---
viewer: false
license: cc-by-nc-sa-4.0
task_categories:
- tabular-classification
- feature-extraction
tags:
- genomics
- bioinformatics
- raw-reads
- unmapped-sequences
- synthetic-biology
pretty_name: CGSC Raw Transcriptome Unmapped Reads
size_categories:
- 100K<n<1M
---

# Dataset Card for CGSC Raw Transcriptome Unmapped Reads

## Dataset Description

- **Homepage:** https://comp-genomics.dev/datasets/transcriptome-raw
- **Repository:** Computational Genomics & Synthetic Bio-Arrays Consortium
- **Paper:** N/A (Internal Lab Protocol: CGSC-SEQ-2026)
- **Point of Contact:** sequence-data@comp-genomics.dev

### 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.

```json
{
  "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.
```