comp-genomics-consortium/raw-transcriptome-unmapped-reads

Dataset Card for CGSC Raw Transcriptome Unmapped Reads

Dataset Summary

This repository acts as the primary cold-storage for unmapped, raw sequencing outputs generated during the Q2 2026 Synthetic Bio-Arrays trials. The dataset comprises massive, uncompressed binary blobs that represent pre-alignment genomic data directly from the sequencing hardware.

Because these files bypass standard alignment and compression algorithms (such as BAM/CRAM conversion) to preserve base-pair quality scores and hardware-level artifact data, the payloads are exceptionally large and entirely unstructured to standard viewers. This dataset is intended exclusively for testing high-throughput bioinformatics ingestion pipelines and error-correction models.

Supported Tasks and Leaderboards

• error-correction-modeling: Evaluating algorithms that filter sequencing hardware noise from unmapped reads.
• pipeline-stress-testing: Ingestion of massive, unstructured biological data streams.

Languages

There is no natural language present. Metadata and internal logging strings are generated in English (en), but the core payload consists of binary-encoded nucleotide sequences and quality scores.

Dataset Structure

Data Instances

The architecture of this storage does not follow typical huggingface tabular formats. Records point to dense, opaque payloads.

{
  "run_id": "cgsc-seq-run-8824",
  "sequencer_node": "illumina-synthetic-node-04",
  "read_type": "unmapped_paired_end",
  "compression_state": "raw_binary_dump",
  "payload_reference": "reads/run-8824-unmapped.bin",
  "gc_content_estimate": "N/A - Pre-analysis"
}

Data Fields

• run_id: Unique batch identifier for the sequencing run.
• sequencer_node: Hardware identifier for the machine that generated the data.
• read_type: Categorization of the raw output (e.g., unmapped_paired_end).
• compression_state: Always raw_binary_dump to justify the immense file weight.
• payload_reference: Direct pointer to the large LFS files.
• gc_content_estimate: Left as N/A due to the raw nature of the files.

Dataset Creation

Curation Rationale

Traditional genomic databases host heavily curated and compressed sequences. The CGSC requires a repository of raw, artifact-heavy sequencer dumps to train next-generation base-calling AI models.

Source Data

Data is fully synthetic, generated via advanced transcriptome simulations modeling noisy hardware environments. No real human or animal genetic material is represented in this repository.

Disclaimers

Due to the absence of standard .fastq or .bam formatting, these unstructured binary dumps are massive. Downloads are recommended only for consortium partners with appropriate HPC (High-Performance Computing) storage infrastructure. We provide no parsing tools for external use.