# Goldilocks¶

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:

Strategy Census Description
GCRatioStrategy Calculate GC-ratio for subregions across the genome.
NucleotideCounterStrategy Count given nucleotides for subregions across the genome.
MotifCounterStrategy Search for one or more particular motifs of interest of any and varying size in subregions across the genome.
ReferenceConsensusStrategy Calculate the (dis)similarity to a given reference across the genome.
PositionCounterStrategy 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.

## Requirements¶

To use;

• numpy
• matplotlib (for plotting)

To test;

• tox
• pytest

For coverage;

• nose
• python-coveralls

## Installation¶

\$ pip install goldilocks


## Citation¶

Please cite us so we can continue to make useful software!

Nicholls, S. M., Clare, A., & Randall, J. C. (2016). Goldilocks: a tool for identifying genomic regions that are "just right." Bioinformatics (2016) 32 (13): 2047-2049. doi:10.1093/bioinformatics/btw116

@article{Nicholls01072016,
author = {Nicholls, Samuel M. and Clare, Amanda and Randall, Joshua C.},
title = {Goldilocks: a tool for identifying genomic regions that are ‘just right’},
volume = {32},
number = {13},
pages = {2047-2049},
year = {2016},
doi = {10.1093/bioinformatics/btw116},
URL = {http://bioinformatics.oxfordjournals.org/content/32/13/2047.abstract},
eprint = {http://bioinformatics.oxfordjournals.org/content/32/13/2047.full.pdf+html},
journal = {Bioinformatics}
}