Getting Started with Bridge PipelineEngine (bPE)

Log in

Log in at app.bridge-horizon.com with your credentials. If this is your first time on the platform, follow the Bridge Horizon getting-started tutorial to activate your account first.

Upload data

Upload your input files from the dashboard's "Upload Data" button before launching a pipeline. Each pipeline accepts different inputs:

Raw reads should be gzipped FASTQ files; feature-count outputs should be TSV; and differential expression inputs such as a DESeq2 results table should include log2 fold-change columns.

Access and launch a pipeline

1. Return to the dashboard home screen. There are two ways to launch the PipelineEngine: click the orange Explore button under "Run pipelines," or open Compute in the left-hand navigation pane and click PipelineEngine.
2. Choose the pipeline you want to run. To learn the workflow, select the NF Core Demo pipeline and click Launch Pipeline.
3. Configure the run: enter a Run Name (for example, "Demo") and a Sample Name (for example, "sample1"), set the Out Directory field to NULL, and upload your two sample FASTQ files (sample1_R1 and sample1_R2).
4. Click Next to skip the parameters, then pick a Speed Tier (Economy, Balanced, or Priority) and click Next again.
5. Review the summary on the final page and click Launch Pipeline to begin the run.
6. Monitor the run from the summary page, which shows its status (in queue, running, or complete) along with execution details, input files, and submission parameters. The platform also sends email notifications when a pipeline starts and when it finishes.

Interpret the results

When a pipeline finishes, it produces an organized output directory. For a typical RNA-seq run it looks like this:

outdir/
├── fastqc_raw/       # Quality control of raw reads
├── trimmomatic/      # Adapter-trimmed reads
├── fastqc_trimmed/   # Quality control of trimmed reads
├── star_align/       # Aligned BAM files from STAR
├── picard_rg/        # BAM files with read groups added
├── mark_duplicates/  # Deduplicated BAM files
├── bam_indices/      # BAM index files (.bai)
├── featurecounts/    # Gene-level read counts
├── salmon/           # Transcript-level quantification
└── reports/          # Pipeline execution reports

• fastqc_raw and fastqc_trimmed hold HTML quality reports for the raw and trimmed reads — open them in a browser and check them before trusting downstream results.
• trimmomatic holds adapter-trimmed, quality-filtered reads, and its log shows trimming efficiency and read retention.
• star_align holds the aligned reads mapped to the reference genome; these BAM files are the input for quantification.
• mark_duplicates flags PCR and optical duplicates — a high duplication rate can indicate low library complexity.
• featurecounts holds the gene-level counts that feed the Differential Gene Expression pipeline, and salmon provides transcript-level quantification.
• reports holds the pipeline execution reports you will need if a run fails or a result needs to be reproduced.

For differential expression output, see our practical guides on DESeq2 differential expression and the nf-core RNA-seq pipeline, or browse sample reports to see what finished runs look like.

Share your results

When a run finishes, open Submission Outputs and click Explore to browse the files. The SecureShare feature — the network icon or the three-dot menu next to a file — lets you share results with collaborators without exporting them: enter your collaborator's name and email, set their permissions and a link expiration date, and they receive an email with a secure link to the file. Large genomic datasets are delivered in seconds rather than in email attachments or file-transfer tickets.