Skip to main content

RT Alignment and Peptide ID Confidence Updating for LC-MS/MS Data

Project description

DART-ID

GitHub release PyPI PyPI - Downloads GitHub

Website: https://dart-id.slavovlab.net

Manuscript: https://www.biorxiv.org/content/10.1101/399121v3


Getting started

Dependencies

DART-ID requires Python >= 3.4 (64-bit - miniconda distribution recommended), and has been tested on Windows 8 / OSX Mojave 10.14 / Centos 7 / Ubuntu 14.04.

Installation

DART-ID is available on PyPI and can be installed with pip.

pip install dart-id

Usage

DART-ID requires a YAML-formatted configuration file to run. An example annotated config file can be found in example/config_annotated.yaml. You can specify input files and the output folder on the command line, if that's what you prefer.

View the command-line arguments anytime by running: dart_id -h.

usage: dart_id [-h] [-i INPUT [INPUT ...]] [-o OUTPUT] [-v] [--version] -c
                 CONFIG_FILE

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT [INPUT ...], --input INPUT [INPUT ...]
                        Input file(s) from search engine output (e.g.,
                        MaxQuant evidence.txt). Not required if input files
                        are specified in the config file
  -o OUTPUT, --output OUTPUT
                        Path to output folder
  -v, --verbose
  --version             Display the program's version
  -c CONFIG_FILE, --config-file CONFIG_FILE
                        Path to config file (required). See
                        example/config_example.yaml

Example runs

An example configuration file can be downloaded from GitHub: https://github.com/SlavovLab/DART-ID/blob/master/config_files/example_sqc_67_95_varied.yaml.

The first few lines of the above configuration file specify the path to the input file:

## Input
## ==========================

input: 
  - /path/to/SQC_67_95_Varied/evidence.txt

You can download the evidence.txt file from MassIVE: ftp://massive.ucsd.edu/MSV000083149/other/MaxQuant/SQC_67_95_Varied/evidence.txt.

Then edit the path to the file downloaded, and run the following command:

dart_id -c config_files/example_sqc_67_95_varied.yaml -o ~/DART_ID/SQC_67_95_varied_20181206

The -o parameter points to the output folder for DART-ID. You can also specify this path in the config file.

An example analysis of the data and configuration file specified above is available publicly at ftp://massive.ucsd.edu/MSV000083149/other/Alignments/SQC_varied_20180711_4/.


About the project

DART-ID is a project developed in the Slavov Laboratory at Northeastern University Bioengineering, and was authored by Albert Tian Chen, Alexander Franks (of UCSB Statistics and Applied Probability), and Nikolai Slavov.

The manuscript for this tool is available on bioRxiv: https://www.biorxiv.org/content/10.1101/399121v3.

Contact the authors by email: nslavov{at}northeastern.edu.

License

DART-ID is distributed by an MIT license.

Contributing

Please feel free to contribute to this project by opening an issue or pull request in the GitHub repository.

Data

All data used for the manuscript is available on UCSD's MassIVE Repository

Figures/Analysis

Scripts for the figures in the DART-ID manuscript are available in a separate GitHub repository, https://github.com/SlavovLab/DART-ID_2018

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dart_id-2.0.7.tar.gz (3.2 MB view details)

Uploaded Source

Built Distribution

dart_id-2.0.7-py3-none-any.whl (3.3 MB view details)

Uploaded Python 3

File details

Details for the file dart_id-2.0.7.tar.gz.

File metadata

  • Download URL: dart_id-2.0.7.tar.gz
  • Upload date:
  • Size: 3.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.1 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.7.3

File hashes

Hashes for dart_id-2.0.7.tar.gz
Algorithm Hash digest
SHA256 d83092a98ab90e37f1a9d42c2d764ade14e555472af80c8e28ce8348b5bb4b6d
MD5 6c33678c330821943de20ef84fd5a1e0
BLAKE2b-256 72179e5b38e89d96f3a38f99dd88abe817e6ee331ed10fbf14624e3eedfde963

See more details on using hashes here.

File details

Details for the file dart_id-2.0.7-py3-none-any.whl.

File metadata

  • Download URL: dart_id-2.0.7-py3-none-any.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.1 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.7.3

File hashes

Hashes for dart_id-2.0.7-py3-none-any.whl
Algorithm Hash digest
SHA256 62065869ed2b60fa4885700da8984ada7b96491a9daba528d9620bc5fc61d3ac
MD5 02021e9425f46ef77cc4fc8a0c648258
BLAKE2b-256 1710306cb84d104bdf27f496e179c4b76945387fedce265f4a4a7c1fc26aa2c7

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page