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Genome Viewer Sequence Alignments Sharing Data Automation Map Viewer

Genome Viewer Sequence Alignments Sharing Data Automation Map Viewer Genetic Marker Correspondences FPC Map FPC I-Map Future Goals EnsEMBL Pipeline. Genome Viewer. Aligned Data Sets:. Rice TIGR GIs Rice BGI EST Clusters Rice dbEST ESTs Rice BGI ESTs Rice Complete CDSs.

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Genome Viewer Sequence Alignments Sharing Data Automation Map Viewer

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  1. Genome Viewer • Sequence Alignments • Sharing Data • Automation • Map Viewer • Genetic Marker Correspondences • FPC Map • FPC I-Map • Future Goals • EnsEMBL Pipeline

  2. Genome Viewer

  3. Aligned Data Sets: Rice TIGR GIs Rice BGI EST Clusters Rice dbEST ESTs Rice BGI ESTs Rice Complete CDSs Maize Unigene Clusters Maize TIGR Gis Maize dbEST ESTs Barley dbEST ESTs Wheat dbEST ESTs Sorghum dbEST ESTs Rice CUGI BACends Rice JRGP/Cornell RFLP Markers Rice Cornell SSRs

  4. Alignment Methods: • Rice ESTs: • BLAT • pslReps • Discard based on percent of EST length matched • Non-Rice ESTs: • BLAT • pslReps • Discard based on hit length and hit frequency • Rice BACends: • BLAT • Discard based on gap length, percent of BACend length matched, percent identity, and hit frequency.

  5. Total BACs/PACs: 1,847 Total bp: 250,879,896 (250MB ) Phase 1: 78 Phase 2: 1,238 Phase 3: 531 Transcripts: 8,034 (~80,000) Phase 3 with genes: 330 (~%18)

  6. Alignment Methods: • Rice Markers: • BLAT • Discard based on percent of marker length matched and the gap length in case of genomic markers. • Utilize loci information, outputting hits only for those whose chromosome matches that of the BAC/PAC. • Rice SSRs: • e-PCR

  7. Alignment Totals

  8. Sharing Results • All processed alignments available under “Downloads”. • Send to GrainGenes a list of BAC/PAC and EST Accessions for Barley and Wheat. • Send to maizdb locations of each mapped Unigene cluster and if available, closest RFLP marker.

  9. Automating Alignments: • For each group of data sets, there is a script to automatically: • Run pslReps • Load results into the database • Discard low-quality matches • Update documentation

  10. Map Viewer

  11. Map Correspondences Same marker on multiple mapping studies • Name-identity • Curated evidence • Sequence-based correspondences for JRGP and Cornell markers: • BLAT • Utilize loci information, discarding matches from different chromosomes or more than 30cM apart.

  12. curator same name sequence-based

  13. same name curator

  14. FPC data from CUGI, synchronized with the latest release.

  15. Discordant

  16. Cornell/JRGP markers mapped to sequenced clones were assigned positions on the FPC contigs.

  17. Total: 2,272 4,417

  18. Future Goals

  19. EnsEMBL Pipeline • Install the EnsEBML annotation pipeline on the Clemson cluster • Use the pipeline to update alignments • Integrate genomic data to produce uniform annotation of rice

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