The document discusses the integration of computation and visualization in genomics, particularly focusing on the use of the software 'abyss-explorer' for genome assembly visualization. It highlights the processes for genome sequencing, resolving ambiguities through paired-end reads, and the effectiveness of clustering for revealing patterns in genomic data. Future work aims to enhance the tool's functionality, including search capabilities and richer analysis features.

1. British Columbia Cancer AgencyGenome Sciences CentreVancouver . British Columbia . CanadaComplementing Computation with Visualization in GenomicsMarch 11, 2010EBI Interfaces Interest ForumCydney Nielsen

2. Discovery pathBiological SampleGenomic DataScientific Insight

3. Discovery pathBiological SampleGenomic DataScientific Insight

4. Components of Data AnalysisAutomationAnalysisGenomic DataScientific InsightHuman Judgment

5. OutlineGenome Assembly VisualizationABySS-ExplorerComplement to genome browsing Using clustering and interactive data exploration

6. OutlineGenome Assembly VisualizationABySS-ExplorerComplement to genome browsing Using clustering and interactive data exploration

7. Genome Sequencingcell populationextracted DNAShotgun approachsheared DNAsequencing readsAGCGGATTGCATGACAGTGTACAGCCTGACAGAAGCGCGCTACGATCAGATCAACATGACAGTCCGAGTACATTCAGAATGGTACAGCAG

8. ABySS – Assembly ByShort SequencesSimpson et al. Genome Res 2009Sequencing read set (read length = 7 nt):GGACATCGGACAGACorresponding de Bruijn graph (k = 5 nt):

9. ABySS – Assembly ByShort SequencesSimpson et al. Genome Res 2009Sequencing read set (read length = 7 nt):GGACATCGGACAGACorresponding de Bruijn graph (k = 5 nt):ABySS merges unambiguously connected vertices to form contigs

10. Assembly AmbiguitiesTrue genome sequenceGGATTGAAAAAAAAAAAAAAAAGTAGCACGAATATACATAGAAAAAAAAAAAAAAAAATTACG

11. Assembly AmbiguitiesTrue genome sequenceGGATTGAAAAAAAAAAAAAAAAGTAGCACGAATATACATAGAAAAAAAAAAAAAAAAATTACGAssembled sequence de Bruijn graph representation

12. Starting PointShaun Jackman

13. Example of existing tools: Consed

14. Example of existing tools: Consed

18. Properties of DNA

19. Capture sequence strandAAAAAT2+1+

20. Capture sequence strandAAAAAT2+1+TTTTTA2-1-

21. Capture sequence strandAAAAAT1+2+TTTTTA

22. Capture sequence strandAAAAAT1-2-TTTTTA

24. Capture sequence lengthone oscillation = 100 nt

25. Genome Sequencingcell populationextracted DNAread pair informationreadsheared DNAdsDNAfragment(known size)sequencing reads(typically produce millions)AGCGGATTGCATGACAGTreadGTACAGCCTGACAGAAGCGCGCTACGATCAGATCAACATGACAGTCCGAGTACATTCAGAATGGTACAGCAG

26. Capture read pair informationAfter building the initial single-end (SE) contigs from k-mer sequences, ABySS uses paired-end reads to resolve ambiguities.

27. Capture read pair informationPaired end read information is used the construct paired end (PE) contigs… 13+ 44- 46+ 4+ 79+ 70+ …blue gradient = paired end contigorange = selected single end contig

28. ABySS-Explorer Visual representation of:

29. contig adjacency information

30. contig strand

31. contig length

32. paired-end relationships

33. paired-end contigs

34. Implemented using the Java Universal Network/Graph Framework (JUNG)

35. Applied the Kamada-Kawai layout algorithm (JUNG implementation)

36. Use ABySS files as input (version 1.1.0 and higher)

37. http://www.bcgsc.ca/platform/bioinfo/software/abyss-explorer

38. Part 1: Conclusions and Future Work Graph encoding provides a integrated display of genome assemblies and associated meta-data

39. This representation is particularly powerful for revealing high-level genome assembly structure, not readily viewable in any other interactive tool

40. Future work includes:

41. support for other assembly algorithm outputs

42. enable flexible annotation display

43. integrate with existing assembly editing toolsOutlineGenome Assembly VisualizationABySS-ExplorerComplement to genome browsing Using clustering and interactive data exploration

44. Genome Sequencingcell populationextracted DNAsheared DNAsequencing reads(typically produce millions)AGCGGATTGCATGACAGTGTACAGCCTGACAGAAGCGCGCTACGATCAGATCAACATGACAGTCCGAGTACATTCAGAATGGTACAGCAG

45. Genome Sequencingcell populationextracted DNAsheared DNAsequencing reads(typically produce millions)AGCGGATTGCATGACAGTGTACAGCCTGACAGAAGCGCGCTACGATCAGATCAACATGACAGTCCGAGTACATTCAGAATGGTACAGCAG

46. Genome Sequencingcell populationChromatin Immunoprecipitationand Sequencing (ChIP-Seq)extracted DNAselectionsheared DNAsequencing reads(typically produce millions)AGCGGATTGCATGACAGTGTACAGCCTGACAGAAGCGCGCTACGATCAGATCAAGTACAGCCTGACAGAAGCCATGACAGTCCGAGTACATTCAGAATGGTACAGCAGTTCAGAATGGTACAGCAG

47. Align sequences to the genomeCCGAGTACAGCCTGACAGAGCATGACAGTCCGAGTACTTGCATGACAGTCCGAGTAGCGGATTGCATGACAGTAGCGGATTGCATGACAGTAGCGGATTGCATGACAGTReference GenomeAGCGGATTGCATGACAGTCCGAGTACAGCCTGACAGARead coverageGenomic coordinate

48. Genome browser can reveal local patternsH3K4me3H3K36me3H3K27me3H3K9me3H3K9AcMRE

49. Difficult to get global overview

50. Focus on regions of interest1. For example, transcriptional start sites (TSS +/- 3000 nt)H3K4me3H3K9AcH3K4me1H3K36me3MeDIPMRE2. Extract data matricesNormalization for bin i, sample h:3. Cluster matrices (k-means clustering with Euclidean distance)