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.

Explore the two main types of sequence alignment—pairwise and multiple—and dive into global and local pairwise alignment, including when to use them based on sequence relatedness and length.

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.

Explore the dot-matrix algorithm for sequence comparison by plotting residues on x and y axes, marking matches with dots, and tracing diagonal lines to reveal sequence similarity.

interpret the two-sequence dot matrix by examining diagonal lines to identify local alignment, where similarity appears in a specific region, and a central diagonal for global alignment.

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.

Learn to perform global pairwise sequence alignment of two DNA sequences using the needle tool on the emboss dispatcher, including input, default metrics, and interpreting identity, similarity, and indels.

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.

Compare exhaustive and heuristic algorithms by exploring sensitivity, specificity, time, and RAM constraints, and learn when to favor heuristic methods under limited resources.

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 to evaluate blast results using query coverage, e-value, and percentage identity. Achieve higher coverage, lower e-values, and higher identities to reduce false positives in protein alignments.

Learn how to use protein BLAST to search protein databases, select NR or PDB databases, filter by organism, and assess results by E-value, identity, and query coverage.

Discover how PSI-BLAST uses an iterative approach to find true homologues, builds a PSM from a multiple sequence alignment, and evaluates results with e-values.

Compare exhaustive dynamic programming with heuristic methods for multiple sequence alignment. Focus on global progressive sequence alignment, noting iterative approaches and local block based methods.

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.

Examine cluster W and t-coffee for multiple sequence alignment, and discover how progressive greedy alignment amplifies errors, while t-coffee fuses global and local alignment via l-align for the final MSA.

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.

Explore genomic bioinformatics in module three by predicting eukaryotic and prokaryotic genes and their regulatory elements with various algorithms, and learn to analyze microarray data.

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.

Compare prokaryotic and eukaryotic gene structure and regulation: prokaryotes lack introns and use operons with Shine-Dalgarno sequences; eukaryotes contain introns, splicing, Kozak promoters, and multiple RNA polymerases with transcription factors.

Explore computational algorithms for gene prediction in bioinformatics, including ab initio, homology-based, and consensus methods, with a focus on machine learning and hidden Markov model.

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

Apply machine learning and neural networks to ab initio gene prediction, training computers with input features through hidden layers to predict genes in eukaryotes and prokaryotes.

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 the Sanger sequencing method, where dideoxy dntps terminate DNA synthesis, allowing primers to amplify fragments and produce a chromatogram read by a sequencing machine with fluorescent probes.

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.

Understand hydrogen bonds stabilizing the protein's 3D structure, including alpha helices and beta sheets, plus wander wall and hydrophobic core interactions that expel water and form a vacuum-like force.

Learn to visualize protein structures in pymol on your local machine, installing pymol, opening pdb files, and switching between wire, sticks, cartoon, ribbon, sphere, and surface representations for docking insights.

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.

Learn to visualize protein structures in Chimera by using the select and action tools to highlight chains, select residues, apply colors and surface models, adjust transparency, and save publication-quality images.

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.

Select a template from the protein data bank using protein blast to model a query protein via a homolog with at least 30% identity, prioritizing resolution alignment with dynamic programming.

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.

Learn to predict a protein’s three-dimensional structure with i-tasser, covering threading, homology-based, and ab initio options, and interpret results like secondary structure, solvent accessibility, and model quality.

Learn to create publication quality images in Pymol with cartoon structures. Set background to white, add a surface, and apply light gray in color space C by K.

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.

Learn to use the Zinc database to download three-dimensional ligand structures, perform similarity searches, and filter purchasable compounds for docking and virtual screening.

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.

Learn how to choose molecular markers for phylogenetic tree construction by comparing DNA and protein sequences, considering coding vs noncoding regions, selection pressures, and organism relatedness.

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.

Explore distance-based and character-based methods to construct phylogenetic trees, detailing upgma and neighbor-joining for distance-based approaches and maximum parsimony and maximum likelihood for character-based methods.

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.