==========
Goldilocks
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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;
For coverage;
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}
}
License
Goldilocks is distributed under the MIT license, see LICENSE.
History
0.1.1 (2016-07-07)
- Updated citation.
Please cite us! <3
- [PR:ar0ch] Add lowercase matching in GCRatioStrategy
Fixes 'feature' where lowercase letters are ignored by GCRatioStrategy.
0.1.0 (2016-03-08)
- Goldilocks is published software!
0.0.83-beta
-l
and -s
CLI arguments and corresponding length
and stride
parameters
to Goldilocks
constructor now support SI suffixes: K
, M
, G
, T
.
util
module contains parse_si_bp
used to parse option strings and return
the number of bases for length and stride.- Add length and stride to x-axis label of plots.
- Add
ignore_query
option to plot
to override new default behaviour of plot
that only plots points for regions remaining after a call to query
. - Remove
profile
function, use plot
with bins=N
instead. - Add binning to
plot
to reduce code duplication. - Add
chrom
kwarg to plot
to allow plotting of a single chromosome across
multiple input genomes. - Fix support for plotting data from multiple contigs or chromosomes of a single
input genome when provided as a FASTA.
- Add
ignore_query
kwarg to plot
for ignoring the results of a query on
the Goldilocks
object when performing a plot afterwards. - Bins no longer have to be specified manually, use
bins=N
, this will create
N+1 bins (a special 0 bin is reserved) between 0 and the largest observed
value unless bin_max
is also provided. - Bins may have a hard upper limit set with
bin_max
. This will override the
default of the largest observed value regardless of whether bin_max
is smaller. - Plots can now be plotted proportionally with
prop=True
. - Improve labels for plotting.
- Reduce duplication of plotting code inside
plot
. - Share Y axis across plot panels to prevent potentially misleading default plots.
- Reduce duplication of code used for outputting metadata:
- Add
fmt
kwarg to export_meta
that permits one of:
- bed
BED format (compulsory fields only)
- circos
A format compatible with the circos plotting tool
- melt
A format that will suit import to an R dataframe without the need
for additional munging with reshape2
- table
A plain tabular format that will suit for quick outputs with
some munging
- Remove
print_melt
, use export_meta
with fmt=melt
. - Add
is_pos_file
kwarg to Goldilocks, allows user to specify position based
variants in the format CHR\tPOS
or CHR:POS
in a newline delimited file. - Changed required
idx
key to file
in sequence dictionaries. - Added custom strategy and plotting examples to the documentation.
- The
Goldilocks
class is now imported as from goldilocks import Goldilocks
. - The
textwrap.wrap
function is used to write out FASTA more cleanly. - A serious regression in the parsing of FASTA files introduced by v0.0.80 has
been closed.
- Improved plotting functionality for co-plotting groups, tracks of chromosome
has been introduced. Tracks can now be plotted together on the same panel by
providing their names as a list to the
tracks
keyword. reset_candidates
allows users to "reset" the Goldilocks object after a
query or sort has been performed on the regions.
0.0.82 (2016-01-29)
- Changed example to use
MotifCounterStrategy
over removed KMerCounterStrategy
. - Fix runtime
NameError
preventing PositionCounterStrategy
from executing correctly. - Fix runtime
NameError
preventing ReferenceConsensusStrategy
from executing correctly. - Add default
count
track to PositionCounterStrategy
to prevent accidental
multiple counting issue encountered when couting with the default
track. - Add LICENSE
- Paper accepted for press!
0.0.81 (2016-01-29)
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
examples.py
. 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)