Project description:Scientific services in the area of OMICS research is becoming increasingly popular. In gen- eral omics research can produce a massive amount of data that can pose a challenge for computing infrastructure. While in the genomics area, many applications can run on Linux nodes the situation in proteomics is different. In proteomics, many applications are optimized to run on Windows computer only. As a sci- entific service provider, a core facility needs reliable, reproducible and easy to use integrated solutions. Liquid chromatography mass spectrometry intensity based label-free quantifica- tion using data-dependent acquisition is a popular approach in proteomics to perform relative quantification of proteins in complex samples. MaxQuant is a widely used software for this type of analysis which has a complex graphical user interface and provides information-rich outputs. We run it in which also includes Scaffold for search result validation and visualization and an R based quality control report generation. Data analysis workflows consists of several components: a workflow engine, compute hosts, and archives. In particular, applications can run on compute hosts, while the data is kept in an archive server. Therefore, the input and output need to be staged to the compute host and the results need to be staged back to the archive. This complexity can be overwhelming for a most common user. These different components have all been integrated into a robust and user-friendly application to process standardized label-free quantification experiments. We integrated MaxQuant as an in-house Software as a Service application so it can be used by any workflow engine in a platform-independent manner. In this manuscript, we provide a technical description of how MaxQuant as software service has been integrated into our heterogeneous compute environment for reproducible and automatic large scale high throughput data processing of label-free quantification experiments. In this Pride dataset we provide four raw files along with the full MaxQuant results, the Scaffold file, the QC-pdf report to have a concrete idea of the potential of our workflow. These data are generated in the FGCZ-course in Nov. 2016 (for further information see: http://www.fgcz.ch/education/genomics-courses01.html).

Project description:Isolated murine kidney glomeruli were lysed in buffer containing 8M urea, and proteins were reduced and alkylated. Proteins were digested with trypsin. After reverse-phase chomatrography, peptides were fractionated using strong cation exchange chromatography. Phosphopeptides were enriched using IMAC (FeNTA) columns. Peptides were analyzed by LC-MS/MS on a Q Exactive mass spectrometer or an LTQ Orbitrap XL mass spectrometer. Metadata for Orbitrap samples (MaxQuant Output files include „Orbi“) 1. General features – 1.1 Global descriptors a. Responsible person or role: Markus Rinschen. markus.rinschen@uk-koeln.de; tobias.lamkemeyer@uni-koeln.de b. Instrument manufacturer, model: LTQ-Orbitrap XL, Thermo Scientific c. customization 2. Ion sources – ESI fed by nLC 2 (Proxeon) 3. Post source component 3.1. Analyser: MS1 survey scan in an Orbitrap and MS2 analysed in Linear Trap. 3.2. Activation/Dissociation: CID 4. Spectrum and peak list generation and annotation 4.1. Data acquisition: MaxQuant v. 1.3.05 Top one method with a cycle of one full MS1 scan in the Orbitrap, followed by the fragmentation with dynamic exclusion window of 60 seconds and followed by the acquisition of 5 product ion scans generated in the LTQ analyser, and detected in the LTQ. Unselected fragmentation. For further details, see .raw files. URL of file: Filename 4.2. Data analysis: MaxQuant v 1.3.05 Parameters used in the generation of peak lists or processed spectra: see annotation in “parameters” file. The mouse uniprot reference database “complete proteome” obtained on January 2nd, 2013 was used. Metadata for QExactive samples (Max Quant output files include „QE“) 1. General features – 1.1 Global descriptors a. Responsible person or role: Markus Rinschen. markus.rinschen@uk-koeln.de; Marcus Krüger marcus.krueger@mpi-bn.mpg.de b. Instrument manufacturer, model: Q Exactive Thermo Scientific c. customization 2. Ion sources – ESI fed by nLC 1000 (Proxeon) 3. Post source component 3.1. Analyser: MS1 survey scan in an Orbitrap and MS2 analysed in Orbitrap/HCD cell 3.2. Activation/Dissociation: HCD 4. Spectrum and peak list generation and annotation 4.1. Data acquisition: MaxQuant v. 1.3.05 Top one method with a cycle of one full MS1 scan in the Orbitrap, followed by the fragmentation with dynamic exclusion window of 20 seconds in the HCD cell and followed by the acquisition of 10 product ion scans generated in the LTQ analyser, and detected in the LTQ. Unselected fragmentation. For further details, see .raw files. URL of file: Filename 4.2. Data analysis: MaxQuant v 1.3.05 Parameters used in the generation of peak lists or processed spectra: see annotation in “parameters” file. The mouse uniprot reference database “complete proteome” obtained on January 2nd, 2013 was used.

Project description:LncRNA expression profiling for liver tissues of mice fed for NFD, LSF and HSF groups Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.

Project description:LncRNA expression profiling for liver tissues of mice fed for a normal diet (NFD, 3mice) and a high-fat diet (HFD, 3mice) Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.

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