Align sequences using WFA2-lib
Project description
A python wrapper for wavefront alignment using WFA2-lib
Installation
To download from pypi:
pip install pywfa
From conda:
conda install -c bioconda pywfa
Build from source:
git clone https://github.com/kcleal/pywfa cd pywfa pip install .
Overview
Alignment of pattern and text strings can be performed by accessing WFA2-lib functions directly:
from pywfa import WavefrontAligner
pattern = "TCTTTACTCGCGCGTTGGAGAAATACAATAGT"
text = "TCTATACTGCGCGTTTGGAGAAATAAAATAGT"
a = WavefrontAligner(pattern)
score = a.wavefront_align(text)
assert a.status == 0 # alignment was successful
assert a.cigarstring == "3M1X4M1D7M1I9M1X6M"
assert a.score == -24
a.cigartuples
>>> [(0, 3), (8, 1), (0, 4), (2, 1), (0, 7), (1, 1), (0, 9), (8, 1), (0, 6)]
a.cigar_print_pretty()
>>> 3M1X4M1D7M1I9M1X6M ALIGNMENT
1X1D1I1X ALIGNMENT.COMPACT
PATTERN TCTTTACTCGCGCGTT-GGAGAAATACAATAGT
||| |||| ||||||| ||||||||| ||||||
TEXT TCTATACT-GCGCGTTTGGAGAAATAAAATAGT
The output of cigar_pretty_print can be directed to a file, rather than stdout using:
a.cigar_print_pretty("file.txt")
To obtain a python str of this print out, access the results object (see below).
Cigartuples follow the convention:
Operation |
Code |
---|---|
M |
0 |
I |
1 |
D |
2 |
N |
3 |
S |
4 |
H |
5 |
= |
7 |
X |
8 |
B |
9 |
For convenience, a results object can be obtained by calling the WavefrontAligner with a pattern and text:
pattern = "TCTTTACTCGCGCGTTGGAGAAATACAATAGT"
text = "TCTATACTGCGCGTTTGGAGAAATAAAATAGT"
a = WavefrontAligner(pattern)
result = a(text) # alignment result
result.__dict__
>>> {'pattern_length': 32, 'text_length': 32, 'pattern_start': 0, 'pattern_end': 32, 'text_start': 0, 'text_end': 32, 'cigartuples': [(0, 3), (8, 1), (0, 4), (2, 1), (0, 7), (1, 1), (0, 9), (8, 1), (0, 6)], 'score': -24, 'pattern': 'TCTTTACTCGCGCGTTGGAGAAATACAATAGT', 'text': 'TCTATACTGCGCGTTTGGAGAAATAAAATAGT', 'status': 0}
# Alignment can also be called with a pattern like this:
a(text, pattern)
# obtain a string in the same format as cigar_print_pretty
a.pretty
>>> 3M1X4M1D7M1I9M1X6M ALIGNMENT
1X1D1I1X ALIGNMENT.COMPACT
PATTERN TCTTTACTCGCGCGTT-GGAGAAATACAATAGT
|||*|||| ||||||| |||||||||*||||||
TEXT TCTATACT-GCGCGTTTGGAGAAATAAAATAGT
Configure
To configure the WaveFrontAligner, options can be provided during initialization:
from pywfa import WavefrontAligner
a = WavefrontAligner(scope="score",
distance="affine2p",
span="end-to-end",
heuristic="adaptive")
Supported distance metrics are “affine” (default) and “affine2p”. Scope can be “full” (default) or “score”. Span can be “ends-free” (default) or “end-to-end”. Heuristic can be None (default), “adaptive” or “X-drop”.
When using heuristic functions it is recommended to check the status attribute:
pattern = "AAAAACCTTTTTAAAAAA"
text = "GGCCAAAAACCAAAAAA"
a = WavefrontAligner(heuristic="adaptive")
a(pattern, text)
a.status
>>> 0 # successful alignment, -1 indicates the alignment was stopped due to the heuristic
Default options
The WavefrontAligner will be initialized with the following default options:
Parameter |
Default value |
---|---|
pattern |
None |
distance |
“affine” |
match |
0 |
gap_opening |
6 |
gep_extension |
2 |
gap_opening2 |
24 |
gap_extension2 |
1 |
scope |
“full” |
span |
“ends-free” |
pattern_begin_free |
0 |
pattern_end_free |
0 |
text_begin_free |
0 |
text_end_free |
0 |
heuristic |
None |
min_wavefront_length |
10 |
max_distance_threshold |
50 |
steps_between_cutoffs |
1 |
xdrop |
20 |
Modifying the cigar
If desired the cigar can be modified so the end operation is either a soft-clip or a match, this makes the alignment cigar resemble those produced by bwa, for example:
pattern = "AAAAACCTTTTTAAAAAA"
text = "GGCCAAAAACCAAAAAA"
a = WavefrontAligner(pattern)
res = a(text, clip_cigar=False)
print(cigartuples_to_str(res.cigartuples))
>>> 4I7M5D6M
res(text, clip_cigar=True)
print(cigartuples_to_str(res.cigartuples))
>>> 4S7M5D6M
An experimental feature is to trim short matches at the end of alignments. This results in alignments that approximate local alignments:
pattern = "AAAAAAAAAAAACCTTTTAAAAAAGAAAAAAA"
text = "ACCCCCCCCCCCAAAAACCAAAAAAAAAAAAA"
a = WavefrontAligner(pattern)
# The unmodified cigar may have short matches at the end:
res = a(text, clip_cigar=False)
res.cigartuples
>>> [(0, 1), (1, 5), (8, 6), (0, 7), (2, 5), (0, 5), (8, 1), (0, 7)]
res.aligned_text
>>> ACCCCCCCCCCCAAAAACCAAAAAAAAAAAAA
res.text_start, res.text_end
>>> 0, 32
# The minimum allowed block of matches can be set at e.g. 5 bp, which will trim off short matches
res = a(text, clip_cigar=True, min_aligned_bases_left=5, min_aligned_bases_right=5)
res.cigartuples
>>> [(4, 12), (0, 7), (2, 5), (0, 5), (8, 1), (0, 7)]
res.aligned_text
>>> AAAAACCAAAAAAAAAAAAA
res.text_start, res.text_end
>>> 12, 32
# Mismatch operations X can also be elided, note this occurs after the clip_cigar stage
res = a(text, clip_cigar=True, min_aligned_bases_left=5, min_aligned_bases_right=5, elide_mismatches=True)
res.cigartuples
>>> [(4, 12), (0, 7), (2, 5), (0, 13)]
res.aligned_text
>>> AAAAACCAAAAAAAAAAAAA
Notes: The alignment score is not modified currently by trimming the cigar, however the pattern_start, pattern_end, test_start and text_end are modified when the cigar is modified.
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