loco-pipe: a Snakemake pipeline for low-coverage whole-genome sequencing

• Key features
• Currently supported functionalities
• Complete pipeline flowchart
• Before you start
• Setting up the pipeline
• Preparing the project directory and required input files
• Launching the pipeline
• Future directions
• Citation

loco-pipe is an automated Snakemake pipeline that streamlines a set of essential population genomic analyses for low-coverage whole genome sequencing (lcWGS) data.

Key features

• Streamlining of several essential population genomic analyses
• Can be launched with a single line of code
• Incorporation of key filtering steps and best practices for low-coverage data
• Key results are plotted automatically for visual inspection
• Easy customization through a configuration file
• A quick start guide with an example dataset
• Extensive in-line annotation along with a detailed user’s manual
• Flexible architecture that allows for the addition of new features
• Inheritance of the many benefits offered by Snakemake, including
  • High computational efficiency achieved through massive parallelization
  • Seamless integration with common job schedulers on computer clusters
  • The ability to automatically continue from the last failed or interrupted job
  • Built-in software management system and robust file structure

Currently supported functionalities

• Depth counting
• SNP calling
• Allele frequency estimation
• Site frequency spectrum (SFS) estimation
• Fixation index (Fst) estimation
• Principal component analysis (PCA)
• Admixture analysis
• Theta estimation
• Neutrality test statistics
• Heterozygosity estimation
• Local PCA analysis

A simplified flowchart of loco-pipe highlighting its key functionalities. The box in dotted lines represents user-provided input files, and boxes in solid lines represent key analytical steps in the pipeline. Plots are generated using our example dataset. Please see our quick start guide for detailed descriptions of the plots.

Complete pipeline flowchart

Each box represents a Snakemake rule and is colored based on the major groups of analyses in the form of separate Snakefiles (shown at the top left corner). The differently colored shades indicate the types of SNPs or sites on which the analyses are conducted. Dashed arrows indicate key modules that can be turned on or off in the configuration file. The four orange boxes at the top right corner (part of the “pipeline_prep.smk” Snakefile) are the starting points of the pipeline, and the “all” box at the bottom is the end point. Please see our user’s manual for detailed descriptions of each Snakemake rule.

Before you start

Reference genome

This pipeline requires a moderately contiguous reference genome for your study system. Currently, it does not support highly fragmented reference genomes, since most analyses are parallelized by scaffolds. Having too many scaffolds or unscaffolded contigs will create too many parallel jobs for Snakemake and a job scheduler to handle. We recommend that 90% of the genome should be consisted of no more than 100 scaffolds (i.e. L90 < 100). Small scaffolds and contigs should be excluded from the analysis (we will ask you to provide a list of scaffolds that you would like to include).

Sequence alignment files

In addition, we assume that properly mapped and filtered bam files are ready to be used as input files for loco-pipe. You can choose your favorite software and/or pipeline to go from fastq to bam, but one pipeline that we particularly recommend is grenepipe.

grenepipe

grenepipe is a Snakemake pipeline for variant calling from raw sequence data. Although it is developed for high-coverage data, you can skip the variant calling step by using the all_qc shortcut and turning the bcftools-stats switch in the config file to false. This way, grenepipe will stop after generating the final bam files and their associated quality reports. (We also recommend turning the clip-read-overlaps switch to true and setting VALIDATION_STRINGENCY=SILENT for picard MarkDuplicates in the configuration file.) grenepipe is very thoughtfully built and extensively documented, and it is a major inspiration for loco-pipe. Familiarizing yourself with grenepipe will also make loco-pipe much easier to learn.

Quick start guide with an example dataset

If you don’t yet have your data ready for loco-pipe, and even if you do, we highly recommend you to first follow our quick start guide which includes a heavily subsetted example dataset. It only takes loco-pipe a few minutes to analyse the example dataset on a computer cluster, making it much easier to learn and troubleshoot.

User’s manual

We also provide an extensive user’s manual with detailed description of each step of the pipeline for easy reference. Tips and suggestions are also included in this document.

We strongly advise users to take advantage of these resources (as well as other resources such as the ANGSD manual and our beginner’s guide to lcWGS) to gain a good understanding of loco-pipe, the software it uses, and lcWGS data analysis in general while you are using loco-pipe. Using this pipeline as a black box can lead to spurious results and erroneous conclusions.

Setting up the pipeline

1. Install mamba or conda if you have not already done so. Note that mamba v2 has some compatibility issues with Snakemake, and the speed of conda has improved significantly in recent updates, so mamba is no longer recommended over conda. If you choose to use mamba, make sure to use a v1 version, and replace all occurrences of conda with mamba in the code below.

2. Download loco-pipe from GitHub (e.g. using git clone https://github.com/sudmantlab/loco-pipe.git). We recommend you to download it to a folder where you store your software programs. We will refer to the full path of the directory that contains the loco-pipe folder as SOFTWARE_DIR.

3. Create the loco-pipe conda environment using conda by running conda env create -f $SOFTWARE_DIR/loco-pipe/workflow/envs/loco-pipe.yaml (replace $SOFTWARE_DIR with a real path).

4. (Optional) If you would like to run local PCA with the lostruct package in R using loco-pipe, you must install lostruct manually as it is not yet available on conda. Please install it to a conda environment named lostruct_lcpipe using the script below.

   # create a conda environment named lostruct_lcpipe and install R and some key R packages
   conda env create -f $SOFTWARE_DIR/loco-pipe/workflow/envs/lostruct.yaml
   # activate the lostruct conda environment
   conda activate lostruct_lcpipe
   # launch R
   R
   # install lostruct
   devtools::install_github("petrelharp/local_pca/lostruct", ref = "93ad59309151e44d2d3d0d7748cdc92f6121f564")
   # quit R
   q()
   # deactivate the conda environment
   conda deactivate  

   Note: depending on your system, you may need to ensure that lostruct is properly installed to the lostruct_lcpipe environment with something like the following

   withr::with_libpaths(new = "/path/to/conda/envs/lostruct_lcpipe/lib/R/library",
                        devtools::install_github("petrelharp/local_pca/lostruct"))

Preparing the project directory and required input files

1. Set up the file structure.

   • First, create a base directory for your project. This folder should be separate from the loco-pipe folder. You can name it however you want (just avoid special characters other than dashes and underscores), but we will refer to the full path of this folder as BASEDIR.

   • Within BASEDIR, create two new folders: docs, and config.

   • You can also have your sequencing data (e.g. fastq and bam files) and the reference genome in separate folders in BASEDIR, but this is not required.

2. Prepare a sample table and a chromosome table. Both of these should be tab separated text files. Store them in the docs folder in BASEDIR. You can name them however you want.

   • Sample table: This table should contain a minimum of three columns in no particularly order. One column should be named exactly as sample_name and it should contain sample IDs of all the samples to be included in the analysis. Another column should be named exactly as bam and it should store the full paths of the bam file for each sample. A third column should specify the grouping information you wish to segregate the samples by. These could be species, subspecies, ecotypes, populations, sampling sites, sex, etc. You will need to enter the name of the third column into the pipeline configuration file config.yaml. Below is an example of a sample table.

     ## Warning: package 'tidyr' was built under R version 4.2.3
     
     ## Warning: package 'readr' was built under R version 4.2.3
     
     ## Warning: package 'dplyr' was built under R version 4.2.3
     
     ## Warning: package 'stringr' was built under R version 4.2.3
     

     sample_name	bam	species	population
     ABLG11920-1	/path/to/loco-pipe/toyfish/bams/ABLG11920-1.bam	sunset	sunset
     ABLG12067-1	/path/to/loco-pipe/toyfish/bams/ABLG12067-1.bam	sunset	sunset
     ABLG11918-1	/path/to/loco-pipe/toyfish/bams/ABLG11918-1.bam	vermilion	vermilion_1
     ABLG11913-1	/path/to/loco-pipe/toyfish/bams/ABLG11913-1.bam	vermilion	vermilion_1
     ABLG9871-1	/path/to/loco-pipe/toyfish/bams/ABLG9871-1.bam	vermilion	vermilion_2
     ABLG11795-1	/path/to/loco-pipe/toyfish/bams/ABLG11795-1.bam	vermilion	vermilion_2
     ABLG11937-1	/path/to/loco-pipe/toyfish/bams/ABLG11937-1.bam	vermilion	vermilion_3
     ABLG11940-1	/path/to/loco-pipe/toyfish/bams/ABLG11940-1.bam	vermilion	vermilion_3

     A few tips about the sample table:

     • Sample names have to be unique, and each sample should correspond to a single bam file (i.e. we assume that if you have multiple bam files for a single sample, they have already been merged). In cases where you would like to keep different bam files from the same sample separate, e.g. for batch effect control, you will need to distinguish them by assigning them different sample names, e.g. by adding a suffix to their original names. If you do this, please also note that including multiple copies of the same sample can severely bias certain analyses, such as PCA, so we recommend you to only do this in the first iteration of the pipeline, and once the risk of batch effect is eliminated, you should merge or remove the duplicated samples.
     • You can have multiple grouping variables in the sample table, although you may need to launch the pipeline more than once in order to conduct analyses based on different grouping variables.
     • In practice, you may need to have multiple versions of the sample table. For example, you may include all samples in the first run, exclude some problematic one and/or change the grouping of others based on the result of the first run, and launch the pipeline again with an updated sample table.
     • In case that you don’t have any grouping information a priori, you still need to have a fake grouping column with all samples having the same entry for plotting purposes (e.g. PCA and admixture). In this case, you will also need to turn off all population-level analyses in the configuration file (see below).

   • Chromosome table: This table should contain one or two columns. These columns should be unnamed. The first column is required, and it records the names of all chromosomes/scaffolds/contigs that you would like to include in the analysis. These should exactly match the names in the reference genome. The second column is optional, and it records shortened or alternative names of the chromosomes/scaffolds/contigs which you would like to show on the plots. If the second column is empty, the original names will be shown. Below is an example of a chromosome table.

     Sebastes_miniatus.Sebrube.F.HiC_scaffold_15	15
     Sebastes_miniatus.Sebrube.F.HiC_scaffold_16	16

     A few tips about the sample table:

     • As mentioned earlier, we recommend against including too many chromosomes/scaffolds/contigs in the chromosome table (e.g. < 100).
     • If you would like to use the plotting modules in loco-pipe to generate Manhattan-like plots automatically, we would also recommend to only include reference sequences of similar sizes (e.g. you shouldn’t include unincorporated contigs in the chromosome table when you have a chromosomal level assembly).

3. Edit the configuration files.

   • The pipeline config file config.yaml: This config file serves as an easily accessible way to control the behavior of loco-pipe. You can use it to 1) specify the location of input files, 2) include or exclude certain analyses from a loco-pipe run, 3) adjust the parameter settings for different analyses. This config file is thoroughly annotated, so please read it through and make edits when needed. Importantly, please copy config.yaml to the config folder in your project directory (i.e. cp $SOFTWARE_DIR/loco-pipe/config.yaml $BASEDIR/config/config.yaml) and make your edits there rather in the loco-pipe directory.

   • (Optional) The cluster config file cluster_config.yaml under workflow/profiles: This config files specifies the resources each step of the pipeline can use on a computer cluster. We have extensively tested loco-pipe with a slurm job scheduler and an example slurm profile is included in loco-pipe (workflow/profiles/slurm), but other job schedulers should also work with Snakemake given an appropriate profile setup (see Snakemake manual for details).

Launching the pipeline

1. Activate the loco-pipe environment with conda activate loco-pipe.

2. You are now ready to launch the pipeline. We recommend to always start with a dry run using the -n flag before actually running it.

   snakemake \
   --use-conda \
   --conda-frontend conda \
   --directory $BASEDIR \
   --rerun-triggers mtime \
   --scheduler greedy \
   --snakefile $SOFTWARE_DIR/loco-pipe/workflow/pipelines/loco-pipe.smk

   When running loco-pipe on a computer cluster, make sure to use the --profile flag to specify your cluster profile. Other flags that may be useful are --conda-prefix, --default-resources, --printshellcmds, --cores, etc.

Future directions

We plan to continue to maintain and develop loco-pipe, by incorporating additional analyses (e.g. GWAS, dxy, LD estimation and pruning) into this pipeline and also enabling more functionalities for the existing software programs (e.g. ANGSD, Ohana). For certain existing analyses, we also hope to provide more software options for users to pick from (e.g. winSFS for SFS estimation, ngsAdmix for admixture analysis). Advanced SNP filtering procedures, such as the one described in Dallaire et al. (2023), can be incorporated into loco-pipe manually (see User’s Manual for instructions), but may be fully automated in the future. In choosing depth filters, we may adopt a mixed model instead of a truncated normal distribution for curve fitting in the future. Docker/singularity support will also be considered.

All kinds of feedback, such as bug reports and feature requests, are welcome on the Issues page. We also encourage users to build on the existing infrastructure and add more functionalities to loco-pipe in the form of pull requests.

Citation

loco-pipe is published in Bioinformatics Advances: https://doi.org/10.1093/bioadv/vbae098 Please cite this paper if you use loco-pipe in your research.