Metagenomics an introduction Katie Lennard Metagenomics vs. - - PowerPoint PPT Presentation

Metagenomics – an introduction

Katie Lennard

Metagenomics vs. amplicon sequencing (16S)

• Metagenomics = Shotgun sequencing of DNA from an environment
• Improved taxonomic resolution compared to 16S
• Depending on the 16S region, some taxa are impossible to resolve
• Limited to analysis of previously identified taxa
• Offers compositional (who is there) AND functional (what are they

doing) information

• In the past few years metagenomic sequencing has been used to
• Identify novel viruses
• Characterize genomic diversity and function of uncultured bacteria
• Reveal novel proteins
• Identify taxa and metabolic pathways that differentiate disease states

Metagenomics analyses challenges

• More expensive and computationally demanding
• Require high performance compute clusters and parallelization
• More complex pipeline that needs to be tailored to study
• The challenge of correct binning – which read belongs to which
• rganism?
• For highly diverse communities (soil, gut) – coverage may be

insufficient to characterize low abundance organisms

• Host DNA (human, plant etc.) needs to be removed – mostly simple

but may require PCR-based enrichment of microbial DNA if majority

• f DNA is host-derived (e.g. certain human samples; plants)
• Contaminant removal tricky – which genes were generated by the

contaminant?

Sequencing technologies overview

Common metagenomic techniques: marker gene analysis

• Marker gene analysis (marker gene database)
• Straightforward + computationally efficient
• Apply to assembled OR unassembled reads
• Two strategies
• sequence similarity to marker gene database + custom classifiers that consider rate of

evolution and read properties (MetaPhyler; MetaPhlAn)

• based on phylogenetic information: identify metagenomic homologs of phylogenetically

informative, single copy protein-coding genes (AMPHORA – Hidden Markov Models) → assemble a marker gene phylogeny (phylotyping)

Common metagenomic techniques: assembly

• Crucial step for:
• Genome reconstruction of

individual organisms AND

• To elucidate taxonomic

and functional diversity of the community

• Challenges:
• Repeat regions
• Co-assembly of reads from

different (related) taxa → chimeric contigs

• All assemblers will make

numerous errors!

• Manual inspection (time)

Common metagenomic techniques: binning

• Reference-based (taxonomy dependent)
• caveat: 3 million of 16S rRNA genes already

sequenced, BUT only around 6000 complete genomes available

• De novo (taxonomy independent)
• Sequence composition based (e.g. %GC

content) – need longer reads for accurate results (contigs); not reliable for complex microbial populations with low abundant

• communities. T
• Abundance based – uses short (raw) reads or

assembled contigs (method dependent)

• Hybrid (sequence+abundance based)
• Result used not only for taxonomic assignment but

also downstream (genome assembly, evaluate functional profiles for each bin)

Common metagenomic techniques: gene prediction

• Functional (what are they doing?)
• Gene prediction (gene calling) – ID coding regions
• Evidence-based (sequence similarity to database gene sequences)
• ab initio (relies on intrinsic factors in DNA sequence to discriminate coding/non-coding)
• Functional annotation

http://envgen.github.io/metagenomics.html

Self study resources

• Sedlar et al. Bioinformatics strategies for taxonomy independent

binning and visualization of sequences in shotgun metagenomics Computational and Structural Biotechnology Journal 15 (2017) 48–55

• Ghurye et al. Metagenomic Assembly: Overview, Challenges and

Applications https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5045144/

• Mande et al. Classification of metagenomic sequences: methods and

challenges https://academic.oup.com/bib/article/13/6/669/193900/Classificatio n-of-metagenomic-sequences-methods