Microarray Data Analysis Workshop. Custom arrays and Probe design Probe design in a pangenomic world. Carsten Friis. MedVetNet Workshop, DTU 2008

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1 Microarray Data Analysis Workshop MedVetNet Workshop, DTU 2008 Custom arrays and Probe design Probe design in a pangenomic world Carsten Friis Media glna tnra GlnA TnrA C2 glnr C3 C5 C6 K GlnR C1 C4 C7

2 Why custom microarrays? Tailored specifically to the relevant organism(s) Better coverage than traditional microarray Compare experiments with different isolates Comparative Genomic Hybridization (CGH) Phylogeny, Stronger than e.g. MLST Identification of infection sources Faster and cheaper than sequencing (for now)

3 Probe design for microarrays What is a Probe Different Probe Types What is a good Probe? Cross Hybridization and Complexity Affinity Position Array Design OligoWiz Custom designed pangenomic arrays 32 genomes on one chip!

4 An Ideal Probe Discriminate well between its intended target and all other targets in the target pool Detect concentration differences under the hybridization conditions

5 Probe Type comparisons Advantages Disadvantages PCR products Inexpensive Linkers can be applied Handling problems Hard to design to avoid cross-hybridization Unequal amplification Oligos Can be designed for many criteria Easy to handle Normalized concentrations Linkers can be applied Expensive Affymetrix GeneChip High quality data Standardized arrays Fast to set up Multiple probes per gene Expensive Arrays available for limited number of species

6 How to avoid cross-hybridization From Kane et al. (2000) we learn that a 50 mer probe can detect significant false signal from a target that has >75-80% homology to a 50 mer oligo or a continuous stretch of >15 complementary bases If we have substantial sequence information on the given organism, we can try to avoid this by choosing oligos that are not similar to any other expressed sequences.

7 Probe specificity by %-Mismatch Hughes et al. 2001

8 Regions without similarity to other transcripts 5 The Sequence we want to design a probe for 3 BLAST hits >75% & longer than 15bp 50 bp Regions suitable for probes

9 Filtering self-detecting BLAST hits out The Sequence we want to design a oligo for 5 3 BLAST hits >75% & longer than 15bp Sequence identical or very similar to the query sequence 50 bp Therefore no BLAST hits with homology > 97% and with a hit length vs. query length ratio > 0.8, are considered.

10 Cross-hybridization as a homology score Only BLAST hits that passed filtering are considered If m is the number of BLAST hits considered in position i. Let h=(h1 i,...,hm i) be the BLAST hits in position i in the oligo BLAST max score = i 1 n = 100 max( h1 i,..., hmi ) n 100 n Where n is the length of the oligo Oligo BLAST hits { Max hit in pos. i 100% 0

11 Similar affinity for all oligos Another way of ensuring an optimal discrimination between target and non-target under hybridization is to design all the oligos on an array with similar affinity for their targets Optimal hybridization conditions for all oligos by choosing the right hybridization temperature and salt concentration Commonly, Melting Temperature (Tm) is used as a measure for DNA:DNA or RNA:DNA hybrid affinity

12 Tm distributions for 30 mers and 50 mers

13 ΔTm Distribution for oligo length intervals

14 Melting temperature difference 1000ΔH Tm( i) = log[ Na+ ] Ct A + ΔS + R ln( ) 4 Where ΔH (Kcal/mol) is the sum of the nearest neighbor enthalpy, A is a constant for helix initiation corrections, ΔS is the sum of the nearest neighbor entropy changes, R is the Gas Constant (1.987 cal deg-1 mol-1) and Ct is the total molar concentration of strands. ΔTm score = Tm(i) - 1 N Tm(i) N Where N is all oligos in all sequences.

15 Self-annealing oligos Probes that form strong hybrids with itself should be avoided But, accurate folding algorithms like the one employed by mfold or RNAfold, is too time consuming, for large scale folding of oligos. Time consumption: mfold ~2 sec / 30 mer Pr. gene (500bp) ~16 min.

16 { { { Folding an oligonucleotide: an approximation The alignment is based on dinucleotides Substitution matrix is based on binding energies { { { AT TG CT...CG GT TT AT TG CT...CG GT TT Dynamic programming: alignment to inverted self Minimal loop size border

17 A fast heuristic implementation AT TG CT...CG GT TT AT TG CT...CG GT TT Full dynamic programming calculation for first probe Dynamic programming calculation for second etc. probe Minimal loop size border Last probe Super-alignment matrix

18 Folding prediction compared to mfold OligoWiz vs mfold (DNA folding energy) mfold: folding energy (kcal/mol) OligoWiz: folding energy (kcal/mol)

19 Common sequences result in unspecific signal If the sub-fractions of an oligo are very common we define it as low-complex Oligo with low-complexity: AAAAAAAGGAGTTTTTTTTCAAAAAACTTTTTAAAAAAGCTTTAGGTTTTTA (Human) Oligo without low-complexity: CGTGACTGACAGCTGACTGCTAGCCATGCAACGTCATAGTACGATGACT (Human)

20 Low-complexity For a given transcriptome a list of information content from all words with length wl (8bp) is calculated: I(w) f(w) f(w) = log 2 tf(w) tf ( w) 4 wl Where f(w) is the number of occurrences of a pattern and tf(w) is the total number of patterns of length wl. A low-complexity score for a given oligo is defined as: Low-complexity = 1-norm d i 1 ( = L-wl+ 1 I(w )) i Where norm is a function that normalizes to between 1 and 0, L is the length of the oligo and Wi is the pattern in position i.

21 Location of Oligo within transcript Labeling include reverse transcription of the mrna and is sensitive to: RNA degradation Premature termination of cdna synthesis Premature termination of crna transcription (IVT) A Position Score reflecting this (eukaryotes): Position score= (1-drp)Δ3 end Where drp is the chance of labeling termination pr. base

22 OligoWiz: A Tool for flexible probe design

23 About OligoWiz How and Who OligoWiz 2.0 is a client-server application for designing oligonucleotides for microarrays The OligoWiz client (the graphical interface) is written in Java 1.4 and runs on virtually all platforms The OligoWiz Server performs the heavy-duty computation and is hosted on a multi-cpu Altix server at CBS. OligoWiz is created by Henrik Bjørn Nielsen and Rasmus Wernersson both at the Center for Biological Sequence Analysis at the Technical University of Denmark.

24 About the OligoWiz scores All scores are normalized to a value between 0.0 (worst) and 1.0 (best). All scores are independent and is assigned a user-adjustable weight. A total score is calculated as the sum of all weighted scores and is normalized to a value between 0.0 and 1.0.

25 Extracting annotation from GenBank files -FeatureExtract server -

26 Sequence Features Intron/Exon structure, UTR regions etc. Special purpose arrays Example: Detecting Differential splicing Exon Intron Exon Exon Exon

27 Species databases The species databases are built from complete genomic sequences or UniGene collections in the case of Vertebrates. The databases are used for: Cross hybridization Low-complexity Several hundred species currently available

28 A microarray from 32 genomes Characterization of probiotic E. coli isolates using a novel pangenome microarray H Willenbrock, PF Hallin, T Wassanar and DW Ussery Background: Based on 32 Escherichia coli and Shigella genome sequences, we have developed an E. coli pan-genome microarray. Publicly available genomes were annotated in a consistent manor to define all currently known genes potentially present in the species. The chip design was evaluated by hybridization of DNA from two sequenced E. coli strains, K-12 MG1655 (a commensal) and O157:H7 EDL933 (an enterotoxigenic E. coli). A dual channel and single channel analysis approach was compared for the comparative genomic hybridization experiments. Moreover, the microarray was used to characterize four unsequenced probiotic E. coli strains, currently marketed for beneficial effects on the human gut flora. Conclusion: This high-density microarray provides an excellent tool for characterizing either DNA content or gene expression from unknown E. coli strains.

29 E. coli core genes

30 Genes added to the E. coli pangenome

31 Designing Probes False positives/false negatives Cover all versions of same gene Distinguish between different genes Conservation is not that well-defined Usually related to the function of the product Genetic mutation is (often) gradual

32 Versions of the same gene or different?

33 Designing Probes False positives/false negatives Cover all versions of same gene Distinguish between different genes Conservation is not that well-defined Usually related to the function of the product Genetic mutation is (often) gradual Distinct versions of the same gene! Mutations giving rise to different phenotypes Bias in the types of strains sequenced Sequencing pathogens seems highly fashionable

34 Benchmarking the chips

35 Out with the bad

36 Realistic design principles Version of the same or different? Let the probes decide! If probes can distinguish, then so do we >90% overall identity, no 15-20bp unique stretch We will never achieve 100% coverage Compromising probe quality gives false signal Filtering probes may eliminate false signals, but does so at the expense of coverage Major remaining issue is speed!

37 To build a web-service An extension of OligoWiz Pangenomics Metagenomics You bring your genome(s) Whole genomes Collection of genes You get a list of probes Ready to put on array