Reference based assembly

1. Reference based assembly Macrogen Inc 김세환

2. Reconstructing transcripts from RNA-Seq

3. Scripture VS Cufflinks SIMILATIRY Both programs then build directed graphs and traverse the graphs to identify distinct transcripts, using paired end information to link sparsely covered transcripts and filter out unlikely isoforms DIFFERENCE - Cufflinks uses a rigorous mathematical model to identify the complete set of alternatively regulated transcripts at each locus - Scripture employs a statistical segmentation model to distinguish expressed loci and filter out experimental noise

4. Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs(Scripture) NATURE BIOTECHNOLOGY MAY 2010

5. 1 2 3 4 5

6. 1. Map Read to Genome • Using Tophat, • since ~30% of 76 base reads are expected on average to span an exon-exon junction • ‘spliced’ reads provide direct information (GT/AG or GC/AG,AT/AC)

7. 2. Construct Connectivity Graph • Use only ‘spliced’ reads for construction of connectivity graph • Splicing motifs provide direct information (GT/AG or GC/AG,AT/AC) • Node = base, edge = connection between base A G T A G T C G A A G T A A C A A A T C A C A G A G A A A A T A A A A A

8. 3. Identify Significantly Enriched Paths • Use a statistical segmentation strategy : • segmentation approach identifies regions of mapped read enrichment compared to the genomic background A G T A G T C G A A G T A A C A A A T C A C A G A G A A A A T A G A C C C C G

9. 4. Construct Transcript Graphs • Each node in a transcript graph is an exon and each edge is a splice junction • A path through the graph represents one isoform of the gene

10. 5. Weighting of Isoforms Isoform 1 Insert size distribution (Σ probability of insert size of paired read) Normalized weighted score of Isoform 1 = (# of paired read) Filter out :: Normalized weighted score < 0.1

11. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation(Cufflinks) NATURE BIOTECHNOLOGY MAY 2010

12. Cufflinks Cufflinks seek an assembly that parsimoniously explains the fragments from the RNA-Seq experiment; => Every fragment in the experiment should have come from a Cufflinks transcript, and Cufflinks should produce as few transcripts as possible with that property

13. Transcript Assembly Isoform 1 Isoform 2

14. Transcript Assembly

15. Transcript Assembly Compatibility Incompatiblity Nested Uncertain : x4 - compatibility & incompatibility -

16. Transcript Assembly Nested incompatible

17. Transcript Assembly Nested incompatible chain

18. Transcript Assembly Bipartite graph Directed Acyclic Graph

19. Transcript Assembly Theorem (Dilworth's theorem) Let P be a finite partially ordered set. The maximum number of elements in any antichainof P equals the minimum number of chains in any partition of P into chains Theorem (Konig's theorem) In a bipartite graph, the number of edges in a maximum matching equals the number of vertices in a minimum vertex cover. Theorem Dilworth's theorem is equivalent to Konig's theorem. Hasse diagram & reachability graph

22. Transcript Assembly Finally, Finding minimum number of chains in directed acyclic graph is reduced to finding maximum matching problem in bipartite graph This can be solved by LEMON and Boost graph library.

23. Conditions for filtering transctript x • x aligns to the genome entirely within an intronic region of the alignment for a transcript y, and the abundance of x is less than 15% of y's abundance. • x is supported by only a single fragment alignment to the genome. • More than 75% of the fragment alignments supporting x, are mappable to multiple genomic loci. • x is an isoform of an alternatively spliced gene, and has an estimated abundance less than 5% of the major isoform of the gene.

24. Keyword for Fresher 1.Reference-based assembly == mapping-first approach

25. Keyword for Intermediate • 1. Graph theory • - reading recommendation : introduction to graph theory

26. Keyword for Expert 1. Scan statistics

27. Transcript Assembly Bipartite graph Directed Acyclic Graph

28. Transcript Assembly