RNASeq pipeline


The following library versions are packaged in Databricks Runtime 7.0 for Genomics. For libraries included in lower versions of Databricks Runtime for Genomics, see the release notes.

The Databricks RNASeq pipeline handles short read alignment and quantification using STAR v2.6.1a and ADAM v0.32.0.


The pipeline is run as an Azure Databricks job. You can set up a cluster policy to save the configuration:

  "num_workers": {
    "type": "unlimited",
    "defaultValue": 13
  "node_type_id": {
    "type": "unlimited",
    "defaultValue": "Standard_F32s_v2"
  "spark_env_vars.refGenomeId": {
    "type": "unlimited",
    "defaultValue": "grch38_star"
  "spark_version": {
    "type": "regex",
    "pattern": ".*-hls.*",
    "defaultValue": "7.0.x-hls-scala2.12"
  • The task should be the RNASeq notebook provided at the bottom of this page.
  • For best performance, use the compute optimized VMs with at least 60GB of memory. We recommend Standard_F32s_v2 VMs.


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.

Parameter Default Description
manifest n/a The manifest describing the input.
output n/a The path where pipeline output should be written.
replayMode skip One of:

* skip: stages are skipped if output already exists.
* overwrite: existing output is deleted.
perSampleTimeout 12h A timeout applied per sample. After reaching this timeout, the pipeline continues on to the next sample. The value of this parameter must include a timeout unit: ‘s’ for seconds, ‘m’ for minutes, or ‘h’ for hours. For example, ‘60m’ will result in a timeout of 60 minutes.

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:

  1. Alignment: Map each short read to the reference genome using the STAR aligner.
  2. Quantification: Count how many reads correspond to each reference transcript.

Additional usage info and troubleshooting

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.

RNASeq pipeline notebook

Get notebook