From ad8a49371ad75edb3078615a4fb2f23adf030ef5 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Sat, 8 Jun 2024 20:53:24 +0000 Subject: [PATCH] build based on ccaa3ac --- dev/404.html | 10 ++-- dev/api.html | 28 ++++----- dev/article01.html | 20 +++---- dev/assets/api.md.BiqC9tVJ.js | 58 +++++++++++++++++++ dev/assets/api.md.BiqC9tVJ.lean.js | 1 + dev/assets/api.md.CM80ZGQK.js | 58 ------------------- dev/assets/api.md.CM80ZGQK.lean.js | 1 - dev/assets/app.DJvmlMci.js | 1 + dev/assets/app.s0WURkb8.js | 1 - dev/assets/article01.md.BL6r3tif.lean.js | 1 - ...d.BL6r3tif.js => article01.md.BrGXw77Y.js} | 6 +- dev/assets/article01.md.BrGXw77Y.lean.js | 1 + ...aXJV.js => biomarkovchains.md.0rg0z1ev.js} | 2 +- ...js => biomarkovchains.md.0rg0z1ev.lean.js} | 2 +- .../chunks/@localSearchIndexroot.Bh2pKAzu.js | 1 - .../chunks/@localSearchIndexroot.Bj-EWWk0.js | 1 + .../chunks/VPLocalSearchBox.CaalGK9M.js | 7 --- .../chunks/VPLocalSearchBox.DqWxI-Ji.js | 7 +++ dev/assets/chunks/framework.Cvv1S4VC.js | 17 ++++++ dev/assets/chunks/framework.D1rHXXmm.js | 17 ------ dev/assets/chunks/theme.BWgOxzZE.js | 7 --- dev/assets/chunks/theme.CNBIs78H.js | 2 + ....BxUYlh-U.js => getstarted.md.EJuNxDSJ.js} | 8 +-- ...lean.js => getstarted.md.EJuNxDSJ.lean.js} | 2 +- ...ome.md.nGdy1Xvl.js => home.md.DYCAAGxb.js} | 2 +- ...y1Xvl.lean.js => home.md.DYCAAGxb.lean.js} | 2 +- dev/assets/index.md.Ba-cgBKx.js | 1 + ...IapY.lean.js => index.md.Ba-cgBKx.lean.js} | 2 +- dev/assets/index.md.mmk1IapY.js | 1 - dev/assets/style.BSicJgYg.css | 1 + dev/assets/style.COODeMdn.css | 1 - dev/biomarkovchains.html | 16 ++--- dev/getstarted.html | 22 +++---- dev/hashmap.json | 2 +- dev/home.html | 16 ++--- dev/index.html | 16 ++--- 36 files changed, 168 insertions(+), 173 deletions(-) create mode 100644 dev/assets/api.md.BiqC9tVJ.js create mode 100644 dev/assets/api.md.BiqC9tVJ.lean.js delete mode 100644 dev/assets/api.md.CM80ZGQK.js delete mode 100644 dev/assets/api.md.CM80ZGQK.lean.js create mode 100644 dev/assets/app.DJvmlMci.js delete mode 100644 dev/assets/app.s0WURkb8.js delete mode 100644 dev/assets/article01.md.BL6r3tif.lean.js rename dev/assets/{article01.md.BL6r3tif.js => article01.md.BrGXw77Y.js} (79%) create mode 100644 dev/assets/article01.md.BrGXw77Y.lean.js rename dev/assets/{biomarkovchains.md.DhkAaXJV.js => biomarkovchains.md.0rg0z1ev.js} (99%) rename dev/assets/{biomarkovchains.md.DhkAaXJV.lean.js => biomarkovchains.md.0rg0z1ev.lean.js} (99%) delete mode 100644 dev/assets/chunks/@localSearchIndexroot.Bh2pKAzu.js create mode 100644 dev/assets/chunks/@localSearchIndexroot.Bj-EWWk0.js delete mode 100644 dev/assets/chunks/VPLocalSearchBox.CaalGK9M.js create mode 100644 dev/assets/chunks/VPLocalSearchBox.DqWxI-Ji.js create mode 100644 dev/assets/chunks/framework.Cvv1S4VC.js delete mode 100644 dev/assets/chunks/framework.D1rHXXmm.js delete mode 100644 dev/assets/chunks/theme.BWgOxzZE.js create mode 100644 dev/assets/chunks/theme.CNBIs78H.js rename dev/assets/{getstarted.md.BxUYlh-U.js => getstarted.md.EJuNxDSJ.js} (88%) rename dev/assets/{getstarted.md.BxUYlh-U.lean.js => getstarted.md.EJuNxDSJ.lean.js} (95%) rename dev/assets/{home.md.nGdy1Xvl.js => home.md.DYCAAGxb.js} (85%) rename dev/assets/{home.md.nGdy1Xvl.lean.js => home.md.DYCAAGxb.lean.js} (85%) create mode 100644 dev/assets/index.md.Ba-cgBKx.js rename dev/assets/{index.md.mmk1IapY.lean.js => index.md.Ba-cgBKx.lean.js} (60%) delete mode 100644 dev/assets/index.md.mmk1IapY.js create mode 100644 dev/assets/style.BSicJgYg.css delete mode 100644 dev/assets/style.COODeMdn.css diff --git a/dev/404.html b/dev/404.html index c370f1a..5bfc900 100644 --- a/dev/404.html +++ b/dev/404.html @@ -5,17 +5,17 @@ 404 | BioMarkovChains.jl - - + + - + -
Skip to content

404

PAGE NOT FOUND

But if you don't change your direction, and if you keep looking, you may end up where you are heading.
- +
+ \ No newline at end of file diff --git a/dev/api.html b/dev/api.html index 9cc57eb..f8753b1 100644 --- a/dev/api.html +++ b/dev/api.html @@ -5,19 +5,19 @@ BioMarkovChains.jl - - + + - + - - - + + + -
Skip to content
# BioMarkovChains.BMCType.
julia
BMC

Alias for the type BioMarkovChain.

source


# BioMarkovChains.BioMarkovChainType.
julia
struct BioMarkovChain{S<:DataType, M<:AbstractMatrix, I<:AbstractVector, N<:Integer} <: AbstractBioMarkovChain

A BioMarkovChain represents a Markov chain used in biological sequence analysis. It contains a transition probability matrix (tpm) and an initial distribution of probabilities (inits) and also the order of the Markov chain.

Fields

  • alphabet::A: Is the state space of the sequence whether DNA, RNA AminoAcid DataTypes.

  • tpm::M: The transition probability matrix.

  • inits::I: The initial distribution of probabilities.

  • n::N: The order of the Markov chain.

Constructors

  • BioMarkovChain(tpm::M, inits::I, n::N=1) where {M<:AbstractMatrix, I<:AbstractVector, N<:Integer}: Constructs a BioMarkovChain object with the provided transition probability matrix, initial distribution, and order.

  • BioMarkovChain(sequence::LongNucOrView{4}, n::Int64=1): Constructs a BioMarkovChain object based on the DNA sequence and transition order.

Example

julia
sequence = LongDNA{4}("ACTACATCTA")
+    
Skip to content
# BioMarkovChains.BMCType.
julia
BMC

Alias for the type BioMarkovChain.

source


# BioMarkovChains.BioMarkovChainType.
julia
struct BioMarkovChain{S<:DataType, M<:AbstractMatrix, I<:AbstractVector, N<:Integer} <: AbstractBioMarkovChain

A BioMarkovChain represents a Markov chain used in biological sequence analysis. It contains a transition probability matrix (tpm) and an initial distribution of probabilities (inits) and also the order of the Markov chain.

Fields

  • alphabet::A: Is the state space of the sequence whether DNA, RNA AminoAcid DataTypes.

  • tpm::M: The transition probability matrix.

  • inits::I: The initial distribution of probabilities.

  • n::N: The order of the Markov chain.

Constructors

  • BioMarkovChain(tpm::M, inits::I, n::N=1) where {M<:AbstractMatrix, I<:AbstractVector, N<:Integer}: Constructs a BioMarkovChain object with the provided transition probability matrix, initial distribution, and order.

  • BioMarkovChain(sequence::LongNucOrView{4}, n::Int64=1): Constructs a BioMarkovChain object based on the DNA sequence and transition order.

Example

julia
sequence = LongDNA{4}("ACTACATCTA")
 
 model = BioMarkovChain(sequence, 2)
 BioMarkovChain of DNAAlphabet{4}() and order 1:
@@ -30,7 +30,7 @@
    0.3333
    0.3333
    0.0
-   0.3333

source


# BioMarkovChains.dnaseqprobabilityMethod.
julia
dnaseqprobability(sequence::LongNucOrView{4}, model::BioMarkovChain)

Compute the probability of a given sequence using a transition probability matrix and the initial probabilities distributions of a BioMarkovModel.

P(X1=i1,,XT=iT)=πi1T1t=1T1ait,it+1

Arguments

  • sequence::LongNucOrView{4}: The input sequence of nucleotides.

  • model::BioMarkovChain: A given BioMarkovChain.

Returns

  • probability::Float64: The probability of the input sequence given the model.

Example

julia
mainseq = LongDNA{4}("CCTCCCGGACCCTGGGCTCGGGAC")
+   0.3333

source


# BioMarkovChains.dnaseqprobabilityMethod.
julia
dnaseqprobability(sequence::LongNucOrView{4}, model::BioMarkovChain)

Compute the probability of a given sequence using a transition probability matrix and the initial probabilities distributions of a BioMarkovModel.

P(X1=i1,,XT=iT)=πi1T1t=1T1ait,it+1

Arguments

  • sequence::LongNucOrView{4}: The input sequence of nucleotides.

  • model::BioMarkovChain: A given BioMarkovChain.

Returns

  • probability::Float64: The probability of the input sequence given the model.

Example

julia
mainseq = LongDNA{4}("CCTCCCGGACCCTGGGCTCGGGAC")
    
 bmc = BioMarkovChain(mainseq)
 
@@ -54,11 +54,11 @@
 
 dnaseqprobability(newseq, bmc)
     
-    0.021739130434782608

source


# BioMarkovChains.initialsMethod.
julia
initials(sequence::SeqOrView{A}) where A

Calculate the estimated initial probabilities for a Markov chain based on a given sequence.

This function takes a sequence of states and calculates the estimated initial probabilities of each state in the sequence for a Markov chain. The initial probabilities are estimated by counting the occurrences of each state at the beginning of the sequence and normalizing the counts to sum up to 1.

πi=P(Xi=i),iTi=1Nπi=1

Now using the dinucleotides counts estimating the initials would follow:

πi^=cikck

Arguments

  • sequence::SeqOrView{A}: The sequence of states representing the Markov chain.

Returns

An Vector{Flot64} of estimated initial probabilities for each state in the sequence.

source


# BioMarkovChains.log_odds_ratio_matrixMethod.
julia
log_odds_ratio_matrix(model1::BioMarkovChain, model2::BioMarkovChain)

Calculates the log-odds ratio between the transition probability matrices of two BioMarkovChain models.

β=logP(x|m1)P(x|m2)

Where m1 and m2 are the two models transition probability matrices.

Arguments

  • model1::BioMarkovChain: The first BioMarkovChain model.

  • model2::BioMarkovChain: The second BioMarkovChain model.

source


# BioMarkovChains.log_odds_ratio_matrixMethod.
julia
log_odds_ratio_matrix(sequence::NucleicSeqOrView{A}, model::BioMarkovChain) where A

Calculates the log-odds ratio between the transition probability matrix of a given DNA sequence and a reference model.

Arguments

  • sequence::NucleicSeqOrView{A}: A DNA, RNA sequence or view with a length of 4 nucleotides.

  • model::BioMarkovChain: A reference BioMarkovChain model.

Examples

julia
sequence = LongNucOrView{4}("ACGT")
+    0.021739130434782608

source


# BioMarkovChains.initialsMethod.
julia
initials(sequence::SeqOrView{A}) where A

Calculate the estimated initial probabilities for a Markov chain based on a given sequence.

This function takes a sequence of states and calculates the estimated initial probabilities of each state in the sequence for a Markov chain. The initial probabilities are estimated by counting the occurrences of each state at the beginning of the sequence and normalizing the counts to sum up to 1.

πi=P(Xi=i),iTi=1Nπi=1

Now using the dinucleotides counts estimating the initials would follow:

πi^=cikck

Arguments

  • sequence::SeqOrView{A}: The sequence of states representing the Markov chain.

Returns

An Vector{Flot64} of estimated initial probabilities for each state in the sequence.

source


# BioMarkovChains.log_odds_ratio_matrixMethod.
julia
log_odds_ratio_matrix(model1::BioMarkovChain, model2::BioMarkovChain)

Calculates the log-odds ratio between the transition probability matrices of two BioMarkovChain models.

β=logP(x|m1)P(x|m2)

Where m1 and m2 are the two models transition probability matrices.

Arguments

  • model1::BioMarkovChain: The first BioMarkovChain model.

  • model2::BioMarkovChain: The second BioMarkovChain model.

source


# BioMarkovChains.log_odds_ratio_matrixMethod.
julia
log_odds_ratio_matrix(sequence::NucleicSeqOrView{A}, model::BioMarkovChain) where A

Calculates the log-odds ratio between the transition probability matrix of a given DNA sequence and a reference model.

Arguments

  • sequence::NucleicSeqOrView{A}: A DNA, RNA sequence or view with a length of 4 nucleotides.

  • model::BioMarkovChain: A reference BioMarkovChain model.

Examples

julia
sequence = LongNucOrView{4}("ACGT")
 model = BioMarkovChain(sequence)  # Provide appropriate initialization for BioMarkovChain
-result = log_odds_ratio_matrix(sequence, model)

source


# BioMarkovChains.log_odds_ratio_scoreMethod.
julia
log_odds_ratio_score(sequence::SeqOrView{A}, model::BioMarkovChain; b::Number =)

Compute the log odds ratio score between a given sequence and a BioMarkovChain model.

S(x)=i=1Lβxix=i=1logai1m1xiai1m2xi

Arguments

  • sequence::SeqOrView{A}: A sequence of elements of type A.

  • model::BioMarkovChain: A BioMarkovChain model.

  • b::Number = ℯ: The base of the logarithm used to compute the log odds ratio.

Returns

The log odds ratio score between the sequence and the model.

Example

source


# BioMarkovChains.perronfrobeniusMethod.
julia
perronfrobenius(sequence::SeqOrView{A}, n::Int64=1) where A

Compute the Perron-Frobenius matrix, a column-stochastic version of the transition probability matrix (TPM), for a given nucleotide sequence.

The Perron-Frobenius matrix captures the asymptotic probabilities of transitioning between nucleotides in the sequence over a specified number of steps n. It provides insight into the long-term behavior of a Markov chain or a dynamical system associated with the sequence.

Arguments

  • sequence::SeqOrView{A}: A nucleotide sequence represented as a NucleicSeqOrView{A} object.

  • n::Int64=1: The number of steps to consider for the transition probability matrix. Default is 1.

Returns

A copy of the Perron-Frobenius matrix. Each column of this matrix corresponds to the probabilities of transitioning from the current nucleotide state to all possible nucleotide states after n steps.

Example

julia
sequence = LongSequence{DNAAlphabet{4}}("ACGTCGTCCACTACGACATCAGC")  # Replace with an actual nucleotide sequence
+result = log_odds_ratio_matrix(sequence, model)

source


# BioMarkovChains.log_odds_ratio_scoreMethod.
julia
log_odds_ratio_score(sequence::SeqOrView{A}, model::BioMarkovChain; b::Number =)

Compute the log odds ratio score between a given sequence and a BioMarkovChain model.

S(x)=i=1Lβxix=i=1logai1m1xiai1m2xi

Arguments

  • sequence::SeqOrView{A}: A sequence of elements of type A.

  • model::BioMarkovChain: A BioMarkovChain model.

  • b::Number = ℯ: The base of the logarithm used to compute the log odds ratio.

Returns

The log odds ratio score between the sequence and the model.

Example

source


# BioMarkovChains.perronfrobeniusMethod.
julia
perronfrobenius(sequence::SeqOrView{A}, n::Int64=1) where A

Compute the Perron-Frobenius matrix, a column-stochastic version of the transition probability matrix (TPM), for a given nucleotide sequence.

The Perron-Frobenius matrix captures the asymptotic probabilities of transitioning between nucleotides in the sequence over a specified number of steps n. It provides insight into the long-term behavior of a Markov chain or a dynamical system associated with the sequence.

Arguments

  • sequence::SeqOrView{A}: A nucleotide sequence represented as a NucleicSeqOrView{A} object.

  • n::Int64=1: The number of steps to consider for the transition probability matrix. Default is 1.

Returns

A copy of the Perron-Frobenius matrix. Each column of this matrix corresponds to the probabilities of transitioning from the current nucleotide state to all possible nucleotide states after n steps.

Example

julia
sequence = LongSequence{DNAAlphabet{4}}("ACGTCGTCCACTACGACATCAGC")  # Replace with an actual nucleotide sequence
 n = 2
-pf = perronfrobenius(sequence, n)

source


# BioMarkovChains.transition_count_matrixMethod.
julia
transition_count_matrix(sequence::LongSequence{DNAAlphabet{4}})

Compute the transition count matrix (TCM) of a given DNA sequence.

Arguments

  • sequence::LongSequence{DNAAlphabet{4}}: a LongSequence{DNAAlphabet{4}} object representing the DNA sequence.

Returns

A Matrix object representing the transition count matrix of the sequence.

Example

seq = LongDNA{4}("AGCTAGCTAGCT")
+pf = perronfrobenius(sequence, n)

source


# BioMarkovChains.transition_count_matrixMethod.
julia
transition_count_matrix(sequence::LongSequence{DNAAlphabet{4}})

Compute the transition count matrix (TCM) of a given DNA sequence.

Arguments

  • sequence::LongSequence{DNAAlphabet{4}}: a LongSequence{DNAAlphabet{4}} object representing the DNA sequence.

Returns

A Matrix object representing the transition count matrix of the sequence.

Example

seq = LongDNA{4}("AGCTAGCTAGCT")
 
 tcm = transition_count_matrix(seq)
 
@@ -66,7 +66,7 @@
  0  0  3  0
  0  0  0  3
  0  3  0  0
- 2  0  0  0

source


# BioMarkovChains.transition_probability_matrixMethod.
julia
transition_probability_matrix(sequence::LongSequence{DNAAlphabet{4}}, n::Int64=1)

Compute the transition probability matrix (TPM) of a given DNA sequence. Formally it construct M^ where:

mij=P(Xt=jXt1=i)=P(Xt1=i,Xt=j)P(Xt1=i)

The transition matrices of DNA and Amino-Acids are arranged sorted and in row-wise matrices:

First the DNA matrix:

MDNA=[AAACAGATCACCCGCTGAGCGGGTTATCTGTT]

And then, the Aminoacids:

MAA=[AAACADAWCACCCDCWDADCDDDWWAWCWDWW]

Arguments

  • sequence::LongNucOrView{4}: a LongNucOrView{4} object representing the DNA sequence.

  • n::Int64=1: The order of the Markov model. That is the M^n

Keywords

  • extended_alphabet::Bool=false: If true will pass the extended alphabet of DNA to search

Returns

A Matrix object representing the transition probability matrix of the sequence.

Example

julia
seq = dna"AGCTAGCTAGCT"
+ 2  0  0  0

source


# BioMarkovChains.transition_probability_matrixMethod.
julia
transition_probability_matrix(sequence::LongSequence{DNAAlphabet{4}}, n::Int64=1)

Compute the transition probability matrix (TPM) of a given DNA sequence. Formally it construct M^ where:

mij=P(Xt=jXt1=i)=P(Xt1=i,Xt=j)P(Xt1=i)

The transition matrices of DNA and Amino-Acids are arranged sorted and in row-wise matrices:

First the DNA matrix:

MDNA=[AAACAGATCACCCGCTGAGCGGGTTATCTGTT]

And then, the Aminoacids:

MAA=[AAACADAWCACCCDCWDADCDDDWWAWCWDWW]

Arguments

  • sequence::LongNucOrView{4}: a LongNucOrView{4} object representing the DNA sequence.

  • n::Int64=1: The order of the Markov model. That is the M^n

Keywords

  • extended_alphabet::Bool=false: If true will pass the extended alphabet of DNA to search

Returns

A Matrix object representing the transition probability matrix of the sequence.

Example

julia
seq = dna"AGCTAGCTAGCT"
 
 tpm = transition_probability_matrix(seq)
 
@@ -74,8 +74,8 @@
  0.0  0.0  1.0  0.0
  0.0  0.0  0.0  1.0
  0.0  1.0  0.0  0.0
- 1.0  0.0  0.0  0.0

source


- + 1.0 0.0 0.0 0.0

source


+ \ No newline at end of file diff --git a/dev/article01.html b/dev/article01.html index f0f4fc7..29ce82c 100644 --- a/dev/article01.html +++ b/dev/article01.html @@ -5,19 +5,19 @@ From BMC to sequence probabilties | BioMarkovChains.jl - - + + - + - - - + + + -
Skip to content

From BMC to sequence probabilties

We can now calculate a transition probability matrix from a LongDNA sequence using the transition_probability_matrix and initials methods:

julia
using BioSequences
+    
Skip to content

From BMC to sequence probabilties

We can now calculate a transition probability matrix from a LongDNA sequence using the transition_probability_matrix and initials methods:

julia
using BioSequences
 
 sequence = dna"CCTCCCGGACCCTGGGCTCGGGAC"
 
@@ -25,7 +25,7 @@
 initials = initials(sequence)
 
 println(tpm)
-println(initials)
4×4 Matrix{Float64}:
+println(initials)
4×4 Matrix{Float64}:
  0.0   1.0       0.0       0.0
  0.0   0.5       0.2       0.3
  0.25  0.125     0.625     0.0
@@ -35,8 +35,8 @@
  0.08695652173913043
  0.43478260869565216
  0.34782608695652173
- 0.13043478260869565
- + 0.13043478260869565
+ \ No newline at end of file diff --git a/dev/assets/api.md.BiqC9tVJ.js b/dev/assets/api.md.BiqC9tVJ.js new file mode 100644 index 0000000..941a69f --- /dev/null +++ b/dev/assets/api.md.BiqC9tVJ.js @@ -0,0 +1,58 @@ +import{_ as T,c as s,j as a,a as t,a6 as Q,o as e}from"./chunks/framework.Cvv1S4VC.js";const c6=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),l={name:"api.md"},n=Q(`
# BioMarkovChains.BMCType.
julia
BMC

Alias for the type BioMarkovChain.

source


# BioMarkovChains.BioMarkovChainType.
julia
struct BioMarkovChain{S<:DataType, M<:AbstractMatrix, I<:AbstractVector, N<:Integer} <: AbstractBioMarkovChain

A BioMarkovChain represents a Markov chain used in biological sequence analysis. It contains a transition probability matrix (tpm) and an initial distribution of probabilities (inits) and also the order of the Markov chain.

Fields

Constructors

Example

julia
sequence = LongDNA{4}("ACTACATCTA")
+
+model = BioMarkovChain(sequence, 2)
+BioMarkovChain of DNAAlphabet{4}() and order 1:
+  - Transition Probability Matrix -> Matrix{Float64}(4 × 4):
+   0.0     0.6667  0.0     0.3333
+   0.3333  0.0     0.0     0.6667
+   0.0     0.0     0.0     0.0
+   0.6667  0.3333  0.0     0.0
+  - Initial Probabilities -> Vector{Float64}(4 × 1):
+   0.3333
+   0.3333
+   0.0
+   0.3333

source


`,4),i={style:{"border-width":"1px","border-style":"solid","border-color":"black",padding:"1em","border-radius":"25px"}},d=a("a",{id:"BioMarkovChains.dnaseqprobability-Union{Tuple{A}, Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}, BioMarkovChain}} where A<:BioSequences.NucleicAcidAlphabet",href:"#BioMarkovChains.dnaseqprobability-Union{Tuple{A}, Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}, BioMarkovChain}} where A<:BioSequences.NucleicAcidAlphabet"},"#",-1),o=a("b",null,[a("u",null,"BioMarkovChains.dnaseqprobability")],-1),m=a("i",null,"Method",-1),r=Q('
julia
dnaseqprobability(sequence::LongNucOrView{4}, model::BioMarkovChain)

Compute the probability of a given sequence using a transition probability matrix and the initial probabilities distributions of a BioMarkovModel.

',2),h={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},p={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-2.819ex"},xmlns:"http://www.w3.org/2000/svg",width:"41.497ex",height:"6.73ex",role:"img",focusable:"false",viewBox:"0 -1728.7 18341.5 2974.6","aria-hidden":"true"},g=Q('',1),c=[g],k=a("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[a("mi",null,"P"),a("mo",{stretchy:"false"},"("),a("msub",null,[a("mi",null,"X"),a("mn",null,"1")]),a("mo",null,"="),a("msub",null,[a("mi",null,"i"),a("mn",null,"1")]),a("mo",null,","),a("mo",null,"…"),a("mo",null,","),a("msub",null,[a("mi",null,"X"),a("mi",null,"T")]),a("mo",null,"="),a("msub",null,[a("mi",null,"i"),a("mi",null,"T")]),a("mo",{stretchy:"false"},")"),a("mo",null,"="),a("msubsup",null,[a("mi",null,"π"),a("mrow",{"data-mjx-texclass":"ORD"},[a("msub",null,[a("mi",null,"i"),a("mn",null,"1")])]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"T"),a("mo",null,"−"),a("mn",null,"1")])]),a("munderover",null,[a("mo",{"data-mjx-texclass":"OP"},"∏"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"t"),a("mo",null,"="),a("mn",null,"1")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"T"),a("mo",null,"−"),a("mn",null,"1")])]),a("msub",null,[a("mi",null,"a"),a("mrow",{"data-mjx-texclass":"ORD"},[a("msub",null,[a("mi",null,"i"),a("mi",null,"t")]),a("mo",null,","),a("msub",null,[a("mi",null,"i"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"t"),a("mo",null,"+"),a("mn",null,"1")])])])])])],-1),u=Q(`

Arguments

Returns

Example

julia
mainseq = LongDNA{4}("CCTCCCGGACCCTGGGCTCGGGAC")
+   
+bmc = BioMarkovChain(mainseq)
+
+    BioMarkovChain of DNAAlphabet{4}() and order 1:
+      - Transition Probability Matrix -> Matrix{Float64}(4 × 4):
+       0.0     1.0     0.0     0.0
+       0.0     0.5     0.2     0.3
+       0.25    0.125   0.625   0.0
+       0.0     0.6667  0.3333  0.0
+      - Initial Probabilities -> Vector{Float64}(4 × 1):
+       0.087
+       0.4348
+       0.3478
+       0.1304
+
+newseq = LongDNA{4}("CCTG")
+
+    4nt DNA Sequence:
+    CCTG
+
+
+dnaseqprobability(newseq, bmc)
+    
+    0.021739130434782608

source

`,7),H=a("br",null,null,-1),x={style:{"border-width":"1px","border-style":"solid","border-color":"black",padding:"1em","border-radius":"25px"}},y=a("a",{id:"BioMarkovChains.initials-Union{Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}}, Tuple{A}} where A<:BioSequences.Alphabet",href:"#BioMarkovChains.initials-Union{Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}}, Tuple{A}} where A<:BioSequences.Alphabet"},"#",-1),L=a("b",null,[a("u",null,"BioMarkovChains.initials")],-1),w=a("i",null,"Method",-1),b=Q('
julia
initials(sequence::SeqOrView{A}) where A

Calculate the estimated initial probabilities for a Markov chain based on a given sequence.

This function takes a sequence of states and calculates the estimated initial probabilities of each state in the sequence for a Markov chain. The initial probabilities are estimated by counting the occurrences of each state at the beginning of the sequence and normalizing the counts to sum up to 1.

',3),_={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},M={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-4.275ex"},xmlns:"http://www.w3.org/2000/svg",width:"24.706ex",height:"9.681ex",role:"img",focusable:"false",viewBox:"0 -2389.4 10919.9 4278.9","aria-hidden":"true"},f=Q('',1),C=[f],D=a("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[a("mtable",{displaystyle:"true",columnalign:"right left",columnspacing:"0em",rowspacing:"3pt"},[a("mtr",null,[a("mtd",null,[a("mi",null,"π"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i")])]),a("mtd",null,[a("mi"),a("mo",null,"="),a("mi",null,"P"),a("mo",{stretchy:"false"},"("),a("msub",null,[a("mi",null,"X"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i")])]),a("mo",null,"="),a("mi",null,"i"),a("mo",{stretchy:"false"},")"),a("mo",null,","),a("mi",null,"i"),a("mo",null,"∈"),a("mi",null,"T")])]),a("mtr",null,[a("mtd",null,[a("munderover",null,[a("mo",{"data-mjx-texclass":"OP"},"∑"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i"),a("mo",null,"="),a("mn",null,"1")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"N")])]),a("msub",null,[a("mi",null,"π"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i")])])]),a("mtd",null,[a("mi"),a("mo",null,"="),a("mn",null,"1")])])])])],-1),A=a("p",null,"Now using the dinucleotides counts estimating the initials would follow:",-1),E={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},Z={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-2.75ex"},xmlns:"http://www.w3.org/2000/svg",width:"12.788ex",height:"4.899ex",role:"img",focusable:"false",viewBox:"0 -950 5652.2 2165.5","aria-hidden":"true"},v=Q('',1),F=[v],B=a("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[a("mrow",{"data-mjx-texclass":"ORD"},[a("mover",null,[a("msub",null,[a("mi",null,"π"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i")])]),a("mo",{stretchy:"false"},"^")])]),a("mo",null,"="),a("msub",null,[a("mi",null,"c"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i")])]),a("munder",null,[a("mo",{"data-mjx-texclass":"OP"},"∑"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"k")])]),a("msub",null,[a("mi",null,"c"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"k")])])])],-1),V=a("p",null,[a("strong",null,"Arguments")],-1),j=a("ul",null,[a("li",null,[a("code",null,"sequence::SeqOrView{A}"),t(": The sequence of states representing the Markov chain.")])],-1),R=a("p",null,[a("strong",null,"Returns")],-1),O=a("p",null,[t("An "),a("code",null,"Vector{Flot64}"),t(" of estimated initial probabilities for each state in the sequence.")],-1),S=a("p",null,[a("a",{href:"https://github.com/camilogarciabotero/BioMarkovChains.jl/",target:"_blank",rel:"noreferrer"},"source")],-1),q=a("br",null,null,-1),X={style:{"border-width":"1px","border-style":"solid","border-color":"black",padding:"1em","border-radius":"25px"}},N=a("a",{id:"BioMarkovChains.log_odds_ratio_matrix-Tuple{BioMarkovChain, BioMarkovChain}",href:"#BioMarkovChains.log_odds_ratio_matrix-Tuple{BioMarkovChain, BioMarkovChain}"},"#",-1),P=a("b",null,[a("u",null,"BioMarkovChains.log_odds_ratio_matrix")],-1),I=a("i",null,"Method",-1),G=Q('
julia
log_odds_ratio_matrix(model1::BioMarkovChain, model2::BioMarkovChain)

Calculates the log-odds ratio between the transition probability matrices of two BioMarkovChain models.

',2),U={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},z={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-2.172ex"},xmlns:"http://www.w3.org/2000/svg",width:"16.289ex",height:"5.475ex",role:"img",focusable:"false",viewBox:"0 -1460 7199.8 2420","aria-hidden":"true"},J=Q('𝓂𝓂',1),W=[J],$=a("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[a("mi",null,"β"),a("mo",null,"="),a("mi",null,"log"),a("mo",{"data-mjx-texclass":"NONE"},"⁡"),a("mfrac",null,[a("mrow",null,[a("mi",null,"P"),a("mo",{stretchy:"false"},"("),a("mi",null,"x"),a("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),a("msub",null,[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"m")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mn",null,"1")])]),a("mo",{stretchy:"false"},")")]),a("mrow",null,[a("mi",null,"P"),a("mo",{stretchy:"false"},"("),a("mi",null,"x"),a("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),a("msub",null,[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"m")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mn",null,"2")])]),a("mo",{stretchy:"false"},")")])])])],-1),K={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},Y={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.452ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.345ex",height:"2.149ex",role:"img",focusable:"false",viewBox:"0 -750 1036.6 950","aria-hidden":"true"},a2=Q('𝓂',1),t2=[a2],Q2=a("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[a("msub",null,[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"m")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mn",null,"1")])])])],-1),s2={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},e2={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.452ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.345ex",height:"2.149ex",role:"img",focusable:"false",viewBox:"0 -750 1036.6 950","aria-hidden":"true"},T2=Q('𝓂',1),l2=[T2],n2=a("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[a("msub",null,[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"m")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mn",null,"2")])])])],-1),i2=a("p",null,[a("strong",null,"Arguments")],-1),d2=a("ul",null,[a("li",null,[a("p",null,[a("code",null,"model1::BioMarkovChain"),t(": The first BioMarkovChain model.")])]),a("li",null,[a("p",null,[a("code",null,"model2::BioMarkovChain"),t(": The second BioMarkovChain model.")])])],-1),o2=a("p",null,[a("a",{href:"https://github.com/camilogarciabotero/BioMarkovChains.jl/",target:"_blank",rel:"noreferrer"},"source")],-1),m2=Q(`
# BioMarkovChains.log_odds_ratio_matrixMethod.
julia
log_odds_ratio_matrix(sequence::NucleicSeqOrView{A}, model::BioMarkovChain) where A

Calculates the log-odds ratio between the transition probability matrix of a given DNA sequence and a reference model.

Arguments

Examples

julia
sequence = LongNucOrView{4}("ACGT")
+model = BioMarkovChain(sequence)  # Provide appropriate initialization for BioMarkovChain
+result = log_odds_ratio_matrix(sequence, model)

source


`,3),r2={style:{"border-width":"1px","border-style":"solid","border-color":"black",padding:"1em","border-radius":"25px"}},h2=a("a",{id:"BioMarkovChains.log_odds_ratio_score-Union{Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}}, Tuple{A}} where A<:BioSequences.Alphabet",href:"#BioMarkovChains.log_odds_ratio_score-Union{Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}}, Tuple{A}} where A<:BioSequences.Alphabet"},"#",-1),p2=a("b",null,[a("u",null,"BioMarkovChains.log_odds_ratio_score")],-1),g2=a("i",null,"Method",-1),c2=Q('
julia
log_odds_ratio_score(sequence::SeqOrView{A}, model::BioMarkovChain; b::Number =)

Compute the log odds ratio score between a given sequence and a BioMarkovChain model.

',2),k2={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},u2={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-3.033ex"},xmlns:"http://www.w3.org/2000/svg",width:"31.936ex",height:"7.198ex",role:"img",focusable:"false",viewBox:"0 -1840.7 14115.8 3181.3","aria-hidden":"true"},H2=Q('𝓂𝓂',1),x2=[H2],y2=a("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[a("mi",null,"S"),a("mo",{stretchy:"false"},"("),a("mi",null,"x"),a("mo",{stretchy:"false"},")"),a("mo",null,"="),a("munderover",null,[a("mo",{"data-mjx-texclass":"OP"},"∑"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i"),a("mo",null,"="),a("mn",null,"1")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"L")])]),a("msub",null,[a("mi",null,"β"),a("mrow",{"data-mjx-texclass":"ORD"},[a("msub",null,[a("mi",null,"x"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i")])]),a("mi",null,"x")])]),a("mo",null,"="),a("munder",null,[a("mo",{"data-mjx-texclass":"OP"},"∑"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i"),a("mo",null,"="),a("mn",null,"1")])]),a("mi",null,"log"),a("mo",{"data-mjx-texclass":"NONE"},"⁡"),a("mfrac",null,[a("mrow",null,[a("msubsup",null,[a("mi",null,"a"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i"),a("mo",null,"−"),a("mn",null,"1")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("msub",null,[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"m")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mn",null,"1")])])])]),a("msub",null,[a("mi",null,"x"),a("mi",null,"i")])]),a("mrow",null,[a("msubsup",null,[a("mi",null,"a"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i"),a("mo",null,"−"),a("mn",null,"1")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("msub",null,[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"m")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mn",null,"2")])])])]),a("msub",null,[a("mi",null,"x"),a("mi",null,"i")])])])])],-1),L2=Q('

Arguments

Returns

The log odds ratio score between the sequence and the model.

Example

source

',6),w2=Q(`
# BioMarkovChains.perronfrobeniusMethod.
julia
perronfrobenius(sequence::SeqOrView{A}, n::Int64=1) where A

Compute the Perron-Frobenius matrix, a column-stochastic version of the transition probability matrix (TPM), for a given nucleotide sequence.

The Perron-Frobenius matrix captures the asymptotic probabilities of transitioning between nucleotides in the sequence over a specified number of steps n. It provides insight into the long-term behavior of a Markov chain or a dynamical system associated with the sequence.

Arguments

Returns

A copy of the Perron-Frobenius matrix. Each column of this matrix corresponds to the probabilities of transitioning from the current nucleotide state to all possible nucleotide states after n steps.

Example

julia
sequence = LongSequence{DNAAlphabet{4}}("ACGTCGTCCACTACGACATCAGC")  # Replace with an actual nucleotide sequence
+n = 2
+pf = perronfrobenius(sequence, n)

source


# BioMarkovChains.transition_count_matrixMethod.
julia
transition_count_matrix(sequence::LongSequence{DNAAlphabet{4}})

Compute the transition count matrix (TCM) of a given DNA sequence.

Arguments

Returns

A Matrix object representing the transition count matrix of the sequence.

Example

seq = LongDNA{4}("AGCTAGCTAGCT")
+
+tcm = transition_count_matrix(seq)
+
+4×4 Matrix{Int64}:
+ 0  0  3  0
+ 0  0  0  3
+ 0  3  0  0
+ 2  0  0  0

source


`,5),b2={style:{"border-width":"1px","border-style":"solid","border-color":"black",padding:"1em","border-radius":"25px"}},_2=a("a",{id:"BioMarkovChains.transition_probability_matrix-Union{Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}}, Tuple{A}, Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}, Int64}} where A<:BioSequences.Alphabet",href:"#BioMarkovChains.transition_probability_matrix-Union{Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}}, Tuple{A}, Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}, Int64}} where A<:BioSequences.Alphabet"},"#",-1),M2=a("b",null,[a("u",null,"BioMarkovChains.transition_probability_matrix")],-1),f2=a("i",null,"Method",-1),C2=Q('
julia
transition_probability_matrix(sequence::LongSequence{DNAAlphabet{4}}, n::Int64=1)
',1),D2={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},A2={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.113ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.751ex",height:"2.577ex",role:"img",focusable:"false",viewBox:"0 -1089 1216 1139","aria-hidden":"true"},E2=Q('',1),Z2=[E2],v2=a("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 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Keywords

Returns

A Matrix object representing the transition probability matrix of the sequence.

Example

julia
seq = dna"AGCTAGCTAGCT"
+
+tpm = transition_probability_matrix(seq)
+
+4×4 Matrix{Float64}:
+ 0.0  0.0  1.0  0.0
+ 0.0  0.0  0.0  1.0
+ 0.0  1.0  0.0  0.0
+ 1.0  0.0  0.0  0.0

source

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# BioMarkovChains.BMCType.
julia
BMC

Alias for the type BioMarkovChain.

source


# BioMarkovChains.BioMarkovChainType.
julia
struct BioMarkovChain{S<:DataType, M<:AbstractMatrix, I<:AbstractVector, N<:Integer} <: AbstractBioMarkovChain

A BioMarkovChain represents a Markov chain used in biological sequence analysis. It contains a transition probability matrix (tpm) and an initial distribution of probabilities (inits) and also the order of the Markov chain.

Fields

Constructors

Example

julia
sequence = LongDNA{4}("ACTACATCTA")
-
-model = BioMarkovChain(sequence, 2)
-BioMarkovChain of DNAAlphabet{4}() and order 1:
-  - Transition Probability Matrix -> Matrix{Float64}(4 × 4):
-   0.0     0.6667  0.0     0.3333
-   0.3333  0.0     0.0     0.6667
-   0.0     0.0     0.0     0.0
-   0.6667  0.3333  0.0     0.0
-  - Initial Probabilities -> Vector{Float64}(4 × 1):
-   0.3333
-   0.3333
-   0.0
-   0.3333

source


`,4),i={style:{"border-width":"1px","border-style":"solid","border-color":"black",padding:"1em","border-radius":"25px"}},d=a("a",{id:"BioMarkovChains.dnaseqprobability-Union{Tuple{A}, Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}, BioMarkovChain}} where A<:BioSequences.NucleicAcidAlphabet",href:"#BioMarkovChains.dnaseqprobability-Union{Tuple{A}, Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}, BioMarkovChain}} where A<:BioSequences.NucleicAcidAlphabet"},"#",-1),o=a("b",null,[a("u",null,"BioMarkovChains.dnaseqprobability")],-1),m=a("i",null,"Method",-1),r=Q('
julia
dnaseqprobability(sequence::LongNucOrView{4}, model::BioMarkovChain)

Compute the probability of a given sequence using a transition probability matrix and the initial probabilities distributions of a BioMarkovModel.

',2),h={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},p={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-2.819ex"},xmlns:"http://www.w3.org/2000/svg",width:"41.497ex",height:"6.73ex",role:"img",focusable:"false",viewBox:"0 -1728.7 18341.5 2974.6","aria-hidden":"true"},g=Q('',1),c=[g],k=a("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[a("mi",null,"P"),a("mo",{stretchy:"false"},"("),a("msub",null,[a("mi",null,"X"),a("mn",null,"1")]),a("mo",null,"="),a("msub",null,[a("mi",null,"i"),a("mn",null,"1")]),a("mo",null,","),a("mo",null,"…"),a("mo",null,","),a("msub",null,[a("mi",null,"X"),a("mi",null,"T")]),a("mo",null,"="),a("msub",null,[a("mi",null,"i"),a("mi",null,"T")]),a("mo",{stretchy:"false"},")"),a("mo",null,"="),a("msubsup",null,[a("mi",null,"π"),a("mrow",{"data-mjx-texclass":"ORD"},[a("msub",null,[a("mi",null,"i"),a("mn",null,"1")])]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"T"),a("mo",null,"−"),a("mn",null,"1")])]),a("munderover",null,[a("mo",{"data-mjx-texclass":"OP"},"∏"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"t"),a("mo",null,"="),a("mn",null,"1")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"T"),a("mo",null,"−"),a("mn",null,"1")])]),a("msub",null,[a("mi",null,"a"),a("mrow",{"data-mjx-texclass":"ORD"},[a("msub",null,[a("mi",null,"i"),a("mi",null,"t")]),a("mo",null,","),a("msub",null,[a("mi",null,"i"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"t"),a("mo",null,"+"),a("mn",null,"1")])])])])])],-1),u=Q(`

Arguments

Returns

Example

julia
mainseq = LongDNA{4}("CCTCCCGGACCCTGGGCTCGGGAC")
-   
-bmc = BioMarkovChain(mainseq)
-
-    BioMarkovChain of DNAAlphabet{4}() and order 1:
-      - Transition Probability Matrix -> Matrix{Float64}(4 × 4):
-       0.0     1.0     0.0     0.0
-       0.0     0.5     0.2     0.3
-       0.25    0.125   0.625   0.0
-       0.0     0.6667  0.3333  0.0
-      - Initial Probabilities -> Vector{Float64}(4 × 1):
-       0.087
-       0.4348
-       0.3478
-       0.1304
-
-newseq = LongDNA{4}("CCTG")
-
-    4nt DNA Sequence:
-    CCTG
-
-
-dnaseqprobability(newseq, bmc)
-    
-    0.021739130434782608

source

`,7),H=a("br",null,null,-1),x={style:{"border-width":"1px","border-style":"solid","border-color":"black",padding:"1em","border-radius":"25px"}},y=a("a",{id:"BioMarkovChains.initials-Union{Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}}, Tuple{A}} where A<:BioSequences.Alphabet",href:"#BioMarkovChains.initials-Union{Tuple{Union{BioSequences.LongSequence{A}, BioSequences.LongSubSeq{A}}}, Tuple{A}} where A<:BioSequences.Alphabet"},"#",-1),L=a("b",null,[a("u",null,"BioMarkovChains.initials")],-1),w=a("i",null,"Method",-1),b=Q('
julia
initials(sequence::SeqOrView{A}) where A

Calculate the estimated initial probabilities for a Markov chain based on a given sequence.

This function takes a sequence of states and calculates the estimated initial probabilities of each state in the sequence for a Markov chain. The initial probabilities are estimated by counting the occurrences of each state at the beginning of the sequence and normalizing the counts to sum up to 1.

',3),_={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},M={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-4.275ex"},xmlns:"http://www.w3.org/2000/svg",width:"24.706ex",height:"9.681ex",role:"img",focusable:"false",viewBox:"0 -2389.4 10919.9 4278.9","aria-hidden":"true"},f=Q('',1),C=[f],D=a("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[a("mtable",{displaystyle:"true",columnalign:"right left",columnspacing:"0em",rowspacing:"3pt"},[a("mtr",null,[a("mtd",null,[a("mi",null,"π"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i")])]),a("mtd",null,[a("mi"),a("mo",null,"="),a("mi",null,"P"),a("mo",{stretchy:"false"},"("),a("msub",null,[a("mi",null,"X"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i")])]),a("mo",null,"="),a("mi",null,"i"),a("mo",{stretchy:"false"},")"),a("mo",null,","),a("mi",null,"i"),a("mo",null,"∈"),a("mi",null,"T")])]),a("mtr",null,[a("mtd",null,[a("munderover",null,[a("mo",{"data-mjx-texclass":"OP"},"∑"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i"),a("mo",null,"="),a("mn",null,"1")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"N")])]),a("msub",null,[a("mi",null,"π"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i")])])]),a("mtd",null,[a("mi"),a("mo",null,"="),a("mn",null,"1")])])])])],-1),A=a("p",null,"Now using the dinucleotides counts estimating the initials would follow:",-1),E={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},Z={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-2.75ex"},xmlns:"http://www.w3.org/2000/svg",width:"12.788ex",height:"4.899ex",role:"img",focusable:"false",viewBox:"0 -950 5652.2 2165.5","aria-hidden":"true"},v=Q('',1),F=[v],B=a("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[a("mrow",{"data-mjx-texclass":"ORD"},[a("mover",null,[a("msub",null,[a("mi",null,"π"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i")])]),a("mo",{stretchy:"false"},"^")])]),a("mo",null,"="),a("msub",null,[a("mi",null,"c"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i")])]),a("munder",null,[a("mo",{"data-mjx-texclass":"OP"},"∑"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"k")])]),a("msub",null,[a("mi",null,"c"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"k")])])])],-1),V=a("p",null,[a("strong",null,"Arguments")],-1),j=a("ul",null,[a("li",null,[a("code",null,"sequence::SeqOrView{A}"),t(": The sequence of states representing the Markov chain.")])],-1),R=a("p",null,[a("strong",null,"Returns")],-1),O=a("p",null,[t("An "),a("code",null,"Vector{Flot64}"),t(" of estimated initial probabilities for each state in the sequence.")],-1),S=a("p",null,[a("a",{href:"https://github.com/camilogarciabotero/BioMarkovChains.jl/",target:"_blank",rel:"noreferrer"},"source")],-1),q=a("br",null,null,-1),X={style:{"border-width":"1px","border-style":"solid","border-color":"black",padding:"1em","border-radius":"25px"}},N=a("a",{id:"BioMarkovChains.log_odds_ratio_matrix-Tuple{BioMarkovChain, BioMarkovChain}",href:"#BioMarkovChains.log_odds_ratio_matrix-Tuple{BioMarkovChain, BioMarkovChain}"},"#",-1),P=a("b",null,[a("u",null,"BioMarkovChains.log_odds_ratio_matrix")],-1),I=a("i",null,"Method",-1),G=Q('
julia
log_odds_ratio_matrix(model1::BioMarkovChain, model2::BioMarkovChain)

Calculates the log-odds ratio between the transition probability matrices of two BioMarkovChain models.

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# BioMarkovChains.log_odds_ratio_matrixMethod.
julia
log_odds_ratio_matrix(sequence::NucleicSeqOrView{A}, model::BioMarkovChain) where A

Calculates the log-odds ratio between the transition probability matrix of a given DNA sequence and a reference model.

Arguments

Examples

julia
sequence = LongNucOrView{4}("ACGT")
-model = BioMarkovChain(sequence)  # Provide appropriate initialization for BioMarkovChain
-result = log_odds_ratio_matrix(sequence, model)

source


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julia
log_odds_ratio_score(sequence::SeqOrView{A}, model::BioMarkovChain; b::Number =)

Compute the log odds ratio score between a given sequence and a BioMarkovChain model.

',2),k2={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},u2={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-3.033ex"},xmlns:"http://www.w3.org/2000/svg",width:"31.936ex",height:"7.198ex",role:"img",focusable:"false",viewBox:"0 -1840.7 14115.8 3181.3","aria-hidden":"true"},H2=Q('𝓂𝓂',1),x2=[H2],y2=a("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[a("mi",null,"S"),a("mo",{stretchy:"false"},"("),a("mi",null,"x"),a("mo",{stretchy:"false"},")"),a("mo",null,"="),a("munderover",null,[a("mo",{"data-mjx-texclass":"OP"},"∑"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i"),a("mo",null,"="),a("mn",null,"1")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"L")])]),a("msub",null,[a("mi",null,"β"),a("mrow",{"data-mjx-texclass":"ORD"},[a("msub",null,[a("mi",null,"x"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i")])]),a("mi",null,"x")])]),a("mo",null,"="),a("munder",null,[a("mo",{"data-mjx-texclass":"OP"},"∑"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i"),a("mo",null,"="),a("mn",null,"1")])]),a("mi",null,"log"),a("mo",{"data-mjx-texclass":"NONE"},"⁡"),a("mfrac",null,[a("mrow",null,[a("msubsup",null,[a("mi",null,"a"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i"),a("mo",null,"−"),a("mn",null,"1")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("msub",null,[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"m")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mn",null,"1")])])])]),a("msub",null,[a("mi",null,"x"),a("mi",null,"i")])]),a("mrow",null,[a("msubsup",null,[a("mi",null,"a"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i"),a("mo",null,"−"),a("mn",null,"1")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("msub",null,[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"m")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mn",null,"2")])])])]),a("msub",null,[a("mi",null,"x"),a("mi",null,"i")])])])])],-1),L2=Q('

Arguments

Returns

The log odds ratio score between the sequence and the model.

Example

source

',6),w2=Q(`
# BioMarkovChains.perronfrobeniusMethod.
julia
perronfrobenius(sequence::SeqOrView{A}, n::Int64=1) where A

Compute the Perron-Frobenius matrix, a column-stochastic version of the transition probability matrix (TPM), for a given nucleotide sequence.

The Perron-Frobenius matrix captures the asymptotic probabilities of transitioning between nucleotides in the sequence over a specified number of steps n. It provides insight into the long-term behavior of a Markov chain or a dynamical system associated with the sequence.

Arguments

Returns

A copy of the Perron-Frobenius matrix. Each column of this matrix corresponds to the probabilities of transitioning from the current nucleotide state to all possible nucleotide states after n steps.

Example

julia
sequence = LongSequence{DNAAlphabet{4}}("ACGTCGTCCACTACGACATCAGC")  # Replace with an actual nucleotide sequence
-n = 2
-pf = perronfrobenius(sequence, n)

source


# BioMarkovChains.transition_count_matrixMethod.
julia
transition_count_matrix(sequence::LongSequence{DNAAlphabet{4}})

Compute the transition count matrix (TCM) of a given DNA sequence.

Arguments

Returns

A Matrix object representing the transition count matrix of the sequence.

Example

seq = LongDNA{4}("AGCTAGCTAGCT")
-
-tcm = transition_count_matrix(seq)
-
-4×4 Matrix{Int64}:
- 0  0  3  0
- 0  0  0  3
- 0  3  0  0
- 2  0  0  0

source


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julia
transition_probability_matrix(sequence::LongSequence{DNAAlphabet{4}}, n::Int64=1)
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Keywords

Returns

A Matrix object representing the transition probability matrix of the sequence.

Example

julia
seq = dna"AGCTAGCTAGCT"
-
-tpm = transition_probability_matrix(seq)
-
-4×4 Matrix{Float64}:
- 0.0  0.0  1.0  0.0
- 0.0  0.0  0.0  1.0
- 0.0  1.0  0.0  0.0
- 1.0  0.0  0.0  0.0

source

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From BMC to sequence probabilties

We can now calculate a transition probability matrix from a LongDNA sequence using the transition_probability_matrix and initials methods:

julia
using BioSequences
+import{_ as s,c as a,o as n,a6 as i}from"./chunks/framework.Cvv1S4VC.js";const g=JSON.parse('{"title":"From BMC to sequence probabilties","description":"","frontmatter":{},"headers":[],"relativePath":"article01.md","filePath":"article01.md","lastUpdated":null}'),e={name:"article01.md"},t=i(`

From BMC to sequence probabilties

We can now calculate a transition probability matrix from a LongDNA sequence using the transition_probability_matrix and initials methods:

julia
using BioSequences
 
 sequence = dna"CCTCCCGGACCCTGGGCTCGGGAC"
 
@@ -6,7 +6,7 @@ import{_ as s,c as a,o as n,a7 as i}from"./chunks/framework.D1rHXXmm.js";const g
 initials = initials(sequence)
 
 println(tpm)
-println(initials)
4×4 Matrix{Float64}:
+println(initials)
4×4 Matrix{Float64}:
  0.0   1.0       0.0       0.0
  0.0   0.5       0.2       0.3
  0.25  0.125     0.625     0.0
@@ -16,4 +16,4 @@ import{_ as s,c as a,o as n,a7 as i}from"./chunks/framework.D1rHXXmm.js";const g
  0.08695652173913043
  0.43478260869565216
  0.34782608695652173
- 0.13043478260869565
`,5),t=[p];function l(c,o,h,r,d,k){return n(),a("div",null,t)}const C=s(e,[["render",l]]);export{g as __pageData,C as default}; + 0.13043478260869565
`,5),p=[t];function l(c,o,h,r,d,k){return n(),a("div",null,p)}const C=s(e,[["render",l]]);export{g as __pageData,C as default}; diff --git a/dev/assets/article01.md.BrGXw77Y.lean.js b/dev/assets/article01.md.BrGXw77Y.lean.js new file mode 100644 index 0000000..0987431 --- /dev/null +++ b/dev/assets/article01.md.BrGXw77Y.lean.js @@ -0,0 +1 @@ +import{_ as s,c as a,o as n,a6 as i}from"./chunks/framework.Cvv1S4VC.js";const g=JSON.parse('{"title":"From BMC to sequence probabilties","description":"","frontmatter":{},"headers":[],"relativePath":"article01.md","filePath":"article01.md","lastUpdated":null}'),e={name:"article01.md"},t=i("",5),p=[t];function l(c,o,h,r,d,k){return n(),a("div",null,p)}const C=s(e,[["render",l]]);export{g as __pageData,C as default}; diff --git a/dev/assets/biomarkovchains.md.DhkAaXJV.js b/dev/assets/biomarkovchains.md.0rg0z1ev.js similarity index 99% rename from dev/assets/biomarkovchains.md.DhkAaXJV.js rename to dev/assets/biomarkovchains.md.0rg0z1ev.js index 47dea7b..07d72f8 100644 --- a/dev/assets/biomarkovchains.md.DhkAaXJV.js +++ b/dev/assets/biomarkovchains.md.0rg0z1ev.js @@ -1 +1 @@ -import{_ as s,c as a,l as t,a as Q,a7 as e,o as T}from"./chunks/framework.D1rHXXmm.js";const g6=JSON.parse('{"title":"Towards Markov Chains","description":"","frontmatter":{},"headers":[],"relativePath":"biomarkovchains.md","filePath":"biomarkovchains.md","lastUpdated":null}'),l={name:"biomarkovchains.md"},o=e('

Towards Markov Chains

DNA as a Markov chain

Several packages (e.g. MarkovChainsHammer.jl, DiscreteMarkovChains.jl, etc.) in the Julia ecosystem have been implemented to work with Markov chains with a state space of integers, those could be efficient in many ways, but they are clumsy to work with a specialized biological types as in the BioJulia ecosystem. Therefore, in the GeneFinder package we dedicated some implementations to work with BioSequence types so that we can expand the functionality in an efficient way (see complete API).

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"),t("em",null,"Comparative Gene Finding"),Q(". Vol. 20. Computational Biology. London: Springer London. "),t("a",{href:"http://link.springer.com/10.1007/978-1-4471-6693-1",target:"_blank",rel:"noreferrer"},"http://link.springer.com/10.1007/978-1-4471-6693-1"),Q(".")],-1);function n6(d6,m6,r6,i6,h6,c6){return T(),a("div",null,[o,t("p",null,[Q("One important step towards many gene finding algorithms is to represent a DNA sequence as a Markov chain. In this representation a DNA sequence of a reduced alphabet "),t("mjx-container",n,[(T(),a("svg",d,r)),i]),Q(" is draw as a four-vertex graph, where each letter of "),t("mjx-container",h,[(T(),a("svg",c,g)),x]),Q(" is a "),w,Q(" and the edges of the graph represent "),u,Q(" from one nucleotide to another in a sequence (e.g. "),t("mjx-container",H,[(T(),a("svg",_,f)),M]),Q(" represent a single nucleotide to nucleotide transition). This is also considered more specifically as a Discrete Markov chain (Axelson-Fisk 2015). The complete set of transitions and states of a DNA sequence of alphabet "),t("mjx-container",k,[(T(),a("svg",y,b)),V]),Q(".")]),t("p",null,[Q("More formally a Markov chain is a random process where each state is a random variable "),t("mjx-container",v,[(T(),a("svg",D,A)),C]),Q(" where "),t("mjx-container",R,[(T(),a("svg",S,P)),X]),Q(" is a discrete time in a finite sequence "),t("mjx-container",E,[(T(),a("svg",I,G)),N]),Q(" and the probability to jump from one state into another is "),z,Q(". Therefore a definition of this "),J,Q(" is given by:")]),t("mjx-container",q,[(T(),a("svg",F,U)),W]),t("p",null,[Q("where "),t("mjx-container",K,[(T(),a("svg",Y,Q1)),a1]),Q(" . This property led us to generalize a way to calculate the probability of a sequence "),t("mjx-container",T1,[(T(),a("svg",e1,l1)),o1]),Q(" from a process "),t("mjx-container",n1,[(T(),a("svg",d1,r1)),i1]),Q(" where each random variable is a nucleotide from "),t("mjx-container",h1,[(T(),a("svg",c1,g1)),x1]),Q(" so that:")]),t("mjx-container",w1,[(T(),a("svg",u1,_1)),L1]),t("p",null,[Q("Note that previous equations has two terms, a initial probability "),t("mjx-container",f1,[(T(),a("svg",M1,y1)),Z1]),Q(" and the the product of all transitions beginning at "),t("mjx-container",b1,[(T(),a("svg",V1,D1)),j1]),Q(". So, to calculate the initial probability distribution of each of the nucleotides of a string "),t("mjx-container",A1,[(T(),a("svg",C1,S1)),O1]),Q(" with the alphabet 𝒜 we can first calculate the transition probability matrix "),t("mjx-container",P1,[(T(),a("svg",X1,I1)),B1]),Q(" out of the frequency count of the transitions. In an alphabet 𝒜 we got "),t("mjx-container",G1,[(T(),a("svg",N1,J1)),q1]),Q(" transitions of one order, that is the "),t("mjx-container",F1,[(T(),a("svg",$1,W1)),K1]),Q(" which coincides with the frequency of the dinucleotides in the sequence. So we can later in fact build a "),t("mjx-container",Y1,[(T(),a("svg",t2,a2)),T2]),Q(" matrix representing all the transitions. For instance in a DNA sequence "),t("mjx-container",e2,[(T(),a("svg",s2,o2)),n2]),Q(" of 24 nucleotides:")]),t("mjx-container",d2,[(T(),a("svg",m2,i2)),h2]),t("p",null,[Q("We can calculate each frequency nucleotide to any other nucleotide "),t("mjx-container",c2,[(T(),a("svg",p2,x2)),w2]),Q(" where "),t("mjx-container",u2,[(T(),a("svg",H2,L2)),f2]),Q(" is the actual count of the dinucleotide, and therefore "),t("mjx-container",M2,[(T(),a("svg",k2,Z2)),b2]),Q(" is the counts of the nucleotide "),t("mjx-container",V2,[(T(),a("svg",v2,j2)),A2]),Q(" to any other nucleotide and build the transition probability matrix:")]),t("mjx-container",C2,[(T(),a("svg",R2,O2)),P2]),t("p",null,[Q("It is noteworthy that initial probabilities can also be obtained from the counts of each nucleotide transitions "),t("mjx-container",X2,[(T(),a("svg",E2,B2)),G2]),Q(" over the total sum of the dinucleotide counts "),t("mjx-container",N2,[(T(),a("svg",z2,q2)),F2]),Q(":")]),t("mjx-container",$2,[(T(),a("svg",U2,K2)),Y2]),t("p",null,[Q("That way for the previous example example we can can calculate the initial probabilities "),t("mjx-container",t6,[(T(),a("svg",Q6,T6)),e6]),Q(". Both set of probabilities composed a "),s6,Q(" that can be used to predict the probability of any DNA sequence using equation (2).")]),l6,o6])}const x6=s(l,[["render",n6]]);export{g6 as __pageData,x6 as default}; +import{_ as s,c as a,j as t,a as Q,a6 as e,o as T}from"./chunks/framework.Cvv1S4VC.js";const g6=JSON.parse('{"title":"Towards Markov Chains","description":"","frontmatter":{},"headers":[],"relativePath":"biomarkovchains.md","filePath":"biomarkovchains.md","lastUpdated":null}'),l={name:"biomarkovchains.md"},o=e('

Towards Markov Chains

DNA as a Markov chain

Several packages (e.g. MarkovChainsHammer.jl, DiscreteMarkovChains.jl, etc.) in the Julia ecosystem have been implemented to work with Markov chains with a state space of integers, those could be efficient in many ways, but they are clumsy to work with a specialized biological types as in the BioJulia ecosystem. Therefore, in the GeneFinder package we dedicated some implementations to work with BioSequence types so that we can expand the functionality in an efficient way (see complete API).

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"),t("em",null,"Comparative Gene Finding"),Q(". Vol. 20. Computational Biology. London: Springer London. "),t("a",{href:"http://link.springer.com/10.1007/978-1-4471-6693-1",target:"_blank",rel:"noreferrer"},"http://link.springer.com/10.1007/978-1-4471-6693-1"),Q(".")],-1);function n6(d6,m6,r6,i6,h6,c6){return T(),a("div",null,[o,t("p",null,[Q("One important step towards many gene finding algorithms is to represent a DNA sequence as a Markov chain. In this representation a DNA sequence of a reduced alphabet "),t("mjx-container",n,[(T(),a("svg",d,r)),i]),Q(" is draw as a four-vertex graph, where each letter of "),t("mjx-container",h,[(T(),a("svg",c,g)),x]),Q(" is a "),w,Q(" and the edges of the graph represent "),u,Q(" from one nucleotide to another in a sequence (e.g. "),t("mjx-container",H,[(T(),a("svg",_,f)),M]),Q(" represent a single nucleotide to nucleotide transition). This is also considered more specifically as a Discrete Markov chain (Axelson-Fisk 2015). The complete set of transitions and states of a DNA sequence of alphabet "),t("mjx-container",k,[(T(),a("svg",y,b)),V]),Q(".")]),t("p",null,[Q("More formally a Markov chain is a random process where each state is a random variable "),t("mjx-container",v,[(T(),a("svg",D,A)),C]),Q(" where "),t("mjx-container",R,[(T(),a("svg",S,P)),X]),Q(" is a discrete time in a finite sequence "),t("mjx-container",E,[(T(),a("svg",I,G)),N]),Q(" and the probability to jump from one state into another is "),z,Q(". Therefore a definition of this "),J,Q(" is given by:")]),t("mjx-container",q,[(T(),a("svg",F,U)),W]),t("p",null,[Q("where "),t("mjx-container",K,[(T(),a("svg",Y,Q1)),a1]),Q(" . This property led us to generalize a way to calculate the probability of a sequence "),t("mjx-container",T1,[(T(),a("svg",e1,l1)),o1]),Q(" from a process "),t("mjx-container",n1,[(T(),a("svg",d1,r1)),i1]),Q(" where each random variable is a nucleotide from "),t("mjx-container",h1,[(T(),a("svg",c1,g1)),x1]),Q(" so that:")]),t("mjx-container",w1,[(T(),a("svg",u1,_1)),L1]),t("p",null,[Q("Note that previous equations has two terms, a initial probability "),t("mjx-container",f1,[(T(),a("svg",M1,y1)),Z1]),Q(" and the the product of all transitions beginning at "),t("mjx-container",b1,[(T(),a("svg",V1,D1)),j1]),Q(". So, to calculate the initial probability distribution of each of the nucleotides of a string "),t("mjx-container",A1,[(T(),a("svg",C1,S1)),O1]),Q(" with the alphabet 𝒜 we can first calculate the transition probability matrix "),t("mjx-container",P1,[(T(),a("svg",X1,I1)),B1]),Q(" out of the frequency count of the transitions. In an alphabet 𝒜 we got "),t("mjx-container",G1,[(T(),a("svg",N1,J1)),q1]),Q(" transitions of one order, that is the "),t("mjx-container",F1,[(T(),a("svg",$1,W1)),K1]),Q(" which coincides with the frequency of the dinucleotides in the sequence. So we can later in fact build a "),t("mjx-container",Y1,[(T(),a("svg",t2,a2)),T2]),Q(" matrix representing all the transitions. For instance in a DNA sequence "),t("mjx-container",e2,[(T(),a("svg",s2,o2)),n2]),Q(" of 24 nucleotides:")]),t("mjx-container",d2,[(T(),a("svg",m2,i2)),h2]),t("p",null,[Q("We can calculate each frequency nucleotide to any other nucleotide "),t("mjx-container",c2,[(T(),a("svg",p2,x2)),w2]),Q(" where "),t("mjx-container",u2,[(T(),a("svg",H2,L2)),f2]),Q(" is the actual count of the dinucleotide, and therefore "),t("mjx-container",M2,[(T(),a("svg",k2,Z2)),b2]),Q(" is the counts of the nucleotide "),t("mjx-container",V2,[(T(),a("svg",v2,j2)),A2]),Q(" to any other nucleotide and build the transition probability matrix:")]),t("mjx-container",C2,[(T(),a("svg",R2,O2)),P2]),t("p",null,[Q("It is noteworthy that initial probabilities can also be obtained from the counts of each nucleotide transitions "),t("mjx-container",X2,[(T(),a("svg",E2,B2)),G2]),Q(" over the total sum of the dinucleotide counts "),t("mjx-container",N2,[(T(),a("svg",z2,q2)),F2]),Q(":")]),t("mjx-container",$2,[(T(),a("svg",U2,K2)),Y2]),t("p",null,[Q("That way for the previous example example we can can calculate the initial probabilities "),t("mjx-container",t6,[(T(),a("svg",Q6,T6)),e6]),Q(". 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"),t("em",null,"Comparative Gene Finding"),Q(". Vol. 20. Computational Biology. London: Springer London. "),t("a",{href:"http://link.springer.com/10.1007/978-1-4471-6693-1",target:"_blank",rel:"noreferrer"},"http://link.springer.com/10.1007/978-1-4471-6693-1"),Q(".")],-1);function n6(d6,m6,r6,i6,h6,c6){return T(),a("div",null,[o,t("p",null,[Q("One important step towards many gene finding algorithms is to represent a DNA sequence as a Markov chain. In this representation a DNA sequence of a reduced alphabet "),t("mjx-container",n,[(T(),a("svg",d,r)),i]),Q(" is draw as a four-vertex graph, where each letter of "),t("mjx-container",h,[(T(),a("svg",c,g)),x]),Q(" is a "),w,Q(" and the edges of the graph represent "),u,Q(" from one nucleotide to another in a sequence (e.g. "),t("mjx-container",H,[(T(),a("svg",_,f)),M]),Q(" represent a single nucleotide to nucleotide transition). This is also considered more specifically as a Discrete Markov chain (Axelson-Fisk 2015). The complete set of transitions and states of a DNA sequence of alphabet "),t("mjx-container",k,[(T(),a("svg",y,b)),V]),Q(".")]),t("p",null,[Q("More formally a Markov chain is a random process where each state is a random variable "),t("mjx-container",v,[(T(),a("svg",D,A)),C]),Q(" where "),t("mjx-container",R,[(T(),a("svg",S,P)),X]),Q(" is a discrete time in a finite sequence "),t("mjx-container",E,[(T(),a("svg",I,G)),N]),Q(" and the probability to jump from one state into another is "),z,Q(". Therefore a definition of this "),J,Q(" is given by:")]),t("mjx-container",q,[(T(),a("svg",F,U)),W]),t("p",null,[Q("where "),t("mjx-container",K,[(T(),a("svg",Y,Q1)),a1]),Q(" . This property led us to generalize a way to calculate the probability of a sequence "),t("mjx-container",T1,[(T(),a("svg",e1,l1)),o1]),Q(" from a process "),t("mjx-container",n1,[(T(),a("svg",d1,r1)),i1]),Q(" where each random variable is a nucleotide from "),t("mjx-container",h1,[(T(),a("svg",c1,g1)),x1]),Q(" so that:")]),t("mjx-container",w1,[(T(),a("svg",u1,_1)),L1]),t("p",null,[Q("Note that previous equations has two terms, a initial probability "),t("mjx-container",f1,[(T(),a("svg",M1,y1)),Z1]),Q(" and the the product of all transitions beginning at "),t("mjx-container",b1,[(T(),a("svg",V1,D1)),j1]),Q(". So, to calculate the initial probability distribution of each of the nucleotides of a string "),t("mjx-container",A1,[(T(),a("svg",C1,S1)),O1]),Q(" with the alphabet 𝒜 we can first calculate the transition probability matrix "),t("mjx-container",P1,[(T(),a("svg",X1,I1)),B1]),Q(" out of the frequency count of the transitions. In an alphabet 𝒜 we got "),t("mjx-container",G1,[(T(),a("svg",N1,J1)),q1]),Q(" transitions of one order, that is the "),t("mjx-container",F1,[(T(),a("svg",$1,W1)),K1]),Q(" which coincides with the frequency of the dinucleotides in the sequence. So we can later in fact build a "),t("mjx-container",Y1,[(T(),a("svg",t2,a2)),T2]),Q(" matrix representing all the transitions. For instance in a DNA sequence "),t("mjx-container",e2,[(T(),a("svg",s2,o2)),n2]),Q(" of 24 nucleotides:")]),t("mjx-container",d2,[(T(),a("svg",m2,i2)),h2]),t("p",null,[Q("We can calculate each frequency nucleotide to any other nucleotide "),t("mjx-container",c2,[(T(),a("svg",p2,x2)),w2]),Q(" where "),t("mjx-container",u2,[(T(),a("svg",H2,L2)),f2]),Q(" is the actual count of the dinucleotide, and therefore "),t("mjx-container",M2,[(T(),a("svg",k2,Z2)),b2]),Q(" is the counts of the nucleotide "),t("mjx-container",V2,[(T(),a("svg",v2,j2)),A2]),Q(" to any other nucleotide and build the transition probability matrix:")]),t("mjx-container",C2,[(T(),a("svg",R2,O2)),P2]),t("p",null,[Q("It is noteworthy that initial probabilities can also be obtained from the counts of each nucleotide transitions "),t("mjx-container",X2,[(T(),a("svg",E2,B2)),G2]),Q(" over the total sum of the dinucleotide counts "),t("mjx-container",N2,[(T(),a("svg",z2,q2)),F2]),Q(":")]),t("mjx-container",$2,[(T(),a("svg",U2,K2)),Y2]),t("p",null,[Q("That way for the previous example example we can can calculate the initial probabilities "),t("mjx-container",t6,[(T(),a("svg",Q6,T6)),e6]),Q(". 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"),t("em",null,"Comparative Gene Finding"),Q(". Vol. 20. Computational Biology. London: Springer London. "),t("a",{href:"http://link.springer.com/10.1007/978-1-4471-6693-1",target:"_blank",rel:"noreferrer"},"http://link.springer.com/10.1007/978-1-4471-6693-1"),Q(".")],-1);function n6(d6,m6,r6,i6,h6,c6){return T(),a("div",null,[o,t("p",null,[Q("One important step towards many gene finding algorithms is to represent a DNA sequence as a Markov chain. In this representation a DNA sequence of a reduced alphabet "),t("mjx-container",n,[(T(),a("svg",d,r)),i]),Q(" is draw as a four-vertex graph, where each letter of "),t("mjx-container",h,[(T(),a("svg",c,g)),x]),Q(" is a "),w,Q(" and the edges of the graph represent "),u,Q(" from one nucleotide to another in a sequence (e.g. "),t("mjx-container",H,[(T(),a("svg",_,f)),M]),Q(" represent a single nucleotide to nucleotide transition). This is also considered more specifically as a Discrete Markov chain (Axelson-Fisk 2015). The complete set of transitions and states of a DNA sequence of alphabet "),t("mjx-container",k,[(T(),a("svg",y,b)),V]),Q(".")]),t("p",null,[Q("More formally a Markov chain is a random process where each state is a random variable "),t("mjx-container",v,[(T(),a("svg",D,A)),C]),Q(" where "),t("mjx-container",R,[(T(),a("svg",S,P)),X]),Q(" is a discrete time in a finite sequence "),t("mjx-container",E,[(T(),a("svg",I,G)),N]),Q(" and the probability to jump from one state into another is "),z,Q(". Therefore a definition of this "),J,Q(" is given by:")]),t("mjx-container",q,[(T(),a("svg",F,U)),W]),t("p",null,[Q("where "),t("mjx-container",K,[(T(),a("svg",Y,Q1)),a1]),Q(" . This property led us to generalize a way to calculate the probability of a sequence "),t("mjx-container",T1,[(T(),a("svg",e1,l1)),o1]),Q(" from a process "),t("mjx-container",n1,[(T(),a("svg",d1,r1)),i1]),Q(" where each random variable is a nucleotide from "),t("mjx-container",h1,[(T(),a("svg",c1,g1)),x1]),Q(" so that:")]),t("mjx-container",w1,[(T(),a("svg",u1,_1)),L1]),t("p",null,[Q("Note that previous equations has two terms, a initial probability "),t("mjx-container",f1,[(T(),a("svg",M1,y1)),Z1]),Q(" and the the product of all transitions beginning at "),t("mjx-container",b1,[(T(),a("svg",V1,D1)),j1]),Q(". So, to calculate the initial probability distribution of each of the nucleotides of a string "),t("mjx-container",A1,[(T(),a("svg",C1,S1)),O1]),Q(" with the alphabet 𝒜 we can first calculate the transition probability matrix "),t("mjx-container",P1,[(T(),a("svg",X1,I1)),B1]),Q(" out of the frequency count of the transitions. In an alphabet 𝒜 we got "),t("mjx-container",G1,[(T(),a("svg",N1,J1)),q1]),Q(" transitions of one order, that is the "),t("mjx-container",F1,[(T(),a("svg",$1,W1)),K1]),Q(" which coincides with the frequency of the dinucleotides in the sequence. So we can later in fact build a "),t("mjx-container",Y1,[(T(),a("svg",t2,a2)),T2]),Q(" matrix representing all the transitions. For instance in a DNA sequence "),t("mjx-container",e2,[(T(),a("svg",s2,o2)),n2]),Q(" of 24 nucleotides:")]),t("mjx-container",d2,[(T(),a("svg",m2,i2)),h2]),t("p",null,[Q("We can calculate each frequency nucleotide to any other nucleotide "),t("mjx-container",c2,[(T(),a("svg",p2,x2)),w2]),Q(" where "),t("mjx-container",u2,[(T(),a("svg",H2,L2)),f2]),Q(" is the actual count of the dinucleotide, and therefore "),t("mjx-container",M2,[(T(),a("svg",k2,Z2)),b2]),Q(" is the counts of the nucleotide "),t("mjx-container",V2,[(T(),a("svg",v2,j2)),A2]),Q(" to any other nucleotide and build the transition probability matrix:")]),t("mjx-container",C2,[(T(),a("svg",R2,O2)),P2]),t("p",null,[Q("It is noteworthy that initial probabilities can also be obtained from the counts of each nucleotide transitions "),t("mjx-container",X2,[(T(),a("svg",E2,B2)),G2]),Q(" over the total sum of the dinucleotide counts "),t("mjx-container",N2,[(T(),a("svg",z2,q2)),F2]),Q(":")]),t("mjx-container",$2,[(T(),a("svg",U2,K2)),Y2]),t("p",null,[Q("That way for the previous example example we can can calculate the initial probabilities "),t("mjx-container",t6,[(T(),a("svg",Q6,T6)),e6]),Q(". Both set of probabilities composed a "),s6,Q(" that can be used to predict the probability of any DNA sequence using equation (2).")]),l6,o6])}const x6=s(l,[["render",n6]]);export{g6 as __pageData,x6 as default}; diff --git a/dev/assets/chunks/@localSearchIndexroot.Bh2pKAzu.js b/dev/assets/chunks/@localSearchIndexroot.Bh2pKAzu.js deleted file mode 100644 index b218c89..0000000 --- a/dev/assets/chunks/@localSearchIndexroot.Bh2pKAzu.js +++ /dev/null @@ -1 +0,0 @@ -const 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t,r,n=this._options.autoVacuum;try{this._options.autoVacuum=!1;try{for(var a=D(e),i=a.next();!i.done;i=a.next()){var s=i.value;this.discard(s)}}catch(u){t={error:u}}finally{try{i&&!i.done&&(r=a.return)&&r.call(a)}finally{if(t)throw t.error}}}finally{this._options.autoVacuum=n}this.maybeAutoVacuum()},o.prototype.replace=function(e){var t=this._options,r=t.idField,n=t.extractField,a=n(e,r);this.discard(a),this.add(e)},o.prototype.vacuum=function(e){return e===void 0&&(e={}),this.conditionalVacuum(e)},o.prototype.conditionalVacuum=function(e,t){var r=this;return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(function(){var n=r._enqueuedVacuumConditions;return 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lt(Se.Layout,null,{})},enhanceApp({app:s,router:e,siteData:t}){Hr(s)}};export{Dr as R,Yn as c,L as u}; diff --git a/dev/assets/getstarted.md.BxUYlh-U.js b/dev/assets/getstarted.md.EJuNxDSJ.js similarity index 88% rename from dev/assets/getstarted.md.BxUYlh-U.js rename to dev/assets/getstarted.md.EJuNxDSJ.js index 8474fee..9db306b 100644 --- a/dev/assets/getstarted.md.BxUYlh-U.js +++ b/dev/assets/getstarted.md.EJuNxDSJ.js @@ -1,8 +1,8 @@ -import{_ as i,c as s,l as a,a as t,a7 as n,o as e}from"./chunks/framework.D1rHXXmm.js";const G=JSON.parse('{"title":"Get started","description":"","frontmatter":{},"headers":[],"relativePath":"getstarted.md","filePath":"getstarted.md","lastUpdated":null}'),o={name:"getstarted.md"},l=n(`

Get started

Installation

You can install BioMarkovChains from the julia REPL. Press ] to enter pkg mode, and enter the following:

julia
]add BioMarkovChains

If you are interested in the cutting edge of the development, please check out the main branch to try new features before release.

Create a BioMarkovChain of DNA

julia
using BioSequences, BioMarkovChains
+import{_ as i,c as s,j as a,a as t,a6 as n,o as e}from"./chunks/framework.Cvv1S4VC.js";const G=JSON.parse('{"title":"Get started","description":"","frontmatter":{},"headers":[],"relativePath":"getstarted.md","filePath":"getstarted.md","lastUpdated":null}'),o={name:"getstarted.md"},l=n(`

Get started

Installation

You can install BioMarkovChains from the julia REPL. Press ] to enter pkg mode, and enter the following:

julia
]add BioMarkovChains

If you are interested in the cutting edge of the development, please check out the main branch to try new features before release.

Create a BioMarkovChain of DNA

julia
using BioSequences, BioMarkovChains
 
 sequence = dna"CCTCCCGGACCCTGGGCTCGGGAC"
 
-BioMarkovChain(sequence)
BioMarkovChain of DNAAlphabet{4}() and order 1:
+BioMarkovChain(sequence)
BioMarkovChain of DNAAlphabet{4}() and order 1:
   - Transition Probability Matrix --> Matrix{Float64}(4 × 4):
    0.0     1.0     0.0     0.0
    0.0     0.5     0.2     0.3
@@ -12,7 +12,7 @@ import{_ as i,c as s,l as a,a as t,a7 as n,o as e}from"./chunks/framework.D1rHXX
    0.087
    0.4348
    0.3478
-   0.1304

Note that, sometimes the dinucleotides transition do not harbor important biological meaning, whereas trinucleotides or codons are, in fact, the building block of proteins. Therefore, sometimes the transition model we want to build is usually a second-order Markov chain, that represents the possible transitions of a trinucleotide.

`,9),r=a("em",null,"n-step transition probability matrix",-1),p={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},d={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.666ex"},xmlns:"http://www.w3.org/2000/svg",width:"14.679ex",height:"2.363ex",role:"img",focusable:"false",viewBox:"0 -750 6487.9 1044.2","aria-hidden":"true"},T=n('𝓂',1),h=[T],Q=a("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[a("msup",null,[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"M")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"n")])]),a("mo",null,"="),a("mo",{stretchy:"false"},"("),a("msub",null,[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"m")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i"),a("mi",null,"j")])]),a("mo",{stretchy:"false"},"("),a("mi",null,"n"),a("mo",{stretchy:"false"},")"),a("mo",{stretchy:"false"},")")])],-1),c={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},m={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.357ex",height:"1.025ex",role:"img",focusable:"false",viewBox:"0 -442 600 453","aria-hidden":"true"},g=a("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[a("g",{"data-mml-node":"math"},[a("g",{"data-mml-node":"mi"},[a("path",{"data-c":"1D45B",d:"M21 287Q22 293 24 303T36 341T56 388T89 425T135 442Q171 442 195 424T225 390T231 369Q231 367 232 367L243 378Q304 442 382 442Q436 442 469 415T503 336T465 179T427 52Q427 26 444 26Q450 26 453 27Q482 32 505 65T540 145Q542 153 560 153Q580 153 580 145Q580 144 576 130Q568 101 554 73T508 17T439 -10Q392 -10 371 17T350 73Q350 92 386 193T423 345Q423 404 379 404H374Q288 404 229 303L222 291L189 157Q156 26 151 16Q138 -11 108 -11Q95 -11 87 -5T76 7T74 17Q74 30 112 180T152 343Q153 348 153 366Q153 405 129 405Q91 405 66 305Q60 285 60 284Q58 278 41 278H27Q21 284 21 287Z",style:{"stroke-width":"3"}})])])],-1),u=[g],x=a("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[a("mi",null,"n")])],-1),_={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},k={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.113ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.751ex",height:"1.744ex",role:"img",focusable:"false",viewBox:"0 -721 1216 771","aria-hidden":"true"},w=n('',1),v=[w],b=a("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"M")])])],-1),f={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},y={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.462ex"},xmlns:"http://www.w3.org/2000/svg",width:"5.985ex",height:"1.957ex",role:"img",focusable:"false",viewBox:"0 -661 2645.6 865","aria-hidden":"true"},C=n('',1),M=[C],L=a("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[a("mi",null,"i"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mo",null," ")]),a("mo",{stretchy:"false"},"→"),a("mi",null,"j")])],-1),H=a("em",null,"n",-1),A=n(`
julia
BioMarkovChain(sequence, 2)
BioMarkovChain of DNAAlphabet{4}() and order 2:
+   0.1304

Note that, sometimes the dinucleotides transition do not harbor important biological meaning, whereas trinucleotides or codons are, in fact, the building block of proteins. Therefore, sometimes the transition model we want to build is usually a second-order Markov chain, that represents the possible transitions of a trinucleotide.

`,9),r=a("em",null,"n-step transition probability matrix",-1),p={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},d={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.666ex"},xmlns:"http://www.w3.org/2000/svg",width:"14.679ex",height:"2.363ex",role:"img",focusable:"false",viewBox:"0 -750 6487.9 1044.2","aria-hidden":"true"},T=n('𝓂',1),h=[T],Q=a("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[a("msup",null,[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"M")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"n")])]),a("mo",null,"="),a("mo",{stretchy:"false"},"("),a("msub",null,[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"m")]),a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",null,"i"),a("mi",null,"j")])]),a("mo",{stretchy:"false"},"("),a("mi",null,"n"),a("mo",{stretchy:"false"},")"),a("mo",{stretchy:"false"},")")])],-1),c={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},m={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.357ex",height:"1.025ex",role:"img",focusable:"false",viewBox:"0 -442 600 453","aria-hidden":"true"},g=a("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[a("g",{"data-mml-node":"math"},[a("g",{"data-mml-node":"mi"},[a("path",{"data-c":"1D45B",d:"M21 287Q22 293 24 303T36 341T56 388T89 425T135 442Q171 442 195 424T225 390T231 369Q231 367 232 367L243 378Q304 442 382 442Q436 442 469 415T503 336T465 179T427 52Q427 26 444 26Q450 26 453 27Q482 32 505 65T540 145Q542 153 560 153Q580 153 580 145Q580 144 576 130Q568 101 554 73T508 17T439 -10Q392 -10 371 17T350 73Q350 92 386 193T423 345Q423 404 379 404H374Q288 404 229 303L222 291L189 157Q156 26 151 16Q138 -11 108 -11Q95 -11 87 -5T76 7T74 17Q74 30 112 180T152 343Q153 348 153 366Q153 405 129 405Q91 405 66 305Q60 285 60 284Q58 278 41 278H27Q21 284 21 287Z",style:{"stroke-width":"3"}})])])],-1),u=[g],x=a("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[a("mi",null,"n")])],-1),_={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},k={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.113ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.751ex",height:"1.744ex",role:"img",focusable:"false",viewBox:"0 -721 1216 771","aria-hidden":"true"},w=n('',1),b=[w],v=a("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[a("mrow",{"data-mjx-texclass":"ORD"},[a("mi",{mathvariant:"script"},"M")])])],-1),f={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},y={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.462ex"},xmlns:"http://www.w3.org/2000/svg",width:"5.985ex",height:"1.957ex",role:"img",focusable:"false",viewBox:"0 -661 2645.6 865","aria-hidden":"true"},C=n('',1),M=[C],L=a("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[a("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[a("mi",null,"i"),a("mrow",{"data-mjx-texclass":"ORD"},[a("mo",null," ")]),a("mo",{stretchy:"false"},"→"),a("mi",null,"j")])],-1),H=a("em",null,"n",-1),A=n(`
julia
BioMarkovChain(sequence, 2)
BioMarkovChain of DNAAlphabet{4}() and order 2:
   - Transition Probability Matrix --> Matrix{Float64}(4 × 4):
    0.0     0.5     0.2     0.3
    0.05    0.475   0.325   0.15
@@ -22,4 +22,4 @@ import{_ as i,c as s,l as a,a as t,a7 as n,o as e}from"./chunks/framework.D1rHXX
    0.087
    0.4348
    0.3478
-   0.1304
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<div class="vp-doc" style="width:90%; margin:auto">

Install BioMarkovChains from the julia REPL. Press ] to enter pkg mode, and enter the add BioMarkovChains command.

julia
(@v1.10) pkg> add BioMarkovChains

For more information, see the Getting Started section.

</div>

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<p style="margin-bottom:2cm"></p>

<div class="vp-doc" style="width:80%; margin:auto">

<h2> Get Started </h2>

Install BioMarkovChains from the julia REPL. Press ] to enter pkg mode, and enter the add BioMarkovChains command.

julia
(@v1.10) pkg> add BioMarkovChains

For more information, see the Getting Started section.

</div>

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var(--vp-c-danger-soft);--vp-code-copy-code-border-color: var(--vp-c-divider);--vp-code-copy-code-bg: var(--vp-c-bg-soft);--vp-code-copy-code-hover-border-color: var(--vp-c-divider);--vp-code-copy-code-hover-bg: var(--vp-c-bg);--vp-code-copy-code-active-text: var(--vp-c-text-2);--vp-code-copy-copied-text-content: "Copied";--vp-code-tab-divider: var(--vp-code-block-divider-color);--vp-code-tab-text-color: var(--vp-c-text-2);--vp-code-tab-bg: var(--vp-code-block-bg);--vp-code-tab-hover-text-color: var(--vp-c-text-1);--vp-code-tab-active-text-color: var(--vp-c-text-1);--vp-code-tab-active-bar-color: var(--vp-c-brand-1)}:root{--vp-button-brand-border: transparent;--vp-button-brand-text: var(--vp-c-white);--vp-button-brand-bg: var(--vp-c-brand-3);--vp-button-brand-hover-border: transparent;--vp-button-brand-hover-text: var(--vp-c-white);--vp-button-brand-hover-bg: var(--vp-c-brand-2);--vp-button-brand-active-border: transparent;--vp-button-brand-active-text: 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Skip to content

Towards Markov Chains

DNA as a Markov chain

Several packages (e.g. MarkovChainsHammer.jl, DiscreteMarkovChains.jl, etc.) in the Julia ecosystem have been implemented to work with Markov chains with a state space of integers, those could be efficient in many ways, but they are clumsy to work with a specialized biological types as in the BioJulia ecosystem. Therefore, in the GeneFinder package we dedicated some implementations to work with BioSequence types so that we can expand the functionality in an efficient way (see complete API).

One important step towards many gene finding algorithms is to represent a DNA sequence as a Markov chain. In this representation a DNA sequence of a reduced alphabet A={A,C,G,T} is draw as a four-vertex graph, where each letter of A is a state (vertex) and the edges of the graph represent transitions from one nucleotide to another in a sequence (e.g. AT represent a single nucleotide to nucleotide transition). This is also considered more specifically as a Discrete Markov chain (Axelson-Fisk 2015). The complete set of transitions and states of a DNA sequence of alphabet A.

More formally a Markov chain is a random process where each state is a random variable Xt where tT is a discrete time in a finite sequence T and the probability to jump from one state into another is only dependent of the current state. Therefore a definition of this Markov property is given by:

P(Xt=j|Xt1=i)

where i,jA . This property led us to generalize a way to calculate the probability of a sequence T from a process (X1...XT) where each random variable is a nucleotide from A so that:

P(X1=i1,...,XT=iT)=P(X1=i1)t=2TP(Xt=it|Xt1=it1)

Note that previous equations has two terms, a initial probability P(X1=i1) and the the product of all transitions beginning at t=2. So, to calculate the initial probability distribution of each of the nucleotides of a string T with the alphabet 𝒜 we can first calculate the transition probability matrix M^ out of the frequency count of the transitions. In an alphabet 𝒜 we got 42 transitions of one order, that is the AA,AC,AG,... which coincides with the frequency of the dinucleotides in the sequence. So we can later in fact build a 4x4 matrix representing all the transitions. For instance in a DNA sequence T of 24 nucleotides:

CCTCCCGGACCCTGGGCTCGGGAC

We can calculate each frequency nucleotide to any other nucleotide m^ij=cijci where cij is the actual count of the dinucleotide, and therefore ci is the counts of the nucleotide i to any other nucleotide and build the transition probability matrix:

[ACGTA0.001.000.000.00C0.000.560.220.30G0.250.120.620.00T0.000.670.330.00]

It is noteworthy that initial probabilities can also be obtained from the counts of each nucleotide transitions cij over the total sum of the dinucleotide counts ck:

π^i=cikck

That way for the previous example example we can can calculate the initial probabilities π^=(0.08,0.43,0.34,0.13). Both set of probabilities composed a transition model that can be used to predict the probability of any DNA sequence using equation (2).

References

Axelson-Fisk, Marina. 2015. Comparative Gene Finding. Vol. 20. Computational Biology. London: Springer London. http://link.springer.com/10.1007/978-1-4471-6693-1.

- +
Skip to content

Towards Markov Chains

DNA as a Markov chain

Several packages (e.g. MarkovChainsHammer.jl, DiscreteMarkovChains.jl, etc.) in the Julia ecosystem have been implemented to work with Markov chains with a state space of integers, those could be efficient in many ways, but they are clumsy to work with a specialized biological types as in the BioJulia ecosystem. Therefore, in the GeneFinder package we dedicated some implementations to work with BioSequence types so that we can expand the functionality in an efficient way (see complete API).

One important step towards many gene finding algorithms is to represent a DNA sequence as a Markov chain. In this representation a DNA sequence of a reduced alphabet A={A,C,G,T} is draw as a four-vertex graph, where each letter of A is a state (vertex) and the edges of the graph represent transitions from one nucleotide to another in a sequence (e.g. AT represent a single nucleotide to nucleotide transition). This is also considered more specifically as a Discrete Markov chain (Axelson-Fisk 2015). The complete set of transitions and states of a DNA sequence of alphabet A.

More formally a Markov chain is a random process where each state is a random variable Xt where tT is a discrete time in a finite sequence T and the probability to jump from one state into another is only dependent of the current state. Therefore a definition of this Markov property is given by:

P(Xt=j|Xt1=i)

where i,jA . This property led us to generalize a way to calculate the probability of a sequence T from a process (X1...XT) where each random variable is a nucleotide from A so that:

P(X1=i1,...,XT=iT)=P(X1=i1)t=2TP(Xt=it|Xt1=it1)

Note that previous equations has two terms, a initial probability P(X1=i1) and the the product of all transitions beginning at t=2. So, to calculate the initial probability distribution of each of the nucleotides of a string T with the alphabet 𝒜 we can first calculate the transition probability matrix M^ out of the frequency count of the transitions. In an alphabet 𝒜 we got 42 transitions of one order, that is the AA,AC,AG,... which coincides with the frequency of the dinucleotides in the sequence. So we can later in fact build a 4x4 matrix representing all the transitions. For instance in a DNA sequence T of 24 nucleotides:

CCTCCCGGACCCTGGGCTCGGGAC

We can calculate each frequency nucleotide to any other nucleotide m^ij=cijci where cij is the actual count of the dinucleotide, and therefore ci is the counts of the nucleotide i to any other nucleotide and build the transition probability matrix:

[ACGTA0.001.000.000.00C0.000.560.220.30G0.250.120.620.00T0.000.670.330.00]

It is noteworthy that initial probabilities can also be obtained from the counts of each nucleotide transitions cij over the total sum of the dinucleotide counts ck:

π^i=cikck

That way for the previous example example we can can calculate the initial probabilities π^=(0.08,0.43,0.34,0.13). Both set of probabilities composed a transition model that can be used to predict the probability of any DNA sequence using equation (2).

References

Axelson-Fisk, Marina. 2015. Comparative Gene Finding. Vol. 20. Computational Biology. London: Springer London. http://link.springer.com/10.1007/978-1-4471-6693-1.

+ \ No newline at end of file diff --git a/dev/getstarted.html b/dev/getstarted.html index d7d02e5..3f35946 100644 --- a/dev/getstarted.html +++ b/dev/getstarted.html @@ -5,23 +5,23 @@ Get started | BioMarkovChains.jl - - + + - + - - - + + + -
Skip to content

Get started

Installation

You can install BioMarkovChains from the julia REPL. Press ] to enter pkg mode, and enter the following:

julia
]add BioMarkovChains

If you are interested in the cutting edge of the development, please check out the main branch to try new features before release.

Create a BioMarkovChain of DNA

julia
using BioSequences, BioMarkovChains
+    
Skip to content

Get started

Installation

You can install BioMarkovChains from the julia REPL. Press ] to enter pkg mode, and enter the following:

julia
]add BioMarkovChains

If you are interested in the cutting edge of the development, please check out the main branch to try new features before release.

Create a BioMarkovChain of DNA

julia
using BioSequences, BioMarkovChains
 
 sequence = dna"CCTCCCGGACCCTGGGCTCGGGAC"
 
-BioMarkovChain(sequence)
BioMarkovChain of DNAAlphabet{4}() and order 1:
+BioMarkovChain(sequence)
BioMarkovChain of DNAAlphabet{4}() and order 1:
   - Transition Probability Matrix --> Matrix{Float64}(4 × 4):
    0.0     1.0     0.0     0.0
    0.0     0.5     0.2     0.3
@@ -31,7 +31,7 @@
    0.087
    0.4348
    0.3478
-   0.1304

Note that, sometimes the dinucleotides transition do not harbor important biological meaning, whereas trinucleotides or codons are, in fact, the building block of proteins. Therefore, sometimes the transition model we want to build is usually a second-order Markov chain, that represents the possible transitions of a trinucleotide.

A very nice nice property of the transition probability matrix is that the n-step transition probability matrix Mn=(mij(n)), that is the nth power of M represents ij transitions in n steps. We can also have higher order transition models as:

julia
BioMarkovChain(sequence, 2)
BioMarkovChain of DNAAlphabet{4}() and order 2:
+   0.1304

Note that, sometimes the dinucleotides transition do not harbor important biological meaning, whereas trinucleotides or codons are, in fact, the building block of proteins. Therefore, sometimes the transition model we want to build is usually a second-order Markov chain, that represents the possible transitions of a trinucleotide.

A very nice nice property of the transition probability matrix is that the n-step transition probability matrix Mn=(mij(n)), that is the nth power of M represents ij transitions in n steps. We can also have higher order transition models as:

julia
BioMarkovChain(sequence, 2)
BioMarkovChain of DNAAlphabet{4}() and order 2:
   - Transition Probability Matrix --> Matrix{Float64}(4 × 4):
    0.0     0.5     0.2     0.3
    0.05    0.475   0.325   0.15
@@ -41,8 +41,8 @@
    0.087
    0.4348
    0.3478
-   0.1304
- + 0.1304
+ \ No newline at end of file diff --git a/dev/hashmap.json b/dev/hashmap.json index 117f06e..d2853f6 100644 --- a/dev/hashmap.json +++ b/dev/hashmap.json @@ -1 +1 @@ -{"home.md":"nGdy1Xvl","index.md":"mmk1IapY","article01.md":"BL6r3tif","getstarted.md":"BxUYlh-U","biomarkovchains.md":"DhkAaXJV","api.md":"CM80ZGQK"} +{"index.md":"Ba-cgBKx","getstarted.md":"EJuNxDSJ","home.md":"DYCAAGxb","article01.md":"BrGXw77Y","api.md":"BiqC9tVJ","biomarkovchains.md":"0rg0z1ev"} diff --git a/dev/home.html b/dev/home.html index aa407ee..a30e90c 100644 --- a/dev/home.html +++ b/dev/home.html @@ -5,20 +5,20 @@ BioMarkovChains.jl - - + + - + - - - + + + -
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Representing biological sequences as Markov chains


Overview

This package aim to represent BioSequences types as Markov chains to perform different operations and predictions

- +
Skip to content


Representing biological sequences as Markov chains


Overview

This package aim to represent BioSequences types as Markov chains to perform different operations and predictions

+ \ No newline at end of file diff --git a/dev/index.html b/dev/index.html index c8bc34b..5988282 100644 --- a/dev/index.html +++ b/dev/index.html @@ -5,20 +5,20 @@ BioMarkovChains.jl - - + + - + - - - + + + -
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BioMarkovChains.jl

Representing biological sequences as Markov chains

BioMarkovChains

<p style="margin-bottom:2cm"></p>

<div class="vp-doc" style="width:80%; margin:auto">

<h2> Get Started </h2>

Install BioMarkovChains from the julia REPL. Press ] to enter pkg mode, and enter the add BioMarkovChains command.

julia
(@v1.10) pkg> add BioMarkovChains

For more information, see the Getting Started section.

</div>

- +
Skip to content

BioMarkovChains.jl

Representing biological sequences as Markov chains

BioMarkovChains

<div class="vp-doc" style="width:90%; margin:auto">

Install BioMarkovChains from the julia REPL. Press ] to enter pkg mode, and enter the add BioMarkovChains command.

julia
(@v1.10) pkg> add BioMarkovChains

For more information, see the Getting Started section.

</div>

+ \ No newline at end of file