Deconvoluting BAC-gene Deconvoluting BAC-gene Relationships Using - PowerPoint PPT Presentation

Deconvoluting BAC-gene Deconvoluting BAC-gene Relationships Using Relationships Using a Physical Map a Physical Map Y. Wu 1 1 , L. Liu , L. Liu 1 1 , T. Close , T. Close 2 2 , S. Lonardi , S. Lonardi 1 1 Y. Wu 1 1 Department of Computer Science & Engineering Department of Computer Science & Engineering 2 Department of Botany & Plant Sciences Department of Botany & Plant Sciences 2

Selective sequencing Selective sequencing • Many organisms are unlikely to be Many organisms are unlikely to be • sequenced in the near future due to the sequenced in the near future due to the large size and highly repetitive content of large size and highly repetitive content of their genomes their genomes • Selective sequencing: Selective sequencing: obtain the sequence obtain the sequence • of a small set of BAC clones that contain a of a small set of BAC clones that contain a specific set of genes of interest specific set of genes of interest • How do we identify these BAC clones? How do we identify these BAC clones? • BAC-gene deconvolution problem BAC-gene deconvolution problem Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

An illustration of the problem An illustration of the problem Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

An illustration of the problem An illustration of the problem Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

An illustration of the problem An illustration of the problem Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

Hybridization with probes Hybridization with probes • The presence of a gene in a BAC can be The presence of a gene in a BAC can be • determined by an hybridization experiment determined by an hybridization experiment (e.g., using a unique unique probe designed from it) probe designed from it) (e.g., using a • Given that typically BAC clones and probes Given that typically BAC clones and probes • could be in the order of tens of thousands, could be in the order of tens of thousands, carrying out an experiment for each pair carrying out an experiment for each pair (BAC,probe) is usually unfeasible (BAC,probe) is usually unfeasible • Group testing Group testing (or (or pooling pooling ) has to be used ) has to be used • Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

Hybridization with pools of probes Hybridization with pools of probes • Probes can be arranged into pools for Probes can be arranged into pools for • group testing. However, i However, in order to achieve n order to achieve group testing. exact deconvolution this strategy could be exact deconvolution this strategy could be still unfeasible due to the large number of still unfeasible due to the large number of pools pools • Question Question : Can we use a small number of : Can we use a small number of • pools (e.g., 1- or 2-decodable pool design) pools (e.g., 1- or 2-decodable pool design) and still achieve accurate deconvolution? and still achieve accurate deconvolution? Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

Dealing with the limitations of pooling Dealing with the limitations of pooling • Answer: Answer: Yes, if one compensates for the Yes, if one compensates for the • lack of information obtained by a weak lack of information obtained by a weak pooling design with the knowledge of the pooling design with the knowledge of the overlapping structure of the BACs overlapping structure of the BACs • In this way, the number of pools required In this way, the number of pools required • is reduced ⇒ less expensive/time- less expensive/time- is reduced ⇒ consuming consuming Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

Hybridization data Hybridization data h(b,p)=1 (pool (pool p p hybridizes to BAC hybridizes to BAC b b ) ) h(b,p)=1 – b b must must contain at least one of the contain at least one of the – probes/genes represented by p p probes/genes represented by – positive information positive information – h(b,p)=0 (pool (pool p p does not hybridize to BAC does not hybridize to BAC b b ) ) h(b,p)=0 – b b cannot cannot contain any of the probes/genes contain any of the probes/genes – represented by p p represented by – negative information negative information – Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

Deconvolution problem Deconvolution problem • Given h(b,p) h(b,p) for all pairs for all pairs (b,p) (b,p) the the • Given deconvolution problem is to establish a is to establish a deconvolution problem one-to-many assignment between the one-to-many assignment between the probes p p and the clones and the clones b b in such a way in such a way probes that it satisfies the value of h h that it satisfies the value of 1. Basic deconvolution: uses only on Basic deconvolution: uses only on 1. information obtained from group testing information obtained from group testing 2. Improved deconvolution: also uses the Improved deconvolution: also uses the 2. physical map physical map Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

Input to the basic deconvolution Input to the basic deconvolution Hybridization table h p 1 p 2 p 3 p 4 h p p p p 1 2 3 4 b 1 1 0 0 0 b 1 0 0 0 1 b 2 1 1 0 0 b 1 1 0 0 2 b 3 0 1 1 0 b 0 1 1 0 3 b 4 0 0 1 1 b 0 0 1 1 4 b 5 0 0 0 1 b 0 0 0 1 5 p i is a pool b j is a BAC u k is a probe/gene Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

Input to the basic deconvolution Input to the basic deconvolution Hybridization table Pool content table h p 1 p 2 p 3 p 4 h p p p p 1 2 3 4 u 1 u 2 u 3 u 4 u 5 u 6 u 7 u 8 u 9 u u u u u u u u u b 1 1 b 1 1 2 3 4 5 6 7 8 9 1 p 1 1 1 1 p 1 1 1 b 2 1 1 b 1 1 1 2 p 2 1 1 1 p 1 1 1 b 3 1 1 b 1 1 2 3 p 3 1 1 1 p 1 1 1 b 4 1 1 b 1 1 3 4 p 4 1 1 1 p 1 1 1 b 5 1 b 1 4 5 p i is a pool b j is a BAC u k is a probe/gene Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

Positive information Positive information u 1 u 2 u 3 u 4 u 5 u 6 u 7 u 8 u 9 u u u u u u u u u 1 2 3 4 5 6 7 8 9 b 1 ,p 1 1 1 1 b 1 ,p 1 1 1 1 b 2 ,p 1 1 1 1 b 2 ,p 1 1 1 1 b 2 ,p 2 1 1 1 b 2 ,p 1 1 1 2 b 3 ,p 2 1 1 1 b 3 ,p 1 1 1 2 b 3 ,p 3 1 1 1 b 3 ,p 1 1 1 3 b 4 ,p 3 1 1 1 b 4 ,p 1 1 1 3 b 4 ,p 4 1 1 1 b 4 ,p 1 1 1 p i is a pool 4 b j is a BAC b 5 ,p 4 1 1 1 b 5 ,p 1 1 1 u k is a probe/gene 4 Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

Negative information Negative information u 9 u u 1 u 2 u 3 u 4 u 5 u 6 u 7 u 8 u u u u u u u u 9 1 2 3 4 5 6 7 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 b 1 b 1 0 0 0 0 0 b 2 0 0 0 0 0 b 2 0 0 0 0 0 0 0 0 0 0 0 0 b 3 b 3 0 0 0 0 0 0 0 0 0 0 b 4 b 4 b 5 0 0 0 0 0 0 0 b 0 0 0 0 0 0 0 5 p i is a pool b j is a BAC u k is a probe/gene Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

Combining positive & negative Combining positive & negative u 1 u 2 u 3 u 4 u 5 u 6 u 7 u 8 u 9 u u u u u u u u u 1 2 3 4 5 6 7 8 9 b 1 ,p 1 1 1 1 b 1 ,p 1 1 1 1 b 2 ,p 1 1 1 1 b 2 ,p 1 1 1 1 b 2 ,p 2 1 1 1 b 2 ,p 1 1 1 2 b 3 ,p 2 1 1 1 b 3 ,p 1 1 1 2 b 3 ,p 3 1 1 1 b 3 ,p 1 1 1 3 b 4 b 4 ,p ,p 3 1 1 1 1 1 1 3 b 4 ,p 4 1 1 1 b 4 ,p 1 1 1 p i is a pool 4 b j is a BAC b 5 ,p 4 1 1 1 b 5 ,p 1 1 1 4 u k is a probe/gene Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

Combining positive & negative Combining positive & negative u 1 u 2 u 3 u 4 u 5 u 6 u 7 u 8 u 9 u u u u u u u u u • Each row represents • Each row represents 1 2 3 4 5 6 7 8 9 a constraint constraint to be to be a b 1 ,p 1 1 1 b 1 ,p 1 1 satisfied 1 satisfied b 2 ,p 1 1 1 1 b 2 ,p 1 1 1 • If a row contains only • If a row contains only 1 one “ “1 1” ”, then the , then the one b 2 ,p 2 1 1 b 2 ,p 1 1 relationship between relationship between 2 the BAC and probe the BAC and probe b 3 ,p 2 1 1 b 3 ,p 1 1 2 is resolved exactly is resolved exactly b 3 ,p 3 1 1 b 3 ,p 1 1 3 b 4 b 4 ,p ,p 3 1 1 1 1 3 b 4 ,p 4 1 1 1 b 4 ,p 1 1 1 p i is a pool 4 b j is a BAC b 5 ,p 4 1 1 b 5 ,p 1 1 4 u k is a probe/gene Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007

Physical map-assisted deconvolution Physical map-assisted deconvolution Contig 1 Contig 2 • Basic deconvolution is not sufficient Basic deconvolution is not sufficient • • BACs are assembled into BACs are assembled into contigs contigs by FPC (a by FPC (a • contig is a set of BAC clones) is a set of BAC clones) contig • We assume the probes are unique We assume the probes are unique ⇒ each probe • ⇒ each probe can belong to exactly one contig contig can belong to exactly one Stefano Lonardi , Department of Computer Science and Engineering, CSB 2007