O PTICAL M APPING AS A M ETHOD OF W HOLE G ENOME A NALYSIS M AY 4, 2009 C OURSE : 22M:151 P RESENTED BY : A USTIN J. R AMME

Presentation Outline Introduction to Optical Mapping Definitions for Understanding Modern Optical Mapping Process Data Analysis ◦ Overview ◦ Steps to Restriction Map Generation Applications of Optical Mapping Conclusions

Optical Mapping (OM) Introduction The number of identified polygenetic diseases is ever increasing Methods to analyze the entire genome will enhance current diagnostic and treatment methods for a variety of diseases Patient-specific genomic analysis has become the goal in genetics-based medical research Optical mapping(OM) is an automated method of ordered restriction map generation with a goal of whole genome analysis that avoids the limitations inherent to traditional techniques

Definitions Restriction Enzymes ◦ Proteins that cleave DNA molecules based on a specific base pair sequence (e.g. ATCG) + =

Definitions Restriction Map ◦ Representation of the cut sites on a given DNA molecule to provide spatial information of genetic loci Optical Mapping ◦ Process to generate ordered restriction maps from single DNA molecules Optical Map ◦ Ordered restriction map of a portion of genomic DNA DNA strand [2]

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Computer Representation of Imaging Data Imaged datasets are converted into barcode patterns corresponding to the cleaved fragments Lengths are determined using an internal λ standard and fluorescence intensity values Computer Representation of Ordered DNA Fragments Imaged Cleaved DNA Fragments [5]

Raw Data Description ◦ Image collection containing genomic restriction fragments of known length deposited in an ordered manner ◦ Fragments represent randomly sheared genomic DNA ◦ Each OM imaging study redundantly represents the entire genomic region of interest Challenges with analyzing individual DNA molecules: ◦ Extra cut sites - physical breakage ◦ Missing cut sites - partial digestion ◦ Loss of small fragments ◦ Sizing error ◦ Chimeric maps- physically overlapped molecules Combining multiple OMs gives more accurate restriction maps Graphing has been used to accomplish this

Steps to Restriction Map Generation 1. Calculation of OM Overlaps 2. Overlap Graph Construction 3. Graph Correction Procedure 4. Identification of Islands 5. Contig Construction 6. Construction of Draft Consensus Map 7. Consensus Map Refinement

Calculation of Overlaps A multitude of OMs are collected per optical mapping experiment Scoring system used to find overlaps between individual optical maps: Scoring system components: Matching sites are rewarded Discordant sites are penalized Length similarity is rewarded [6]

Overlap Graph Construction Overlap Graph = G(V,E) ◦ Literature describes it as a graph, but its technically a digraph ◦ The set of nodes (V) represent individual optical maps ◦ The set of edges (E) represent high quality overlaps between pairs of maps Weighting and orienting the edges of the graph ◦ Edge weights correspond to genomic distances of the overlapping map regions ◦ Orientation based on the sign of distance measurements from neighboring map centerpoints Goal: Heaviest weight path represents the most comprehensive genomic restriction map OM 1 OM 2 OM 3 OM 4 … Graph Construction Optical Mapping Data

Graph Correction Procedure (1) False edges correspond to falsely identified overlaps ◦ Spurious edges  Connect two nodes forming a cycle which is not possible in linear DNA ◦ Orientation consistent false overlaps (cut edge)  Edges that connect two unrelated portions of the genome [4]

Graph Correction Procedure (2) False Nodes  Chimeric maps ◦ Consist of two groups of nodes only connected via a single node (cut vertex) ◦ Connect two unrelated portions of the genome [4]

Identification of Islands Islands correspond to genomic regions spanned by multiple overlapping optical maps Contig Construction For each island, “contigs” are defined as paths from sources to sinks within the overlap graph for the island The most complete representation of the genomic region is represented by the heaviest edge path from source to sink Island 1Island 2Island 3 [4]

Construction of Draft Consensus Map Using the determined paths, the nodes and edges are used to merge the individual optical maps corresponding to each chosen island component Each of the individual composite optical maps are stored for further analysis [4]

Consensus Map Refinement (1) The draft map may contain errors: ◦ Missing cut sites ◦ False cut sites Hidden Markov Model (HMM) for map refinement ◦ Compares draft map to many other optical maps ◦ Statistics used to identify matching, deleted, and inserted cut sites Hidden Markov Model [7]

Consensus Map Refinement (2) The corrected consensus map for each island pieced back together to form a complete genomic restriction map Typically takes iterations for statistical correction of the draft map Sample HMM Path [7]

Applications of Optical Mapping Identification of genetic insertions, deletions, inversions, and repeats Establish genotype-phenotype correlations for advancements in diagnosis and treatment of genetic disorders Reduction of the time needed and the cost to sequence entire strands of DNA In the future: Patient-specific whole genome analysis

Conclusions Optical mapping is a method of restriction map generation for whole genome analysis Applications range from clinical phenotype- genotype correlations to identification of polymorphisms in a variety of diseases In the future, optical mapping technology will help to realize the goal of patient-specific whole genomic analysis Optical Mapping is a modern application of discrete mathematics with potential to change medicine

References 1. Samad A, Huff EF, Cai W, Schwartz DC. Optical mapping: A novel, single- molecule approach to genomic analysis. Genome Res. 1995;5: Ramme AJ. Personal image collection.. 3. Schwartz DC, Samad A. Optical mapping approaches to molecular genomics. Curr Opin Biotechnol. 1997;8: Valouev A, Schwartz DC, Zhou S, Waterman MS. An algorithm for assembly of ordered restriction maps from single DNA molecules. Proc Natl Acad Sci U S A. 2006;103: Aston C, Mishra B, Schwartz DC. Optical mapping and its potential for large-scale sequencing projects. Trends Biotechnol. 1999;17: Valouev A, Li L, Liu YC, et al. Alignment of optical maps. J Comput Biol. 2006;13: Valouev A, Zhang Y, Schwartz DC, Waterman MS. Refinement of optical map assemblies. Bioinformatics. 2006;22:

Questions? Further information available from: 1.) Laboratory for Molecular and Computational Genetics ( 2.) Opgen (