hmmix 0.8.1

Find introgressed segments

Introgression detection

Contact

https://sites.google.com/view/laurits-skov

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Helpful files

The outgroup files, mutation rate files, reference genomes, ancestral alleles and callability files and ancestral allele files are now premade!

https://doi.org/10.5281/zenodo.11212339 (hg19 and hg38)

Using these files I have already called archaic segments in 1000 genomes and HDGP datasets (hg38 reference coordinate system)

https://doi.org/10.5281/zenodo.14136628 (archaic introgression callsets for HGDP and 1000genomes in hg38)

VCF file containing 4 high coverage archaic genomes (Altai, Vindija and Chagyrskaya Neanderthals and Denisovan) here:

https://zenodo.org/records/7246376 (hg19)

https://zenodo.org/records/13368126 (hg38)

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If you are working with archaic introgression into present-day humans of non-African ancestry you can use these files and skip the following steps: Find derived variants in outgroup and Estimate local mutation rate.

These are the scripts needed to infere archaic introgression in modern populations using an unadmixed outgroup.

1. Installation
2. Usage
3. Quick tutorial
4. 1000 genomes tutorial
   • Get data
   • Find derived variants in outgroup
   • Estimate local mutation rate
   • Find variants in ingroup
   • Train the HMM
   • Decoding
   • Phased data
   • Annotate
5. Run in python

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Installation

Run the following command to install:

pip install hmmix 

If you want to work with bcf/vcf files you should also install vcftools and bcftools. You can either use conda or visit their websites.

conda install -c bioconda vcftools bcftools

The way the model works is by removing variation found in an outgroup population and then using the remaining variants to group the genome into regions of different variant density. If the model works well we would expect that introgressed regions have higher variant density than non-introgressed - because they have spend more time accumulation variation that is not found in the outgroup.

An example on simulated data is provided below:

In this example we zoom in on 1 Mb of simulated data for a haploid genome. The top panel shows the coalescence times with the outgroup across the region and the green segment is an archaic introgressed segment. Notice how much more deeper the coalescence time with the outgroup is. The second panel shows that probability of being in the archaic state. We can see that the probability is much higher in the archaic segment, demonstrating that in this toy example the model is working like we would hope. The next panel is the snp density if you dont remove all snps found in the outgroup. By looking at this one cant tell where the archaic segments begins and ends, or even if there is one. The bottom panel is the snp density when all variation in the outgroup is removed. Notice that now it is much clearer where the archaic segment begins and ends!

The method is now published in PlosGenetics and can be found here: Detecting archaic introgression using an unadmixed outgroup This paper is describing and evaluating the method.

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Usage

Script for identifying introgressed archaic segments

> Turorial:
hmmix make_test_data 
hmmix train  -obs=obs.txt -weights=weights.bed -mutrates=mutrates.bed -param=Initialguesses.json -out=trained.json 
hmmix decode -obs=obs.txt -weights=weights.bed -mutrates=mutrates.bed -param=trained.json


Different modes (you can also see the options for each by writing hmmix make_test_data -h):
> make_test_data        
    -windows            Number of Kb windows to create (defaults to 50,000 per chromosome)
    -chromosomes        Number of chromosomes to simulate (defaults to 2)
    -nooutfiles         Don't create obs.txt, mutrates.bed, weights.bed, Initialguesses.json, simulated_segments.txt (defaults to yes)
    -param              markov parameters file (default is human/neanderthal like parameters)

> mutation_rate         
    -outgroup           [required] path to variants found in outgroup
    -out                outputfile (defaults to mutationrate.bed)
    -weights            file with callability (defaults to all positions being called)
    -window_size        size of bins (defaults to 1 Mb)

> create_outgroup       
    -ind                [required] ingroup/outgrop list (json file) or comma-separated list e.g. ind1,ind2
    -vcf                [required] path to list of comma-separated vcf/bcf file(s) or wildcard characters e.g. chr*.bcf
    -weights            file with callability (defaults to all positions being called)
    -out                outputfile (defaults to stdout)
    -ancestral          fasta file with ancestral information - comma-separated list or wildcards like vcf argument (default none)
    -refgenome          fasta file with reference genome - comma-separated list or wildcards like vcf argument (default none)

> create_ingroup        
    -ind                [required] ingroup/outgrop list (json file) or comma-separated list e.g. ind1,ind2
    -vcf                [required] path to list of comma-separated vcf/bcf file(s) or wildcard characters e.g. chr*.bcf
    -outgroup           [required] path to variant found in outgroup
    -weights            file with callability (defaults to all positions being called)
    -out                outputfile prefix (default is a file named obs.<ind>.txt where ind is the name of individual in ingroup/outgrop list)
    -ancestral          fasta file with ancestral information - comma-separated list or wildcards like vcf argument (default none)

> train                 
    -obs                [required] file with observation data
    -chrom              Subset to chromosome or comma separated list of chromosomes e.g chr1 or chr1,chr2,chr3 (default is use all chromosomes)
    -weights            file with callability (defaults to all positions being called)
    -mutrates           file with mutation rates (default is mutation rate is uniform)
    -param              markov parameters file (default is human/neanderthal like parameters)
    -out                outputfile (default is a file named trained.json)
    -window_size        size of bins (default is 1000 bp)
    -haploid            Change from using diploid data to haploid data (default is diploid)

> decode                
    -obs                [required] file with observation data
    -chrom              Subset to chromosome or comma separated list of chromosomes e.g chr1 or chr1,chr2,chr3 (default is use all chromosomes)
    -weights            file with callability (defaults to all positions being called)
    -mutrates           file with mutation rates (default is mutation rate is uniform)
    -param              markov parameters file (default is human/neanderthal like parameters)
    -out                outputfile prefix <out>.hap1.txt and <out>.hap2.txt if -haploid option is used or <out>.diploid.txt (default is stdout)
    -window_size        size of bins (default is 1000 bp)
    -haploid            Change from using diploid data to haploid data (default is diploid)
    -admixpop           Annotate using vcffile with admixing population (default is none)
    -extrainfo          Add variant position for each SNP (default is off)
    -viterbi            decode using the viterbi algorithm (default is posterior decoding)

> inhomogeneous                
    -obs                [required] file with observation data
    -chrom              Subset to chromosome or comma separated list of chromosomes e.g chr1 or chr1,chr2,chr3 (default is use all chromosomes)
    -weights            file with callability (defaults to all positions being called)
    -mutrates           file with mutation rates (default is mutation rate is uniform)
    -param              markov parameters file (default is human/neanderthal like parameters)
    -out                outputfile prefix <out>.hap1_sim(0-n).txt and <out>.hap2_sim(0-n).txt if -haploid option is used or <out>.diploid_(0-n).txt (default is stdout)
    -window_size        size of bins (default is 1000 bp)
    -haploid            Change from using diploid data to haploid data (default is diploid)
    -samples            Number of simulated paths for the inhomogeneous markov chain (default is 100)
    -admixpop           Annotate using vcffile with admixing population (default is none)
    -extrainfo          Add variant position for each SNP (default is off)

---

Quick tutorial

Here is how we can simulate test data using hmmix. Lets make some test data and start using the program.

> hmmix make_test_data
> creating 2 chromosomes each with 50000 kb of test data with the following parameters..
> hmm parameters file: None
> state_names = ['Human', 'Archaic']
> starting_probabilities = [0.98, 0.02]
> transitions = [[1.0, 0.0], [0.02, 0.98]]
> emissions = [0.04, 0.4]
> Seed is 42

This will generate 5 files, obs.txt, weights.bed, mutrates.bed, simulated_segments.txt and Initialguesses.json. obs.txt. These are the mutations that are left after removing variants which are found in the outgroup.

chrom  pos     ancestral_base  genotype
chr1   17102   C               CT
chr1   34435   C               CT
chr1   69860   T               TA
chr1   122270  C               CA
chr1   181106  G               GC
chr1   218071  A               AC
chr1   220700  T               TG
chr1   231020  A               AG
chr1   235614  T               TG

weights.bed. This is the parts of the genome that we can accurately map to - in this case we have simulated the data and can accurately access the entire genome.

chr1   0   50000000
chr2   0   50000000

mutrates.bed. This is the normalized mutation rate across the genome.

chr1   0   50000000   1
chr2   0   50000000   1

Initialguesses.json. This is our initial guesses when training the model - note these are different from those we simulated from.

{
  "state_names": ["Human","Archaic"],
  "starting_probabilities": [0.5,0.5],
  "transitions": [[0.99,0.01],[0.02,0.98]],
  "emissions": [0.03,0.3]
}

The simulated_segments.txt contains the simulated states which generated the data (you can compare this to the decoded results later and see that it matches).

chrom  start     end       length    state
chr1   0         22980000  22980000  Human
chr1   22980000  23071000  91000     Archaic
chr1   23071000  43905000  20834000  Human
chr1   43905000  43911000  6000      Archaic
chr1   43911000  47419000  3508000   Human
chr1   47419000  47443000  24000     Archaic
chr1   47443000  50000000  2557000   Human
chr2   0         16378000  16378000  Human
chr2   16378000  16492000  114000    Archaic
chr2   16492000  19478000  2986000   Human
chr2   19478000  19512000  34000     Archaic
chr2   19512000  37728000  18216000  Human
chr2   37728000  37751000  23000     Archaic
chr2   37751000  46777000  9026000   Human
chr2   46777000  46791000  14000     Archaic
chr2   46791000  50000000  3209000   Human

We can find the best fitting parameters using BaumWelsch training. Here is how you use it: - note you can try to ommit the weights and mutrates arguments. Since this is simulated data the mutation is constant across the genome and we can asses the entire genome. Also notice how the parameters approach the parameters the data was generated from (jubii).

> hmmix train  -obs=obs.txt -weights=weights.bed -mutrates=mutrates.bed -param=Initialguesses.json -out=trained.json
----------------------------------------
> state_names = ['Human', 'Archaic']
> starting_probabilities = [0.5, 0.5]
> transitions = [[0.99, 0.01], [0.02, 0.98]]
> emissions = [0.03, 0.3]
> chromosomes to use: All
> number of windows: 99982. Number of snps = 4116
> total callability: 99982000 bp (100.0 %)
> average mutation rate per bin: 1.0
> Output is trained.json
> Window size is 1000 bp
> Haploid False
----------------------------------------
iteration  loglikelihood  start1  start2  emis1   emis2   trans1_1  trans2_2
0          -17904.2589    0.5     0.5     0.03    0.3     0.99      0.98
1          -17259.0236    0.96    0.04    0.0346  0.2009  0.9968    0.9217
2          -17243.7038    0.969   0.031   0.0365  0.1861  0.9971    0.9105
...
29         -17195.4135    0.997   0.003   0.0401  0.4477  0.9999    0.9803
30         -17195.4098    0.997   0.003   0.0401  0.4482  0.9999    0.9806
31         -17195.409     0.997   0.003   0.0401  0.4485  0.9999    0.9808

# run without mutrate and weights (only do this for simulated data)
> hmmix train  -obs=obs.txt -param=Initialguesses.json -out=trained.json

We can now decode the data with the best parameters that maximize the likelihood and find the archaic segments. Please note that even thought we simulated 50 Mb of sequence the last window in the decoded output is at window 49,985,000 for chromosome 1 and 49,997,000 for chromosome 2. This is because hmmix uses the position of the last SNP to determine the length of the chromosome. The last SNP on chromosome 1 is 49,984,119 and the last SNP on chromosome 2 is 49,996,253.

> hmmix decode -obs=obs.txt -weights=weights.bed -mutrates=mutrates.bed -param=trained.json
----------------------------------------
> state_names = ['Human', 'Archaic']
> starting_probabilities = [0.997, 0.003]
> transitions = [[1.0, 0.0], [0.019, 0.981]]
> emissions = [0.04, 0.448]
> chromosomes to use: All
> number of windows: 99982. Number of snps = 4116
> total callability: 99982000 bp (100.0 %)
> average mutation rate per bin: 1.0
> Output prefix is /dev/stdout
> Window size is 1000 bp
> Haploid False
> Decode with posterior decoding
----------------------------------------
chrom  start     end       length    state    mean_prob  snps
chr1   0         22979000  22979000  Human    0.99989    903
chr1   22979000  23066000  87000     Archaic  0.96368    32
chr1   23066000  47418000  24352000  Human    0.99975    935
chr1   47418000  47443000  25000     Archaic  0.88224    10
chr1   47443000  49985000  2542000   Human    0.99933    96
chr2   0         16381000  16381000  Human    0.99981    653
chr2   16381000  16492000  111000    Archaic  0.99166    60
chr2   16492000  19478000  2986000   Human    0.99883    134
chr2   19478000  19512000  34000     Archaic  0.96454    18
chr2   19512000  49997000  30485000  Human    0.99981    1275

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Example with 1000 genomes data

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The whole pipeline we will run looks like this. In the following section we will go through all the steps on the way

NOTE: The outgroup files, mutation rate files, reference genomes, ancestral alleles and callability files and ancestral allele files are now premade! They can be downloaded in hg38 and hg19 here: https://doi.org/10.5281/zenodo.11212339

But keep reading along if you want to know HOW the files were generated! Another important thing to note is that hmmix is relying on VCFtools which only support VCF files up to format V4.2 - so if you have VCFfiles in version 4.3 you will need to change this in your header!

hmmix create_outgroup -ind=individuals.json -vcf=*.bcf -weights=strickmask.bed -out=outgroup.txt -ancestral=hg19_ancestral/homo_sapiens_ancestor_*.fa -refgenome=hg19_refgenome/*fa
hmmix mutation_rate -outgroup=outgroup.txt  -weights=strickmask.bed -window_size=1000000 -out mutationrate.bed
hmmix create_ingroup  -ind=individuals.json -vcf=*.bcf -weights=strickmask.bed -out=obs -outgroup=outgroup.txt -ancestral=hg19_ancestral/homo_sapiens_ancestor_*.fa
hmmix train  -obs=obs.HG00096.txt -weights=strickmask.bed -mutrates=mutationrate.bed -out=trained.HG00096.json 
hmmix decode -obs=obs.HG00096.txt -weights=strickmask.bed -mutrates=mutationrate.bed -param=trained.HG00096.json 

Getting data

I thought it would be nice to have an entire reproduceble example of how to use this model. From a common starting point such as a VCF file (well a BCF file in this case) to the final output. The reason for using BCF files is because it is MUCH faster to extract data for each individual. You can convert a vcf file to a bcf file like this:

bcftools view file.vcf -l 1 -O b > file.bcf
bcftools index file.bcf

In this example I will analyse an individual (HG00096) from the 1000 genomes project phase 3. All analysis are run on my lenovo thinkpad (8th gen) computer so it should run on yours too!

First we will need to know which 1) bases can be called in the genome and 2) which variants are found in the outgroup. So let's start out by downloading the files from the following directories. To download callability regions, ancestral alleles information, ingroup outgroup information call this command:

# bcffiles (hg19)
ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/bcf_files/

# callability (remember to remove chr in the beginning of each line to make it compatible with hg19 e.g. chr1 > 1)
ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/accessible_genome_masks/20141020.strict_mask.whole_genome.bed
sed 's/^chr\|%$//g' 20141020.strict_mask.whole_genome.bed | awk '{print $1"\t"$2"\t"$3}' > strickmask.bed

# outgroup information
ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/integrated_call_samples_v3.20130502.ALL.panel

# Ancestral information
ftp://ftp.ensembl.org/pub/release-74/fasta/ancestral_alleles/hg19_ancestral.tar.bz2

# Reference genome
wget 'ftp://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/chromFa.tar.gz' -O chromFa.tar.gz

# Archaic variants (Altai, Vindija, Chagyrskaya and Denisova in hg19)
https://zenodo.org/records/7246376

For this example we will use all individuals from 'YRI','MSL' and 'ESN' as outgroup individuals. While we will only be decoding HG00096 in this example you can add as many individuals as you want to the ingroup.

{
  "ingroup": [
    "HG00096",
    "HG00097"
  ],
  "outgroup": [
    "HG02922",
    "HG02923",
    ...
    "HG02944",
    "HG02946"]
}

---

Finding snps which are derived in the outgroup

First we need to find a set of variants found in the outgroup. We can use the wildcard character to loop through all bcf files. It is best if you have files with the ancestral alleles (in FASTA format) and the reference genome (in FASTA format) but the program will run without.

Something to note is that if you use an outgroup vcffile (like 1000 genomes) and an ingroup vcf file from a different dataset (like SGDP) there is an edge case which could occur. There could be recurrent mutations where every individual in 1000 genome has the derived variant and one individual in SGDP where the derived variant has mutated back to the ancestral allele. This means that this position will not be present in the outgroup file. However if a recurrent mutation occurs it will look like multiple individuals in the ingroup file have the mutation. This does not happen often but that is why I recommend having files with the ancestral allele and reference genome information.

# Recommended usage (if you want to remove sites which are fixed derived in your outgroup/ingroup). This is the file from zenodo. 
(took two hours) > hmmix create_outgroup -ind=individuals.json -vcf=*.bcf -weights=strickmask.bed -out=outgroup.txt -ancestral=hg19_ancestral/homo_sapiens_ancestor_*.fa -refgenome=hg19_refgenome/*fa
----------------------------------------
> Outgroup individuals: 292
> Using vcf and ancestral files
vcffile: chr1.bcf ancestralfile:  hg19_ancestral/homo_sapiens_ancestor_1.fa reffile:  hg19_refgenome/chr1.fa
vcffile: chr2.bcf ancestralfile:  hg19_ancestral/homo_sapiens_ancestor_2.fa reffile:  hg19_refgenome/chr2.fa
vcffile: chr3.bcf ancestralfile:  hg19_ancestral/homo_sapiens_ancestor_3.fa reffile:  hg19_refgenome/chr3.fa
vcffile: chr4.bcf ancestralfile:  hg19_ancestral/homo_sapiens_ancestor_4.fa reffile:  hg19_refgenome/chr4.fa
vcffile: chr5.bcf ancestralfile:  hg19_ancestral/homo_sapiens_ancestor_5.fa reffile:  hg19_refgenome/chr5.fa
vcffile: chr6.bcf ancestralfile:  hg19_ancestral/homo_sapiens_ancestor_6.fa reffile:  hg19_refgenome/chr6.fa
vcffile: chr7.bcf ancestralfile:  hg19_ancestral/homo_sapiens_ancestor_7.fa reffile:  hg19_refgenome/chr7.fa
vcffile: chr8.bcf ancestralfile:  hg19_ancestral/homo_sapiens_ancestor_8.fa reffile:  hg19_refgenome/chr8.fa
vcffile: chr9.bcf ancestralfile:  hg19_ancestral/homo_sapiens_ancestor_9.fa reffile:  hg19_refgenome/chr9.fa
vcffile: chr10.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_10.fa reffile: hg19_refgenome/chr10.fa
vcffile: chr11.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_11.fa reffile: hg19_refgenome/chr11.fa
vcffile: chr12.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_12.fa reffile: hg19_refgenome/chr12.fa
vcffile: chr13.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_13.fa reffile: hg19_refgenome/chr13.fa
vcffile: chr14.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_14.fa reffile: hg19_refgenome/chr14.fa
vcffile: chr15.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_15.fa reffile: hg19_refgenome/chr15.fa
vcffile: chr16.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_16.fa reffile: hg19_refgenome/chr16.fa
vcffile: chr17.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_17.fa reffile: hg19_refgenome/chr17.fa
vcffile: chr18.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_18.fa reffile: hg19_refgenome/chr18.fa
vcffile: chr19.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_19.fa reffile: hg19_refgenome/chr19.fa
vcffile: chr20.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_20.fa reffile: hg19_refgenome/chr20.fa
vcffile: chr21.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_21.fa reffile: hg19_refgenome/chr21.fa
vcffile: chr22.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_22.fa reffile: hg19_refgenome/chr22.fa
vcffile: chrX.bcf ancestralfile:  hg19_ancestral/homo_sapiens_ancestor_X.fa reffile:  hg19_refgenome/chrX.fa

> Callability file: strickmask.bed
> Writing output to: outgroup.txt
----------------------------------------

Here it is important to check that hmmix matches up the reference, ancestral and vcffiles correctly e.g. chr1.bcf should fit with hg19_ancestral/homo_sapiens_ancestor_1.fa and hg19_refgenome/chr1.fa for instance. If you see an issue here its better to give the files as commaseparated values.

hmmix create_outgroup -ind=individuals.json \
-vcf=chr1.bcf,chr2.bcf,chr3.bcf,chr4.bcf,chr5.bcf,chr6.bcf,chr7.bcf,chr8.bcf,chr9.bcf,chr10.bcf,chr11.bcf,chr12.bcf,chr13.bcf,chr14.bcf,chr15.bcf,chr16.bcf,chr17.bcf,chr18.bcf,chr19.bcf,chr20.bcf,chr21.bcf,chr22.bcf,chrX.bcf \
-ancestral=hg19_ancestral/homo_sapiens_ancestor_1.fa,hg19_ancestral/homo_sapiens_ancestor_2.fa,hg19_ancestral/homo_sapiens_ancestor_3.fa,hg19_ancestral/homo_sapiens_ancestor_4.fa,hg19_ancestral/homo_sapiens_ancestor_5.fa,hg19_ancestral/homo_sapiens_ancestor_6.fa,hg19_ancestral/homo_sapiens_ancestor_7.fa,hg19_ancestral/homo_sapiens_ancestor_8.fa,hg19_ancestral/homo_sapiens_ancestor_9.fa,hg19_ancestral/homo_sapiens_ancestor_10.fa,hg19_ancestral/homo_sapiens_ancestor_11.fa,hg19_ancestral/homo_sapiens_ancestor_12.fa,hg19_ancestral/homo_sapiens_ancestor_13.fa,hg19_ancestral/homo_sapiens_ancestor_14.fa,hg19_ancestral/homo_sapiens_ancestor_15.fa,hg19_ancestral/homo_sapiens_ancestor_16.fa,hg19_ancestral/homo_sapiens_ancestor_17.fa,hg19_ancestral/homo_sapiens_ancestor_18.fa,hg19_ancestral/homo_sapiens_ancestor_19.fa,hg19_ancestral/homo_sapiens_ancestor_20.fa,hg19_ancestral/homo_sapiens_ancestor_21.fa,hg19_ancestral/homo_sapiens_ancestor_22.fa,hg19_ancestral/homo_sapiens_ancestor_X.fa \
-refgenome=hg19_refgenome/chr1.fa,hg19_refgenome/chr2.fa,hg19_refgenome/chr3.fa,hg19_refgenome/chr4.fa,hg19_refgenome/chr5.fa,hg19_refgenome/chr6.fa,hg19_refgenome/chr7.fa,hg19_refgenome/chr8.fa,hg19_refgenome/chr9.fa,hg19_refgenome/chr10.fa,hg19_refgenome/chr11.fa,hg19_refgenome/chr12.fa,hg19_refgenome/chr13.fa,hg19_refgenome/chr14.fa,hg19_refgenome/chr15.fa,hg19_refgenome/chr16.fa,hg19_refgenome/chr17.fa,hg19_refgenome/chr18.fa,hg19_refgenome/chr19.fa,hg19_refgenome/chr20.fa,hg19_refgenome/chr21.fa,hg19_refgenome/chr22.fa,hg19_refgenome/chrX.fa \
-weights=strickmask.bed \
-out=outgroup.txt 

---

Estimating mutation rate across genome

We can use the number of variants in the outgroup to estimate the substitution rate as a proxy for mutation rate.

(took 30 sec) > hmmix mutation_rate -outgroup=outgroup.txt  -weights=strickmask.bed -window_size=1000000 -out mutationrate.bed
----------------------------------------
> Outgroupfile: outgroup.txt
> Outputfile is: mutationrate.bed
> Callability file is: strickmask.bed
> Window size: 1000000
----------------------------------------

---

Find a set of variants which are not derived in the outgroup

Keep variants that are not found to be derived in the outgroup for each individual in ingroup. You can also speficy a single individual or a comma separated list of individuals.

(took 20 min) > hmmix create_ingroup  -ind=individuals.json -vcf=*.bcf -weights=strickmask.bed -out=obs -outgroup=outgroup.txt -ancestral=hg19_ancestral/homo_sapiens_ancestor_*.fa
----------------------------------------
> Ingroup individuals: 2
> Using vcf and ancestral files
vcffile: chr1.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_1.fa
vcffile: chr2.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_2.fa
vcffile: chr3.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_3.fa
vcffile: chr4.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_4.fa
vcffile: chr5.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_5.fa
vcffile: chr6.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_6.fa
vcffile: chr7.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_7.fa
vcffile: chr8.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_8.fa
vcffile: chr9.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_9.fa
vcffile: chr10.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_10.fa
vcffile: chr11.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_11.fa
vcffile: chr12.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_12.fa
vcffile: chr13.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_13.fa
vcffile: chr14.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_14.fa
vcffile: chr15.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_15.fa
vcffile: chr16.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_16.fa
vcffile: chr17.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_17.fa
vcffile: chr18.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_18.fa
vcffile: chr19.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_19.fa
vcffile: chr20.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_20.fa
vcffile: chr21.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_21.fa
vcffile: chr22.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_22.fa
vcffile: chrX.bcf ancestralfile: hg19_ancestral/homo_sapiens_ancestor_X.fa

> Using outgroup variants from: outgroup.txt 
> Callability file: strickmask.bed 
> Writing output to file with prefix: obs.<individual>.txt

----------------------------------------
Running command:
bcftools view -m2 -M2 -v snps -s HG00096 -T strickmask.bed chr1.bcf | vcftools --vcf - --exclude-positions outgroup.txt --recode --stdout 
...
bcftools view -m2 -M2 -v snps -s HG00097 -T strickmask.bed chr22.bcf | vcftools --vcf - --exclude-positions outgroup.txt --recode --stdout 


# Different way to define which individuals are in the ingroup
(took 20 min) > hmmix create_ingroup  -ind=HG00096,HG00097 -vcf=*.bcf -weights=strickmask.bed -out=obs -outgroup=outgroup.txt -ancestral=hg19_ancestral/homo_sapiens_ancestor_*.fa

---

Training

Now for training the HMM parameters and decoding

(took 2 min) > hmmix train  -obs=obs.HG00096.txt -weights=strickmask.bed -mutrates=mutationrate.bed -out=trained.HG00096.json 
----------------------------------------
> state_names = ['Human', 'Archaic']
> starting_probabilities = [0.98, 0.02]
> transitions = [[1.0, 0.0], [0.02, 0.98]]
> emissions = [0.04, 0.4]
> chromosomes to use: All
> number of windows: 3032224 . Number of snps =  129803
> total callability: 0.72
> average mutation rate per bin: 1.0
> Output is trained.HG00096.json
> Window size is 1000 bp
> Haploid False
----------------------------------------
iteration  loglikelihood  start1  start2  emis1   emis2   trans1_1  trans2_2
0          -495665.7379   0.98    0.02    0.04    0.4     0.9999    0.98
1          -493092.9242   0.964   0.036   0.0459  0.3894  0.9995    0.9859
2          -492917.6235   0.959   0.041   0.0455  0.3847  0.9993    0.9834
...
20         -492775.4072   0.954   0.046   0.0442  0.3725  0.9989    0.9768
21         -492775.4058   0.954   0.046   0.0442  0.3725  0.9989    0.9768
22         -492775.4049   0.954   0.046   0.0442  0.3725  0.9989    0.9768

---

Decoding

(took 30 sec) > hmmix decode -obs=obs.HG00096.txt -weights=strickmask.bed -mutrates=mutationrate.bed -param=trained.HG00096.json 
----------------------------------------
> state_names = ['Human', 'Archaic']
> starting_probabilities = [0.954, 0.046]
> transitions = [[0.999, 0.001], [0.023, 0.977]]
> emissions = [0.044, 0.372]
> chromosomes to use: All
> number of windows: 3032224 . Number of snps =  129803
> total callability: 0.72
> average mutation rate per bin: 1.0
> Output prefix is /dev/stdout
> Window size is 1000 bp
> Haploid False
----------------------------------------
chrom  start    end      length   state    mean_prob  snps
1      0        2988000  2988000  Human    0.98429    91
1      2988000  2997000  9000     Archaic  0.76217    6
1      2997000  3425000  428000   Human    0.98776    30
1      3425000  3452000  27000    Archaic  0.95816    22
1      3452000  4302000  850000   Human    0.97917    36
1      4302000  4361000  59000    Archaic  0.867      20
1      4361000  4500000  139000   Human    0.96854    4
1      4500000  4510000  10000    Archaic  0.8552     7

You can also save to an output file with the command:

hmmix decode -obs=obs.HG00096.txt -weights=strickmask.bed -mutrates=mutationrate.bed -param=trained.HG00096.json -out=HG00096.decoded

This will create a file named HG00096.decoded.diploid.txt because the default option is treating the data as diploid (more on haploid decoding in next chapter)

---

Training and decoding with phased data

It is also possible to tell the model that the data is phased with the -haploid parameter. For that we first need to train the parameters for haploid data and then decode. Training the model on phased data is done like this - and we also remember to change the name of the parameter file to include phased so future versions of ourselves don't forget. Another thing to note is that the number of snps is bigger than before 135483 vs 129803. This is because the program is counting snps on both haplotypes and homozygotes will be counted twice!

(took 4 min) > hmmix train  -obs=obs.HG00096.txt -weights=strickmask.bed -mutrates=mutationrate.bed -out=trained.HG00096.phased.json -haploid
----------------------------------------
> state_names = ['Human', 'Archaic']
> starting_probabilities = [0.98, 0.02]
> transitions = [[1.0, 0.0], [0.02, 0.98]]
> emissions = [0.04, 0.4]
> chromosomes to use: All
> number of windows: 6064448 . Number of snps =  135483
> total callability: 0.72
> average mutation rate per bin: 1.0
> Output is trained.HG00096.phased.json
> Window size is 1000 bp
> Haploid True
----------------------------------------
iteration  loglikelihood  start1  start2  emis1   emis2   trans1_1  trans2_2
0          -605433.1928   0.98    0.02    0.04    0.4     0.9999    0.98
1          -589492.2      0.985   0.015   0.0248  0.3999  0.9998    0.9852
2          -588823.124    0.98    0.02    0.0238  0.3671  0.9996    0.9825
...
20         -588456.6027   0.973   0.027   0.0228  0.3267  0.9993    0.9755
21         -588456.6015   0.973   0.027   0.0228  0.3266  0.9993    0.9755
22         -588456.6009   0.973   0.027   0.0228  0.3266  0.9993    0.9755

Below I am only showing the first archaic segments on chromosome 1 for each haplotype (note you have to scroll down after chrom X before the new haplotype begins). The seem to fall more or less in the same places as when we used diploid data.

(took 30 sec) > hmmix decode -obs=obs.HG00096.txt -weights=strickmask.bed -mutrates=mutationrate.bed -param=trained.HG00096.phased.json -haploid
----------------------------------------
> state_names = ['Human', 'Archaic']
> starting_probabilities = [0.973, 0.027]
> transitions = [[0.999, 0.001], [0.024, 0.976]]
> emissions = [0.023, 0.327]
> chromosomes to use: All
> number of windows: 6064448 . Number of snps =  135483
> total callability: 0.72
> average mutation rate per bin: 1.0
> Output prefix is /dev/stdout
> Window size is 1000 bp
> Haploid True
----------------------------------------
hap1
chrom  start    end      length  state    mean_prob  snps
1      2156000  2185000  29000   Archaic  0.64711    6
1      3425000  3452000  27000   Archaic  0.96701    22

...
hap2
1      2780000  2803000  23000   Archaic  0.68285    7
1      4302000  4337000  35000   Archaic  0.94244    13
1      4500000  4511000  11000   Archaic  0.87938    7
1      4989000  5001000  12000   Archaic  0.61874    5

You can also save to an output file with the command:

hmmix decode -obs=obs.HG00096.txt -weights=strickmask.bed -mutrates=mutationrate.bed -param=trained.HG00096.phased.json -haploid -out=HG00096.decoded

This will create two files named HG00096.decoded.hap1.txt and HG00096.decoded.hap2.txt

---

Annotate with known admixing population

Even though this method does not use archaic reference genomes for finding segments you can still use them to annotate your segments. I have uploaded a VCF file containing 4 high coverage archaic genomes (3 Neanderthals and 1 Denisovan) here:

https://zenodo.org/records/7246376 (hg19 - the one I use in this example)

https://zenodo.org/records/13368126 (hg38)

If you have a vcf from the population that admixed in VCF/BCF format you can write this:

> hmmix decode -obs=obs.HG00096.txt -weights=strickmask.bed -mutrates=mutationrate.bed -param=trained.HG00096.json -admixpop=archaicvar/*bcf
----------------------------------------
> state_names = ['Human', 'Archaic']
> starting_probabilities = [0.954, 0.046]
> transitions = [[0.999, 0.001], [0.023, 0.977]]
> emissions = [0.044, 0.372]
> chromosomes to use: All
> number of windows: 3032224 . Number of snps =  129803
> total callability: 0.72
> average mutation rate per bin: 1.0
> Output prefix is /dev/stdout
> Window size is 1000 bp
> Haploid False
----------------------------------------
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_9.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_19.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_7.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_21.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_20.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_15.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_10.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_3.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_17.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_6.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_X.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_16.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_1.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_18.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_14.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_4.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_2.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_22.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_5.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_8.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_11.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_12.bcf
bcftools view -a -R obs.HG00096.txttemp archaicvar/highcov_ind_13.bcf

chrom  start     end       length  state    mean_prob  snps  admixpopvariants  AltaiNeandertal  Vindija33.19  Denisova  Chagyrskaya-Phalanx
1      2988000   2997000   9000    Archaic  0.76217    6     4                 4                4             1         4
1      3425000   3452000   27000   Archaic  0.95816    22    17                17               15            3         17
1      4302000   4361000   59000   Archaic  0.867      20    12                11               12            11        11
1      4500000   4510000   10000   Archaic  0.8552     7     5                 4                5             4         5
1      5306000   5319000   13000   Archaic  0.55584    4     1                 1                1             0         1
1      5338000   5348000   10000   Archaic  0.65033    5     3                 2                3             0         3
1      9321000   9355000   34000   Archaic  0.86414    9     0                 0                0             0         0
1      12599000  12655000  56000   Archaic  0.91159    18    11                4                11            0         10

For the first segment there are 6 derived snps. Of these snps 4 are shared with Altai,Vindija, Denisova and Chagyrskaya. Only 1 is shared with Denisova so this segment likeli introgressed from Neanderthals

---

And that is it! Now you have run the model and gotten a set of parameters that you can interpret biologically (see my paper) and you have a list of segments that belong to the human and Archaic state.

If you have any questions about the use of the scripts, if you find errors or if you have feedback you can contact my here (make an issue) or write to: [e-mail]