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()

>>> ALIGNMENT       3M1X4M1D7M1I9M1X6M
  ALIGNMENT.COMPACT 1X1D1I1X
  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:

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”. Heuristics can be None (default), “adaptive”, “X-drop”, “Z-drop”, “banded-static”, “banded-adaptive” or “wfmash”. The memory footprint can be selected with memory_mode: “high” (default), “medium”, “low” or “biwfa” (bidirectional WFA, lowest memory). The maximum number of alignment steps and the memory budget (in bytes) can be capped with max_steps and max_memory, respectively.

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
a.status_message
>>> [WFA] Alignment completed successfully

To inspect which WFA variant performed the alignment (e.g. when using BiWFA), check the align_mode attribute:

a = WavefrontAligner(pattern, memory_mode="biwfa")
a(text)
a.align_mode
>>> 'biwfa'

Default options

The WavefrontAligner will be initialized with the following default options:

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 = a(text, clip_cigar=True)
print(cigartuples_to_str(res.cigartuples))
>>> 4S7M5D6M