Index Page
ZENBU Interfaces
- ZENBU Genome Browser (gLyphs)
- Data Explorer (DEX)
- User profile system
- Data download : processed or raw data export
Data stream processing
- ZENBU on demand data processing
- Processing modules
- Infrastructure modules
- Proxy: provide security-checked access to data sources loaded into ZENBU
- FeatureEmitter: create regular grids of features dynamically
- Clustering and collation
- TemplateCluster: use side-chain-stream as template to collate expression.
- UniqueFeature: cluster and count features matching 'unique' criteria
- Paraclu: hierarchical clustering algorithm http://www.cbrc.jp/paraclu/
- Filtering
- TemplateFilter: use side-chain-stream as mask to filter primary stream features
- CutoffFilter: filter features using simple cutoff filters (high pass, low pass, band pass)
- ExpressionDatatypeFilter: filter expression from features based on datatype
- FeatureLengthFilter:
- TopHits:
- NeighborCutoff: noise filtering relative to strongest signal within a neighborhood
- Data normalization and rescaling
- General manipulation
- CalcFeatureSignificance:
- CalcInterSubfeatures:
- StreamSubfeatures:
- FilterSubfeatures: rebuild a feature/subfeature structure by filtering subfeatures
- ResizeFeatures:
- MakeStrandless:
- RenameExperiments:
- FeatureRename:
- Infrastructure modules
Metadata and search systems
Track, View and Script configurations
Experimental data types
Case studies
- Case studies overview
- Uploading annotation from other sources : UCSC repetitive elements track
- RNAseq overview
- shortRNA overview
- CHiP-seq overview
- Processing CAGE data
- Clustering CAGE along predefined regions
In this case study we will illustrate how to vizualize CAGE based expression (yellow background track) along predefined clusters (blue background track) and collate expression onto themc (green background track). The example below uses the FANTOM4 CAGE data and significant clusters boundaries geenrated by the FANTOM4 consortium in The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line Nat Genet. 2009 May;41(5):553-62.
http://fantom.gsc.riken.jp/zenbu/gLyphs/#config=hXV1oi_zCdOjH3oPeHd2VC;loc=hg18::chr19:54860670..54861358 - Clustering CAGE along gene models
- de-novo CAGE signal clustering with "Paraclu"
- Repeat associated Transcription Start Sites
- Clustering CAGE along predefined regions
- Combining multi datatypes into a meta-analysis