Bioinformatics with Python

Explore handling sequences with BioPython to create, analyze, and visualize DNA and RNA sequences, including counting nucleotides, slicing, joining, locating motifs, and simulating transcription and translation.

Explore protein synthesis with Biopython by translating and transcribing DNA to mRNA and amino acids, and creating codons, complements, and custom stop codes.

Explore DNA composition by calculating GC content, AT content, and their frequencies across sequences using Python, and learn how GC content influences DNA stability, melting point, and PCR considerations.

Learn how to perform sequence alignment with Biopython, comparing DNA or protein sequences to reveal matches, mismatches, and gaps, and choose global or local alignment via dynamic programming.

Learn the difference between sequence similarity and sequence identity, and measure them using edit distance, Hamming distance, and Levenshtein distance, with examples of identity versus similarity in sequence comparisons.

Explore how to quantify sequence similarity with distance measures in bioinformatics, using Python and methods like Hamming distance and other edit distances.

BioPython dot plots visually compare two biological sequences to reveal regions of close similarity, direct or indirect repeats, and rearrangements.

Explore how to read and parse biological data formats like FASTA and GenBank with Biopython, loading sequences from files or databases and accessing descriptions for analysis.

Explore three-dimensional structures with BioPython by downloading PDB files from protein data banks, parsing them, and visualizing models, chains, residues, and atoms in notebooks.

Visualize protein 3D structures directly in Python using pi 3D model, by querying a PDB database to render structures without downloading files, with adjustable spectrum color schemes and interactive rotation.

Learn how to visualize and render 3d structures from pdb files with nglview in a Jupyter notebook, including activating extensions, adjusting views, and trying the pi trajectory method.

Learn to load pdb files with pytraj in python, visualize and render 3d protein structures, and display models using multiple view methods.

This lecture shows how to use Biopython to run blast queries, manage large data efficiently, choose query types, and save results to disk to avoid data loss.

Apply real-world BioPython tools to fetch a Covid-19 DNA sequence, perform transcription, translation, nucleotide frequency analysis, GC content, molecular weight, and stop-codon stripping to interpret viral sequence data.

Biopython alphabets are deprecated after version 1.78; construct sequences by passing the raw sequence directly without an alphabet, and use a try/except to preserve backward compatibility with older code.

Explore Biotite for handling DNA sequences, alphabets, and protein synthesis; learn sequence creation, transcription and translation, reverse complements, codon tables, and basic sequence analysis.

Explore sequence alignment with Biotite: understand global and local alignments, scoring with substitution matrices, gaps, and determining similarity and identity between DNA sequences.

learn to download genome data from Biotite, choose GenBank or FASTA formats, read and print sequences, extract annotations, and convert between formats for analysis.

Biotite guides reading data from bioinformatic databases by performing simple and composite queries, searching nucleotide databases, fetching and downloading results, and reading byte type files in a working space.

Learn to fetch PDB files from the Protein Data Bank using Biotite and a database workflow, query and download PDB formats for 3D structures, and save them locally.

Fetch and query multiple PDB files with Biotite by supplying a single id or a list, enabling batch retrieval and analysis of structures.

Discover how to work with protein 3D structures using Biotite: read and parse PDB files with a file reader, extract structures and coordinates, and visualize or print key details.

Explore phylogenetics with Biotite by building reference objects and creating trees and networks. Compute distances between nodes and visualize the tree with graphics.

Biotite - applications subpackage demonstrates building a simple blast app to align a query sequence against a database, managing initialization, execution, and result retrieval with alignments and scores.

Use Biotite in Python to analyze dna sequences, compute gc content and nucleotide frequencies, and perform global and local alignments to compare coronavirus and mers genomes.

Explore how the scikit-bio library handles DNA, RNA, and protein sequences, including reading, creating, and analyzing sequences with features like GC content and motifs.

Explore dna transcription and rna translation to proteins using scikit-bio, converting dna to rna and amino acids to build sequences.

Explore DNA composition with scikit-bio by calculating GC content, GC frequency, and AT content, comparing content vs frequency, and visualizing nucleotide frequencies for DNA sequences.

Explains how to assess similarity between sequences using hamming distance and kmer-distance in scikit-bio. Demonstrates identifying mismatches and gaps and building custom distance functions for sequence comparisons.

Learn how to compute nucleotide and k-mer frequencies using scikit-bio, including counting specific k-mers, relative frequencies, and simple scripting to inspect DNA sequences.

Explore sequence alignment with scikit-bio, comparing global and local alignments, understanding matches, mismatches, and gaps, and learning how to interpret alignment scores.

Explore scikit-bio's tools for reading FASTA and GenBank files, loading sequences, and performing basic analyses such as translation, transcription, and frame checks.

Explore phylogenetics with scikit-bio by learning the Newick format to encode trees, including roots, children, and common ancestors, and practice generating and visualizing simple trees.

Define and use custom Python functions to compute nucleotide frequencies, GC content, and AT content, generate complements and reverse complements, transcribe DNA to RNA, and translate to amino acids.

Build a bioinformatics package with Python using Poetry to manage dependencies and packaging; implement DNA and sequence classes, transcription, translation, GC content, and comprehensive tests.

Explore building and using a bioinformatics package mbai with neatbio, covering dna, rna, protein sequences, transcription, translation, and gc content. Also learn dot plots and distance metrics for sequence comparison.

Learn to build a simple bioinformatics app with streamlit that analyzes DNA sequences, computes GC and AT content, transcribes, translates, and visualizes frequencies with interactive plots.

Explore bioinformatics in Julia by installing and using packages to build and analyze DNA sequences, perform transcription and translation, compute GC content, and visualize 3D structures with PDB and Blink.