Dictionaries

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Mandatory Lesson Feedback Survey

Overview

Questions

  • How is a dictionary defined in Python?
  • What are the ways to interact with a dictionary?
  • Can a dictionary be nested?

Objectives

  • Understanding the structure of a dictionary.
  • Accessing data from a dictionary.
  • Practising nested dictionaries to deal with complex data.

This chapter assumes that you are familiar with the following concepts in Python 3:

Dictionary

Mapping Types – dict

Google search

StackOverflow python-3.x dictionaries

YouTube Tutorial Dictionaries

One of the most useful built-in tools in Python, dictionaries associate a set of values with a number of keys.

Think of an old fashion, paperback dictionary where we have a range of words with their definitions. The words are the keys, and the definitions are the values that are associated with the keys. A Python dictionary works in the same way.

Consider the following scenario:

Suppose we have a number of protein kinases, and we would like to associate them with their descriptions for future reference.

This is an example of association in arrays. We may visualise this problem as displayed in Figure.

Illustrative diagram of associative arrays, showing the sets of keys and their association with some of the values. One way to associate the proteins with their definitions would be to use nested arrays. However, it would make it difficult to retrieve the values at a later time. This is because to retrieve the values, we would need to know the index at which a given protein is stored.

Instead of using normal arrays, in such circumstances, we use associative arrays. The most popular method to create construct an associative array in Python is to create dictionaries or dict.

Remember

To implement a dict in Python, we place our entries in curly bracket, separated using a comma. We separate keys and values using a colon — e.g. {‘key’: ‘value’}. The combination of dictionary key and its associating value is known as a dictionary item.

Note

When constructing a long dict with several items that span over several lines, it is not necessary to write one item per line or use indentations for each item or line. All we must is to write the as {‘key’: ‘value’} in curly brackets and separate each pair with a comma. However, it is good practice to write one item per line and use indentations as it makes it considerably easier to read the code and understand the hierarchy.

We can therefore implement the diagram displayed in Figure in Python as follows:

PYTHON 

protein_kinases = {
  'PKA': 'Involved in regulation of glycogen, sugar, and lipid metabolism.',
  'PKC': 'Regulates signal transduction pathways such as the Wnt pathway.',
  'CK1': 'Controls the function of other proteins through phosphorylation.'
  }

print(protein_kinases)

OUTPUT 

{'PKA': 'Involved in regulation of glycogen, sugar, and lipid metabolism.', 'PKC': 'Regulates signal transduction pathways such as the Wnt pathway.', 'CK1': 'Controls the function of other proteins through phosphorylation.'}

PYTHON 

print(type(protein_kinases))

OUTPUT 

<class 'dict'>

Do it Yourself

Use Universal Protein Resource (UniProt) to find the following proteins for humans: - Axin-1 - Rhodopsin

Construct a dictionary for these proteins and the number amino acids for each of them. The keys should represent the name of the protein. Display the result.

PYTHON 

proteins = {
  'Axin-1': 862,
  'Rhodopsin': 348
  }

print(proteins)

OUTPUT 

{'Axin-1': 862, 'Rhodopsin': 348}

Now that we have created a dictionary; we can test whether or not a specific key exists our dictionary:

PYTHON 

'CK1' in protein_kinases

OUTPUT 

True

PYTHON 

'GSK3' in protein_kinases

OUTPUT 

False

Do it Yourself

Using the dictionary you created in Do it Yourself, test to see whether or not a protein called ERK exists as a key in your dictionary? Display the result as a Boolean value.

PYTHON 

print('ERK' in proteins)

OUTPUT 

False

Interacting with a dictionary

We have already learnt that in programming, the more explicit our code, the better it is. Interacting with dictionaries in Python is very easy, coherent, and explicit. This makes them a powerful tool that we can exploit for different purposes.

In arrays, specifically in list and tuple, we routinely use indexing techniques to retrieve values. In dictionaries, however, we use keys to do that. Because we can define the keys of a dictionary ourselves, we no longer have to rely exclusively on numeric indices.

As a result, we can retrieve the values of a dictionary using their respective keys as follows:

PYTHON 

print(protein_kinases['CK1'])

OUTPUT 

Controls the function of other proteins through phosphorylation.

However, if we attempt to retrieve the value for a key that does not exist in our dict, a KeyError will be raised:

PYTHON 

'GSK3' in protein_kinases

OUTPUT 

False

PYTHON 

print(protein_kinases['GSK3'])

ERROR 

Error: KeyError: 'GSK3'

Do it Yourself

Implement a dict to represent the following set of information:

Cystic Fibrosis:

Full Name Gene Type
Cystic fibrosis transmembrane conductance regulator CFTR Membrane Protein

Using the dictionary you implemented, retrieve and display the gene associated with cystic fibrosis.

PYTHON 

cystic_fibrosis = {
  'full name': 'Cystic fibrosis transmembrane conductance regulator',
  'gene': 'CFTR',
  'type': 'Membrane Protein'
  }

print(cystic_fibrosis['gene'])

OUTPUT 

CFTR

Remember

Whilst the values in a dict can be of virtually any type supported in Python, the keys may only be defined using immutable types.

To find out which types are immutable, see Table. Additionally, the keys in a dictionary must be unique.

If we attempt to construct a dict using a mutable value as key, a TypeError will be raised.

For instance, list is a mutable type and therefore cannot be used as a key:

PYTHON 

test_dict = {
  ['a', 'b']: 'some value'
  }

ERROR 

Error: TypeError: unhashable type: 'list'

But we can use any immutable type as a key:

PYTHON 

test_dict = {
  'ab': 'some value'
  }

print(test_dict)

OUTPUT 

{'ab': 'some value'}

PYTHON 

test_dict = {
  ('a', 'b'): 'some value'
  }

print(test_dict)

OUTPUT 

{('a', 'b'): 'some value'}

If we define a key more than once, the Python interpreter constructs the entry in dict using the last instance.

In the following example, we repeat the key ‘pathway’ twice; and as expected, the interpreter only uses the last instance, which in this case represents the value ‘Canonical’:

PYTHON 

signal = {
  'name': 'Wnt',
  'pathway': 'Non-Canonical',  # first instance
  'pathway': 'Canonical'  # second instance
  }

print(signal)

OUTPUT 

{'name': 'Wnt', 'pathway': 'Canonical'}

Mutability

Dictionaries are mutable. This means that we can alter their contents. We can make any alterations to a dictionary as long as we use immutable values for the keys.

Suppose we have a dictionary stored in a variable called protein, holding some information about a specific protein:

PYTHON 

protein = {
  'full name': 'Cystic fibrosis transmembrane conductance regulator',
  'alias': 'CFTR',
  'gene': 'CFTR',
  'type': 'Membrane Protein',
  'common mutations': ['Delta-F508', 'G542X', 'G551D', 'N1303K']
  }

We can add new items to our dictionary or alter the existing ones:

PYTHON 

# Adding a new item:
protein['chromosome'] = 7

print(protein)

print(protein['chromosome'])

OUTPUT 

{'full name': 'Cystic fibrosis transmembrane conductance regulator', 'alias': 'CFTR', 'gene': 'CFTR', 'type': 'Membrane Protein', 'common mutations': ['Delta-F508', 'G542X', 'G551D', 'N1303K'], 'chromosome': 7}
7

We can also alter an existing value in a dictionary using its key. To do so, we simply access the value using its key, and treat it as a normal variable; i.e. the same way we do with members of a list:

PYTHON 

print(protein['common mutations'])

OUTPUT 

['Delta-F508', 'G542X', 'G551D', 'N1303K']

PYTHON 

protein['common mutations'].append('W1282X')
print(protein)

OUTPUT 

{'full name': 'Cystic fibrosis transmembrane conductance regulator', 'alias': 'CFTR', 'gene': 'CFTR', 'type': 'Membrane Protein', 'common mutations': ['Delta-F508', 'G542X', 'G551D', 'N1303K', 'W1282X'], 'chromosome': 7}

Do it Yourself

Implement the following dictionary:

signal = {'name': 'Wnt', 'pathway': 'Non-Canonical'}}

with respect to signal:

  • Correct the value of pathway to “Canonical”;
  • Add a new item to the dictionary to represent the receptors for the canonical pathway as “Frizzled” and “LRP”.

Display the altered dictionary as the final result.

PYTHON 

signal = {'name': 'Wnt', 'pathway': 'Non-Canonical'}

signal['pathway'] = 'Canonical'
signal['receptors'] = ('Frizzled', 'LRP')

print(signal)

OUTPUT 

{'name': 'Wnt', 'pathway': 'Canonical', 'receptors': ('Frizzled', 'LRP')}

Advanced Topic

Displaying an entire dictionary using the print() function can look a little messy because it is not properly structured. There is, however, an external library called pprint (Pretty-Print) that behaves in very similar way to the default print() function, but structures dictionaries and other arrays in a more presentable way before displaying them. We do not discuss ``Pretty-Print’’ in this course, but it is a part of Python’s default library and is therefore installed with Python automatically. To learn more it, have a read through the official documentations for the library and review the examples.

Because the keys are immutable, they cannot be altered. However, we can get around this limitation by introducing a new key and assigning the values of the old key to the new one. Once we do that, we can go ahead and remove the old item. The easiest way to remove an item from a dictionary is to use the syntax del:

PYTHON 

# Creating a new key and assigning to it the
# values of the old key:
protein['human chromosome'] = protein['chromosome']

print(protein)

OUTPUT 

{'full name': 'Cystic fibrosis transmembrane conductance regulator', 'alias': 'CFTR', 'gene': 'CFTR', 'type': 'Membrane Protein', 'common mutations': ['Delta-F508', 'G542X', 'G551D', 'N1303K', 'W1282X'], 'chromosome': 7, 'human chromosome': 7}

PYTHON 

# Now we remove the old item from the dictionary:
del protein['chromosome']

print(protein)

OUTPUT 

{'full name': 'Cystic fibrosis transmembrane conductance regulator', 'alias': 'CFTR', 'gene': 'CFTR', 'type': 'Membrane Protein', 'common mutations': ['Delta-F508', 'G542X', 'G551D', 'N1303K', 'W1282X'], 'human chromosome': 7}

We can simplify the above operation using the .pop() method, which removes the specified key from a dictionary and returns any values associated with it:

PYTHON 

protein['common mutations in caucasians'] = protein.pop('common mutations')

print(protein)

OUTPUT 

{'full name': 'Cystic fibrosis transmembrane conductance regulator', 'alias': 'CFTR', 'gene': 'CFTR', 'type': 'Membrane Protein', 'human chromosome': 7, 'common mutations in caucasians': ['Delta-F508', 'G542X', 'G551D', 'N1303K', 'W1282X']}

Do it Yourself

Implement a dictionary as:

PYTHON 

signal = {'name': 'Beta-Galactosidase', 'pdb': '4V40'}

with respect to signal:

  • Change the key name from ‘pdb’ to ‘pdb id’ using the .pop() method.

  • Write a code to find out whether the dictionary:

    • contains the new key (i.e. ‘pdb id’).
    • confirm that it no longer contains the old key (i.e. ‘pdb’)

If both conditions are met, display:

Contains the new key, but not the old one.

Otherwise:

Failed to alter the dictionary.

PYTHON 

signal = {
		    'name': 'Beta-Galactosidase',
		    'pdb': '4V40'
	}

signal['pdb id'] = signal.pop('pdb')

if 'pdb id' in signal and 'pdb' not in signal:
    print('Contains the new key, but not the old one.')
else:
    print('Failed to alter the dictionary.')

OUTPUT 

Contains the new key, but not the old one.

Nested dictionaries

As explained earlier the section, dictionaries are amongst the most powerful built-in tools in Python. It is possible to construct nested dictionaries to organise data in a hierarchical fashion. This useful technique is outlined extensively in example.

It is very easy to implement nested dictionaries:

PYTHON 

# Parent dictionary
pkc_family = {
    # Child dictionary A:
    'conventional': {
      'note': 'Require DAG, Ca2+, and phospholipid for activation.',
      'types': ['alpha', 'beta-1', 'beta-2', 'gamma']
    },
    # Child dictionary B:
    'atypical': {
        'note': (
            'Require neither Ca2+ nor DAG for'
            'activation (require phosphatidyl serine).'
        ),
        'types': ['iota', 'zeta']
    }
}

and we follow similar principles to access, alter, or remove the values stored in nested dictionaries:

PYTHON 

print(pkc_family)

OUTPUT 

{'conventional': {'note': 'Require DAG, Ca2+, and phospholipid for activation.', 'types': ['alpha', 'beta-1', 'beta-2', 'gamma']}, 'atypical': {'note': 'Require neither Ca2+ nor DAG foractivation (require phosphatidyl serine).', 'types': ['iota', 'zeta']}}

PYTHON 

print(pkc_family['atypical'])

OUTPUT 

{'note': 'Require neither Ca2+ nor DAG foractivation (require phosphatidyl serine).', 'types': ['iota', 'zeta']}

PYTHON 

print(pkc_family['conventional']['note'])

OUTPUT 

Require DAG, Ca2+, and phospholipid for activation.

PYTHON 

print(pkc_family['conventional']['types'])

OUTPUT 

['alpha', 'beta-1', 'beta-2', 'gamma']

PYTHON 

print(pkc_family['conventional']['types'][2])

OUTPUT 

beta-2

PYTHON 

apkc_types = pkc_family['conventional']['types']
print(apkc_types[1])

OUTPUT 

beta-1

Do it Yourself

Implement the following table of genetic disorders as a nested dictionary:

Full Name Gene Type
Cystic fibrosis Cystic fibrosis transmembrane conductance regulator CFTR Membrane Protein
Xeroderma pigmentosum A DNA repair protein complementing XP-A cells XPA Nucleotide excision repair
Haemophilia A Haemophilia A F8 Factor VIII Blood-clotting protein

Using the dictionary, display the gene for Haemophilia A.

PYTHON 

genetic_diseases = {
    'Cystic fibrosis': {
        'name': 'Cystic fibrosis transmembrane conductance regulator',
        'gene': 'CFTR',
        'type': 'Membrane Protein'
    },
    'Xeroderma pigmentosum A': {
        'name': 'DNA repair protein complementing XP-A cells',
        'gene': 'XPA',
        'type': 'Nucleotide excision repair'
    },
    'Haemophilia A': {
        'name': 'Haemophilia A',
        'gene': 'F8',
        'type': 'Factor VIII Blood-clotting protein'
    }
}

print(genetic_diseases['Haemophilia A']['gene'])

OUTPUT 

F8

EXAMPLE: Nested dictionaries in practice

We would like to store and analyse the structure of several proteins involved in the Lac operon. To do so, we create a Python dict to help us organise our data.

We start off by creating an empty dictionary that will store our structures:

PYTHON 

structures = dict()

We then move onto depositing our individual entries to structure by adding new items to it.

Each item has a key that represents the name of the protein we are depositing, and a value that is itself a dictionary consisting of information regarding the structure of that protein:

PYTHON 

structures['Beta-Galactosidase'] = {
    'pdb id': '4V40',
    'deposit date': '1994-07-18',
    'organism': 'Escherichia coli',
    'method': 'x-ray',
    'resolution': 2.5,
    'authors': (
        'Jacobson, R.H.', 'Zhang, X.',
        'Dubose, R.F.', 'Matthews, B.W.'
    )
}

PYTHON 

structures['Lactose Permease'] = {
    'pdb id': '1PV6',
    'deposit data': '2003-06-23',
    'organism': 'Escherichia coli',
    'method': 'x-ray',
    'resolution': 3.5,
    'authors': (
        'Abramson, J.', 'Smirnova, I.', 'Kasho, V.',
        'Verner, G.', 'Kaback, H.R.', 'Iwata, S.'
    )
}

Dictionaries don’t have to be homogeneous. In other words, there can be different items in each entry.

For instance, the ‘LacY’ protein contains an additional key entitled ‘note’:

PYTHON 

structures['LacY'] = {
    'pdb id': '2Y5Y',
    'deposit data': '2011-01-19',
    'organism': 'Escherichia coli',
    'method': 'x-ray',
    'resolution': 3.38,
    'note': 'in complex with an affinity inactivator',
    'authors': (
        'Chaptal, V.', 'Kwon, S.', 'Sawaya, M.R.',
        'Guan, L.', 'Kaback, H.R.', 'Abramson, J.'
    )
}

The variable structure which is an instance of type dict, is now a nested dictionary:

PYTHON 

print(structures)

OUTPUT 

{'Beta-Galactosidase': {'pdb id': '4V40', 'deposit date': '1994-07-18', 'organism': 'Escherichia coli', 'method': 'x-ray', 'resolution': 2.5, 'authors': ('Jacobson, R.H.', 'Zhang, X.', 'Dubose, R.F.', 'Matthews, B.W.')}, 'Lactose Permease': {'pdb id': '1PV6', 'deposit data': '2003-06-23', 'organism': 'Escherichia coli', 'method': 'x-ray', 'resolution': 3.5, 'authors': ('Abramson, J.', 'Smirnova, I.', 'Kasho, V.', 'Verner, G.', 'Kaback, H.R.', 'Iwata, S.')}, 'LacY': {'pdb id': '2Y5Y', 'deposit data': '2011-01-19', 'organism': 'Escherichia coli', 'method': 'x-ray', 'resolution': 3.38, 'note': 'in complex with an affinity inactivator', 'authors': ('Chaptal, V.', 'Kwon, S.', 'Sawaya, M.R.', 'Guan, L.', 'Kaback, H.R.', 'Abramson, J.')}}

We know that we can extract information from our nested dict just like we would with any other dict:

PYTHON 

print(structures['Beta-Galactosidase'])

OUTPUT 

{'pdb id': '4V40', 'deposit date': '1994-07-18', 'organism': 'Escherichia coli', 'method': 'x-ray', 'resolution': 2.5, 'authors': ('Jacobson, R.H.', 'Zhang, X.', 'Dubose, R.F.', 'Matthews, B.W.')}

PYTHON 

print(structures['Beta-Galactosidase']['method'])

OUTPUT 

x-ray

PYTHON 

print(structures['Beta-Galactosidase']['authors'])

OUTPUT 

('Jacobson, R.H.', 'Zhang, X.', 'Dubose, R.F.', 'Matthews, B.W.')

PYTHON 

print(structures['Beta-Galactosidase']['authors'][0])

OUTPUT 

Jacobson, R.H.

Sometimes, especially when creating longer dictionaries, it might be easier to store individual entries in a variable beforehand and add them to the parent dictionary later on.

Note that our parent dictionary in this case is represented by the variable structure.

PYTHON 

entry = {
    'Lac Repressor': {
        'pdb id': '1LBI',
        'deposit data': '1996-02-17',
        'organism': 'Escherichia coli',
        'method': 'x-ray',
        'resolution': 2.7,
        'authors': (
            'Lewis, M.', 'Chang, G.', 'Horton, N.C.',
            'Kercher, M.A.', 'Pace, H.C.', 'Lu, P.'
        )
    }
}

We can then use the .update() method to update our structures dictionary:

PYTHON 

structures.update(entry)

print(structures['Lac Repressor'])

OUTPUT 

{'pdb id': '1LBI', 'deposit data': '1996-02-17', 'organism': 'Escherichia coli', 'method': 'x-ray', 'resolution': 2.7, 'authors': ('Lewis, M.', 'Chang, G.', 'Horton, N.C.', 'Kercher, M.A.', 'Pace, H.C.', 'Lu, P.')}

We sometimes need to see what keys our dictionary contains. To obtain an array of keys, we use the method .keys() as follows:

PYTHON 

print(structures.keys())

OUTPUT 

dict_keys(['Beta-Galactosidase', 'Lactose Permease', 'LacY', 'Lac Repressor'])

Likewise, we can also obtain an array of values in a dictionary using the .values() method:

PYTHON 

print(structures['LacY'].values())

OUTPUT 

dict_values(['2Y5Y', '2011-01-19', 'Escherichia coli', 'x-ray', 3.38, 'in complex with an affinity inactivator', ('Chaptal, V.', 'Kwon, S.', 'Sawaya, M.R.', 'Guan, L.', 'Kaback, H.R.', 'Abramson, J.')])

We can then extract specific information to conduct an analysis. Note that the len() function in this context returns the number of keys in the parent dictionary only.

PYTHON 

sum_resolutions = 0
res = 'resolution'

sum_resolutions += structures['Beta-Galactosidase'][res]
sum_resolutions += structures['Lactose Permease'][res]
sum_resolutions += structures['Lac Repressor'][res]
sum_resolutions += structures['LacY'][res]

total_entries = len(structures)

average_resolution = sum_resolutions / total_entries

print(average_resolution)

OUTPUT 

3.0199999999999996

Useful methods for dictionary

Now we use some snippets to demonstrate some of the useful methods associated with dict in Python.

Given a dictionary as:

PYTHON 

lac_repressor = {
	    'pdb id': '1LBI',
	    'deposit data': '1996-02-17',
	    'organism': 'Escherichia coli',
	    'method': 'x-ray',
	    'resolution': 2.7,
}

We can create an array of all items in the dictionary using the .items() method:

PYTHON 

print(lac_repressor.items())

OUTPUT 

dict_items([('pdb id', '1LBI'), ('deposit data', '1996-02-17'), ('organism', 'Escherichia coli'), ('method', 'x-ray'), ('resolution', 2.7)])

Similar to the enumerate() function (discussed in subsection DIY ), the .items() method also returns an array of tuple members. Each tuple itself consists of 2 members, and is structured as (‘key’: ‘value’). On that account, we can use its output in the context of a for–loop as follows:

PYTHON 

for key, value in lac_repressor.items():
    print(key, value, sep=': ')

OUTPUT 

pdb id: 1LBI
deposit data: 1996-02-17
organism: Escherichia coli
method: x-ray
resolution: 2.7

Do it Yourself

Try .items() on a nested dict and see how it works.

PYTHON 

nested_dict = {
    'L1-a': {
        'L2-Ka': 'L2_Va',
        'L2-Kb': 'L2_Vb',
    },
    'L1-b': {
        'L2-Kc': 'L2_Vc',
        'L2-Kd': 'L3_Vd'
    },
    'L3-c': 'L3_V'
}

print(nested_dict.items())

OUTPUT 

dict_items([('L1-a', {'L2-Ka': 'L2_Va', 'L2-Kb': 'L2_Vb'}), ('L1-b', {'L2-Kc': 'L2_Vc', 'L2-Kd': 'L3_Vd'}), ('L3-c', 'L3_V')])

We learned earlier that if we ask for a key that is not in the dict, a KeyError will be raised. If we anticipate this, we can handle it using the .get() method. The method takes in the key and searches the dictionary to find it. If found, the associating value is returned. Otherwise, the method returns None by default. We can also pass a second value to .get() to replace None in cases that the requested key does not exist:

PYTHON 

print(lac_repressor['gene'])

ERROR 

Error: KeyError: 'gene'

PYTHON 

print(lac_repressor.get('gene'))

OUTPUT 

None

PYTHON 

print(lac_repressor.get('gene', 'Not found...'))

OUTPUT 

Not found...

Do it Yourself

Implement the lac_repressor dictionary and try to extract the values associated with the following keys:

  • organism
  • authors
  • subunits
  • method

If a key does not exist in the dictionary, display No entry instead.

Display the results in the following format:

organism: XXX
authors: XXX

PYTHON 

lac_repressor = {
    'pdb id': '1LBI',
    'deposit data': '1996-02-17',
    'organism': 'Escherichia coli',
    'method': 'x-ray',
    'resolution': 2.7,
}

requested_keys = ['organism', 'authors', 'subunits', 'method']

for key in requested_keys:
    lac_repressor.get(key, 'No entry')

OUTPUT 

'Escherichia coli'
'No entry'
'No entry'
'x-ray'

for-loop and dictionary

Dictionaries and for-loops create a powerful combination. We can leverage the accessibility of dictionary values through specific keys that we define ourselves in a loop to extract data iteratively and repeatedly.

One of the most useful tools that we can create using nothing more than a for-loop and a dictionary, in only a few lines of code, is a sequence converter.

Here, we are essentially iterating through a sequence of DNA nucleotides (sequence), extracting one character per loop cycle from our string (nucleotide). We then use that character as a key to retrieve its corresponding value from our a dictionary (dna2rna). Once we get the value, we add it to the variable that we initialised using an empty string outside the scope of our for-loop (rna_sequence) as discussed in subsection. At the end of the process, the variable rna_sequence will contain a converted version of our sequence.

PYTHON 

sequence = 'CCCATCTTAAGACTTCACAAGACTTGTGAAATCAGACCACTGCTCAATGCGGAACGCCCG'

dna2rna = {"A": "U", "T": "A", "C": "G", "G": "C"}

rna_sequence = str()  # Creating an empty string.

for nucleotide in sequence:
    rna_sequence += dna2rna[nucleotide]

print('DNA:', sequence)
print('RNA:', rna_sequence)

OUTPUT 

DNA: CCCATCTTAAGACTTCACAAGACTTGTGAAATCAGACCACTGCTCAATGCGGAACGCCCG
RNA: GGGUAGAAUUCUGAAGUGUUCUGAACACUUUAGUCUGGUGACGAGUUACGCCUUGCGGGC

Do it Yourself

We know that in reverse transcription, RNA nucleotides are converted to their complementary DNA as shown:

Type Direction Nucleotides
RNA 5’…’ U A G C
cDNA 5’…’ A T C G

with that in mind:

  1. Use the table to construct a dictionary for reverse transcription, and another dictionary for the conversion of cDNA to DNA.

  2. Using the appropriate dictionary, convert the following mRNA (exon) sequence for human G protein-coupled receptor to its cDNA.

PYTHON 

human_gpcr = (
    'AUGGAUGUGACUUCCCAAGCCCGGGGCGUGGGCCUGGAGAUGUACCCAGGCACCGCGCAGCCUGCGGCCCCCAACACCACCUC'
    'CCCCGAGCUCAACCUGUCCCACCCGCUCCUGGGCACCGCCCUGGCCAAUGGGACAGGUGAGCUCUCGGAGCACCAGCAGUACG'
    'UGAUCGGCCUGUUCCUCUCGUGCCUCUACACCAUCUUCCUCUUCCCCAUCGGCUUUGUGGGCAACAUCCUGAUCCUGGUGGUG'
    'AACAUCAGCUUCCGCGAGAAGAUGACCAUCCCCGACCUGUACUUCAUCAACCUGGCGGUGGCGGACCUCAUCCUGGUGGCCGA'
    'CUCCCUCAUUGAGGUGUUCAACCUGCACGAGCGGUACUACGACAUCGCCGUCCUGUGCACCUUCAUGUCGCUCUUCCUGCAGG'
    'UCAACAUGUACAGCAGCGUCUUCUUCCUCACCUGGAUGAGCUUCGACCGCUACAUCGCCCUGGCCAGGGCCAUGCGCUGCAGC'
    'CUGUUCCGCACCAAGCACCACGCCCGGCUGAGCUGUGGCCUCAUCUGGAUGGCAUCCGUGUCAGCCACGCUGGUGCCCUUCAC'
    'CGCCGUGCACCUGCAGCACACCGACGAGGCCUGCUUCUGUUUCGCGGAUGUCCGGGAGGUGCAGUGGCUCGAGGUCACGCUGG'
    'GCUUCAUCGUGCCCUUCGCCAUCAUCGGCCUGUGCUACUCCCUCAUUGUCCGGGUGCUGGUCAGGGCGCACCGGCACCGUGGG'
    'CUGCGGCCCCGGCGGCAGAAGGCGCUCCGCAUGAUCCUCGCGGUGGUGCUGGUCUUCUUCGUCUGCUGGCUGCCGGAGAACGU'
    'CUUCAUCAGCGUGCACCUCCUGCAGCGGACGCAGCCUGGGGCCGCUCCCUGCAAGCAGUCUUUCCGCCAUGCCCACCCCCUCA'
    'CGGGCCACAUUGUCAACCUCACCGCCUUCUCCAACAGCUGCCUAAACCCCCUCAUCUACAGCUUUCUCGGGGAGACCUUCAGG'
    'GACAAGCUGAGGCUGUACAUUGAGCAGAAAACAAAUUUGCCGGCCCUGAACCGCUUCUGUCACGCUGCCCUGAAGGCCGUCAU'
    'UCCAGACAGCACCGAGCAGUCGGAUGUGAGGUUCAGCAGUGCCGUG'
)

PYTHON 

mrna2cdna = {
    'U': 'A',
    'A': 'T',
    'G': 'C',
    'C': 'G'
}

cdna2dna = {
    'A': 'T',
    'T': 'A',
    'C': 'G',
    'G': 'C'
}

Q2

PYTHON 

cdna = str()
for nucleotide in human_gpcr:
    cdna += mrna2cdna[nucleotide]

print(cdna)

OUTPUT 

TACCTACACTGAAGGGTTCGGGCCCCGCACCCGGACCTCTACATGGGTCCGTGGCGCGTCGGACGCCGGGGGTTGTGGTGGAGGGGGCTCGAGTTGGACAGGGTGGGCGAGGACCCGTGGCGGGACCGGTTACCCTGTCCACTCGAGAGCCTCGTGGTCGTCATGCACTAGCCGGACAAGGAGAGCACGGAGATGTGGTAGAAGGAGAAGGGGTAGCCGAAACACCCGTTGTAGGACTAGGACCACCACTTGTAGTCGAAGGCGCTCTTCTACTGGTAGGGGCTGGACATGAAGTAGTTGGACCGCCACCGCCTGGAGTAGGACCACCGGCTGAGGGAGTAACTCCACAAGTTGGACGTGCTCGCCATGATGCTGTAGCGGCAGGACACGTGGAAGTACAGCGAGAAGGACGTCCAGTTGTACATGTCGTCGCAGAAGAAGGAGTGGACCTACTCGAAGCTGGCGATGTAGCGGGACCGGTCCCGGTACGCGACGTCGGACAAGGCGTGGTTCGTGGTGCGGGCCGACTCGACACCGGAGTAGACCTACCGTAGGCACAGTCGGTGCGACCACGGGAAGTGGCGGCACGTGGACGTCGTGTGGCTGCTCCGGACGAAGACAAAGCGCCTACAGGCCCTCCACGTCACCGAGCTCCAGTGCGACCCGAAGTAGCACGGGAAGCGGTAGTAGCCGGACACGATGAGGGAGTAACAGGCCCACGACCAGTCCCGCGTGGCCGTGGCACCCGACGCCGGGGCCGCCGTCTTCCGCGAGGCGTACTAGGAGCGCCACCACGACCAGAAGAAGCAGACGACCGACGGCCTCTTGCAGAAGTAGTCGCACGTGGAGGACGTCGCCTGCGTCGGACCCCGGCGAGGGACGTTCGTCAGAAAGGCGGTACGGGTGGGGGAGTGCCCGGTGTAACAGTTGGAGTGGCGGAAGAGGTTGTCGACGGATTTGGGGGAGTAGATGTCGAAAGAGCCCCTCTGGAAGTCCCTGTTCGACTCCGACATGTAACTCGTCTTTTGTTTAAACGGCCGGGACTTGGCGAAGACAGTGCGACGGGACTTCCGGCAGTAAGGTCTGTCGTGGCTCGTCAGCCTACACTCCAAGTCGTCACGGCAC

Summary

In this section we talked about dictionaries, which are one the most powerful built-in types in Python. We learned:

  • how to create dictionaries in Python,
  • methods to alter or manipulate normal and nested dictionaries,
  • two different techniques for changing an existing key,
  • examples on how dictionaries help us organise our data and retrieve them when needed,

Finally, we also learned that we can create an iterable (discussed in section) from dictionary keys or values using the .key(), the .values(), or the .items() methods.

Exercises

End of chapter Exercises

We know that the process of protein translation starts by transcribing a gene from DNA to RNA nucleotides, followed by translating the RNA codons to protein.

Conventionally, we write a DNA sequence from the 5’-end to the 3’-end. The transcription process, however, starts from the 3’-end of a gene to the 5’-end (anti-sense strand), resulting in a sense mRNA sequence complementing the sense DNA strand. This is because RNA polymerase can only add nucleotides to the 3’-end of the growing mRNA chain, which eliminates the need for the Okazaki fragments as seen in DNA replication.

Example: The DNA sequence ATGTCTAAA is transcribed into AUGUCUAAA.

Given a conversion table:

and this 5’- to 3’-end DNA sequence of 717 nucleotides for the Green Fluorescent Protein (GFP) mutant 3 extracted from Aequorea victoria:

PYTHON 

dna_sequence = (
    'ATGTCTAAAGGTGAAGAATTATTCACTGGTGTTGTCCCAATTTTGGTTGAATTAGATGGTGATGTTAATGGT'
    'CACAAATTTTCTGTCTCCGGTGAAGGTGAAGGTGATGCTACTTACGGTAAATTGACCTTAAAATTTATTTGT'
    'ACTACTGGTAAATTGCCAGTTCCATGGCCAACCTTAGTCACTACTTTCGGTTATGGTGTTCAATGTTTTGCT'
    'AGATACCCAGATCATATGAAACAACATGACTTTTTCAAGTCTGCCATGCCAGAAGGTTATGTTCAAGAAAGA'
    'ACTATTTTTTTCAAAGATGACGGTAACTACAAGACCAGAGCTGAAGTCAAGTTTGAAGGTGATACCTTAGTT'
    'AATAGAATCGAATTAAAAGGTATTGATTTTAAAGAAGATGGTAACATTTTAGGTCACAAATTGGAATACAAC'
    'TATAACTCTCACAATGTTTACATCATGGCTGACAAACAAAAGAATGGTATCAAAGTTAACTTCAAAATTAGA'
    'CACAACATTGAAGATGGTTCTGTTCAATTAGCTGACCATTATCAACAAAATACTCCAATTGGTGATGGTCCA'
    'GTCTTGTTACCAGACAACCATTACTTATCCACTCAATCTGCCTTATCCAAAGATCCAAACGAAAAGAGAGAC'
    'CACATGGTCTTGTTAGAATTTGTTACTGCTGCTGGTATTACCCATGGTATGGATGAATTGTACAAATAA'
)

Use the DNA sequence and the conversion table to:

  1. Write a Python script to transcribe this sequence to mRNA as it occurs in a biological organism. That is, determine the complimentary DNA first, and use that to work out the mRNA.

  2. Use the following dictionary in a Python script to obtain the translation (protein sequence) of the Green Fluorescent Protein using the mRNA sequence you obtained.

PYTHON 

codon2aa = {
    "UUU": "F", "UUC": "F", "UUA": "L", "UUG": "L", "CUU": "L",
    "CUC": "L", "CUA": "L", "CUG": "L", "AUU": "I", "AUC": "I",
    "AUA": "I", "GUU": "V", "GUC": "V", "GUA": "V", "GUG": "V",
    "UCU": "S", "UCC": "S", "UCA": "S", "UCG": "S", "AGU": "S",
    "AGC": "S", "CCU": "P", "CCC": "P", "CCA": "P", "CCG": "P",
    "ACU": "T", "ACC": "T", "ACA": "T", "ACG": "T", "GCU": "A",
    "GCC": "A", "GCA": "A", "GCG": "A", "UAU": "Y", "UAC": "Y",
    "CAU": "H", "CAC": "H", "CAA": "Q", "CAG": "Q", "AAU": "N",
    "AAC": "N", "AAA": "K", "AAG": "K", "GAU": "D", "GAC": "D",
    "GAA": "E", "GAG": "E", "UGU": "C", "UGC": "C", "UGG": "W",
    "CGU": "R", "CGC": "R", "CGA": "R", "CGG": "R", "AGA": "R",
    "AGG": "R", "GGU": "G", "GGC": "G", "GGA": "G", "GGG": "G",
    "AUG": "<Met>", "UAA": "<STOP>", "UAG": "<STOP>", "UGA": "<STOP>"
}

Q1

PYTHON 

dna_sequence = (
    'ATGTCTAAAGGTGAAGAATTATTCACTGGTGTTGTCCCAATTTTGGTTGAATTAGATGGTGATGTTAATGGT'
    'CACAAATTTTCTGTCTCCGGTGAAGGTGAAGGTGATGCTACTTACGGTAAATTGACCTTAAAATTTATTTGT'
    'ACTACTGGTAAATTGCCAGTTCCATGGCCAACCTTAGTCACTACTTTCGGTTATGGTGTTCAATGTTTTGCT'
    'AGATACCCAGATCATATGAAACAACATGACTTTTTCAAGTCTGCCATGCCAGAAGGTTATGTTCAAGAAAGA'
    'ACTATTTTTTTCAAAGATGACGGTAACTACAAGACCAGAGCTGAAGTCAAGTTTGAAGGTGATACCTTAGTT'
    'AATAGAATCGAATTAAAAGGTATTGATTTTAAAGAAGATGGTAACATTTTAGGTCACAAATTGGAATACAAC'
    'TATAACTCTCACAATGTTTACATCATGGCTGACAAACAAAAGAATGGTATCAAAGTTAACTTCAAAATTAGA'
    'CACAACATTGAAGATGGTTCTGTTCAATTAGCTGACCATTATCAACAAAATACTCCAATTGGTGATGGTCCA'
    'GTCTTGTTACCAGACAACCATTACTTATCCACTCAATCTGCCTTATCCAAAGATCCAAACGAAAAGAGAGAC'
    'CACATGGTCTTGTTAGAATTTGTTACTGCTGCTGGTATTACCCATGGTATGGATGAATTGTACAAATAA'
)


codon2aa = {
    "UUU": "F", "UUC": "F", "UUA": "L", "UUG": "L", "CUU": "L",
    "CUC": "L", "CUA": "L", "CUG": "L", "AUU": "I", "AUC": "I",
    "AUA": "I", "GUU": "V", "GUC": "V", "GUA": "V", "GUG": "V",
    "UCU": "S", "UCC": "S", "UCA": "S", "UCG": "S", "AGU": "S",
    "AGC": "S", "CCU": "P", "CCC": "P", "CCA": "P", "CCG": "P",
    "ACU": "T", "ACC": "T", "ACA": "T", "ACG": "T", "GCU": "A",
    "GCC": "A", "GCA": "A", "GCG": "A", "UAU": "Y", "UAC": "Y",
    "CAU": "H", "CAC": "H", "CAA": "Q", "CAG": "Q", "AAU": "N",
    "AAC": "N", "AAA": "K", "AAG": "K", "GAU": "D", "GAC": "D",
    "GAA": "E", "GAG": "E", "UGU": "C", "UGC": "C", "UGG": "W",
    "CGU": "R", "CGC": "R", "CGA": "R", "CGG": "R", "AGA": "R",
    "AGG": "R", "GGU": "G", "GGC": "G", "GGA": "G", "GGG": "G",
    "AUG": "<Met>", "UAA": "<STOP>", "UAG": "<STOP>", "UGA": "<STOP>"
}

dna2cdna = {
    'A': 'T',
    'C': 'G',
    'G': 'C',
    'T': 'A'
}


dna2mrna = {
    'A': 'U',
    'T': 'A',
    'G': 'C',
    'C': 'G'
}


# Transcription
# -----------------------------------------------------------------
m_rna = str()

for nucleotide in dna_sequence:
    # DNA to cDNA
    c_dna = dna2cdna[nucleotide]

    # cDNA to mRNA
    m_rna += dna2mrna[c_dna]


print('mRNA:', m_rna)


# Translation:
# -----------------------------------------------------------------

OUTPUT 

mRNA: AUGUCUAAAGGUGAAGAAUUAUUCACUGGUGUUGUCCCAAUUUUGGUUGAAUUAGAUGGUGAUGUUAAUGGUCACAAAUUUUCUGUCUCCGGUGAAGGUGAAGGUGAUGCUACUUACGGUAAAUUGACCUUAAAAUUUAUUUGUACUACUGGUAAAUUGCCAGUUCCAUGGCCAACCUUAGUCACUACUUUCGGUUAUGGUGUUCAAUGUUUUGCUAGAUACCCAGAUCAUAUGAAACAACAUGACUUUUUCAAGUCUGCCAUGCCAGAAGGUUAUGUUCAAGAAAGAACUAUUUUUUUCAAAGAUGACGGUAACUACAAGACCAGAGCUGAAGUCAAGUUUGAAGGUGAUACCUUAGUUAAUAGAAUCGAAUUAAAAGGUAUUGAUUUUAAAGAAGAUGGUAACAUUUUAGGUCACAAAUUGGAAUACAACUAUAACUCUCACAAUGUUUACAUCAUGGCUGACAAACAAAAGAAUGGUAUCAAAGUUAACUUCAAAAUUAGACACAACAUUGAAGAUGGUUCUGUUCAAUUAGCUGACCAUUAUCAACAAAAUACUCCAAUUGGUGAUGGUCCAGUCUUGUUACCAGACAACCAUUACUUAUCCACUCAAUCUGCCUUAUCCAAAGAUCCAAACGAAAAGAGAGACCACAUGGUCUUGUUAGAAUUUGUUACUGCUGCUGGUAUUACCCAUGGUAUGGAUGAAUUGUACAAAUAA

PYTHON 

mrna_len = len(m_rna)
codon_len = 3

protein = str()

for index in range(0, mrna_len, codon_len):
    codon = m_rna[index: index + codon_len]
    protein += codon2aa[codon]

print('Protein:', protein)

# -----------------------------------------------------------------
# INTERMEDIATE-LEVEL TWIST (Alternative answer):
# One can also combine the two processes.
#
# Advantages:
#   - One for-loop.
#   - No use of `range()`.
#   - Almost twice as fast (half as many iterations).
# -----------------------------------------------------------------

OUTPUT 

Protein: <Met>SKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFGYGVQCFARYPDH<Met>KQHDFFKSA<Met>PEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYI<Met>ADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDH<Met>VLLEFVTAAGITHG<Met>DELYK<STOP>

PYTHON 

m_rna = str()
protein = str()
codon = str()

for nucleotide in dna_sequence:
    # DNA to cDNA
    c_dna = dna2cdna[nucleotide]

    # Transcription:
    transcribed_nucleotide = dna2mrna[c_dna]
    m_rna += transcribed_nucleotide

    # Translation process:
    # Retaining the residue to construct triplets.
    codon += transcribed_nucleotide

    # Check if this is a triplet (a codon):
    if len(codon) == 3:
        # Convert to amino acid and store:
        protein += codon2aa[codon]

        # Reset the codon to an empty string:
        codon = str()
print('mRNA:', m_rna)

OUTPUT 

mRNA: AUGUCUAAAGGUGAAGAAUUAUUCACUGGUGUUGUCCCAAUUUUGGUUGAAUUAGAUGGUGAUGUUAAUGGUCACAAAUUUUCUGUCUCCGGUGAAGGUGAAGGUGAUGCUACUUACGGUAAAUUGACCUUAAAAUUUAUUUGUACUACUGGUAAAUUGCCAGUUCCAUGGCCAACCUUAGUCACUACUUUCGGUUAUGGUGUUCAAUGUUUUGCUAGAUACCCAGAUCAUAUGAAACAACAUGACUUUUUCAAGUCUGCCAUGCCAGAAGGUUAUGUUCAAGAAAGAACUAUUUUUUUCAAAGAUGACGGUAACUACAAGACCAGAGCUGAAGUCAAGUUUGAAGGUGAUACCUUAGUUAAUAGAAUCGAAUUAAAAGGUAUUGAUUUUAAAGAAGAUGGUAACAUUUUAGGUCACAAAUUGGAAUACAACUAUAACUCUCACAAUGUUUACAUCAUGGCUGACAAACAAAAGAAUGGUAUCAAAGUUAACUUCAAAAUUAGACACAACAUUGAAGAUGGUUCUGUUCAAUUAGCUGACCAUUAUCAACAAAAUACUCCAAUUGGUGAUGGUCCAGUCUUGUUACCAGACAACCAUUACUUAUCCACUCAAUCUGCCUUAUCCAAAGAUCCAAACGAAAAGAGAGACCACAUGGUCUUGUUAGAAUUUGUUACUGCUGCUGGUAUUACCCAUGGUAUGGAUGAAUUGUACAAAUAA

PYTHON 

print('Protein:', protein)

OUTPUT 

Protein: <Met>SKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFGYGVQCFARYPDH<Met>KQHDFFKSA<Met>PEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYI<Met>ADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDH<Met>VLLEFVTAAGITHG<Met>DELYK<STOP>

Key Points

  • Dictionaries associate a set of values with a number of keys.
  • keys are used to access the values of a dictionary.
  • Dictionaries are mutable.
  • Nested dictionaries are constructed to organise data in a hierarchical fashion.
  • Some of the useful methods to work with dictionaries are: .items(), .get()