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Project description
lshmm
lshmm is a Python library for prototyping, experimenting, and testing implementations of algorithms using the Li & Stephens (2003) Hidden Markov Model.
Usage
Inputs
Data
- Sample and/or ancestral haplotypes comprising a reference panel.
- Query haplotypes.
In the haploid mode, the alleles in haplotypes can be represented by any integer value (besides -1
and -2
, which are special values). In the diploid mode, the genotypes (encoded as allele dosages) can be 0
(homozygous for the reference allele), 1
(heterozygous for the alternative allele), or 2
(homozygous for the alternative allele). Currently, multiallelic sites are supported in the haploid mode, but not the diploid mode.
Note that there are two special values NONCOPY
and MISSING
. NONCOPY
(or -2
) represent non-copiable states, and can only be found in partial ancestral haplotypes in the reference panel. MISSING
(or -1
) representing missing data, and can be found only in query haplotypes.
Parameters
- Recombination probabilities.
- Mutation probabilities.
Models and algorithms
- Haploid LS HMM
- Forward-backward algorithm
- Viterbi algorithm
- Diploid LS HMM
- Forward-backward algorithm
- Viterbi algorithm
Features
- Scaling of mutation rate by the number of distinct alleles per site.
- Non-copiable state in the reference panel (
NONCOPY
). - Missing state in the query (
MISSING
). - Multiallelic sites (haploid only).
Project details
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