Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this presentation? Why not share!

- 2013 pag-poultry-workshop by c.titus.brown 1096 views
- Similarity search in large sets of ... by Australian Bioinf... 840 views
- 2014 abic-talk by c.titus.brown 1129 views
- Visualizing the genome: Techniques ... by Ann Loraine 176 views
- Linking IGB with Galaxy by Ann Loraine 954 views
- A Survey of Bioinformatics Training... by Australian Bioinf... 774 views

472 views

329 views

329 views

Published on

No Downloads

Total views

472

On SlideShare

0

From Embeds

0

Number of Embeds

7

Shares

0

Downloads

5

Comments

0

Likes

1

No embeds

No notes for slide

- 1. Genometry Gregg Helt Cyrus Harmon
- 2. Genometry • Motivation and Purpose • Points of Reference • Genometry interfaces • Genometry manipulations • Genometry implementation • Representation examples • Prototype apps • Current status, future work
- 3. Motivation and Goals • Desire for a more unified data model to represent relationships between biological sequences, such as: – Annotations – Alignments – Sequence composition • More networked, less hierarchical (genome-centric, transcript-centric) • Simplicity • Expressivity / Flexibility • Memory and Computational Efficiency • Use by others to provide core functionality for various Affy projects
- 4. Points of Reference • com.neomorphic.bio models • Genisys DB and Genisys IDL • EBI mapping models • Apollo data models • BioPerl • BioJava • Closest similarity to bio alignment models and Genisys alignment models
- 5. Basic Annotations Transcript T Genome G Transcript T G: 1000..5000 Exon E1 G:1000..1200 Exon E2 G:3000..3500 Exon E3 G:4500..5000
- 6. Genometry Annotations – Specify All Coordinates Transcript T Genome G Transcript T G: 1000..5000 T:0..1200 Exon E1 G:1000..1200 T:0..200 Exon E2 G:3000..3500 T:200..700 Exon E3 G:4500..5000 T:700..1200
- 7. Genometry Annotations – All coordinates are relative to BioSeqs Transcript T Genome G TranscriptAnnot T1 G: 1000..5000 T:0..1200 ExonAnnot E1 G:1000..1200 T:0..200 ExonAnnot E2 G:3000..3500 T:200..700 ExonAnnot E3 G:4500..5000 T:700..1200 Transcript T Genome G
- 8. Genometry Annotations – SeqSpans encapsulate a range along a BioSeq Transcript T Genome G TranscriptAnnot T1 ExonAnnot E1 ExonAnnot E2 ExonAnnot E3 Transcript T Genome G G: 1000..5000 T: 0..200 G:1000..1200 T:0..200 G:3000..3500 T:200..700 G:4500..5000 T:700..1200
- 9. Genometry Core Core • BioSeq – length, residues (optional) • SeqSpan – start, end, BioSeq • SeqSymmetry – SeqSpans (breadth) – SeqSymmetry parent / child hierarchy (depth)
- 10. Expressiveness of Core Core • “Standard” annotations • Singleton annotations • Alternative Splicing • Pairwise alignments • Annotations with depth > 2 • Annotations with breadth > 2 • Indels • Structure of analyzed sequence • Fuzzy locations • All without explicit pointers from BioSeq to annotation
- 11. Genometry Modelling of Insertions and Deletions #1a G:1000..1006 T:7..18 G:1000..1017 T:0..6 G:1006..1017 T:0..18 …AGGCAATTAATTGATCCAGGTG……GAGTCCGAATAGGGTTAGCG… GCAATTCAATTGATCCAG TCCGAATAGGTTAGCG G:2000..2017 T:18..34 G:2000..2010 T:28..34T:18..28 G:2011..2017 G:1000..2017 T:0..34 insertion in transcript relative to genome (deletion in genome relative to transcript) deletion in transcript relative to genome (insertion in genome relative to transcript) Genome G Transcript T
- 12. Genometry Modelling of Insertions and Deletions #1b G: g0..g2 T:t0..t2 …AGGCAATTAATTGATCCAGGTG……GAGTCCGAATAGGGTTAGCG… GCAATTCAATTGATCCAG TCCGAATAGGTTAGCG G:g3..g5 T:t3..t5 G:g3..g4 T:t4..t5T:t3..t4 G:g4+1..g5G:g0..g1 T:t0..t1 T:t1+1..t2 G:g1..g2 G:g0..g5 T:t0..t5 insertion in transcript relative to genome (deletion in genome relative to transcript) deletion in transcript relative to genome (insertion in genome relative to transcript) Genome G Transcript T t0 t1 t1+1 t2 g0 g1 g2 g3 g4 g4+1 g5 t3 t4 t5
- 13. Genometry Modelling of Insertions and Deletions #2 G:g0..g1 T:t0..t1 T:t1+1..t2 G:g1..g2 G: g0..g2 T:t0..t2 …AGGCAATTAATTGATCCAGGTG……GAGTCCGAATAGGGTTAGCG… GCAATTCAATTGATCCAG TCCGAATAGGTTAGCG G:g3..g5 T:t3..t5 G:g3..g4 T:t3..t4 T:t4..t5 G:g4+1..g5 G:g0..g5 T:t0..t5 insertion in transcript relative to genome (deletion in genome relative to transcript) deletion in transcript relative to genome (insertion in genome relative to transcript) Genome G Transcript T T:t1..t1+1 “C” :0..1 t0 t1 t1+1 t2 g0 g1 g2 g3 g4 g4+1 g5 t3 t4 t5 G:g4..g4+1 “G” :0..1
- 14. Genometry Modelling of Insertions and Deletions #3 G:g0..g1 T:t0..t1 T:t1+1..t2 G:g1..g2 G: g0..g2 T:t0..t2 …AGGCAATTAATTGATCCAGGTG……GAGTCCGAATAGGGTTAGCG… GCAATTCAATTGATCCAG TCCGAATAGGTTAGCG G:g3..g5 T:t3..t5 G:g3..g4 T:t3..t4 T:t4..t5 G:g4+1..g5 G:g0..g5 T:t0..t5 insertion in transcript relative to genome (deletion in genome relative to transcript) deletion in transcript relative to genome (insertion in genome relative to transcript) Genome G Transcript T T:t1..t1+1 G:g1..g1 t0 t1 t1+1 t2 g0 g1 g2 g3 g4 g4+1 g5 t3 t4 t5 G:g4..g4+1 T:t4..t4
- 15. Genometry Modelling of Insertions and Deletions #4 G:g0..g1 T:t0..t1 T:t1+1..t2 G:g1..g2 G: g0..g2 T:t0..t2 …AGGCAATTAATTGATCCAGGTG……GAGTCCGAATAGGGTTAGCG… GCAATTCAATTGATCCAG TCCGAATAGGTTAGCG G:g3..g5 T:t3..t5 G:g3..g4 T:t3..t4 T:t4..t5 G:g4+1..g5 G:g0..g5 T:t0..t5 insertion in transcript relative to genome (deletion in genome relative to transcript) deletion in transcript relative to genome (insertion in genome relative to transcript) Genome G Transcript T t0 t1 t1+1 t2 g0 g1 g2 g3 g4 g4+1 g5 t3 t4 t5 T:t1..t1+1 G:g1..g1 “C”:0..1 T:t4..t4 G:g4..g4+1 “G”:0..1
- 16. Modelling SNPs with Genometry: Two Approaches SeqB : 0..n SeqA : 0..x SeqB : 0..x “T” : 0..1 SeqB : x..x+1 SeqA : 0..m SeqA : x+1..m SeqB : x+1..n SeqA : x..x+1…GGCAAGGAATGATC…SeqA x x+1 …GGCAAGGAATGATC…SeqA SeqB …GGCAAGTAATGATC… x x+1 SeqA = reference chromosome SeqB = exactly same as reference chromosome, except for one SNP I. SNPs as annotations of differences between sequences II. SNPs as gaps in similarity between two sequences T SeqB : x..x+1 SeqA : x..x+1…GGCAAGGAATGATC…SeqA SeqB …GGCAAGTAATGATC… x x+1 “T” : 0..1 SeqA : x..x+1…GGCAAGGAATGATC…SeqA T x x+1 I.a. annotation of just reference seq I.b. annotation of reference seq w/ variant base I.c. annotation of reference and variant seq
- 17. Modelling SNPs with Genometry: Two Approaches SeqB : 0..n SeqA : 0..x SeqB : 0..x “T” : 0..1 SeqB : x..x+1 SeqA : 0..m SeqA : x+1..m SeqB : x+1..n SeqA : x..x+1…GGCAAGGAATGATC…SeqA x x+1 …GGCAAGGAATGATC…SeqA SeqB …GGCAAGTAATGATC… x x+1 SeqA = reference chromosome SeqB = exactly same as reference chromosome, except for one SNP I. SNPs as annotations of differences between sequences II. SNPs as gaps in similarity between two sequences T SeqB : x..x+1 SeqA : x..x+1…GGCAAGGAATGATC…SeqA SeqB …GGCAAGTAATGATC… x x+1 “T” : 0..1 SeqA : x..x+1…GGCAAGGAATGATC…SeqA T x x+1 I.a. annotation of just reference seq I.b. annotation of reference seq w/ variant base I.c. annotation of reference and variant seq
- 18. Sequence-oriented annotations • AnnotatedBioSeq – Contains a collection of SeqSymmetries that annotate the sequence – Interfaces to retrieve annotations covered by a span within the sequence
- 19. Annotation Networks • Can traverse networks of annotations, alternating between AnnotatedBioSeqs and SeqSymmetries protein2mRNA proteinSpanB mrnaSpanB mRNA2genomic genomicSpanC mrnaSpanC Annotated GenomicSeq G Annotated mRNASeq M Annotated ProteinSeq P m2gSub0 gSpanC0 mSpanC0 m2gSub1 gSpanC1 mSpanC1 m2gSub2 gSpanC2 mSpanC2 domainOnProtein proteinSpanA = AnnotatedBioSeq = SeqSymmetry
- 20. Sequence Composition • CompositeBioSeq – Contains a SeqSymmetry describing the mapping of BioSeqs used in composition to the CompositeBioSeq itself
- 21. Sequence Composition Representations • Sequence Assembly / Golden Path / etc. • Piecewise data loading / lazy data loading • Genotypes • Chromosomal Rearrangements • Primer construction • Reverse Complement • Coordinate Shifting
- 22. Genometry Modelling of Reverse Complement Sequence B = reverse complement of Sequence A BioSeq A length: x Composite BioSeq B length: x A:0..x B:x..0 Sym AB composition AGGCAATTAATTGATCCAGGTGGAGTCCGAATAGGGTTAGCGA TCGCTAACCCTATTCGGACTCCACCTGGATCAATTAATTGCCT SeqA SeqB
- 23. MultiSequence Alignments • MultiSeqAlignment – Alignments sliced “horizontally” -- each “row” in an alignment is a CompositeBioSeq whose composition maps another BioSeq to the same coord space as the alignment • Can also slice vertically (synteny)
- 24. Alignment Representations • Can represent same alignment as either MultiSeqAlignment or Synteny • Transformation from horizontal slicing (MultiSeqAlignment) to vertical slicing (Synteny)
- 25. Complete Genometry Core Models • Mutability • Curations
- 26. Genometry Manipulations • Symmetry Intersection (AND) • Symmetry Union (OR) • Symmetry Inverse (NOT) • Symmetry Mutual Exclusion (XOR) • Symmetry Transformation / Mapping
- 27. Symmetry Combination Operations SymA SymB XOR(A, B) AND(A, B) OR(A, B) NOT(A) NOT(B)
- 28. Genometry Transformations • Every symmetry of breadth > 1 describes a mapping between different sequences • Therefore every symmetry can be used to transform coordinates of other symmetries from one sequence to another • Because sequence annotations, alignments, and composition are all based on symmetries, can use any of them as mappings • Discontiguous linear mapping algorithm • Results of transformation are also symmetries
- 29. Coordinate Mapping (note that domain mapped to spliced transcript only overlaps two of the three exons, hence only end up with two children for resulting domain2genomic symmetry) Example – mapping domain from protein coords to genomic coords protein2mRNA proteinSpanB mrnaSpanB mRNA2genomic genomicSpanC mrnaSpanC Annotated GenomicSeq G Annotated mRNASeq M Annotated ProteinSeq P m2gSub0 gSpanC0 mSpanC0 domain2genomic proteinSpanA d2gSub0 pSpanA0 mSpanA0 gSpanA0 domain2genomic proteinSpanA mrnaSpanA domain2genomic proteinSpanA mrnaSpanA genomicSpanA d2gSub1 pSpanA1 mSpanA1 gSpanA1 transform via protein2mRNA transform via mRNA2genomic m2gSub1 gSpanC1 mSpanC1 m2gSub2 gSpanC2 mSpanC2 domainOnProtein proteinSpanA = AnnotatedBioSeq (BioSeq) = SeqSymmetry (SeqAnnot) “Growing” domain2genomic result = MutableSeqSymmetry
- 30. mRNA2genomic genomicSpanC mrnaSpanC m2gSub0 gSpanC0 mSpanC0 m2gSub1 gSpanC1 mSpanC1 m2gSub2 gSpanC2 mSpanC2 domain2genomic proteinSpanA mrnaSpanA domain2genomic proteinSpanA mrnaSpanA d2gSub0 mSpanA0 domain2genomic proteinSpanA mrnaSpanA d2gSub0 mSpanA0 pSpanA0 domain2genomic proteinSpanA mrnaSpanA d2gSub0 mSpanA0 pSpanA0 gSpanA0 d2gSub0 pSpanA0 mSpanA0 gSpanA0 domain2genomic proteinSpanA mrnaSpanA genomicSpanA d2gSub1 pSpanA1 mSpanA1 gSpanA1 domain2genomic proteinSpanA mrnaSpanA d2gSub0 mSpanA0 pSpanA0 gSpanA0 d2gSub1 mSpanA1 pSpanA1 gSpanA1 step1b step1cstep1a step 2 step1 (loop2) [a,b,c] Step 2 “roll up” Step 1a “sit still” Step1b “roll back” Step1c “roll forward” Step 1 Details of “split” mapping
- 31. Transformations Applications • Mapping Affy probes to genome • Mapping contig annotations to larger genomic assemblies • Mapping protein annotations to genome • Mapping genomic annotations to proteins and transcripts (SNPs, for example) • Sequence slice-and-dice with annotation propagation • Propagation of annotations across versioned sequences (such as Golden Path) • Deep mappings (for example, SNP to genomeA to transcriptB to proteinC to homolog proteinD to transcriptE to genomeF to putative SNP location in genomeF – symmetry path of depth 5) • Etc., etc.
- 32. Prototypes & Applications • GenometryTest • Generic Genometry Viewer • ProtAnnot (Ann) • GPView (Cyrus) • AlignView (Eric) • ContigViewer (Peter, Barry) • Unibrow (Transcriptome Group)
- 33. Genometry Summary • Genometry presents a unified model for location-based sequence relationships • Sequence annotation, composition, and alignment are all based on SeqSymmetry • Provides powerful genometry manipulations -- any SeqSymmetry can be used to map other SeqSymmetries across sequences / coordinate spaces • Work in progress

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment