Shotgun Analyses

Shotgun metagenomics & metatranscriptomics

  • To assemble or not?
  • To gene call or not?
  • Kmers (“reference-free”)
  • Taxonomic profiling
  • Coverage estimation
  • Reference mapping / alignment
  • Gene Catalogues
  • Marker-Based approaches
  • Read-based approaches
  • Functional profiling/annotation
  • Functional hierarchies / ontologies
  • Depth comparisons
  • Binning
  • Whole genome assembly and evaluation
  • Pathway Analysis / Metabolic modeling
  • Cross assembly

Learning objectives

Shotgun metagenomics & metatranscriptomics (Intro):

  • Discern the difference between genetic potential and expressed genes.
  • Evaluate the advantages and disadvantages between the metaT and metaG
  • Aware of the cost and depth of sequencing differences for metaT and metaG

Reference mapping / alignment:

  • Understand approaches to the elimination of host-associated or contaminating material
  • Apply reference mapping to assess community structure
  • Interpret the mapping outcomes as representative abundance of taxa.
  • Compare the mapping approach with the “reference-free”.

Taxonomic profiling:

  • Compare taxonomic profiling using metagenomics and amplicon-based approaches.
  • Utilise both marked based and all-read methods for determining taxonomic profiles from metagenomic data
  • Apply one or more methods to create a taxonomic profile
  • Distinguish reference taxonomies and database limitations.
  • Apply appropriate statistical methods for comparative analysis.
  • Demonstrate knowledge of the limitations of quantifying microbes as relative abundances

Functional profiling:

  • Recognise the role and application of different reference databases in functional assignments
  • Use tools to perform functional annotations of nucleic acid and protein sequences found in metagenomics datasets.
  • Demonstrate an understanding of the relationship between ontologies (e.g. GO) and functional assignments as a means of hierarchically viewing the data.
  • Critically assess the functional assignments in terms of confidence, with clear understanding about the limitations of the algorithms and databases used.
  • Apply pathway gap-filling methods to overcome low coverage sequencing