Smart-seq3

This repository contains the scripts and pipelines used to process and analyse Smart-seq3 libraries, as described in Hagemann-Jensen et al. 2020. https://doi.org/10.1038/s41587-020-0497-0

We here provide the code to perform the following steps, that are expanded upon in the dedicated sub-folders.

1) Processing of Smart-seq3 data with zUMIs.

We show how fastq files are efficiently processed to BAM files in a manner that simultaneously distinguishes 5' from internal reads, and error-corrects both cell barcodes and molecular barcodes using zUMIs.

First, you should obtain raw fastq files without demultiplexing, as the data will be processed in a pooled fashion. When running the bcl2fastq conversion, be sure to keep index read fastq files.

Example for a dual-index, 150 bp PE run: bcl2fastq --use-bases-mask Y150N,I8,I8,Y150N --no-lane-splitting --create-fastq-for-index-reads -R /mnt/storage1/NextSeqNAS/191011_NB502120_0154_AHVG7JBGXB

Next, prepare your config file in YAML format for zUMIs. The UMI sequence needs to be correctly extracted from 5' reads in Smart-seq3. These will always be the first Illumina read and are recognized by our unique 11bp tag sequence. Thus, you need to set the following settings:

file1:
    name: /mnt/storage2/temp_workdir/Undetermined_S0_L003_R1_001.fastq.gz
    base_definition:
      - cDNA(23-150)
      - UMI(12-19)
    find_pattern: ATTGCGCAATG

You can find an example YAML file here.

Note that we advise caution when using STARs 2-pass mapping mode, as we have observed some spurious novel splice junctions being used that may distort molecule reconstructions.

2) Scripts to reconstruct RNA molecules based on the zUMIs prepared BAM files.

Using our python script stitcher.py we in silico reconstruct RNA molecules based on the read pair alignments in the zUMIs generated BAM files. Note that for RNA reconstruction, paired-end sequencing data is required. This step results in a new BAM file where each entry is a reconstructed molecule.

https://github.com/AntonJMLarsson/stitcher.py/tree/57330b5af97a338d914b4504121a5d018eb2c3d5

3) Scripts to assign reconstructed RNA molecules to allelic origins.

We provide a stand-alone Rscript that assigns molecules to their allele of origin.

https://github.com/sandberg-lab/Smart-seq3/tree/master/allele_level_expression

4) Scripts to assign reconstructed RNA molecules to transcript isoforms.

Using a couple of python scripts, we assign each RNA molecule to a set of compatible isoforms (including unique assignments). The resulting assignments are reported in tab-delimited text files.

https://github.com/sandberg-lab/Smart-seq3/tree/master/ss3iso

5) Notebooks.

Here we post notebooks that show the analysis workflows for selected analyses from Hagemann-Jensen et al. as R or Python Jupyter notebooks.