Twinscan-mars презентация

Содержание

1. Twinscan-mars
2. Twinscan Twinscan incorporates a pair wise alignment
3. Goals & Methods Exploit the differences in
4. Mm Gg Xt
5. Results Successes Successfully predicts alternatively spliced transcripts
6. Results Challenges Transcript clustering in gene dense

Twinscan Twinscan incorporates a pair wise alignment net (with an informant sequence) into its algorithm Twinscan “works” for human gene prediction with almost any mammalian informant (but mice and rats are

Слайд 1Twinscan - MARS
Paul Flicek
Washington University EBI

Слайд 2Twinscan
Twinscan incorporates a pair wise alignment net (with an informant sequence)

into its algorithm
Twinscan “works” for human gene prediction with almost any mammalian informant (but mice and rats are the most useful)
Prediction characteristics are dependent on the informant

Слайд 3Goals & Methods
Exploit the differences in gene characteristics resulting from each

individual informant genome
Create closely-related prediction by incorporating mixtures of pair-wise alignment nets into Twinscan
Cluster these predictions as a way to find multiple transcripts from a single gene

Слайд 4

Mm
Gg
Xt
Rn
Cf
Pair-wise Informant Set
Mouse Informant Set
Human Target Sequence

Слайд 5Results
Successes
Successfully predicts alternatively spliced transcripts de novo
Scales well
Approximately 1.67 transcripts per

gene with the mouse informant set
Sensitive…

Слайд 6Results
Challenges
Transcript clustering in gene dense regions (like the manually picked ENCODE

regions) can be problematic

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Twinscan-mars презентация

Содержание

Слайд 1Twinscan - MARS
Paul Flicek
Washington University EBI

Слайд 2Twinscan
Twinscan incorporates a pair wise alignment net (with an informant sequence)

Слайд 3Goals & Methods
Exploit the differences in gene characteristics resulting from each

Слайд 4

Mm
Gg
Xt
Rn
Cf
Pair-wise Informant Set
Mouse Informant Set
Human Target Sequence

Слайд 5Results
Successes
Successfully predicts alternatively spliced transcripts de novo
Scales well
Approximately 1.67 transcripts per

Слайд 6Results
Challenges
Transcript clustering in gene dense regions (like the manually picked ENCODE

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Twinscan-mars презентация

Содержание

Слайд 1Twinscan - MARSPaul FlicekWashington University EBI

Слайд 2TwinscanTwinscan incorporates a pair wise alignment net (with an informant sequence)

Слайд 3Goals & MethodsExploit the differences in gene characteristics resulting from each

Слайд 4MmGgXtRnCfPair-wise Informant SetMouse Informant SetHuman Target Sequence

Слайд 5ResultsSuccessesSuccessfully predicts alternatively spliced transcripts de novoScales wellApproximately 1.67 transcripts per

Слайд 6ResultsChallengesTranscript clustering in gene dense regions (like the manually picked ENCODE

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Обратная связь

Что такое ThePresentation.ru?

Слайд 1Twinscan - MARS
Paul Flicek
Washington University EBI

Слайд 2Twinscan
Twinscan incorporates a pair wise alignment net (with an informant sequence)

Слайд 3Goals & Methods
Exploit the differences in gene characteristics resulting from each

Слайд 4

Mm
Gg
Xt
Rn
Cf
Pair-wise Informant Set
Mouse Informant Set
Human Target Sequence

Слайд 5Results
Successes
Successfully predicts alternatively spliced transcripts de novo
Scales well
Approximately 1.67 transcripts per

Слайд 6Results
Challenges
Transcript clustering in gene dense regions (like the manually picked ENCODE