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bcbio-nextgen runs in a temporary work directory which contains a number of processing intermediates. Pipeline completion extracts the final useful output files into a separate directory, specified by the :ref:`upload-configuration`. This configuration allows upload to local directories, Galaxy, or Amazon S3. Once extracting and confirming the output files, you can delete the temporary directory to save space.

Common files

The output directory contains sample specific output files labeled by sample name and a more general project directory. The sample directories contain all of the sample specific output files, while the project directory contains global files like project summaries or batched population level variant calls. See the :ref:`teaching` documentation for a full variant calling example with additional details about configuration setting and resulting output files.

Project directory

  • project-summary.yaml -- Top level YAML format summary file with statistics on read alignments and duplications as well as analysis specific metrics.
  • programs.txt -- Program versions for bcbio-nextgen and software run in the pipeline. This enables reproduction of analyses.
  • multiqc run MultiQC to gather all QC metrics from different tools, such as, cutadapt, featureCounts, samtools, STAR ... into an unique HTML report.

Sample directories

  • SAMPLE/qc -- Directory of quality control runs for the sample. These include charts and metrics for assessing quality of sequencing and analysis.
  • SAMPLE-ready.bam -- A prepared BAM file of the aligned reads. Depending on the analysis used, this may include trimmed, recalibrated and realigned reads following alignment.

Variant calling

Project directory

  • grading-summary.csv -- Grading details comparing each sample to a reference set of calls. This will only have information when providing a validation callset.
  • BATCH-caller.vcf -- Variants called for a population/batch of samples by a particular caller.
  • BATCH-caller.db -- A GEMINI database associating variant calls with a wide variety of third party annotations. This provides a queryable framework for assessing variant quality statistics.

Sample directories

  • SAMPLE-caller.vcf -- Variants calls for an individual sample.


Project directory

  • annotated_combined.counts -- featureCounts counts matrix with gene symbol as an extra column.
  • combined.counts -- featureCounts counts matrix with gene symbol as an extra column.
  • combined.dexseq -- DEXseq counts matrix with exonID as first column.
  • combined.gene.sf.tmp -- Sailfish gene count matrix normalized to TPM.
  • combined.isoform.sf.tpm -- Sailfish transcript count matix normalized to TPM.
  • combined.sf -- Sailfish raw output, all samples files are pasted one after another.
  • tx2gene.csv -- Annotation file needed for DESeq2 to use Sailfish output.

Sample directories

  • SAMPLE-transcriptome.bam -- BAM file aligned to transcriptome.
  • SAMPLE-ready.counts -- featureCounts gene counts output.
  • sailfish -- Sailfish output.

small RNA-seq

Project directory

  • counts_mirna.tsv -- miRBase miRNA count matrix.
  • counts.tsv -- miRBase isomiRs count matrix. The ID is made of 5 tags: miRNA name, SNPs, additions, trimming at 5 and trimming at 3. Here there is detail explanation of the naming .
  • counts_mirna_novel.tsv -- miRDeep2 miRNA count matrix.
  • counts_novel.tsv -- miRDeep2 isomiRs. See counts.tsv explanation for more detail. count matrix.
  • seqcluster -- output of seqcluster tool. Inside this folder, counts.tsv has count matrix for all clusters found over the genome.
  • seqclusterViz -- input file for interactive browser at
  • report -- Rmd template to help with downstream analysis like QC metrics, differential expression, and clustering.

Sample directories

  • SAMPLE-mirbase-ready.counts -- counts for miRBase miRNAs.
  • SAMPLE-novel-ready -- counts for miRDeep2 novel miRNAs.
  • tRNA -- output for tdrmapper.

Downstream analysis

This section collects useful scripts and tools to do downstream analysis of bcbio-nextgen outputs. If you have pointers to useful tools, please add them to the documentation.

  • Calculate and plot coverage with matplolib, from Luca Beltrame.
  • Another way to visualize coverage for targeted NGS (exome) experiments with bedtools and R, from Stephen Turner
  • assess the efficiency of targeted enrichment sequencing with ngscat