1 / 20

Overview

Prediction of non-coding RNA and proteins involved in RNAi Michael Muratet, Gopi Podila 3 rd Workshop of the Laccaria Genome Consortium INRA-Nancy 4-5 April, 2006. Overview. Search for non-protein coding RNAs tRNAs, rRNAs, etc. Search for components of RNAi

zach
Download Presentation

Overview

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Prediction of non-coding RNA and proteins involved in RNAiMichael Muratet, Gopi Podila3rd Workshop of the Laccaria Genome ConsortiumINRA-Nancy4-5 April, 2006

  2. Overview • Search for non-protein coding RNAs • tRNAs, rRNAs, etc. • Search for components of RNAi • Proteins in the miRNA biogenesis pathways • miRNA candidate screening • Summary & conclusions • Plans

  3. Non-coding RNA (INFERNAL & RFAM) • INFERNAL is a covariance model for identifying profiles that are indicative of RNA secondary structure • Has analogies to Hidden Markov models • INFERNAL was used in the generation of the RFAM database of non-protein coding RNAs • 280,000 regions in 379 families • We are using INFERNAL and seed data from RFAM to search for non-protein coding RNAs in L. bicolor • Currently in the process of building models appropriate for L. bicolor • Algorithm is finding regions that are close sequence matches but different foldings SEED BEST MATCH (62.5 BITS) Griffiths-Jones, S., Moxon, S., Marshall, M., Khanna, A., Eddy, S., Bateman, A., (2005). Nucleic Acid Research, 33:D121-D124 Eddy, S., and Durbin, R., (1994). Nucleic Acid Research, 22:2079-2088

  4. Where are the fungal miRNAs? http://tolweb.org/Eukaryotes/3 • The existence of miRNA in fungi has yet to be confirmed • Hertel et al report “No plausible [animal] orthologs in Schizosaccharomyzes pombe or Encephalitozoon cuniculi” • Our own search between plants & fungi was unsuccessful • Presents interesting evolutionary questions, but the question here is: are fungal miRNAs more plant-like or animal-like and how does that affect a search strategy? Hertel, J., Lindemeyer, M., Missal, K., Fried, C., Tanzer, A., Flamm, C., Hofacker, I., Stadler, P. (2006) BMC Genomics, 7:25 Kidner, C.A., and Martienssen, R.A. (2005) Current Opinion in Plant Biology, 8:38-44 Du, T., and Zamore, P.D., (2005) Development, 132:4645-4652

  5. Metazoan RNAi Pathways • Overlap exists in the miRNA and siRNA biogenesis pathways • There are key proteins that are diagnostic of animal pathway versus plant pathways • Adopted a working hypothesis that fungal miRNA are more animal-like Filipowicz et al (2005), Curr Op Struct Bio Used with permission

  6. Search Strategy • Search for proteins diagnostic of the pathway • pgpblast with varying threshold • Search for potential genes based on the minimal set of structural features of the transcript • Stem-loop is apparently a requirement for recognition by Exportin-5 and nuclear export • Basis for the very successful mirSeeker and MirScan tools • Search within other fungi • Conservation of pri-miRNA/hairpin • Conservation of UTRs that match mature miRNA • Use the larger length parameters associated with plants

  7. Dicer Genes • Two Dicers known in N. crassa • both are necessary for quelling to occur • We have found two likely matches in L. bicolor Catalanotto, C., Pallotta, M., ReFalo, P., Sachs, M.S., Vayssie, L., Macino, G., Cogoni, C., (2004) Molecular and Cellular Biology, 24:2536-2545

  8. Dicer candidate estExt_fgenesh2_pg.C_120274 domain diagram Dicer 1

  9. Dicer candidate fgenesh3_pg.C_scaffold_6000369 domain diagram Dicer 2

  10. Drosha Drosha candidate eu2.Lbscf0004g06770 domain diagram

  11. Pasha • Several putative Pasha genes have ‘WW’ domains • The ‘WW’ domain was shown to be unnecessary for proper function Pasha candidate fgenesh3_pg.C_scaffold_1000691 domain diagram Han, J., Lee, Y., Yeom, K-H., Kim, Y-K., Jin, H., and Kim, V.N., (2004) Genes and Development, 18: 3016-3027;

  12. Exportin 5

  13. VMATCH PALINDROME SEARCH SQL COLLATION MFOLD 6144 TWINSCAN GENE SEARCH MARCH 2005 SCAFFOLDS SQL COLLATION UTR ESTIMATES miRNA CANDIDATES PATSCAN POLY-A SEARCH Mature miRNA matching UTRs BLAST BLAST utrDB FUNGAL UTRs MIT/BROAD FUNGAL DB Pri- miRNA matching other fungi Screening miRNA Candidates

  14. Hairpin search criteria with VMATCH Kurtz, S., (2003). “The Vmatch large scale sequence analysis software:A Manual”, Center for Bioinformatics, University of Hamburg, Hamburg, Germany 30 nt  L  400 nt 15 nt  Lm 30 nt Nmismatch  3

  15. L. bicolor miRNA Predictions

  16. miRNA Search Results 100 nt matches 239 miRNA/UTR matches 41 miRNA/gene matches 5

  17. Candidate on scaffold_612 3SCE000420: S. cerevisiae SCH9 gene eu2.Lbscf0003g05650: ? • Region predicted as ‘intergenic’ by TwinScan • Other models predict a gene, but the putative miRNA is on the opposite strand

  18. scaffold_319_224 (likely rRNA)

  19. Summary & Conclusions • INFERNAL model is expected to produce satisfactory estimates of non-protein coding RNAs in Laccaria once new seeds are calculated • Aided by rRNA discoveries from miRNA searches • Observed miRNA candidate short stem-loops are consistent with metazoan miRNA model • Observed miRNA candidate long stem-loops are more likely to be rRNA

  20. Plans • Continue INFERNAL searches • Building new seed alignments • 1 pass per week • Revise UTR estimates based on ‘all model’ estimates using BLAT (or GFF files if available) • Initiate biochemical miRNA analyses • Initiate microarray detection assay • Continue computational miRNA searches with novel techniques • SVM

More Related