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Data first vs Hypothesis first Alan Ward. Data first vs Hypothesis first Hypothesis driven approach Look at the data we have Formulate an hypothesis about..

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Presentation on theme: "Data first vs Hypothesis first Alan Ward. Data first vs Hypothesis first Hypothesis driven approach Look at the data we have Formulate an hypothesis about.."— Presentation transcript:

1 Data first vs Hypothesis first Alan Ward

2 Data first vs Hypothesis first Hypothesis driven approach Look at the data we have Formulate an hypothesis about.. Do experiments to test the hypothesis As a byproduct, collect more data Weinberg R (2010) Point: Hypotheses first. NATURE 464, 678

3 Data first vs Hypothesis first Data driven approach Identify a system of interest Identify an approach to measure/describe attributes of the system Collect and organise the data Golub T (2010) Counterpoint: Data first. NATURE 464, 679

4 Data first vs Hypothesis first “Reports that say that something hasn't happened are always interesting to me, because as we know, there are known knowns; there are things we know that we know. There are known unknowns; that is to say, there are things that we now know we don't know. But there are also unknown unknowns – there are things we do not know we don't know.” —United States Secretary of Defense, Donald RumsfeldUnited States Secretary of Defense, Donald Rumsfeld

5 Data first vs Hypothesis first The Black Swan: The Impact of the Highly Improbable. Nassim Taleb

6 Data first vs Hypothesis first known Hypothesis driven research unknown Enzyme activity Feedback inhibition Allosteric regulation Transcriptional regulation - Inducers and repressors Non-coding short RNAs

7 Data first vs Hypothesis first Breadth first vs Depth first A slice up and down A slice across

8 Observation has always been part of biology as in the imatinib example (Golub, 2010) but DNA sequencing technology has revolutionized observational data collection. You can see that Weinberg (2010) is arguing that ‘cheap sequencing’ on a massive scale = too much funding for data collection. And, he doesn’t argue it but you might spend all your time managing the data 1 Data first vs Hypothesis first 1 Marx, V (2013) Biology: The big challenges of big data. Nature 498, 255–260

9 Depth first or breadth first Two different strategies for computer search algorithms Which is best? That heavily depends on the structure of the search tree and the number and location of solutions. If you know a solution is not far from the root of the tree, a breadth first search (BFS) might be better. If the tree is very deep and solutions are rare, depth first search (DFS) might rootle around forever, but BFS could be faster. If the tree is very wide, a BFS might need too much memory, so it might be completely impractical. If solutions are frequent but located deep in the tree, BFS could be impractical. If the search tree is very deep you will need to restrict the search depth for depth first search (DFS), anyway. Data first vs Hypothesis first

10 Data first vs Hypothesis first EST database dbEST release Summary by Organism - 01 January 2013 Number of public entries: 74,186,692 Homo sapiens (human) 8,704,790 Mus musculus + domesticus (mouse) 4,853,570 Zea mays (maize) 2,019,137 Sus scrofa (pig) 1,669,337 Bos taurus (cattle) 1,559,495 Arabidopsis thaliana (thale cress) 1,529,700 Danio rerio (zebrafish) 1,488,275 Glycine max (soybean) 1,461,722 Triticum aestivum (wheat) 1,286,372 Xenopus (Silurana) tropicalis (western clawed frog) 1,271,480 Oryza sativa (rice) 1,253,557 Ciona intestinalis 1,205,674 Rattus norvegicus + sp. (rat) 1,162,136 Drosophila melanogaster (fruit fly) 821,005 ….. Salmonella enterica subsp. enterica serovar Typhi 217 Mycobacterium smegmatis str. MC Mycobacterium tuberculosis 30

11 DbEST references Boguski, MS, Lowe, TMJ, Tolstoshev, CM (1993) DbEST - Database For Expressed Sequence Tags. Nature Genetics 4, Boguski, MSS (1994) Gene discovery in dbEST. Science 265, Boguski, MSS (1995) The turning point in genome research. Trends in Biochemical Sciences 20, Nagaraj, S (2007) A hitchhiker's guide to expressed sequence tag (EST) analysis. Briefings in Bioinformatics 8, 6-21 Data first vs Hypothesis first

12 Why DNA? An example: Species and strain identification in prokaryotes DNA:DNA similarity MLEE (MultiLocus Enzyme Electrophoresis) MLST (MultiLocus Sequence Typing) ANI (Average Nucleotide Identity) Data first vs Hypothesis first

13 Defining species The modern concept of species dates back to: Mayr, E. (1942) Systematics and the Origin of Species(Columbia Univ. Press, New York) Biological species concept: Species are groups of actually or potentially interbreeding natural populations, which are reproductively isolated from other such groups de Queiroz K (2005) Ernst Mayr and the modern concept of species. Proc Natl Acad Sci U S A. 102 Suppl 1:

14 Bacterial species Bacteria do not interbreed in the same way so defining species in bacteria remained an exercise in clustering organisms with similar, initially phenotypic, characters Stanier RY. Adaptation, evolutionary and physiological: Or Darwinism among the microorganisms. In: Davies R, Gale EF, editors. Adaptation in Microorganisms, Third Symposium of the Society for General Microbiology. Cambridge: Cambridge University Press; 1953 Goldner M (2007) The genius of Roger Stanier Can J Infect Dis Med Microbiol 18, 193–194

15 DNA:DNA similarity From the 1960s there was a consensus that all taxonomic information about a bacterium is incorporated in the complete nucleotide sequence of its genome Wayne et al., in 1987 correlated the measurement of the similarity of DNA of two strains with then currently defined species and concluded that: A DNA:DNA similarity of 70% and a ΔTm of > 5°C, both are important, marks the boundary of a group of strains which belong to the same species Wayne, L. G., Brenner, D. J., Colwell, R. R., Grimont, P. A. D., Kandler, O., Krichevsky, M. I., Moore, L. H., Moore, W. E. C., Murray, R. G. E. & other authors (1987). Report of the ad hoc committee on reconciliation of approaches to bacterial systematics. Int J Syst Bacteriol 37, 463–464.

16 DNA-DNA similarity Measuring DNA similarity by hybridisation is not the same as DNA sequence similarity and it is measured using a number of different techniques % Similarity De Ley – rate of renaturation Ezaki – microplate binding ΔTm DNA melting Elution from hydroxyapatite The methods are not robust and few labs can do: Stackebrandt et al. (2002) Report of the Ad Hoc Committee for the re- evaluation of the species definition in bacteriology. Intl J Systematic Evol Microbiol 52,

17 Melting Temperature analysis

18 DNA Melting

19 Using RT-PCR and Syber Green for DNA melt curve analysis Gonzalez, JM & Saiz-Jimenez, C (2005) A simple fluorimetric method for the estimation of DNA–DNA relatedness between closely related microorganisms by thermal denaturation temperatures. Extremophiles 9, 75–79

20 ΔTm determination Exactly the same melting program, but this time the DNA from Organism 1 and Organism 2 has been mixed, denatured and then renatured at the optimum temperature for renaturation T OR calculated from the %GC (Tor=0.51(%GC)+47.0) before adding Syber Green and melting

21 Disadvantages of DNA-DNA similarity Because DNA:DNA hybridisation compares the whole genome it has remained the “Gold standard” for species delineation but it has several disadvantages: It requires large amounts of high quality DNA The methods are difficult to do Different methods can different results Reciprocal measurements can be very different (amount of A binding to B is different from amount of B binding to A) The experimental measurement has to be made between 2 strains – so to obtain DNA-DNA similarity for 5 strains requires 20 experimental determinations and if a 6 th strain needs to be compared another 5 experiments are needed You can’t build an incremental database

22 Disadvantages of DNA-DNA similarity

23 Multilocus Enzyme Electrophoresis MLEE Selander, RK, Caugant, DA, Ochman, H, Musser, JM, Gilmour, MN and Whittam, TS (1986) Methods of multilocus enzyme electrophoresis for bacterial population genetics and systematics. Appl. Environ. Microbiol 51,

24 Multilocus sequence typing MLST Maiden, MCJ, Bygraves, JA, Feil, E, Morelli, G, Russell, JE, Urwin, R, Zhang, Q, Zhou, J, Zurth, K, Caugant, DA, Feavers, IM, Achtman, M, and Spratt, BG (1998) Multilocus sequence typing: A portable approach to the identification of clones within populations of pathogenic microorganisms. Proc. Natl. Acad. Sci. USA 95, 3140–3145 Staphylococcus aureus

25 Portable Unambiguous Reproducible Cumulative Scalable Multilocus sequence typing MLST

26 The traditional method of data reduction is publication —results are summarized in peer- reviewed journals. Publications include only the most important results, from experiments that may have been performed over many years. The published paper is a concise compilation of the data, an interpretation of the results, and a comparison with results obtained by others. Data first vs Hypothesis first A significant fraction of experiments from academic laboratories cannot be repeated in industry 1. Reflecting inadequate description of experiments performed on different equipment and on biological samples that were produced with disparate methods. 1 Begley CG & Ellis LM (2012) Drug development: Raise standards for preclinical cancer research Nature 483, 531–3

27 Data first vs Hypothesis first In 1991 the GenBank On-line Service utilized a Solbourne 5/800 running OS/MP 4.0C. The database work was done on a Sun network 4/490 server and workstations running SunOS UNIX version 4.1. The GenBank database was maintained on Sybase relational database management system (RDBMS). Software was developed in ' C language. In 1990s NCBI scanned the literature for sequences and manually typed them into the database.

28 Data first vs Hypothesis first Benson, DA, Cavanaugh, M, Clark, K, Karsch-Mizrachi, I, Lipman, DJ, Ostell J and Sayers EW (2013) Genbank Nucleic Acids Research 41, D36–D42

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