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Wassinki

Mapping OligoNucleotides to an assembly (7)

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Version info The former version of the workflow expected that results from BioMART only report transcripts when the query (the probe in our case) are entirely encapsulated in an exon of that transcript. However, the BioMart service also returns transcripts when the query is not or only partially overlapping with an exon in the stretch on the assembly on which a transcript is defined. This resulted in too many oligos classified as having multiple transcripts or having multiple genes. ...

Created: 2009-02-13 | Last updated: 2009-02-13

Credits: Wassinki Pieter Neerincx

Attributions: Blat against ENSEMBLE Danio_rerio_Genome BlatBlastCombi Blast against ENSEMBLE Danio_rerio_Genome AppendToFile Test Input for Mapping oligonucleotides to an assembly Input for Mapping oligonucleotides to an assembly

Taverna 2

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Robert Kulawik

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BioVeL ESW DIFF - ENM Statistical Workflow... (16)

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The ENM Statistical Difference Workflow (ESW DIFF) allows the computation of the extent and intensity of change in species potential distribution through computation of the differences between two raster layers using the R statistical environment (R Core Team 2013). The difference file is computed from two input files (in this case present projection and 2050 projection) coming from the Ecological Niche Modelling (ENM) Workflow (http://www.myexperiment.org/workflows/3355). The difference bet...

Created: 2013-07-08 | Last updated: 2016-06-22

Credits: Robert Kulawik

Taverna 1

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Peter Li

Identification of differential genes using... (1)

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This workflow starts by retrieving the names of microarray datasets from the Maxd database. The user has to select sets of control and test data which are then analysed by the LIMMA Bioconductor package in an R script. This produces a list of significant differentially expressed genes which is then analysed using the Go Term Finder tool to generate a PDF report of the common GO terms associated with the genes. A CSV file containing the list of sign...

Created: 2008-07-02 | Last updated: 2008-07-02

Credits: Peter Li

Taverna 1

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Peter Li

Identification of differential genes using... (2)

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This workflow starts by retrieving the names of microarray datasets from the Maxd database. The user has to select sets of control and test data for analysis using t-tests by R. A list of significant differentially expressed genes is then analysed using the Go Term Finder tool which generates a list of GO terms associated with the genes. A CSV file containing the list of significant genes is also generated.

Created: 2008-04-15 | Last updated: 2008-07-01

Credits: Peter Li

Taverna 1

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Pieter Neerincx

AffyArrayNormalization (2)

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The AffyArrayNormalization web services normalise raw Affymetrix GeneChip data. They are wrappers around Philip de Groot's normalization R script to provide remote programmatic access. This example workflow demonstrates the use of the AffyArrayNormalization services. The flow is as follows: A client executes the AffyArrayNormalization_submit service with two inputs: a User object and a collection of URLs linking to CEL files. The User object contains a user ID, a password and an ...

Created: 2009-02-16 | Last updated: 2009-02-16

Credits: Pieter Neerincx Philipg

Taverna 1

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Pieter Neerincx

AffyArrayQualityAnalysis (2)

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The AffyArrayQualityAnalysis web services provide quality control for raw Affymetrix GeneChip data. They are wrappers around Philip de Groot's quality control R script to provide remote programmatic access. This example workflow demonstrates the use of the AffyArrayQualityAnalysis services. The flow is as follows: A client executes the AffyArrayQualityAnalysis_submit service with two inputs: a User object and a collection of URLs linking to CEL files. The User object contains a u...

Created: 2008-03-13 | Last updated: 2009-02-16

Credits: Pieter Neerincx Philipg

Taverna 2

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Robert Kulawik

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BioVeL ESW STACK - ENM Statistical Workflo... (5)

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The ENM Statistical Stack Workflow (ESW STACK) allows the computation of the extent, intensity and a cummulated potential species distribution through computation of an average sum layer from the input raster layers using the R statistical environment (R Core Team 2013). The sum layer is computed from all input files. e.g from different distribution of species as a mean value from each corresponding raster cell values, coming from the Ecological Niche Modelling (ENM) Workflow (http://www.myex...