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A pipeline for fingerprinting data analysis

A pipeline for fingerprinting data analysis. DiMI. University of Udine. Simone Scalabrin. Department of Matemathics and Computer Science. Physical Map. Set of contigs Each contig is a set of partially overlapping genomic clones Minimal tiling path. B. BAC clone. Digestion.

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A pipeline for fingerprinting data analysis

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  1. A pipeline for fingerprinting data analysis DiMI University of Udine Simone Scalabrin Department of Matemathics and Computer Science

  2. Physical Map Set of contigs Each contig is a set of partially overlapping genomic clones Minimal tiling path

  3. B BAC clone Digestion Separation Detection Band calling 20,000 bp 10,000 bp 4,500 bp 4,000 bp 2,000 bp 1,200 bp 800 bp BAC clone library, 7-30 genome equivalents, inserts produced with one or more restriction enzymes A C Pairwise comparisons High-stringency Assembly E D Verification & Map Alignment Low-stringency And Manual Re-assembly Meyers, Scalabrin, Morgante 2004 Nature Reviews Genetics, 2004

  4. A pipeline for fingerprinting preculture culture miniprep fingerprint sequencer

  5. Process and people A B B A C Samples 1600 1600 1600 1600 1600 • Day 1: cellular preculture • Day 2: cellular culture • Day 3: DNA isolation (miniprep) • Day 4: DNA fragmentation • Day 5: separation on sequencer All 5 phases were carried on in parallel leading to 8000 DNA samples processed weekly by 3 people

  6. Automation 48 DNA samples every 35 minutes, 2000 per day Almost everything automated

  7. DNA fingerprinting of BAC clones. B BAC clone Digestion Separation Detection Band calling 20,000 bp 10,000 bp 4,500 bp 4,000 bp 2,000 bp 1,200 bp 800 bp

  8. Blunt end enzymes 5’AATGCATAGTACACATGTACTACAGATACGTACACAT 3’ 3’TTACGTATCATGTGTACATGATGTCTATGCATGTGTA 5’ Blunt ends

  9. Blunt end cut 5’AATGCATAGT 3’ 5’ACACAT 3’ 3’TTACGTATCA 5’ 3’TGTGTA 5’ 5’ACACATGTACTACAGATACGT 3’ 3’TGTGTACATGATGTCTATGCA 5’

  10. Sticky end enzymes 5’ACTGAATGCATACTTAAGACATAGAGT 3’ 3’TGACTTACGTATGAATTCTGTATCTCA 5’ Sticky ends

  11. Sticky end cut 5’ACTGAATGCATACT 3’ 5’TAAGACATAGAGT 3’ 3’TGACTTACGTATGAAT 5’ 3’TCTGTATCTCA 5’

  12. Fluorescent fingerprinting 5’ACTGAATGCATACTT 3’ 3’TGACTTACGTATGAAT 5’ Different dyes

  13. Markers VV132 VV132 How to detect overlaps

  14. VV132 VV132 How to detect overlaps Markers

  15. How to detect overlaps Markers VV132

  16. How to detect overlaps Markers VV132 Fingerprinting EcoRI

  17. How to detect overlaps Markers VV132 Fingerprinting

  18. How to detect overlaps Markers VV132 Fingerprinting

  19. How to detect overlaps Markers VV132 Fingerprinting

  20. Fluorescent DNA Fingerprinting

  21. Data Analisys SOFTWARE ACTIVITIES Electrochromatograms ABI Prism 3730 Peaks detection GeneMapper Background and vector removal Script in PERL Genoprofiler Contigs assembly FPC

  22. Peaks detection Tabular text table (GeneMapper)

  23. Electrochromatograms Composition (per color/dye): • At least 200 peaks • 30 – 50 true bands (as expected from previous simulations) • Minimum height

  24. Threshold BACKGROUND REMOVAL True bands Background

  25. Dirtier data

  26. f(avg) Background Removal (1)

  27. Genoprofiler 1.10 http://wheat.pw.usda.gov/PhysicalMapping/tools/genoprofiler/genoprofiler.html

  28. f(ratio) Background Removal 2 Scalabrin and Morgante

  29. UA UA UL UL end LL LL LA LA Background Removal 3 IG = UA1 – LA1 UL = UA – 0,3 * IG LL = LA + 0,15 * IG Scalabrin and Morgante Perl script

  30. Electrochromatograms → text • Divide by colors • High sensibility (FPC deals with integers 0-64k) • 4 dyes → 4 zones 50 500 50 500 50 500 50 500 0 15000 30000 45000 60000

  31. Electrochromatograms → text 1028_B10 14 1526,7 1739,1 5867,4 6664,5 7170,6 7319,1 16500,0 18532,8 20370,9 20919,6 21139,5 22703,7 24783,3 50414,1 BLUE GREEN RED

  32. Automated assembly Pairwise comparisons High-stringency Assembly

  33. FALSE POSITIVE Fingerprinting Techniques A B A B Digestion with Fluorescence Simple Digestion

  34. Contigs assembly

  35. http://www.agcol.arizona.edu/software/fpcFPC 8.1 Basics - FingerPrinted Contigs - Designed for restriction digest fingerprints - Assembles clones into contigs in 2 steps: 1) Clustering- based on the # of shared bands 2) Ordering- finds best solution to maximize overlap Two Key Parameters Tolerance = bin size Cutoff: probability that the match between any 2 BAC clones is due to chance alone (and not a real overlap) Lower cutoff: higher stringency in the assembly

  36. nL [ ] ( ) å - nL m nL m - (( 1 p ) p ) m = m M Statistics to build the map Sulston cutoff score where nL and nH are the min and max number of bands among the two clones and M is the minimum number of shared bands, p =(1-b)nH, b=2t/gellen, t is the tolerance, gellen. t t 0 60000=gellen b represents the probability that one band of one clone matches with another band of the other clone. p represents the probability that none of the nH bands of the “bigger” clone match with a single band of the “smaller” clone.

  37. Mapping BACs within contigs

  38. CB Maps FPC tries to order clones based on Consensus Bands Clone order Clone name Bands Extra bands + = shared band o = missing band x = 2 tolerance bin

  39. Q Clones Qs: enough shared bands to cluster into a contig, but do not fit nicely into a CB map (many extra bands) 4 Types of Qs 1) Bad fingerprint 2) Clone doesn’t belong there -duplicated,repetitive region 3) Suboptimal solution 4) Allelic diversity

  40. Manual curation and assembly Low-stringency And Manual Re-assembly

  41. Map alignment and verification

  42. Confirmation of contigs: Fingerprinting with a second enzyme Fingerprinting of 15 contigs with a second set of enzymes • Contigs are unchanged (cut-off)? • Confirming linear order of BACs within contig

  43. Genetic and Physical Map Integration Chr10 GR0568 0,0 GR0176 7,2 BA0025 17,6 BA0003 21,1 F20236b 21,8 IN0126 23,4 GR0409 24,4 GR0280 25,5 F20681 26,1 E39/M49-114 26,7 E32/M62-282 30,5 F20236a 33,7

  44. Linking of Physical Map to Genetic Map • How many contigs from the physical map? • in theory one per chromosome • a realistic goal is around 2000 (in the grapevine project) • Each contig needs to be linked to the genetic map • 1 marker per contig provides position • 2 markers per contig provide orientation

  45. Heterozygosity impact 1200 CBu 2200 CBu 50% shared fragments 50% shared fragments and 4 clones of type B missing

  46. Acknowledgements • Prof. Michele Morgante • Doct. Riccardo Velasco • Doct. Marco Moroldo • Prof. Alberto Policriti • Doct. Giacomo Prete • Doct. Raffaella Marconi • Doct. Nicoletta Felice • Doct. Massimo Pindo • Doct. Michela Troggio • Doct. Cinzia Segala • Doct. Paolo Fontana

  47. Literature • Mapping and sequencing complex genomes: Let’s get physical!, Meyers, Scalabrin, Morgante, Nature Reviews Genetics, 2004 • FPC: a system for building contigs from restriction fingerprinted clones, Soderlund, Longden, Mott, 1997 • Whole-Genome Validation of High-Information-Content Fingerprinting, Nelson, Soderlund et al. 2005 • Mapping Sequence to Rice FPC, Soderlund, Wing et al.2005

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