IggNition

Ultra-fast nucleotide-level Aho numbering for antibody variable domains.

IggNition replaces ANARCI/HMMER with a purpose-built Rust aligner against pre-numbered human germline V/D/J genes. It assigns Aho scheme positions to every nucleotide in an antibody variable domain sequence, producing a fixed-length coordinate frame suitable for repertoire analysis and AbLLM training.

	ANARCI (Python/HMMER)	IgNition
Throughput (single core)	~4 seq/s	>50,000 seq/s
Language	Python + HMMER	Rust
Output	amino-acid level	nucleotide level
Parallelism	external (shell)	built-in (Rayon)

Installation

pip install iggnition

Pre-compiled wheels are available for Linux (x86_64, aarch64), macOS (Apple Silicon + Intel), and Windows.

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Quick Start

Single sequence

import iggnition

df = iggnition.run(
    nt_seq="CAGGTGCAGCTGGTGCAGTCTGGAGCT...",
    aa_seq="QVQLVQSGAE...",
)
print(df)
# shape: (447, 7)  ← 149 Aho positions × 3 nucleotides for a heavy chain
# ┌─────────────┬───────┬─────────────┬──────────────┬────────────────┬────────────┬───────────┐
# │ sequence_id ┆ chain ┆ nt_position ┆ aho_position ┆ codon_position ┆ nucleotide ┆ amino_acid│
# │ u32         ┆ str   ┆ u32         ┆ u32          ┆ u32            ┆ str        ┆ str       │

AIRR-format DataFrame (single chain)

import polars as pl
import iggnition

df = pl.read_csv("airr_table.tsv", separator="\t")

results, errors = iggnition.run(
    df,
    nt_col="sequence",
    aa_col="sequence_aa",
    locus_col="locus",   # e.g. "IGH", "IGK", "IGL"
)

Paired heavy + light (PairPlex-style)

results, errors = iggnition.run(
    df,
    paired=True,
    nt_col_heavy="sequence:0",
    aa_col_heavy="sequence_aa:0",
    nt_col_light="sequence:1",
    aa_col_light="sequence_aa:1",
)

File paths

# FASTA → DataFrame
results, errors = iggnition.run("input.fasta")

# Parquet → Parquet
iggnition.run("input.parquet", output="numbered.parquet")

# TSV → TSV
iggnition.run("input.tsv", output="numbered.tsv")

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Output Formats

iggnition.run(df) returns a (results, errors) tuple for batch input.

Per-nucleotide (default)

One row per nucleotide position — the most detailed output:

results, errors = iggnition.run(df)
# shape: (n_sequences × 447, 7) for heavy chains
# columns: sequence_id, chain, nt_position, aho_position, codon_position, nucleotide, amino_acid

Column	Type	Description
sequence_id	u32	Row index from input
chain	str	H, K, or L
nt_position	u32	Absolute nucleotide position (1-based)
aho_position	u32	Aho amino acid position (1-based)
codon_position	u32	Position within codon (1, 2, or 3)
nucleotide	str	A/T/G/C or - for gaps
amino_acid	str	Single-letter AA or - for gaps

Per-codon

One row per Aho position:

results, errors = iggnition.run(df, per_codon=True)
# columns: sequence_id, chain, aho_position, codon, amino_acid

Wide format — recommended for AI/ML

Wide format is the natural input shape for machine learning models: each sequence becomes a single fixed-length row with one column per nucleotide position (H1…H447 for heavy chains, L1…L444 for light chains). Both Kappa and Lambda light chains use the L prefix.

results, errors = iggnition.run(df, wide=True)

Output shape: (n_sequences, 1 + 447 + 444) for paired heavy + light, with columns:

seq_name | H1 | H2 | … | H447 | L1 | L2 | … | L444

The first column is always seq_name. If you supply name_col, it takes the values from that column; otherwise it contains the integer row index.

Human-readable vs numeric encoding

By default (human_readable=True) nucleotide positions are stored as single-character strings — what you would expect:

results, errors = iggnition.run(df, wide=True)
# H1 column: "C", "A", "G", "-", ...  (pl.Utf8)

For large-scale work (millions of sequences) switch to the raw ASCII byte encoding with human_readable=False. Each position becomes a pl.UInt8 value (65=A, 84=T, 71=G, 67=C, 45=gap), which is the most memory-efficient representation and enables direct numerical operations without any decoding step:

results, errors = iggnition.run(df, wide=True, human_readable=False)
# H1 column: 67, 65, 71, 45, ...  (pl.UInt8)

# Decode a column back to characters when needed:
results.with_columns(pl.col("H1").map_elements(chr, return_dtype=pl.Utf8))

# Or work directly with the numbers — e.g. one-hot encode, pass to PyTorch, etc.

Memory note: The UInt8 wide path uses a compact Rust byte-array representation (~2.5 GB for 2.4 M paired antibodies) vs. ~400+ GB peak via a naive pivot on per-nucleotide rows. Always prefer wide=True over post-hoc pivoting.

Per-chain wide format

By default paired results are merged so each antibody occupies a single row (H columns + L columns). Set per_chain=True to keep one row per chain:

results, errors = iggnition.run(df, wide=True, per_chain=True)
# One row per chain; a "chain" column indicates H or L.
# H rows have L columns filled with null, and vice versa.

Propagating sequence names

Use name_col to carry an identifier from the input DataFrame into the results as seq_name:

results, errors = iggnition.run(
    df,
    wide=True,
    name_col="clone_id",   # any column in df
)
# First column: seq_name (values from df["clone_id"])
# Then: H1…H447, L1…L444

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Region Annotations

IgNition ships a complete structural annotation layer accessible without running any alignment. All positions are in the Aho coordinate system.

import iggnition

# CDR / FR boundaries — Aho AA positions (1-based, inclusive)
iggnition.CDR_REGIONS["H"]["CDR3"]   # → (109, 137)
iggnition.CDR_REGIONS["L"]["CDR1"]   # → (26, 38)

# Same boundaries expressed as IgNition nt positions
iggnition.CDR_REGIONS_NT["H"]["CDR3"]  # → (325, 411)
iggnition.CDR_REGIONS_NT["L"]["FR4"]   # → (415, 444)

Helper functions

# Convert Aho AA position → 3 IgNition nt positions
iggnition.aho_to_nt(106)       # → (316, 317, 318) — the conserved FR3 Cys

# Convert an Aho range → (first_nt, last_nt)
iggnition.aho_range_to_nt(26, 38)  # → (76, 114)  — VH CDR1

# Which region does an Aho position fall in?
iggnition.region_of(43, "H")   # → "FR2"
iggnition.region_of(120, "H")  # → "CDR3"

# Convert a wide-format column name → (aho_pos, codon_pos)
iggnition.nt_col_to_aho("H76")   # → (26, 1) — first nt of Aho pos 26 (CDR1 start)
iggnition.nt_col_to_aho("L444")  # → (148, 3) — last nt of VL

# Build a list of column names for a given CDR/FR (for masking a wide DataFrame)
iggnition.cdr_mask("H", "CDR3")  # → ['H325', 'H326', ..., 'H411']
iggnition.cdr_mask("L", "CDR1")  # → ['L76', 'L77', ..., 'L114']

Structural landmarks

Exact Aho positions for absolutely conserved residues:

iggnition.LANDMARKS["H"]["Cys_disulfide_N"]["aho"]  # → 23  (IMGT 23 / Kabat ~22)
iggnition.LANDMARKS["H"]["Cys_disulfide_C"]["aho"]  # → 106 (IMGT 104 / Kabat 92)
iggnition.LANDMARKS["H"]["Trp_FR2"]["aho"]          # → 43  (IMGT 41 / Kabat 36)

These positions are derived directly from the germline database embedded in the binary — they are guaranteed to be consistent with the numbering IgNition assigns.

Vernier zone and canonical positions

# Vernier zone Aho positions (Foote & Winter 1992)
iggnition.VERNIER_ZONE_AHO["H"]   # → [2, 27, 29, 30, 43, 54, 55, 56, 66, 68, 70, 77, 92, 93]
iggnition.VERNIER_ZONE_AHO["L"]   # → [2, 25, 26, 27, 29, 33, 49, 51, 52, 73, 77, 78, 80]

# Full detail including Kabat cross-reference and notes
iggnition.VERNIER_ZONE["H"][71]   # → {"aho": 70, "kabat": 71, "region": "FR3", "note": "..."}

# Chothia canonical structural positions (Al-Lazikani et al. 1997)
iggnition.CHOTHIA_CANONICAL["H"]["CDR1_H1"]
iggnition.CHOTHIA_CANONICAL["L"]["CDR2_L2"]

Numbering scheme cross-reference

# Verified Aho ↔ IMGT ↔ Kabat correspondences at landmark positions
iggnition.NUMBERING_CROSSREF["H"]
# [{"aho": 23, "imgt": 23, "kabat_h": 22, "residue": "Cys (intrachain SS, N-terminal)"}, ...]

CDR boundaries in Aho — quick reference

Region	VH Aho	VH nt	VK/VL Aho	VK/VL nt
FR1	1–25	1–75	1–25	1–75
CDR1	26–38	76–114	26–38	76–114
FR2	39–49	115–147	39–49	115–147
CDR2	50–64	148–192	50–66	148–198
FR3	65–108	193–324	67–108	199–324
CDR3	109–137	325–411	109–138	325–414
FR4	138–149	412–447	139–148	415–444

VH CDR2 is shorter in the Aho frame (slots 62–64 for insertions) while VK/VL CDR2 has a larger insertion slot (59–66), reflecting the structural reality that H2 loops occupy different insertion positions than L2 loops.

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CLI

# FASTA → TSV (stdout)
iggnition run input.fasta

# FASTA → TSV (file)
iggnition run input.fasta output.tsv

# Parquet → Parquet
iggnition run input.parquet output.parquet

# TSV (AIRR) → TSV, per-codon
iggnition run input.tsv output.tsv --per-codon

# Wide format, 8 threads, with progress bar
iggnition run input.fasta output.tsv --wide --threads 8 --verbose

CLI options

Option	Default	Description
--per-codon	off	One row per codon instead of per nucleotide
--wide	off	Pivot to wide format
--nt-col	sequence	NT column name (TSV/Parquet)
--aa-col	sequence_aa	AA column name
--locus-col	locus	Chain/locus column name
--nt-col-heavy	sequence:0	Heavy chain NT column (paired mode)
--aa-col-heavy	sequence_aa:0	Heavy chain AA column (paired mode)
--nt-col-light	sequence:1	Light chain NT column (paired mode)
--aa-col-light	sequence_aa:1	Light chain AA column (paired mode)
--no-aa	off	Auto-translate NT (fallback, emits warning)
--threads	all cores	Rayon worker threads
--verbose / -v	off	Show progress bar and summary statistics

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Aho Position Ranges

Chain	Max Aho position	Max NT columns
H (heavy)	149	447 (H1…H447)
K (kappa)	148	444 (L1…L444)
L (lambda)	148	444 (L1…L444)

Kappa and Lambda share the L prefix in wide format — they map to the same coordinate frame.

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How It Works

1. All human V/D/J germline genes are pre-numbered with Aho positions and embedded in the binary at compile time — no external database files.
2. For each query: find the closest germline via Needleman-Wunsch alignment (amino acid level), then transfer Aho positions from the germline to the query.
3. Map each occupied Aho position to its codon from the nucleotide sequence: position N → nucleotides (N-1)*3+1, (N-1)*3+2, (N-1)*3+3. Unoccupied positions become gaps (-).
4. Rayon parallelises across sequences in the batch; the Python GIL is never held during alignment.

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Building from Source

git clone https://github.com/bnemoz/iggnition
cd iggnition
pip install .

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License

MIT. If you use IggNition in published research, please cite accordingly.

References

• Honegger, A. & Plückthun, A. (2001). Yet another numbering scheme for immunoglobulin variable domains. J Mol Biol, 309(3), 657–670.
• Dunbar, J. & Deane, C.M. (2016). ANARCI: antigen receptor numbering and receptor classification. Bioinformatics, 32(2), 298–300.