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SouthGreenPlatform / VcfHunter

VcfExplorer regroups several programs which principal aims are to map DNA and RNAseq data onto reference genome sequence, perform variant calling, manipulate vcf files, perform chromosome painting of accessions based on the contribution of ancestral groups, select marker for genetic map analysis and perform pairwise chromosome linkage of ordered markers.
GNU General Public License v3.0
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Purpose of VcfHunter

VcfHunter regroups several programs which principal aims are to map DNA and RNAseq data onto reference genome sequence, perform variant calling, manipulate vcf files, perform chromosome painting of accessions based on the contribution of ancestral groups, select marker for genetic map analysis and perform pairwise chromosome linkage of ordered markers.

Installation

All proposed tools described here are written in python and work on linux system To install the tools:

  1. Open the loca_programs.conf file loacted in bin folder
  2. Set the path to each required program. If the program is loaded in the environment (available in $PATH), the complete path is not required and then only program name can be set in loca_programs.conf. For example if bwa is already loaded in the environment, put bwa = bwa. If bam-readcound is not loaded in the environment put bamreadcount = /toto/tartenpion/programmes/bam-readcount/bin/bam-readcount

Dependencies

  1. BWA, https://bio-bwa.sourceforge.net/
  2. STAR, https://github.com/alexdobin/STAR
  3. PicarTools, https://broadinstitute.github.io/picard/
  4. GATK, https://software.broadinstitute.org/gatk/
  5. Samtools, https://github.com/samtools/samtools
  6. Bamtools, https://github.com/pezmaster31/bamtools
  7. bam-readcount, https://github.com/genome/bam-readcount
  8. gnuplot, http://www.gnuplot.info/
  9. circos-0.67 or greater, http://circos.ca/software/download/circos/
  10. umi tools, https://umi-tools.readthedocs.io/en/latest/

Python3 (tested with 3.4.10), Java and Biopython are also required.

How to cite

Depending on the tool you use (see Description section) please cite either:

Martin et al., in prep. G. Martin, B. Istace, F.C. Baurens, C. Belser, C Hervouet, K. Labadie, C. Cruaud, B. Noel, C. Guiougou, F. Salmon, J. Mahadeo, F. Ahmad, H. A. Volkaert, G. Droc, M. Rouard, J. Sardos, P. Wincker, N Yahiaoui, J.M. Aury, A D’Hont. in prep. Chromosome evolution in Musa species: Insights from genome assemblies of wild contributors to banana cultivars.

or

Martin et al., 2023b. Martin G, Baurens F-C, Labadie K, Hervouet C, Salmon F, Marius F, Paulo-de-la-Reberdiere N, Van den Houwe I, Aury J-M, D'Hont A, Yahiaoui N. 2023. Shared pedigree relationships and transmission of unreduced gametes in cultivated banana. Annals of Botany. XX:1–13 https://doi.org/10.1093/aob/mcad065

or

Martin et al., 2023a. Martin G, Cottin A, Baurens F-C, Labadie K, Hervouet C, Salmon F, Paulo-de-la-Reberdiere N, Van den Houwe I, Sardos J, Aury J-M, et al. 2023. Interspecific introgression patterns reveal the origins of worldwide cultivated bananas in New Guinea. Plant J. 113:802–818 https://doi.org/10.1111/tpj.16086

or

Martin et al., 2020b. Martin G, Baurens F-C, Hervouet C, Salmon F, Delos J-M, Labadie K, Perdereau A, Mournet P, Blois L, Dupouy M, et al. 2020. Chromosome reciprocal translocations have accompanied subspecies evolution in bananas. Plant J. 104:1698–1711 https://doi.org/10.1111/tpj.15031

or

Martin et al., 2020a. Martin G, Cardi C, Sarah G, Ricci S, Jenny C, Fondi E, Perrier X, Glaszmann J-C, D’Hont A, Yahiaoui N. 2020. Genome ancestry mosaics reveal multiple and cryptic contributors to cultivated banana. Plant J. 102:1008–1025. https://doi.org/10.1111/tpj.14683

or

Baurens et al., 2019. Baurens F-C, Martin G, Hervouet C, Salmon F, Yohomé D, Ricci S, Rouard M, Habas R, Lemainque A, Yahiaoui N, et al. 2019. Recombination and large structural variations shape interspecific edible bananas genomes. Mol. Biol. Evol. https://academic.oup.com/mbe/article/36/1/97/5162481

or

Garsmeur et al., 2018. Garsmeur O, Droc G, Antonise R, Grimwood J, Potier B, Aitken K, Jenkins J, Martin G, Charron C, Hervouet C, et al. 2018. A mosaic monoploid reference sequence for the highly complex genome of sugarcane. Nat. Commun. 9:2638. https://www.nature.com/articles/s41467-018-05051-5

Referring person of the deposit

Guillaume Martin (CIRAD)

License

Licencied under GPLv3

Description

The package provided comprised 46 programs listed here:

49 programs run using the following command: python program-name <--options-name value> 1 program (PaintAssembly.sh) run using the following command: bash PaintAssembly.sh <--options-name value>


Programs



process_RNAseq.1.0.py

This program takes a reference DNA sequence multifasta file and several fastq files and returns a bam file for each accessions and a final VCF file containing alleles count at each variant site having at least one variant allele supported by at least one read.

Options:

--conf: A configuration file containing path to references sequence (multifasta file) and RNAseq reads (fastq files).
--thread: Number of processor to use (i.e. Max number of accessions treated at the same time). Do not exceed the number of processors available! [default: 1] 
--queue: If you are using SGE scheduler: the queue name for the job to perform parallelization. If not do not fill.
--prefix: Prefix for vcf file and statistics folders.
--steps: A string containing steps to perform:
    a: STAR indexing reference,
    b: Merging fastq from first mapping step to identify splicing sites,
    c: STAR Indexing reference with identified splicing sites,
    d: Second mapping step,
    e: Merging bam libraries with identical identifier,
    f: Removing duplicates,
    g: Reordering reads,
    h: Splitting and trimming reads,
    i: Indel realignment,
    j: Allele counting,
    k: Genotype calling,
    l: Merging genotype calling,
    m: Gene exon coverage statistics calculation.

Configuration file description:

The configuration file should contain 4 sections and must be formated as followed:

[Libraries]
lib1 = genome_name path_to_mate1 path_to_mate2 ploidy
lib2 = genome_name path_to_single ploidy
...
[Reference]
genome = path_to_the_reference_sequence
[star]
options = additional_options_to_pass
[General]
max_size = max_read_length
gff3 (optional) = path to a gff3 file used to calculate statistics on genes coverage in step "m"

Output:

Warning: This program need to create a .dict and a .fai file in the folder were the reference sequence is stored if they do not already exist. Make sure that you have right to write in this folder!

Warning: If you use the STAR aligner for the read mapping onto the reference sequence, it only works on uncompressed fastq files (gunzip fastq.gz)

Outputs are dependent of the steps you are running and each steps use the output of the preceding one.

process_reseq.1.0.py

This program takes a reference DNA sequence multifasta file and several fastq files and returns a bam file for each accessions and a final VCF file containing alleles count at each variant site having at least one variant allele supported by at least one read. The genotypes found in the output vcf are indicative and may not reflect the correct genotype! For example, only two allele are authorized in one genotype to gain computation time. This program must be used in conjunction with VcfPreFilter.1.0.py which have been specifically designed to perform a variant calling on a selected set of polymorph markers based on user specification.

Options:

--conf: A configuration file containing path to references sequence (multifasta file) and DNAseq reads (fastq files).
--thread: Max number of accessions treated at the same time. Do not exceed the number of processors available! [default: 1] 
--queue: If you are using SGE scheduler: the queue name for the job to perform parallelization. If not do not fill.
--prefix: Prefix for vcf file and statistics folders.
--chrom: Chromosomes to work with (only for step f). If "all" : all chromosomes will be used for calling. Otherwise : a list of chromosome names separated by ":" [Default:all]
--steps: A string containing steps to perform:
    a: Aligning libraries
    b: Removing duplicates
    c: Indel realignment
    d: Bases recalibration
    e: Allele counting
    f: Genotype calling
    g: Merging genotype calling
    h: Mapping statistics calculation

Configuration file description:

The configuration file should contain 2 sections and must be formated as followed:

[Libraries]
lib1 = genome_name path_to_mate1 path_to_mate2 ploidy
lib2 = genome_name path_to_single ploidy
...
[Reference]
genome = path_to_the_reference_sequence

An additional section named [RmUMI] can be added. If so, aligned reads will be processed using umi_tools dedup.

Output:

Warning: This program need to create a .dict and a .fai file un the folder were the reference sequence is stored if they do not already exist. Make sure that you have right to write in this folder!

Outputs are dependent of the steps you are running and each steps use the output of the preceding one.

VcfPreFilter.1.0.py

This script filter VCF file generated by Process_RNAseq.1.0.py by removing homozygous sites for all accessions based on accession minimal and maximal coverage, accession minimal allele coverage and frequency parameters passed. The idea of this tool is to filter out variant lines resulting from sequencing errors. Filter are performed as follows:

Options:

--vcf: The VCF file
--MinCov: Minimal read coverage for site. [Default: 10]
--MaxCov: Maximal read coverage for site. [Default: 1000]
--minFreq: Minimal allele frequency in an accession to keep the allele for calling in the row
--MinAlCov: Minimal read number of minor allele to call variant heterozygous (between 1 and infinity). [Default: 3]
--dial: Perform only a bi-allelic calling. i.e Only two allele are possible in a genotype if "y" is passed to this argument. Possible values "y" or "n". [Default: y]
--out: Prefix for output files. [Default: Pop]
--outgzip: Output files in gzip format. [Default: n]



vcf2struct.1.0.py

This program has been designed to perform statistics, filter, manipulate vcf files as well as analyzing the mosaic structure of genomes.

Mandatory Options:

--type: A string corresponding to the type of analysis performed. 
Possible values are: 
    RANDOM_SUB_SET 
    STAT
    FILTER
    COMPARE
    ADD_REF
    AL_IDENTITY
    FACTORIAL
    VISUALIZE_VAR_3D
    VISUALIZE_VAR_2D
    SNP_CLUST-Kmean
    SNP_CLUST-MeanShift
    FILTER_ON_MAX_GP_PROP
    MERGE_VCF
    ALL_PROP
    GET_GENOTYPE
    GET_GENOTYPE_AND_GROUP

Options and outputs depend on analysis type:

Options:

--vcf: A vcf file.
--nRand: Number of variant site to get randomly from the vcf file (integer). [Default: 1000]
--prefix: The prefix for output files. [Default: WorkOnVcf]

Outputs:

**_subset.vcf:* A vcf file corresponding to a random line subset of the first vcf

Options:

--vcf: A vcf file.
--names: A one column file containing accession names to treat.
--prefix: The prefix for output files. [Default: WorkOnVcf]
--gff3: (optional) A gff3 file containing gene annotation. If not filled, the statistics will return 0 for these coding sequence values.

Outputs:

**_general.stat:* A file containing vcf global statistics such as number of indel and SNP sites, number of different tags in the FILTER columns, number of sites with 1,2,3,4, ... variants, number of transitions, transversions and variant in annotated regions.

**_accession.stat:* A file containing for each accessions missing data number, number of alleles specific to this accession in the vcf, number of homozygous sites identical to the reference, number of homozygous sites different from the reference and number of heterozygous sites.

Options:

--vcf: A vcf file.
--names: A one column file containing accession names to treat.
--outgroup: (optional) A one column file containing accession names that will not be used for filtering but will remain in the
 output file.
--prefix: The prefix for output files. [Default: WorkOnVcf]
--MinCov: Minimal coverage by accession to keep genotype calling (integer). If the value is lower, genotype will be converted to
 unknown for the concerned accession. [Default: 10]
--MinAl: Minimal allele coverage by accession to keep genotype calling (integer). If the value is lower for at least one allele,
 genotype will be converted to unknown for the concerned accession. [Default: 3]
--nMiss: Maximal number of missing genotype in a line to keep the line (integer). [Default: 0]
--RmAlAlt: (optional) Number of alleles at the site in filtered accessions to remove the variant site (several values can be
 passed and should be separated by ":"). Values: 1,2,3,...,n
--RmType: (optional) Variant status to filter out (several values can be passed in this case they should be separated by ":"). 
    Possible values: 
        *Values which can be found in the FILTER column: PASS, DP_FILTER, QD_FILTER, SnpCluster, 
        *Other values: INDELS, SNP, AUTAPO (accession specific variant site).

Outputs:

**_filt.vcf:* a filtered vcf file based on passed options.

Options:

--vcf: A vcf file.
--comp1: Accession name to compare. If 2 vcf files are provided and only one accession name is passed, this name will be
 searched in both vcf files.
--vcf2: (optional if --comp2 filled) A second vcf file.
--comp2:(optional if --vcf2 filled) Second accession to compare.
If 2 vcf and 2 names are passed, comp1 will be searched in vcf and comp2 will be searched in vcf2.

Outputs: Print to standard output statistics on the comparison.

Options:

--vcf: A vcf file.
--prefix: The prefix for output files. [Default: WorkOnVcf]
--ref_cov: Value to put to AD and DP flags (integer). [Default: 1000]

Outputs:

**_add_ref.vcf:* A new vcf file in which the reference has been added as an haploid accession named ref_silico_call.

Options:

--vcf: A vcf file.
--prefix: The prefix for output files. [Default: WorkOnVcf]

Outputs:

**_ident.mat:* A matrix containing genotype pairwise identity.

1- First the vcf file is recoded as followed: For each allele at each variants site two markers were generated; One marker for the presence of the allele (0/1 coded) and one for the absence of the allele (0/1 coded).

Only alleles present or absent in part (not all) of selected accessions were included in the final matrix file.

If groups information was passed to the script, alleles groups were attributed based on the following rule: the allele is attributed to a group if it is only present in this group but not in other defined groups. If no grouping information the GROUP column is filled with UN value. This grouping value doesn't have any influence on the analysis, it only allows to add colors graphs drawn. It will also help to validate if the structure of your data correspond to the one you suspect.

2- The factorial analysis was performed on the transposed matrix using R. Graphical outputs of the analysis were draw and for example accessions and alleles can be projected along axis in the following picture.

In this example allele projected along synthetic axis were colored if group informations were passed to the program.

Options:

--vcf: A vcf file.
--names: A one column file containing accession names to work with.
--prefix: The prefix for output files. [Default: WorkOnVcf]
--nAxes: Axis number to keep for the factorial analysis (integer). [Default: 4]
--mulType: Multivariate analysis type. Possible values: coa, pca and pca_normed [Default: coa]
--group: (optional) A file containing two sections: A section[group] with in col 1 accession name ; col 2 group (UN for
 unknown group). All group should be in capital letters. A section [color], that define for each group a color for pca
 drawing (in RGB+alpha percentage, ex: red=1:green=0:blue=0:alpha=0.1)
Optional and dependent parameters:
--dGroup: If passed, all alleles belonging to groups passed to this option will be removed.
--mat: Matrix of grouped alleles (with either a GROUP or a K-mean_GROUP column). If a K-mean_GROUP column is found, the
 filter will be performed on this column, else it will be performed on the GROUP one.

Outputs:

*_axis**XvsY_accessions.pdf:** Several pdf files showing accessions projected along X and Y axis.

*_axis**XvsY.pdf:** Several pdf files showing accessions projected along X and Y synthetic axis in a first graph and allele projection along X and Y synthetic axis in a second graph.

**_inertia.pdf:* A pdf files showing axis inertia.

**_matri_4_PCA.tab:* A tabulated file of the recoded vcf file passed to R for the factorial analysis.

**_multivariate.R:* The R script file passed to R to do the analysis.

**_multivariate.Rout:* The R script log file.

**_individuals_coordinates.tab:* A tabulated file of individuals coordinates along synthetic axis.

**_variables_coordinates.tab:* A tabulated file of allele coordinates along synthetic axis.

**_variables_coordinates_scaled.tab:* A tabulated file of allele scaled coordinates (columns centered and reduced) along synthetic axis.

Options:

--VarCoord: The *_variables_coordinates.tab file generated by FACTORIAL.
--dAxes: Axes to use in kmean clustering. Axis should be separated by ":".
--nGroup: Group number for the k-mean algorithm that will cluster variables (alleles) based on their coordinates. [Default: 2]
--mat: The *_matrix_4_PCA.tab tabulated file containing variant allele encoded generated by FACTORIAL.
--prefix: The prefix for output files. [Default: WorkOnVcf]
--thread: Number of processor available. [Default: 1]
--iter: Parallel k-mean clustering different star-points performed. [Default: 100]
--AP: Cluster absent (A) and present (P) lines. Possible values, "y" or "n" [Default: y]

Outputs:

**_centroid_coordinates.tab:* Coordinates of the distinct final centroids calculated for the X k-mean random star-points.

**_centroid_iteration_grouping.tab:* A tabulated file indicating centroid group attribution.

**_kMean_allele.tab:* A tabulated file equivalent to *_matrix_4_PCA.tab in which an column (named K-mean_GROUP) containing k-mean allele grouping has been added.

**_group_color.tab:* Color file in which a RGB color as been attributed to each k-mean cluster group. This file can be used for VISUALIZE_VAR_2D and VISUALIZE_VAR_3D tools.

**_kMean_gp_prop.tab:* A tabulated file reporting for each allele the probability to be in each groups. This is not a "real" probability, the idea was to have a statistics in case you want to filter alleles. This value was calculated as the inverse of the euclidean distance of one point and each centroids and these values were normalized so that the sum is equal to 1.

Options:

--VarCoord: The *_variables_coordinates.tab file generated by FACTORIAL.
--dAxes: Axes to use in mean shift clustering. Axis should be separated by ":".
--quantile: The quantile value to estimate the bandwidth parameters used in the MeanShift. Value should be in [0:1]. [Default: 0.2]
--mat: The *_matrix_4_PCA.tab tabulated file containing variant allele encoded generated by FACTORIAL.
--prefix: The prefix for output files. [Default: WorkOnVcf]
--thread: Number of processor available. [Default: 1]
--MeanShiftAll: Cluster all point in the MeanShift. Possible values, "y" or "n" [Default: y]
--bandwidth: Bandwidth value used for mean shift. If filled, the --quantile parameter is ignored. [Default: None]
--AP: Cluster absent (A) and present (P) lines. Possible values, "y" or "n" [Default: y]

Outputs:

**_centroid_coordinates.tab:* Coordinates of centroids calculated.

**_centroid_iteration_grouping.tab:* A tabulated file indicating centroid group attribution.

**_kMean_allele.tab:* A tabulated file equivalent to *_matrix_4_PCA.tab in which an column (named K-mean_GROUP) containing k-mean allele grouping has been added.

**_group_color.tab:* Color file in which a RGB color as been attributed to each k-mean cluster group. This file can be used for VISUALIZE_VAR_2D and VISUALIZE_VAR_3D tools.

**_kMean_gp_prop.tab:* A tabulated file reporting for each allele the probability to be in each groups. This is not a "real" probability, the idea was to have a statistics in case you want to filter alleles. This value was calculated as the inverse of the euclidian distance of one point and each centroids and these values were normalized so that the sum is equal to 1.

Options:

--VarCoord: The *_variables_coordinates.tab file generated by FACTORIAL.
--dAxes: Axes to plot. Axis should be separated by ":". All combination between axis will be drawn.
--mat: The *_matrix_4_PCA.tab or *_kMean_allele.tab.
--group: (optional) A file containing at least the a section [color], that define for each group a color
 (in RGB+alpha percentage, ex: red=1:green=0:blue=0:alpha=0.1). This file can be the *_group_color.tab
 generated by SNP_CLUST.
--dGroup: Groups IDs to draw. Groups names should be separated by ":". Groups names will be searched in
 the file provided in --mat argument in the "K-mean_GROUP" column, and if such column is not present, they
 will be searched in "GROUP" columns. If none of these columns are found the program will exit without finishing.
--prefix: The prefix for output files. [Default: WorkOnVcf]

Outputs:

*_axis**X_vs_axisY.png:** Several png files showing grouped alleles projected along X and Y synthetic axes.

Options:

--VarCoord: The *_variables_coordinates.tab file generated by FACTORIAL.
--dAxes: 3 axes to plot. Axis should be separated by ":". All combination between axis will be drawn.
--mat: The *_matrix_4_PCA.tab or *_kMean_allele.tab.
--dGroup: Groups IDs to draw. Groups names should be separated by ":". Groups names will be searched in the
 file provided in --mat argument in the "K-mean_GROUP" column, and if such column is not present, they will
 be searched in "GROUP" columns. If none of these columns are found the program will exit without finishing.
--group: (optional) A file containing at least the a section [color], that define for each group a color
 (in RGB+alpha percentage, ex: red=1:green=0:blue=0:alpha=0.1).

Outputs:

Display to standard output an interactive plot.

Options:

--gpPropFile: The group file proportion, *_kMean_gp_prop.tab file generated by SNP_CLUST.
--mat: The *_kMean_allele.tab file generated by SNP_CLUST.
--gpPropValue: Minimal value to keep the allele grouped. This value should be comprised between 0 and 1 [Default: 0.95]
--prefix: The prefix for output files. [Default: WorkOnVcf]

Outputs:

\_kMean_allele_filtered_with_x_value.tab:** A filtered file of \_kMean_allele.tab** file in which ambiguous (according to --gpPropValue = x) alleles were removed.

Options:

--vcf: A vcf file in which you want to add an accession.
--vcf2: A vcf file containing accession you want to add to the fist vcf file.
--comp1: Accession name to add to the first vcf.
--prefix: The prefix for output files. [Default: WorkOnVcf]

Outputs:

**_merged.vcf:* A new vcf file with the accessions variant calling added.

Options:

--vcf: A vcf file.
--mat: A matrix file containing allele grouping with either "K-mean_GROUP" or "GROUP" columns, if "K-mean_GROUP" does not
 exists the "GROUP" column will be used. These files are those generated by FACTORIAL (*_matrix_4_PCA.tab), SNP_CLUST (*_kMean_allele.tab)
 or  FILTER_ON_MAX_GP_PROP (*_kMean_allele_filtered_with_x_value.tab). 
--prefix: The prefix for output files. [Default: WorkOnVcf]
--names: (optional) A one column file containing accession names to calculate proportion on (it can be accessions that were
 not used for PCA analysis but originating from the same original vcf).
--dGroup: (optional) Groups IDs to calculate proportion on. Groups names should be separated by ":". Groups names will be searched
 in the file provided in --mat argument in the "K-mean_GROUP" column, and if such column is not present, they will be searched in
 "GROUP" columns. If none of these columns are found the program will exit without finishing.
--ExclChr: (optional) A list of chromosome to exclude from the analysis separated by ":".

Outputs:

*_gp_prop.tab: A tabulated file containing for each accessions the number of grouped allele for each groups.

Options:

--vcf: A vcf file.
--names: A one column file containing accession names to plot genotypes
--prefix: The prefix for output files. [Default: WorkOnVcf]

Outputs:

**_genotype.gen:* A tabulated file containing, for each accessions (columns) and at each positions (rows) along chromosomes, the genotype.

Options:

--vcf: A vcf file.
--names: A one column file containing accession names to plot genotypes
--mat: A matrix file containing allele grouping with either "K-mean_GROUP" or "GROUP" columns, if "K-mean_GROUP" does not
 exists the "GROUP" column will be used. These files are those generated by FACTORIAL (*_matrix_4_PCA.tab), SNP_CLUST (*_kMean_allele.tab)
 or  FILTER_ON_MAX_GP_PROP (*_kMean_allele_filtered_with_x_value.tab).
--dGroup: Groups IDs to calculate proportion on. Groups names should be separated by ":". Groups names will be searched
 in the file provided in --mat argument in the "K-mean_GROUP" column, and if such column is not present, they will be searched
 in "GROUP" columns. If none of these columns are found the program will exit without finishing.
--prefix: The prefix for output files. [Default: WorkOnVcf]

Outputs:

**_genotype.gen:* A tabulated file containing, for each accessions (2 columns per accessions) and at each positions (rows) along chromosomes, the genotype (first of the two columns) and allele grouping (second of the two columns).

vcf2linear.1.1.py

This programs aims at performing a chromosome painting of accessions along chromosome based on the allele grouping corresponding to the ancestral groups. The idea is to search, for distinct regions (along the chromosomes) of the accession we want to study, the probability of an ancestral origin (homozygous or heterozygous). Because, distinct ancestral groups can contain different specific alleles, show distinct heterozygosity levels and allele fixation (du to distinct reproductive mode for example) such probability is not straightforward. An idea can be to calculate for each regions, the expected ancestral allele grouping proportion (based on ancestral accessions) and compare this value to the observed one. Because dataset can be small and/or representatives of ancestral group are very few, the expected ancestral allele grouping proportion can be difficult to estimate.

This programs solves the problem by simulating ancestral population from accessions identified as representatives of the ancestral groups under panmixiea hypothesis. For each ancestral populations a total of 100 individuals were simulated. The same was performed for hybrids between two populations. At the and of this simulation process mean and standard deviation of allele grouping proportions were calculated for each ancestral group on sliding windows of size n overlapping of n-1. Because the simulation process and access to the simulation is a long process, we also implemented a function that estimated grouping proportions based on sums of binomial densities. The mean values estimated are exact but standard deviations are approximated.

These grouping proportions were then calculated on tested accessions and estimated proportion were used to calculate the ancestry probability as followed.

With:

Hmu: Mean gX expected number for the group gX based on simulations on homozygous groups,

Hsd: Maximal standard deviation value calculated for all groups based on simulations on homozygous groups,

Emu: Mean gX expected number for the group gX based on simulations on heterozygous groups,

Esd: Maximal standard deviation value calculated for all groups based on simulations on heterozygous groups,

Nmu: Mean noisy allele grouped based on simulations (calculated as the number of alleles not from the right group according to the simulated population),

Nsd: Noise standard deviation,

obs: gX number observed for the accession on the window,

PgX: Probability to be at least gX,

PgN: Probability to be in the noise (unknown/additional ancestor),

N(x, mu, sd): Probability density function of the normal distribution for mean = mu and standard deviation = sd.

Genotype grouping at the window was then attributed based on the maximal probability and haplotypes representation was performed trying to minimize the recombination events.

Options:

--vcf: A vcf file.
--mat: A matrix file containing allele grouping with either "K-mean_GROUP" or "GROUP" columns, if "K-mean_GROUP" does not
 exists the "GROUP" column will be used. These files are those generated by FACTORIAL (*_matrix_4_PCA.tab), SNP_CLUST (*_kMean_allele.tab)
 or  FILTER_ON_MAX_GP_PROP (*_kMean_allele_filtered_with_x_value.tab).
--names: A one column file containing accession names to treat (it is not necessarily those used in the Factorial analysis
but they must share variant sites).
--namesH: A two column file containing accession names used to simulate populations. 
--chr: Chromosomes names to work with (each chromosome names should be separated by ":"). If not filled, all chromosomes will be used.
--win: Half window size (allele number) around a variant site to evaluate the structure at the site (integer). [Default: 25]
--ploidy: Ploidy level (integer). [Default: 2]
--thread: Number of processors to use (integer), [default: 1]
--type: Type of estimation performed: "Simul", "Binom". If "Simul", a total of 100 individuals are simulated for
 each combinations of haplotype and mean values and sd values are estimated based on these simulation. If
 "Binom", mean value is calculated as the sum of binomial mean at each point (exact estimator) and sd
 value is estimated as sqrt(sum variance at each point). This is not the exact sd but the analysis is a
 lot more faster! If you do not trust this sd estimation, you can choose to change this estimator
 by filling a value between ]0,1] to --prop parameter. In this case, the program will use the
 maximal_expected_value*prop_argument instead of using the maximal sd observed for all groups for probability
 calculation [default: Binom]
--prop: Estimator different from sd calculated as mean_value*--prop. Value should be comprised in ]0,1].
 A value of 0, means that this parameter is not used. [default: 0]
--gcol: A file containing at least the a section [color], that define for each group a color (in RGB+alpha percentage,
ex: red=1:green=0:blue=0:alpha=0.1). This file can be the *_group_color.tab generated by SNP_CLUST.
--Ambiguous: A 2 column file containing accession names used to simulate populations and their group.
 In this file we only pass admixed representative of a group not represented in nameH. These accessions will
 be used to infer expected allele of a group at the observed position. This inference is calculated as followed:
 a probability to have an allele of this group in an accession of this group is of 1 an allele of the group is
 found in the passed accession. Accessions passed to this option are not used for noise inference.
--FormerFolder: Folder containing previous chromosome painting. This chromosome painting will be used to better
estimate the expected number of alleles based on admixed accessions representative of a group. If only the ancestral
 group is found in the haplotypes of the admixed accessions (passed to --Ambiguous), the probability is calculated has
 the number of alleles of the group observed at this position. In others cases, the probability is still 1 if an alleles
 of the group is observed.
--sdMult: Multiplier of standard deviation for probability calculation of a segment to be of a group. [Default: 1]
--prefix: The prefix for output files. [Default: WorkOnVcf]

Output

A folder with the name passed in --prefix options which contained several files for each accessions and each chromosomes:

*_Accession_chromosome.tab: A tabulated file with for each window around a given position:

Accession_chromosome_haplo1.tab: A tabulated file listing grouped region blocs for haplotype1

Accession_chromosome_haplo2.tab: A tabulated file listing grouped region blocs for haplotype2

Accession_chromosome_density.pdf: A pdf file summarizing all statistics (heterozygosity, expected and observed ancestries along chromosomes, accessions ancestry probabilities and inferred accession haplotypes)

haplo2kar.1.0.py

This program perform a synthesis of the haplotypes reconstructed by vcf2linear.1.0.py by drawing for an accession the chromosomal painting for all its chromosomes.

Options:

--acc: Accession name.
--chr: Chromosomes list to draw (separated by ":")
--gcol: A file containing at least the a section [color], that define for each group a color (in RGB+alpha percentage,
 ex: red=1:green=0:blue=0:alpha=0.1). This file can be the *_group_color.tab generated by SNP_CLUST.
--dg: Groups to draw, separated by ":". Groups not passed here will be replaced by grey.
--centro: A tabulated file locating pericentromeric regions. In column 1: chromosome name, column 2: start, column 3: end.

Output:

Accession.pdf: A pdf file with chromosome painting for all chromosomes of this accession.

haplo2karByChr.1.0.py

This program perform a synthesis of the haplotypes reconstructed by vcf2linear.1.0.py by drawing for a chromosome the chromosomal painting for all selected accessions.

Options:

--acc: Accession names configuration file with Column1: accession name and column2: ploidy level.
--chr: Chromosome to draw
--gcol: A file containing at least the a section [color], that define for each group a color (in RGB+alpha percentage,
 ex: red=1:green=0:blue=0:alpha=0.1). This file can be the *_group_color.tab generated by SNP_CLUST.
--dg: Groups to draw, separated by ":". Groups not passed here will be replaced by grey.
--centro: A tabulated file locating pericentromeric regions. In column 1: chromosome name, column 2: start, column 3: end.
--prefix: Prefix for output files. [Default: All_acc]
--maxChr: Maximal haplotype number to draw in a pdf. [Default: 53]

Output:

*_chromosme_X.pdf: As much pdf file as necessary with chromosome painting for all chromosomes of this accession.

haplo2Circos.1.0.py

This program perform a synthesis of the haplotypes reconstructed by vcf2linear.1.0.py by drawing a circos representation of the chromosomal painting for all selected accessions and chromosomes.

Options:

--acc: Accession names configuration file with Column1: accession name and column2: ploidy level.
--chr: Chromosome(s) to draw, separated by ":"
--gcol: A file containing at least the a section [color], that define for each group a color (in RGB+alpha percentage,
 ex: red=1:green=0:blue=0:alpha=0.1). This file can be the *_group_color.tab generated by SNP_CLUST.
--dg: Groups to draw, separated by ":". Groups not passed here will be replaced by grey.
--centro: A tabulated file locating pericentromeric regions. In column 1: chromosome name, column 2: start, column 3: end.
--prefix: Prefix for output files. [Default: All_acc]

Output:

*.conf: the configuration file used by circos. I choose to keep this file so that it can be edited to change aspect of the Figure if you want. After editing this file you will only have to run the following command line to have your new Figure : circos -conf Circos_All_Admix.conf -noparanoid

*_housekeeping.conf: a second file used by circos

*.kar: a third file also used by circos

*.png: the circos Figure

vcfFilter.1.0.py

This program filter a vcf file based on several criteria. This is a improved version of FILTER option of vcf2struct.1.0.py

Options:

--vcf: A standard vcf file
--names: A one column file containing accessions to treat.
--outgroup: (optional) A one column file containing accession names that will not be used for filtering but will remain in the
 output file.
--RmType: (optional) Variant status to filter out (several values can be passed in this case they should be separated by ","). 
    Possible values: 
        *Values which can be found in the FILTER column: PASS, DP_FILTER, QD_FILTER, SnpCluster, 
        *Other values: INDELS, SNP, AUTAPO (accession specific variant site).
--RmAlAlt: (optional) Number of alleles at the site in filtered accessions to remove the variant site (several values can be
 passed and should be separated by ":"). Values: 1,2,3,...,n
--MinCov: Minimal coverage by accession to keep genotype calling (integer). If the value is lower, genotype will be converted to
 unknown for the concerned accession. [Default: 10]
--MaxCov: Maximal coverage by accession to keep genotype calling (integer). If the value is greater, genotype will be converted to
 unknown for the concerned accession. [Default: 1000]
--MinFreq: Minimal allele frequency to keep genotype calling (float). If the value is lower, genotype will be converted to unknown
 for the concerned accession. [Default: 0.05]
--MinAl: Minimal allele coverage by accession to keep genotype calling (integer). If the value is lower for at least one allele,
 genotype will be converted to unknown for the concerned accession. [Default: 3]
--nMiss: Maximal number of missing genotype in a line to keep the line (integer). [Default: 0]
--prefix: The prefix for output files. [Default: WorkOnVcf]

Output:

*_filt.vcf: a filtered vcf file based on passed options.

vcf2allPropAndCov.py

This program perform two things based on a vcf. 1) It plots for an accession, the allele coverage along its chromosomes. 2) It identify, based on known ancestral accessions in the vcf, the alleles specific to each groups and plot the alleles proportion at a site in the accession along chromosomes.

Options:

--conf: Conf file containing vcf location (one per chromosome or a single vcf for all chromosomes),
--origin: A 2 column file containing accession name (col1), origin/group (Col2),
--acc: Accession to work with,
--ploidy: Accession ploidy (integer),
--NoMiss: No missing data are allowed in accessions used to attribute alleles to group,
--all: Allele should be present in all accessions of the group.

Output:

*Cov.png: a png file presenting SNP coverage along the chromosomes.

*Ratio.png: a png file presenting ancestral allele proportion at a site along the chromosomes.

*_AlleleOriginAndRatio.tab: a tabulated file reporting for each sites were an ancestral allele has been attributed, its origin and the proportion of reads supporting this allele. This files contains chromosome (col1), position (col2), allele (col3), ancestral origin (col4) and allele ratio (col5).

*_stats.tab: a tabulated file reporting various statistics on the alleles of the accession.

vcf2allPropAndCovByChr.py

This program perform two things based on a vcf. 1) It plots for a chromosome of all accessions in a vcf, the allele coverage along its chromosomes. 2) It identify,based on known ancestral accessions in the vcf, the alleles specific to each groups and plot the alleles proportion at a site along chromosomes for all accessions.

Options:

--conf: Conf file containing vcf location (one per chromosome or a single vcf for all chromosomes),
--origin: A 2 column file containing accession name (col1), origin/group (Col2),
--ploidy: Accession ploidy (integer). If not all accessions have the same ploidy, this is not a problem. This ploidy information
 is only used to draw vertical lines in the coverage plot that help to identify ploidy change,
--NoMiss: No missing data are allowed in accessions used to attribute alleles to group,
--all: Allele should be present in all accessions of the group,
--acc: Accession to work with. If ignored, all accessions in the vcf will be used. Else accessions should be separated by ",",
--prefix: Prefix for output files. [Default: RatioAndCov]

Output:

prefix_chromosomeN_X_Cov.png: X png files presenting SNP coverage along chromosomeN.

prefix_chromosomeN_X_Ratio.png: X png files presenting ancestral allele proportion at a site along chromosomeN.

vcf2pop.1.0.py

This program will select markers for genetic mapping analysis from a vcf file based on several criteria. It will outpout coded markers for two genetic mapping software (onemap and JoinMap).

Options:

--vcf: The vcf file
--MinCov: Minimal read coverage for a marker in an accession (integer). If a lower value is
 found data point is converted to missing. [Default: 10]
--MaxCov: Maximal read coverage for a marker in an accession (integer). If a greater value is
 found data point is converted to missing. [Default: 1000]
--WinFreq: Window for minority allele coverage frequency to be insufficient to call a
 heterozygous but to high to call an homozygous (example: "0.05:0.1"). With the example if
 minority allele is in ]0.05:0.1] calling will become missing for this data point.
--MinAlCov: Minimal read number of minor allele to call variant heterozygous (between 1 and
 infinity). [Default: 1]
--miss: Maximal missing data proportion in the progeny (Excluding parents) (between 0 and 1).
 greater missing proportion will result in removing the marker. [Default: 0.2]
--pValue: P-value threshold to keep marker (between 0 and 1). This p-value is calculated to
 based on a Khi2 test comparing the marker segregation to expected segregation. [Default: 0.0001]
--pop: Population type (Possible values: SELFPOL, SELF, BiP). [Default: BiP]
    Possible values:
        BiP: bi-parental cross. Expected segregation tested: 0.5/0.5 (parental markers) and 0.25/0.5/0.25 (bridge markers).
        SELF: selfing population. Expected segregation tested: 0.25/0.5/0.25 (bridge markers).
--prefix: Prefix for output files. [Default: Pop]
--addcov: A tabulated file containing genotype of all markers passing filter is printed.
 If this option is passed, in addition to genotypes, alleles coverage information is also filled.
    Possible values:
        y: add this information
        n: do not add this information
    [default: n]
--drawplot: Draw statistic plot (y or n).
    Possible values:
        y: add this information
        n: do not add this information
    [Default: n]
--parent: (optional) Names of the parents of the population (separated by ":"). If passed,
 these names will be used to parse marker depending of there segregation and heterozygosity
 in the parents.
--NoUsed: (optional) A tabulated file containing in one column, names of accessions to exclude from the
 filtration (based on missing data and p-value) but which will be kept in final files.
--exclude: (optional) A tabulated file containing in one column, names of accessions to exclude from the
 analysis and the files.
--ref: (optional) The reference fasta file. If passed, a tag associated to the marker will be
 printed in a fasta file. This tag will contained 125 bases before the marker and 125 bases
 after.
--remove: (optional) For some programs, marker name length is limited. This option helps you to reduce marker
 names. By default marker name is "chromosome name"+"M"+"site position". A string can be passed
 that will be searched and removed from all marker name. This is not necessary if your chromosome
 name is not to long.

Outputs:

*_JM_Bridge.loc: A .loc file that can be passed to JoinMap that contained bridge markers.

*JMParent.loc: Two .loc file that can be passed to JoinMap that contained parent1 and parent2 markers respectively. Only if parent option is filled.

*_JM_unknown.loc: A .loc file that can be passed to JoinMap that contained unknown parent markers (missing data for both parents). Only if parent option is filled.

*_onemap_Bridge.tab: A .tab file that can be passed to onemap that contained bridge markers.

*onemapParent.tab: Two .tab file that can be passed to onemap that contained parent1 and parent2 markers respectively. Only if parent option is filled.

*_onemap_unknown.tab: A .tab file that can be passed to onemap that contained unknown parent markers (missing data for both parents). Only if parent option is filled.

*_tab_Bridge.tab: A .tab file corresponding to a simplified JoinMap format that contained bridge markers.

*tabParent.tab: Two .tab file corresponding to a simplified JoinMap format that contained parent1 and parent2 markers respectively. Only if parent option is filled.

*_tab_unknown.tab: A .tab file corresponding to a simplified JoinMap format that contained unknown parent markers (missing data for both parents). Only if parent option is filled.

*_report.tab: A report file.

*_sub.vcf: A sub vcf corresponding to the original vcf with only lines corresponding to conserved markers (no filtering applied in this vcf).

*.tab: A file containing for aech selected marker, the genotype of each accessions based on filter applied. Two additional values are added at the end of the file: the Khi-Square value and the P-value of the test.

*.pdf: A pdf file containing various statistics on the vcf filtrations. Only if --drawplot=y.

*_tags.fasta: A fasta file containing marker tags (to align against another reference genome for example). Only if --ref option is filled.

vcf2popNew.1.0.py

This program will select markers for genetic mapping analysis from a vcf file based on several criteria including segregation ratio. It will outpout coded markers as requested by the user.

Options:

--vcf: The vcf file
--seg: Segregation tested. Several segregations can be passed and should be separated by "/".
 A segregation should look like as follows: Name:Parents:MarkerCoding:MarkerSegregation:PvalueForTest.
 With a real example: SimpleDose:P1,P2:Ho,He@nn,np:0.5,0.5:1e-5/Bridge:P1,P2:Ho,He,Ho@hh,hk,kk:0.25,0.5,0.25:1e-5
 (Ho for homozygous, He for heterozygous)
--MinCov: Minimal read coverage for a marker in an accession (integer). If a lower value is
 found data point is converted to missing. [Default: 10]
--MaxCov: Maximal read coverage for a marker in an accession (integer). If a greater value is
 found data point is converted to missing. [Default: 1000]
--WinFreq: Window for minority allele coverage frequency to be insufficient to call a
 heterozygous but to high to call an homozygous (example: "0.05:0.1"). With the example if
 minority allele is in ]0.05:0.1] calling will become missing for this data point.
--MinAlCov: Minimal read number of minor allele to call variant heterozygous (between 1 and
 infinity). [Default: 1]
--miss: Maximal missing data proportion in the progeny (Excluding parents) (between 0 and 1).
 greater missing proportion will result in removing the marker. [Default: 0.2]
--prefix: Prefix for output files. [Default: Pop]
--addcov: A tabulated file containing genotype of all markers passing filter is printed.
 If this option is passed, in addition to genotypes, alleles coverage information is also filled.
    Possible values:
        y: add this information
        n: do not add this information
    [default: n]
--NoUsed: (optional) A tabulated file containing in one column, names of accessions to exclude from the
 filtration (based on missing data and p-value) but which will be kept in final files.
--exclude: (optional) A tabulated file containing in one column, names of accessions to exclude from the
 analysis and the files.
--ref: (optional) The reference fasta file. If passed, a tag associated to the marker will be
 printed in a fasta file. This tag will contained 125 bases before the marker and 125 bases
 after.
--remove: (optional) For some programs, marker name length is limited. This option helps you to reduce marker
 names. By default marker name is "chromosome name"+"M"+"site position". A string can be passed
 that will be searched and removed from all marker name. This is not necessary if your chromosome
 name is not to long.

Outputs:

*_tab_Bridge.tab: A .tab file corresponding to a simplified JoinMap format that contained bridge markers.

*tab\**SegregationName_Parent.tab:** Two .tab file corresponding to a simplified JoinMap format that contained parent1 and parent2 markers respectively. Only if parent option is filled.

*_tab_unknown.tab: A .tab file corresponding to a simplified JoinMap format that contained unknown parent markers (missing data for both parents). Only if parent option is filled.

*_report.tab: A report file.

*_sub.vcf: A sub vcf corresponding to the original vcf with only lines corresponding to conserved markers (no filtering applied in this vcf).

*.tab: A file containing for each selected marker, the genotype of each accessions based on filter applied. Two additional values are added at the end of the file: the Khi-Square value and the P-value of the test.

*_tags.fasta: A fasta file containing marker tags (to align against another reference genome for example). Only if --ref option is filled.

RecombCalculatorDDose.py

This program perform is designed to calculate frequencies of recombination observed between two pairs of markers. It can also calculate marker segregation distortion.

Options:

--matrix: The marker file matrix (output of vcf2pop.1.0.py or vcf2popNew.1.0.py),
--output: Prefix for output files,
--phased: Are marker phased,
    Possible values:
        y: markers are phased
        n: markers are not phased
    [default: n]
--steps: Analysis to perform,
    Possible values:
        R: Calculate recombination rate
        S: Calculate segregation distortions

Output:

_REC.tab: A tabulated file of pairwise marker recombination rate (--setp R).

_SegDist.tab: A tabulated file of marker segregation distortions (--setp S).

Draw_dot_plot.py

This program draw a dotplot based on marker pairwise recombination file obtained from RecombCalculatorDDose.

Options:

--matrix: The pairwise matrix marker file (generated by RecombCalculatorDDose.py),
--loc: Loci to plot with their locations (col1: marker name, col2: chromosome, col3: position),
--chr: (Optional) List of chromosomes to draw in this order (separated by ":"),
--agp: (Optional) Agp file locating scaffolds in the reference sequence,
--stat: (Optional) A two column file with column 1: marker name, column2: statistics,
--phys: A value specifying if the marker position should be defined based on physical position or not.
    Possible
        y: yes
        n: no: markers are ordered along chromosomes with positions incremented by 1 
    [default: y]
--output: Name of output file. Extension (.png, .svg, .pdf) of the outpout is automatically identified by the script.

Output:

A dotplot file representing pairwise marker linkage named according to --outpout option.

A heatmap file representing pairwise statistic coding named according to --outpout option.

vcfIdent.1.0.py

This program test, along chromosome, the proportion of alleles shared by one accession, against another one. For example when comparing a triploid accession against a diploid one, if the diploid is A/T and the tetraploid one is A/T/T, the proportion of shared alleles at the given position is 1. If the tetraploid is A/A/A, then the proportion is equal to 0.5. A pdf of this statistic along chromosome is generated as well as a density plot of these values over a given window size. In addition, for each potential values (0 and 1 for haploid comparison, 0, 1 and 2 for diploid comparison), a file summarizing the density of this value on a given windows, that can be loaded to circos to perform a circular representation of this statistics is generated.

Options:

--vcf: The vcf file containing accessions to compare
--acc: Accession name to compare to others.
--comp: Accession name(s) to be compared. If several accessions should be compared, they should be separated by ":".
--win: Half window (unit = variant site) to compute density along chromosomes. (Default value: 0)
--draw: Draw a pdf presenting sites scoring along chromosomes. (Default: y)
--out: Prefix for output files names.

Output:

*_X-acc.densityY.txt: Several tabulated file corresponding to the density value for each possible values (Y) resulting from the comparison of the given accession (X) to the one given by --acc option.\ *_X-acc.scatter.txt: Several files (one for each accession passed to --comp option) that contained the proportion of shared alleles at each compared sites of the vcf.\ *.pdf: A pdf file plotting the allele comparison along chromosome of each accessions passed to --comp option to the one passed to --acc option.\ *_OK_prop.tab: A tabulated file containing the proportion of shared allele on slidding windows of 2x(--win)+1.\ *_NoOK_prop.tab: A tabulated file containing the proportion of not shared allele on slidding windows of 2x(--win)+1.

vcfRemove.1.0.py

This program remove alleles in a given accession if they are shared by a second accession. For Example, if an accession in which we will remove allele is A/A/T and a second one which is the one that will be used to remove is A/A, the result for the first accession will be T in the final vcf. If the accession used to remove is T/T, the result will be A/A as only T is common between the two accessions.

Options:

--vcf: The vcf file containing accessions to compare
--acc: Accession name in which allele will be removed in the vcf.
--remove: Name of the accession which will be used to remove allele.
--out: Prefix for output files names.

Output:

*.vcf: A vcf file containing all accession in the former vcf but in which the alleles of the accession passed in --acc option have alleles common with accession passed to --remove option removed.\ *_haplo.vcf: A parsed vcf corresponding to the *.vcf file with only line in which the accession passed to --acc option is now haploid.\ *_MoreThanHaplo.vcf: The parsed vcf corresponding to the complement of *_haplo.vcf.

vcf2cov.py

This program draw coverage from accessions found in a vcf file and output figures in png format. Several options are available to custom figures.

Options:

--vcf: The vcf file containing accessions to compare
--acc: List of accessions to draw in a 2 column file (c1 = acc name, c2 = ploidy)
--minCov: Minimal coverage to keep the data point
--maxCov: Maximal coverage to keep the data point
--psize: Dot size in graph.
--lsize: Size of the line of the mean value curve.
--win: Size of half sliding window that allow to draw mean value curve. The mean value attributed to a dot will be calculated on [dot - win_dot ; dot + win_dot]
--loc: Regions to locate by vertical line. This should be formated this way: Chromosome_name:position,chromosome_name:position, ...
--out: Prefix for file name output.

Output:

*.png: Png figures files. One file for each accessions passed in --acc option.

CaReRa.py

This program calculate, on a given window, the recombination rate observed based on segregating matrix. Options allowed to slightly change the way recombination rate is calculated.

Options:

--fasta: The multifasta file containing reference sequence on which tags were located.
--mat: Phased and corrected matrix file. 
  col 1: markers name (named "marker"), 
  col 2: marker coding ("nn,np" or "hh,hk,kk" or "hh,k-" or "ll,lm") (named "coding"),
  col 3 and 4 are necessary but not used by the program (named "ratio" and "rephased", respectively),
  col 5 to end : individual genotypes.
 First line contain header.
 Redundant names (markers and individuals) are not allowed
--win: The windows (in bases) in which the number of recombinations will be calculated.
--chr: List of chromosomes to draw separated by ",". If omitted, all chromosomes will be drawn.
--excl: (optional) List of marker couple to exclude. A couple of marker should have this structure (Name1:Name2) and each couple should be separated by ",". This should be used in case of structural variations.
--rmun: (optional) Remove regions in which recombination rate has been estimated based on markers of surrounding windows. This only remove values in tab files. Values: "y" or "n".
--prefix: Prefix for the output files.

Output:

*.tab: The statistics of recombination rate observed in the windows.\ *_high.tab: The list of regions in which recombination rate has been estimated based on markers of surrounding windows.\ *.png: The statistics plotted in a png figure. Red curve represent values attributed to central position of the window. Blue curved represented smoothed values. Red areas represent regions in which recombination rate was estimated based on surrounding windows due to absence of markers in the windows.\ *.svg: The statistics plotted in a svg figure. Red curve represent values attributed to central position of the window. Blue curved represented smoothed values. Red areas represent regions in which recombination rate was estimated based on surrounding windows due to absence of markers in the windows.

VcfAndCarto2haplo.py

This program use a vcf file and a phased genotyping matrix to generate haplotypes of the parent of individuals used to create the genotyping matrix.

Options:

--vcf: The vcf file containing individuals and sites used to create the genotyping matrix
--matrix: A matrix file containing phased markers. This file contained several mandatory columns:
  col 1: markers name (named "marker"), 
  col 2: marker coding ("nn,np" or "hh,hk,kk") (named "coding"),
  col 3 and 4 are necessary but not used by the program (named "ratio" and "rephased", respectively),
  col 5 to end : individual genotypes.
 First line contain header.
 Redundant names (markers and individuals) are not allowed
--marker: A list of additional markers file (in one column). If these markers are not found in the matrix, the program will try to phases these markers if possible (i.e. the marker is homozygous in all individuals).
--output: The name of the output file

Output:

A tabulated file containing on column 1: marker_name and on column 2: both haplotypes separated by "|"

HaploProp.py

This program calculate the proportion of alleles of an haplotype found in an accession genotyped in a vcf file.

Options:

--vcf: The vcf file containing individuals in which we want to calculate the proportion of sites having the searched haplotype.
--haplo: A tabulated file containing three columns:
  col 1: chromosome, 
  col 2: position,
  col 3 Allele.
 This file does not contain header.
--out: The name of the output file

Output:

A tabulated file containing four columns:\ col 1: individual name\ col 2: proportion of sites having an allele shared with searched haplotype (calculated by dividing col3 by col4)\ col 3: number of sites having an allele shared with searched haplotype\ col 4: number of sites with data on both haplotype and individual.

vcf2dis.py

This program calculate a simple matching dissimilarity between accessions passed in a vcf. The dissimilarity is calculated as the sum of 1-(shared alleles between two individuals) divided by the lower ploidy (of the two individuals) multiplication of the number of compared sites.

Options:

--vcf: The vcf file containing individuals in which we want to calculate the dissimilarity index.
--names: A file containing accessions we wanted to calculate dissimilarity on. Accessions should be listed in one column, additional informations can be passed in additional columns. This file should contain an header.
--prefix: Prefix for the output files.

Output:

*.dis: A tabulated file containing the square matrix containing individual dissimilarity. The first line and column contained individual recoded name.\ *.cor: A tabulated file corresponding to the file passed in --names argument with an additional first column making the correspondence between individuals and IDs in the .dis file

vcfAndConsToRatio.py

This program looks for specific alleles of a consensus haplotype found in one specific group of accessions and absent from another group of accessions. The read allelic ratio of these specific alleles was then searched and plotted along chromosome for each studied accession. This program only work on one chromosome and several chromosomes should not be passed in the consensus.

Options:

--haplo: A tabulated file containing three columns:
 col 1: chromosome, 
 col 2: position,
 col 3 Allele.
 This file does not contain header.
--With: A list of accessions with the searched haplotype. Individuals should be listed in one column.
--Without: A list of accessions without the searched haplotype. Individuals should be listed in one column.
--acc: (optional) A list of accessions in which we want to draw the allelic read ratio. Individuals should be listed in one column.
--vcf: A vcf file containing individuals passed to --With and --Without arguments
--VCF: A vcf file containing individuals passed to --acc argument. If the individuals passed to --acc argument are in the vcf passed to --vcf, repeat the name of the vcf file in --VCF argument.
--out: Prefix for the output files.

Output:

*.png: A list of png files containing consensus accessions specific group alleles proportions along the studied chromosome. At most 15 individuals were reported per png file.

vcfFormatForVcftools.py

This program reformat the vcf file in order to remove variant alleles that are not found in the individual. REF allele can be changed is the allele of reference sequence is not found in genotypes in the vcf. In this case, the REF allele become the first allele (ASCII sorted) found in individuals of the vcf. Coverage of removed allele is also removed from the AD section but not from the DP section of the individual. Monomorphous sites are also removed.

Options:

--vcf: The vcf file containing genotyped individuals.
--out: Name for the output file.
--outgzip: Output file in gzip format. Possible values: 'y' or 'n'.

Output:

*.vcf or *.vcf.gz: A vcf file in which alleles that are not found in the vcf are removed.\

DrawRatio.py

This program draw observed allele ratios generated by allele_ratio_per_acc. In addition, the normalized ratio calculated by PaintArp could be added to figure.

Options:

--chr: Path to single vcf file on vcf for all chromosomes is available.
--origin: A 5 column file containing chromosome (col1), position (Col2), allele (Col3), origin (Col4) and allele ratio (Col 5).
--NormOri: A 4+n column file containing chromosome name chromosome (CHR), median position (POS), start region (Start), end region (End) and after one column for each origin.
--Unknown: A 4 column file containing chromosome (col1), position (Col2), allele (Col3), origin (Col4). This file will be used to draw density along chromosomes.
--acc: Accession to work with.
--ploidy: Accession ploidy
--dcurve: Draw mean curve for ratio. Possible values: y or n [Default: n]
--psize: Dot size in graph. [Default: 1.5]
--lsize: Size of the line of the mean value curve. [Default: 1]
--win: Size of half sliding window that allow to draw mean value curve [Default: 10]
--loc: Regions to locate by vertical line. This should be formated this way: Chromosome_name,position:chromosome_name,position: ...
--col: A color file with 4 columns: col1=group and the three last column corresponded to RGB code.
--figsize: Figure size (in inches). [Default: 10.5,14.85]
--MinMax: Additional variations added to values equal to 1 to have a better idea of dot density. Do not exceed 1.1 in maximal value. Example: 0.9,1.1 [Default: 1,1]
--prefix: Prefix for output files. Not required

Output:

*.png: A png file containing allele ratio of studied acession plotted along chromosome of reference sequence.\

DrawRatioDetailInterractive.py

This program is an interractive version of DrawRatio.py that draw observed allele ratios generated by allele_ratio_per_acc. In addition, the normalized ratio calculated by PaintArp could be added to figure.

Options:

--chr: Path to a file containing chromosome information. Two columns are required: col1 -> chromosome, col2 -> size.
--origin: Either a 5 column file containing chromosome (col1), position (Col2), allele (Col3), origin (Col4) and allele ratio (Col 5) or the output file of allele_ratio_per_acc.py.
--NormOri: A 4+n column file containing chromosome name chromosome (CHR), median position (POS), start region (Start), end region (End) and after one column for each origin.
--acc: Accession to work with.
--ploidy: Accession ploidy
--dcurve: Draw mean curve for ratio. Possible values: y or n [Default: n]
--psize: Dot size in graph. [Default: 1.5]
--lsize: Size of the line of the mean value curve. [Default: 1]
--win: Size of half sliding window that allow to draw mean value curve [Default: 10]
--loc: Regions to locate by vertical line. This should be formated this way: Chromosome_name,position:chromosome_name,position: ...
--col: A color file with 4 columns: col1=group and the three last column corresponded to RGB code.
--figsize: Figure size (in inches). [Default: 10.5,14.85]
--MinMax: Additional variations added to values equal to 1 to have a better idea of dot density. Do not exceed 1.1 in maximal value. Example: 0.9,1.1 [Default: 1,1]
--reg: Region to draw. This could be a chromosome or a chromosome region. To specify a chromosome, just put the name. If it is a region, it should be formated as follows: name,start,end. [Default: none]

Output:

An interractive window showing allele ratio.\

GroupBasedOnDistanceToCentroids.py

This program use the output of clustering of vcf2struct to cluster dots based on a maximal distance from calculated centroids.

Options:

--centroid: The centroid coordinate file
--centcores: The centroid correspondence with group
--VarCoord: The tabulated file of variables coordinates in new axis. (The --prefix + _variables_coordinates.tab file generated when running this script with PCA type)
--MaxDist: Maximal distance between allele and centroid
--Axes: Axes to use. Axis should be separated by ":".
--groups: Groups to keep. Groups should be separated ":".
--mat: Allele file in which group will be re-attributed.
--eval: If yes this program only calculate mean distances between centroids. Possible values: "y", or "n". [Default: n]
--prefix: Prefix for output files. [Default: Clustered]

Output:

*_Clust.tab: A tabulated file in which alleles were grouped according to their maximal distance to centroids.\

IdentOtherAncestry.py

This program look for the proportion of allele present on a studied accession that are not present in set of accessions identified as ancestor. It also look for the proportion of these alleles not present defined ancestors in potential other (new) ancestors.

Options:

--vcf: The vcf file.
--name: Accession name in which alleles will be compared
--ancestor: A tabulated file that contain on column 1 the name of accessions used to look for unspecific alleles
--newancestor: A tabulated file that contain on column 1 the name of accessions in which specific alleles from the searched accession will be compared
--out: The output file. [Default: Specific.tab]

Output:

*.tab: A tabulated file counting the total number of alleles in the accession, the number of alleles not found in ancestors and their number in tested potential other (new ancestors).\

IdentPrivateAllele.py

This program run vcf2allPropAndCov in chain in order to identify specific alleles from distinct genetic groups.

Options:

--conf: Path to a file containing path to one or multiple vcf files (one per line)
--group: A two column file with accession in the first column and group tag (i.e. origin) in the second column
--outdir: Path to the output directory, where the program will put the subdirectories per accession [Default: step1]
--thread: Number of processors available. [Default: 1]
--param_v2apac_all: vcf2allPropAndCov parameter --all: allele should be present in all accessions of the group. Possible values "y", or "n". [Default: n]
--param_v2apac_introg: vcf2allPropAndCov parameter --excl: a tabulated file locating introgression in ancestral accessions
--param_v2apac_prop: vcf2allPropAndCov parameter --prop: allele proportion in ancestral accessions. Value comprised between 0 and 1 or "n" if not using this parameter. [Default: n]
--param_v2apac_NoMiss: vcf2allPropAndCov parameter --NoMiss: No missing data are allowed in accessions used to group alleles. Value "y" for not allowing missing data, "n" for allowing missing data. [Default: y]

Output:

A folder, by default step1 that contains a sub folder per accession with the outputs of vcf2allPropAndCov (Cov.png, Ratio.png, _stats.tab and a _ratio.tab.gz file corresponding to *_AlleleOriginAndRatio.tab).\

PaintArp.py

This program use allele specific ratio calculated in one accession to characterize the ancestry along chromosomes of studied accession. It also calculate normalized value for each group along chromosomes. The normalization is calculated as follows: In a slidding windows and for each group, the total of observed ratios is divided by the total of expected ratio (expected ratios being calculated on observed ratio in accession(s) representatives of a group).

Options:

--ratio: The ratio file. Tabulated file with 6 columns with headers. Col1: chr, col2: pos, col3: allele, col4: obs_ratio, col5: exp_ratio, col6: grp
--color: Color file name. Tabulated file with 5 columns with header. Col1: group, col2: name, col3: r, col4: g, col5: b
--ploidy: Ploidy of studied accession
--win: Half window size of markers
--overlap: Overlap between two windows (number of SNP positions). If not filled, the overlap between two windows of size n will be of n-1.
--noise: Maximal mean read proportion threshold in which absence of haplotype probability is put to 1
--threshold: Minimal mean read proportion threshold relative to the expected proportion in which haplotype probability is put to 1
--size: A file containing chromosome size. 2 columns are required: col1 : chromosome name, col2 : chromosome size
--MinAll: Minimal allele number of an origin to keep this region for prediction for the origin
--acc: Accession name
--out: Prefix for output files

Output:

Output are prefixed by the information provied to --out argument.\ *.tab: Several tabulated files, one for each pseudo haplotypes of each chromosomes, containing identified segments ancestry along chromosomes.\ *_win_ratio.tab.gz: A gzipped tabulated file containing, on slidding windows, the normalized values for each genetic group.

PhaseInVcf.py

This program phases the genotypic information contained in a vcf file. Phasing is performed on individuals forming a parents-child trio (a child and its parents). With this approach, only sites that are not heterozygous in all individuals can be phased, as well as those that are not consistent with the trio. Phasing is therefore performed appart from recombination events. The output is individuals haplotypes into a vcf.

Options:

--vcf: The vcf file.
--names: A 3 column file containing in this order F1 P1 P2 (F1=child, P1=parent1, P2=parent2).
--prefix: The prefix for output files. [Default: WorkOnVcf]

Output:

*_Phase.vcf.gz: A gzipped vcf file containing phased individuals. Phased individuals are provided at the end of the vcf columns as a new individual per haplotypes and are named according to the trio (provided to --names argument) following the rule:

PhaseInVcfToFasta.2.0.py

This program phases the genotypic information contained in a vcf file. Phasing is performed on individuals forming a parents-child trio (a child and its parents). With this approach, only sites that are not heterozygous in all individuals can be phased, as well as those that are not consistent with the trio. Phasing is therefore performed appart from recombination events. The output is a multifasta containing cumulated sites as sequence for each individuals. Additional individuals for which no trio are available can be passed, a IUPAC code is applied for heterozygous sites in these accessions. A IUPAC code is also applied for sites that could not be phased in parents-child trios.

REMARK: This program will also try to run PHYML using SLURM scheduler. This will probably return an error, but fasta file(s) should be generated anyway.

Options:

--vcf: The vcf file.
--names: A 3 column file containing in this order F1 P1 P2.
--prefix: The prefix for output files. [Default: WorkOnVcf]
--region: Region to work with. Region should be specified as follows : "chrX:start:end"
--outgp: Accessions for which parent child trio is not available but genotype should be obtained. Accessions should be passed in a one column file.
--win: If a value is passed, a multifasta will be generated for SNPs comprised in window size passed to argument. Window size for each sub fasta [Default: False]. 
--MinLength: Minimal length of the alignment (in bp) to keep the alignment for PHYML.
--maxIUP: Minimal proportion of IUPAC letters in a sequence to keep the sequence in the alignment. Values comprised between 0 and 1. [Default: 1]

Output:

*_Phase.fasta: A multifasta file (corresponding to a succession of sites from the vcf) containing phased sequences from parents-child trios (only the parents haplotypes, that are identical to child, of the trios are reported) and consensus of additional individuals. In cases where the argument --win is filled several files named *_PhaseXXX_XXX.fasta are generated (XXX_XXX corresponding to the region along reference sequence).\ *_Phase.phy: A phylip file (corresponding to a succession of sites from the vcf) containing phased sequences from parents-child trios (only the parents haplotypes, that are identical to child, of the trios are reported) and consensus of additional individuals. In cases where the argument --win is filled several files named *_PhaseXXX_XXX.phy are generated (XXX_XXX corresponding to the region along reference sequence).

ReformatTree.py

This program reformat tree output from PHYML to generate a tree file with color that could be loaded to FigTree (tested on FigTree_v1.4.4).

Options:

--tree: The tree file obtained from PhyMl.
--fasta: The corresponding fasta file.
--color: A color file with column 1 = accession name, column 2 = color in hexadecimal.
--layout: The layout of the tree. Possible values: "RECTILINEAR", "RADIAL" or "POLAR" [Default: RECTILINEAR]
--fsize: Labels font size. [Default: 12]

Output:

(--tree)-(--layout).tree: A tree file that could be read and drawn with FigTree (https://github.com/rambaut/figtree).

TotalRecal.1.0.py

This program recalculate genotype calling in specified regions of specified accessions according to given ploidy.

Options:

--vcf: The VCF file
--table: A table file with column1: accession name, column2: chromosome name; column3: start region, column4: end region, column 5: ploidy
--MinCov: Minimal read coverage for site. [Default: 10]
--MaxCov: Maximal read coverage for site. [Default: 1000]
--minFreq: Minimal allele frequency in an accession to keep the allele for calling in the row. [Default: 0.05]
--MinAlCov: Minimal read number of minor allele to call variant heterozygous (between 1 and infinity). [Default: 3]
--dial:  only a diallelic calling. i.e Only two allele are possible in a genotype if "y" is passed to this argument. Possible values "y" or "n". [Default: y]
--out: Prefix for output files. [Default: Pop]
--outgzip: Output files in gzip format. [Default: n]

Output:

(--out): A vcf file in which the genotype calling was recalculated accoring to new ploidy in specified accession regions.

allele_ratio_group.py

This program use the outputs of IdentPrivateAllele to calculate, for each allele attributed too a genetic group, the mean proportion of reads supporting this allele in accessions from this genetic group. The obtained value can be seen as the allele fixation level in the genetic group.

Options:

--group-file: A two column file with accession in the first column and group tag (i.e. origin) in the second column
--stat-file-pattern: pattern of the statistics file from vcf2allPropAndCov
--input-dir: input directory. [Default: ./step1]
--excl: A file containing region to exclude for ancestry attribution in some introgressed accessions.
--output-dir: output directory. [Default: ./step2]

Output:

A folder, by default step2 that contains several _ratio.tab.gz files (one per genetic group).

allele_ratio_per_acc.py

This program use the output of allele_ratio_group and a vcf to calculate, in one accession, the allelic ratio of alleles attributed to genetic groups.

Options:

--conf-file: conf file, list of vcf path, one per line
--group-file: group file, with a pair accession - group per line
--input-dir: input directory. [Default: ./step2]
--output-dir: output directory. [Default: ./step3]
--accession: accession to scan from vcf
--depth: Minimal depth to consider a site. value comprised between 1 to infinite. If only considering sites with genotype, omit this argument.

Output:

A folder, by default step3 that contains a *_ratio.tab.gz file continaing counted allele ratio according to genetic group. If the folder already exists, it will not be erased.

convertForIdeo.py

This program convert outputs from PaintArp or vcf2linear to inputs for GEMO (https://github.com/SouthGreenPlatform/GeMo).

Options:

--name: The name of the accession. It corresponds to a pattern that will be searched to look for files.
--dir: The directory that contained the accession.
--col: A color file that will be used to paint accessions
--size: A file containing chromosome size. 2 columns are required: col1 : chromosome name, col2 : chromosome size
--plo: The ploidy of studied individual. [Default: 2]
--chro: List of chromosomes to draw separated by ",". If omitted, all chromosomes will be drawn. [Default: all]
--prefix: Prefix for the output files

Output:

*_ideo.tab: A file that contained block determined with GEMO's algorithm with default parameters (parametric approach).\ *_ideoProb.tab: A file that contained blocks determined by PaintArp (probalistic approach).\ *_chrom.tab: A file that contained information required to draw chromosomes.\ *_color.tab: A file that contained color information that could be used to draw blocks with custom color.

plot_allele_normalized_mean_ratio_per_acc.py

This program draw normalized origin ratio along chromosomes obtained after PaintArp.

Options:

--color: A color file, five columns: code, complete name, red, green, blue.
--chr: A chromosome file, two columns: chromosome, size.
--acc: An accession file, one columns: accession name.
--ratio: The ratio files containing on the four first column: chromosome (CHR), median position (POS), start region (Start), end region (End) and after one column for each origin.
--loc: Regions to locate by vertical line. This should be formated this way: Chromosome_name:position,chromosome_name:position, ...
--prefix: Prefix for output files. [Default: Out]
graph: graphic output. possible options: pdf, png, svg [Default: png]

Output:

*.png: A png file drawing normalized ratio origins for an acession along chromosome of reference sequence.

ACRO.py

This program calculated the number and proportion of sites in accordance with parentage between two potential parents and a child or one potential parent and a child. This program does not work on phased vcf file.

Options:

--parent1: Potential parent 1 and ploidy of the generated gamete. Parent and ploidy should be separated by ","
--parent2: Potential parent 2 and ploidy of the generated gamete. Parent and ploidy should be separated by ","
--vcf: Path to the vcf file
--acc: Accession name to calculate parentage. Only one accession allowed
--window: Half window to calculate site proportion in accordance with tested parentage (unit = variant site)
--WINDOW: The window to calculate site proportion in accordance with tested parentage (unit base pair)
--fasta: The multifasta reference file
--chr: Chromosome to work with. They should be separated by ":". If omitted, all chromosomes will be used
--prefix: Prefix for output files

Output:

*_OK.tab.gz: A tabulated file containing the list of sites ("Chr", "Pos", "1") in accordance with tested parentage.\ *_noOK.tab.gz: A tabulated file containing the list of sites ("Chr", "Pos", "1") not in accordance with tested parentage.\ *_OK_prop_NUCwin.tab: A tabulated file containing the proportion of sites in accordance with tested parentage in slidding windows of size passed to --WINDOW.\ *_NoOK_prop_NUCwin.tab: A tabulated file containing the proportion of sites not in accordance with tested parentage in slidding windows of size passed to --WINDOW.\ *_OK_prop_SNPwin.tab: A tabulated file containing the proportion of sites in accordance with tested parentage in slidding windows of size 2*(value passed to --window)+1.\ *_NoOK_prop_SNPwin.tab: A tabulated file containing the proportion of sites not in accordance with tested parentage in slidding windows of size 2*(value passed to --window)+1.

APAR.py

This programme goes through all the steps of parent-child trio analysis. It includes a chromosomal analysis of SNPs validating the trio and the duos (child, parent), a search for the complete genome of the parent in the child if appropriate (ploidy of the parent gamete is equal to the ploidy of the parent), an attempt to remove the complete genome of the parent from the child if appropriate (ploidy of the parent gamete is equal to the ploidy of the parent) and a file preparation for circos visualisation.

Required arguments:

--parent1: Potential parent 1 its ploidy and ploidy of the generated gamete. Format should be as follows: ParentName,ParentPloidy,GametePloidy
--parent2: Potential parent 2 its ploidy and ploidy of the generated gamete. Format should be as follows: ParentName,ParentPloidy,GametePloidy
--child: Potential child its ploidy. Format should be as follows: ParentName,ParentPloidy. A folder with the acession name will be generated.
--crossname: Name of the cross. All generated files will be prefixed with this name.
--vcf: Path to the vcf file containing parents and child studied.
--fasta: The multifasta reference file.
--painting: A folder containing chromosome painting for all accessions studied from vcfhunter tools.
--color: A file containing color code for painting.

Optional arguments:

--chr: Chromosome to work with. They should be separated by ":". If omitted, all chromosomes will be used.
--window: Half window to calculate site proportion in accordance with tested parentage (unit = variant site). [Default: 100]
--WINDOW: The window to calculate site proportion in accordance with tested parentage (unit base pair). [Default: 100000]

Output:

DrawCircos.py

This program takes as input tile and curve files as well as karyotype file and generate configuration files required to draw circos representation.

Options:

--Files: Files used in the circos, the type of layer their order from outer to inner they should appear in the circos. Argument should be formated as follows: File1,type,order:File2,type,order:... Where possible types are "tile" or "line" and order is an interger 1, 2, 3, ... Two files can have the same order, they will thus be drawn over each other.
--outfolder: The output folder that will contain circos files and pictures.
--prefix: Prefix for output files generated: "_housekeeping.conf", ".conf", ".png" and ".svg" files will be generated.
--karyotype: Path to karyotype file
--color: A file containing color code for painting.

Output:

*.conf: Two configuration files required for circos to work.

DrawStackedDensity.py

This program draw several statistics along chromosomes. Statistics are stacked.

Required arguments:

--files: List of density files separated by ",". These files should be formated as follows: ID1:File1,ID2:File2,...
--FillUnder: Fill the curve under, (y or n)
--chrToRm: List of chromosome to exclude from drawing separated with ","
--color: A color file in tabulated format (in RGB). Col1: RefxName, Col2: Red, Col3: Green, Col4: Blue
--Ylim: Set Y limit for all plots. Recommended to compare genomes with the same graphic scale --> if omitted each graph may have different Yscale (adjusted from each chromosome)
--draw: Drawing output. Possible options: One figure per genome (g), one figure per chromosomes (i), scaled image (s) - only with (g), in this case chromosomes are drawn scaled. Options can be combined
--negative: Draw also negative curve: y or n
--graph: Graphic output format. possible options: pdf, png, svg
--prefix: Prefix for the output file(s)

Output:

A figure named according to --prefix and suffixed with --graph.

FormatHaplo.py

This program takes the mosaic painting from PaintArp and convert them to tile files for circos representation.

Required arguments:

--indiv: Individual for which the haplotypes should be reformated for Circos drawing ans its ploidy. Format should be as follows: Name,Ploidy.
--vcf: Path to the vcf file containing individual studied.
--painting: A folder containing chromosome painting for all accessions studied from vcfhunter tools.
--color:  A file containing color code for painting.

Optional arguments:

--chr: Chromosome to work with. They should be separated by ":". If omitted, all chromosomes will be used.
--outfolder: Folder in which output should be written.

Output:

circos.*.haplo*.tab: Several tabulated files (as much as the ploidy of the individuals) containing chromosome painting information for circos drawing.

SPRH.py

This program remove the complete genome of a parent from a child, paint the remaining haplotype/genotype and prepare files for circos visualization.

Required arguments:

--parent1: Potential parent 1 its ploidy and ploidy of the generated gamete. Format should be as follows: ParentName,ParentPloidy,GametePloidy
--child: Potential child its ploidy. Format should be as follows: ParentName,ParentPloidy. A folder with the acession name will be generated.
--outfolder: Outfolder in which vcf and painting and circos will be put.
--vcf: Path to the vcf file containing parents and child studied.
--groupFile: Group file, with a pair accession - group per line.
--inputDir: Input directory containing grouped alleles and their expected ratio.
--color: Color file name. Tabulated file with 5 columns with header. Col1: group, col2: name, col3: r, col4: g, col5: b
--size: A file containing chromosome size. 2 columns are required: col1 : chromosome name, col2 : chromosome size.
--win: --win option of PaintArp.py: Half window size of markers to use for origin region attribution
--noise: --noise option of PaintArp.py: Maximal mean read proportion threshold in which absence of haplotype probability is put to 1.
--threshold: --threshold option of PaintArp.py: Minimal mean read proportion threshold relative to the expected proportion in which haplotype probability is put to 1.
--chro: Chromosome to work with. They should be separated by ":".
--centro: File locating (peri)centromeric positions. It contained 3 columns: col1 -> chromosome, col2 -> start, col3 -> end

Output:

ValPar.py

This program look for direct parentage between genotyped individuals.

Options:

--parent: Path to one column file containing parent names
--vcf: Path to the vcf file
--acc: Accession name to calculate parentage. Only one accession allowed
--output: Output file name

Output:

A tabulated file named according to --output. First line contained name of tested parents and ploidy and second line contained the proportion of sites validating the trios.

vcfSelect.py

This program look for direct parentage between genotyped individuals.

Options:

--vcf: Vcf file.
--sites: Sites to keep in a 2 column file (c1 = chromosome, c2 = position).
--out=OUT     Output file name. Output will be gzipped.

Output:

A gzipped vcf file named according to --out.

calcul_pileup_count.py

This program count from a pileup like file (columns 1 (chr), 2 (pos) and 4 (value) of pileup), the coverage sum in a sliding window.

Options:

--pileup: The pileup column selected file (tabulated)
--window: The window to calculate sum
--fasta: The multifasta reference file
--FillUnder: Fill the curve under, (y or n)
--Ylim: Set Y limit. Can be omitted
--chr: Chromosome to work with. They should be separated by ":". If omitted, all chromosomes will be used
--title: Add title to figure: y or n
--negative: Draw also negative curve: y or n
--out: Prefix for the output files
--outtype: Output file type: png or svg
--draw: Draw a figure. Possible values "y" or "n".

Output:

calcul_pileup_mean.py

This program count from a pileup like file (columns 1 (chr), 2 (pos) and 4 (value) of pileup), the average of values reported in a sliding window.

Options:

--pileup: The pileup column selected file (tabulated)
--window: The window to calculate mean value
--fasta: The multifasta reference file
--FillUnder: Fill the curve under, (y or n)
--Ylim: Set Y limit. Can be omitted
--chr: Chromosome to work with. They should be separated by ":". If omitted, all chromosomes will be used
--title: Add title to figure: y or n
--negative: Draw also negative curve: y or n
--out: Prefix for the output files
--outtype: Output file type: png or svg
--draw: Draw a figure. Possible values "y" or "n".

Output:

PaintAssembly.sh

This program perform ancestry chromosome painting of genome assembly according to tags specific of ancestral origin. A tag file in the form of a fastq file should be provided for each origin. All fastq files should be placed in the folder passed to --read-folder argument.

Options:

--reference: Path to the reference fasta file
--directory: A directory that will contains outputs
--read-folder: A folder containing origin reads. A file per origin named ORIGIN.fastq.gz
--window: Windows in which the number of mean number of reads hits will be counted
--include-pattern: Pattern(s) to select chromosomes into the figure. Each patterns should be separated by '|'
--exclude-pattern: Pattern(s) to exclude some chromosomes. Each patterns should be separated by '|'
--color-file: A color file with 4 columns: col 1 = group name and col 2 to 4 = RGB color code
--prefix: Prefix for the output figure and intermediate output files
--steps: Steps of the analysis to perform:
    1: Working folder creation, copy of the reference and indexation
    2: Origin read libraries mapping
    3: Mapping read filtration
    4: Calculating the number of hits on non overlapping sliding windows
    5: Selecting chromosomes on pattern and drawing curve figures
    6: Attributing origin based on majority rule
    7: Data formating for GEMO analysis

Output:

Outpouts depends on steps performed:



ParseReadsOnHaplo.py

This program use a bi-allelic haplotype file to sort aligned reads according to haplotype information. This program does not work with INDELS.

Options:

--haplo: The haplotype file. File should be tabulated with two columns with header "Marker" and "FinalPhase". In the "Marker" column, chromosome and position should be formated as follows "Sequence name"+M+"position" (e.g. chr01M20620). In the "FinalPhase" column, haplotypes are separated by "|" (e.g. C|A).
--conf: Configuration file locating bam files to work with (one line per path to bam files). 
--prefix: Prefix for output files

Output: