ArraySplitter: De Novo Decomposition of Satellite DNA Arrays

Decomposes satellite DNA arrays into monomers within telomere-to-telomere (T2T) assemblies. Ideal for analyzing centromeric and pericentromeric regions on monomeric level.

Status: Production ready. Successfully handles arrays from kilobase to megabase scale.

Key Features:

• De novo monomer identification without prior knowledge
• Autocorrelation-based period detection for robust periodicity analysis
• Automatic orientation to canonical form (A>T, C>G)
• Deterministic output sorted by genomic coordinates
• Multi-threaded processing

Performance: CHM13v2.0 assembly (~1300 alpha satellite arrays) processes in ~3.5 minutes (16 threads)

Installation

pip install arraysplitter

Or build from source:

cd src/rust/arraysplitter
cargo build --release

Quick Start

# Basic decomposition
arraysplitter -i arrays.fa -o output_prefix -t 16

# With predefined cut sequences
arraysplitter -i arrays.fa -o output_prefix -c ATG,CGCG -t 16

# Show version
arraysplitter --version

Output Files

All output is deterministically sorted by chromosome and genomic position (chr1 → chr22 → chrX → chrY → chrM).

File	Description
.decomposed.fasta	Monomers with orientation info in headers
.hors.tsv	HOR-level decomposition with metrics per HOR monomer
.monomers.tsv	Base-level monomers from recursive HOR decomposition
.lengths	Fragment lengths for each array

HORs TSV Columns (.hors.tsv)

Contains the primary decomposition into HOR (Higher Order Repeat) monomers.

Column	Description
array_id	Array identifier (chr_start_end_len_period_type)
type	pred_array, monomer, flank, array, consensus
idx	Monomer index within array
length	Sequence length
source	Detection method (anchor, split_2x, etc.)
ed_tmpl	Edit distance to consensus template
ed_prev	Edit distance to previous monomer
ed_next	Edit distance to next monomer
period	Detected repeat period
autocorr	Autocorrelation value at period
cut_sequence	Anchor sequence used for splitting
orientation	fwd or rev (reverse complemented)
sequence	Actual DNA sequence

Monomers TSV Columns (.monomers.tsv)

Contains base-level monomers after recursive decomposition of HORs. Each HOR is recursively decomposed until no further periodicity is detected (autocorrelation ≤ 0.5) or minimum length (5bp) is reached.

Column	Description
array_id	Array identifier
hor_idx	Index of parent HOR from primary decomposition
sub_idx	Index within parent HOR (hierarchical for nested decomposition)
level	Recursion depth (1 = direct child of HOR)
length	Sequence length
period	Detected period at this level (0 if base monomer)
autocorr	Autocorrelation value at detected period
source	recursive_anchor, recursive_split, base, recursive_flank
sequence	Actual DNA sequence

Algorithm

ArraySplitter employs an autocorrelation-based algorithm for detecting repeat periods and decomposing satellite DNA arrays.

1. Canonical Orientation

Arrays are oriented to canonical form:

• Primary rule: A > T (more A's than T's)
• Secondary rule: C > G (if A=T)
• Non-canonical arrays are reverse complemented

2. Period Detection via Autocorrelation

The algorithm computes sequence autocorrelation to detect periodicity:

autocorr(offset) = matches / comparisons

Where matches counts identical nucleotides at positions i and i + offset.

Key innovations:

• Random expectation correction: Subtracts expected random match rate based on nucleotide composition
• Refined period search: Uses FFT-like peak detection to find true period vs harmonics
• Confidence scoring: Autocorrelation excess over random indicates detection confidence

3. Anchor Selection

For the detected period, finds optimal anchor (cut sequence) using:

1. K-mer enumeration: Extract all k-mers (k=10 by default) from the sequence
2. Position analysis: For each k-mer, record all occurrence positions
3. Scoring metrics:
   • Uniqueness: Fraction of occurrences exactly period apart
   • Regularity: How evenly spaced the occurrences are
4. Combined score: uniqueness × regularity
5. Deterministic selection: K-mers sorted lexicographically for reproducible tie-breaking

4. Array Decomposition

Using the selected anchor:

1. Split array at all anchor occurrences
2. First fragment → left flank (if < 70% of period)
3. Middle fragments → monomers
4. Last fragment → right flank (if < 70% of period)
5. Apply heuristics for multiplet splitting (doublets, triplets, etc.)

5. Output Generation

Results are:

• Sorted by chromosome (natural order: 1, 2, ..., 22, X, Y, M)
• Within chromosome, sorted by start position
• Fully deterministic across runs

Methods

autocorr (Default)

Uses autocorrelation for period detection. Best for:

• Regular tandem repeats
• Alpha satellite arrays
• HOR (Higher Order Repeat) structures

classic

Uses frequency suffix tree approach. Better for:

• Irregular or degenerate repeats
• Very short arrays
• Arrays with high mutation rates

both

Tries autocorrelation first, falls back to classic if autocorr fails.

Command Line Options

arraysplitter --help

Options:
  -i, --input <FILE>       Input FASTA file
  -o, --output <PREFIX>    Output prefix
  -t, --threads <N>        Number of threads [default: all cores]
  -c, --cuts <SEQ,SEQ>     Predefined cut sequences (comma-separated)
  -d, --depth <N>          Max depth for cut search [default: 100]
  --method <METHOD>        Detection method: autocorr, classic, both [default: autocorr]
  --max-ed-len <N>         Max monomer length for edit distance [default: 10000]
  --stats                  Print detailed statistics
  --top-outliers <N>       Number of outliers to show [default: 10]
  -V, --version            Print version

Citation

If you use ArraySplitter in your research, please cite: [Publication pending]

Contact

For questions or support: ad3002@gmail.com