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Python代写 | COMPSCI369 – S1 2020 Assignment 3

这个作业是用Python解决DNA序列相关的编程
COMPSCI369 – S1 2020
Assignment 3
0 Instructions
This assignment is worth 7.5% of the final grade. It is marked out of 75 points.
Provide a solution as a Python notebook that includes well documented code with the code/test
calls to reproduce your results. Include markdown cells with explanation of the results for each
question. Submit to Canvas
• the .ipynb file with outputs from the executed code
• a .html version of the notebook with all outputs of executed code showing. (To get this
format, export from the notebook viewer or use nbconvert.)
Within the notebook, set the random seed to some integer of your choosing (using random.seed)
so that the marker can recreate the same output that you get. You can reset the seed before each
question if you like.
1 Genome Assembly
Due to limitations of the modern sequencing technology, we can only obtain short continuous sequence fragments (or reads) from the genome sequences at a time. Given millions of such short
reads, can we reconstruct the full DNA sequences as close as possible? This is essentially the DNA
sequence assembly problem, that can be approximated to the Shortest Common Superstring (SCS)
problem: Given a set of strings s1, s2, . . . , sn, find a shortest string s that contains all of them as
substrings.
SCS problem is a NP-complete problem, so the early DNA sequences algorithms used a simple
greedy strategy to the assembly problem, that find the SCS by iteratively joining and merging
overlapping reads until all reads have been merged.
In this assignment you will implement the greedy sequence assembler. You will also implement the
read generator, i.e simulate the shotgun sequencing process, that randomly partitions a genome
sequence S into fragments of certain length l. The process is repeated a large number of times
(also known as cloning) to amplify the biological signal, needed for proper identification of the
short reads. In the lecture, you saw example of greedy DNA reconstruction given all the l-length
reads defined over the original DNA sequence for a fixed value of l. Here, we will not assume that
all possible reads of length l will be given (generated), and we will allow for reads with different
length. But, we will keep the assumption that the reads will be error free.
Question 1: Shotgun Sequencing (10 Points)
Implement a class ShotgunSequencing with the following attributes and methods:
(a) sequence
stores a DNA sequence.
(b) readSequence(filename, i=1)
reads only the i-th sequence from the filename, a file in fasta format, and stores
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the sequence in sequence. The parameter i can take only positive values; if the
file does not have i sequences, then return None.
(c) shotgun(l min,l max)
returns the sequence sequence as an array of fragments with random length
len(f) that is uniformly distributed between l min and l max. Note that the
last generated fragment can be shorter than l min. You can safely assume that
the fragments cover the whole sequence, and no fragment gets lost.
Example: S=‘ATTCGGT’ could be fragmented as ‘AT’, ‘TCGG’, ‘T’ for 2 ≤
len(f) ≤ 4.
(d) cloning(l min,l max,n)
calls the function shotgun(min, max) n times and returns one sorted array
with all fragments.
Question 2: Sequence Assembler (35 Points)
Implement a class SequenceAssembler which assembles the fragments from the above
exercise, with the following attributes and methods:
(a) graph
stores the overlap graph.
Hint: Think about the below given methods which you have to implement
before you decide on how to store your graph. You need to have easy access
to all in-coming and out-going edges of a node. You will need to remove nodes
and their corresponding edges. Why are adjacency lists the best representation
for graphs to use for this problem?
(b) calculateOverlap(fragmentA,fragmentB)
returns the length of the overlap if fragmentB follows fragmentA.
Example: calculateOverlap(‘AAT’,‘ATG’) should return 2, while calculateOverlap(‘ATG’, ‘AAT’) should return 0.
(c) createOverlapGraph(fragments)
creates an overlap graph where each fragment is considered as node. Edges
are directed and weighted, with the weights representing length of fragment
overlaps. Keep edges that have non-zero weights. Nodes representing fragments
that are substrings of other fragments (nodes) can be removed from the graph.
(d) getMaxEdge()
returns the edge with highest weight.
(e) assembling()
implements the greedy algorithm for Shortest Common Superstring where you
merge the nodes until one is left and return the final sequence.
Test your solution for different combinations of l min, l max and n
2
Sequence l min l max n
5 10
Test 10 20 3,5,10
20 30
50 100
Real mRNA 100 200 5,10,15
200 500
and interpret the result for the reconstruction of the two sequences (short-length
test sequence, and real-length mRNA from the human genome) provided in the file
A3 DNAs.fasta. When interpreting the results, take into consideration the following
questions:
• How well is the sequence reconstructed as the fragments vary in size?
• Does the reconstructed sequence have a similar length to the original sequence?
• What is the effect of cloning on the two sequences?
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2 Stochastic simulation
A standard model in epidemiology is the SIR model of infectious disease spread. It has a population
of N hosts is divided into 3 compartments, so is known as a compartmental model:
• the S compartment of those who are susceptible to the disease
• the I compartment of those who are infectious with the disease
• the R compartment of those who are recovered from the disease and now immune (or, more
generally, those who are removed from the epidemic either through recovery with immunity
or due to death).
We assume that S + I + R = N.
The model can be thought of as deterministic or stochastic. We consider the stochastic version
here. Times between all events are exponentially distributed with the following rates which depend
on the current state of the outbreak, assumed to be (S, I, R):
• the rate of transmissions is βSI and the new state is (S − 1, I + 1, R), and
• the rate of recoveries is γI and the new state is (S, I − 1, R + 1).
Question 3: Simulating outbreaks (30 Points)
(a) At what point will the epidemic finish?
(b) Write method rand exp that takes a rate parameter λ as input and produces
as output an exponentially distributed random variable with rate parameter λ.
(c) Write method sim SIR that takes as inputs N, S0, β, γ and produces as output
a list of the event times and the number susceptible, infected and recovered at
each time point. All outbreaks start at time t = 0.
(d) Run a simulation with N = 1000, S0 = 10, β = 2.2, γ = 2 and plot the number
infected through time.
(e) Run an experiment and report the results to approximate the probability that
a large outbreak occurs using the same parameters as above but with only one
initial infected. What has usually happened if there is no outbreak?
(f) The reproduction number R0 = β/γ of the epidemic is the mean number of
transmissions by a single infected in an otherwise susceptible population. Using
the same parameters as above but allowing β to vary, select five values of R0
near to or at 1 and explore whether or not you get an outbreak. Report and
explain your results.
(g) Suppose now that the infectious period is fixed, so that hosts are infectious
for exactly 1 time unit. Is the process still Markov? How would you go about
writing code to simulate such an epidemic? (You do not have to actually write
the code here.)
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