An improved statistical model for taxonomic assignment of metagenomics
Abstract Background With the advances in the next-generation sequencing technologies, researchers can now rapidly examine the composition of samples from humans and their surroundings. To enhance the accuracy of taxonomy assignments in metagenomic samples, we developed a method that allows multiple...
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| Format: | Article |
| Language: | English |
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BMC
2018-10-01
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| Series: | BMC Genetics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12863-018-0680-1 |
| _version_ | 1818216722181652480 |
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| author | Yujing Yao Zhezhen Jin Joseph H Lee |
| author_facet | Yujing Yao Zhezhen Jin Joseph H Lee |
| author_sort | Yujing Yao |
| collection | DOAJ |
| description | Abstract Background With the advances in the next-generation sequencing technologies, researchers can now rapidly examine the composition of samples from humans and their surroundings. To enhance the accuracy of taxonomy assignments in metagenomic samples, we developed a method that allows multiple mismatch probabilities from different genomes. Results We extended the algorithm of taxonomic assignment of metagenomic sequence reads (TAMER) by developing an improved method that can set a different mismatch probability for each genome rather than imposing a single parameter for all genomes, thereby obtaining a greater degree of accuracy. This method, which we call TADIP (Taxonomic Assignment of metagenomics based on DIfferent Probabilities), was comprehensively tested in simulated and real datasets. The results support that TADIP improved the performance of TAMER especially in large sample size datasets with high complexity. Conclusions TADIP was developed as a statistical model to improve the estimate accuracy of taxonomy assignments. Based on its varying mismatch probability setting and correlated variance matrix setting, its performance was enhanced for high complexity samples when compared with TAMER. |
| first_indexed | 2024-12-12T06:56:29Z |
| format | Article |
| id | doaj.art-f29fdcd7b2ba46999dfa8db3090753df |
| institution | Directory Open Access Journal |
| issn | 1471-2156 |
| language | English |
| last_indexed | 2024-12-12T06:56:29Z |
| publishDate | 2018-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genetics |
| spelling | doaj.art-f29fdcd7b2ba46999dfa8db3090753df2022-12-22T00:33:57ZengBMCBMC Genetics1471-21562018-10-0119111110.1186/s12863-018-0680-1An improved statistical model for taxonomic assignment of metagenomicsYujing Yao0Zhezhen Jin1Joseph H Lee2Department of Biostatistics, Columbia UniversityDepartment of Biostatistics, Columbia UniversitySergievsky Center, Taub Institute, and Departments of Epidemiology and Neurology, Columbia UniversityAbstract Background With the advances in the next-generation sequencing technologies, researchers can now rapidly examine the composition of samples from humans and their surroundings. To enhance the accuracy of taxonomy assignments in metagenomic samples, we developed a method that allows multiple mismatch probabilities from different genomes. Results We extended the algorithm of taxonomic assignment of metagenomic sequence reads (TAMER) by developing an improved method that can set a different mismatch probability for each genome rather than imposing a single parameter for all genomes, thereby obtaining a greater degree of accuracy. This method, which we call TADIP (Taxonomic Assignment of metagenomics based on DIfferent Probabilities), was comprehensively tested in simulated and real datasets. The results support that TADIP improved the performance of TAMER especially in large sample size datasets with high complexity. Conclusions TADIP was developed as a statistical model to improve the estimate accuracy of taxonomy assignments. Based on its varying mismatch probability setting and correlated variance matrix setting, its performance was enhanced for high complexity samples when compared with TAMER.http://link.springer.com/article/10.1186/s12863-018-0680-1EM algorithmMetagenomicsTaxonomic assignment |
| spellingShingle | Yujing Yao Zhezhen Jin Joseph H Lee An improved statistical model for taxonomic assignment of metagenomics BMC Genetics EM algorithm Metagenomics Taxonomic assignment |
| title | An improved statistical model for taxonomic assignment of metagenomics |
| title_full | An improved statistical model for taxonomic assignment of metagenomics |
| title_fullStr | An improved statistical model for taxonomic assignment of metagenomics |
| title_full_unstemmed | An improved statistical model for taxonomic assignment of metagenomics |
| title_short | An improved statistical model for taxonomic assignment of metagenomics |
| title_sort | improved statistical model for taxonomic assignment of metagenomics |
| topic | EM algorithm Metagenomics Taxonomic assignment |
| url | http://link.springer.com/article/10.1186/s12863-018-0680-1 |
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