Python for Biologists and Beginners

Explains the central dogma, detailing how DNA stores information, RNA carries it to synthesize proteins via transcription and translation, and how reverse transcription fits into this flow.

Explore RNA structure, transcription and reverse transcription, including coding and template strands, RNA's uracil, and cDNA synthesis.

Explain how proteins arise from amino acids, the 20 amino acids with their one-letter codes, and translation from DNA to protein via mRNA codons.

Explore fasta and gff formats used in bioinformatics. Fasta stores multiple DNA or protein sequences with headers, while GFF describes gene features and coordinates.

Begin your journey in Python for biologists and beginners, learning to write code in Jupyter Notebook with a text editor and interpreter, and install Anaconda Navigator to manage tools.

Learn to download and install Anaconda Navigator on macOS or Windows, launch JupyterLab, rename notebooks, and explore code, markdown, and raw string editors, with Google Colab as an alternative.

Learn how to use variables in Python as containers for different data types, including strings, numbers, booleans, and common data structures, while applying naming rules and the print function.

Explore numeric types in Python for biologists and beginners—integers and floats—perform arithmetic like add, subtract, multiply, and divide, and learn quotient, modulus, and complex numbers in a Jupyter notebook.

Learn how strings work in python, including initialization with single or double quotes, printing, and explicit type casting with int() and str().

Explore Python string analysis with real protein sequences: measure length, slice substrings using zero-based indexing, employ negative indexing to flip sequences, and concatenate with plus or f-strings, including type casting.

Learn how to manipulate string case in Python using lower, upper, title, and capitalize, including sentence mode formatting and f-string printing with dynamic substitutions.

Explore the Python string replace method to substitute substrings in any string, including creating mutants of wild type DNA, RNA, or protein sequences.

Explore dot find and dot count for string analysis. Dot find locates the first occurrence; dot count tallies all occurrences in Python and protein examples, including -1 when not found.

Explore the split method for string analysis in Python, turning a sentence into a list of words using a separator, then reassemble them with join and a chosen delimiter.

Learn to clean strings with strip, lstrip, and rstrip to remove leading and trailing patterns, compare with replace, and understand when to apply patterns like spaces or semicolons.

Explore string analysis techniques by implementing reverse complements and gc content calculations on dna sequences with python, through hands-on activities that reinforce sequencing concepts.

Open a file, retrieve its contents, and analyze them using Python for biologists and beginners. Learn to write a new file to store modified data.

Explore read methods: read, read line, and read lines as approaches to reading a fasta file in Python, plus path handling and proper closing.

Learn the second approach to opening and reading files in Python with a context manager using with open, which auto-closes the file while performing read, read line, or read lines.

Copy a file path on Windows, then handle path issues with two approaches: escape backslashes by doubling them or replace with forward slashes, and read the file in a notebook.

learn how to read files in Google Colab by uploading them to the current environment, copying the file path, and reading and printing the content.

Learn to read a DNA sequencing file, clean headers and line breaks, and analyze gene length and GC content. Write results to a file and compare write versus append modes.

Learn to append files in Python by comparing read, write, and append modes, preserving data with append instead of overwriting, and using lower() to modify sequences.

Explore initializing lists and tuples in Python, covering empty and non-empty cases, square vs round brackets, range-based creation, and one-line loops, while comparing mutability and efficiency.

Learn how to convert between lists and tuples in Python, using list() and tuple(), and observe how data types change from list to tuple and back.

Use the len function to determine the length of lists and tuples by counting elements. Compare this to string length, which counts characters including spaces and special characters.

Learn to convert a string to a list or tuple by characters or words, split to create lists, and join to recover the original string.

Learn to access list and tuple elements by index or value, and to slice lists and tuples with start, end, and step, including forward and negative indexing.

Explore the count method on lists and tuples to measure element frequency, and note that individual element data types remain intact. Also see how case sensitivity affects counts.

Learn list concatenation in Python by appending elements or lists and inserting them at specific positions, then compare creating a new list to modifying the original.

Learn how to concatenate a new list to an existing list in Python, using a new list without tampering the original and using extend or slicing for in‑place updates.

Explore when to append, extend, and insert in Python lists, compare with tuples, and learn how to build and access nested lists for biological data.

Learn to clone a list in Python by value or by address; value copies with list.copy to a new list, address copies via assignment, and id shows memory location.

Tuples are immutable, so copying by value isn’t possible; use assignment for a copy by address or convert to a list to modify, then convert back to a tuple.

Delete list elements by index with dot pop or del, or by value with remove; learn copying lists and negative indexing, and that tuples are immutable.

Explore sorting lists with the sort and sorted methods, compare ascending and descending orders, and learn how tuples remain immutable while using sorted and optional conversion back to a tuple.

Explore how the membership operator in Python uses in and not in to test presence in lists and strings, returning true or false for element matches and character subparts.

Explore mathematical operations on lists in Python: use max and min on numeric lists, sum for aggregation, and flatten nested lists by using sum with an empty list.

Use all and any to test boolean lists for all or any true values. Apply in and not in, and max, min, and sum on lists and tuples, including flattening.

Learn how to pair two equal-length lists into a single list of tuples using zip, and how to unzip to recover separate gene and expression lists.

Explore loops in Python for biologists and beginners, focusing on one-liner for loops to build lists, power operations, and basic list operations with range objects.

Learn the conventional for loop in Python by mastering its syntax, iterating over lists, ranges, and strings, and printing formatted outputs, with index-based access and a preview of nested loops.

Master nested loops in Python by counting bases in a DNA sequence and computing the lengths of inner lists without using len, with debugging via break.

Understand how while loops control iteration by condition, compare them to for loops, and learn syntax and updates to avoid infinite loops, illustrated with a dna sequence example.

Explore conditional testing in Python for biologists, mastering if, elif, and else, and building decision making with logical operations, comparison operators, and boolean concepts.

Master decision making in Python by using if, elif, and else, including nested ifs, to evaluate gene expression and validate coding sequences through clear, condition-driven logic.

Explore Biopython as an external package to analyze DNA and protein data by converting strings to Seq objects, performing transcription, translation, reverse complement, and computing GC fraction and molecular weight.

Analyze fasta files efficiently with Biopython by parsing headers and sequences, then compute gc content and molecular weight, and optionally write results to a file.

Learn to format biopython printouts by left, right, and center alignment using the less-than, greater-than, and hat symbols after the colon, improving fasta analysis visuals.

Explore dictionaries in Python, a mutable unordered data structure of key-value pairs enclosed in curly brackets, including unique keys, potentially repeated values, and common dictionary methods.

Initialize dictionaries in Python with curly braces or the dict constructor, creating empty and non-empty dictionaries via key–value pairs or zip of lists.

Explore dictionary operations by calculating length, copying by value or by address, and extracting keys and values, then convert between lists and dictionaries using zip.

Learn how to access dictionary values by key, recognize that dictionaries are unsorted with no indices, and prevent key errors using get with a default value.

Learn to add or update a dictionary key's value in Python using assignment for new or existing keys. See how dictionaries differ from lists, which require append for new elements.

Explore how dictionaries are iterable like strings and lists, and learn three ways to iterate: by keys, by values, and by key-value pairs using for loops and dict methods.

Learn to delete dictionary elements in Python using pop, popitem, del, and clear, and see how each method modifies the original and when to build a new dict with one-liner.