Do not use any modules except the math module, or any pieces of codeyou did not writeFor each question, your function will be automatically tested on a varietyof test cases, which will account for 75% of theFor each question, 25% of the mark will be assigned by the TA based on (i) your appropriate naming of variables; (ii) commenting of your program; (iii) simplicity of your program (simpler =better)Background information: Python代写Transcription factors (TF) are proteins that recognize and bind to DNA, and in doing so regulate the expression of a nearby gene. Each type of TF has an affinity for a particular short DNA pattern. For example, the E-­‐box TF likes to bind the sequence CAGCTG, whereas the Runx TF likes to bind CCACA. However, every transcription factor has a certain flexibility in the DNA sequences it can bind to. For example, the E-­‐box TF will sometimes bind CAGGTG, or CAGCAG. The Runx TF may also bind CCAGA or ACACA. Biologists can identify DNA sequences bound by a given transcription factor using experiments such as chromatin immunoprecipitation followed by sequence (ChIP-­‐seq). The outcome of such an experiment is a set of DNA sequences from the genome that are bound by the TF. For example, a ChIP-­‐seq experiment on transcription factor E2F1 may produce the following set of 10 sequences:ACGATGACAATGACGATCACGATCTCGATCTCGAGCTAGATCTAAATCAAAATCACGATAThese DNA sequences are probably only a small subset of all the sites bound by E2F1 in the genome. We would like to be able to learn a model of what type of pattern E2F1 likes to bind, in order to predict, in the human genome, sites that may have been missed by the experiment. In order to do so, bioinformaticians have developed a model called Position Weight Matrix (PWM). Here, you will learn how this model works, and you will have to implement it in Python.Python代写To build a PWM, Python代写Python代写one first needs to build a Position Frequency Matrix (PFM). A PFM is matrix (table) of 4 rows and L columns, where L is the length of the binding site sequences. In the E2F1 example, L=6. The entry in row i and column j of the PFM, which we denote PFM[i][j], is the frequency of nucleotide i at position j of the binding sites. For E2F1, based on the 10 sites listed above, the PFM isYou will notice that large positive values in the PWM (e.g. 0.59) correspond to positions where the TF has a strong preference for a particular nucleotide (e.g. an A at position 3).We can now use a PWM to calculate the score of a match between a candidate sequence of L nucleotides and the PWM. For example, the score of candidate sequence TCGATC is obtained by summing up the appropriate values from the PWM:Score(TCGATG) = PWM[T][0] + PWM[C][1] + PWM[G][2] + PWM[A][3] + PWM[T][4] + PWM[G][5]Python代写= 0.19 + 0.43 + 0.43 + 0.59 + 0.54 + (-­‐0.09) = 1.66whereas for sequence ACATAG, we getScore(ACATAG) = PWM[A][0] + PWM[C][1] + PWM[A][2] + PWM[T][3] + PWM[A][4] + PWM[G][5]= 0.37 + 0.43 + 0.07 + (-­‐1.41) + (-­‐1.41) + (-­‐0.09) = -­‐0.53The larger the score, the higher the affinity of the transcription factor for the sequence.Finally, we can use a PWM to scan a longer sequence to identify potential binding sites for the TF. This is done by calculating the PWM score for every possible portion of L nucleotides of the sequence. For example:Sequence = ATCGATGAGACTGA Score(0) = Score(ATCGAT) = -­‐3.87… Score(1) = Score(TCGATG) = 1.65… Score(2) = Score(CGATGA) = -­‐4.16… Score(3) = Score(GATGAG) = -­‐4.16… Score(4) = Score(ATGAGA) = -­‐0.01… Score(5) = Score(TGAGAC) = -­‐2.55……Python代写Positions whose score exceeds a user-­‐chosen threshold are reported as a putative binding sites for the TF. For example, if we choose a threshold of -­‐0.2, then positions 1 and 4 would be reported as putative sites.Download http://www.cs.mcgill.ca/~blanchem/204/hw2/PWM.pyYour work will consist in completing the each of the functions given in this program.You must not change the function names or their arguments. Python代写For each question, it is your responsibility to test your program to make sure it works on all possible cases, not just those provided as examples.Python代写Question 1 (10 points). Encoding of DNA nucleotides into integers.It is often convenient to represent a nucleotide (“A”,”C”,”G”, or “T”) as a number between 0 and 3, where “A” is represented as 0, “C” as 1, “G” as 2, and “T” as 3.Complete the encode function that takes as argument a string of one character, and returns an integer between 0 and 3. If the string provided as argument is not one of “A”,”C”,”G”, or “T”, the function should return -­‐1.Python代写Example of execution: code = encode(“G”) print(code) èè 2code = encode(“A”) print(code) èè 0 code = encode(“X”) print(code) èè -­‐1Question 2 (20 points). Building a PFM. Python代写Write a function called buildPFM that takes as input a list of DNA sequences identified as binding sites for a given transcription factor, and returns a Position Frequency Matrix (PFM), represented as a list of lists of integers, where PFM[nuc][pos] = number of sequences that have nucleotide nuc at position pos. The function can assume that the input sequences all have the same length, but that length is not necessarily 6 (like in our example).Python代写Example of execution:sites = [ ACGATG , ACAATG , ACGATC , ACGATC , TCGATC , TCGAGC , TAGATC , TAAATC , AAAATC , ACGATA ]PFM = buildPFM(sites)print(PFM) èè [[6, 3, 3, 10, 0, 1], [0, 7, 0, 0, 0, 7], [0, 0, 7, 0, 1, 2], [4, 0, 0, 0, 9, 0]]Note: In your code, you may find it useful to be able to create a PFM with all entries initialized to zero. If L is the length of the desired PFM, this can be done as follows:PFM = [[0 for i in range(L)] for j in range(4)]Question 3 (10 points). Building a PWM from a PFM Python代写Write the function getPWMfromPFM, which takes as argument a PFM and a real number called the pseudocount and returns a PWM, according to the formula given above.Python代写Example of execution:PFM = [[6, 3, 3, 10, 0, 1], [0, 7, 0, 0, 0, 7], [0, 0, 7, 0, 1, 2], [4, 0, 0, 0, 9, 0]] PWM = getPWMfromPFM(PFM, 0.1)print(PWM)èè Output:[[0.370356487039949, 0.07638834586345467, 0.07638834586345467, 0.5893480258118245, -­‐1.4149733479708178, -­‐0.37358066281259295], [-­‐1.4149733479708178, 0.43628500074825727, -­‐1.4149733479708178, -­‐1.4149733479708178, -­‐1.4149733479708178, 0.43628500074825727], [-­‐1.4149733479708178, -­‐1.4149733479708178, 0.43628500074825727, -­‐1.4149733479708178, -­‐ Python代写0.37358066281259295, -­‐0.09275405323689867], [0.19781050874891748, -­‐1.4149733479708178,-­‐1.4149733479708178, -­‐1.4149733479708178, 0.5440680443502756, -­‐1.4149733479708178]]Question 4 (20 points). Scoring a sequence of length LWrite a function score that takes as argument a sequence and a PWM (both of the same length L), and calculates the score of the sequence with that PWM.Example of execution:PWM=[[0.370356487039949, 0.07638834586345467, 0.07638834586345467, 0.5893480258118245, -­‐1.4149733479708178, -­‐0.37358066281259295], [-­‐1.4149733479708178,0.43628500074825727, -­‐1.4149733479708178, -­‐1.4149733479708178, -­‐1.4149733479708178,0.43628500074825727], [-­‐1.4149733479708178, -­‐1.4149733479708178, 0.43628500074825727, Python代写-­‐1.4149733479708178, -­‐0.37358066281259295, -­‐0.09275405323689867], [0.19781050874891748, -­‐1.4149733479708178, -­‐1.4149733479708178, -­‐1.4149733479708178, 0.5440680443502756, -­‐1.4149733479708178]]s= score( TCGATG ,PWM) print(s) èè 1.6597285360572567s=score( ACATAG ,PWM)print(s) èè -­‐0.5319435143191569Question 5 (20 points). Identifying matches in longer sequencesPython代写Write a function predictSites that takes as input a DNA sequence and a PWM as positional arguments, as well a floating point number called threshold as keyword argument, and returns a list of the starting positions of substrings whose score with the given PWM is larger or equal to the threshold.Python代写Example of execution:PWM=[[0.370356487039949, 0.07638834586345467, 0.07638834586345467, 0.5893480258118245, -­‐1.4149733479708178, -­‐0.37358066281259295], [-­‐1.4149733479708178,0.43628500074825727, -­‐1.4149733479708178, -­‐1.4149733479708178, -­‐1.4149733479708178,0.43628500074825727], [-­‐1.4149733479708178, -­‐1.4149733479708178, 0.43628500074825727,-­‐1.4149733479708178, -­‐0.37358066281259295, -­‐0.09275405323689867], [0.19781050874891748, -­‐1.4149733479708178, -­‐1.4149733479708178, -­‐1.4149733479708178, 0.5440680443502756, -­‐1.4149733479708178]]sequence = GCAGACTCAGCAGCGACTACAGCGCTACTACAGCGGAGACGATGCGACAAT hits = predictSites(sequence, PWM) print(hits) èè [12, 38, 43, 46]Question 6 (20 points): Identification of putative target genesGenes are portions of DNA sequences. The transcription start site of a gene is the position of the start of the gene in the sequence. Suppose that you have a dictionary of gene names and associated transcription start site position. For examplegenes = { BRCA1 :3, MYC :23, RUNX : 45}Python代写Our goal is to count the number of predicted binding sites located in the neighborhood of the transcription start site of each gene. For example, if we allow a maximum distance of 10 and the list of PWM hits is [12, 38, 43, 46], then the BRCA1 gene has 1 hit, the MYC gene has 0, and the RUNX gene has 3.Python代写Your task is to write the function countHitsPerGene, which takes as positional arguments the genes dictionary and the list of hits, as well as keyword argument maxDist, and returns a new dictionary with genes as keys and hit count as values.Example of execution:genes = { BRCA1 :3, MYC :23, RUNX : 45} hits = [12, 38, 43, 46]geneHits = countHitsPerGene(genes, hits, maxDist = 10) print(geneHits) èè { BRCA1 : 1, MYC : 0, RUNX : 3} geneHits = countHitsPerGene(genes, hits, maxDist = 20) print(geneHits) èè { BRCA1 : 1, MYC : 3, RUNX : 3}Python代写更多其他：C++代写 考试助攻 C语言代写 finance代写  计算机代写 report代写 project代写 物理代写 数学代写 经济代写  java代写 程序代写 algorithm代写 作业代写合作平台：Essayghost 315代写 写手招聘 Essay代写

所有的编程代写范围：essayghost为美国、加拿大、英国、澳洲的留学生提供C语言代写、代写C语言、C语言代做、代做C语言、数据库代写、代写数据库、数据库代做、代做数据库、Web作业代写、代写Web作业、Web作业代做、代做Web作业、Java代写、代写Java、Java代做、代做Java、Python代写、代写Python、Python代做、代做Python、C/C++代写、代写C/C++、C/C++代做、代做C/C++、数据结构代写、代写数据结构、数据结构代做、代做数据结构等留学生编程作业代写服务。