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Configuration cheat sheet

This contains the same information largely as the configuration docs, but in an interactive config.yaml file. This can be helpful if you have your config in front of you and want to check what a setting does. Just click the + next to each line to get info on that setting.

config/config.yaml
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#=====================Dataset Configuration============================#

samples: config/samples.tsv # (1)

units: config/units.tsv # (2)

dataset: "dataset-name" # (3)

#===================== Reference Configuration ========================#

chunk_size: 50000000 # (4)

reference:
  name: "dataset-ref" # (5)
  fasta: "resources/ref/reference-genome.fa" # (6)
  mito: ["mitochondrion"] # (7)
  sex-linked: ["Z"] # (8)
  exclude: [] # (9)
  min_size: 1000000 # (10)

ancestral: # (11)

#===================== Sample Set Configuration =======================#

exclude_ind: [] # (12)

excl_pca-admix: [] # (13)

#====================== Analysis Selection ============================#

populations: [] # (14)

analyses:
  # mapping options
  mapping:
    historical_only_collapsed: true # (15)
    historical_collapsed_aligner: "aln" # (16)
  # sites file filters
  pileup-mappability: # (17)
  repeatmasker: # (18)
    bed: # (19)
    local_lib: # (20)
    build_lib: true # (21)
  extreme_depth: true # (22)
  dataset_missing_data: # (23)
  population_missing_data: # (24)
  # quality control
  qualimap: true # (25)
  ibs_ref_bias: true # (26)
  damageprofiler: true # (27)
  mapdamage_rescale: # (28)
  # population genomic analyses
  estimate_ld: # (29)
  ld_decay: true # (30)
  pca_pcangsd: true # (31)
  admix_ngsadmix: true # (32)
  relatedness: # (33)
    ibsrelate_ibs: # (34)
    ibsrelate_sfs: # (35)
    ngsrelate_ibsrelate-only: true # (36)
    ngsrelate_freqbased: # (37)
  1dsfs: # (38)
  1dsfs_boot: # (39)
  2dsfs: # (40)
  2dsfs_boot: # (41)
  thetas_angsd: true # (42)
  heterozygosity_angsd: true # (43)
  fst_angsd: # (44)
    populations: true # (45)
    individuals: # (46)
  inbreeding_ngsf-hmm: true # (47)
  ibs_matrix: # (48)
  pop_allele_freqs: true # (49)

#==================== Downsampling Configuration ======================#

subsample_dp: [4] # (50)

subsample_by: "sitefilt" # (51) three options: unfilt, mapqbaseq, sitefilt; sitefilt recommended (subsamples bams to `subsample_dp` within filtered regions)
redo_depth_filts: false # (52) has no effect when `subsample_by` == "sitefilt"
drop_samples: [] # (53) These samples won't be included in the downsampled dataset

subsample_analyses: # (54)
  estimate_ld:
  ld_decay:
  pca_pcangsd:
  admix_ngsadmix:
  relatedness:
    ibsrelate_ibs:
    ibsrelate_sfs:
    ngsrelate_ibsrelate-only:
    ngsrelate_freqbased:
  1dsfs:
  1dsfs_boot:
  2dsfs:
  2dsfs_boot:
  thetas_angsd: true
  heterozygosity_angsd: true
  fst_angsd:
    populations: true
    individuals:
  inbreeding_ngsf-hmm: true
  ibs_matrix:
  pop_allele_freqs: true

#=========================== Filter Sets ==============================#

filter_beds: #(55)
  example:

only_filter_beds: false # (56)

#===================== Software Configuration =========================#

mapQ: 30 # (57)
baseQ: 20 # (58)

params:
  clipoverlap:
    clip_user_provided_bams: false # (59)
  fastp:
    extra: "-p -g" # (60) don't put --merge or --overlap_len_require here, they're implicit
    min_overlap_hist: 30 # (61)
  bwa_aln:
    extra: "-l 16500 -n 0.01 -o 2" # (62)
  picard:
    MarkDuplicates: "--REMOVE_DUPLICATES true --OPTICAL_DUPLICATE_PIXEL_DISTANCE 2500" #(63)
  damageprofiler:
    profile_modern: false # (64)
  genmap: # (65)
    K: "30" # (66)
    E: "2" # (67)
    map_thresh: 1 # (68)
  extreme_depth_filt: # (69)
    method: "percentile" # (70)
    bounds: [ 0.025, 0.975 ] # (71)
    filt-on-dataset: true # (72)
    filt-on-depth-classes: true # (73)
  samtools:
    subsampling_seed: "$RANDOM" # (74)
  angsd: # (75)
    gl_model: 2 # (76) gl_model - 1=SAMtools, 2=GATK, 3=SOAPsnp, 4=SYK
    maxdepth: 100 # (77) for depth calc only
    mindepthind: 1 # (78)
    minind_dataset: 0.75 # (79) as a proportion of N
    minind_pop: 0.75 # (80) as a proportion of N
    rmtrans: true # (81)
    extra: "" # (82) goes to all ANGSD runs
    extra_saf: "" # (83) goes to all -doSaf runs
    extra_beagle: "" # (84) goes to all -doGlf 2 runs
    domajorminor: 1 # (85) Currently only compatible with values 1, 2, 4, 5
    domaf: 1 # (87) Currently only compatible with values 1 & 2
    snp_pval: "1e-6" # (86)
    min_maf: 0.05 # (88) Set -1 to disable
    mindepthind_heterozygosity: 1 # (89)
  ngsld: # (90)
    max_kb_dist_est-ld: 4000 # (91)
    rnd_sample_est-ld: 0.001 # (92)
    max_kb_dist_decay: 100 # (93)
    rnd_sample_decay: 0.01 # (94)
    fit_LDdecay_extra: "--fit_level 1 --plot_size 3,6 --plot_y_lim 1" # (95)
    fit_LDdecay_n_correction: true # (96)
    max_kb_dist_pruning_dataset: 25 # (97) used for PCA, Admix, not ngsF-HMM
    pruning_min-weight_dataset: 0.2 # (98) used for PCA, Admix, not ngsF-HMM
  ngsrelate: # (99)
    ibsrelate-only-extra: "" #(100)
    freqbased-extra: "" # (101)
  ngsf-hmm: # (102)
    estimate_in_pops: true # (103)
    prune: true # (104)
    max_kb_dist_pruning_pop: 25 # (105) used only for ngsf-hmm
    pruning_min-weight_pop: 0.4 # (106) used only for ngsf-hmm
    min_roh_length: 50000 # (107)
    roh_bins: [ 100000, 250000, 500000, 1000000, 2500000, 5000000 ] # (108) in ascending order, bins are between adjacent values and between min_roh_length and value 1 and the last value and infinity
  realsfs: # (109)
    fold: 1 # (110) Should only be set to 1, unless ancestral reference is given above
    sfsboot: 50 # (111)
  fst: # (112)
    whichFst: 1 # (113)
    win_size: 25000 # (114)
    win_step: 10000 # (115)
  thetas: # (116)
    win_size: 25000 # (117)
    win_step: 10000 # (118)
    minsites: 1000 # (119)
  ngsadmix: # (120)
    kvalues: [1,2,3,4,5] # (121)
    reps: 100 # (122)
    minreps: 20 # (123)
    thresh: 2 # (124)
    conv: 3 # (125)
    extra: "-maxiter 4000" # (126)
  ibs: # (127)
    doibs: 1 # (128)
  1. An absolute or relative path from the working directory to the samples.tsv file.
  2. An absolute or relative path from the working directory to the units.tsv file.
  3. A name for this dataset run - an identifier for a batch of samples to be analysed together with the same configuration.
  4. A size in bp (integer). Your reference will be analyzed in 'chunks' of contigs of this size to parallelize processing. This size should be larger than the largest contig in your genome. A larger number means fewer jobs that run longer. A smaller number means more jobs that run shorter. The best fit will depend on the reference and the compute resources you have available. Leaving this blank will not divide the reference up into chunks.
  5. A name for your reference genome, will go in the file names and be used to organize the reference filter outputs.
  6. A path to the reference fasta file (currently only supports uncompressed fasta files).
  7. Mitochondrial contig name(s), will be removed from analysis. Should be listed as a list of strings within brackets [].
  8. Sex-linked contig name(s), will be removed from analysis. Should be listed as a list of strings within brackets [].
  9. Additional contig name(s) to exclude from analysis. Should be listed as a list of strings within brackets [].
  10. A size in bp (integer). All contigs below this size will be excluded from analysis.
  11. A path to a fasta file containing the ancestral states in your reference genome. This is optional, and is used to polarize allele frequencies in SAF files to ancestral/derived. If you leave this empty, the reference genome itself will be used as ancestral, and you should be sure the [params] [realSFS] [fold] is set to 1. If you put a fasta here, you can set that to 0.
  12. Sample name(s) that will be excluded from the workflow. Should be a list in []. As an alternative, putting a # in front of the sample in the sample list also will remove it. Mainly used to drop samples with poor quality after initial processing.
  13. Sample name(s) that will be excluded only from PCA and Admixture analyses. Useful for close relatives that violate the assumptions of these analyses, but that you want in others. Should be a list in []. If you want relatives out of all downstream analyses, not just PCA/Admix, put them in exclude_ind instead. Note this will trigger a re-run for relatedness analyses, but you can just disable them now as they've already been run.
  14. A list of populations found in your sample list to limit population analyses to. Might be useful if you want to perform individual analyses on some samples, but not include them in any population level analyses. Leave blank ([]) if you want population level analyses on all the populations defined in your samples.tsv file.
  15. Historical samples are expected to have fragmented DNA. For this reason, overlapping (i.e. shorter, usually <270bp) read pairs are collapsed in this workflow for historical samples. Setting this option to true will only map only these collapsed reads, and is recommended to target primarily endogenous content. However, in the event you want to map both the collapsed and uncollapsed reads, you can set this to false. (true/false)
  16. Aligner used to map collapsed historical sample reads. aln is the recommended for this, but this is here in case you would like to select mem instead. Uncollapsed historical reads will be mapped with mem if historical_only_collapsed is set to false, regardless of what is put here. (aln/mem)
  17. Filter out sites with low 'pileup mappability', which describes how uniquely fragments of a given size can map to the reference (true/false)
  18. (NOTE: Only one of the three options should be filled/true)
  19. Supply a path to a bed file that contains regions with repeats. This is for those who want to filter out repetitive content, but don't need to run Repeatmodeler or masker in the workflow because it has already been done for the genome you're using. Be sure the contig names in the bed file match those in the reference supplied. GFF or other filetypes that work with bedtools subtract may also work, but haven't been tested.
  20. Filter repeats by supplying a repeat library you have locally (such as ones downloaded for Darwin Tree of Life genomes) and using it to identify repeats with RepeatMasker. Should be file path, not a URL.
  21. Use RepeatModeler to build a library of repeats from the reference itself, then identify them with RepeatMasker and filter them out (true/false).
  22. Filter out sites with extremely high or low global sequencing depth. Set the parameters for this filtering in the params section of the yaml. (true/false)
  23. A floating point value between 0 and 1. Sites with data for fewer than this proportion of individuals across the whole dataset will be filtered out in all analyses using the filtered sites file. (This is only needed if you need to ensure all your analyses are using exactly the same sites, which I find may result in coverage biases in results, especially heterozygosity. Unless you explicitly need to ensure all groups and analyses use the same sites, I would leave this blank, instead using the [params][angsd][minind_dataset] to set a minimum individual threshold for dataset level analyses, allowing analyses to maximize sites per group/sample. This is how most papers do it.)
  24. A floating point value between 0 and 1. Sites with data for fewer than this proportion of individuals in any population will be filtered out in all populations using the filtered sites file. (This is only needed if you need to ensure all your populations are using exactly the same sites, which I find may result in coverage biases in results, especially heterozygosity. Unless you explicitly need to ensure all groups and analyses use the same sites, I would leave this blank, instead using the [params][angsd][minind_pop] to set a minimum individual threshold for each analyses, allowing analyses to maximize sites per group/sample. This is how most papers do it.)
  25. Perform Qualimap bamqc on bam files for general quality stats (true/false)
  26. Enable reference bias calculation. For each sample, one read is randomly sampled at each position and compared to the reference base. These are summarized as the proportion of the genome that is identical by state to the reference for each sample to quantify reference bias. This is done for all filter sets as well as for all sites without site filtering. If transition removal or other arguments are passed to ANGSD, they are included here. (true/false)
  27. Estimate post-mortem DNA damage on historical samples with Damageprofiler (true/false)
  28. Rescale base quality scores using MapDamage2 to help account for post-mortem damage in analyses (if you only want to assess damage, use damageprofiler instead, they return the same results) (true/false) [docs]
  29. Estimate pairwise linkage disquilibrium between sites with ngsLD for each popualation and the whole dataset. Note, only set this if you want to generate the LD estimates for use in downstream analyses outside this workflow. Other analyses within this workflow that require LD estimates (LD decay/pruning) will function properly regardless of the setting here. (true/false) [docs]
  30. Use ngsLD to plot LD decay curves for each population and for the dataset as a whole (true/false) [docs]
  31. Perform Principal Component Analysis with PCAngsd. Currently requires at least 4 samples to finish, as it will by default try to plot PCs1-4. (true/false) [docs]
  32. Perform admixture analysis with NGSadmix (true/false) [docs]
  33. Relatedness is estimated using two methods: IBSrelate and NgsRelate v2. IBSrelate does not require allele frequencies, which is useful if you do not have sufficient sample size to confidently estimate allele frequencies for your populations. In this pipeline, it is can be run three ways: using the (1) IBS and (2) SFS based methods described in Waples et al. 2019, Mol. Ecol. using ANGSD or (3) using the SFS based method's implementation in NgsRelate v2 (which still does not require allele frequencies). NgsRelate v2 also offers an allele frequency based method, which enables co-inference of inbreeding and relatedness coefficients. If using this method, PopGLen will calculate the allele frequencies for your populations and input them into NgsRelate. These different methods have trade-offs in memory usage and run time in addition to the methodological effects on the estimates themselves. Generally, I recommend starting with NgsRelate, using IBSrelate only (ngsrelate_ibsrelate-only), adding the other approaches as you need them.
  34. Estimate pairwise relatedness with the IBS based method from. This can use a lot of memory, as it has genotype likelihoods for all sites from all samples loaded into memory, so it is done per 'chunk', which still takes a lot of time and memory. NOTE: For those removing transitions, this method does not include transition removal. All other relatedness methods here do. (true/false) [docs]
  35. Estimate pairwise relatedness with the SFS based method. Enabling this can greatly increase the time needed to build the workflow DAG if you have many samples. As a form of this method is implemented in NGSrelate, it may be more efficient to only enable that. (true/false) [docs]
  36. Performs the IBSrelate SFS method, but on SNPs only using NgsRelate. Does not need to estimate allele frequencies. (true/false) [docs]
  37. Performs the allele frequency based co-inference of relatedness and inbreeding that NgsRelate is primarly intended for. Will estimate allele frequencies per population and use them in the analysis. Also runs the IBSrelate SFS method in NgsRelate. (true/false) [docs]
  38. Generates a one dimensional site frequency spectrum for all populations in the sample list. Automatically enabled if thetas_angsd is enabled. (true/false) [docs]
  39. Generates N bootstrap replicates of the 1D site frequency spectrum for each population. N is determined from the sfsboot setting below (true/false) [docs]
  40. Generates a two dimensional site frequency spectrum for all unique populations pairings in the sample list. Automatically enabled if fst_angsd is enabled. (true/false) [docs]
  41. Generates N bootstrap replicates of the 2D site frequency spectrum for each population pair. N is determined from the sfsboot setting below (true/false) [docs]
  42. Estimate nucleotide diversity (pi), Watterson's theta, and Tajima's D for each population in windows across the genome using ANGSD. If polarized to an ancestral reference, additional measures of theta and neutrality statistics in the output will be relevant (see 'Unknown ancestral state (folded sfs)' in the ANGSD docs for more details on this) (true/false) [docs]
  43. Estimate individual genome-wide heterozygosity using ANGSD. Calculates confidence intervals from bootstraps. (true/false) [docs]
  44. Estimate pairwise FST using ANGSD. Set one or both of the below options. Estimates both globally and in windows across the genome.
  45. Pairwise FST is calculated between all possible population pairs (true/false) [docs]
  46. Pairwise FST is calculated between all possible individual pairs. NOTE: This can be really intensive on the DAG building process, so I don't recommend enabling unless you're certain you want this (true/false) [docs]
  47. Estimates inbreeding coefficients and runs of homozygosity using ngsF-HMM. Inbreeding coefficients are output in both the model based form from ngsF-HMM, as well as converted into FRoH, which describes the proportion of the genome in runs of homozygosity over a certain length. (true/false) [docs]
  48. Estimate pairwise identity by state distance between all samples using ANGSD. (true/false) [docs]
  49. Estimates population-specific minor allele frequencies for each population in the dataset using ANGSD. Two outputs are generated per population: (1) population-specific minor allele frequencies, where only sites variable in the population are included and the minor allele is set to the minor of the population, and (2) dataset-wide minor allele frequencies, where the minor allele is set to the minor of the entire dataset and includes sites that are fixed within the population if they are variable in the dataset. Alternatively, major alleles can be polarized to the reference or ancestral state in both outputs if domajorminor is set to 4 or 5. [docs]
  50. A list of mean depths to subsample your reads to. This will be done per sample, and subsample from all the reads. Leaving the list empty disables subsampling. The list can contain multiple depths to subsample to. If a sample already has the same, or lower, depth than this number, it will just be used as is in the analysis. (List [] of integers)
  51. This determines how the 'full' sequencing depth of a sample is calculated to determine the amount of subsampling needed to reach the target depth. This should be one of three options: (1) "unfilt" will treat the sequencing depth of all (unfiltered) reads and sites as the 'full' depth; (2) "mapqbaseq" will filter out reads that don't pass the configured mapping or base quality, then calculate depth across all sites as the 'full' depth, (3) "sitefilt" will filter reads just as "mapqbaseq" does, but will only calculate the 'full' depth from sites passing the sites filter. As the main goal of subsampling is to make depth uniform for analyses, this latter option is preferred, as it will most accurately bring the depth of the samples to the target depth for analyses. ("unfilt"/"mapqbaseq"/"sitefilt")
  52. If subsample_by is set to "unfilt" or "mapqbaseq", then it would be possible to recaculate extreme depth filters for the subsampled dataset. Enable this to do so, otherwise, the depth filters from the full depth bams will be used. If subsample_by is set to "sitefilt" this will have no effect, as the subsampling is already in reference to a set site list. (true/false)
  53. When performing depth subsampling, you may want to leave some samples out that you kept in your 'full' dataset. These can be listed here and they will be removed from ALL depth subsampled analyses. A use case for this might be if you have a couple samples that are below your targeted subsample depth, and you don't want to include them. Note that if you configure multiple subsample_dp, these samples will be dropped from all of them. If you need to perform mutliple depth subsamplings with different subsets of samples, its best to run each depth individually. Alternatively, a config file can be made for each subsampled depth, however you may run into issues of file locking blocking both from running at the same time. (list of strings: [])
  54. Individually enable analyses to be performed with the subsampled data. These have the same function as described in the [analyses]section above. Enabling here will only run the analysis for the subsampled data, if you want to run it for the full data as well, you need to enable it in the analyses section as well. (true/false)

  55. By default, this workflow will perform all analyses requested in the above section on all sites that pass the filters set in the above section. These outputs will contain allsites-filts in the filename and in the report. However, many times, it is useful to perform an analysis on different subsets of sites, for instance, to compare results for genic vs. intergenic regions, neutral sites, exons vs. introns, etc. Here, users can set an arbitrary number of additional filters using BED files. For each BED file supplied, the contents will be intersected with the sites passing the filters set in the above section, and all analyses will be performed additionally using those sites.

    For instance, given a BED file containing putatively neutral sites, one could set the following:

    filter_beds:
  neutral: "resources/neutral_sites.bed"

    In this case, for each requested analysis, in addition to the allsites-filts output, a neutral-filts (named after the key assigned to the BED file in config.yaml) output will also be generated, containing the results for sites within the specified BED file that passed any set filters. More than one BED file can be set, up to an arbitrary number:

    filter_beds:
  neutral: "resources/neutral_sites.bed"
  intergenic: "resources/intergenic_sites.bed"
  introns: "resources/introns.bed"

  56. It may also sometimes be desireable to skip analyses on allsites-filts, say if you are trying to only generate diversity estimates or generate SFS for a set of neutral sites you supply.

    To skip running any analyses for allsites-filts and only perform them for the BED files you supply, you can set only_filter_beds: true in the config file. This may also be useful in the event you have a set of already filtered sites, and want to run the workflow on those, ignoring any of the built in filter options by setting them to false.

  57. Phred-scaled mapping quality filter. Reads below this threshold will be filtered out. (integer)

  58. Phred-scaled base quality filter. Reads below this threshold will be filtered out. (integer)
  59. Determines whether overlapping read pairs will be clipped in BAM files supplied by users. This is useful as many variant callers will account for overlapping reads in their processing, but ANGSD will double count overlapping reads. If BAMs were prepped without this in mind, it can be good to apply before running through ANGSD. However, it essentially creates a BAM file of nearly equal size for every sample, so it may be nice to leave it off by applying it to the BAMs you feed into the pipeline. (true/false)
  60. Additional options to pass to fastp trimming. (string) [docs]
  61. Minimum overlap to collapse historical reads. Default in fastp is 30. This effectively overrides the --length_required option if it is larger than that. (INT) [docs]
  62. Additional options to pass to bwa aln for mapping of historical sample reads. (string) [docs]
  63. Additional options to pass to Picard MarkDuplicates. --REMOVE_DUPLICATES true is recommended. (string) [docs]
  64. Enable to run damage profiler on modern samples in addition to historical (true/false)
  65. Parameters for pileup mappability analysis, see GenMap's documentation for more details.
  66. Length of k-mers to calculate mappability for. (integer)
  67. Allowed mismatches per k-mer. (integer)
  68. A threshold mappability score. Each site gets an average mappability score taken by averaging the mappability of all K-mers that would overlap it. A score of 1 means all K-mers are uniquely mappable, allowing for E mismatches. This is done via a custom script, and may eventually be replaced by the SNPable method, which is more common. (float, 0-1)
  69. Parameters for excluding sites based on extreme high and/or low global depth. The final sites list will contain only sites that pass the filters for all categories requested (i.e the whole dataset and/or the depth categories set in samples.tsv).
  70. Whether you will generate extreme thresholds as a multiple of the median global depth ("median") or as percentiles of the global depth distribution ("percentile")
  71. The bounds of the depth cutoff, defined as a numeric list. For the median method, the values will be multiplied by the median of the distribution to set the thresholds (i.e. [0.5,1.5] would generate a lower threshold at 0.5*median and an upper at 1.5*median). For the percentile method, these define the lower and upper percentiles to filter out (i.e [0.01,0.99] would remove the lower and upper 1% of the depth distributions). ([FLOAT, FLOAT])
  72. Whether to perform this filter on the dataset as a whole (may want to set to false if your dataset global depth distribution is multi-modal). (true/false)
  73. Whether to perform this filter on the depth classes defined in the samples.tsv file. This will generate a global depth distribution for samples in the same category, and perform the filtering on these distributions independently. Then, the sites that pass for all the classes will be included. (true/false)
  74. Seed to use when subsampling bams to lower depth. "$RANDOM" can be used to set a random seed, or any integer can be used to set a consistent seed. (string or int) [docs]
  75. General options in ANGSD, relevant doc pages are linked
  76. Genotype likelihood model to use in calculation (-GL option in ANGSD, [docs])
  77. When calculating individual depth, sites with depth higher than this will be binned to this value. Should be fine for most to leave at 100. (integer, [docs])
  78. Individuals with sequencing depth below this value at a position will be treated as having no data at that position by ANGSD. ANGSD defaults to 1 for this. Note that this can be separately set for individual heterozygosity estimates with mindepthind_heterozygosity below. (integer, -setMinDepthInd option in ANGSD (INT) [docs])
  79. Used to fill the -minInd option for any dataset wide ANGSD outputs (like Beagles for PCA/Admix). Should be a floating point value between 0 and 1 describing what proportion of the dataset must have data at a site to include it in the output. (FLOAT) [docs])
  80. Used to fill the -minInd option for any population level ANGSD outputs (like SAFs or Beagles for ngsF-HMM). Should be a floating point value between 0 and 1 describing what proportion of the population must have data at a site to include it in the output. (FLOAT) [docs])
  81. Removes transitions using ANGSD, effectively removing them from downstream analyses. This is useful for removing DNA damage from analyses, and will automatically set the appropriate ANGSD flags (i.e. using -noTrans 1 for SAF files and -rmTrans 1 for Beagle files.) (true/false)
  82. Additional options to pass to ANGSD during genotype likelihood calculation at all times. This is primarily useful for adding BAM input filters. Note that --remove_bads and -only_proper_pairs are enabled by default, so they only need to be included if you want to turn them off or explicitly show they are enabled. I've also found that for some datasets, -C 50 and -baq 1 can create a strong relationship between sample depth and detected diversity, effectively removing the benefits of ANGSD for low/variable depth data. I recommend that these aren't included unless you know you need them. Since the workflow uses BWA to map, -uniqueOnly 1 doesn't do anything if your minimum mapping quality is > 0, but you may wish to add it if you're bringing in your own files from another mapper. Mapping and base quality thresholds are also not needed, it will use the ones defined above automatically. If you prefer to correct for historical damage by trimming the ends of reads, this is where you'd want to put -trim INT. (string) (string, [docs])
  83. Same as extra, but only used when making SAF files (used for SFS, thetas, FST, IBSrelate (not NgsRelate version), heterozygosity). Doesn't require options already in extra or defined via other params in the YAML (such as notrans, minind, GL, etc.) (string)
  84. Same as extra, but only used when making Beagle and Maf files (used for PCA, Admix, ngsF-HMM, doIBS, NgsRelate, allele frequencies). Doesn't require options already in extra or defined via other params in the YAML (such as rmtrans, minind, GL, etc.) (string)
  85. Method for inferring the major and minor alleles. Set to 1 to infer from the genotype likelihoods, see documentation for other options. 1, 2, and 4 can be set without any additional configuration. 5 must also have an ancestral reference provided in the config, otherwise it will be the same as 4. 3 is currently not possible, and is used for generating the dataset polarized MAFs. If you have a use for 3 that PopGLen is suited for, please open an issue and I will look into it. (integer)
  86. The p-value to use for calling SNPs (float or string) [docs]
  87. Method for inferring minor allele frequencies. Set to 1 to infer from genotype likelihoods using a known major and minor from the domajorminor setting above. Set to 2 to assume the minor is unknown. See [docs] for more information. 3 is possible, and will estimate the frequencies both assuming a known and unknown minor. If you choose this option, you'll get both in the MAF outputs, but only the known will be passed to NgsRelate if you also use that. Other values are currently unsupported in PopGLen. (integer)
  88. The minimum minor allele frequency required to call a SNP. This is set when generating the Beagle file, so will filter SNPs for PCAngsd, NGSadmix, ngsF-HMM, and NgsRelate. If you would like each tool to handle filtering for maf on its own you can set this to -1 (disabled). (float, [docs])
  89. When estimating individual heterozygosity, sites with sequencing depth lower than this value will be dropped. (integer, -setMinDepthInd option in ANGSD) (integer)
  90. Settings for ngsLD [docs]
  91. For the LD estimates generated when setting estimate_ld: true above, set the maximum distance between sites in kb that LD will be estimated for (--max_kb_dist in ngsLD, integer)
  92. For the LD estimates generated when setting estimate_ld: true above, randomly sample this proportion of pairwise linkage estimates rather than estimating all (--rnd_sample in ngsLD, float)
  93. The same as max_kb_dist_est-ld:, but used when estimating LD decay when setting ld_decay: true above (integer)
  94. The same as rnd_sample_est-ld:, but used when estimating LD decay when setting ld_decay: true above (float)
  95. Additional plotting arguments to pass to fit_LDdecay.R when estimating LD decay (string)
  96. When estimating LD decay, should the sample size corrected r2 model be used? (true/false, true is the equivalent of passing a sample size to fit_LDdecay.R in ngsLD using --n_ind)
  97. The same as max_kb_dist_est-ld:, but used when linkage pruning SNPs as inputs for PCAngsd and NGSadmix. Pruning is performed on the whole dataset. Any positions above this distance will be assumed to be in linkage equilibrium during the pruning process. (integer)
  98. The minimum r2 to assume two positions are in linkage disequilibrium when pruning for PCAngsd and NGSadmix analyses. (float)
  99. Settings for NGSrelate
  100. Any extra command line parameters to be passed to NgsRelate when performing an IBSrelate only (no allele frequencies) run. (string)
  101. Any extra command line parameters to be passed to NgsRelate when performing a standard, allele frequency based, run. (string)
  102. Settings for ngsF-HMM [docs]
  103. Set to true to run ngsF-HMM separately for each population in your dataset. Set to false to run for whole dataset at once. ngsF-HMM assumes Hardy-Weinberg Equilibrium (aside from inbreeding) in the input data, so select the option that most reflects this in your data. (true/false)
  104. Whether or not to prune SNPs for LD before running the analysis. ngsF-HMM assumes independent sites, so it is preferred to set this to true to satisfy that expectation. (true/false)
  105. The maximum distance between sites in kb that will be treated as in LD when pruning for the ngsF-HMM input. (INT)
  106. The minimum r2 to assume two positions are in linkage disequilibrium when pruning for the ngsF-HMM input. Note, that this likely will be substantially higher for individual populations than for the whole dataset, as background LD should be higher when no substructure is present. (float)
  107. Minimum ROH size in base pairs to include in inbreeding coefficient calculation. Set if short ROH might be considered low confidence for your data. (integer)
  108. A list of integers that describe the size classes in base pairs you would like to partition the inbreeding coefficient by. This can help visualize how much of the coefficient comes from ROH of certain size classes (and thus, ages). List should be in ascending order and the first entry should be greater than min_roh_length. The first bin will group ROH between min_roh_length and the first entry, subsequent bins will group ROH with sizes between adjacent entries in the list, and the final bin will group all ROH larger than the final entry in the list. (list [] of integers)
  109. Settings for realSFS
  110. Whether or not to fold the produced SFS. Set to 1 if you have not provided an ancestral-state reference (0 or 1, [docs])
  111. Determines number of bootstrap replicates to use when requesting bootstrapped SFS. Is used for both 1dsfs and 2dsfs (this is very easy to separate, open an issue if desired). Automatically used for heterozygosity analysis to calculate confidence intervals. (integer)
  112. Settings for FST calculation in ANGSD
  113. Determines which FST estimator is used by ANGSD. With 0 being the default Reynolds 1983 and 1 being the Bhatia-Hudson 2013 estimator. The latter is preferable for small or uneven sample sizes (0 or 1, [docs])
  114. Window size in bp for sliding window analysis (integer)
  115. Window step size in bp for sliding window analysis (integer)
  116. Settings for pi, theta, and Tajima's D estimation
  117. Window size in bp for sliding window analysis (integer)
  118. Window step size in bp for sliding window analysis (integer)
  119. Minimum sites to include window in report plot. This does not remove them from the actual output, just the report plot and averages.
  120. Settings for admixture analysis with NGSadmix. This analysis is performed for a set of K groupings, and each K has several replicates performed. Replicates will continue until a set of N highest likelihood replicates converge, or the number of replicates reaches an upper threshold set here. Defaults for reps, minreps, thresh, and conv can be left as default for most. Based on method described by Pečnerová et al. 2021, Curr. Biol.
  121. A list of values of K to fit the data to (list [] of integers)
  122. The maximum number of replicates to perform per K. Default is 100. (integer)
  123. The minimum number of replicates to perform, even if replicates have converged. Default is 20. (integer)
  124. The convergence threshold - the top replicates must all be within this value of log-likelihood units to consider the run converged. Default is 2. (integer)
  125. The number of top replicates to include in convergence assessment. Default is 3. (integer)
  126. Additional arguments to pass to NGSadmix (for instance, increasing -maxiter). (string, [docs])
  127. Settings for identity by state calculation with ANGSD
  128. Whether to use a random (1) or consensus (2) base in IBS distance calculation ([docs])