parasail 1.1.5

pairwise sequence alignment library

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Author: Jeff Daily ([e-mail])

Table of Contents

• Installation

  • Using pip

  • Building from Source

• Quick Example

• Standard Function Naming Convention

• Profile Function Naming Convention

• Substitution Matrices

• SSW Library Emulation

• Banded Global Alignment

• File Input

• Tracebacks

• Citing parasail

• License: Battelle BSD-style

This package contains Python bindings for parasail. Parasail is a SIMD C (C99) library containing implementations of the Smith-Waterman (local), Needleman-Wunsch (global), and semi-global pairwise sequence alignment algorithms.

Installation

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Using pip

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The recommended way of installing is to use the latest version available via pip.

pip install parasail

Binaries for Windows and OSX should be available via pip. Using pip on a Linux platform will first download the latest version of the parasail C library sources and then compile them automatically into a shared library. For an installation from sources, or to learn how the pip installation works on Linux, please read on.

Building from Source

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The parasail python bindings are based on ctypes. Unfortunately, best practices are not firmly established for providing cross-platform and user-friendly python bindings based on ctypes. The approach with parasail-python is to install the parasail shared library as “package data” and use a relative path from the parasail/__init__.py in order to locate the shared library.

There are two approaches currently supported. First, you can compile your own parasail shared library using one of the recommended build processes described in the parasail C library README.md, then copy the parasail.dll (Windows), libparasail.so (Linux), or libparasail.dylib (OSX) shared library to parasail-python/parasail – the same folder location as parasasail-python/parasail/__init__.py.

The second approach is to let the setup.py script attempt to download and compile the parasail C library for you using the configure script that comes with it. This happens as a side effect of the bdist_wheel target.

python setup.py bdist_wheel

The bdist_wheel target will first look for the shared library. If it exists, it will happily install it as package data. Otherwise, the latest parasail master branch from github will be downloaded, unzipped, configured, made, and the shared library will be copied into the appropriate location for package data installation.

Quick Example

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The Python interface only includes bindings for the dispatching functions, not the low-level instruction set-specific function calls. The Python interface also includes wrappers for the various PAM and BLOSUM matrices included in the distribution.

Gap open and extension penalties are specified as positive integers.

import parasail
result = parasail.sw_scan_16("asdf", "asdf", 11, 1, parasail.blosum62)
result = parasail.sw_stats_striped_8("asdf", "asdf", 11, 1, parasail.pam100)

Standard Function Naming Convention

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To make it easier to find the function you’re looking for, the function names follow a naming convention. The following will use set notation {} to indicate a selection must be made and brackets [] to indicate an optional part of the name.

• Non-vectorized, reference implementations.

  • Required, select one of global (nw), semi-global (sg), or local (sw) alignment.

  • Optional return alignment statistics.

  • Optional return DP table or last row/col.

  • Optional use a prefix scan implementation.

  • parasail. {nw,sg,sw} [_stats] [{_table,_rowcol}] [_scan]

• Non-vectorized, traceback-capable reference implementations.

  • Required, select one of global (nw), semi-global (sg), or local (sw) alignment.

  • Optional use a prefix scan implementation.

  • parasail. {nw,sg,sw} _trace [_scan]

• Vectorized.

  • Required, select one of global (nw), semi-global (sg), or local (sw) alignment.

  • Optional return alignment statistics.

  • Optional return DP table or last row/col.

  • Required, select vectorization strategy – striped is a good place to start, but scan is often faster for global alignment.

  • Required, select solution width. ‘sat’ will attempt 8-bit solution but if overflow is detected it will then perform the 16-bit operation. Can be faster in some cases, though 16-bit is often sufficient.

  • parasail. {nw,sg,sw} [_stats] [{_table,_rowcol}] {_striped,_scan,_diag} {_8,_16,_32,_64,_sat}

• Vectorized, traceback-capable.

  • Required, select one of global (nw), semi-global (sg), or local (sw) alignment.

  • Required, select vectorization strategy – striped is a good place to start, but scan is often faster for global alignment.

  • Required, select solution width. ‘sat’ will attempt 8-bit solution but if overflow is detected it will then perform the 16-bit operation. Can be faster in some cases, though 16-bit is often sufficient.

  • parasail. {nw,sg,sw} _trace {_striped,_scan,_diag} {_8,_16,_32,_64,_sat}

Profile Function Naming Convention

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It has been noted in literature that some performance can be gained by reusing the query sequence when using striped [Farrar, 2007] or scan [Daily, 2015] vector strategies. There is a special subset of functions that enables this behavior. For the striped and scan vector implementations only, a query profile can be created and reused for subsequent alignments. This can noticeably speed up applications such as database search.

• Profile creation

  • Optional, prepare query profile for a function that returns statistics. Stats require additional data structures to be allocated.

  • Required, select solution width. ‘sat’ will allocate profiles for both 8- and 16-bit solutions.

  • parasail.profile_create [_stats] {_8,_16,_32,_64,_sat}

• Profile use

  • Vectorized.

    • Required, select one of global (nw), semi-global (sg), or local (sw) alignment.

    • Optional return alignment statistics.

    • Optional return DP table or last row/col.

    • Required, select vectorization strategy – striped is a good place to start, but scan is often faster for global alignment.

    • Required, select solution width. ‘sat’ will attempt 8-bit solution but if overflow is detected it will then perform the 16-bit operation. Can be faster in some cases, though 16-bit is often sufficient.

    • parasail. {nw,sg,sw} [_stats] [{_table,_rowcol}] {_striped,_scan} _profile {_8,_16,_32,_64,_sat}

  • Vectorized, traceback-capable.

    • Required, select one of global (nw), semi-global (sg), or local (sw) alignment.

    • Required, select vectorization strategy – striped is a good place to start, but scan is often faster for global alignment.

    • Required, select solution width. ‘sat’ will attempt 8-bit solution but if overflow is detected it will then perform the 16-bit operation. Can be faster in some cases, though 16-bit is often sufficient.

    • parasail. {nw,sg,sw} _trace {_striped,_scan} _profile {_8,_16,_32,_64,_sat}

This is a sample function signature of one of the profile creation functions.

profile = parasail.profile_create_8("asdf", parasail.blosum62)
result1 = parasail.sw_trace_striped_profile_16(profile, "asdf", 10, 1)
result2 = parasail.nw_scan_profile_16(profile, "asdf", 10, 1)

Substitution Matrices

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parasail bundles a number of substitution matrices including PAM and BLOSUM. To use them, look them up by name (useful for command-line parsing) or use directly. For example

print(parasail.blosum62)
matrix = parasail.Matrix("pam100")

You can also create your own matrices with simple match/mismatch values. For more complex matrices, you can start by copying a built-in matrix or start simple and modify values as needed. For example

# copy a built-in matrix, then modify like a numpy array
matrix = parasail.blosum62.copy()
matrix[2,4] = 200
matrix[3,:] = 100
user_matrix = parasail.matrix_create("ACGT", 2, -1)

You can also parse simple matrix files using the function if the file is in the following format:

#
# Any line starting with '#' is a comment.
#
# Needs a row for the alphabet.  First column is a repeat of the
# alphabet and assumed to be identical in order to the first alphabet row.
#
        A   T   G   C   S   W   R   Y   K   M   B   V   H   D   N   U
A   5  -4  -4  -4  -4   1   1  -4  -4   1  -4  -1  -1  -1  -2  -4
T  -4   5  -4  -4  -4   1  -4   1   1  -4  -1  -4  -1  -1  -2   5
G  -4  -4   5  -4   1  -4   1  -4   1  -4  -1  -1  -4  -1  -2  -4
C  -4  -4  -4   5   1  -4  -4   1  -4   1  -1  -1  -1  -4  -2  -4
S  -4  -4   1   1  -1  -4  -2  -2  -2  -2  -1  -1  -3  -3  -1  -4
W   1   1  -4  -4  -4  -1  -2  -2  -2  -2  -3  -3  -1  -1  -1   1
R   1  -4   1  -4  -2  -2  -1  -4  -2  -2  -3  -1  -3  -1  -1  -4
Y  -4   1  -4   1  -2  -2  -4  -1  -2  -2  -1  -3  -1  -3  -1   1
K  -4   1   1  -4  -2  -2  -2  -2  -1  -4  -1  -3  -3  -1  -1   1
M   1  -4  -4   1  -2  -2  -2  -2  -4  -1  -3  -1  -1  -3  -1  -4
B  -4  -1  -1  -1  -1  -3  -3  -1  -1  -3  -1  -2  -2  -2  -1  -1
V  -1  -4  -1  -1  -1  -3  -1  -3  -3  -1  -2  -1  -2  -2  -1  -4
H  -1  -1  -4  -1  -3  -1  -3  -1  -3  -1  -2  -2  -1  -2  -1  -1
D  -1  -1  -1  -4  -3  -1  -1  -3  -1  -3  -2  -2  -2  -1  -1  -1
N  -2  -2  -2  -2  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -2
U  -4   5  -4  -4  -4   1  -4   1   1  -4  -1  -4  -1  -1  -2   5

matrix_from_filename = parasail.Matrix("filename.txt")

SSW Library Emulation

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The SSW library (https://github.com/mengyao/Complete-Striped-Smith-Waterman-Library) performs Smith-Waterman local alignment using SSE2 instructions and a striped vector. Its result provides the primary score, a secondary score, beginning and ending locations of the alignment for both the query and reference sequences, as well as a SAM CIGAR. There are a few parasail functions that emulate this behavior, with the only exception being that parasail does not calculate a secondary score.

score_size = 1 # 0, use 8-bit align; 1, use 16-bit; 2, try both
profile = parasail.ssw_init("asdf", parasail.blosum62, score_size)
result = parasail.ssw_profile(profile, "asdf", 10, 1)
print(result.score1)
print(result.cigar)
print(result.ref_begin1)
print(result.ref_end1)
print(result.read_begin1)
print(result.read_end1)
# or skip profile creation
result = parasail.ssw("asdf", "asdf", 10, 1, parasail.blosum62)

Banded Global Alignment

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There is one version of banded global alignment available. Though it is not vectorized, it might still be faster than using other parasail global alignment functions, especially for large sequences. The function signature is similar to the other parasail functions with the only exception being k, the band width.

band_size = 3
result = parasail.nw_banded("asdf", "asdf", 10, 1, band_size, matrix):

File Input

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Parasail can parse FASTA, FASTQ, and gzipped versions of such files. The function parasail.sequences_from_file will return a list-like object containing Sequence instances. A parasail Sequence behaves like an immutable string but also has extra attributes name, comment, and qual. These attributes will return an empty string if the input file did not contain these fields.

Tracebacks

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Parasail supports accessing a SAM CIGAR string from a result. You must use a traceback-capable alignment function. Refer to the C interface description above for details on how to use a traceback-capable alignment function.

result = parasail.sw_trace("asdf", "asdf", 10, 1, parasail.blosum62)
cigar = result.cigar
# cigars have seq, len, beg_query, and beg_ref properties
# the seq property is encoded
print(cigar.seq)
# use decode method to return a decoded cigar string
print(cigar.decode())

Citing parasail

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If needed, please cite the following paper.

Daily, Jeff. (2016). Parasail: SIMD C library for global, semi-global, and local pairwise sequence alignments. BMC Bioinformatics, 17(1), 1-11. doi:10.1186/s12859-016-0930-z

http://dx.doi.org/10.1186/s12859-016-0930-z

License: Battelle BSD-style

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Copyright (c) 2015, Battelle Memorial Institute

1. Battelle Memorial Institute (hereinafter Battelle) hereby grants permission to any person or entity lawfully obtaining a copy of this software and associated documentation files (hereinafter “the Software”) to redistribute and use the Software in source and binary forms, with or without modification. Such person or entity may use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and may permit others to do so, subject to the following conditions:

   • Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimers.

   • Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

   • Other than as used herein, neither the name Battelle Memorial Institute or Battelle may be used in any form whatsoever without the express written consent of Battelle.

   • Redistributions of the software in any form, and publications based on work performed using the software should include the following citation as a reference:

   Daily, Jeff. (2016). Parasail: SIMD C library for global, semi-global, and local pairwise sequence alignments. BMC Bioinformatics, 17(1), 1-11. doi:10.1186/s12859-016-0930-z

2. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL BATTELLE OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.