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Biodiversity and Evolution - Metabarcoding

Biodiversity and Evolution - Metabarcoding

Environmental Metagenomics: Metabarcoding and Microbes at Lake Gollin.

Authors: Fritz Lekschas, Annkatrin Bressin, Melanie Liedtke, Nina Kersten

Source code: https://github.com/flekschas/bio-div-metabarcording

Fritz Lekschas

July 08, 2014
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  1. 08.07.2014 Bressin, Kersten, Lekschas, Liedtke Environmental Metagenomics: Metabarcoding and Microbes

    at Lake Gollin Biodiversity and Evolution M.Sc. Bioinformatics SS14
  2. 08.07.2014 Bressin, Kersten, Lekschas, Liedtke Metagenomics Source: http://www.vaiomer.com/sites/default/files/Fig1%20Metagenomic%20workflow_2.png • study

    of genetic material recovered directly from environmental samples • to reveal the biodiversity of an ecosystem • samples: water, soil
  3. 08.07.2014 Bressin, Kersten, Lekschas, Liedtke Material Samples: Source: http://www.dorfschule-gollin.de/wp-content/uploads/2014/03/Home-21. jpg

    • Lake Gollin (Schorfheide: April, May, June 2010) • Samples from 8 different locations • LSU gene as marker • 4-5-4 sequencing
  4. 08.07.2014 Bressin, Kersten, Lekschas, Liedtke Aim / Goal • Biodiversity:

    ◦ number of fungal taxa • Dominance: ◦ relative proportion • Habitat / Season: ◦ distribution among habitat / season Source:http://www.mycolog.com/chapter11b.htm
  5. 08.07.2014 Bressin, Kersten, Lekschas, Liedtke Expectations • Terrestrial zone: ◦

    passively introduced via wind, rainwater, inflowing streams • Littoral zone: ◦ hotspot for all kinds of fungi • Pelagic zone: ◦ only specialized species Source: (1) Christian M. Wurzbacher et al. (2010) Fungi in lake ecosystems (2) Sébastien Monchy et al. (2011) Exploring and quantifying fungal diversity in freshwater lake ecosystems using rDNA cloning/sequencing and SSU tag pyrosequencing
  6. First Approach: BLAST • Demultiplex Reads • Quality Control •

    Barcode, Primer removal & trimming • BLAST reads against SILVA • Taxonomy analysis BASH ➔ SFF to FASTQ ➔ FASTX Toolkit ➔ Trimmomatic ➔ Create BLAST DB ➔ BLAST (Subsamples) ➔ MEGAN ➔ R
  7. Second Approach: OTU Generation • Same read preparation • OTU

    Clustering • Creating OTU table • Taxonomic classification with SILVA BASH ➔ SFF to FASTQ ➔ FASTX Toolkit ➔ Trimmomatic ➔ UPARSE ➔ SINA ➔ R
  8. 08.07.2014 Bressin, Kersten, Lekschas, Liedtke Important parameters • Trimming: window

    min score of 15 • BLAST: 500 reads per month per habitat • MEGAN with SILVA taxonomy ◦ LCA: min support 5 and min score 50 • UPARSE: 97% sequence similarity • SINA: ◦ 70% sequence similarity and 10 reads support
  9. 08.07.2014 Bressin, Kersten, Lekschas, Liedtke Habitat specifity • similar at

    benthic • but overall wide variations • more specific classification at blast ◦ less reads mapped to fungal taxa ◦ max 40 reads per sample Discussion: Biodiversity
  10. 08.07.2014 Bressin, Kersten, Lekschas, Liedtke Discussion: Dominance • Expect high

    percentage in: ◦ littoral, reed, benthic, biofilm • low percentage: ◦ pelagic, sediment, sed, plankton
  11. 08.07.2014 Bressin, Kersten, Lekschas, Liedtke Discussion • Only few fungi

    detected with Blast (SILVA) ◦ Same with SINA (online) ◦ Sub sampling may be problematic ◦ too specific classification • Bacteria found although eucaryotic primer • Pipeline (without MEGAN) at https://bitbucket.org/flekschas/biodivex2
  12. 08.07.2014 Bressin, Kersten, Lekschas, Liedtke Look Out • OTU -

    Pipeline ◦ Similarity threshold ◦ NCBI database instead of SILVA • Blast - Pipeline ◦ SILVA LSU taxonomy good but could be better ◦ SILVA to NCBI mapping kind of old (2 years)