Schemas › Biology & Biotechnology › Bulk RNA-seq Library Preparation and Sequencing

Bulk RNA-seq Library Preparation and Sequencing

A SciSchema.org Process Schema

Domain: Biology & Biotechnology
Schema: Bulk RNA-seq Library Preparation and Sequencing Process
Current version: 1.0.0

RNA sequencing library preparation is the process used to turn RNA from biological samples into a form that can be read by sequencing machines.

Properties from Bulk RNA-seq Library Preparation and Sequencing

The table below lists the top-level properties of this master schema. Each property may contain more detailed nested fields in the JSON Schema representation.

Property Expected Type Description Required
sample Object Biological sample identity and source information, separated from extraction and QC as an independent workflow stage. Yes
rna_extraction Object RNA isolation methodology and associated parameters. Post-extraction quality assessment is handled exclusively in the quality_control block to avoid duplication. Yes
rna_input Object RNA input quantity used for library preparation — documented as a separate workflow stage from extraction to capture the specific amount committed to library construction, which may differ from total yield. Yes
quality_control Object Comprehensive two-stage quality control block covering post-extraction RNA integrity assessment and post-library-preparation library quality assessment. Both sub-blocks are required to fully document sample suitability for sequencing. Yes
library_preparation Object Complete library construction workflow from transcript enrichment through PCR amplification. Transcript enrichment is nested here as an intrinsic construction step rather than a standalone block. Yes
sequencing Object Sequencing instrument configuration, run parameters, and metadata documenting the technical specifications of the sequencing experiment. Yes
primary_data_processing Object Computational analysis pipeline applied to raw sequencing data to produce gene-level expression estimates. All required fields are mandatory for FAIR computational reproducibility. Yes
experimental_design Object Study-level experimental design parameters critical for statistical analysis model specification, batch effect correction, and accurate result interpretation. Yes
metadata Object Study-level metadata required for FAIR data compliance, public repository submission, and long-term data provenance tracking. Yes

Machine-readable schema representations

This schema is available in two machine-readable representations: as an ORKG template, which can be exported as SHACL, and as a JSON Schema document for download and reuse.