MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data
While metagenome sequencing may provide insights on the genome sequences and composition of microbial communities, metatranscriptome analysis can be useful for studying the functional activity of a microbiome. RNA-Seq data provides the possibility to determine active genes in the community and how t...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2022.981458/full |
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| author | Daria Shafranskaya Varsha Kale Rob Finn Alla L. Lapidus Anton Korobeynikov Andrey D. Prjibelski Andrey D. Prjibelski |
| author_facet | Daria Shafranskaya Varsha Kale Rob Finn Alla L. Lapidus Anton Korobeynikov Andrey D. Prjibelski Andrey D. Prjibelski |
| author_sort | Daria Shafranskaya |
| collection | DOAJ |
| description | While metagenome sequencing may provide insights on the genome sequences and composition of microbial communities, metatranscriptome analysis can be useful for studying the functional activity of a microbiome. RNA-Seq data provides the possibility to determine active genes in the community and how their expression levels depend on external conditions. Although the field of metatranscriptomics is relatively young, the number of projects related to metatranscriptome analysis increases every year and the scope of its applications expands. However, there are several problems that complicate metatranscriptome analysis: complexity of microbial communities, wide dynamic range of transcriptome expression and importantly, the lack of high-quality computational methods for assembling meta-RNA sequencing data. These factors deteriorate the contiguity and completeness of metatranscriptome assemblies, therefore affecting further downstream analysis.Here we present MetaGT, a pipeline for de novo assembly of metatranscriptomes, which is based on the idea of combining both metatranscriptomic and metagenomic data sequenced from the same sample. MetaGT assembles metatranscriptomic contigs and fills in missing regions based on their alignments to metagenome assembly. This approach allows to overcome described complexities and obtain complete RNA sequences, and additionally estimate their abundances. Using various publicly available real and simulated datasets, we demonstrate that MetaGT yields significant improvement in coverage and completeness of metatranscriptome assemblies compared to existing methods that do not exploit metagenomic data. The pipeline is implemented in NextFlow and is freely available from https://github.com/ablab/metaGT. |
| first_indexed | 2024-04-13T17:32:22Z |
| format | Article |
| id | doaj.art-9ef2074a7aee4e6bb073df2d16b72d7e |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-04-13T17:32:22Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-9ef2074a7aee4e6bb073df2d16b72d7e2022-12-22T02:37:31ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2022-10-011310.3389/fmicb.2022.981458981458MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic dataDaria Shafranskaya0Varsha Kale1Rob Finn2Alla L. Lapidus3Anton Korobeynikov4Andrey D. Prjibelski5Andrey D. Prjibelski6Center for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg, RussiaEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United KingdomEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United KingdomCenter for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg, RussiaCenter for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg, RussiaCenter for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg, RussiaDepartment of Computer Science, University of Helsinki, Helsinki, FinlandWhile metagenome sequencing may provide insights on the genome sequences and composition of microbial communities, metatranscriptome analysis can be useful for studying the functional activity of a microbiome. RNA-Seq data provides the possibility to determine active genes in the community and how their expression levels depend on external conditions. Although the field of metatranscriptomics is relatively young, the number of projects related to metatranscriptome analysis increases every year and the scope of its applications expands. However, there are several problems that complicate metatranscriptome analysis: complexity of microbial communities, wide dynamic range of transcriptome expression and importantly, the lack of high-quality computational methods for assembling meta-RNA sequencing data. These factors deteriorate the contiguity and completeness of metatranscriptome assemblies, therefore affecting further downstream analysis.Here we present MetaGT, a pipeline for de novo assembly of metatranscriptomes, which is based on the idea of combining both metatranscriptomic and metagenomic data sequenced from the same sample. MetaGT assembles metatranscriptomic contigs and fills in missing regions based on their alignments to metagenome assembly. This approach allows to overcome described complexities and obtain complete RNA sequences, and additionally estimate their abundances. Using various publicly available real and simulated datasets, we demonstrate that MetaGT yields significant improvement in coverage and completeness of metatranscriptome assemblies compared to existing methods that do not exploit metagenomic data. The pipeline is implemented in NextFlow and is freely available from https://github.com/ablab/metaGT.https://www.frontiersin.org/articles/10.3389/fmicb.2022.981458/fullmetatranscriptomicsmetagenomicsRNA-Seqde novo assemblycomputational pipeline |
| spellingShingle | Daria Shafranskaya Varsha Kale Rob Finn Alla L. Lapidus Anton Korobeynikov Andrey D. Prjibelski Andrey D. Prjibelski MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data Frontiers in Microbiology metatranscriptomics metagenomics RNA-Seq de novo assembly computational pipeline |
| title | MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data |
| title_full | MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data |
| title_fullStr | MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data |
| title_full_unstemmed | MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data |
| title_short | MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data |
| title_sort | metagt a pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data |
| topic | metatranscriptomics metagenomics RNA-Seq de novo assembly computational pipeline |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2022.981458/full |
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