Learn Bioinformatics From Scratch (Theory & Practical)

Explore sequence alignment as the heart of bioinformatics and learn how it reveals evolutionary similarities and differences in DNA from a common ancestor.

Define algorithm as a logical method to solve problems and apply it to bioinformatics for sequence comparison using dot matrix qualitative, dynamic programming quantitative, and word method for database searching.

The dot matrix offers visual sequence comparison, identifies repeats, and supports global and local alignment, but is qualitative and does not show deletions or insertions.

Explore the dot matrix algorithm with the emboss dot matcher to perform qualitative alignment of two DNA sequences, adjusting window size to reduce noise in the diagonal lines.

Apply dynamic programming to build a two dimensional matrix to align sequences, using a scoring scheme of 1 for matches and 0 for mismatches, filling matrix with highest neighboring score.

Transform a scoring matrix into a meaningful alignment by tracing the maximally diagonal path from bottom-right to top-left, noting matches, mismatches, and gaps.

Explore dynamic programming in sequence alignment, including gap opening, gap closing, and how gaps represent insertions or deletions, with a note on scoring matrices like BLOSUM and PAM.

Discover how BLAST enables pairwise sequence alignment for searching massive databases like the NCBI nucleotide database and Protein Data Bank, balancing high sensitivity, high specificity, and less time.

Learn six steps of the word algorithm for bioinformatics: break queries into words, scan databases for matches, score with a matrix, and extend alignments by dynamic programming to a threshold.

Explore the highest scoring pair (HSP) in word algorithm for bioinformatics, showing how a query sequence matches a database via a local alignment, forming the basic local alignment search tool.

Learn how progressive sequence alignment builds a final multiple sequence alignment by pairwise alignments, a distance matrix, a guide tree, and consensus sequences used as a reference.

Learn to perform a multiple sequence alignment using Clustal W in bio edit, loading sequences, running with default values, and viewing colored amino acid alignments.

Learn dna basics: explore the double stranded, helical polymer of nucleotides, including deoxyribose, phosphate, and bases a, t, c, g, linked by phosphodiester bonds and stabilized by a-t and c-g hydrogen bonds.

The lecture explains how coding DNA, via transcription and translation, controls phenotypes by producing proteins that shape structure and function.

Explore how coding DNA uses open reading frames, genetic codes, and codons to specify amino acids, including degeneracy, and examine exons, introns, promoters, and start and stop signals.

Examine the first ab initio algorithm, GC bias and test code, and understand how degeneracy and open reading frames influence coding region predictions.

Learn to predict prokaryotic genes using ProcSeq with FASTA files from NCBI; upload, create a map, inspect CDSs, annotate genomes, and explore GC content, GC skew, and circular plots.

Learn to predict eukaryotic genes with Augustus by uploading a FASTA sequence, selecting Homo sapiens, and reviewing predicted genes, transcripts (including alternative transcripts), CDS, introns, and protein sequences.

Explore Sanger sequencing basics, including fasta format with header and sequence, assess data quality via Abi chromatograms and peaks, and note limitations like slow runs and noisy starts and ends.

Explore structural bioinformatics foundations, from amino acids and peptide bonds to Ramachandran plot, protein structures, and practical PyMOL visualization, plus protein-ligand docking and molecular dynamics.

Explore how amino acids connect through a peptide bond formed by condensation, releasing water. This bond has double bond-like character; the lecture explains the linkage and upcoming details.

Learn how the peptide bond exhibits a double bond like character, restricting phi and psi rotations, and how Ramachandran plots map these angles for protein validation.

Discover how to install and use UCSF Chimera to visualize protein structures, open local PDB files, fetch by ID from the Protein Data Bank, and save PDB files.

Explore how the Chou–Fasman algorithm uses amino acid propensities to predict alpha helix, beta sheet, and loops in globular proteins, and note the neighboring effect and the Gore algorithm.

Explore how transmembrane proteins achieve secondary structure, comparing beta-barrel channels and helical receptors, and learn to predict them with tm m by pasting sequences to view graphical and tabular results.

Explore the homology based algorithm for protein 3d structure prediction, known as comparative modeling, detailing six steps and postulate that structures are more conserved than sequences, with a modeler tutorial.

Assess your optimized protein model with a Ramachandran plot, checking phi and the size of alpha helix and beta sheets to validate accuracy and anticipate threading if templates are unavailable.

Explore threading algorithm for 3d protein structure prediction beyond sequence similarity by fitting sequences into a structural database to identify fold. Learn how to use I-TASSER and interpret results.

Visualize docking data to identify where the ligand binds the receptor and the interactions formed, using pymol and ligplot to analyze hydrophobic contacts and hydrogen bonds in a three-dimensional view.

Explore the definition of evolution through natural selection and DNA mutations, linking genotype to phenotype, and compare fossil-based and molecular phylogenetics to study evolutionary history.

Contrast phylograms and cladograms by comparing unequal and equal branch lengths; understand how scaled trees express evolutionary divergence and why phylograms reveal both relationships and divergence among taxa.

Perform multiple sequence alignment using progressive sequence alignment to build a phylogenetic tree, since alignment quality directly affects the accuracy of tree construction.

Calculate the evolutionary distance between two aligned sequences by correcting observed substitutions with models such as Jukes-Cantor and Kimura, accounting for homoplasy, transitions, and transversions.

Apply bootstrapping to validate phylogenetic trees by perturbing the multiple sequence alignment and reconstructing the tree each time to assess whether the original topology remains stable across repeated changes.

Learn to construct a phylogenetic tree from 16s ribosomal RNA by identifying homologs with BLASTn, selecting sequences, and using a one-click phylogeny server to build and bootstrap the tree.

Learn to download and install Chemsketch and Open Babel from free sources, provide your credentials to receive download links by email, and complete the setup on Windows, Linux, or Mac.

Use AI-powered tools like Semantic Scholar and SciSpace to search, summarize abstracts, and quickly review literature. Compare results with Google Scholar and explore papers on alpha amylase protein engineering.