1. Reference based assembly
Macrogen Inc
김세환

2. Reconstructing transcripts from RNA-Seq
Denovo assembly는 발현이 많은 transcript만이 fully assembly 되는 경향이 있다.

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. NATURE BIOTECHNOLOGY MAY 2010
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
1. Unique mapped read를 사용하고, splice junction에 align 된 read, 곧 aligned spliced read 사용. 
2. Aligned spliced read로 connectivity graph 구축(canonical intron2 donor 랑 acceptor를 찾는다(GT-AG) 로 connectivity의 방향성을 선택)
3. statistical segmentation approach로 significant path를 찾는다.
4. 3번의 path로 부터 transcript graph를 구축
5. Paired-end read의 connection 정보와 그들의 distance constraint로 부터 떨어져 있던 exon을 합치기도 하고 말도 안되 보이는 isoform들은 제거 하기도 한다. 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 G C C

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
Single isoform 이 있는 유전자들의 paired end를 가지고 insert size distribution을 구한다.
각 path에 떨어지는 paired end들의 I (inferred insert size – average insert size) 를 구한다.
I의 확률 값을 insert size distribution의 면적으로 구한다.
각 path 마다의 paired end의 확률값의 평균을 구한다.
0.1 보다 작은 isoform은 버린다.
(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. NATURE BIOTECHNOLOGY MAY 2010
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 & incompatibility - Compatibility
Incompatiblity
Nested
Uncertain : x4
assembly that parsimoniously explains the fragments from the RNA-Seq experiment : aligned 된 모든 fragment를 설명할 수 있는 최소한의 transcript를 assembly 하는 것이 목표
- compatibility & incompatibility -

16. Transcript Assembly
Nested
incompatible

17. Transcript Assembly
Nested
incompatible
chain

18. Transcript Assembly
Bipartite graph
Directed Acyclic Graph

19. Transcript Assembly Hasse diagram & reachability graph
Theorem (Dilworth's theorem) Let P be a finite partially ordered set. The maximum number of elements in any antichain of 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.
하세 diagram도 그리고 DAG(direct acyclic graph)도 그린다. 그리고 이것의 transitive closure(바로 연결된 edge 뿐만 아니라 a->b, b->c를 통해 a->c 인 edge까지 포함하는 것)인 reachability graph를 그린다
Dilworth’s theorem 문제를 konig’s theorem문제로 바꾸는 이유는 시간 절약(maximum matching problem이 polynomial time algorithm 이라서.)
Hasse diagram & reachability graph

20. the percent-spliced-in x

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
Likelihood 와 probability의 차이 :

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