Pack: useful_for_sysmo

Description:

This service takes an SBML file (for example from the Biomodels database) and extracts synonyms for each of the SBML species in the file, using ChEBI. Service is written and maintained by Neil Swainston at the MCISB.

Description:

This workflow performs a reaction enumeration. Therefore it loads a generic reaction rxn file and two educt lists. This SD-Files contains molecules for the enumeration. The products of the enumerated reaction are stored as Mol Files and a PDF will be created which contains the product molecules.

Description:

This workflow performs a topological substructure search. The molecules to be searched are loaded from a MDL SD file. The substructure is loaded from a SMILES (workflow input). The matched molecules are converted to CML and for the molecules which do not contain the substructure the InChI is generated.

Description:

This workflow load molecules into a database. The molecules originally are stored in a MDL SD file. As workflow input an origin is added to each molecule which gets stored into the database. The output of the workflow shows logs from the database insert process.

Description:

This workflow reads the molecules form the an MDL SD File and stores them into a database. The database used here is a Postgres SQL database which uses the PGChem::Tigress chemoinformtic extension.

To run this workflow you have to install the CDK-Taverna Plug-in for Taverna 1.7.1.0 from http://cdk-taverna.de/plugin

Description:

Get details of an ontology term given its identifer.

This workflow uses the EBI's Ontology Lookup Service (OLS) to get the details of the ontology term. The OLS suports a wide range of biological and bioinformatic ontologies. See http://www.ebi.ac.uk/ontology-lookup/ for more information.

Description:

This workflow takes in a CDNA raw file and a normalisation method then returns a series of images/graphs which represent the same output obtained using the R and bioconductor. Also retruned by this workflow are a list of the top differentialy expressed genes (size dependant on the number specified as input - geneNumber), which are then used to find the candidate pathways which may be influencing the observed changes in the microarray data. By identifying the candidate pathways, more detailed insights into the gene expression data can be obtained. These pathways are subsequently used to obtain a corpus of published abstracts (from the PubMed database) relating to each biological pathway identified. These pathways are subsequently used to obtain a corpus of published abstracts (from the PubMed database) relating to each biological pathway identified. Also it generates a pie chart which, indicates the number of genes in a dataset that are regulated by a known transcriptional regulator, or by combination of regulators, and can suggest previously unknown regulatory interactions. The information for each regulon comes from files that are created manually from the EcoCyc database.

NOTE - You will also need to install R and Rserv on your machine and install the libaries required by the R script into you R library directory

The example inputs for this workflow are as follows:

geneNumber = the number of differentialy expressed gene to be returned above a given p-value, e.g. 20 path = the direct path to the raw data file location, e.g. C:/Microarray_Data/FILES/ - note the forward slashes p-value = the p-value cut-off value for the array data, e.g. 0.05 foldChange = the fold change value for the microarray data, e.g. 1 (means greater than 1 or less than -1) regulonDir = the direct path to the regulon file.

Description:

This workflow takes a spreadsheet exported as a tab delimited file, such as an Excel spreadsheet. The style is expected as a matrix of data with the first row containing the column names and the first column containing the row names (see below). The top left cell is discarded.

The workflow outputs three lists. A list of column names, a list of row names, and a two deep list of data points. The first list is a list of the rows and the sublist is a list of values of the cells from the columns.

Description:

uses InChI's retrieved from a MassBank peaklist query to get compound information about those compounds via querying ChemSpider for information and displaying those results with image

Description:

updated version of Download from ChemSpider using Accurate Mass to make use of the new ChemSpider services

Description:

No description available

Description:

A (very) simple demo pipeline that takes an experiment ID, gets the data from the (currently in beta) metware metabolomics datawarehouse and runs XCMS.

Description:

This workflow takes in probesets from and AffyMetrix micorarray experiment and returns: the genes ; gene start and end positions; chromosome where genes reside; ensembl trasncripts; SwissProt ids; affy probeset identifiers for chips Mouse430_2 and Mouse430a_2.

Example ids from the Mouse430_2 affymetrix array are as follows (newline separated):

1447227_at
1440624_at
1436240_at
1454904_at
1435665_at
1418148_at
1429831_at

Description:

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 significant genes is also generated.

Description:

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.

Description:

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 e-mail address. Currently the user ID and password can be any characters [a-zA-Z0-9]. Just pick something, there's no need to register them first. They are only used to make sure that the one who tries to download the results is the same person as the one who submitted the job.
• Your job will be submitted to the SUN Grid Engine on the NuGO R-server. The e-mail address is used by the Sun Grid Engine to notify you when your job is done. We might also use it to send you feedback in case something goes wrong with your job, but it won't be used for anything else and will only be stored for a maximum of 7 days (together with your job's results).
• The URLs to the CEL files must use either the http or https protocol. You can restrict access to these URLs using basic authentication and putting the username and password in the URL. For example if the user is pieter and the password is test you could have a URL like this: https://pieter:test@lab5.bioinformatics.nl/phenolink/home/TisMix_mix5a_01_v1_U133plus2.CEL. Hence you have to put the CEL files somewhere on a web server, so the AffyArrayNormalization_submit service can download them.
• The AffyArrayNormalization_submit service returns a job ID and a link to the results. Once the job is done this link can be used to download the results. Results will be available for 7 days after which they will be deleted automatically.
• The job ID is used to execute the AffyArrayNormalization_poll service inside a nested workflow. AffyArrayNormalization_poll returns the job status and unless the status is "finished" the entire nested workflow will fail. If the nested CheckStatus workflow fails, Taverna will automatically retry until it succeeds and hence the job has finished (or until the maximum number of retries is reached).
• The nested DownloadFile workflow depends on successful completion of the nested CheckStatus workflow. The name says it all: It downloads the result, which is a single ZIP file. This workflow does not take care of unzipping the archive. You have to do that yourself.

AffyArrayNormalization uses a secure connection over HTTPS. To make this work you *must* import our SSL certificates in your local Java keystores. You can do this either manually as described at https://www.bioinformatics.nl/phenolink/home/JavaAndHTTPS.html or use a Java tool to do the job as described at http://www.myexperiment.org/files/148

Description:

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 user ID, a password and an e-mail address. Currently the user ID and password can be any characters [a-zA-Z0-9]. Just pick something, there's no need to register them first. They are only used to make sure that the one who tries to download the results is the same person as the one who submitted the job.
• Your job will be submitted to the SUN Grid Engine on the NuGO R-server. The e-mail address is used by the Sun Grid Engine to notify you when your job is done. We might also use it to send you feedback in case something goes wrong with your job, but it won't be used for anything else and will only be stored for a maximum of 7 days (together with your job's results).
• The URLs to the CEL files must use either the http or https protocol. You can restrict access to these URLs using basic authentication and putting the username and password in the URL. For example if the user is pieter and the password is test you could have a URL like this: https://pieter:test@lab5.bioinformatics.nl/phenolink/home/TisMix_mix5a_01_v1_U133plus2.CEL. Hence you have to put the CEL files somewhere on a web server, so the AffyArrayQualityAnalysis_submit service can download them.
• The AffyArrayQualityAnalysis_submit service returns a job ID and a link to the results. Once the job is done this link can be used to download the results. Results will be available for 7 days after which they will be deleted automatically.
• The job ID is used to execute the AffyArrayQualityAnalysis_poll service inside a nested workflow. AffyArrayQualityAnalysis_poll returns the job status and unless the status is "finished" the entire nested workflow will fail. If the nested CheckStatus workflow fails, Taverna will automatically retry until it succeeds and hence the job has finished (or until the maximum number of retries is reached).
• The nested DownloadFile workflow depends on successful completion of the nested CheckStatus workflow. The name says it all: It downloads the result, which is a single ZIP file. This workflow does not take care of unzipping the archive. You have to do that yourself.

AffyArrayQualityAnalysis uses a secure connection over HTTPS. To make this work you *must* import our SSL certificates in your local Java keystores. You can do this either manually as described at https://www.bioinformatics.nl/phenolink/home/JavaAndHTTPS.html or use a Java tool to do the job as described at http://www.myexperiment.org/files/148

Description:

This workflow takes in a CEL file and a normalisation method then returns a series of images/graphs which represent the same output obtained using the MADAT software package (MicroArray Data Analysis Tool) http://www.bioinf.manchester.ac.uk/MADAT/index.html. Also retruned by this workflow are a list of the top differentialy expressed genes (size dependant on the number specified as input - geneNumber), which are then used to find the candidate pathways which may be influencing the observed changes in the microarray data. By identifying the candidate pathways, more detailed insights into the gene expression data can be obtained. NOTE - You will also need to install R and Rserv on your machine and install the libaries required by the R script into you R library directory. For basic info: http://www.cs.man.ac.uk/~fisherp/rlib.html.

The example inputs for this workflow are as follows: Samples1 = one or more CEL files for cross-correlating with Samples2 CEL files (new line separated): Liver_Day1_Mouse.CEL Liver_Day2_Mouse.CEL Samples2 = one or more CEL files for cross-correlating with Samples1 CEL files (new line separated): Kideny_Day1_Mouse.CEL Kidney_Day2_Mouse.CEL geneNumber = the number of differentialy expressed gene to be returned above a given p-value, e.g. 20 arrayTypeAffy = the name of the Mouse AffyMetrix array used, e.g. mouse4302 path = the direct path to the CEL file location, e.g. C:\Microarray_Data\CEL_FILES\ - note the forward slashes NormalizationMethod = the type of normalisation to perfrom, e.g. rma, gcrma or mmgmos testMethod = e.g. limma, mmtest or pplr p-value = the p-value cut-off value for the array data, e.g. 0.05 foldChange = the fold change value for the microarray data, e.g. 1 (means greater than 1 or less than -1)

Description:

This workflow maps microarray data onto metabolic pathway diagrams represented as SBML models drawn using Cell Designer. To run this workflow requires libsbml to be installed into taverna - see http://www.mcisb.org/software/taverna/libsbml/index.html

Description:

This workflow takes in a CEL file and a normalisation method then returns a series of images/graphs which represent the same output obtained using the MADAT software package (MicroArray Data Analysis Tool) [http://www.bioinf.manchester.ac.uk/MADAT/index.html]. Also returned by this workflow are a list of the top differentialy expressed genes (size dependant on the number specified as input - geneNumber), which are then used to find the candidate pathways which may be influencing the observed changes in the microarray data. By identifying the candidate pathways, more detailed insights into the gene expression data can be obtained. NOTE - You will also need to install R and Rserv on your machine and install the libaries required by the R script into you R library directory (see for basic info: http://www.cs.man.ac.uk/~fisherp/rlib.html) The example inputs for this workflow are as follows: Samples1 = one or more CEL files for cross-correlating with Samples2 CEL files (new line separated including the .CEL): Liver_Day1_Mouse.CEL Liver_Day2_Mouse.CEL Samples2 = one or more CEL files for cross-correlating with Samples1 CEL files (new line separated including the .CEL): Kideny_Day1_Mouse.CEL Kidney_Day2_Mouse.CEL geneNumber = the number of differentialy expressed gene to be returned above a given p-value, e.g. 20 arrayTypeAffy = the name of the Mouse AffyMetrix array used, e.g. mouse4302, hgu133a... path = the direct path to the CEL file location, e.g. C:/Microarray_Data/CEL_FILES/ - note the forward slashes NormalizationMethod = the type of normalisation to perfrom, e.g. rma, gcrma or mmgmos testMethod = e.g. limma, mmtest or pplr p-value = the p-value cut-off value for the array data, e.g. 0.05 foldChange = the fold change value for the microarray data, e.g. 1 (means greater than 1 or less than -1)

Description: