- Clone the project directory.
git clone https://github.com/marschall-lab/strand-seq-graph-phasing.git- Create the working directory. This is where the intermediate and final outputs will be generated.
mkdir wd/- Create the conda environment that will be used to run snakemake. Keep this environment in the project directory.
conda env create -p strand-seq-graph-phasing/env/snakemake_runner/ -f strand-seq-graph-phasing/workflow/envs/env_snakemake.yaml-
Adjust config file and sample sheet. Template config and sample sheets are
config/config.yamlandconfig/samples.tsvrespectively -
Before running Graphasing, activate the environment created in step 3. Once activated, you should be ready to go!
cd strand-seq-graph-phasing/
conda activate env/snakemake_runner/If there are difficulties in installing R Bioconductor packages in your execution environment, a Singularity container may be used. You can use the definition file provided at workflow/envs/Renv2_singularity_definition.def to build the required Singularity container. Once built, copy the .sif contained into the working directory created in step 2.
The sample sheet is a .tsv file with the following columns:
sample: str Sample ID. This will serve as the identifier for the sample outputs.
strandseq_dir: str Path to directory containing Strand-seq .fasta.gz/fastq.gz files.
gfa: str Path to unphased input assembly graph .gfa file
coverage: str, NA Path to coverage file used for rukki path calculations. The path coverage file is a tsv file with three columns 'node', 'coverage', and 'length'. For Verkko assemblies, this is the assembly.hifi-coverage.csv file. For hifiasm use "NA", and coverage will be extracted from the input p_utg.gfa.
hpc: [TRUE, FALSE] Is the unphased input assembly graph gfa homopolymer compressed?
assembler: [verkko, hifiasm] Which genome assembly program was used to create the input assembly graph.
samples: str Path to sample sheet.
scripts_dir: str Path to folder containing Graphasing R and python scripts. This is the folder scripts in the project folder.
reference: str (optional, default:None) Path to reference genome. If provided, the input assembly will be aligned to the reference using minimap2, and used to supplement the output summaries and evaluation plots.
segmentLengthThreshold: int Filtration parameter. Unitigs in the input assembly graph .gfa less than segmentLengthThreshold in basepairs will be filtered out.
tangleSegmentLengthThreshold: int (optional, default: 250000) Filtration parameter. Unitigs in the input assembly graph .gfa less than tangleSegmentLengthThreshold in basepairs will be considered during large tangle removal.
per_thread_memory: bool (optional, default:False) If True, memory specified for each rule is divided by the number of threads.
calc_breakpoints bool (optional, default:False) If True, Graphasing will attempt to locate haplotype switches in the input assembly. Detected peaks, as well as plots, can be found in breakpoints/
plot_evaluation bool (optional, default:True) If True, evaluation plots will be made for all input samples. If a reference is provided, evaluation plots will include reference-derived evaluations. Without a reference, some plots will be left blank. Evaluation plots can be found in plots/.
plot_width, plot_height int (optional, default:20,15)
Width and height of evaluation plots .pdf output file.
plot_output_suffix str (optional, default:'')
Suffix to output file name. May be useful if using multiple configs within the same working directory
The following options are only needed if a reference is provided for the evaluation plots:
dip_chroms_regex: str (optional, default: '(chr|CHR)[0-9]+')
hap_chroms_regex: str (optional, default: '(chr|CHR)[yY]'
hem_chroms_regex: str (optional, default: '(chr|CHR)[xX]'
hta_chroms_regex: str (optional, default: '(chr|CHR)(13|14|15|21|22)'
These regexs control what is plotted, as well as additional plot annotations. Annotations are made for chromosomes that are diploid, haploid, hemiploid (sometimes diploid or haploid eg, chr X in humans), and hard-to-assemble. Only chromosomes matching the diploid, haploid, and hemiploid regexs are plotted. Defaults are set for human genomes.
snakemake \\
-d ../wd/ \\
--configfiles config/config.yaml \\
--config samples=/path/to/samples.tsv \\
--restart-times 3-d Path to working directory
--configfiles Path to config .yaml
--config samples= Path to sample sheet. Overrides the config file.
--restart-times While the resource allocation for each rule generally works on the first attempt, some rules occasionally need more memory. 3 restarts generally works to provide enough memory for the workflow to finish.
To generate phased assemblies with Verkko, the output .gaf file, found at rukki/{sample}/{sample}_rukki_paths.gaf can be passed to the --paths argument. At the same time, the folder containing the unphased assembly, as well as paths to the hifi and ONT reads used to constuct the unphased assembly, need to be input as well, eg:
verkko
# Other Arguments
...
# Same as for unphased assembly
--assembly {unphased_assembly_folder}
--hifi {hifi_reads}
--nano {nano_reads}
# Pass the paths .gaf here:
--paths {Graphasing_paths_gaf}To generate phased assemblies with hifiasm, the output Yak databases files, found at yak/{sample}_hap[12].yak can be passed to the -1 and -2 arguments of hifiasm trio mode assembly. Read overlaps from the unphased assembly can be reused by passing the same output prefix as the unphased assembly, eg:
hifiasm -o {unphased_output_prefix} ... -1 {Graphasing_yak_database_1} -2 {Graphasing_yak_database_2}See the trio-phasing documentation for more.
Graphasing outputs bandage-friendly haplotype marker summaries, which can be found in haplotype_marker_counts/. Each chromosome cluster is assigned two opposing colors, one for each haplotype, and each unitig is shaded according to the haplotype markers that align to it, with grey representing a 50/50 balance of markers. If a reference genome was provided, then summaries, containing additional reference-alignment-derived annotations can be found in rmc/.
An important step in the workflow is to cluster unitigs from the input assembly .gfa into groups corresponding to the same chromosome. Part of this step is achieved using a stochastic ensemble clustering algorithm from the contiBAIT package (See figure 7.3). Though contiBAIT parameters are set to attempt to limit variation in clustering, it is possible that the clustering of unitigs, as well as all downstream haplotype inference, will vary even if the pipeline is run multiple times with the same configuration.
Strand-seq alignments to haploid chromosomes have been observed to occasionally behave strangely, especially when they are already full phased in the input assembly .gfa; while normally unitigs from haploid chromosomes would each display a haploid alignment signal and cluster together, it has been observed that some or all haploid can display a diploid alignment signal and cluster separately. When this happens, it is possible for all haploid chromosomes, eg X and Y, to be assigned to the same haplotype.