PatchWorkPlot

A tool for visualization of pairwise alignments of multiple sequences as dot plots arranged into an upper or lower triangular matrix.

Dependencies

• Pandas
• BioPython
• LASTZ and/or YASS

Input Parameters

Required parameters

-i INPUT_CONFIG.CSV: a configuration file containing information about input sequences in the CSV format. The configuration file contains the following columns:

• SampleID: a unique identifier of each sequence (required).
• Fasta: a complete path to each sequence in FASTA format (required).
• Label: labels will be used in the output plot and, unlike SampleIDs, do not have to be unique to a sequence and can be empty (required).
• Annotation: a complete path to annotation in BED format (optional).
• Strand: an orientation of the sequence with respect to the first one. Values can be + and - (optional).

An example of the configuration file can be found here.

-o OUTPUR_DIR: the name of the output directory. If the directory does not exist, it will be created.

Optional parameters

--aligner NAME: the name of a tool used for pairwise alignment sequences. lastz (LASTZ) and yass (YASS) options are available. Default: lastz.

--cmap NAME: the name of a coloring map used for visualization of alignments. The minimum and maximum values of percent identity thresholds (min-pi and max-pi) will be used to determine the color of the alignment:

• Alignments with percent identity below min-pi will be shown using the leftmost color in the coloring cmap.
• Alignments with percent identity above max-pi will be shown using the rightmost color in the coloring map.
• Alignments with percent identity between min-pi and max-pi will be projected onto the coloring map and colored accordingly.

For the list of available coloring maps, please refer to the Matplotlib documentation. Default: Spectral.

--reverse-cmap BOOLEAN: if true, then min-pi and max-pi values of the percent identities will correspond to the rightmost and leftmost colors of the coloring map, respectively. In the case of Spectral map, alignments with high and low percent identity will be colored in red and blue, respectively. Default: true.

--color STRING: alignments will be colored in the specified color. The value can be a color name (e.g., black) or a HEX code (e.g., #000000). If both --color and --cmap are specified, then the --color option will be used.

--min-pi FLOAT: the alignment percent identity value that will be used to determine the color of the least similar alignments. Default: 85.

--max-pi FLOAT: the alignment percent identity value that will be used to determine the color of the most similar alignments. Default: 100.

--min-len INT: only alignments of lengths exceeding min-len will be visualized. Default: 5000.

--lwidth INT: the width of lines showing alignments on the final plot. Default: 1.

--lower: if specified, alignments will be visualized as a lower triangular matrix instead of an upper triangular matrix.

--show-annot: if specified, annotations will be extracted from INPUT_CONFIG.CSV (column Annotation) and shown on the side of the plot.

--transparent: if specified, the .PNG version of the plot will have a transparent background.

--help: print help.

Default values of parameters

Default values of input parameters are stored in default_params.txt. It covers the parameters described above as well as the parameters of LASTZ alignments. The file can be modified to change the default values and avoid passing the arguments through the arguments of the command line.

Usage

An example of visualization of a lower-triangular patchwork plot using the PuBuGn coloring map (the direct orientation) with an increased line width and without gene positions:

python PatchWorkPlot.py -i input_config.csv -o patchwork_output --cmap PuBuGn --reverse-cmap false --lwidth 2 --lower

Please note that the pairwise alignment is the most time-consuming step. If you want to change visualization of previously aligned sequences, you can rerun PatchWorkPlot specifying the existing output directory through -o and changing the desired visualization parameters. E.g.: the command line:

python PatchWorkPlot.py -i input_config.csv -o patchwork_output --cmap jet --reverse-cmap false --min-len 10000 --show-annot

will use alignments in the patchwork_output direcitory and modify the patchworkplot by:

• changing the coloring map to jet,
• discarding alignments shorter than 10 kbp,
• using the default parameter to draw lines,
• showing gene positions,
• reporting an upper triangular matrix.

Visualization of IgDetective results

PatchWorkPlot is useful to visualize highly repetitive sequences or sequences with a high density of structural variations such as immunoglobulin (IG) and T-cell receptor (TCR) loci. A script generate_igdetective_config.py simplifies analysis of adaptive immune loci annotated using the IgDetective tool and generates a config file that can be used as an input to PatchWorkPlot. To run the script, use the following command line:

python generate_igdetective_config.py PATHS_TO_IGDETECTIVE_DIRS LOCUS OUTPUT_DIR

where:

• PATHS_TO_IGDETECTIVE_DIRS is a space- or comma-separated paths to output directories of IgDetective. If the paths are separated by spaces, make sure to put them in double quotes: "PATH_1 PATH_2 ... PATH_N".
• LOCUS: a type of adaptive immune locus for which the config will be generated. Available options are IGH, IGK, IGL, TRA, TRB, TRG.
• OUTPUT_DIR: the name of the output directory. If the directory does not exist, it will be created.

Example of joint usage of IgDetective & PatchWorkPlot

The directory test_dataset includes five IgDetective directories containing results of IG/TR locus annotation for five cat genomes:

• test_dataset/01_mPumCon1.1_hap1_igdetective: the mountain lion (Puma concolor), haplotype 1, accession: GCA_028749985.3.
• test_dataset/02_mPumCon1.1_hap2_igdetective: the mountain lion (Puma concolor), haplotype 2, accession: GCA_028749965.3.
• test_dataset/03_mNeoNeb1_igdetective: the clouded leopard (Neofelis nebulosa), accession: GCA_028018385.1.
• test_dataset/04_mLynRuf1_igdetective: the bobcat (Lynx rufus), accession: GCF_022079265.1.
• test_dataset/05_mFelCat1_igdetective: the domestic cat (Felis catus), accession: .

The following command lines generates a configuration file for immunoglobulin heavy chain (IGH) loci and converts IGH gene files to BED format:

python generate_igdetective_config.py "test_dataset/01_mPumCon1.1_hap1_igdetective test_dataset/02_mPumCon1.1_hap2_igdetective test_dataset/03_mNeoNeb1_igdetective test_dataset/04_mLynRuf1_igdetective test_dataset/05_mFelCat1_igdetective" IGH cats_IGH_configuration

Then, PatchWorkPlot takes the compiled configuration file and visualizes pairwise alignments of the IGH loci. The --show-annot option is used to illustrate positions of IGH genes predicted by IgDetective:

python PatchWorkPlot.py -o cats_IGH_configuration/config.csv -o cats_IGH_patchworkplot --show-annot

Gallery

Annotation	Upper triangle	Lower triangle
No annotation
Parameters	default	--lwidth 3 --min-len 20000 --lower --min-pi 80 --max-pi 95
With annotation
Parameters	--show-annot --lwidth 2 --min-len 10000 --cmap Greens --reverse-cmap false --min-pi 80	--show-annot --lower --lwidth 2 --min-len 15000 --cmap viridis --min-pi 75

Citation

To be added