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Computational identification of genes

Computational identification of genes. Genís Parra. ab initio and comparative approaches. PhD Thesis. Barcelona, December 2004. Summary. General introduction Objectives Ab initio gene prediction methods Comparative gene prediction methods Exon structure conservation Conclusions.

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Computational identification of genes

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  1. Computational identification of genes Genís Parra ab initio and comparative approaches PhD Thesis Barcelona, December 2004

  2. Summary • General introduction • Objectives • Ab initio gene prediction methods • Comparative gene prediction methods • Exon structure conservation • Conclusions

  3. Summary • General introduction • Objectives • Ab initio gene prediction methods • Comparative gene prediction methods • Exon structure conservation • Conclusions

  4. Genetic information

  5. Central dogma:from DNA to proteins

  6. DNA structure A T G G C C C G T G C G A C G

  7. Central dogma:post genomic era view ACTGATGACGTACATGACGTATGACGTAGAC ACTGATGACGTACATGACGTATGACGTAGAC ACTGATGACGTACATGACGTATGACGTAGAC

  8. Why it is so difficult?

  9. Summary • General introduction • Objectives • Ab initio gene prediction methods • Comparative gene prediction methods • Exon structure conservation • Conclusions

  10. Objectives • To develop and test a generic parameter file structure for the new version of geneid. • To build a sets of sequences for different species and develop parameter files from each one. • To analize the signals and the intrisec properties of gene codification in eukariotes. • To develop a method to incorporate genomic comparative information into geneid framework into sgp2. • To provide and distribute both, predicted genes and the developed programs.

  11. Summary • General introduction • Objectives • Ab initio gene prediction methods • Comparative gene prediction methods • Exon structure conservation • Conclusions

  12. ATG STOP AG AG GT AG GT AG GT GT Genome sequence Non-coding exons Coding exons Signals Gene codification

  13. Modeling Biological Signals GAGGTAAAC TCCGTAAGT CAGGTTGGA ACAGTCAGT TAGGTCATT TAGGTACTG ATGGTAACT CAGGTATAC TGTGTGAGT AAGGTAAGT

  14. Coding statistics

  15. Geneid general structure

  16. Final assembly

  17. Different species

  18. Final accuracy

  19. Summary • General introduction • Objectives • Ab initio gene prediction methods • Comparative gene prediction methods • Exon structure conservation • Conclusions

  20. Comparative gene prediction

  21. aplots

  22. Sgp2

  23. Human mouse predictions

  24. Summary • General introduction • Objectives • Ab initio gene prediction methods • Comparative gene prediction methods • Exon structure conservation • Conclusions

  25. Exon structural conservation

  26. Filtering process

  27. Experimental verification

  28. Novel genes

  29. Summary • General introduction • Objectives • Ab initio gene prediction methods • Comparative gene prediction methods • Exon structure conservation • Conclusions

  30. Conclusions • Current version of geneid shows an accuracy comparable, and often superior, to the mos currently used methods. • The generation of geneid parameters files for different species showed an important improvement in the accuracy of predictions. • Our experiments demonstrate that integrating genomic similarity to geneid significantly improves accuracy over standard gene prediction methods.

  31. Conclusions • The enrichement protocol, based on exonic structure conservation between closely related species, has led to an increase of the amplification success ratio. • The synergy

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