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Jeff

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Information about Jeff
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Published on December 28, 2007

Author: Roxie

Source: authorstream.com

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PREditor Predictive RNA Editor for Plant Mitochondrial Genes:  PREditor Predictive RNA Editor for Plant Mitochondrial Genes Jeff Mower Slide2:  What is RNA Editing? A process that alters the RNA sequence Nt insertion, deletion, or conversion Does not include RNA maturation processes Slide3:  RNA Editing in Plants Occurs in mitochondria and chloroplasts C to U and U to C conversions Mechanism is not known Slide4:  RNA Editing in Plants In seed plants (conifers, flowering plants, etc.) Widespread in mitochondrion Rare in chloroplast Predominantly C to U In non-seed plants (mosses, ferns, etc.) Frequent in mitochondrion and chloroplast Both C to U and U to C are common Slide5:  AUG UGA AGACGGUC CAA AAU CGU UCU UGC GGC GUA M R N S V G C Q * UGGC Slide6:  AUG UGA AGACGGUC CAA AAU CGU UCU UGC GGC GUA M R N S V G C Q * UGGC AUG AUG CAA AAU CGU UCU UGC GGC GUA M V M R N S V G C Q * GUC Creation of new start codon AG UGA UGGC Slide7:  AUG UGA AGACGGUC CAA AAU CGU UCU UGC GGC GUA M R N S V G C Q * UGGC AUG AUG CAA AAU UGU UUU UGC GGC GUA M V M C N F V G C Q * GUC Alteration of protein sequence Creation of new start codon AG UGA UGGC Slide8:  AUG UGA AGACGGUC CAA AAU CGU UCU UGC GGC GUA M R N S V G C Q * UGGC AUG AUG CAA AAU UGU UUU UGC GGU GUA M V M C N F V G C Q * GUC Alteration of protein sequence Creation of new start codon AG UGA UGGC No effect on protein sequence Slide9:  AUG UGA AGACGGUC CAA AAU CGU UCU UGC GGC GUA M R N S V G C Q * UGGC AUG UGAUGGC AUG UAA AAU UGU UUU UGC GGU GUA M V M C N F V G C * GUC Alteration of protein sequence Creation of new stop codon Creation of new start codon AG No effect on protein sequence Slide10:  Identifying Edit Sites Determine experimentally Need to isolate and reverse transcribe RNA Need multiple reads (editing is not always complete) Slide11:  Identifying Edit Sites Determine experimentally Need to isolate and reverse transcribe RNA Need multiple reads (editing is not always complete) Predict based on sequence context Upstream and downstream regions are important Unambiguous motifs have not been identified Slide12:  Identifying Edit Sites Determine experimentally Need to isolate and reverse transcribe RNA Need multiple reads (editing is not always complete) Predict based on sequence context Upstream and downstream regions are important Unambiguous motifs have not been identified Predict based on protein conservation Proteins are more conserved after editing Editing tends to “correct” amino acid sequences Slide13:  PREditor Methodology Aligned sequence database (ASD) Construction 363 DNA sequences RNA editing information is known Organisms do not perform RNA editing Proteins were translated using the editing information Homologous proteins were aligned 42 different alignments All known mt proteins are covered Slide14:  PREditor Methodology Input Sequence Manipulation Accept a protein-coding DNA sequence as input Translate input sequence Align translation to homologous proteins in ASD Slide15:  PREditor Methodology The Underlying Principle ASD sequences translated from edited RNA Input sequence translated from unedited DNA “Where can RNA editing in the input sequence increase conservation to the ASD sequences?” Slide22:  Performance Analysis Remove one protein sequence from the database Use the unedited DNA sequence as input Calculate statistics Accuracy = (TP + TN) / (TP + FP + TN + FN) Sensitivity = TP / (TP + FN) Specificity = TN / (TN + FP) Repeat for each sequence Slide23:  Performance Analysis Total # C’s = 58,982 True edited sites = 3,548 (6.0%) TP = 2,922 FN = 626 True non-edited sites = 55,434 TN = 54,829 FP = 605 Slide24:  Performance Analysis Sensitivity = 82.4% Proportion of true edited sites that were predicted correctly Increases to 94.6% if you ignore missed silent edited sites Specificity = 98.9% Proportion of true non-edited sites that were predicted correctly Accuracy = 97.9% Proportion of all sites that were predicted correctly Increases to 98.7% if you ignore missed silent edited sites Slide25:  Limitations Cannot predict editing at silent sites 458 of 626 FN are at silent sites Slide26:  Limitations Cannot predict editing at silent sites 458 of 626 FN are at silent sites Not a major problem in practice Silent editing sites do not affect the protein sequence Many silent sites are only occasionally edited Only 13% of editing sites are silent (expect ~38%) Slide27:  Limitations Aligned sequence database is skewed Origin of RNA editing Angiosperms 266 (74%) Gymnosperms 2 (1%) Ferns 0 Horsetails 0 Hornworts 0 Mosses 0 Liverworts 32 (9%) Charophytes 59 (16%) Chlorophytes ― Slide28:  Limitations The skewed database effect ASD1 Z ASD2 Z ASD3 Z ASD4 Z ASD4 X Input X or Z? Slide29:  Ongoing Work Reducing the skewed database effect Weighted sequences and phylogenetics ASD1 Z ASD2 Z ASD3 Z ASD4 Z ASD4 X Input X! Slide30:  Ongoing Work Making the online resource more appealing Making the online resource more user-friendly Slide31:  Future Directions Increase diversity in the ASD Analyze sequence context using the ASD Apply methodology to editing in chloroplasts Apply methodology to U to C editing Slide32:  Thanks Jeff Palmer Sun Kim Danny Rice and other members of the Palmer lab

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