Skip to main content

Locating genomic regions that are "just right".

Project description Join the chat at

Locating genomic regions that are “just right”.

What is it?

Goldilocks is a Python package providing functionality for locating ‘interesting’ genomic regions for some definition of ‘interesting’. You can import it to your scripts, pass it sequence data and search for subsequences that match some criteria across one or more samples.

Goldilocks was developed to support our work in the investigation of quality control for genetic sequencing. It was used to quickly locate regions on the human genome that expressed a desired level of variability, which were “just right” for later variant calling and comparison.

The package has since been made more flexible and can be used to find regions of interest based on other criteria such as GC-content, density of target k-mers, defined confidence metrics and missing nucleotides.

What can I use it for?

Given some genetic sequences (from one or more samples, comprising of one or more chromosomes), Goldilocks will shard each chromosome in to subsequences of a desired size which may or may not overlap as required. For each chromosome from each sample, each subsequence or ‘region’ is passed to the user’s chosen strategy.

The strategy simply defines what is of interest to the user in a language that Goldilocks can understand. Goldilocks is currently packaged with the following strategies:


Census Description


Calculate GC-ratio for subregions across the genome.


Count given nucleotides for subregions across the genome.


Search for one or more particular motifs of interest of any and varying size in subregions across the genome.


Calculate the (dis)similarity to a given reference across the genome.


Given a list of base locations, calculate density of those locations over subregions across the genome.

Once all regions have been ‘censused’, the results may be sorted by one of four mathematical operations: max, min, median and mean. So you may be interested in subregions of your sequence(s) that feature the most missing nucleotides, or subregions that contain the mean or median number of SNPs or the lowest GC-ratio.

Why should I use it?

Goldilocks is hardly the first tool capable of calculating GC-content across a genome, or to find k-mers of interest, or SNP density, so why should you use it as part of your bioinformatics pipeline?

Whilst not the first program to be able to conduct these tasks, it is the first to be capable of doing them all together, sharing the same interfaces. Every strategy can quickly be swapped with another by changing one line of your code. Every strategy returns regions in the same format and so you need not waste time munging data to fit the rest of your pipeline.

Strategies are also customisable and extendable, those even vaguely familiar with Python should be able to construct a strategy to meet their requirements.

Goldilocks is maintained, documented and tested, rather than that hacky perl script that you inherited years ago from somebody who has now left your lab.


To use;

  • numpy

  • matplotlib (for plotting)

To test;

  • tox

  • pytest

For coverage;

  • nose

  • python-coveralls


$ pip install goldilocks


Citation pending…


Goldilocks is distributed under the MIT license, see LICENSE.


0.0.80 (2015-08-10)

  • Added multiprocessing capabilities during census step.

  • Added a simple command line interface.

  • Removed prepare-evaluate paradigm from strategies and now perform counts directly on input data in one step.

  • Skip slides (and set all counts to 0) if their end_pos falls outside of the region on that particular genome’s chromosome/contig.

  • Rename KMerCounterStrategy to MotifCounterStrategy

  • Fixed bug causing use_and to not work as expected for chromosomes not explicitly listed in the exceptions dict when also using use_chrom.

  • Support use of FASTA files which must be supplied with a samtools faidx style index.

  • Stopped supporting Python 3 due to incompatability with buffer and memoryview.

  • Prevent query from deep copying itself on return. Note this means that a query will alter the original Goldilocks object.

  • Now using a 3D numpy matrix to store counters with memory shared to support multiprocessing during census.

  • Removed StrategyValue as these cannot be stored in shared memory. This makes ratio-based strategies a bit of a hack currently (but still work…)

  • tldr; Goldilocks is at least 2-4x faster than previously, even without multiprocessing

0.0.71 (2015-07-11)

  • Officially add MIT license to repository.

  • Deprecate _filter.

  • Update and tidy

  • is_seq argument to initialisation removed and replaced with is_pos.

  • Use is_pos to indicate the expected input is positional, not sequence.

  • Force use of PositionCounterStrategy when is_pos is True.

  • Sequence data now read in to 0-indexed arrays to avoid the overhead of string

    re-allocation by having to append a padding character to the beginning of very long strings.

  • Region metadata continues to use 1-indexed positions for user output.

  • VariantCounterStrategy now PositionCounterStrategy.

  • PositionCounterStrategy expects 1-indexed lists of positions;

    prepare populates the listed locations with 1 and then evaluate returns the sum as before.

  • test_regression2 updated to account for converting 1-index to 0-index when

    manually handling the sequence for expected results.

  • query accepts gmax and gmin arguments to filter candidate regions by the group-track value.

  • CandidateList removed and replaced with simply returning a new Goldilocks.

0.0.6 (2015-06-23)

  • Goldilocks.sorted_regions stores a list of region ids to represent the result of a sorting operation following a call to query.

  • Regions in Goldilocks.regions now always have a copy of their “id” as a key.

  • __check_exclusions now accepts a group and track for more complex exclusion-based operations.

  • region_group_lte and region_group_gte added to usable exclusion fields to remove regions where the value of the desired group/track combination is less/greater than or equal to the value of the group/track set by the current query.

  • query now returns a new Goldilocks instance, rather than a CandidateList.

  • Goldilocks.candidates property now allows access to regions, this property will maintain the order of sorted_regions if it has one.

  • export_meta now allows group=None

  • CandidateList class deleted.

  • Test data that is no longer used has been deleted.

  • Scripts for generating test data added to test_gen/ directory.

  • Tests updated to reflect the fact CandidateList lists are no longer returned by query.

  • _filter is to be deprecated in favour of query by 0.0.7

Beta (2014-10-08)

  • Massively updated! Compatability with previous versions very broken.

  • Software retrofitted to be much more flexible to support a wider range of problems.

0.0.2 (2014-08-18)

  • Remove incompatible use of print

0.0.1 (2014-08-18)

  • Initial package

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

goldilocks-0.0.8.tar.gz (52.8 kB view hashes)

Uploaded Source

Built Distribution

goldilocks-0.0.8-py2-none-any.whl (41.2 kB view hashes)

Uploaded Python 2

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