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de-goulash: Bioinformatics Pipeline for Single-cell mixed deconvolution

Lucie Kulhankova, Diego Montiel González, Eskeatnaf Mulugeta, Manfred Kayser

Erasmus MC University Medical Center Rotterdam, Department of Genetic Identification, Rotterdam, The Netherlands.

de-goulash is a bioinformatics pipeline build in Snakemake which allows clustering mixed individuals using 10x single-cell RNA-seq.

The pipeline is divided in two main steps.

1) Deconvolution of mixed single-cell samples, including genotyping and clustering.

The following inputs are needed:

  1. Possorted_genome_bam.bam
  2. barcodes.tsv as output from 10x. this file contains the cells to use onwards.
  3. genome.fasta (Human reference genome e.g. hg19 or hg38 in fasta format)
  4. *MT.fasta (mitochondrial DNA sequence in fasta format, same build as genome)
  5. *region.txt
  6. *MT_regions.txt

Input files with asterik * [4, 5, 6] can be generated with the python script.

python process_reference.py [path/genome.fasta]

2) Individual genetic identification and biogeographical ancestry assigment. It requires the output variant calling for each assignated cluster from step 1 and it will calculate likelihood of forensic parameters, population assignation, execute haplogrep and finally Yleaf v.2.2.

The inputs needed includes the following:

  1. Exone reference: exome_96_remmapedto38.vcf.gz
  2. Reference population based on 1000G project: 100G_populations.txt
  3. Path where the chromosomes for 1000G variant calling: /single-cell/input/1000G/ [https://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20130502/]

All input parameters need to be within the config.yaml file

Snakemake step 1

Inputs

  • sample_bam: /single-cell/input/1/possorted_genome_trimmed.bam
  • barcodes: /single-cell/input/1/barcodes_reduced.txt
  • reference: /single-cell/input/reference/genome.fasta
  • regions: /single-cell/input/reference/regions.txt #for parallel freebayes, region file can be generated with https://github.com/nh13/freebayes/blob/master/scripts/fasta_generate_regions.py
  • reference_MT: /single-cell/input/reference/MT.fasta
  • regions_MT: /single-cell/input/reference/MT_regions.txt

Snakemake settings

  • cores: 4
  • dp: 50 # SNP filtering depth
  • qual: 60 # SNP filtering quality

threshold for iteration 1

  • thr_cell_1: 10 #Minimal number of SNPs per cell

threshold for iteration 2

  • thr_cell_2: 20 #Minimal number of SNPs per cell

rule for merging cells python

  • threshold_coverage: 10 #treshhold total coverage of selected SNPs per cell
  • threshold_coverage_pos: 5 #treshold coverage per selected SNP per cell
  • threshold_base_calling: 90

rule for for clustering Rscript

  • n_neighbors: 5 #setting for UMAP clustering
  • n_components: 300
  • clusters: 0 # if clusters > 1 then nBclust is executed to predict number of clusters to use

Snakemake analysis step 2

Inputs

  • ref_exome: /single-cell/input/exome_96_remmapedto38.vcf.gz
  • ref_population: /single-cell/input/1000G/1000G_populations.txt
  • dirpath_1000G: /single-cell/input/1000G/
  • dirpath_analysis: output
  • dp_2: 50 #SNP filtering depth
  • qual_2: 60 #SNP filtering quality

Yleaf parameters

  • read_depth: 1
  • quality: 20
  • base_calling: 90
  • positions_file: /single-cell/software/Yleaf/Position_files/WGS_hg38.txt

Usage in Docker (Linux) (recommended)

We provided a docker image where you can run the pipeline without having to install any other dependency than docker. Although you need root permissions to proceed.

Download docker image (2.03gb)

docker pull geniderasmusmc/de-goulash:1

Tested in Docker version 19.03.2, build 6a30dfc

docker --version

You can execute de-goulash Snakemake pipeline throught docker image-container. You have to manually mount the current directory where input files are located.

1) de-goulash deconvolution

  • Current directory where input files are located -> /current/directory/de-goulash/
  • Default root location inside the container (do not change) -> :/single-cell
  • Container name -> geniderasmusmc/de-goulash:1
  • Target file [only change output name e.g. output_test/iter2/cells_merge_clusters.vcf] -> output/iter2/cells_merge_clusters.vcf

docker run -it -v /current/directory/de-goulash/:/single-cell geniderasmusmc/de-goulash:1 output/iter2/cells_merge_clusters.vcf --snakefile Snakefile --configfile config.yaml --cores 1

2) de-goulash statistical analysis

docker run -it -v /current/directory/de-goulash/:/single-cell geniderasmusmc/de-goulash:1 --snakefile Snakefile_analysis --configfile config.yaml --cores 1

Manual installation

Instead of using docker container you can install everything independently and run Snakemake directly

Tested in:

  • R 3.6.1 -- "Action of the toes"
  • Python 3.7.3
  • Linux Ubuntu 18.04
  • Java Run time environment 8

Recommended use conda or Python3 venv

Install libraries

pip3 install requirements.txt

Rscript requirements.R

git clone https://github.com/genid/de-goulash.git

To run through Snakemake pipeline

Step 1

snakemake output/iter2/cells_merge_clusters.vcf --snakefile Snakefile --configfile config.yaml --cores 1

Step 2

snakemake --snakefile Snakefile_analysis --configfile config.yaml --cores 1