Tools for dealing with Unique Molecular Identifiers


Important update: We now recommend the use of alevin for 10x Chromium and Drop-seq droplet-based scRNA-Seq. 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 and alevin pre-print for more information

Welcome to the UMI-tools documentation. UMI-tools contains tools for dealing with Unique Molecular Identifiers (UMIs)/Random Molecular Tags (RMTs) and single cell RNA-Seq cell barcodes.

UMI-tools was published in Genome Research on 18 Jan ‘17 (open access)


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. The whitelist can then be used to filter cell barcodes 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. For more deails about the deduplication schemes see The network-based deduplication methods

  • 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 or perform bespoke downstream processing such as generating consensus sequences
  • 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

Each tool has a set of Common options for input/output, profiling and debugging.

See Quick start guide 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

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

Blog post discussing network-based methods.


If you’re using Conda, you can use:

$ conda install -c bioconda -c conda-forge umi_tools

Or pip:

$ pip install umi_tools

Or if you’d like to work directly from the git repository:

$ git clone

Enter repository and run:

$ python install

For more detail see Installation Guide

To get detailed help on umi_tools run

$ umi_tools --help

To get help on a specific [COMMAND] run

$ umi_tools [COMMAND] --help


umi_tools is dependent on numpy, pandas, scipy, cython, pysam, future, regex and matplotlib

Indices and tables