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PAVE Overview Assembler and SNP finderWill WEB annotateWill WEB displayWill Java annotateCari Java viewPAVECari and Mark Java cmpPAVEMark Data organization.

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Presentation on theme: "PAVE Overview Assembler and SNP finderWill WEB annotateWill WEB displayWill Java annotateCari Java viewPAVECari and Mark Java cmpPAVEMark Data organization."— Presentation transcript:

1 PAVE Overview Assembler and SNP finderWill WEB annotateWill WEB displayWill Java annotateCari Java viewPAVECari and Mark Java cmpPAVEMark Data organization Overall design Cari 1

2 www.plantrhizomes.org/progress.html 2

3 454 MSU 3

4 Illumina NCGR 4

5 Assemblies 5

6 454+Ilm RedRice( OlR) 6 Total 454: 17,624 Total Illumina: 21,083 Need to compute Illimina low coverage (~singletons) from exp level.

7 7

8 454+Ilm Ginger (ZoR) 8

9 PAVE assembler Assemble Sanger with mate-pairs – retaining mate-pairs in contigs Assemble 454 – can handle ~500,000 easily by burying redundancy Assemble consensus sequences from 454 and Illumina SNPs – most use two confirming bases, but with 454 there is way too many false-positives due to homopolymers. So a ‘p-value’ is computed based on number of confirming ESTs, depth of ESTs at the base, and estimated base-call error. Script to add expression level 9

10 Web PAVE 10

11 viewPAVE 11

12 12

13 13

14 14

15 15 Ginger with Illumina expression levels

16 cmpPAVE 16

17 UniProt table 17

18 18 Unitrans tables

19 19

20 Immediate Future Streamline and combine web and java annotation cmpPAVE Show alignments of: a protein to all its UniTrans a UniTrans to all its proteins Self-blast all UniTrans, create clusters of cliques, display and filters similar to UniProt Incorporate GO, EC, etc viewPAVE Show alignment of all proteins for a UniTran Show coverage of reads as histogram Cluster similar UniTrans (instead of Pairs) Incorporate GO and EC (i.e. same functionality as Web) Web PAVE Remove count of ESTs - instead indicate protein coverage How much we further extend….. 20

21 21

22 22 NRO: would need to reduce hits to D6NDn_9POAL because one could have best be 42 and another 43….

23 Clustering Best clustering for paralogs in viewPAVE and orthologs in cmpPAVE: Same protein region Prodom and/or Motif EC GO/GoSlim 23


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