Posted: February 21st, 2023

Homework #2

Homework #2
Due February 28th, 11:59pm
Each homework submission must include:
• An archive (.zip or .gz) file of the source code containing:
o The makefile used to compile the code on Monsoon (5pts)
o All .cpp and .h files (5pts)
• A full write-up (.pdf of .doc) file containing answers to homework’s questions (5pts), including
the exact command line needed to execute every subproblem of the homework
The source code must follow the following guidelines:
• No external libraries that implement data structures discussed in class are allowed, unless
specifically stated as part of the problem definition. Standard input/output and utilities libraries
(e.g. math.h) are ok.
• All external data sources (e.g. input data) must be passed in as a command line argument (no
hardcoded paths within the source code (5pts).
• Solutions to sub-problems must be executable separately from each other. For example, via a
special flag passed as command line argument (5pts)
For this homework, you will use the query dataset located on Monsoon:
/common/contrib/classroom/inf503/human_reads.fa.
For this homework, you will also need to use the subject dataset (human genome assembly that you
used in HW#1). Recall that it is located at: /common/contrib/classroom/inf503/genomes/human.txt
• This file contains multiple scaffolds
that comprise the human genome
• The genome is in FASTA format (see
insert)
o The headers are unique and
always begin with the “>”
character. These can be
discarded for this homework.
Each line of genome file is exactly 80 characters long (plus carriage return character)
o The genomic sequences consist of the following alphabet {A, C, G, T, N}
Problem #1 (of 1)
Create a class called Queries_AR. The purpose of the class will be to contain a dataset of genomic
sequences (queries) and all of the functions needed to operate on this set. Use the 2D array datastructure to store the genomic sequences of the dataset. For this assignment, you can completely
disregard the headers of the sequence fragments. At minimum, the class must contain (15pts):
• A default constructor (that zeroes everything out)
• At least one custom constructor (e.g. one taking a file path or ifstream as input)
• A function to read the query dataset file
• A search function designed to find a sequence fragment within class’s data
• A function to sort the fragments of the Queries_AR object
• A destructor
A. (30 pts) Read in the entire query dataset and store it in an instance of the Queries_AR class. Read in
the entire subject dataset into a single, concatenated character array (same way you did it in HW#1).
Implement a search function which would search for 32 character fragments of the subject sequence
within the Queries_AR object. The search function should return the location (index) of the match OR
a negative value if a ‘hit’ was not found. Iterate through 32-character long fragments of the subject
dataset, searching for each one in the query dataset.
• How long did it take you to search for the first 10K, 100K, and 1M 32-character long fragments
of the subject dataset within the query dataset?
• How long would it take to search for every possible 32-character long fragment of the subject
dataset within the query dataset? Please note that depending on the efficiency of your
algorithm, this step may take a long time. If the total time is greater than 24 CPU hours,
provide an estimate rather than exact number.
• Print the first 10 fragments of the subject dataset that you found within the Query AR object
(if any).
B. (30 pts) Read in the entire query dataset and store it in an instance of the Queries_AR class. Sort all
character fragments in alphabetic (lexographic) order. Any sorting algorithm will do. Read in the
entire subject dataset into a single, concatenated character array (same way you did it in HW#1).
Implement a search function which would search for 32 character fragments of the subject sequence
within the Queries_AR object. The search function should return the location (index) of the match OR
a negative value if a ‘hit’ was not found. Iterate through 32-character long fragments of the subject
dataset, searching for each one in the query dataset.
• How long did it take you to search for the first 10K, 100K, and 1M 32-character long fragments
of the subject dataset within the query dataset?
• How long would it take to search for every possible 32-character long fragment of the subject
dataset within the query dataset? Please note that depending on the efficiency of your
algorithm, this step may take a long time. If the total time estimate is greater than 24 CPU
hours, provide estimate rather than exact number.
• Print the first 10 fragments that you found within the Query AR object (if any).

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