FreeSyntheticGenomicReads200M

A free dataset of 200 million fully synthetic short-read DNA sequences, built for developers, researchers, and students who need realistic sequencing-style data at scale — for testing bioinformatics pipelines, sequence-alignment tools, read-processing systems, or teaching computational biology. No real genomes, organisms, or individuals are represented in this data; every sequence is randomly generated.

Schema

Column	Type	Description
read_id	string	Unique read identifier
sequence	string	150 bp read sequence (A/C/G/T)
quality_scores	string	Per-base Phred+33 quality string (150 chars)
chromosome	string	Mapped chromosome (chr1-chr22, chrX, chrY, chrM), or * if unmapped
position	int	1-based reference position, or 0 if unmapped
strand	string	+ or - for mapped reads, * if unmapped
mapping_quality	int	MAPQ score (0-60)
gc_content	float	GC fraction of the read (0-1)
is_mapped	bool	Whether the read is mapped
read_length	int	Read length in bases (150)

Format

Single Parquet file, Snappy compression, ~44 GB, 200,000,000 rows.

Quick Start

Note: this dataset is ~44 GB and 200M rows. Do not load it all at once — stream it in batches or query it with a columnar engine.

duckdb (recommended)

import duckdb
duckdb.sql("SELECT * FROM 'genomic_200M.parquet' LIMIT 10").show()

pyarrow batches

import pyarrow.parquet as pq
pf = pq.ParquetFile("genomic_200M.parquet")
for batch in pf.iter_batches(batch_size=100000):
    ...  # process each batch

datasets (streaming)

from datasets import load_dataset
ds = load_dataset("ziadatalabs/FreeSyntheticGenomicReads200M", streaming=True)

Notes

Sequences are 150 bp random A/C/G/T with per-base Phred+33 quality strings skewed toward high quality, mirroring the shape of real short-read output. Reads are mapped (~94%) across chromosomes weighted by real human chromosome sizes, with mapping quality, position, strand, and GC content populated accordingly; unmapped reads carry the standard * / 0 placeholders. All entirely synthetic — no real biological sequence is represented.

License & Usage

Released under CC BY-NC 4.0 — personal, research, and educational use permitted, attribution required, no commercial use.

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Created by Zia Data Labs. Questions or feedback: zia.data.team@protonmail.com