Set techniques - Revision history

WikiSysop: /* Exercises to be handed in */

2025-10-03T13:57:50Z

Exercises to be handed in

← Older revision		Revision as of 15:57, 3 October 2025
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	== Exercises to be handed in ==		== Exercises to be handed in ==
	Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC. The first 4 exercises works with k-mers and sets and leads up to an algorithm for find the sequence in a group that looks most like a target sequence. Would they be homologs? Good bet.		Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC. The first 4 exercises works with k-mers and sets and leads up to an algorithm for find the sequence in a group that looks most like a target sequence. Would they be homologs? Good bet.
	# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your python knowledge.
	# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.		# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your python knowledge.<br><br>
	# Make a program that asks for a number (integer) then reads the ''dna7.fsa'' file (which contains insulin-like genes) and saves the entry selected by the number in ''selected.fsa'' and the rest of the entries into the file ''rest.fsa'' This should be a fairly easy task since you have your '''fastaread''' and '''fastawrite''' functions from lesson 8, exercise 5 and 6.		# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.<br><br>
	# Now that you can select a fasta entry, then read the ''selected.fsa'' and create 5-mers from the sequence. Now read the entries from ''rest.fsa'' and for every entry create the 5-mers from the sequence. Report which sequence in ''rest.fsa'' had the greatest overlap (and how much overlap) with the selected sequence. This must be the sequence that looks most like the selected one.		# Make a program that asks for a number (integer) then reads the ''dna7.fsa'' file (which contains insulin-like genes) and saves the entry selected by the number in ''selected.fsa'' and the rest of the entries into the file ''rest.fsa'' This should be a fairly easy task since you have your '''fastaread''' and '''fastawrite''' functions from lesson 8, exercise 5 and 6.<br><br>
	# Read the sequences in the file ''dna7.fsa''. Find out which and how many of the 64 codons are '''not''' used somewhere in the sequences. Print the unused codons.		# Now that you can select a fasta entry, then read the ''selected.fsa'' and create 5-mers from the sequence. Now read the entries from ''rest.fsa'' and for every entry create the 5-mers from the sequence. Report which sequence in ''rest.fsa'' had the greatest overlap (and how much overlap) with the selected sequence. This must be the sequence that looks most like the selected one.<br><br>
	# <font color="#AA00FF">You have made a program (let's call it the X-program), which as input takes a file of accession numbers, ''start10.dat'' and produces some output, which is in ''res10.dat''. Now you count the lines in your input file and your output file and you discover that the line numbers do not match. Horror - your program does not produce output for some input. Now the assignment is to discover which accession numbers did not produce output. This can be done in various ways, but now you have to use a set. Print the results.</font>		# Read the sequences in the file ''dna7.fsa''. Find out which and how many of the 64 codons are '''not''' used somewhere in the sequences. Print the unused codons.<br><br>
	# <font color="#AA00FF">In the file ''ex5.acc'' are a lot of accession numbers, where some are duplicates. We have earlier cleaned this file of duplicates. Let's do that again using a set. Make a program that reads the file once, finds the unique accession numbers and write them to the file ''uniq5.acc''</font>		# <font color="#AA00FF">You have made a program (let's call it the X-program), which as input takes a file of accession numbers, ''start10.dat'' and produces some output, which is in ''res10.dat''. Now you count the lines in your input file and your output file and you discover that the line numbers do not match. Horror - your program does not produce output for some input. Now the assignment is to discover which accession numbers did not produce output. This can be done in various ways, but now you have to use a set. Print the results.</font><br><br>
			# <font color="#AA00FF">In the file ''ex5.acc'' are a lot of accession numbers, where some are duplicates. We have earlier cleaned this file of duplicates. Let's do that again using a set. Make a program that reads the file once, finds the unique accession numbers and write them to the file ''uniq5.acc''</font><br><br>
	# In the ''data1.gb'' file there are 6 references (to articles). Make a program that extracts all authors from the references, eliminates those that are duplicates and print the list of authors. You will notice that some authors seems to be the same person using different initials. You should only consider a person a duplicate if the name matches exactly. This should also work for the other Genbank entries: ''data2.gb'', ''data3.gb'' & ''data4.gb''.<br>Beware: there traps in this exercise, check your output properly.		# In the ''data1.gb'' file there are 6 references (to articles). Make a program that extracts all authors from the references, eliminates those that are duplicates and print the list of authors. You will notice that some authors seems to be the same person using different initials. You should only consider a person a duplicate if the name matches exactly. This should also work for the other Genbank entries: ''data2.gb'', ''data3.gb'' & ''data4.gb''.<br>Beware: there traps in this exercise, check your output properly.

	== Exercises for extra practice ==		== Exercises for extra practice ==

WikiSysop: /* Required course material for the lesson */

2025-09-22T10:03:39Z

Required course material for the lesson

← Older revision		Revision as of 12:03, 22 September 2025
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	\|}		\|}
	== Required course material for the lesson ==		== Required course material for the lesson ==
	Powerpoint: [https://teaching.healthtech.dtu.dk/material/22116/~~22116_12~~-Sets.ppt Sets]<br>		Powerpoint: [https://teaching.healthtech.dtu.dk/material/22116/22116_10-Sets.ppt Sets]<br>
	Video: [https://panopto.dtu.dk/Panopto/Pages/Viewer.aspx?id=e69a7b62-4f83-4b92-b254-af27012c464e Sets]<br>		Video: [https://panopto.dtu.dk/Panopto/Pages/Viewer.aspx?id=e69a7b62-4f83-4b92-b254-af27012c464e Sets]<br>

WikiSysop: /* Exercises to be handed in */

2025-09-03T14:47:14Z

Exercises to be handed in

← Older revision		Revision as of 16:47, 3 September 2025
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	== Exercises to be handed in ==		== Exercises to be handed in ==
	Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC. The first 4 exercises works with k-mers and sets and leads up to an algorithm for find the sequence in a group that looks most like a target sequence. Would they be homologs? Good bet.		Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC. The first 4 exercises works with k-mers and sets and leads up to an algorithm for find the sequence in a group that looks most like a target sequence. Would they be homologs? Good bet.
	# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your ~~biological background~~.		# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your python knowledge.
	# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.		# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.
	# Make a program that asks for a number (integer) then reads the ''dna7.fsa'' file (which contains insulin-like genes) and saves the entry selected by the number in ''selected.fsa'' and the rest of the entries into the file ''rest.fsa'' This should be a fairly easy task since you have your '''fastaread''' and '''fastawrite''' functions from lesson 8, exercise 5 and 6.		# Make a program that asks for a number (integer) then reads the ''dna7.fsa'' file (which contains insulin-like genes) and saves the entry selected by the number in ''selected.fsa'' and the rest of the entries into the file ''rest.fsa'' This should be a fairly easy task since you have your '''fastaread''' and '''fastawrite''' functions from lesson 8, exercise 5 and 6.

WikiSysop: /* Exercises to be handed in */

2025-09-03T10:49:21Z

Exercises to be handed in

← Older revision		Revision as of 12:49, 3 September 2025
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	== Exercises to be handed in ==		== Exercises to be handed in ==
	Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC. The first 4 exercises works with k-mers and sets and leads up to an algorithm for find the sequence in a group that looks most like a target sequence. ~~Homologs, anyone~~?		Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC. The first 4 exercises works with k-mers and sets and leads up to an algorithm for find the sequence in a group that looks most like a target sequence. Would they be homologs? Good bet.
	# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your biological background.		# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your biological background.
	# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.		# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.

WikiSysop: /* Subjects covered */

2025-09-02T14:20:16Z

Subjects covered

← Older revision		Revision as of 16:20, 2 September 2025
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	== Subjects covered ==		== Subjects covered ==
	* Sets, which are unordered data collections with no duplicates.<br>		* Sets, which are unordered data collections with no duplicates.<br>
	* ~~Methods relevant to sets:~~		* Set methods<br>
	** '''clear''', clears a set		* Set uses
	** '''add''', adds and element to the set,
	** '''update''', adds several elements to the set, performance,
	** '''remove''', removes an element from the set, error if not present,
	** '''discard''', removes an element from the set, no error,
	** '''in''', determines if an element exist,
	** mathematical set operations, intersection (&), union (\|)

	== Exercises to be handed in ==		== Exercises to be handed in ==

WikiSysop: /* Exercises to be handed in */

2025-08-31T19:58:56Z

Exercises to be handed in

← Older revision		Revision as of 21:58, 31 August 2025
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	# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your biological background.		# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your biological background.
	# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.		# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.
	# Make a program that asks for a number (integer) then reads the ''dna7.fsa'' file (which contains insulin-like genes) and saves the entry selected by the number in ''selected.fsa'' and the rest of the entries into the file ''rest.fsa'' This should be a fairly easy task since you have your fastaread and fastawrite functions from lesson 8, exercise 5 and 6.		# Make a program that asks for a number (integer) then reads the ''dna7.fsa'' file (which contains insulin-like genes) and saves the entry selected by the number in ''selected.fsa'' and the rest of the entries into the file ''rest.fsa'' This should be a fairly easy task since you have your '''fastaread''' and '''fastawrite''' functions from lesson 8, exercise 5 and 6.
	# Now that you can select a fasta entry, then read the ''selected.fsa'' and create 5-mers from the sequence. Now read the entries from ''rest.fsa'' and for every entry create the 5-mers from the sequence. Report which sequence in ''rest.fsa'' had the greatest overlap (and how much overlap) with the selected sequence. This must be the sequence that looks most like the selected one.		# Now that you can select a fasta entry, then read the ''selected.fsa'' and create 5-mers from the sequence. Now read the entries from ''rest.fsa'' and for every entry create the 5-mers from the sequence. Report which sequence in ''rest.fsa'' had the greatest overlap (and how much overlap) with the selected sequence. This must be the sequence that looks most like the selected one.
	# Read the sequences in the file ''dna7.fsa''. Find out which and how many of the 64 codons are '''not''' used somewhere in the sequences. Print the unused codons.		# Read the sequences in the file ''dna7.fsa''. Find out which and how many of the 64 codons are '''not''' used somewhere in the sequences. Print the unused codons.

WikiSysop: /* Exercises to be handed in */

2025-08-31T19:58:21Z

Exercises to be handed in

← Older revision		Revision as of 21:58, 31 August 2025
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	== Exercises to be handed in ==		== Exercises to be handed in ==
	Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC. The first 4 exercises works with k-mers and sets and leads up to ~~aalgorithm~~ for find the sequence in a group that looks most like a target sequence. Homologs, anyone?		Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC. The first 4 exercises works with k-mers and sets and leads up to an algorithm for find the sequence in a group that looks most like a target sequence. Homologs, anyone?
	# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your biological background.		# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your biological background.
	# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.		# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.

WikiSysop: /* Exercises to be handed in */

2025-08-31T19:56:34Z

Exercises to be handed in

← Older revision		Revision as of 21:56, 31 August 2025
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	== Exercises to be handed in ==		== Exercises to be handed in ==
	Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC.		Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC. The first 4 exercises works with k-mers and sets and leads up to aalgorithm for find the sequence in a group that looks most like a target sequence. Homologs, anyone?
	# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your biological background.		# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your biological background.
	# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.		# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.

WikiSysop: /* Exercises to be handed in */

2025-08-31T19:53:07Z

Exercises to be handed in

← Older revision		Revision as of 21:53, 31 August 2025
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	Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC.		Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC.
	# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your biological background.		# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your biological background.
	# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.# Make a program that asks for a number (integer) then reads the ''dna7.fsa'' file (which contains insulin-like genes) and saves the entry selected by the number in ''selected.fsa'' and the rest of the entries into the file ''rest.fsa''.		# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.
			# Make a program that asks for a number (integer) then reads the ''dna7.fsa'' file (which contains insulin-like genes) and saves the entry selected by the number in ''selected.fsa'' and the rest of the entries into the file ''rest.fsa'' This should be a fairly easy task since you have your fastaread and fastawrite functions from lesson 8, exercise 5 and 6.
	# Now that you can select a fasta entry, then read the ''selected.fsa'' and create 5-mers from the sequence. Now read the entries from ''rest.fsa'' and for every entry create the 5-mers from the sequence. Report which sequence in ''rest.fsa'' had the greatest overlap (and how much overlap) with the selected sequence. This must be the sequence that looks most like the selected one.		# Now that you can select a fasta entry, then read the ''selected.fsa'' and create 5-mers from the sequence. Now read the entries from ''rest.fsa'' and for every entry create the 5-mers from the sequence. Report which sequence in ''rest.fsa'' had the greatest overlap (and how much overlap) with the selected sequence. This must be the sequence that looks most like the selected one.
	# Read the sequences in the file ''dna7.fsa''. Find out which and how many of the 64 codons are '''not''' used somewhere in the sequences. Print the unused codons.		# Read the sequences in the file ''dna7.fsa''. Find out which and how many of the 64 codons are '''not''' used somewhere in the sequences. Print the unused codons.

WikiSysop: /* Exercises to be handed in */

2025-08-31T19:49:28Z

Exercises to be handed in

← Older revision		Revision as of 21:49, 31 August 2025
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	== Exercises to be handed in ==		== Exercises to be handed in ==
	Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC.		Many modern bioinformatic algorithms utilizes '''k-mers'''. A k-mer is a piece of sequence k units long. The k in the k-mer usually changes size, i.e. length of the sequence depending on the algorithm. It can be both protein and DNA sequence. As an example I have this DNA sequence: GACTAC. It contains the following 4-mers: GACT, ACTA, CTAC.
	# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true, why?		# From a genbank file like ''data1-4.gb'' extract the DNA sequence (been there - done that). Now insert all 5-mers in the DNA sequence into a set. Compute the following numbers: how many did you insert in the set, how many are in the set, how many different 5-mers could there possibly be. Display. It seems like the two first numbers are the same, but this is not guaranteed to be true. Explain why using your biological background.
	# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.# Make a program that asks for a number (integer) then reads the ''dna7.fsa'' file (which contains insulin-like genes) and saves the entry selected by the number in ''selected.fsa'' and the rest of the entries into the file ''rest.fsa''.		# Calculate the overlap of 5-mers between any two of the ''data1-4.gb'' files. Just ask for two filenames and calculate for these.# Make a program that asks for a number (integer) then reads the ''dna7.fsa'' file (which contains insulin-like genes) and saves the entry selected by the number in ''selected.fsa'' and the rest of the entries into the file ''rest.fsa''.
	# Now that you can select a fasta entry, then read the ''selected.fsa'' and create 5-mers from the sequence. Now read the entries from ''rest.fsa'' and for every entry create the 5-mers from the sequence. Report which sequence in ''rest.fsa'' had the greatest overlap (and how much overlap) with the selected sequence. This must be the sequence that looks most like the selected one.		# Now that you can select a fasta entry, then read the ''selected.fsa'' and create 5-mers from the sequence. Now read the entries from ''rest.fsa'' and for every entry create the 5-mers from the sequence. Report which sequence in ''rest.fsa'' had the greatest overlap (and how much overlap) with the selected sequence. This must be the sequence that looks most like the selected one.