1. Toward Next Generation Biodiversity Research
Morne Du Plessis1,2, Monica Mwale1, Essa Suleman1, Emily Mitchell1, Kim Labuschagne1,
Desire Dalton1,3 and Antoinette Kotze1,4
1 National Zoological Gardens of South Africa, Research and Scientific Services Department, Pretoria, 0001
2 Department of Biotechnology, University of the Western Cape, Bellville, 7530
3 Department of Zoology, University of Venda, Thohoyandou, South Africa
4 Genetics Department, University of the Free State, Bloemfontein, South Africa

2. Introduction
Merging NGS + eDNA + Bioinformatics = Next Generation Biodiversity assessment
Techniques – Variations of metagenomics approaches
- Microbiome analysis – 16S
- Barcoding - Animals – COI Plants – rbcL, matk trnH-psbA, ITS
- Shotgun sequencing – direct environmental sequencing
- Transcriptome analysis –
eDNA – Direct from environment (soil, plant matter, animal matter, water)
Indirect from environ – Fecal matter – host and what they consume
Indirect parasites or insects that feed on other animals (eg bloodfeed analysis)

3. The Technology – Why the hype
Capillary electrophoresis – gold standard for seq
avg 800bp generated per sequence
NGS on ION S massive parallel sequencing
can generate 200bp / 400 bp / 600bp reads
Can therefore generate up to 15 Gb of sequence data = bp
Additionally has a significant capacity for multiplexing

4. The Technology – How is this ridiculously large volume of data possible
Chip contains millions of wells
How it actually works

5. Data Analysis – What sort of data is generated and how much

6. The Technology – Getting maximum value
DNA – Site 1
DNA – Site 2
DNA – Site 3
Shear DNA across all samples seperately
Size select across all samples seperately
Library prep across all samples seperately
Add unique (barcode A) to Site 1 sample
Add unique (barcode B) to Site 2 sample
Add unique (barcode C) to Site 3 sample
Barcodes = short seqs to uniquely tag your respective experiments
Merge and NGS together
Separate according to barcode
Seqs from site 1
Seqs from site 2
Seqs from site 3

7. The Technology – What does the data look like

8. Data Analysis – What happens downstream
Eg. Shotgun sequencing of environments (getting an idea of the diversity we might encounter)
Perform QC of sequences
Trim sequences
Redo QC
Optimize assembly of sequences
Generate assemblies
Merge into larger scaffolds
Group by similarity
Generate a reference database for comparison
Align scaffolds to references
Annotate the aligned hits
Categorize
Evaluate abundance and diversity

9. Data Analysis – Checking quality and cleaning
Before Trim Sequences After

10. The Analysis - Assembly of sequence reads

11. The Analysis – Making biological sense
Assembled Sequences
Reference database
Contig A
Gene K from Org A
Contig B
Gene L from Org B
Contig C
Whole genome from Org C
Mitochondrial genome from Org D
Raw sequence data from Org E
Annotated sequences
Organism A
Contig A =
Organism C
Contig B =
Organism E
Contig C =

12. The Analysis – Bioinformatics resources
Parallel Processing
NZG Bioinformatics server and storage server
Also use the Centre for High Performance Computing (CHPC) - DST

13. What happens to all of the data
The raw sequence data is typically stored on the NCBI SRA (sequence retrieval archive) system
All assembled genes / molecules (eg. mitochondria) / genomes on NCBI nucleotide / genome database
The incidental assembled barcode data will feed back into the relevant barcode projects
There is an evolution of specialised databases
eg. Qiita – managing microbial studies (Microbiomes)
Also keep back-ups of all datasets on our server at NZG

14. Summary What do we have / what can we supply: Access to: NGS resources
Environmental samples / sampling
Bioinformatics resources
Bioinformatics training for students
Expertise in related studies and techniques
What we need next:
Understand requirements of SANBI in terms of the diversity assessment
Evaluate which Next Generation strategies are possible and feasible
Strengthening partnerships - shared environments and shared spp.
Build bioinformatics capacity in terms of students
Benchmarking the next generation strategies vs traditional
Adequate mathematical and statistical models to accurately reflect biodiversity

15. Acknowledgements
NRF
SANBI
DST
NZG