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pyabpoa: abPOA Python interface

Introduction

pyabpoa provides an easy-to-use interface to abPOA, it contains all the APIs that can be used to perform MSA for a set of sequences and consensus calling from the final alignment graph.

Installation

Install pyabpoa with pip

pyabpoa can be installed with pip:

pip install pyabpoa

Install pyabpoa from source

Alternatively, you can install pyabpoa from source (cython is required):

git clone https://github.com/yangao07/abPOA.git
cd abPOA
make install_py

Examples

The following code illustrates how to use pyabpoa.

import pyabpoa as pa
a = pa.msa_aligner()
seqs=[
'CCGAAGA',
'CCGAACTCGA',
'CCCGGAAGA',
'CCGAAGA'
]
res=a.msa(seqs, out_cons=True, out_msa=True, out_pog='pog.png', incr_fn='') # perform multiple sequence alignment 
                                                                # generate a figure of alignment graph to pog.png

for seq in res.cons_seq:
    print(seq)  # print consensus sequence

res.print_msa() # print row-column multiple sequence alignment in PIR format

You can also try the example script provided in the source folder:

python ./python/example.py

APIs

Class pyabpoa.msa_aligner

pyabpoa.msa_aligner(aln_mode='g', ...)

This constructs a multiple sequence alignment handler of pyabpoa, it accepts the following arguments:

  • aln_mode: alignment mode. 'g': global, 'l': local, 'e': extension; default: 'g'
  • match: match score; default: 2
  • mismatch: match penaty; default: 4
  • score_matrix: scoring matrix file, match and mismatch are not used when score_matrix is used; default: ''
  • gap_open1: first gap opening penalty; default: 4
  • gap_ext1: first gap extension penalty; default: 2
  • gap_open2: second gap opening penalty; default: 24
  • gap_ext2: second gap extension penalty; default: 1
  • extra_b: first adaptive banding paremeter; set as < 0 to disable adaptive banded DP; default: 10
  • extra_f: second adaptive banding paremete; the number of extra bases added on both sites of the band is b+f*L, where L is the length of the aligned sequence; default : 0.01
  • is_diploid: set as 1 if input is diploid datal default: 0
  • min_freq: minimum frequency of each consensus to output for diploid datal default: 0.3

The msa_aligner handler provides one method which performs multiple sequence alignment and takes four arguments:

pyabpoa.msa_aligner.msa(seqs, out_cons, out_msa, out_pog='', incr_fn='')
  • seqs: a list variable containing a set of input sequences; positional
  • out_cons: a bool variable to ask pyabpoa to generate consensus sequence; positional
  • out_msa: a bool variable to ask pyabpoa to generate RC-MSA; positional
  • out_pog: name of a file (.png or .pdf) to store the plot of the final alignment graph; optional, default: ''
  • incr_fn: name of an existing graph (GFA) or MSA (FASTA) file, incrementally align sequence to this graph/MSA; optional, default: ''

Class pyabpoa.msa_result

pyabpoa.msa_result(seq_n, cons_n, cons_len, ...)

This class describes the information of the generated consensus sequence and the RC-MSA. The returned result of pyabpoa.msa_aligner.msa() is an object of this class that has the following properties:

  • seq_n: number of input aligned sequences
  • cons_n: number of generated consensus sequences (generally 1, could be 2 if input is specified as diploid)
  • cons_len: an array of consensus sequence length(s)
  • cons_seq: an array of consensus sequence(s)
  • msa_len: size of each row in the RC-MSA
  • msa_seq: an array containing seq_n strings that demonstrates the RC-MSA, each consisting of one input sequence and several - indicating the alignment gaps.

pyabpoa.msa_result() has a function of print_msa which prints the RC-MSA to screen.

pyabpoa.msa_result().print_msa()