mutation_motif, software for naalyses of point mutations, see https://www.ncbi.nlm.nih.gov/pubmed/27974498
Project description
Mutation Motif
mutation_motif
provides capabilities for analysis of point mutation counts data. It includes commands for preparing sequence data, log-linear analyses of the resulting counts and sequence logo style visualisations. Two different analysis approaches are supported:
- log-linear analysis of neighbourhood base influences on mutation coupled with a sequence logo like representation of influences (illustrated above)
- log-linear analysis of mutation spectra, the relative proportions of different mutation directions from a starting base. A logo-like visualisation of the latter is also supported.
The description of the models and applications of them are described in Zhu, Neeman, Yap and Huttley 2017 Statistical methods for identifying sequence motifs affecting point mutations.
Installation
You can just do a pip install
$ pip install mutation_motif
The commands
The primary tool is installed as a command line executable, mm
.
Preparing data for analyses
The input sequence file format
At present, mm
reads in a fasta formatted file where each sequence has identical length. The length is an odd number and where the mutation occurred at the middle base. mm
assumes each sequence file contains sequences that experienced the same point mutation at this central position, e.g. seqs-CtoT.fasta
contains only sequences that have experienced a C → T mutation at the central position and the sequences have a C at that position. The sequence flanking the mutated base is used to derive a paired "unmutated" reference. The details of this sampling are in Zhu et al.
Two data preparatory subcommands are available: prep-nbr
and prep-spectra
.
prep-nbr: converts aligned sequences to counts
prep-nbr
converts a fasta formatted alignment of equal length sequences to the required counts table format.
Usage: mm prep-nbr [OPTIONS]
Export tab delimited counts table from alignment centred on SNP position.
Output file is written to the same path with just the file suffix changed from
fasta to txt.
Options:
-a, --align_path TEXT fasta aligned file centred on mutated
position. [required]
-o, --output_path TEXT Path to write data. [required]
-f, --flank_size INTEGER Number of bases per side to include.
[required]
--direction [AtoC|AtoG|AtoT|CtoA|CtoG|CtoT|GtoA|GtoC|GtoT|TtoA|TtoC|TtoG]
Mutation direction. [required]
-S, --seed TEXT Seed for random number generator (e.g. 17, or
2015-02-13). Defaults to system time.
-R, --randomise Randomises the observed data, observed and
reference counts distributions should match.
--step [1|2|3] Specifies a "frame" for selecting the random
base. [default: 1]
-D, --dry_run Do a dry run of the analysis without writing
output.
-F, --force_overwrite Overwrite existing files.
--help Show this message and exit.
prep-spectra: combining mutation counts from multiple files
This command combines the separate counts tables of prep-nbr
into a larger table suitable for analyses by ll-spectra
.
Usage: mm prep-spectra [OPTIONS]
export tab delimited combined counts table by appending the 12 mutation
direction tables, adding a new column ``direction``.
Options:
-c, --counts_pattern TEXT glob pattern uniquely identifying all 12 mutation
counts files.
-o, --output_path TEXT Path to write combined_counts data.
-s, --strand_symmetric produces table suitable for strand symmetry test.
-p, --split_dir TEXT path to write individual direction strand symmetric
tables.
-D, --dry_run Do a dry run of the analysis without writing
output.
-F, --force_overwrite Overwrite existing files.
--help Show this message and exit.
The output counts table format
The counts table format has a simple structure, illustrated by the following:
count | pos0 | pos1 | pos2 | pos3 | mut |
---|---|---|---|---|---|
5663 | C | T | T | T | M |
2639 | G | C | A | T | M |
2425 | G | C | A | G | M |
... | ... | ... | ... | ... | ... |
882 | G | G | G | T | R |
6932 | A | G | T | G | R |
10550 | A | A | A | A | R |
The mutation status must be indicated by R
(reference) and M
(mutated). In this instance, the flank size is 2 and mutation was between pos1
and pos2
. Tables with this format are generated by prep-nbr
.
Statistical analyses of mutations
The log-linear analyses requires a counts table from the prep steps. The table contains counts for a specified flank size (maximum of 2 bases, assumed to be either side of the mutated base). It assumes the counts all reflect a specific mutation direction (e.g. AtoG) and that counts from a control distribution are also included.
Two subcommands are available: ll-nbr
and ll-spectra
.
ll-nbr: for detecting the influence of neighbouring bases on mutation
The first examines the influence of neighbouring bases up to fourth order interactions.
Usage: mm ll-nbr [OPTIONS]
log-linear analysis of neighbouring base influence on point mutation
Writes estimated statistics, figures and a run log to the specified directory
outpath.
See documentation for count table format requirements.
Options:
-1, --countsfile TEXT tab delimited file of counts.
-o, --outpath TEXT Directory path to write data.
-2, --countsfile2 TEXT second group motif counts file.
--first_order Consider only first order effects. Defaults to
considering up to 4th order interactions.
-s, --strand_symmetry single counts file but second group is strand.
-g, --group_label TEXT second group label.
-r, --group_ref TEXT reference group value for results presentation.
-v, --verbose Display more output.
-D, --dry_run Do a dry run of the analysis without writing output.
--help Show this message and exit.
ll-spectra: detect differences in mutation spectra between groups
Contrasts the mutations from specified starting bases between groups.
Usage: mm ll-spectra [OPTIONS]
log-linear analysis of mutation spectra between groups
Options:
-1, --countsfile TEXT tab delimited file of counts.
-o, --outpath TEXT Directory path to write data.
-2, --countsfile2 TEXT second group motif counts file.
-s, --strand_symmetry single counts file but second group is strand.
-F, --force_overwrite Overwrite existing files.
-D, --dry_run Do a dry run of the analysis without writing output.
-v, --verbose Display more output.
--help Show this message and exit.
Visualisation of mutation motifs, or mutation spectra, in a grid is provided by the draw-
subcommands.
Evaluating the effect of neighbours on mutation
Sample data files are included as tests/data/counts-CtoT.txt
and tests/data/counts-CtoT-ss.txt
with the latter being appropriate for analysis of the occurrence of strand asymmetric neighbour effects.
The simple analysis is invoked as:
$ mm ll-nbr -1 path/to/tests/data/counts-CtoT.txt -o path/for/results/
This will write 11 files into the results directory. Files such as 1.pdf
and 2.pdf
are the mutation motifs for the first and second order effects from the log-linear models. Files ending in .json
contain the raw data used to produce these figures and may be used for subsequent analyses, such as generating grids of mutation motifs. The summary files include the full log-linear modelling hierarchy. The .log
files track the command used to generate these files, including
the input files and the settings used.
Testing for strand symmetry (or asymmetry) is done as:
$ mm ll-nbr -1 path/to/tests/data/counts-CtoT.txt -o path/for/results/ --strand_symmetry
Similar output to the above is generated. The difference here is that the reference group for display are bases on the +
strand.
If comparing between groups, such as patient cohorts or chromosomal regions, then there are two separate counts files and the second count file is indicated using a -2
command line option.
Testing Full Spectra
Testing for strand symmetry requires the combined counts file, produced using the provided all_counts
script. A sample such file is included as tests/data/counts-combined.txt
. In this instance, a test of consistency in mutation spectra between strands is specified.
This analysis is run as:
$ mm ll-spectra -1 path/to/tests/data/counts-combined.txt -o another/path/for/results/ --strand_symmetry
Drawing
mm
provides support for drawing either spectra or neighbour mutation motif logos.
Interpreting logo's
If the plot is derived from a group comparison, the relative entropy terms (which specify the stack height, letter size and orientation) are taken from the mutated class belonging to group 1 (which is the counts file path assigned to the -1
option). For example, if you specified -1 file_a.txt -2 file_b.txt
, then large upright letters in the display indicate an excess in the mutated class from file_a.txt
relative to file_b.txt
.
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