.. image:: https://user-images.githubusercontent.com/6096414/93030687-c7cf7300-f61c-11ea-92b8-102ec17ef6aa.png
UMI-tools was published in Genome Research <http://genome.cshlp.org/content/early/2017/01/18/gr.209601.116.abstract>_ on 18 Jan '17 (open access)
For full documentation see https://umi-tools.readthedocs.io/en/latest/
This repository contains tools for dealing with Unique Molecular Identifiers (UMIs)/Random Molecular Tags (RMTs) and single cell RNA-Seq cell barcodes. Currently there are 6 commands.
The extract and whitelist commands are used to prepare a
fastq containg UMIs +/- cell barcodes for alignment.
whitelist: Builds a whitelist of the 'real' cell barcodes This is useful for droplet-based single cell RNA-Seq where the identity of the true cell barcodes is unknown. Whitelist can then be used to filter with extract (see below)
extract: Flexible removal of UMI sequences from fastq reads. UMIs are removed and appended to the read name. Any other barcode, for example a library barcode, is left on the read. Can also filter reads by quality or against a whitelist (see above)
The remaining commands, group, dedup and count/count_tab, are used to
identify PCR duplicates using the UMIs and perform different levels of
analysis depending on the needs of the user. A number of different UMI
deduplication schemes are enabled - The recommended method is
directional.
dedup: Groups PCR duplicates and deduplicates reads to yield one read per group Use this when you want to remove the PCR duplicates prior to any downstream analysis
group:
Groups PCR duplicates using the same methods available through dedup.
This is useful when you want to manually interrogate the PCR duplicates
count: Groups and deduplicates PCR duplicates and counts the unique molecules per gene Use this when you want to obtain a matrix with unique molecules per gene, per cell, for scRNA-Seq.
count_tab: As per count except input is a flatfile
See QUICK_START.md <./doc/QUICK_START.md>_ for a quick tutorial on
the most common usage pattern.
If you want to use UMI-tools in single-cell RNA-Seq data processing,
see Single_cell_tutorial.md <./doc/Single_cell_tutorial.md>_
Important update: We now recommend the use of alevin for droplet-based
scRNA-Seq (e.g 10X, inDrop etc). alevin is an accurate, fast and convenient end-to-end tool to go from fastq -> count matrix and extends the UMI error correction in UMI-tools within a framework that also enables quantification of droplet scRNA-Seq without discarding multi-mapped reads. See alevin documentation <https://salmon.readthedocs.io/en/latest/alevin.html> and alevin pre-print <https://www.biorxiv.org/content/10.1101/335000v2> for more information
The dedup, group, and count / count_tab commands make use of network-based methods to resolve similar UMIs with the same alignment coordinates. For a background regarding these methods see:
Genome Research Publication <http://genome.cshlp.org/content/early/2017/01/18/gr.209601.116.abstract>_
Blog post discussing network-based methods <https://cgatoxford.wordpress.com/2015/08/14/unique-molecular-identifiers-the-problem-the-solution-and-the-proof/>_.
If you're using Conda, you can use:
.. code:: bash
$ conda install -c bioconda -c conda-forge umi_tools
Or pip:
.. code:: bash
$ pip install umi_tools
Or if you'd like to work directly from the git repository:
.. code:: bash
$ git clone https://github.com/CGATOxford/UMI-tools.git
Enter repository and run:
.. code:: bash
$ python setup.py install
For more detail see INSTALL.rst <./doc/INSTALL.rst>_
Help
For full documentation see https://umi-tools.readthedocs.io/en/latest/
See QUICK_START.md <./doc/QUICK_START.md>_ and
Single_cell_tutorial.md <./doc/Single_cell_tutorial.md>_ for tutorials on the most common usage patterns.
To get help on umi_tools run
.. code:: bash
$ umi_tools --help
To get help on the options for a specific [COMMAND], run
.. code:: bash
$ umi_tools [COMMAND] --help
umi_tools is dependent on python>=3.5, numpy, pandas, scipy, cython, pysam,
future, regex and matplotlib