1. Using NGS to answer biological questions Usadellab.org @ RWTH Aachen, Forschungszentrum Jülich

2. You have heard it all before Open platform Good for SNP calling Higher dynmic range Better reflects RT-PCR data Was considered big data once Microarrays and RNA Seq the old and the new

3. Next generation sequencing publications in pubmed The goldrush is still in its high steam But all that glitters is not gold Sometimes a closed platform is not too bad, this also means standardization and of course microarrays take much less time to download Did you ever ask yourself: Oh let’s have a brief look a this dataset… All that glitters is not gold

4. Next generation sequencing publications in pubmed The goldrush is still in its high steam But all that glitters is not gold Sometimes a closed platform is not too bad, this also means standardization and of course microarrays take much less time to download And then there is still mapping and stats All that glitters is not gold

5. Next generation sequencing publications in pubmed The goldrush is still in its high steam But all that glitters is not gold Sometimes a closed platform is not too bad, this also means standardization and of course microarrays take much less time to download And storage Btw did you buy that new storage pod? Another 180TB Another 20k€ Or worse you can‘t build yourself All that glitters is not gold

6. So does it all come to naught? The goldrush is still in its high steam, so there is of course something Did you ever think it were possible that you yourself can sequence a full genome, de novo of course? Well that‘s a PhD topic now. (Within reason, up to medium sized plants, bacteria can be dealt with in a Bsc if you gt lucky) So where are good claims to be had and what can one do about it? Can Bioinformatics help biologists? But the goldrush is still on

7. Wild relative of S. lycopersicum Moyle 2008Source: Tomato Genome Resource Centre (TGRC) Grows in Peru and Northern Chile (TGRC Accessions shown) Solanum pennellii - a wild tomato relative

8. Schauer et al., 2006 Metabolites Introgression Lines Solanum pennellii - a great source of gentic variation x x S.lyc M82S. pennellii S.lyc M82 F1 Introgression Line Population

9. Trimmomatic fast & precise

10. Filtering Effects

11. ScaffoldsTotalN50 (>2000) S. pennellii (V2.00) 943M1,741,129 S. lycopersicum (V2.4) 781M16,467,796 S. pimpinellifolium (A-1.0) -- S. tuberosum (V3) 715M1,354,002 Final ContigsTotal SizeN50 S. pennellii (V2.00) ~870M45,7k S. lycopersicum (V2.4) 738M86,9k S. pimpinellifolium (A- 1.0) 689M6k S. tuberosum (V3) 683M31,4k Split on ‘N’s SNP small indel <0.03% Solanum pennellii Assembly

12. Schauer et al., 2006 Metabolites Introgression Lines Solanum pennellii - a great source of gentic variation x x S.lyc M82S. pennellii S.lyc M82 F1 Introgression Line Population Unfortunately it was the cultivar Heinz and not M82 that was sequenced Luckily re-sequencing is relatively straight forward (sometimes)

13. Solanacae..... What makes them what they are Physalis alkengi (Chinese lantern) Physalis peruviana (Cape gooseberry) Physalis ixocarpia (tomatillo)

14. More than 2000 terms Redundancy reduced terms for better visualization and statistical analysis ~ 20 plant species Automatic tool for whole transcriptome annotation The MapMan Plant Ontology

15. Mercator Data Submission Mercator is an online resource allowing to submit large FASTA files containing plant sequences Mercator compares the sequences to in-house annotated and classified plant sequences and searches for domains Mercator then classifies all genes/proteins Mercator typically processes one genome equivalent in 2-3 days in acurate mode (and faster in draft mode) FASTA Sequence Results Summary and Tables Mercator: Bulk Sequence classification

16. MapMan MapMan: Omics on Plant Pathway visualization, testing Pathway Visualization MapMan is a graphical tool allowing Pathway visualization for more about 20 plant species including all major crops Testing for enriched pathways and processes Interactice data exploration and visualization e.g. Venn Diagrams, Clustering,… Expression DataEnrichment testingInteractive data Exploration

17. Physalis alkengi leaf versus root Something was done right Bringing it together

18. Carbon Status ArraysRNA Seq Metabolic profiling day night extended night Diurnal Cycles and an Extended Night across species

19. Carbon Status ArraysRNA Seq Metabolic profiling day night extended night Diurnal Cycles and an Extended Night across species The mciroarray was pretty useless <10k genes

20. Peak times seem to be conserved If you are a cycling gene it seems to be good to peak around midday or midnight

21. Phases for orthologs seem to do much worse..... Genes ordered by phase in Arabidopsis, if you are very far away you might see some conservation

22. Diurnal Cycles and an Extended Night across species Looking at individual genes can help....

23. Myo Inositol pathway (MIOX) shows a conserved response Arabidopsis Tomato Blue up Red down UDP-Glucose UDP-Glucuronic Acid Glucuronic Acid-1-P Glucuronic Acid Myo-Inositol Miox UGD UDP-Glucose UDP-Glucuronic Acid Glucuronic Acid-1-P Glucuronic Acid Myo-Inositol Miox UGD Maize UDP-Glucose UDP-Glucuronic Acid Glucuronic Acid-1-P Glucuronic Acid Myo-Inositol Miox UGD CELL WALL Conserved Pathways

24. Miox Pathway shows a correlated change in metabolites and transcripts Blue up Red down UDP-Glucose UDP-Glucuronic Acid Glucuronic Acid-1-P Glucuronic Acid Myo-Inositol Miox UGD Glucuronokinase CELL WALL Conserved Pathways... And metabolites

25. UDP-sugars drop in response to Carbon depletion EDENXN EDENXN UGD MIOX GK Carbon and the Wall

26. UDP-sugars drop in Carbon depletion EDENXN EDENXN UGD GK Carbon and the Wall

27. Miox Mutants show a stronger drop in UDP-sugars EDENXN EDENXN EDENXN UGD GK Carbon and the Wall

28. Not all that glitters is gold, but well treated you can find much more unexpected stories from NGS data (S.pimp) NGS does allow us to actually get a handle on genomes and transcritomes we couldn’t dream of before (S.penn Physalis) Using the openness of NGS one starts seeing new things and can compare between species Summary

29. Zhangjun Fei, Jim Giovannoni, Cornell University Raimund Tenhaken, Salzburg University Alisdair Fernie, Mark Stitt MPI Golm Detlef Weigel, MPI Tübingen Acknowledgements Thomas Herter LC-MS usadellab.org