Generate random genome sequences

Generates random DNA sequences based on nucleotide probabilities without using a trained Markov model. Each nucleotide is sampled independently according to the specified probabilities.

gsynth.random(
  intervals = NULL,
  output_path = NULL,
  output_format = c("misha", "fasta", "vector"),
  nuc_probs = c(A = 0.25, C = 0.25, G = 0.25, T = 0.25),
  mask_copy = NULL,
  preserve_n = TRUE,
  seed = NULL,
  n_samples = 1,
  iterator = 1
)

Arguments

intervals

Genomic intervals to sample. If NULL, uses all chromosomes.

output_path

Path to the output file (ignored when output_format = "vector")

output_format

Output format:

• "misha": .seq binary format (default)

• "fasta": FASTA text format

• "vector": Return sequences as a character vector (does not write to file)

nuc_probs

Nucleotide probabilities. Can be specified as:

• A named vector: c(A = 0.3, C = 0.2, G = 0.2, T = 0.3)

• An unnamed vector in A, C, G, T order: c(0.3, 0.2, 0.2, 0.3)

Probabilities are automatically normalized to sum to 1. Default is uniform (0.25 each).

mask_copy

Optional intervals to copy from the original genome instead of random sampling. Use this to preserve specific regions exactly as they appear in the reference.

preserve_n

Logical; default TRUE. When TRUE, positions whose original reference is N (or n) are written to the output verbatim rather than filled with a random ACGT base. Same semantics as in gsynth.sample; mask_copy intervals take precedence.

seed

Random seed for reproducibility. If NULL, uses current random state.

n_samples

Number of samples to generate per interval. Default is 1.

iterator

Iterator for position resolution. Default is 1 (base-pair resolution). Larger values may speed up processing but are typically not needed for random sampling.

Value

When output_format is "misha" or "fasta", returns invisible NULL and writes the random sequences to output_path. When output_format is "vector", returns a character vector of sequences (length = n_intervals * n_samples).

Details

Unlike gsynth.sample which uses a trained Markov model to generate sequences that preserve k-mer statistics, gsynth.random generates purely random sequences where each nucleotide is sampled independently. This is useful for generating baseline random sequences or sequences with specific GC content.

Nucleotide ordering: When using an unnamed vector for nuc_probs, the order is A, C, G, T. Named vectors can be in any order.

See also

gsynth.sample, gsynth.train

Examples

gdb.init_examples()

# Generate random sequences with uniform nucleotide probabilities
seqs <- gsynth.random(
    intervals = gintervals(1, 0, 1000),
    output_format = "vector",
    seed = 42
)
#> Setting up random sampling positions...
#> Generating random sequences (1 samples per interval)...
#> Generated 1 random sequence(s)

# Generate GC-rich sequences (60% GC)
gc_rich <- gsynth.random(
    intervals = gintervals(1, 0, 1000),
    output_format = "vector",
    nuc_probs = c(A = 0.2, C = 0.3, G = 0.3, T = 0.2),
    seed = 42
)
#> Setting up random sampling positions...
#> Generating random sequences (1 samples per interval)...
#> Generated 1 random sequence(s)

# Generate AT-rich sequences
at_rich <- gsynth.random(
    intervals = gintervals(1, 0, 1000),
    output_format = "vector",
    nuc_probs = c(A = 0.35, C = 0.15, G = 0.15, T = 0.35),
    seed = 42
)
#> Setting up random sampling positions...
#> Generating random sequences (1 samples per interval)...
#> Generated 1 random sequence(s)