Next-Generation Informatics
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Talk from the Bioinformatics session of the Advances in Genome Biology and Technology 2009 meeting.
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Next-Generation Informatics
- 1. Next-Generation Informatics David Dooling <ddooling@wustl.edu> AGBT Bioinformatics 2009-02-05
- 3. Framing the problem ,--./01#234# 567# 89-.3:/#;<=># 8/?@/AB/# 6/.1-AA/C# !quot;quot;quot;# !quot;quot;$# !quot;quot;!# !quot;quot;%# !quot;quot;&# !quot;quot;'# !quot;quot;(# !quot;quot;)# !quot;quot;*# !quot;quot;+# !quot;$quot;# ddooling@wustl.edu
- 9. Analysis - cDNA Solexa cDNA reads Maq/Tophat [Transcriptome] OR [Genome + SpliceJunctions (SJs)] OR [Genome] Maq Reads Reads Read SNPs map to map to depth Indels novel SJs or “non-genic” introns regions Velvet GenScan Gene Variant Splice Novel expression discovery/ isotypes Genes (to exquisite ASE sensitivity) ddooling@wustl.edu
- 12. Changing pipelines - LIMS Tech-Specific Primary Prep Submission Prep /Detection Analysis PCR (Technology- Solexa specific) NCBI SRA Hybrid 454 Selection Flow-space NCBI Medical cDNAs SOLiD Color-space Archive . Bisulfite Church Project . Polony(?) Jumping Archives . Libraries (e.g., DCC) Helicos(?) Sample Pooling 3730 Phred NCBI Trace … WGS Courtesy of Toby Bloom ddooling@wustl.edu
- 13. Changing pipelines - Analysis BLAST Phrap BLAT Arachne PASH PCAP ssaha Phusion runMapping Assemblers ELAND Euler Aligners mapreads ATLAS Arachne Newbler MAQ Velvet exonerate Forge SHRiMP SPLIGN SSAKE Mosaik VCAKE SLIM Search Euler-USR SXOligoSearch SHARCGS SOAP2 CABOG NovoCraft Bowtie Tophat ddooling@wustl.edu
- 20. UR • Object-relational mapping (ORM) layer – Interact with persistence layer (e.g., relational database) through objects and methods – Automatic, dynamic class definitions – Moose1-like object definition syntax • Object context – In-memory transactions (even across databases) – Caching/deferred loading • Dynamic command-line interface • Integrated documentation system 1 - http://www.iinteractive.com/moose/ ddooling@wustl.edu
- 24. … but with a wrinkle • Lab personnel accept the software you give them • Analysts are more than happy to develop their own • We need to make it easy for analysts to build tools within the system ddooling@wustl.edu
- 30. Assembly and Annotation Pipeline ddooling@wustl.edu
- 31. Challenges • There is still much more work to do • Sequencing is demolishing Moore’s law • The cult of traces • The richness of data • Visualization ddooling@wustl.edu
- 33. Thanks Web Site http://genome.wustl.edu/ Blog http://www.politigenomics.com/ LIMS Paper http://www.biomedcentral.com/1471-2105/8/362 UR Presentation http://www.media-landscape.com/yapc/2006-06-27.ScottSmith/ ddooling@wustl.edu
Editor's Notes
- There is too much data 4 genomes to more than an order of magnitude increase Move from processing regions to single genomes to multi-genome comparisons This is a story about how we are trying to deal with this problem
- This creates tension
- Sample in -> answer out Don’t care how the sausage was made.
- Never the same pipe twice (TJ Max)
- And expanding beyond the laboratory
- Different aligners, genotypers
- How do we even begin to tackle this problem? How do we resolve the tension between changing pipelines and production systems?
- Metadata Store DNA types, equipment, reagents, even process steps as rows rather than tables So maq is not maq, it is an aligner Standards like SAM help
- Solexa/Maq specific commands
- Generic medical resequencing pipeline
- Never write SQL
- XML and flow chart
- Click on any box to see processing details including file system location
- Screenshot of script vs. module
- photograph
- What I have talked about here is automation There is still much work to do in data reduction
- How do you compare more than three genomes? How do you track all the analysis? So that’s one problem