compomics / peptide-shaker

Interpretation of proteomics identification results
http://compomics.github.io/projects/peptide-shaker.html
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bioinformatics java protein-identification proteomics

PeptideShaker


PeptideShaker Publication:


download v3.0.11 - All platforms ReleaseNotes

(Click on an image to see the full size version)


Introduction

PeptideShaker is a search engine independent platform for interpretation of proteomics identification results from multiple search and de novo engines, currently supporting X!Tandem, MS-GF+, MS Amanda, OMSSA, MyriMatch, Comet, Tide, Mascot, Andromeda, MetaMorpheus, Sage, Novor, DirecTag and mzIdentML.

PeptideShaker aggregates the results in a single identification set, annotates spectra, computes a consensus score, maps sequences and performs protein inference, scores post-translational modification localization, runs statistical validation, quality control, and annotates the results using multiple sources of information like Gene Ontology, UniProt and Ensembl annotation, and protein structures.

PeptideShaker can be used in command line and comes with rich visualization capabilities to navigate the results. It can be used on local data as well as on data sets deposited to the ProteomeXchange PRIDE repository.

PeptideShaker currently supports nine different analysis tasks:

All data can also easily be exported for follow up analysis in other tools.

For further help see the Bioinformatics for Proteomics Tutorial.

If you have any questions, suggestions or remarks, feel free to contact us via the PeptideShaker Google Group. For specific bug reports or issues please use the issues tracker.

To start using PeptideShaker, unzip the downloaded file, and double-click the PeptideShaker-X.Y.Z.jar file. No additional installation required!

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Read Me


Minimum Requirements

It should be possible to run PeptideShaker on almost any computer where Java 1.8 or newer is installed. We recommend always using the latest version of Java.

However to get the best out of PeptideShaker a newer machine with at least 4 GB of memory is recommended. If parsing big datasets even more memory is required: the bigger the dataset the more memory you need.

Note that in order to use more than 1500 MB of memory you need to install the 64 bit version of Java. See our Java Troubleshooting for help.

The minimum screen resolution for PeptideShaker is 1280 x 800, but it is highly recommended to use at least 1680 x 1050. Again, the bigger the better.

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From the Command Line

The main purpose of PeptideShaker is to make it simpler to process and display the results of multiple search engines. A graphical user interface is the best choice for smaller projects. PeptideShaker can also be used via the command line, and be incorporated in different analysis pipelines.

For details about the command line see: PeptideShakerCLI.

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Bioconda

install with bioconda install with bioconda install with bioconda

PeptideShaker is available as a Miniconda package in the bioconda channel here.

You can install PeptideShaker with:

conda install -c conda-forge -c bioconda peptide-shaker

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Docker

A Docker container is available via the biocontainers project. You can make use of the container via:

docker run quay.io/biocontainers/peptide-shaker:X.Y.Z--1 peptide-shaker eu.isas.peptideshaker.cmd.PeptideShakerCLI 

Replace X.Y.Z with the wanted PeptideShaker version number.

In case you need to use your own files, you will need to map (using -v Docker parameter) your local folders containing them into the Docker internal file system, like

docker run -v /home/my_user/resources:/myresources 
quay.io/biocontainers/peptide-shaker:X.Y.Z--1 
peptide-shaker eu.isas.peptideshaker.cmd.PeptideShakerCLI 
...

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SearchGUI

PeptideShaker has a strong connection to the SearchGUI project.

SearchGUI is a user-friendly, lightweight and open-source graphical user interface for configuring and running proteomics identification search engines, namely X! Tandem, MyriMatch, MS Amanda, MS-GF+, OMSSA, Comet, Tide, Andromeda, MetaMorpheus, Sage, Novor and DirecTag.

Importing output from SearchGUI is especially simple in PeptideShaker as the parameters and files used for the search is easily available.

For more information on SearchGUI see http://compomics.github.io/projects/searchgui.html.

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User Defined Modifications

To add user defined modifications see User Defined Modifications in SearchGUI.

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Database Help

For help on obtaining a valid sequence database see the Database Help.

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Mascot Support

PeptideShaker supports the import of data from Mascot dat files. Make sure that the spectra are available in the mgf format where every spectrum should have a unique title.

Mascot's randomised protein sequences decoy option is not compatible with PeptideShaker. The reason being that this is a random decoy approach and not a reverse decoy approach. When combining results from different search engines it is important that the database and decoys used are identical, something that cannot be guaranteed when using the random approach.

To combine Mascot results with your results from SearchGUI you therefore have to use the same target-decoy database as the one used in the SearchGUI search and not select the decoy option when performing the Mascot search. It may also be possible to use the reversed protein sequences decoy option in Mascot, but then there is no guarantee that the generated decoys are identical and the comparison against the other search engines may be less straightforward.

To get target-decoy databases that are fully compatible with PeptideShaker see the Decoy Databases section below.

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mzIdentML Support

PeptideShaker can load results from virtually any identification algorithm in the mzIdentML format as long as the minimal peptide to spectrum match information is present in the file.

The following is required:

If you have mzIdentML files that fulfill these criteria but do not load in PeptideShaker, please let us know.

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Decoy Databases

In order for PeptideShaker to be able validate your identifications you need to provide the sequence database (i.e., the FASTA file) as a concatenated target-decoy database.

This is achieved by adding non-existing protein sequences (so-called decoy sequences) to the original protein database. There are various ways of creating the artificial sequences. In the context of PeptideShaker, it is recommended to use reversed versions of the actual sequences. When working with multiple search engines, make sure that they use the exact same database.

This means that whenever a mistake is made when searching in the combined database, it is as likely to happen in the real database (called the target database) as it is in the artificial database (called the decoy database).

Note that we only guarantee the performance of PeptideShaker when using concatenated forward and reversed sequences. If you use other types of databases it is at your own risks!

Target-decoy database compatible with PeptideShaker can be created using SearchGUI.

Note that PeptideShaker will load search results from searches not using decoy databases, but this is not recommended as this makes it impossible to statistically validate the identifications!

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Converting Spectrum Data

PeptideShaker supports mgf and mzML files as the input format for the spectra. To convert your raw data we recommend using ThermoRawFileParser for Thermo raw files and msconvert from ProteoWizard for other raw file formats.

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Troubleshooting

            >sw|P60323|NANO3_HUMAN Nanos homolog 3 OS=Homo sapiens GN=NANOS3 PE=2 SV=1
            MGTFDLWTDYLGLAHLVRALSGKEGPETRLSPQPEPEPMLEPDQKRSLESSPAPERLCSFCKHNGESRAIYQSHV
            LKDEAGRVLCPILRDYVCPQCGATRERAHTRRFCPLTGQGYTSVYSHTTRNSAGKKLVRPDKAKTQDTGHRRGGG
            GGAGFRGAGKSEPSPSCSPSMST
            -Dhttp.proxyHost=<myproxyserver.com>
            -Dhttp.proxyPort=<proxy port>
            -Dhttp.proxyUser=<user>
            -Dhttp.proxyPassword=<password>

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