I previously created a guide called Bash Fundamentals for Bioinformatics, which provided a crash course on essential Bash commands, including how to navigate a file system and move, copy, edit files, and more. After publishing this guide I received a number of messages from students and wet lab biologists who want to break into bioinformatics, but are daunted because they lack a programming background.
This guide is for those who are interested in learning bioinformatics but are overwhelmed by the sheer volume of coding instructionals available. This guide aims to highlight key concepts without overwhelming beginners with minutiae or glossing over critical concepts.
Importantly, this guide assumes you are familiar with basic Python data types and syntax. With that foundation, you'll be ready to learn key programming concepts that will appear time and time again. From understanding the logic behind loops and conditionals, to deciphering the power of functions, and creating clean data visualizations, this guide is your short go-to resource, equipping you with the tools to decipher and compose code that unlocks the mysteries hidden in biological data.
Lists and dictionaries play pivotal roles in managing and manipulating biological data. In simple terms, a list is like an ordered checklist where each item has a discrete position, and a dictionary is like a set of labeled boxes where each item has a unique label. Lists are good for storing sequences of items, while dictionaries are great for associating information with specific labels or keys. Bioinformatics tasks, such as sequence analysis, rely heavily on these fundamental data structures to navigate and manipulate biological datasets efficiently.
A list is a built-in data structure that allows you to store and organize a collection of objects, including integers, strings, and even other lists. In bioinformatics, DNA, RNA, and protein sequences are commonly stored as lists. Lists are defined with square brackets, [ ], and filled with objects separated by commas. Additionally, lists are ordered and mutable, meaning you can change their content by adding or removing elements.
Bioinformatics tasks frequently involve using loops to iterate over large datasets. Lists are well-suited for these loop operations because of their sequential structure, which enables efficient processing of large datasets. In the code sample below, I'll show you how to create a list:
list_1 = [1,2,3,4,5] # a list of integers
print('List of Integers:', list_1)
list_2 = ['A', 'T', 'G', 'C'] # a list of strings
print('List of Strings:', list_2)
list_3 = [[1,2,3], ['A', 'T', 'G', 'C']] # a list of lists
print('List of Lists:', list_3)Which produces the following output:
The code above shows you how to create lists containing integers, strings, and other lists. Importantly, lists do not need to contain just one data type. You can have a single list that includes integers, strings, and more. Additionally, you can add two lists together to produce a single unified list. For example, if you were to write the code new_list = list_1 + list_2, then print(new_list), the resultant output would be as follows: [1, 2, 3, 4, 5, 'A', 'T', 'G', 'C']. Next, I'll demonstrate how to add and remove elements from a list:
list = [1,2,3]
print('Here is a list:', list)
list.append(4)
print('Here is a list with a new item:', list)
ist.remove(2)
print('Here is a list with the second item removed:', list)Which produced the following output:
In the code above, I used the .append( ) function to add a new object to my list and the .remove( ) function to remove an item. To call the append function, you type your list's name followed by a period, the word append, and open and closed parentheses without spaces. You then type the object you wish to add to the list inside the parentheses. If you're adding an integer, you type the number as is, whereas if you add a string, it must be in quotation marks. The remove function works similarly, except you type the object as it appears in your list, within the parentheses, as demonstrated above. Next, i’ll show you how to call items from a list and slice lists:
list = ['a', 'b', 'c', 'd', 'e']
print(list([3]) # retrieve the item in '3' position
print(list[1:4] # retrieve items in positions 1-3
print(list[0:4:2] # retrieve items in positions 0-3, skipping every other item
print(list[:4] # retreive items 0-3Which produces the following output:
In the code above, I show you how to call items from a list, which involves writing the list name followed by square brackets and the index number of the item you wish to retrieve from the list. You’ll notice that I write list[3] in my example, but the resultant output is the letter d, which is the fourth item in my list. The reason for this is that list indexing in Python follows zero-based indexing, where the first element in a list is at index 0, the second element is at index 1, and so on.
Next, I show you how to slice lists, which is the process of extracting a portion of a given data sequence using the following notation: [start: stop: step]. This notation creates a new list with elements from the index start point up to, but not including, the stop point, with an optional step specifying the increment. For example, the code list[1:4] retrieves items in positions 1-3, whereas the code list[0:4:2] retrieves times 0-3 in increments of two, which in practice means items 0 and 2. In code line 5, in the example above, you’ll see that I failed to input a stop position for my list. This retrieves all items from my specified start index onward. Alternatively, in line 6, I failed to input a start position, which means starting at index 0 and retrieving all items unto, but not including my specified stop position.
In the following block of code I’ll show you how to loop through lists:
dna_sequences = ['ACTGGT', 'TGGCAG', 'CGTTAA']
for sequence in dna_sequences:
print(sequence)Which produces the folowing output:
Looping through lists enables the repeated execution of operations across numerous data points. In general, looping through a list takes the following form:
for item in list:
# code to execute for each item In essence, the code above works because the loop processes each item in the list and then performs the specified operations on that element. Later in this guide, I’ll provide a more comprehensive overview of for loops and while loops under the Control Flow section. Now, we’ll wrap this sub-section on lists up with the following demonstration on list comprehensions:
dna_sequences = ['ACTGGT', 'TGGCAG', 'CGTTAA']
gc_content = [(seq.count('G') + seq.count('C')) / len(seq) for seq in dna_sequences]
print('The GC content of each sequence is:', gc_count)Which produces the following output:
List comprehensions provide a concise and readable way to create lists and perform operations on existing ones. In bioinformatics, where data manipulation and analysis are frequent tasks, list comprehensions offer a compact syntax for tasks like filtering, transforming, or generating lists of data. For example, the list comprehension above calculates the GC content in various DNA sequences stored in a list by counting the occurrences of ‘G’ and ‘C’ and dividing that sum by the sequence length.
A Python dictionary is a built-in data type that stores and organizes data in key-value pairs. In other words, it's a collection of items, each identified by a unique key, and each key is associated with a corresponding value. Keys must be strings, numbers, or tuples, and values can be of any data type, including numbers, strings, lists, or even other dictionaries. In the code block below I'll show you how to create a dictionary:
patient_info = {'Patient 1':{'Height':170, 'Weight':60}, 'Patient 2':{'Height':160, 'Weight':50}}
print(patient_info) # prints contents of dictionary
dna_to_rna = {'A':'A', 'T':'U', 'C':'C', 'G':'G'}
print(dna_to_rna['T']) # retrieve value associated with the key 'T'Which produces the following output:
As previously stated, dictionaries excel at storing data in key-value pairs. In bioinformatics, this mirrors the relationship between biological entities and their associated attributes. For example, a dictionary can contain patient data, such as in the example above. In this case, the key is the patient's ID, and the value is another dictionary containing information about the patient. In the second example above, we have a dictionary called dna_to_rna, where each key is a DNA nucleotide, and the value is the associated RNA nucleotide. After defining a dictionary, you can retrieve information based on a specific identifier (i.e., key).
Dictionaries are mutable, meaning you can modify their content by adding, removing, or updating key-value pairs. In the code below, I'll demonstrate how to add and remove key-value pairs from a dictionary:
dna_to_rna = {'A':'A', 'T':'U', 'C':'C', 'G':'G'}
print(dna_to_rna)
dna_to_rna.pop('T') # remove value by key
print(dna_to_rna)
dna_to_rna['T'] = 'U' # add key-value pair to dictionary
print(dna_to_rna)Which produces the following output:
In the code above, I use the .pop( ) function to remove a key-value pair from my dictionary. To call the pop function, you type your dictionary name followed by a period, the word pop, and open and closed parentheses without spaces. You then type the key for the key-value pair you wish to remove within the parenthesis. To add a new key-value pair to your dictionary, you write the name of your dictionary followed by close brackets and an equal sign. You then type the key for your new key-value pair within the closed brackets and the new value after the equal sign, as demonstrated in the code above.
In the final demonstration in this section i’ll show you how to convert a tuple into a dictionary:
dictionary = dict([('GeneA','protein_coding'), ('GeneB','non_coding),('GeneC','protein_coding')])
print(dictionary)Which produces the following output:
The sample code above is a simple example of converting a list of tuples into a dictionary. However, in bioinformatics, you might encounter more complex scenarios where converting tuples to a dictionary is beneficial for better data representation, organization, and analysis, as demonstrated in the code below:
gene_annotations = [
('GeneA',1000,2000,'protein_coding'),
('GeneB',3000,4000,'non_coding'),
('GeneC',5000,6000,'protein_coding')]
gene_dictionary = [{'gene_name':name, 'start_position':start, 'end_position':end, 'gene_type',gene_type} for name, start, end, gene_type in gene_annotations]In the code above we have gene annotations provided as a list of tuples, where each tuple represents information about a gene, such as gene name, start position, end position, and gene type. We then use a list comprehension to convert the list of tuples store in gene_annotation into a dictionary called gene_dictionary.
Control flow is crucial in bioinformatics for navigating and processing diverse biological data by controlling the order in which statements are executed in a code block. Additionally, control flow provides the flexibility to handle different conditions, iterate over data, implement algorithms, and automate tasks, contributing to the efficiency and effectiveness of bioinformatics analyses and computational biology research.
In this section, I’ll cover the two most common types of control flow you’ll encounter: conditionals and loops. Conditionals help you make decisions in your code by executing specific blocks based on whether a condition is true or false. Loops, on the other hand, enable you to repeat a set of instructions multiple times, making it easier to process data or perform repetitive tasks.
Conditionals in Python are structures that allow you to execute certain code blocks based on whether a specified condition is true or false. In the sample code below I’ll demonstrate the simplest type of conditional, called conditional execution:
x=15
if x>10:
print('x is greater than 10')In the code above, we have a simple example of conditional execution. If the logical statement x > 10 is true, then the code in the indented statement below it is executed (thus, in the code block above the print statement would be executed). If the logical statement is not true, then the indented statement is skipped, and nothing happens. Next, I’ll demonstrate another type of conditional called alternative execution:
x=5
if x>10:
print('x is greater than 10')
else:
print('x is less than or equal to 10') # in this example this second print statement is executedThe fundamental logic behind alternative execution is similar to conditional execution, with the slight difference that code is executed whether or not the logic statement is true. For example, we have a logical statement if >10 in the code block above. If the logical statement is true, the indented code on line 4 is executed. Alternatively, the indented code on line 6 is executed if the logic statement is false. Once you understand alternative execution, the next type of conditional, called a nested conditional, is easy to comprehend:
x=5
y=6
if x==y:
print('x and y are equal')
else:
if x<y:
print('x is less than y') # in this case, this second print statement is executed.
else:
print('x is greater than y')A nested conditional refers to a situation where one or more conditional statements are nested inside another conditional statement. In other words, there is a conditional statement within the block of code controlled by another conditional statement. This nesting can occur at any level, creating a hierarchy of conditions. The example above shows an outer if statement that checks if x and y are equal. If that statement is true, the code prints ‘x and y are equal.’ If the outer statement is false, the else block containing another if/else alternative conditional is executed.
Now, before we review loops there is one more type of conditional to review, called a chained conditional:
x=6
y=6
if x>y:
print('x is greater than y')
elif x<y:
print('x is less than y')
else:
print('x and y are equal') # in this case this third print statement is executed.Whereas a nested conditional refers to one or more conditional statements nested inside another conditional statement, a chained conditional is a conditional statement that contains a series of alternative branches using if, elif, and else statements that are all indented at the same depth, as demonstrated in the code block above.
In Python, loops enable you to repeat a block of code multiple times, making it easier to process data or perform repetitive tasks. There are two types of loops you should be familiar with: for loops and while loops. A for loop iterates over a sequence (such as a list, tuple, string, or range) and executes a code block for each element in that sequence. The syntax of a for loop is as follows:
for variable in sequence:
# code to execute for each element in the sequenceIn bioinformatics, a common scenario involves iterating over a collection of biological sequences, such as DNA, RNA, or protein sequences. In the example below, I’ll show you how to use a for loop for this task:
dna_sequences = ['ACTGGT', 'TGGCAG', 'CGTTAA']
for sequence in dna_sequences:
length = len(sequence)
print(f'The length of the DNA sequence '{sequence}' is: '{length} bases')Which produces the folowing output:
In the example above, the code for sequence in dna_sequences: initiates the for loop. For each item (sequence) in our list, the code then calculates the length and prints the length of each DNA sequence and the sequence itself.
Note: There is nothing special about the word 'sequence' in the code above. It could just as easily say
for peanut_butter in dna_sequences:, and in the subsequent lines of code you'd need to replace every other instance of the wordsequencewithpeanut_butter.
Whereas for loops iterates over a sequence and executes a code block for each element in that sequence, while loops repeatedly execute a block of code as long as a specified condition is true. The syntax of a while loop is as follows:
while condition:
# code to execute as long as the condition is TrueAs previously mentioned, for loops are commonly used in bioinformatics for iterating over sequences or datasets. While loops, on the other hand, are useful in scenarios where the number of iterations is not known beforehand or when iterating until a specific condition is met.
In bioinformatics, a while loop might be used to simulate a scenario where a biological process continues until a certain condition is met. Let's consider a simplified example where we simulate a mutation in a DNA sequence until a specific mutation pattern is achieved:
import random
dna_seq = 'TGGATCCATGCA'
target_mutation = 'TGGATCCATGCT'
mutations = 0
while dna_seq != target_mutation:
position = random.randint(0, len(dna_seq)-1)
mutated_base = random.choice('ATCG'.replace(dna_seq[position],''))
dna_seq = dna_seq[:position]+mutated_base+dna_seq[position+1:]
mutations +=1
print(f'Target mutation achieved after {mutations} mutations.')
print('Final DNA sequence:', dna_seq)Which produces the following result:
In the code block above, the while loop is active as long as dna_seq and target_mutation are not equal. In each iteration through the while loop, a random position in dna_seq is chosen and the base at that position is mutated. The while loop tracks the number of mutations performed until dna_seq =target_mutation.
A function is a reusable block of code that performs a specific set of tasks. For example, in bioinformatics, functions are often used for tasks such as sequence manipulation, statistical analysis, or data preprocessing. Functions enhance the readability of code, allowing for the reuse of code snippets and facilitating the development of robust and modular bioinformatics workflows. The code block below provides you with the basic Python function syntax:
def function_name(parameter_1, parameter_2, ...etc.):
# code to perform a task
# optionally, return a resultThere are a few key components in the code example above. First, the word def is the keyword used to define a function in Python. Next, function_name is the function's name, which you'll use to call the function later on. Ideally, the name should be descriptive. After that, we have a set of parentheses followed by a colon. You can declare one or more parameters or inputs the function accepts within the parentheses. Then, the indented code blocks contain the statements that define what the function does. Often, functions will end with a command to return a specific value using the return statement. Below is a simple example of a function that takes a dna_sequence as its single input and then returns the corresponding rna_sequence:
dna_seq = 'ACGTGTCAGTGGGAC
def dna_to_rna(any_dna_sequence):
rna_sequence = dna_sequence.repace('T':'U')
return rna_sequence
rna_seq = dna_to_rna(dna_seq)
print(rna_seq) # the code will return the rna sequence ACGUGUCAGUGGGACIn the example code above, the name of my function is dna_to_rna, and the function takes a DNA sequence as input. Inside the function, my code creates a new object named rna_sequence, which is defined by the code dna_sequence.replace('T', 'U'). My function then returns the rna_sequence using the return statement.
I then call my function with the code rna_seq = dna_to_rna(dna_seq). Importantly, when you call a function, you must write the function's name, in this case, dna_to_rna( ), and then put the input parameter within the parenthesis. You'll note that the parameter I inputted when I called the function is different than when I defined the function. This is okay as long as the input parameter I choose is formatted correctly for the operations my function performs.
I provided an example of a simple function in the code above. However, in bioinformatics, functions often call other functions to create modular and reusable code. In the code below, i'll provide an example where one function calculates the GC content of a DNA sequence, and another function uses this information to determine whether the GC content is low, moderate, or high:
def get_gc_content(any_dna_sequence):
gc_count = dna_seq.count('G')+dna_seq.count('C')
nucleotide_count = len(dna_seq)
gc_content = (gc_count/nucleotide_count)*100
return gc_content
def analyze_gc_content(any_dna_seq):
gc_content = get_gc_content(any_dna_seq)
if gc_content > 65:
return f'DNA sequence has high GC content: {gc_content}%'
elif gc_content < 40:
return f'DNA sequence has low GC content: {gc_content}%'
else:
return f'DNA sequence has moderate GC content: {gc_content}%'
dna_seq = 'ATGCCGTTCAATAGACGTA'
result = analyze_gc_content(dna_seq)
print(result)Which produces the following output:
In the code above, the first function, get_gc_content( ), computes the GC content of a given DNA sequence, then the second function, analyze_gc_content( ), calls the first function to get the GC content of a DNA sequence and then analyzes the result.
If you're interested in learning more about functions, you can checkout my systems_biology GitHub repository, which contains assorted functions and programs for using numerical methods, quantitative modeling, and solving common problems in systems biology.
Pandas is a powerful open-source data manipulation and analysis library for Python. It provides data structures, such as series and data frames, designed to efficiently handle and analyze structured data. Pandas is widely used in various fields, including bioinformatics, for data cleaning, exploration, and analysis tasks. In the code below I’ll show you how to import the Pandas library, how to load a CSV file as a Panda’s data frame, and how to display the data frame as a table:
I import the Pandas library in the code block above by typing import pandas as pd. I then load a CSV file with information about different viruses using Panda’s pd.read_csv( ) function, which allows me to store the CSV file as a data frame in the df variable. I then use IPython’s display( ) function to display my datagrams, resulting in the above table. You can also choose to display a specified number of the first or last set of rows using [name of data frame].head( ) and [name of data frame].tail( ), respectively. For example, if you only want to view the first ten rows of data, you can type [name of data frame].head(10). Now, let’s say that when viewing your data, you notice missing values. In that case, you can use the [name of data frame].dropna( ) command, which removes all null values from your data.
After viewing your data frame, you can perform more complex exploratory data analysis. For example, in the code below, i’ll show you how to generate summary statistics for the data in your data frame using the .describe( ) command:
After using Pandas to load a data frame, you can also use additional libraries such as matplotlib.pyplot) for performing exploratory data analysis with various visualization tools. For example, in the code below I'll demonstrate how to generate a scatter plot to better understand the relationship between viruses inner and outer radius:
This second section a compliment to the guide above on Python Fundamentals For Biologists. In this section, I'll provide an introduction to data visualization for bioinformatics without overwhelming beginners with minutiae or glossing over critical concepts. In bioinformatics, data visualization is crucial for interpreting complex biological datasets, making it easier for researchers to identify patterns, trends, and insights. Additionally, effective visualization enhances data-driven decision-making, aids in hypothesis generation, and facilitates communication of findings. Let's begin!
Matplotlib and Seaborn are two of the most commonly used libraries for data visualization in Python. Matplotlib provides a MATLAB-like interface and is widely used to create publication-quality visualizations of varying types. Seaborn is built atop Matplotlib and is specialized for statistical data visualization. Additionally, Seaborn provides more aesthetically pleasing defaults compared to Matplot lib. Before diving into specific data visualization techniques, you can install Matploblib and Seaborn in your Python IDE using the following code:
import matplotlib.pyplot as plt
import seaborn as snsA scatter plot is a type of data visualization that displays individual data points on a two-dimensional (x-y coordinate) or three-dimensional (x-y-z coordinate) graph where each point on the plot represents the values of all variables. Scatter plots make it easy to examine the relationship between variables, and as a result, they are useful for identifying patterns, correlations, and outliers in data. Importantly, scatter plots are only appropriate when all variables are numerical. The code below demonstrates the general syntax for producing scatter plots with Seaborn:
sns.scatterplot(x='Column 1', y='Column 2', data=dataset_name)
plt.xlabel('Column 1')
plt.ylabel('Column 2')
plt.title('Scatter Plot')
plt.show()In the first line of code 'Column 1' and 'Column 2' must match the actual column names in your dataset. Additionally, dataset_name should be replaced with the variable name you used to store your CSV file. The code sample below demonstrates a real-world example of what this may look like:
cancer_by_state = read_csv('actual file path...')
sns.scatterplot(x='Skin Cancer', y='UV Index', data=cancer_by_state)
plt.xlabel('Rate of skin cancer per 1000 people by State')
plt.ylabel('Average UV index by state')
plt.title('Relationship b/w Skin Cancer and UV Index')
plt.show()In bioinformatics, scatter plots are commonly used to visualize the relationship between biological variables. For instance, scatter plots may be used to examine the relationship between gene expression levels before and after a specific intervention. For example, the image below is a figure reproduced from a study titled, Role and mechanism of the AMPK pathway in waterborne Zn exposure influencing the hepatic energy metabolism of Synechogobius hasta:
The scatterplot above shows the correlation of gene expression profiles between a control group (y-axis) and a waterborne zinc-exposed (x-axis) group of Synechogobius hasta, a small edible fish species. Genes that are up-regulated in the control group as compared to the zinc-exposed group are indicated in orange, whereas genes that are down-regulated in the control group as compared to the zinc-exposed group are indicated in blue. The genes indicated in brown weren't differentially expressed between the two groups.
A line plot is a type of data visualization that displays data points connected by straight-line segments. Line plots are essentially scatter plots where the points are ordered and connected. However, whereas scatter plots are used to understand the relationship between two independent variables, line plots are appropriate when the y-variable is a continuous function of the x-variable. As a result, line plots are commonly used to represent the change in a variable over time. The code below demonstrates the general syntax for producing line plots with Matplotlib:
plt.plot('x_values', 'y_values', data=data=dataset_name)
plt.xlabel('X-axis Label')
plt.ylabel('Y-axis Label')
plt.title('Line Plot Title')
plt.show()In the first line of code 'x_values' and 'y_values ' must match the actual column names in your dataset. Additionally, dataset_name should be replaced with the variable name you used to store your CSV file. The code below provides a real-world example:
muscle_oxygen = read_csv('actual file path...')
plt.plot('Time', 'SmO2', data=muscle_oxygen, color= 'Green')
plt.xlabel('Time (Seconds)')
plt.ylabel('Muscle Oxygenation (%)')
plt.title('Muscle Oxygenation During Exericse')
plt.show()Which produces the following figure:
The image above shows muscle oxygenation data recorded with a NNOXX wearable device plotted over time. However, in bioinformatics, line plots are commonly utilized to illustrate trends in gene expression levels over time or under different experimental conditions. This type of plot allows researchers to observe how the expression of a gene evolves across various biological states, as demonstrated in the image below, which appears in a study titled, Gene Expression Trees In Lymphoid Development:
The image above represents the expression pattern of a specific gene across different time points. On the left side of the image, we have a simple development tree, where arrows represent the dependencies between variables. Then, on the right side of the image, we can see gene expression values represented on the y-axis for distinct developmental stages on the x-axis. Additionally, each line on the graph corresponds to the developmental profile of a given gene following one of two developmental paths depicted on the tree (A->B->C or A->B->D). The resulting line graph provides a clear visual depiction of how the gene expression changes throughout development and by superimposing the lines that correspond to path B->C (green) and B->D )reg) we can easily contrast the differences in expression values of genes in these two alternate differentiation pathways.
Bar charts are graphical representations used to show the relationship between categorical and numeric variables. The length of each bar on a bar chart is proportional to the value it represents, making it effective for comparing the magnitudes of different categories. The code below demonstrates the general syntax for producing bar charts with Seaborn:
sns.barplot(x='Column 1', y='Column 2', data=dataset_name)
plt.xlabel('Column 1')
plt.ylabel('Column 2')
plt.title('Scatter Plot')
plt.xticks(rotation=70)
plt.show()In the first line of code 'Column 1' and 'Column 2' must match the actual column names in your dataset. Additionally, dataset_name should be replaced with the variable name you used to store your CSV file. The code sample below demonstrates a real-world example of what this may look like:
Virus_database = read_csv('actual file path...')
sns.barplot(x ='Family', y = 'Average Radius', data = Virus_database)
plt.xlabel('Virus Family')
plt.ylabel('Average Radius')
plt.title('Average Radius Per Virus Family')
plt.xticks(rotation=80)
sns.set(style="darkgrid")#Change background color
plt.show()Which produces the following figure:
In bioinformatics, bar charts are used to compare the abundance or frequency of different biological entities, such as genes, proteins, or mutations, across various conditions or samples or to compare a specific, quantifiable characteristic between different organisms, as demonstrated in the image above. Alternatively, bar charts can represent the mean expression of varying genes under different treatment conditions. In this example, each bar corresponds to a specific gene, and the height of the bar indicates the average expression level as demonstrated in the image below, which appears in a study titled, LRRTM3 interacts with APP and BACE1 and has variants associating with late-onset Alzheimer's disease:
The image above compares the expression level of six different genes, listed on the x-axis, in the brains of wild-type (WT), Lrrtm3 knock-out (LRRTM3 K/O), and heterozygote (LRRTM3 HET) mice. The three mouse genotypic groups are color-coded, as shown in the upper left corner of the image. Additionally, the vertical displacement of each bar depicts the mean gene expression level for that group, and the error bars represent the standard deviations from the average of all measurements made in all of the mice in each group.
Histograms are graphical representations of how numerical or categorical data are distributed in a dataset. When working with numerical data, data is binned into continuous ranges, and then bars are used to represent the frequency or count of data points within each bin. When working with categorical data, the bins are instead composed of the varying categories in the dataset. The code below demonstrates the general syntax for producing bar histograms with Seaborn:
sns.histplot(dataset_name['column'], bins=20, kde=True)
plt.xlabel('Bin names')
plt.ylabel('Frequency')
plt.title('Histogram')
plt.show()In the first line of code, dataset_name should be replaced with the variable name you used to store your data and 'column' should be replaced with an actual column name in your dataset. If your data is numerical, the code bins = # determines how many bins the data is split into (this code does not need to be included if the data is categorical). Then, the code kde=True computes a kernel density estimate to smooth the distribution and show on the plot as (one or more) line(s). However, this is only relevant to univariate data. The code sample below demonstrates a real-world example of what this may look like:
Virus_database = read_csv('actual file path...')
sns.histplot(Virus_database['Average Radius'], bins=20, kde=True)
plt.xlabel('Average Radius')
plt.ylabel('Frequency')
plt.title('Distriution Average Virus Radius')
plt.xticks(rotation=90)
plt.show()Which produces the following chart:
The image above represents the distribution of the average virus radius in a database containing information about various virus families. You’ll note that the data is positively skewed (right-skewed), which is when the distributions tail is longer on the right side and its peak is on the left side.
In bioinformatics, histograms are often used to visualize the distribution of gene expression levels, protein concentrations, or sequence lengths and provide insights into the underlying patterns and frequencies within a dataset, as demonstrated in the figure below, which is from a study titled, Transcriptome Sequencing Revealed Significant Alteration of Cortical Promoter Usage and Splicing in Schizophrenia:
The histogram in the image above shows the distribution of gene expression value for genes in representative samples C26 and S26. The gene expression levels were first normalized to RPKM (reads per Kilobase of transcription per million mapped reads) values and then log2 transformed as shown on the x-axis. The y-axis, on the other hand, represents the frequency of genes with expression values within the specific log2 RPKM intervals represented on the x-axis.
A boxplot, also known as a box-and-whisker plot, is a graphical representation of the distribution of a dataset that summarizes key statistical measures, including the median, quartiles, and potential outliers, as demonstrated in the image below:
Note: Data points within the box fall within the interquartile range (IQR). The whiskers extend from the box to show the range of data, and datapoints beyond the reach of the whiskers are outliers.
Now, the code below demonstrates the general syntax for box and whisker plots with Seaborn:
sns.boxplot(x='category_column', y='numeric_column', data=dataset_name)
plt.xlabel('Category')
plt.ylabel('Numeric Value')
plt.title('Box Plot')
plt.show()In the first line of code 'category_column' and 'numeric_column' must match the actual column names in your dataset. Additionally, dataset_name should be replaced with the variable name you used to store your CSV file. The code sample below demonstrates a real-world example of what this may look like:
dementia_dataset = read_csv('actual file path...')
sns.boxplot(x='CDR', y='Age', data= dementia_dataset)
plt.xlabel('Clinical Dementia Rating')
plt.ylabel('Age')
plt.title('Age Distribution Based On CDR')
plt.xticks(rotation=90)
plt.show()Which produces the following figure:
The figure above shows the distribution of ages for individuals with clinical dementia ratings (CDR) between 0, indicating no dementia, and 2.0, indicating moderate cognitive impairment. Notice that the median age increases from a CDR rating of 0.0 (~71 years) to 2.0 (~82 years). Additionally, you can see that the interquartile range and size of the whiskers decrease as you move from a low to a high CDR rating. In essence, this tells us that there is much more age variability among individuals with a low CDR rating than individuals with a high CDR rating.
In bioinformatics, boxplots may be used to compare the distribution of a quantitative variable across different experimental groups or conditions. For example, imagine a study examining the expression levels of a set of genes under various treatment conditions. A boxplot can be created for each gene to visualize the distribution of expression levels in response to different treatments, allowing researchers to compare the variability in gene expression between conditions, as demonstrated in the figure below from a study titled, Deconstructing transcriptional variations and their effects on immunomodulatory function among human mesenchymal stromal cells:
In the study referenced above, the research investigated variations in gene expression among mesenchymal stem cells (MSCs) exposed to inflammatory cytokine INFγ to understand how these gene expression changes influence MSCs' ability to regulate or modify the activity of the immune system. The boxplot above shows the baseline (labeled Untreat) expression level for eight genes compared to the level of expression following INFγ exposure (labeled Treat). Notably, the expression level of all genes increased in response to INFγ exposure, but the magnitude of change varied from gene to gene. The box and whisker plot demonstrates the interquartile range, range of data points, and outliers from each gene under each condition.
A heatmap presents data in a matrix format, where values are represented as colors, and the intensity of the color indicates the strength of a correlation between two variables. The code block below demonstrates the general syntax for producing a heatmap:
correlation_matrix = dataset_name.corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()In the first line of code 'dataset_name' should be replaced with the variable name you used to store your CSV file. The code sample below demonstrates a real-world example of what this may look like:
dementia_dataset = read_csv('actual file path...')
correlation_matrix = dementia_dataset.corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Oasis-1 Brain Dataset Heatmap')
plt.show()Which produces the following figure:
The heat map in the image above shows the correlations between variables in the Oasis-1 brains dataset. The more intensely warm a cell in the matrix is, the stronger the positive association between two variables, and the more intensely cool a cell in the matrix is, the stronger the negative association between two variables. For example, we can see that the cell at the intersection between AFF and eTIV is dark blue, and within the cell, we see a correlation value of -0.98, which is a near-linear inverse correlation.
In bioinformatics, heatmaps are commonly used to represent large-scale datasets, such as gene expression profiles, where rows correspond to genes and columns correspond to samples or experimental conditions. Each cell in the heatmap represents the expression of a specific gene in a specific sample, with colors indicating the expression intensity. This visual representation enables researchers to quickly identify patterns of upregulation, downregulation, or co-regulation across multiple genes and experimental conditions, as demonstrated in the figure below from a paper titled, Maternal Experience with Predation Risk Influences Genome-Wide Embryonic Gene Expression in Threespined Sticklebacks (Gasterosteus aculeatus):
In the study referenced above, researchers sought to investigate the effect of maternal exposure to predation risk on the embryonic transcriptome in stickleback fish. To do this, the researchers used RNA-sequencing to compare genome-wide transcription in three-day post-fertilization embryos of both control and predator-exposed stickleback mothers. The image above depicts a heatmap showing the general pattern of gene regulation for 295 genes that were differentially expressed due to maternal exposure to predation risk. For example, we can see that the genes in the upper left quadrant of the heatmap were up-regulated in the control group but down-regulated in the predator-exposed group (upper right quadrant). In contrast, the genes in the lower left quadrant were down-regulated in the control group but up-regulated in the predator-exposed group (lower right quadrant). Thus, by performing this analysis, biological pathways involved in metabolism, epigenetic inheritance, and neural proliferation and differentiation that differed between treatments were revealed.
Often, when visualizing complex biological data, you'll want to combine multiple subplots into a single graph so you can view data side-by-side and better understand the story it's telling. The code block below demonstrates the generic syntax for producing subplots within a figure:
plt.subplot(2, 2, 1) #Two plots, 2 columns, first plot
plt.plot(x='column_1', y='column_2', data=dataset_name)
plt.subplot(2, 2, 2) #Two plots, 2 columns, second plot
plt.plot(x2, y2)
plt.show()In the first/third line of code you should list the number of plots and columns that are relevant to your analysis. If you want more than two subplots you’ll need to repeat the plt.subplot( ) code for each newly added plot. Additionally, 'dataset_name' should be replaced with the variable name you used to store your CSV file. The code block below demonstrates a real-world example of what this may look like:
#Load dataset
dementia_dataset = read_csv('actual file path...')
#Arrange sizing and spacing of subplots
plt.subplots(figsize=(12, 8)) #
plt.subplots_adjust(hspace = .2, wspace=.2)
#Create first subplot
plt.subplot(2, 2, 1)
correlation_matrix = dementia_dataset.corr()
sns.heatmap(correlation_matrix, annot=False, cmap='coolwarm')
plt.xlabel('Features')
plt.ylabel('Features')
plt.title('Oasis-1 Brain Dataset Heatmap')
#Create second subplot
plt.subplot(2, 2, 2) # Two plots, one column, second plot
sns.boxplot(x='CDR', y='Age', data= dementia_dataset)
plt.xlabel('Clinical Dementia Rating')
plt.ylabel('Age')
plt.title('Age Distribution Based On CDR')
plt.xticks(rotation=90)
#Show subplots
plt.show()Which, produces the following figure:
After completing this guide and Bash Fundamentals For Bioinformatics, you should have a solid foundation in basic Python and Bash programming, as well as data visualization skills relevant to bioinformatics. If you're eager to dive deeper, I recommend checking out my systems_biology code repository. It includes a variety of functions designed for numerical methods and solving common problems in systems biology. Experiment with the code—deconstruct it, modify variables, and explore how different functions work. For those interested in learning about machine learning, I’ve also created beginner-friendly tutorials that you can find here.
Finally, I strongly encourage you to start your own projects. Whether it’s reproducing a figure from a paper using publicly available data or completing an end-to-end project on a topic of personal interest, this hands-on approach is invaluable. While I’ve spent countless hours exploring courses, tutorials, and books to build my computational biology skills, I’ve found that tackling real projects, troubleshooting challenges, and learning on the fly has been the most effective method for both myself and others.
If you’re unsure where to begin, Dean Lee’s Figure One Lab is an excellent starting point and a resource I’ve drawn inspiration from. Additionally, Tommy Tang's work has been incredibly helpful, and I highly recommend exploring it. You can also check out some projects I've put together with reproducible code below:
- BLAST - The Biological Search Engine
- Decoding The Viral Proteome
- Amiloride in Multiple Myeloma: A Deep Dive into RNA-Seq Data Analysis
- Investigating Transcriptional Changes in Alzheimer's Disease Using a 3xTg-AD Mouse Model
📫 If you have any questions with the material in this code repository you can shoot me an email at evanpeikon@gmail.com.