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# Spoa
[](https://github.com/rvaser/spoa/releases/latest)
[](https://doi.org/10.1101/gr.214270.116)
Spoa (SIMD POA) is a c++ implementation of the partial order alignment (POA) algorithm (as described in 10.1093/bioinformatics/18.3.452) which is used to generate consensus sequences (as described in 10.1093/bioinformatics/btg109). It supports three alignment modes: local (Smith-Waterman), global (Needleman-Wunsch) and semi-global alignment (overlap), and three gap modes: linear, affine and convex (piecewise affine). It also supports Intel SSE4.1+ and AVX2 vectorization (marginally faster due to high latency shifts), [SIMDe](https://github.com/simd-everywhere/simde) and dispatching.
## Build
### Dependencies
- gcc 7+ | clang 4+
- (spoa_exe)(spoa_test) zlib 1.2.8+
#### Hidden
- \[optional\] USCiLab/cereal 1.3.0
- \[optional\] simd-everywhere/simde 0.7.6
- \[optional\] google/cpu_features 0.6.0
- (spoa_exe)(spoa_test) rvaser/bioparser 3.1.0
- (spoa_exe)(spoa_test) rvaser/biosoup 0.11.0
- (spoa_test) google/googletest 1.10.0
### CMake (3.12+)
```bash
git clone https://github.com/rvaser/spoa && cd spoa
cmake -B build -DCMAKE_BUILD_TYPE=Release
make -C build
```
#### Options
- `spoa_install`: generate library install target
- `spoa_build_exe`: build executable
- `spoa_build_tests`: build unit tests
- `spoa_optimize_for_native`: build with `-march=native`
- `spoa_optimize_for_portability`: build with `-msse4.1`
- `spoa_use_cereal`: use cereal library
- `spoa_use_simde`: build with SIMDe for porting vectorized code
- `spoa_use_simde_nonvec`: use SIMDe library for nonvectorized code
- `spoa_use_simde_openmp`: use SIMDe support for OpenMP SIMD
- `spoa_generate_dispatch`: use SIMDe to generate x86 dispatch
### Meson (0.60.0+)
```bash
git clone https://github.com/rvaser/spoa && cd spoa
meson setup build
ninja -C build
```
#### Options
- `exe`: build executable
- `tests`: build unit tests
- `avx2`: build with `-mavx2`
- `sse41`: build with `-msse4.1`
- `cereal`: build serialization funcitons
- `simde`: build with SIMDe
- `simde_nonvec`: use SIMDe for nonvectorized code
- `simde_openmp`: use SIMDe support for OpenMP SIMD
- `dispatch`: use SIMDe to generate x86 dispatch
## Usage
```bash
usage: spoa [options ...] <sequences>
# default output is stdout
<sequences>
input file in FASTA/FASTQ format (can be compressed with gzip)
options:
-m <int>
default: 5
score for matching bases
-n <int>
default: -4
score for mismatching bases
-g <int>
default: -8
gap opening penalty (must be non-positive)
-e <int>
default: -6
gap extension penalty (must be non-positive)
-q <int>
default: -10
gap opening penalty of the second affine function
(must be non-positive)
-c <int>
default: -4
gap extension penalty of the second affine function
(must be non-positive)
-l, --algorithm <int>
default: 0
alignment mode:
0 - local (Smith-Waterman)
1 - global (Needleman-Wunsch)
2 - semi-global
-r, --result <int> (option can be used multiple times)
default: 0
result mode:
0 - consensus (FASTA)
1 - multiple sequence alignment (FASTA)
2 - 0 & 1 (FASTA)
3 - partial order graph (GFA)
4 - 0 & 3 (GFA)
-d, --dot <file>
output file for the partial order graph in DOT format
-s, --strand-ambiguous
for each sequence pick the strand with the better alignment
--version
prints the version number
-h, --help
prints the usage
gap mode:
linear if g >= e
affine if g <= q or e >= c
convex otherwise (default)
```
## Examples
```cpp
#include <iostream>
#include "spoa/spoa.hpp"
int main(int argc, char** argv) {
std::vector<std::string> sequences = {
"CATAAAAGAACGTAGGTCGCCCGTCCGTAACCTGTCGGATCACCGGAAAGGACCCGTAAAGTGATAATGAT",
"ATAAAGGCAGTCGCTCTGTAAGCTGTCGATTCACCGGAAAGATGGCGTTACCACGTAAAGTGATAATGATTAT",
"ATCAAAGAACGTGTAGCCTGTCCGTAATCTAGCGCATTTCACACGAGACCCGCGTAATGGG",
"CGTAAATAGGTAATGATTATCATTACATATCACAACTAGGGCCGTATTAATCATGATATCATCA",
"GTCGCTAGAGGCATCGTGAGTCGCTTCCGTACCGCAAGGATGACGAGTCACTTAAAGTGATAAT",
"CCGTAACCTTCATCGGATCACCGGAAAGGACCCGTAAATAGACCTGATTATCATCTACAT"
};
auto alignment_engine = spoa::AlignmentEngine::Create(
spoa::AlignmentType::kNW, 3, -5, -3); // linear gaps
spoa::Graph graph{};
for (const auto& it : sequences) {
auto alignment = alignment_engine->Align(it, graph);
graph.AddAlignment(alignment, it);
}
auto consensus = graph.GenerateConsensus();
std::cerr << ">Consensus LN:i:" << consensus.size() << std::endl
<< consensus << std::endl;
auto msa = graph.GenerateMultipleSequenceAlignment();
for (const auto& it : msa) {
std::cerr << it << std::endl;
}
return 0;
}
```
## Acknowledgement
This work has been supported in part by Croatian Science Foundation under projects UIP-11-2013-7353 and IP-2018-01-5886.
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