pairwise-alignment

Name: pairwise-alignment
Author: GPTomics

1.1kstars185forks

How to install pairwise-alignment?

gptomics/bioskills/pairwise-alignment

$npx -y skills add GPTomics/bioSkills --skill pairwise-alignment

Installs into the current project.

Summary

Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences.

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: BioPython 1.83+
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7
8	If code throws ImportError, AttributeError, or TypeError, introspect the installed
9	package and adapt the example to match the actual API rather than retrying.
10
11	# Pairwise Sequence Alignment
12
13	"Align two sequences" -> Compute an optimal alignment between a pair of sequences using dynamic programming.
14	- Python: `PairwiseAligner()` (BioPython Bio.Align)
15	- CLI: `needle` (global) or `water` (local) from EMBOSS
16	- R: `pairwiseAlignment()` (Biostrings)
17
18	Align two sequences using dynamic programming algorithms (Needleman-Wunsch for global, Smith-Waterman for local).
19
20	## Required Import
21
22	Goal: Load modules needed for pairwise alignment operations.
23
24	Approach: Import the PairwiseAligner class along with sequence and I/O utilities from Biopython.
25
26	```python
27	from Bio.Align import PairwiseAligner
28	from Bio.Seq import Seq
29	from Bio import SeqIO
30	```
31
32	## Pairwise Library Selection
33
34	`Bio.Align.PairwiseAligner` is the right default for interactive use, scripting, and pair sizes up to a few thousand residues, but it is not the fastest or most scalable option. For high-throughput screens, very long sequences, or production pipelines, switch to a SIMD-accelerated or specialised library.
35
36	\| Library \| Speed vs Bio.Align \| Alphabet \| Scoring \| Vectorization \| When to use \|
37	\|---------\|-------------------\|----------\|---------\|---------------\|-------------\|
38	\| `Bio.Align.PairwiseAligner` (BioPython) \| 1x baseline \| DNA / RNA / protein \| Matrix + affine \| C-backed Gotoh \| Default, <10 kb pairs, interactive use \|
39	\| `parasail` (Daily 2016 BMC Bioinf) \| 10-100x \| DNA / protein \| Matrix + affine \| SSE / AVX SIMD \| High-throughput SW or NW; benchmark loops \|
40	\| `edlib` (Sosic & Sikic 2017 Bioinf) \| 100-1000x \| DNA only \| Edit distance only \| Bit-parallel Myers \| Read mapping, k-mer search, primer placement \|
41	\| `pywfa` / WFA2 (Marco-Sola 2021 Bioinformatics 37:456; BiWFA: Marco-Sola 2023 Bioinformatics 39:btad074) \| Best for low-divergence \| DNA \| Matrix + affine \| Wavefront, O(s) memory \| Long, near-identical sequences (>10 kb, <5% diverged) \|
42	\| `mappy` / minimap2 (Li 2018 Bioinf) \| Production reads-to-genome \| DNA \| Chain + base-level \| k-mer chain \| Long-read mapping, splice-aware DNA \|
43	\| `Bio.pairwise2` \| DEPRECATED \| -- \| -- \| -- \| Migrate to `PairwiseAligner` (deprecated in BioPython 1.80; not yet removed; migrate proactively) \|
44	\| EMBOSS `needle` / `water` \| ~Bio.Align \| DNA / protein \| Matrix + affine \| None \| Reproducibility, audit trails (fixed, documented default parameters) \|
45
46	Speed numbers in the table are rough; benchmark on representative inputs before committing. Critical caveats:
47	- WFA / BiWFA: 10-100x faster than Gotoh below 5% divergence; above ~10% it converges to Gotoh complexity. Right tool for PacBio HiFi self-similarity or assembly-vs-reference; not for distant homologs.
48	- edlib: 100-1000x faster than Gotoh at low divergence (<5% errors); above ~50% divergence the effective speedup drops to ~64x. For high-divergence DNA (<70% nucleotide identity), prefer parasail's SIMD score-only mode.
49	- parasail: SIMD only realises its advantage on long sequences in amortised batch loops.
50
51	When uncertain which algorithm Biopython's aligner selected internally, inspect `aligner.algorithm` after configuration -- it returns the resolved variant ("Needleman-Wunsch", "Smith-Waterman", "Gotoh global alignment algorithm", "Gotoh local alignment algorithm", "Waterman-Smith-Beyer global alignment algorithm", "Waterman-Smith-Beyer local alignment algorithm") for deterministic auditing.
52
53	## Core Concepts
54
55	\| Mode \| Algorithm \| Use Case \|
56	\|------\|-----------\|----------\|
57	\| `global` \| Needleman-Wunsch \| Full-length alignment, similar-length sequences \|
58	\| `local` \| Smith-Waterman \| Find best matching regions, different-length sequences \|
59	\| `global` + free end gaps \| Semi-global \| Overlap detection, fragment-to-reference alignment \|
60
61	### Choosing the Right Mode
62
63	- Global: Both sequences are expected to be homologous over their full length (e.g., two orthologs of similar size). Forces end-to-end alignment.
64	- Local: Conserved domains or motifs within otherwise dissimilar sequences. BLAST uses local alignment internally. Preferred when protein termini are highly divergent (termini accumulate mutations faster than core regions).
65	- Semi-global: One sequence is a fragment or subsequence of the other (e.g., primer to template, read to reference, detecting overlap betw

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pairwise-alignment

1.1kstars185forks

View on GitHub

How to install pairwise-alignment?

gptomics/bioskills/pairwise-alignment

$npx -y skills add GPTomics/bioSkills --skill pairwise-alignment

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: BioPython 1.83+
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7
8	If code throws ImportError, AttributeError, or TypeError, introspect the installed
9	package and adapt the example to match the actual API rather than retrying.
10
11	# Pairwise Sequence Alignment
12
13	"Align two sequences" -> Compute an optimal alignment between a pair of sequences using dynamic programming.
14	- Python: `PairwiseAligner()` (BioPython Bio.Align)
15	- CLI: `needle` (global) or `water` (local) from EMBOSS
16	- R: `pairwiseAlignment()` (Biostrings)
17
18	Align two sequences using dynamic programming algorithms (Needleman-Wunsch for global, Smith-Waterman for local).
19
20	## Required Import
21
22	Goal: Load modules needed for pairwise alignment operations.
23
24	Approach: Import the PairwiseAligner class along with sequence and I/O utilities from Biopython.
25
26	```python
27	from Bio.Align import PairwiseAligner
28	from Bio.Seq import Seq
29	from Bio import SeqIO
30	```
31
32	## Pairwise Library Selection
33
34	`Bio.Align.PairwiseAligner` is the right default for interactive use, scripting, and pair sizes up to a few thousand residues, but it is not the fastest or most scalable option. For high-throughput screens, very long sequences, or production pipelines, switch to a SIMD-accelerated or specialised library.
35
36	\| Library \| Speed vs Bio.Align \| Alphabet \| Scoring \| Vectorization \| When to use \|
37	\|---------\|-------------------\|----------\|---------\|---------------\|-------------\|
38	\| `Bio.Align.PairwiseAligner` (BioPython) \| 1x baseline \| DNA / RNA / protein \| Matrix + affine \| C-backed Gotoh \| Default, <10 kb pairs, interactive use \|
39	\| `parasail` (Daily 2016 BMC Bioinf) \| 10-100x \| DNA / protein \| Matrix + affine \| SSE / AVX SIMD \| High-throughput SW or NW; benchmark loops \|
40	\| `edlib` (Sosic & Sikic 2017 Bioinf) \| 100-1000x \| DNA only \| Edit distance only \| Bit-parallel Myers \| Read mapping, k-mer search, primer placement \|
41	\| `pywfa` / WFA2 (Marco-Sola 2021 Bioinformatics 37:456; BiWFA: Marco-Sola 2023 Bioinformatics 39:btad074) \| Best for low-divergence \| DNA \| Matrix + affine \| Wavefront, O(s) memory \| Long, near-identical sequences (>10 kb, <5% diverged) \|
42	\| `mappy` / minimap2 (Li 2018 Bioinf) \| Production reads-to-genome \| DNA \| Chain + base-level \| k-mer chain \| Long-read mapping, splice-aware DNA \|
43	\| `Bio.pairwise2` \| DEPRECATED \| -- \| -- \| -- \| Migrate to `PairwiseAligner` (deprecated in BioPython 1.80; not yet removed; migrate proactively) \|
44	\| EMBOSS `needle` / `water` \| ~Bio.Align \| DNA / protein \| Matrix + affine \| None \| Reproducibility, audit trails (fixed, documented default parameters) \|
45
46	Speed numbers in the table are rough; benchmark on representative inputs before committing. Critical caveats:
47	- WFA / BiWFA: 10-100x faster than Gotoh below 5% divergence; above ~10% it converges to Gotoh complexity. Right tool for PacBio HiFi self-similarity or assembly-vs-reference; not for distant homologs.
48	- edlib: 100-1000x faster than Gotoh at low divergence (<5% errors); above ~50% divergence the effective speedup drops to ~64x. For high-divergence DNA (<70% nucleotide identity), prefer parasail's SIMD score-only mode.
49	- parasail: SIMD only realises its advantage on long sequences in amortised batch loops.
50
51	When uncertain which algorithm Biopython's aligner selected internally, inspect `aligner.algorithm` after configuration -- it returns the resolved variant ("Needleman-Wunsch", "Smith-Waterman", "Gotoh global alignment algorithm", "Gotoh local alignment algorithm", "Waterman-Smith-Beyer global alignment algorithm", "Waterman-Smith-Beyer local alignment algorithm") for deterministic auditing.
52
53	## Core Concepts
54
55	\| Mode \| Algorithm \| Use Case \|
56	\|------\|-----------\|----------\|
57	\| `global` \| Needleman-Wunsch \| Full-length alignment, similar-length sequences \|
58	\| `local` \| Smith-Waterman \| Find best matching regions, different-length sequences \|
59	\| `global` + free end gaps \| Semi-global \| Overlap detection, fragment-to-reference alignment \|
60
61	### Choosing the Right Mode
62
63	- Global: Both sequences are expected to be homologous over their full length (e.g., two orthologs of similar size). Forces end-to-end alignment.
64	- Local: Conserved domains or motifs within otherwise dissimilar sequences. BLAST uses local alignment internally. Preferred when protein termini are highly divergent (termini accumulate mutations faster than core regions).
65	- Semi-global: One sequence is a fragment or subsequence of the other (e.g., primer to template, read to reference, detecting overlap betw

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