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Analytics toolkit for Illumina sequencer metrics.

Project description

Python module and utilities to parse the metrics binaries output by Illumina sequencers.

Illuminate parses the metrics binaries that result from Illumina sequencer runs, and provides usable data in the form of python dictionaries and dataframes. Intended to emulate the output of Illumina SAV, illuminate allows you to print sequencing run metrics to the command line as well as work with the data programmatically.

This package was built with versatility in mind. There is a section in this README for each of the following typical use cases:

Running illuminate on the command line
Using illuminate as a python module
Parsing orphan binaries (e.g. just ErrorMetrics.bin)

Also, as of version 0.5, Illuminate supports the reading of active (in-progress) sequencing runs for Tile, Index, and Quality metrics.

But first you’ll need to get set up. Jump to “Requirements” below.

Supported machines and files

Currently, the following Illumina machines are supported (any number of indices):


The integrated command-line reporter currently serves the following xml files:


…and the following binary files:

tile (InterOp/TileMetrics.bin)
quality (InterOp/QMetrics.bin)
index (InterOp/IndexMetrics.bin)
control (InterOp/ControlMetrics.bin)
corrected intensity (InterOp/CorrectedIntensityMetrics.bin)
extraction (InterOp/ExtractionMetrics.bin)
error (InterOp/ErrorMetrics.bin)

(Note: binaries may also be named “XxXxOut.bin”; this is an alias.)


You’ll need a UNIX-like environment to use this package. Both OS X and Linux have been confirmed to work. Illuminate relies on four open-source packages available through the Python cheeseshop:


Please let the maintainer of this package ( know if any of these requirements make it difficult to use and integrate Illuminate in your software; this is useful feedback.

How To Install Illuminate via Pip

The latest most stable version of Illuminate can be installed from the Python cheeseshop However, some vagaries of python package management make automatic installaion of all of the dependencies a bit problematic.

You’ll need to explicitly install numpy and pandas first:

$ sudo pip install numpy pandas

Once this completes, you can try:

$ sudo pip install illuminate

The remaining requirements (bitstring and docopt) should come along for the ride, and you’ll be good to go. Jump down to “Illuminate as a Command Line Tool” to immediately start illuminating your own data.

If you want some sample data to play with, grab Illuminate from its mercurial repository on (see next section).

How To Install Illuminate from BitBucket

The latest evelopment versions of illuminate come from its repository on

Clone this repository using Mercurial (hg):

$ hg clone

For integrated use in other code as well as for running the command-line utilities, it is recommended (though not required) to use virtualenv to create a virtual Python environment in which to set up this package’s dependencies.

Follow the directions on this page ( for virtualenv, then, within your intended working directory, type:

$ virtualenv ve
$ source ve/bin/activate

Now run the following command within the same directory:

(ve) $ pip install numpy pandas

The above process can take many minutes (cup of tea, perhaps?) and throw off many warnings, but in the end it should say this:

Successfully installed numpy pandas python-dateutil pytz six
Cleaning up...

If you get an error saying you are missing Python.H, you will need to install the python development package for your system. For example, on Ubuntu or Debian, you’d do:

$ sudo apt-get install python-dev

With numpy and pandas installed, now type:

(ve) $ python build install

When these commands complete, you should be ready to roll.

Illuminate as a Command Line Tool

Illuminate contains a simple command-line utility that prints out the most commonly desired statistics from Illumina SAV.

This package includes some MiSeq and HiSeq data (metrics and metadata only) from live sequencing runs so you can see how things work.

Activate your virtualenv (if you haven’t already):

$ source ve/bin/activate

Now enter the following to run the integrated parser against one of the test datasets:

(ve) $ python illuminate --tile --quality --index sampledata/MiSeq-samples/2013-04_01_high_PF/

You can also output to a file by using the –dump=filename option:

(ve) $ python illuminate --dump=RU1234.txt /path/to/dataset

And you can suppress command-line output by using the –quiet option.

Finally, a fun way to explore the data is to use the –interactive option to load the dataset object directly into iPython. (This suppresses the normal printouts.)

(ve) $ python illuminate -i /path/to/dataset

Within iPython, you’ll have the myDataset object at your disposal. This leads us naturally to a discussion of how to use illuminate in code.

Using Illuminate as a Python Module

Illuminate was made to be integrated in code to make it easy to report on sequencing runs.

The usual way to start is to instantiate a “dataset” through the InteropDataset class, providing it with a valid run path, like so:

from illuminate import InteropDataset
myDataset = InteropDataset('/path/to/data/')

When this class is built, the RunInfo.xml or CompletedJobInfo.xml metadata files will be read, filling important variables like Flowcell Layout and Read Configuration.

The binary parsers are not run until they are specifically requested. Many parsing operations can take several seconds, depending on the size of the binary file.

tilemetrics = myDataset.TileMetrics()
qualitymetrics = myDataset.QualityMetrics()
indexmetrics = myDataset.IndexMetrics()
controlmetrics = myDataset.ControlMetrics()
corintmetrics = myDataset.CorrectedIntensityMetrics()
extractionmetrics = myDataset.ExtractionMetrics()
errormetrics = myDataset.ErrorMetrics()

Note that not all run data will contain all binaries. Particularly, ErrorMetrics.bin will be missing if no errors were recorded / reported by the sequencer.

In the vast majority of cases, variables and data structures closely resemble the names and structures in the XML and BIN files that they came from. All XML information comes through the InteropMetadata class, which can be accessed through the meta attribute of InteropDataset:

metadata = myDataset.meta

InteropDataset caches parsing data after the first run. To get a fresh re-parse of any file, supply “True” as the sole parameter to any parser method:

tm = myDataset.TileMetrics(True)

Using the Results

The two main methods you have access to in every parser class are the data dictionary and the DataFrame, accessed as .data and .df respectively.

Each parser produces a “data” dictionary from the raw data. The data dict reflects the format of the binary itself, so each parser has a slightly different set of keys. For example:


['tile', 'lane', 'code', 'value']

This dictionary is used to set up a pandas DataFrame, a tutorial for which is outside the scope of this document, but here’s an introduction to data structures in Pandas to get you going.

Parsing Orphan Binaries

If you just have a single binary file, you can run the matching parser from the command line:

$ ipython -i illuminate/ sampledata/MiSeq-samples/2013-04_10_has_errors/InterOp/TileMetricsOut.bin

The parsers are designed to exist apart from their parent dataset, so it’s possible to call any one of them without having the entire dataset directory at hand. However, some parsers (like TileMetrics and QualityMetrics) rely on information about the Read Configuration and/or Flowcell Layout (both pieces of data coming from the XML).

Illuminate has been seeded with some typical defaults for MiSeq, but if you are using a HiSeq, or you know you have a different configuration, supply read_config and flowcell_layout as named arguments to these parsers, like so:

from illuminate import InteropTileMetrics
tilemetrics = InteropTileMetrics('/path/to/TileMetrics.bin',
                       read_config = [{'read_num': 1, 'cycles': 151, 'is_index': 0},
                                      {'read_num': 2, 'cycles': 6, 'is_index': 1},
                                      {'read_num': 3, 'cycles': 151, 'is_index':0}],
                       flowcell_layout = { 'lanecount': 1, 'surfacecount': 2,
                                           'swathcount': 1, 'tilecount': 14 } )

More Sample Data

More sample data from MiSeq and HiSeq machines will be found in the Downloads section of this bitbucket repository.

If you’d like to contribute sample data, contact the maintainer of this repository ( along with a brief description.

Support and Maintenance

Illumina’s metrics data, until recently, could only be parsed and interpreted via Illumina’s proprietary “SAV” software which only runs on Windows and can’t be sourced programmatically.

This library was developed in-house at InVitae, a CLIA-certified genetic diagnostics company that offers customizable, clinically-relevant sequencing panels, as a response to the need to emulate Illumina SAV’s output in a program-accessible way.

Invitae currently uses these parsers in conjunction with site-specific reporting scripts to produce automated sequencing run metrics as a check on the health of the run and the machines themselves.

This tool was intended from the beginning to be generalizable and open-sourced to the public. It comes with the MIT License, meaning you are free to modify it for commercial and non- commercial uses; just don’t try to sell it as-is.

Contributions, extensions, bug reports, suggestions, and swear words all happily accepted, in that order. Spring 2013

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