Community DNA fingerprinting

Getting more out of Sanger sequencing.
The idea came from looking at Sanger sequence traces from heterozygote organisms. In these traces it was possible to identify the loci of heterozygocity and determine the two types present visually. We though and still wonder wether it would be possible to extract the species or type composition and possibly frequency from sanger sequencing of community amplicons.
This idea is currently shelved.
https://github.com/low-decarie/community-DNA-fingerprinting

Transcript

1. Getting more out of Sanger sequencing Etienne Low-Decarie Corey Chivers Community DNA fingerprinting
2. Microbial community composition (phyto)plankton images: NHGRI and NASA soil human microbiome experimental ecology and evolution
3. Community fingerprint ›  Determine community composition from a community level characteristic that results from the proportional sum of characteristics of its constituent types. ›  Syn. community profiling 3x ½ x 2x
4. Pigment fingerprinting bbe FluoroProbe Manual Page 10 DETERMINATION OF DIFFERENT ALGAE The division of chlorophyceae (green algae) shows a broad maximum of fluorescence at the 470nm LED, which is caused by chlorophyll-a and -b. The cyanophyceae (blue-green algae) have their maximum at 610nm due to the photosynthetic antenna pigment phycocyanin. Cyanophyceae also contain chlorophyll-a if there is low intensity at 470nm. This is due to the masking effect of the phycocyanin. Furthermore, the high peak at the 525nm region for the bacillariophyceae originates from xanthophyll fucoxanthin and for the dinophyceae from peridin. The maxima at 470nm are caused by chlorophyll-a and -c. In our last analysed group, cryptophyceae, a significant maximum can be found at 570nm, which originates from phycoerythrin. It is obvious from the figure below that it is not possible to distinguish bacillariophyceae and dinophyceae by their “fluorescent fingerprints”. But it can be clearly seen that it is possible to distinguish five groups of algae: chlorophyceae, cyanophyceae, dinophyceae and bacillariophyceae, cryptophyceae. Additionally, it should be mentioned that sometimes the phycocyanin content per cell in cyanophyceae varies. Nevertheless, the average fingerprint can be used to differentiate this division. 0 0,2 0,4 0,6 0,8 1 1,2 440 460 480 500 520 540 560 580 600 Excitation-Wavelength/1nm Relative Fluorescence Intensity scenedesmus chlamydomonas monoraphidium rhodomin chlorella scenedesmus fal. micractinium scenedesmus quad. scenedesmus subs scenedesmus ob. microcystis viridis synnechocochus nrc-1 aphanizomenon cyclotella nitzschia synedra cryptomonas pl cryptomonas a cryptomonas ceratien peridinium Micro2 Micro7 Micro8 Micro9 Micro12 Micro19 Micro13 Micro18 Micro15 Micro16 Micro23 Micro25 Micro26 Micro20 Micro27 Micro21 Micro11 Micro17 Micro22 Micro10 Micro24 Micro14 Micro4 Chlorophyceae Dinophyceae Bacillariophyceae Cryptophyceae Cyanophyceae The fluorescence intensities of the 5 divisions divided by the intensity of the LED and normalised to the maximum intensity of each division. In this measurement, several species of the mycrocystis cyanobacteria (abbreviation micro) were tested as well. images: Academy of Natural Sciences, BBE moldaenke and others
5. Physiological fingerprint ›  Sole carbon source utilization ›  BIOLOG eco-plates ›  Toxicological ›  Antibiotic ›  Toxin and metals
6. Chemical fingerprinting ›  Fatty acid ›  Isotope ›  Trophic structure signal images:Rebecca E. Drenovsky, Roger A. Duncan, Kate M. Scow,SPC ocean fisheries programme,
7. DNA fingerprints ›  Non sequencing based ›  Terminal Restriction Fragment Length Polymorphism (T-RFLP) ›  Denaturing Gradient Gel Electrophoresis (DGGE) ›  Ribosomal Intergenic Spacer Analysis (RISA) ›  Sequencing based ›  High-throughput sequencing images: Xia Zhou, Gregory W. Schmidt, Roberto Danovaro and Antonio Pusceddu, Stephan Gantnera, Anders F. Anderssona, Laura Alonso-Sáeza, Stefan Bertilsson
8. Chain-termination (Sanger) sequencing images: Helmut Kae NAGTCGTCCA
9. Heterozygote trace 0.00 0.05 0.10 0.15 5 10 15 20 25 datum value Homozygote A 0.00 0.05 0.10 0.15 5 10 15 20 25 datum value Homozygote B 0.00 0.05 0.10 0.15 5 10 15 20 25 datum value Homozygote A+B
10. Our naïve logical leap If we think it has not been done, chances are: •  Wrong search terms •  Can’t be done •  Shouldn’t be done
11. Two type community trace 0.00 0.05 0.10 0.15 5 10 15 20 25 datum value Species A 0.00 0.05 0.10 0.15 5 10 15 20 25 datum value Species B 0.00 0.05 0.10 0.15 5 10 15 20 25 datum value Community A+B variable A T C G type: any taxonomic level with common primers and distinguishing loci genotype, species, genus…
12. Two type trace
13. Quantification ›  Sum of square optimization on base ratio (Community - (quantity • Type 1 + quantity • Type 2))2
14. Simulation ›  Random species sequences ›  Sum species values for community ›  Vary ›  Number of species ›  Number of variable loci ›  Proportion of species available (sequenced) for fitting ›  Noise ›  …
15. Simulation Number of species in the community Number of variable loci •  Robust if: •  number of variable loci > number of species •  Better at predicting species with larger frequencies •  Can withstand 10% noise with little loss.
16. Quantification of visually aligned traces 0.519 0.453 R2>0.95
17. Traces are shifted ›  Alignment ›  Segment shift through 0 padding ›  Discontinuous/rough surface ›  Grid search ›  Very costly (time~ rangeparameters) ›  potential alternatives ›  human eye ›  differential evolution ›  particle swarm ›  integer programing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18. Traces contain drift ›  Only segments can be aligned ›  Trace segmentation ›  Provides population with distribution as potential measure of error
19. Distribution of quantification with segmentation and alignment Segmentation size
20. Next steps ›  Get your expert opinion/feedback ›  many potential issues but worth pursuing? ›  … ›  Show proportion calculated=true proportion ›  Dilution series ›  Volunteers for trace data? ›  …
21. Acknowledgements BELL LAB FUSSMANN LAB LEUNG LAB etienne.webhop.org etienne.low-decarie@mail.mcgill.ca corey.chivers@mail.mcgill.ca Contacts and code Adam Herman https://github.com/edielivon/community-DNA-fingerprinting Thomas Bureau Genome Quebec