Correlation Engine 2.0
Clear Search sequence regions


  • across (12)
  • algorithm (1)
  • benchmark (1)
  • biomass (1)
  • c 45 (1)
  • cell numbers (1)
  • cluster (2)
  • Conc (1)
  • conflict interest (1)
  • DADA2 (1)
  • data analysis (1)
  • data file (1)
  • direct (1)
  • elements (1)
  • files (8)
  • forest (7)
  • functions (9)
  • gene (5)
  • help (1)
  • improves (1)
  • NIRD (1)
  • process (7)
  • proteobacteria (1)
  • proxies (1)
  • python (1)
  • RAMBL (1)
  • random (1)
  • research (3)
  • rna (23)
  • rna sequence (2)
  • rna viruses (2)
  • rrna gene (8)
  • seed (1)
  • signal (1)
  • SILVA (1)
  • size decreases (1)
  • t mcs (1)
  • taxa (8)
  • taxonomy (6)
  • tRNA (2)
  • wood (5)
  • Sizes of these terms reflect their relevance to your search.

    Technological advances in meta-transcriptomics have enabled a deeper understanding of the structure and function of microbial communities. ‘Total RNA’ meta-transcriptomics, sequencing of total reverse transcribed RNA, provides a unique opportunity to investigate both the structure and function of active microbial communities from all three domains of life simultaneously. A major step of this approach is the reconstruction of full-length taxonomic marker genes such as the small subunit ribosomal RNA. However, current tools for this purpose are mainly targeted towards analysis of amplicon and metagenomic data and thus lack the ability to handle the massive and complex datasets typically resulting from total RNA experiments.

    Citation

    Yaxin Xue, Anders Lanzén, Inge Jonassen, Jan Gorodkin. Reconstructing ribosomal genes from large scale total RNA meta-transcriptomic data Bioinformatics. 2020 Mar 13;36(11):3365-3371


    PMID: 32167532

    View Full Text