The pipeline is run as a Databricks job. Most likely, a Databricks solutions architect will set up the initial job for you. The necessary details are:
- The task should be the RNASeq notebook provided at the bottom of this page.
- For best performance, use compute optimized instances with at least 60GB of memory. We recommend
- To reduce costs, use all spot workers with the
Spot fall back to On-demandoption selected.
The pipeline accepts a number of parameters that control its behavior. The most important and commonly changed parameters are documented here; the rest can be found in the RNASeq notebook. All parameters can be set for all runs or per-run.
|manifest||n/a||The path of the manifest file describing the input.|
|output||n/a||The path where pipeline output should be written.|
In addition, you must configure the reference genome using environment variables. To use Grch37, set the environment variable:
To use Grch38 instead, set an environment variable like this:
The pipeline consists of two steps:
- Alignment: Map each short read to the reference genome using the STAR aligner.
- Quantification: Count how many reads correspond to each reference transcript.
The operational aspects of the RNASeq pipeline are very similar to the DNASeq pipeline. For more information about manifest format, output structure, programmatic usage, and common issues, see DNASeq Pipeline.