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
Links to resources that cover several for the above topics¶
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