Haskell Meets the Central Dogma

This post will introduce some Haskell code to perform sequence operations relevant to the central dogma of molecular biology. The code here should be relatively simple, but some slight familiarity with Haskell or functional programming is assumed.

The treatment of biology in this post is also kept simple, though if you don’t know what the central dogma of molecular biology is or could do with a brief review, I have written a bit on it; it is meant to give a little background for some of the molecular biology terms used in this post, and to describe its context in the scope of biology today.

Here’s the code from this post if you’d like to follow along. In ghci you can load the file via :l centraldogma.hs to use the functions defined here..

Some helpful type synonyms

I’ll be working with sequences here using Haskell’s native String type. Though DNA, RNA, and protein sequences will all share this same underlying type it will be helpful, in terms of documenting the code, to be able to convey whether a function is meant to work with a particular kind of sequence or another. To do this let’s make use of the type declaration which creates type synonyms. These should help clarify the code’s intent. Note that in Haskell a string is a special case of a list, [] , one that contains Char elements: [Char] . These will be more informative than just Char and String later on:

type Element = Char -- A unitary object type Seq = [ Element ] -- An ordered collection of Elements type DNABase = Element -- DNA variant of Element type DNASeq = [ DNABase ] -- DNA variant of Sequence type RNABase = Element -- RNA variant of Element type RNASeq = [ RNABase ] -- RNA variant of Sequence type AminoAcid = Element -- Protein variant of Element type ProteinSeq = [ AminoAcid ] -- Protein variant of Sequence

Replication, Replication

So let’s get started with replication, one of the basic operations of DNA and RNA in which an exact copy (in its own medium) is produced. Since replication (when we ignore the possibility of error) can be modeled by defining a function that simply returns its own input, it’s rather trivial to implement. Here I’ll choose to define a function that works element-wise, and a sequence-wise function which is the mapping of the element-wise one over all of its elements. This pattern will be employed throughout.

--function that gives whatever sequence element it receives replicateBase :: Element -> Element replicateBase x = x --function that maps the replicateBase function over its input replication :: Seq -> Seq replication s = map replicateBase s

When we supply a sequence string to the replication function like replication "GATTACA" , it should give us "GATTACA" when it evaluates. You could try this out with various sequence strings, the empty string is an important test as well. Here’s a little function to help test if the results are good.

--True if the result of the replication function is the same as the input validReplication :: Seq -> Bool validReplication s = s == replication s

Everybody likes complements

Now let’s take a look at how we can write code to get the complementary sequence for a DNA or RNA sequence. Recall that DNA and RNA form double stranded structures according to nucleobase complementarity: A pairs with T (or U in RNA) and C pairs with G . Here’s a representation of this in code:

complementDNABase :: DNABase -> DNABase complementDNABase 'A' = 'T' complementDNABase 'C' = 'G' complementDNABase 'G' = 'C' complementDNABase 'T' = 'A' complementDNABase x = x -- do not change unexpected characters complementDNA :: DNASeq -> DNASeq complementDNA s = map complementDNABase s complementRNABase :: RNABase -> RNABase complementRNABase 'A' = 'U' complementRNABase 'C' = 'G' complementRNABase 'G' = 'C' complementRNABase 'U' = 'A' complementRNABase x = x -- do not change unexpected characters complementRNA :: RNASeq -> RNASeq complementRNA s = map complementRNABase s

These examples leverage pattern matching on their input. Note that we also have the option of writing it with guards, though I don’t prefer guard syntax for pattern matching. Here’s complementDNABase using guards just to demonstrate:

complementDNABase :: DNABase -> DNABase complementDNABase x | x == 'A' = 'T' | x == 'C' = 'G' | x == 'G' = 'C' | x == 'T' = 'A' | otherwise = x

Giving "GATTACA" to complementDNA should yield "CTAATGT" and giving the corresponding "GAUUACA" to complementRNA should yield "CUAAUCU" .

Complementation is a reciprocal process, meaning that if we produce s' as the complement of s , then s is also the complement of s' . This is an important property of the function, so let’s test it by making sure that if either of the functions reproduce the input when executed twice in succession.

testComplementDNA :: DNASeq -> Bool testComplementDNA s = s == ( complementDNA $ complementDNA s ) testComplementRNA :: RNASeq -> Bool testComplementRNA s = s == ( complementRNA $ complementRNA s )

Use these functions on any sequence string to see if the function was correctly written.

DNA and RNA sequences are traditionally presented in a specific direction: 5’ to 3’ (details here). It is important to remember that the complementing strand of DNA or RNA generally “runs” in the opposite direction. That is to say, if you take GATTACA which is reading 5’ to 3’, and produce it’s complement CTAATGT , the complement produced is reading 3’ to 5’. It is common then to refer to the “reverse complement” of a sequence as the complement read in the conventional 5’ to 3’ fashion. Producing such a reverse complement function in Haskell is simple:

reverseComplementDNA :: DNASeq -> DNASeq reverseComplementDNA = reverse . complementDNA reverseComplementRNA :: RNASeq -> RNASeq reverseComplementRNA = reverse . complementRNA

Here I’ve used function composition with Haskell’s reverse function to easily define the reverse complement functions. That’s pretty handy.

Transcription and noitpircsnarT

The process of transcription refers to the production of RNA from a DNA template and reverse transcription the production of DNA from an RNA template. This is again mediated by base complementarity, and the code for it can be readily made by adapting our complementing functions from above. When a DNA sequence, s , is transcribed, a copy of that sequence is made in RNA using base complementarity to the complementing DNA strand, s' . Since RNA nucleobases differ in the use of Uracil instead of Thymine, the matter of transcription involves replacing our 'T' s with 'U' s and vice versa for reverse transcription.

transcribeBase :: DNABase -> RNABase transcribeBase 'T' = 'U' transcribeBase x = x -- leave other bases, or unexpected chars, alone transcribe :: DNASeq -> RNASeq transcribe s = map transcribeBase s reverseTranscribeBase :: RNABase -> DNABase reverseTranscribeBase 'U' = 'T' reverseTranscribeBase x = x -- leave other bases, or unexpected chars, alone reverseTranscribe :: RNASeq -> DNASeq reverseTranscribe s = map reverseTranscribeBase s

Transcription and reverse transcription functions should be symmetrical. By which I mean to say that if one transcribes a DNA sequence and then reverse transcribes the result, it should reproduce the input. The same is true for reverse transcription of an RNA sequence then transcription of the result. We can make some fun functions to test whether this holds true:

pingPong :: DNASeq -> Bool pingPong s = s == ( reverseTranscribe $ transcribe s ) thereAndBackAgain :: RNASeq -> Bool thereAndBackAgain s = s == transcribe $ reverseTranscribe s

These only really work if the inputs are valid. Passing it a mixed sequence like “GATUACA” should evaluate False in both functions.

Translation -> Traducción

Translation is the process of creating proteins from messenger RNA sequences. RNA is read in chunks of three nucleotides, which constitute a codon, to determine which amino acid to add to a growing protein chain. The “Genetic Code” is the (mostly universal) coding scheme used in biology that maps codons to amino acids. Without further ado, here’s the genetic code in haskell:

--Codons are just special cases (length 3) of their basic types type DNACodon = DNASeq type RNACodon = RNASeq --Using RNA codons, if we want to use DNA, we can just transcribe first --There are 4^3 = 64 combinations, so 64 codons. geneticCode :: RNACodon -> AminoAcid geneticCode "AAA" = 'K' -- Lysine geneticCode "AAC" = 'N' -- Asparagine geneticCode "AAG" = 'K' -- Lysine geneticCode "AAU" = 'N' -- Asparagine geneticCode "ACA" = 'T' -- Threonine geneticCode "ACC" = 'T' -- Threonine geneticCode "ACG" = 'T' -- Threonine geneticCode "ACU" = 'T' -- Threonine geneticCode "AGA" = 'R' -- Arginine geneticCode "AGC" = 'S' -- Serine geneticCode "AGG" = 'R' -- Arginine geneticCode "AGU" = 'S' -- Serine geneticCode "AUA" = 'I' -- Isoleucine geneticCode "AUC" = 'I' -- Isoleucine geneticCode "AUG" = 'M' -- Methionine geneticCode "AUU" = 'I' -- Isoleucine geneticCode "CAA" = 'Q' -- Glutamine geneticCode "CAC" = 'H' -- Histidine geneticCode "CAG" = 'Q' -- Glutamine geneticCode "CAU" = 'H' -- Histidine geneticCode "CCA" = 'P' -- Proline geneticCode "CCC" = 'P' -- Proline geneticCode "CCG" = 'P' -- Proline geneticCode "CCU" = 'P' -- Proline geneticCode "CGA" = 'R' -- Arginine geneticCode "CGC" = 'R' -- Arginine geneticCode "CGG" = 'R' -- Arginine geneticCode "CGU" = 'R' -- Arginine geneticCode "CUA" = 'L' -- Leucine geneticCode "CUC" = 'L' -- Leucine geneticCode "CUG" = 'L' -- Leucine geneticCode "CUU" = 'L' -- Leucine geneticCode "GAA" = 'E' -- Glutamate geneticCode "GAC" = 'D' -- Aspartate geneticCode "GAG" = 'E' -- Glutamate geneticCode "GAU" = 'D' -- Aspartate geneticCode "GCA" = 'A' -- Alanine geneticCode "GCC" = 'A' -- Alanine geneticCode "GCG" = 'A' -- Alanine geneticCode "GCU" = 'A' -- Alanine geneticCode "GGA" = 'G' -- Glycine geneticCode "GGC" = 'G' -- Glycine geneticCode "GGG" = 'G' -- Glycine geneticCode "GGU" = 'G' -- Glycine geneticCode "GUA" = 'V' -- Valine geneticCode "GUC" = 'V' -- Valine geneticCode "GUG" = 'V' -- Valine geneticCode "GUU" = 'V' -- Valine geneticCode "UAA" = '*' -- Stop (Ochre) geneticCode "UAC" = 'Y' -- Tyrosine geneticCode "UAG" = '*' -- Stop (Amber) geneticCode "UAU" = 'Y' -- Tyrosine geneticCode "UCA" = 'S' -- Serine geneticCode "UCC" = 'S' -- Serine geneticCode "UCG" = 'S' -- Serine geneticCode "UCU" = 'S' -- Serine geneticCode "UGA" = '*' -- Stop (Opal) geneticCode "UGC" = 'C' -- Cysteine geneticCode "UGG" = 'W' -- Tryptophan geneticCode "UGU" = 'C' -- Cysteine geneticCode "UUA" = 'L' -- Leucine geneticCode "UUC" = 'F' -- Phenylalanine geneticCode "UUG" = 'L' -- Leucine geneticCode "UUU" = 'F' -- Phenylalanine geneticCode x = '-' -- Anything else corresponds to nothing

When it comes to expressing coding schemes, there’s just no way to cut corners so that got a little verbose. Writing that sort of code is no fun, but there was no way around it1. So now that we’ve got our genetic code, let’s put it to work translating some codons to a protein sequence.

import Data.List.Split -- provides chunksOf function; package: split translate :: RNASeq -> ProteinSeq translate s = map geneticCode $ chunksOf 3 s

chunksOf 3 creates a function that takes a list, and returns a list of lists of length 3, though the last item might be shorter if the input list is not evenly divisible by 3. We can feed our sequence string to this, strings are just lists of characters after all, and get a list of codons back. chunksOf 3 "GAUUACA" would produce ["GAU", "UAC", "A"] . Mapping the genetic code function over that list of strings would give "DY-" .

If we want to translate DNA sequences, we could go the hard way and write a new function, or we could go the smart way and create easily with function composition:

translateDNA :: DNASeq -> ProteinSeq translateDNA = translate . transcribe

Now we can do translateDNA "GATTACA" to get "DY-" .

For a change of pace, let’s do something a bit less frivolous to test our work. Let’s visit the NCBI Gene page of a gene I like and snag the sequences of an mRNA and the protein it produces. Here’s the mRNA sequence file and the protein sequence file. For simplicity I’ll put them here:

>gi|357933616|ref|NM_001252617.1| Homo sapiens lipocalin 1 (LCN1), transcript variant 2, mRNA ACAGCCTCTCCCAGCCCCAGCAAGCGACCTGTCAGGCGGCCGTGGACTCAGACTCCGGAGATGAAGCCCC TGCTCCTGGCCGTCAGCCTTGGCCTCATTGCTGCCCTGCAGGCCCACCACCTCCTGGCCTCAGACGAGGA GATTCAGGATGTGTCAGGGACGTGGTATCTGAAGGCCATGACGGTGGACAGGGAGTTCCCTGAGATGAAT CTGGAATCGGTGACACCCATGACCCTCACGACCCTGGAAGGGGGCAACCTGGAAGCCAAGGTCACCATGC TGATAAGTGGCCGGTGCCAGGAGGTGAAGGCCGTCCTGGAGAAAACTGACGAGCCGGGAAAATACACGGC CGACGGGGGCAAGCACGTGGCATACATCATCAGGTCGCACGTGAAGGACCACTACATCTTTTACTGTGAG GGCGAGCTGCACGGGAAGCCGGTCCGAGGGGTGAAGCTCGTGGGCAGAGACCCCAAGAACAACCTGGAAG CCTTGGAGGACTTTGAGAAAGCCGCAGGAGCCCGCGGACTCAGCACGGAGAGCATCCTCATCCCCAGGCA GAGCGAAACCTGCTCTCCAGGGAGCGATTAGGGGGACACCTTGGCTCCTCAGCAGCCCAAGGACGGCACC ATCCAGCACCTCCGTCATTCACAGGGACATGGAAAAAGCTCCCCACCCCTGCAGAACGCGGCTGGCTGCA CCCCTTCCTACCACCCCCCGCCTTCCCCCTGCCCTGCGCCCCCTCTCCTGGTTCTCCATAAAGAGCTTCA GCAGTTCCCAGTGA >gi|357933617|ref|NP_001239546.1| lipocalin-1 isoform 1 precursor [Homo sapiens] MKPLLLAVSLGLIAALQAHHLLASDEEIQDVSGTWYLKAMTVDREFPEMNLESVTPMTLTTLEGGNLEAK VTMLISGRCQEVKAVLEKTDEPGKYTADGGKHVAYIIRSHVKDHYIFYCEGELHGKPVRGVKLVGRDPKN NLEALEDFEKAAGARGLSTESILIPRQSETCSPGSD

The idea now is to make sure that the results of our translation function when fed the mRNA sequence jives with the reference implementation’s results. Let’s get down to work! I don’t want to have to type or copy the sequence into the source, so let’s make a function to read the sequences out of the files.

--A very simple, non-general function to get the sequence simpleSeqGetter :: FilePath -> IO Seq simpleSeqGetter fp = do unmunged <- readFile fp let linedropped = drop 1 $ lines unmunged return $ foldr ( ++ ) "" linedropped

This is not a proper FASTA parsing job, but it serves for now. I’ve dropped the first line describing the sequence and gotten rid of the newline characters. So now I can do the following in ghci :

ghci > mrna <- simpleSeqGetter "lipocalin_mRNA.fa" ghci > prot <- simpleSeqGetter "lipocalin_protein.fa" ghci > translateDNA mrna "TASPSPSKRPVRRPWTQTPEMKPLLLAVSLGLIAALQAHHLLASDEEIQDVSGTWYLKAMTVDREFPEMNLESVTPMTLTTLEGGNLEAKVTMLISGRCQEVKAVLEKTDEPGKYTADGGKHVAYIIRSHVKDHYIFYCEGELHGKPVRGVKLVGRDPKNNLEALEDFEKAAGARGLSTESILIPRQSETCSPGSD*GDTLAPQQPKDGTIQHLRHSQGHGKSSPPLQNAAGCTPSYHPPPSPCPAPPLLVLHKELQQFPV-" ghci > prot == translate mrna False

It’s pretty clear that our results don’t match. Some readers might have seen this coming, because I’ve not prepared to handle an important detail! If you look carefully you’ll see that the expected sequence is there, but sandwiched between some other protein sequence. Without getting into specifics, mRNA sequences contain untranslated regions (UTRs) at both the 5’ and 3’ ends of the sequence. These untranslated regions are a part of the sequence file; to figure out what the ribosome would actually produce from this particular mRNA we’ll need to know how to drop them (UTRs are totally interesting in their own right, but that’s a story for another time).

To determine where the translated section begins, we must look for the first (5’ most) AUG codon, this is the start codon and codes for Methionine. Everything from that point on is translated until we reach a stop codon: one of UAA , UAG , UGA . The region from the start codon to the stop codon is the coding region. Let’s implement these rules in code now:

translateCodingRegion :: RNASeq -> ProteinSeq translateCodingRegion s = takeWhile ( '*' /= ) $ dropWhile ( 'M' /= ) $ translateDNA s

This now drops everything before the start codon, and keeps everything until the stop codon. Now we can try this function out and test its results in ghci :

ghci > mrna <- simpleSeqGetter "lipocalin_mRNA.fa" ghci > prot <- simpleSeqGetter "lipocalin_protein.fa" ghci > translateCodingRegion mrna "MKPLLLAVSLGLIAALQAHHLLASDEEIQDVSGTWYLKAMTVDREFPEMNLESVTPMTLTTLEGGNLEAKVTMLISGRCQEVKAVLEKTDEPGKYTADGGKHVAYIIRSHVKDHYIFYCEGELHGKPVRGVKLVGRDPKNNLEALEDFEKAAGARGLSTESILIPRQSETCSPGSD" ghci > prot == translateCodingRegion mrna True

Tada! We can translate a given DNA or RNA sequence as well as fish out the first coding region from a sequence. By the way, have you thought it’s weird that I’ve been using translateDNA on an RNA sequence? Yeah, it’s not just you. Nucleic acid sequences are often stored in the DNA alphabet, regardless of whether it makes more physical/biological sense as RNA. It’s a bit odd, but common enough to gloss over transcription and reverse transcription since they directly correlate.

Translation Party: Reverse Translation

In my previous post, I made something of a big deal about how reverse translation (also sometimes called “back translation”) has never been observed and we’ve got pretty good reasons to never expect it to occur. But it turns out that there are plenty of times where we might want to address reverse translation computationally.

Most amino acids have more than one codon that codes for them, but no codon codes for more than one amino acid. In this way, the genetic code is degenerate but not ambiguous. In other words, a given nucleic acid sequence passed through the genetic code will only produce one protein sequence, but a given protein sequence could be decoded to multiple nucleic acid sequences.

This leads to a very hard problem in trying to represent all the possible encoding sequences. Only for rather short protein sequences is it computationally feasible to consider representing all the combinations in memory or storage, or to iteratively consider all of the possibilities.

I think that looking deeper into reverse translation applications is worthy of one or more focused posts, but it’d be good to give a short treatment here. I’ll define a function that decodes a protein sequence to its possible DNA sequences. Might get a little lengthy again…

codonsFor :: AminoAcid -> [ DNACodon ] codonsFor 'A' = [ "GCA" , "GCC" , "GCG" , "GCT" ] -- Alanine codonsFor 'C' = [ "TGC" , "TGT" ] -- Cysteine codonsFor 'D' = [ "GAC" , "GAT" ] -- Aspartate codonsFor 'E' = [ "GAA" , "GAG" ] -- Glutamate codonsFor 'F' = [ "TTC" , "TTT" ] -- Phenylalanine codonsFor 'G' = [ "GGA" , "GGC" , "GGG" , "GGT" ] -- Glycine codonsFor 'H' = [ "CAC" , "CAT" ] -- Histidine codonsFor 'I' = [ "ATA" , "ATC" , "ATT" ] -- Isoleucine codonsFor 'K' = [ "AAA" , "AAG" ] -- Lysine codonsFor 'L' = [ "CTA" , "CTC" , "CTG" , "CTT" , "TTA" , "TTG" ] -- Leucine codonsFor 'M' = [ "ATG" ] -- Methionine codonsFor 'N' = [ "AAC" , "AAT" ] -- Asparagine codonsFor 'P' = [ "CCA" , "CCC" , "CCG" , "CCT" ] -- Proline codonsFor 'Q' = [ "CAA" , "CAG" ] -- Glutamine codonsFor 'R' = [ "AGA" , "AGG" , "CGA" , "CGC" , "CGG" , "CGT" ] -- Arginine codonsFor 'S' = [ "AGC" , "AGT" , "TCA" , "TCC" , "TCG" , "TCT" ] -- Serine codonsFor 'T' = [ "ACA" , "ACC" , "ACG" , "ACT" ] -- Threonine codonsFor 'V' = [ "GTA" , "GTC" , "GTG" , "GTT" ] -- Valine codonsFor 'W' = [ "TGG" ] -- Tryptophan codonsFor 'Y' = [ "TAC" , "TAT" ] -- Tyrosine codonsFor '*' = [ "TAA" , "TAG" , "TGA" ] -- Stop codonsFor x = [] -- If for some reason we get bad input, treat as empty

With this element-wise function in hand, we can create a reverse translation function to work on a protein sequence:

reverseTranslate :: ProteinSeq -> [[ DNACodon ]] reverseTranslate s = map codonsFor s

For each amino acid in the protein sequence this function will produce a list of the codons that can code for it, resulting in a list of codon options. If we wanted to ask how many different ways the protein sequence could be encoded, we could do the following:

import Data.List uniqueCodingsCount :: ProteinSeq -> Integer uniqueCodingsCount s = foldr ( \ x -> ( * ) genericLength x ) 1 $ reverseTranslate s

This folds our list up by taking the length of each sub-list and multiplying it by the accumulated value which I initiated at 1 . Note that I used genericLength from Data.List so that it would return the length as type Integer which can hold an arbitrarily large number (these numbers get big). Let’s give it a couple tries:

ghci > uniqueCodingsCount "" 1 ghci > uniqueCodingsCount "V" 4 ghci > uniqueCodingsCount "VR" 24 ghci > prot <- simpleSeqGetter "lipocalin1_protein.fa" ghci > uniqueCodingsCount prot 179208254265065853973421853282336100254440836495296107948284025895512055446786937609781248 ghci > uniqueCodingsCount "ABCDE" 0

Damn, that’s a lot of ways that this short sequence (only 176 amino acids) could be represented in DNA. Also interesting is that giving uniqueCodingsCount a sequence with an invalid amino acid such as 'B' correctly reports that there are 0 ways to encode this protein.

Now let’s set to work creating a function that will actually generate the unique DNA sequence combinations that can code for a protein sequence. The signature for this function should look like this:

uniqueCodings :: ProteinSeq -> [ DNASeq ]

It’s clear that this function will make use of reverseTranslate whose result type is [[DNACodon]] , so we can begin to think how we might convert that to [DNASeq] . To do this we can make use of a fold function (like I have already above) with a function and an accumulator to reduce the list. So what kind of function might we be interested in? Consider the behavior of list comprehensions:

ghci > [ x + y | x <- [ 10 , 20 , 30 ], y <- [ 1 , 2 , 3 ]] [ 11 , 12 , 13 , 21 , 22 , 23 , 31 , 32 , 33 ] ghci > [ x y | x <- [( * 1 ), ( * 2 ), ( * 3 )], y <- [ 10 , 15 ]] [ 10 , 15 , 20 , 30 , 40 , 60 ] ghci > [ (,) x y | x <- [ "x1" , "x2" ], y <- [ "y1" , "y2" , "y3" , "y4" ]] [( "x1" , "y1" ),( "x1" , "y2" ),( "x1" , "y3" ),( "x2" , "y1" ),( "x2" , "y2" ),( "x2" , "y3" )]

When we draw from two lists in the creation of a new list, the list is produced as an operation with all the combinations of the elements in the drawn lists. To help illustrate how we can make use of this, here’s one more example:

ghci > [ x ++ y | x <- [ "orange" , "apple" ], y <- [ " juice" , " concentrate" ]] [ "orange juice" , "orange concentrate" , "apple juice" , "apple concentrate" ]

Putting the pieces together:

uniqueCodings :: ProteinSeq -> [ DNASeq ] uniqueCodings s = foldl combine [ "" ] $ reverseTranslate s where combine xs ys = [ x ++ y | x <- xs , y <- ys ]

Remember that massive number we got from uniqueCodingsCount on the lipocalin protein sequence? We really don’t want to go printing that many of anything… let’s keep the tests small and reasonable.

ghci > uniqueCodings "" [ "" ] ghci > uniqueCodings "H" [ "CAC" , "CAT" ] ghci > uniqueCodings "HAM" [ "CACGCAATG" , "CACGCCATG" , "CACGCGATG" , "CACGCTATG" , "CATGCAATG" , "CATGCCATG" , "CATGCGATG" , "CATGCTATG" ] ghci > uniqueCodings "HAMBO" []

Pretty cool, the empty protein sequence can only be encoded as an empty DNA sequence. For a lone histidine "H" we essentially get codonsFor 'H' and for "HAM" we get a list of 9 unique combinations of codons. When I gave it bad amino acids it tells me there is no way to encode such a sequence. We expect length uniqueCodings s should be equal to uniqueCodingsCount s :

ghci > let testCounts s = ( uniqueCodingsCount s ) == ( length $ uniqueCodings s ) ghci > testCounts "" True ghci > testCounts "MARCELINE" True ghci > testCounts "BMO" -- Not valid protein sequence True

It looks like our function checks out, we now have a way to work with the DNA sequences that can code for a specified protein. To wrap this post up, I’ll put that function to work solving a real world problem (I’ve done this in my own research).

Storytime

Imagine you are a scientist who has a protein you would like to produce and study. You know the amino acid sequence for the protein and you are ordering the synthesis of a DNA sequence to code for your protein. For reasons you try valiantly (but unsuccessfully) to explain to your great uncle Max during Thanksgiving dinner, you wish to incorporate a specific DNA sequence in a particular segment of your protein’s coding region. This is to allow a restriction enzyme to cut your DNA sequence for important and inscrutable purposes.

Now your DNA sequence design has a constraint. If you can help it, you’d prefer not to mutate the protein sequence. Perhaps you can take advantage of the degeneracy of the genetic code to choose codons that provide your restriction enzyme site without modifying any amino acids.

You select a small region of your protein that is suitable for the site: “ALGYL”

You ask, “Can I put an EcoRI or EcoRV site in here?”

The solution

You’re smart; you decide to let a computer do the investigation.

--produce only the possible coding DNA sequences that contain the constraint constrainedUniqueCodings :: ProteinSeq -> DNASeq -> [ DNASeq ] constrainedUniqueCodings s constraint = filter ( isInfixOf constraint ) $ uniqueCodings s

ghci > let ecoRISeq = "GAATTC" ghci > let ecoRVSeq = "GATATC" ghci > let proteinSegment = "ALGYL" ghci > constrainedUniqueCodings proteinSeqment ecoRISeq [] ghci > constrainedUniqueCodings proteinSeqment ecoRVSeq [ "GCACTAGGATATCTA" , "GCACTAGGATATCTC" , ... , "GCTTTGGGATATCTG" , "GCTTTGGGATATCTT" ]

Sadly you can’t put in an EcoRI site without changing amino acids, but you can choose your codons to incorporate the EcoRV site. I greatly abbreviated the output of that final evaluation, it actually gives 96 result sequences! If the sequence search space were much larger, it could be hard to even find the codons of note. A good way to improve the code would be to have the code identify them. In this case the EcoRV site spans three codons for “GYL”. If we narrow the search to just those three we get an exact notion of our options:

ghci > constrainedUniqueEncodings "GYL" ecoRVSeq [ "GGATATCTA" , "GGATATCTC" , "GGATATCTG" , "GGATATCTT" ]

With that bit of practical problem solving, we conclude our demonstration.

1: I could employ the IUPAC compression format to make this shorter, but that would just pass the buck and I’d have to write the details in an IUPAC function. It would be reusable though, and might be worth doing for that.