3’ DGE

3’ DGE has some additional complexity when compared to standard bulk RNA-seq experiments. 3’ DGE is sequencing just the 3’ end of each transcript, so quantification needs to proceed differently. 3’ DGE also often incorporates UMIs, which need to be accounted for during the quantification step of RNA-seq. Different kits have different ways of incorporating the UMI and may or may not include optional well barcodes for plates and sample barcodes for individual samples. bcbio can be extended to handle arbitrary kits, and comes with support for several commonly used DGE kits out of the box.

Description of example dataset

This example takes a small sample of reads generated from the QIAseq UPX 3’ Transcriptome kit. This particular kit has 96 well and 384 well versions, which are supported in bcbio via the umi_type: qiaseq-upx-96 and umi_type: qiaseq-upx-384 options.

1. Download the example data and configuration files

This downloads the input data, creates the project structure and example configuration files.

1.1 Create input directory and download FASTQ files.

mkdir qiaseq-upx-96-example
cd qiaseq-upx-96-example
mkdir -p fastq
cd fastq
wget --no-check-certificate http://s3.amazonaws.com/bcbio-nextgen/dge_userstory_data/fastq/qiaseq-upx_R1.fastq.gz
wget --no-check-certificate http://s3.amazonaws.com/bcbio-nextgen/dge_userstory_data/fastq/qiaseq-upx_R2.fastq.gz
cd ..

1.2 Download template YAML file describing 3’ DGE analysis

wget --no-check-certificate http://s3.amazonaws.com/bcbio-nextgen/dge_userstory_data/qiaseq-upx.yaml


  - analysis: scrna-seq
    genome_build: hg38
      umi_type: qiaseq-upx-96
      cellular_barcode_correction: 1
      minimum_barcode_depth: 0
  dir: ../final

1.3 Create a sample sheet

wget --no-check-certificate http://s3.amazonaws.com/bcbio-nextgen/dge_userstory_data/example_dge.csv



2. Generate YAML config file for analysis

bcbio_nextgen.py -w template qiaseq-upx.yaml example_dge.csv fastq

In the result you should see a folder structure:


example_dge/config/example_dge.yaml is the main config file to run the bcbio project. You will see this file has a copy of the parameters in qiaseq-upx.yaml for each sample.

3. Run the analysis

This will run the analysis on a local machine, using just one core.

cd example_dge/work
bcbio_nextgen.py ../config/example_dge.yaml -n 1


  • umi_type DGE kit: [harvard-scrb, qiaseq-upx-96, qiaseq-upx-384]

  • minimum_barcode_depth=0 Cellular barcodes with less reads are discarded. This should be 0 if you are using one of these plate-based kits.

  • singlecell_quantifier=rapmap Quantifier to use for single-cell RNA-sequencing. Supports rapmap or kallisto. We recommend using rapmap.

  • optional transcriptome_fasta alternative transcriptome reference.

  • optional transcriptome_gtf An optional GTF file of the transcriptome to quantitate, rather than the bcbio installed version.


Project directory:

├── bcbio-nextgen-commands.log -- commands run by bcbio
├── bcbio-nextgen.log -- logging information from bcbio run
├── cb-histogram.txt -- histogram of reads per cellular barcode
├── data_versions.csv -- version information for data used by bcbio
├── metadata.csv -- provided metadata about each sample
├── programs.txt -- program versions of tools run
├── project-summary.yaml -- YAML description of project with derived metadata
├── tagcounts-dupes.mtx -- Matrix Market of gene counts without UMI duplicate removed 
├── tagcounts-dupes.mtx.colnames -- column names to go with tagcounts-dupes.mtx
├── tagcounts-dupes.mtx.rownames -- row names to go with tagcounts-dupes.mtx
├── tagcounts.mtx -- Matrix Market of gene counts, use these for downstream analyses
├── tagcounts.mtx.colnames -- column names to go with tagcounts.mtx
├── tagcounts.mtx.metadata -- optional sample-level metadata for samples in tagcounts.mtx
├── tagcounts.mtx.rownames -- row names to go with tagcounts.mtx
└── transcriptome
    └── mm10.fa -- transcriptome used for quantification

Sample directories:

├── testrun-barcodes-filtered.tsv -- filtered list of cell/well barcodes
├── testrun-barcodes.tsv -- list of cell/well barcodes with number of reads assigned to each barcode
└── testrun-transcriptome.bam -- transcriptome alignments 

Downstream analysis

The starting point for downstream analyses will be the count table of counts per gene per cell/well in the tagcounts.mtx, tagcounts.mtx.rownames and tagcounts.mtx.colnames files. You can load these into R using the readMM function from the Matrix package.

You can use any standard count-based differential RNA-seq differential expression tool to operate on these count tables such as DESeq2/edgeR/limma and the analysis will be similar to a bulk RNA-seq experiment. With a large number of samples you will find making UMAP plots a useful way to visualize the relationships between your samples.