feat: Improve scoring explanation in docs

docs/src/features.md

@@ -1,6 +1,8 @@
 ## The ORF features
 
-The `ORF` type is designed to be flexible and can store various types of information about the ORF. This versatility allows it to hold data such as a score of an ORF based on a scoring function, the sequence of the ORF, or even the translated amino acid sequence. For example, in the `NaiveFinder` method, the `score` subfield is used to store the score of the ORF using an scoring scheme (i.e. a function).
+The `ORF` type is designed to be flexible and can store various types of information about the ORF. This versatility lies on the `Features` field and allows every instance of the `ORF` to hold data such as a score of an ORF based on a scoring scheme, the sequence of the ORF, or even the translated amino acid sequence. For example, in the `NaiveFinder` method, the `score` subfield is used to store the score of the ORF using an scoring scheme (i.e. a function).
+
+Take the following example:
 
 ```julia
 phi = dna"GTGTGAGGTTATAACGCCGAAGCGGTAAAAATTTTAATTTTTGCCGCTGAGGGGTTGACCAAGCGAAGCGCGGTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACTTTTATTTCTCGCCATAATTCAAACTTTTTTTCTGATAAGCTGGTTCTCACTTCTGTTACTCCAGCTTCTTCGGCACCTGTTTTACAGACACCTAAAGCTACATCGTCAACGTTATATTTTGATAGTTTGACGGTTAATGCTGGTAATGGTGGTTTTCTTCATTGCATTCAGATGGATACATCTGTCAACGCCGCTAATCAGGTTGTTTCTGTTGGTGCTGATATTGCTTTTGATGCCGACCCTAAATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTTCGGTTCCGACTACCCTCCCGACTGCCTATGATGTTTATCCTTTGAATGGTCGCCATGATGGTGGTTATTATACCGTCAAGGACTGTGTGACTATTGACGTCCTTCCCCGTACGCCGGGCAATAACGTTTATGTTGGTTTCATGGTTTGGTCTAACTTTACCGCTACTAAATGCCGCGGATTGGTTTCGCTGAATCAGGTTATTAAAGAGATTATTTGTCTCCAGCCACTTAAGTGAGGTGATTTATGTTTGGTGCTATTGCTGGCGGTATTGCTTCTGCTCTTGCTGGTGGCGCCATGTCTAAATTGTTTGGAGGCGGTCAAAAAGCCGCCTCCGGTGGCATTCAAGGTGATGTGCTTGCTACCGATAACAATACTGTAGGCATGGGTGATGCTGGTATTAAATCTGCCATTCAAGGCTCTAATGTTCCTAACCCTGATGAGGCCGCCCCTAGTTTTGTTTCTGGTGCTATGGCTAAAGCTGGTAAAGGACTTCTTGAAGGTACGTTGCAGGCTGGCACTTCTGCCGTTTCTGATAAGTTGCTTGATTTGGTTGGACTTGGTGGCAAGTCTGCCGCTGATAAAGGAAAGGATACTCGTGATTATCTTGCTGCTGCATTTCCTGAGCTTAATGCTTGGGAGCGTGCTGGTGCTGATGCTTCCTCTGCTGGTATGGTTGACGCCGGATTTGAGAATCAAAAAGAGCTTACTAAAATGCAACTGGACAATCAGAAAGAGATTGCCGAGATGCAAAATGAGACTCAAAAAGAGATTGCTGGCATTCAGTCGGCGACTTCACGCCAGAATACGAAAGACCAGGTATATGCACAAAATGAGATGCTTGCTTATCAACAGAAGGAGTCTACTGCTCGCGTTGCGTCTATTATGGAAAACACCAATCTTTCCAAGCAACAGCAGGTTTCCGAGATTATGCGCCAAATGCTTACTCAAGCTCAAACGGCTGGTCAGTATTTTACCAATGACCAAATCAAAGAAATGACTCGCAAGGTTAGTGCTGAGGTTGACTTAGTTCATCAGCAAACGCAGAATCAGCGGTATGGCTCTTCTCATATTGGCGCTACTGCAAAGGATATTTCTAATGTCGTCACTGATGCTGCTTCTGGTGTGGTTGATATTTTTCATGGTATTGATAAAGCTGTTGCCGATACTTGGAACAATTTCTGGAAAGACGGTAAAGCTGATGGTATTGGCTCTAATTTGTCTAGGAAATAACCGTCAGGATTGACACCCTCCCAATTGTATGTTTTCATGCCTCCAAATCTTGGAGGCTTTTTTATGGTTCGTTCTTATTACCCTTCTGAATGTCACGCTGATTATTTTGACTTTGAGCGTATCGAGGCTCTTAAACCTGCTATTGAGGCTTGTGGCATTTCTACTCTTTCTCAATCCCCAATGCTTGGCTTCCATAAGCAGATGGATAACCGCATCAAGCTCTTGGAAGAGATTCTGTCTTTTCGTATGCAGGGCGTTGAGTTCGATAATGGTGATATGTATGTTGACGGCCATAAGGCTGCTTCTGACGTTCGTGATGAGTTTGTATCTGTTACTGAGAAGTTAATGGATGAATTGGCACAATGCTACAATGTGCTCCCCCAACTTGATATTAATAACACTATAGACCACCGCCCCGAAGGGGACGAAAAATGGTTTTTAGAGAACGAGAAGACGGTTACGCAGTTTTGCCGCAAGCTGGCTGCTGAACGCCCTCTTAAGGATATTCGCGATGAGTATAATTACCCCAAAAAGAAAGGTATTAAGGATGAGTGTTCAAGATTGCTGGAGGCCTCCACTATGAAATCGCGTAGAGGCTTTGCTATTCAGCGTTTGATGAATGCAATGCGACAGGCTCATGCTGATGGTTGGTTTATCGTTTTTGACACTCTCACGTTGGCTGACGACCGATTAGAGGCGTTTTATGATAATCCCAATGCTTTGCGTGACTATTTTCGTGATATTGGTCGTATGGTTCTTGCTGCCGAGGGTCGCAAGGCTAATGATTCACACGCCGACTGCTATCAGTATTTTTGTGTGCCTGAGTATGGTACAGCTAATGGCCGTCTTCATTTCCATGCGGTGCACTTTATGCGGACACTTCCTACAGGTAGCGTTGACCCTAATTTTGGTCGTCGGGTACGCAATCGCCGCCAGTTAAATAGCTTGCAAAATACGTGGCCTTATGGTTACAGTATGCCCATCGCAGTTCGCTACACGCAGGACGCTTTTTCACGTTCTGGTTGGTTGTGGCCTGTTGATGCTAAAGGTGAGCCGCTTAAAGCTACCAGTTATATGGCTGTTGGTTTCTATGTGGCTAAATACGTTAACAAAAAGTCAGATATGGACCTTGCTGCTAAAGGTCTAGGAGCTAAAGAATGGAACAACTCACTAAAAACCAAGCTGTCGCTACTTCCCAAGAAGCTGTTCAGAATCAGAATGAGCCGCAACTTCGGGATGAAAATGCTCACAATGACAAATCTGTCCACGGAGTGCTTAATCCAACTTACCAAGCTGGGTTACGACGCGACGCCGTTCAACCAGATATTGAAGCAGAACGCAAAAAGAGAGATGAGATTGAGGCTGGGAAAAGTTACTGTAGCCGACGTTTTGGCGGCGCAACCTGTGACGACAAATCTGCTCAAATTTATGCGCGCTTCGATAAAAATGATTGGCGTATCCAACCTGCAGAGTTTTATCGCTTCCATGACGCAGAAGTTAACACTTTCGGATATTTCTGATGAGTCGAAAAATTATCTTGATAAAGCAGGAATTACTACTGCTTGTTTACGAATTAAATCGAAGTGGACTGCTGGCGGAAAATGAGAAAATTCGACCTATCCTTGCGCAGCTCGAGAAGCTCTTACTTTGCGACCTTTCGCCATCAACTAACGATTCTGTCAAAAACTGACGCGTTGGATGAGGAGAAGTGGCTTAATATGCTTGGCACGTTCGTCAAGGACTGGTTTAGATATGAGTCACATTTTGTTCATGGTAGAGATTCTCTTGTTGACATTTTAAAAGAGCGTGGATTACTATCTGAGTCCGATGCTGTTCAACCACTAATAGGTAAGAAATCATGAGTCAAGTTACTGAACAATCCGTACGTTTCCAGACCGCTTTGGCCTCTATTAAGCTCATTCAGGCTTCTGCCGTTTTGGATTTAACCGAAGATGATTTCGATTTTCTGACGAGTAACAAAGTTTGGATTGCTACTGACCGCTCTCGTGCTCGTCGCTGCGTTGAGGCTTGCGTTTATGGTACGCTGGACTTTGTGGGATACCCTCGCTTTCCTGCTCCTGTTGAGTTTATTGCTGCCGTCATTGCTTATTATGTTCATCCCGTCAACATTCAAACGGCCTGTCTCATCATGGAAGGCGCTGAATTTACGGAAAACATTATTAATGGCGTCGAGCGTCCGGTTAAAGCCGCTGAATTGTTCGCGTTTACCTTGCGTGTACGCGCAGGAAACACTGACGTTCTTACTGACGCAGAAGAAAACGTGCGTCAAAAATTACGTGCGGAAGGAGTGATGTAATGTCTAAAGGTAAAAAACGTTCTGGCGCTCGCCCTGGTCGTCCGCAGCCGTTGCGAGGTACTAAAGGCAAGCGTAAAGGCGCTCGTCTTTGGTATGTAGGTGGTCAACAATTTTAATTGCAGGGGCTTCGGCCCCTTACTTGAGGATAAATTATGTCTAATATTCAAACTGGCGCCGAGCGTATGCCGCATGACCTTTCCCATCTTGGCTTCCTTGCTGGTCAGATTGGTCGTCTTATTACCATTTCAACTACTCCGGTTATCGCTGGCGACTCCTTCGAGATGGACGCCGTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGTGGCCTTGCTATTGACTCTACTGTAGACATTTTTACTTTTTATGTCCCTCATCGTCACGTTTATGGTGAACAGTGGATTAAGTTCATGAAGGATGGTGTTAATGCCACTCCTCTCCCGACTGTTAACACTACTGGTTATATTGACCATGCCGCTTTTCTTGGCACGATTAACCCTGATACCAATAAAATCCCTAAGCATTTGTTTCAGGGTTATTTGAATATCTATAACAACTATTTTAAAGCGCCGTGGATGCCTGACCGTACCGAGGCTAACCCTAATGAGCTTAATCAAGATGATGCTCGTTATGGTTTCCGTTGCTGCCATCTCAAAAACATTTGGACTGCTCCGCTTCCTCCTGAGACTGAGCTTTCTCGCCAAATGACGACTTCTACCACATCTATTGACATTATGGGTCTGCAAGCTGCTTATGCTAATTTGCATACTGACCAAGAACGTGATTACTTCATGCAGCGTTACCATGATGTTATTTCTTCATTTGGAGGTAAAACCTCTTATGACGCTGACAACCGTCCTTTACTTGTCATGCGCTCTAATCTCTGGGCATCTGGCTATGATGTTGATGGAACTGACCAAACGTCGTTAGGCCAGTTTTCTGGTCGTGTTCAACAGACCTATAAACATTCTGTGCCGCGTTTCTTTGTTCCTGAGCATGGCACTATGTTTACTCTTGCGCTTGTTCGTTTTCCGCCTACTGCGACTAAAGAGATTCAGTACCTTAACGCTAAAGGTGCTTTGACTTATACCGATATTGCTGGCGACCCTGTTTTGTATGGCAACTTGCCGCCGCGTGAAATTTCTATGAAGGATGTTTTCCGTTCTGGTGATTCGTCTAAGAAGTTTAAGATTGCTGAGGGTCAGTGGTATCGTTATGCGCCTTCGTATGTTTCTCCTGCTTATCACCTTCTTGAAGGCTTCCCATTCATTCAGGAACCGCCTTCTGGTGATTTGCAAGAACGCGTACTTATTCGCCACCATGATTATGACCAGTGTTTCCAGTCCGTTCAGTTGTTGCAGTGGAATAGTCAGGTTAAATTTAATGTGACCGTTTATCGCAATCTGCCGACCACTCGCGATTCAATCATGACTTCGTGATAAAAGATTGA"
@@ -41,9 +43,21 @@ phiorfs = findorfs(phi, finder=NaiveFinder, minlen=75, scheme=lors)
  ORF{NaiveFinder}(5164:5325, '+', 1)
 ```
 
-In the example above we calculated a score using the `lors` scoring scheme (see [lors](https://github.com/camilogarciabotero/BioMarkovChains.jl/blob/533e53d97cf5951f1ca050454bce1423ec8d7c36/src/transitions.jl#L179) from the [BioMarkovChains.jl](https://camilogarciabotero.github.io/BioMarkovChains.jl/dev/) package). The score is stored in the `score` subfield of the `ORF` .
+In the example above we calculated a score using the `lors` (`logg_odds_ratio_score`) scoring scheme (see [lors](https://github.com/camilogarciabotero/BioMarkovChains.jl/blob/533e53d97cf5951f1ca050454bce1423ec8d7c36/src/transitions.jl#L179) from the [BioMarkovChains.jl](https://camilogarciabotero.github.io/BioMarkovChains.jl/dev/) package). The score is stored in the `score` subfield of the `ORF.Features`.
+
+Briefly, a sequence of DNA could be scored using a Markov model of the transition probabilities of a known sequence. This could be done using a *log-odds ratio score*, which is the logarithm of the ratio of the transition probabilities of the sequence given two models. The log-odds ratio score is defined as:
+
+```math
+\begin{align}
+S(x) = \sum_{i=1}^{L} \beta_{x_{i}x} = \sum_{i=1} \log \frac{a^{\mathscr{m}_{1}}_{i-1} x_i}{a^{\mathscr{m}_{2}}_{i-1} x_i}
+\end{align}
+```
+
+Where the ``a^{\mathscr{m}_{1}}_{i-1} x_i`` is the transition probability of the first model (in this case the calculated for the given sequence) from the state ``x_{i-1}`` to the state ``x_i`` and ``a^{\mathscr{m}_{2}}_{i-1} x_i`` is the transition probability of the second model from the state ``x_{i-1}`` to the state ``x_i``. The score is the sum of the log-odds ratio of the transition probabilities of the sequence given the two models.
+
+In the `lors` case, the two models are the coding and non-coding models of the E. coli genome. The coding model is a Markov model of the transition probabilities of the coding regions of the E. coli genome, and the non-coding model is a Markov model of the transition probabilities of the non-coding regions of the E. coli genome.
 
-All features can be accesed using a conviniente funciton called `features` that returns a `NamedTuple` with the features of the `ORF` and can be broadcasted to the entire collection of `ORF`s using the `.` syntax.
+All features can be accesed using a conviniente funciton called `features` that returns a `NamedTuple` with the features of the `ORF` and can be broadcasted to the entire collection of `ORF`s using the `.` (broadcast) syntax.
 
 ```julia
 features.(phiorfs)
@@ -84,7 +98,7 @@ features.(phiorfs)
 
 ## Analysing Lambda ORFs
 
-In this case the `lors` calculates the log odds ratio of the ORF sequence given two Markov models (by default: [ECOLICDS](https://github.com/camilogarciabotero/BioMarkovChains.jl/blob/533e53d97cf5951f1ca050454bce1423ec8d7c36/src/models.jl#L3) and [ECOLINOCDS](https://github.com/camilogarciabotero/BioMarkovChains.jl/blob/533e53d97cf5951f1ca050454bce1423ec8d7c36/src/models.jl#L16)), one for the coding region and one for the non-coding region. The score is stored in the `score` field of the `NamedTuple` returned by the `features` function. By default the `lors` function return the base 2 logarithm of the odds ratio, so it is analogous to the bits of information that the ORF sequence is coding.
+As mentioned above the `lors` calculates the log odds ratio of the ORF sequence given two Markov models (by default: [ECOLICDS](https://github.com/camilogarciabotero/BioMarkovChains.jl/blob/533e53d97cf5951f1ca050454bce1423ec8d7c36/src/models.jl#L3) and [ECOLINOCDS](https://github.com/camilogarciabotero/BioMarkovChains.jl/blob/533e53d97cf5951f1ca050454bce1423ec8d7c36/src/models.jl#L16)), one for the coding region and one for the non-coding region. The score is stored in the `score` field of the `NamedTuple` returned by the `features` function. By default the `lors` function return the base 2 logarithm of the odds ratio, so it is analogous to the bits of information that the ORF sequence is coding.
 
 Now we can even analyse how is the distribution of the ORFs' scores as a function of their lengths compared to random sequences.
 
@@ -136,6 +150,4 @@ f
 
 ![](assets/lors-lambda.png)
 
-What this plot shows is that the ORFs in the lambda genome have a higher scores than random sequences of the same length. The score is a measure of how likely a sequence given the coding model is compared to the non-coding model. In other words, the higher the score the more likely the sequence is coding. So, the plot shows that the ORFs in the lambda genome are more likely to be coding regions than random sequences. It also shows that the longer the ORF the higher the score, which is expected since longer ORFs are more likely to be coding regions than shorter ones.
-
-
+What this plot shows is that the ORFs in the lambda genome have a higher scores than random sequences of the same length. The score is a measure of how likely a sequence given the coding model is compared to the non-coding model. In other words, the higher the score the more likely the sequence is coding. So, the plot shows that the ORFs in the lambda genome are more likely to be coding regions than random sequences. It also shows that the longer the ORF the higher the score, which is expected since longer ORFs are more likely to be coding regions than shorter ones.

docs/src/simplecodingrule.md

@@ -1,36 +1,3 @@
-## Scoring a sequence using a Markov model
-
-A sequence of DNA could be scored using a Markov model of the transition probabilities of a known sequence. This could be done using a *log-odds ratio score*, which is the logarithm of the ratio of the transition probabilities of the sequence given a model and. The log-odds ratio score is defined as:
-
-```math
-\begin{align}
-S(x) = \sum_{i=1}^{L} \beta_{x_{i}x} = \sum_{i=1} \log \frac{a^{\mathscr{m}_{1}}_{i-1} x_i}{a^{\mathscr{m}_{2}}_{i-1} x_i}
-\end{align}
-```
-
-Where the ``a^{\mathscr{m}_{1}}_{i-1} x_i`` is the transition probability of the first model (in this case the calculated for the given sequence) from the state ``x_{i-1}`` to the state ``x_i`` and ``a^{\mathscr{m}_{2}}_{i-1} x_i`` is the transition probability of the second model from the state ``x_{i-1}`` to the state ``x_i``. The score is the sum of the log-odds ratio of the transition probabilities of the sequence given the two models.
-
-In the current implementation the second model is a CDS transition probability model of *E. coli*. This classification score is implemented in the `naivescorefinder` method. This method will return ORFs with the associated score of the sequence given the CDS model of *E. coli*.
-
-```julia
-using GeneFinder, BioSequences
-
-seq = dna"TTCGTCAGTCGTTCTGTTTCATTCAATACGATAGTAATGTATTTTTCGTGCATTTCCGGTGGAATCGTGCCGTCCAGCATAGCCTCCAGATATCCCCTTATAGAGGTCAGAGGGGAACGGAAATCGTGGGATACATTGGCTACAAACTTTTTCTGATCATCCTCGGAACGGGCAATTTCGCTTGCCATATAATTCAGACAGGAAGCCAGATAACCGATTTCATCCTCACTATCGACCTGAAATTCATAATGCATATTACCGGCAGCATACTGCTCTGTGGCATGAGTGATCTTCCTCAGAGGAATATATACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAGCAGGAACAGGATTGCCAGGGTGATATAGGAAATATTCAGCAGGTTGTTACAGGATTTCTGAATATCATTCATATCAGTATGGATGACTACATAGCCTTTTACCTTGTAGTTGGAGGTAATGGGAGCAAATACAGTAAGTACATCCGAATCAAAATTACCGAAGAAATCACCAACAATGTAATAGGAGCCGCTGGTTACGGTCGAATCAAAATTCTCAATGACAACCACATTCTCCACATCTAAGGGACTATTGGTATCCAGTACCAGTCGTCCGGAGGGATTGATGATGCGAATCTCGGAATTCAGGTAGACCGCCAGGGAGTCCAGCTGCATTTTAACGGTCTCCAAAGTTGTTTCACTGGTGTACAATCCGCCGGCATAGGTTCCGGCGATCAGGGTTGCTTCGGAATAGAGACTTTCTGCCTTTTCCCGGATCAGATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAACAATGATGAAACCAAATACACCAAAAATAAAATATGCGAGTATAAATTTTAGATAAAGTGTTTTTTTCATAACAAATCCTGCTTTTGGTATGACTTAATTACGTACTTCGAATTTATAGCCGATGCCCCAGATGGTGCTGATCTTCCAGTTGGCATGATCCTTGATCTTCTC"
-
-findorfs(seq, minlen=75, finder=NaiveFinder)
-
-9-element Vector{ORF{4, NaiveFinder}}:
- ORF{NaiveFinder}(37:156, '+', 1,)
- ORF{NaiveFinder}(194:268, '-', 2)
- ORF{NaiveFinder}(194:283, '-', 2)
- ORF{NaiveFinder}(249:347, '+', 3)
- ORF{NaiveFinder}(426:590, '+', 3)
- ORF{NaiveFinder}(565:657, '+', 1)
- ORF{NaiveFinder}(650:727, '-', 2)
- ORF{NaiveFinder}(786:872, '+', 3)
- ORF{NaiveFinder}(887:976, '-', 2)
-```
-
 ## The *log-odds ratio* decision rule
 
 The sequence probability given a transition probability model could be used as the source of a sequence classification based on a decision rule to classify whether a sequence correspond to a model or another. Now, imagine we got two DNA sequence transition models, a CDS model and a No-CDS model. The *log-odds ratio* decision rule could be establish as:
@@ -46,7 +13,11 @@ Where the ``P_{C}`` is the probability of the sequence given a CDS model, ``P_{N
 In this package we have implemented this rule and call some basic models of CDS and No-CDS of *E. coli* from Axelson-Fisk (2015) work (implemented in `BioMarkovChains.jl` package). To check whether a random sequence could be coding based on these decision we use the predicate `log_odds_ratio_decision_rule` with the `ECOLICDS` and `ECOLINOCDS` models:
 
 ```julia
-orfsdna = findorfs(seq, minlen=75, alternative_start=true) .|> sequence
+using GeneFinder, BioSequences
+
+seq = dna"TTCGTCAGTCGTTCTGTTTCATTCAATACGATAGTAATGTATTTTTCGTGCATTTCCGGTGGAATCGTGCCGTCCAGCATAGCCTCCAGATATCCCCTTATAGAGGTCAGAGGGGAACGGAAATCGTGGGATACATTGGCTACAAACTTTTTCTGATCATCCTCGGAACGGGCAATTTCGCTTGCCATATAATTCAGACAGGAAGCCAGATAACCGATTTCATCCTCACTATCGACCTGAAATTCATAATGCATATTACCGGCAGCATACTGCTCTGTGGCATGAGTGATCTTCCTCAGAGGAATATATACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAGCAGGAACAGGATTGCCAGGGTGATATAGGAAATATTCAGCAGGTTGTTACAGGATTTCTGAATATCATTCATATCAGTATGGATGACTACATAGCCTTTTACCTTGTAGTTGGAGGTAATGGGAGCAAATACAGTAAGTACATCCGAATCAAAATTACCGAAGAAATCACCAACAATGTAATAGGAGCCGCTGGTTACGGTCGAATCAAAATTCTCAATGACAACCACATTCTCCACATCTAAGGGACTATTGGTATCCAGTACCAGTCGTCCGGAGGGATTGATGATGCGAATCTCGGAATTCAGGTAGACCGCCAGGGAGTCCAGCTGCATTTTAACGGTCTCCAAAGTTGTTTCACTGGTGTACAATCCGCCGGCATAGGTTCCGGCGATCAGGGTTGCTTCGGAATAGAGACTTTCTGCCTTTTCCCGGATCAGATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAACAATGATGAAACCAAATACACCAAAAATAAAATATGCGAGTATAAATTTTAGATAAAGTGTTTTTTTCATAACAAATCCTGCTTTTGGTATGACTTAATTACGTACTTCGAATTTATAGCCGATGCCCCAGATGGTGCTGATCTTCCAGTTGGCATGATCCTTGATCTTCTC"
+
+orfsdna = findorfs(seq, finder=NaiveFinder, minlen=75, alternative_start=true) .|> sequence
 
 20-element Vector{NucSeq{4, DNAAlphabet{4}}}
  ATGTATTTTTCGTGCATTTCCGGTGGAATCGTGCCGTCC…CGGAAATCGTGGGATACATTGGCTACAAACTTTTTCTGA
@@ -125,7 +96,6 @@ orfs[iscoding.(orfsdna)]
  ORF{NaiveFinder}(650:727, '-', 2, -0.04303976584597201)
 ```
 
-
 Or in a single line using another genome sequence:
 
 ```julia