limited. Deep whole-genome sequencing (WGS) has been proved highly sensitive and discriminative for studying population heterogeneity of MTB. Here, we developed a phylogenetic-based method to detect MTB mixed infections using WGS data. We collected published WGS data of 782 global MTB strains from public database. We called homogeneous and heterogeneous single nucleotide variations (SNVs) of individual strains by mapping short reads to the ancestral MTB reference genome. We constructed a phylogenomic database based on 68,639 homogeneous SNVs of 652 MTB strains. Mixed infections were determined if multiple evolutionary paths were identified by mapping the SNVs of individual samples to the phylogenomic database. By simulation, our method could specifically detect mixed infections when the sequencing depth of minor strains was as low as 1× coverage, and when the genomic distance of two mixed strains was as small as 16 SNVs. By applying our methods to all 782 samples, we detected 47 mixed infections and 45 of them were caused by locally endemic strains. The results indicate that our method is highly sensitive and discriminative for identifying mixed infections from deep WGS data of MTB isolates.http://europepmc.org/articles/PMC4938208?pdf=render |
| spellingShingle | Mingyu Gan Qingyun Liu Chongguang Yang Qian Gao Tao Luo Deep Whole-Genome Sequencing to Detect Mixed Infection of Mycobacterium tuberculosis. PLoS ONE |
| title | Deep Whole-Genome Sequencing to Detect Mixed Infection of Mycobacterium tuberculosis. |
| title_full | Deep Whole-Genome Sequencing to Detect Mixed Infection of Mycobacterium tuberculosis. |
| title_fullStr | Deep Whole-Genome Sequencing to Detect Mixed Infection of Mycobacterium tuberculosis. |
| title_full_unstemmed | Deep Whole-Genome Sequencing to Detect Mixed Infection of Mycobacterium tuberculosis. |
| title_short | Deep Whole-Genome Sequencing to Detect Mixed Infection of Mycobacterium tuberculosis. |
| title_sort | deep whole genome sequencing to detect mixed infection of mycobacterium tuberculosis |
| url | http://europepmc.org/articles/PMC4938208?pdf=render |
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