Learn Bioinformatics - Beginner to Master Through Projects

Explore bioinformatics databases, including primary, secondary, and drive databases, and learn how they organize, store, and analyze biological data for practical applications.

Primary databases like GenBank, EMBL, and DDBJ store raw biological data submitted by researchers. Learn to search, retrieve accession numbers, and download sequences in FASTA, CSV, or JSON using APIs.

Explore EMBL and DNA Data Bank of Japan (DDBJ) databases, retrieve accession numbers, search protein sequences, and download sequences while navigating the European Nucleotide Archive and UniProt.

Explore secondary databases as curated extensions of primary databases, offering annotation and context for protein sequences, structures, and families with examples like UniProt, Pfam, PDB, and RefSeq.

Explore the PDB database to search for and view protein structures, download PDB files, and access PDB 101 and other resources for visualization and analysis.

Explore the UniProt database to search for insulin, view protein information, download data, and examine annotations and their features, GO terms, and related diseases.

Explore the GEO database, the NCBI gene expression omnibus for microarray data, search datasets, view expression profiles, download data, and run differential expression analysis.

Explore drive databases that integrate primary and secondary data to provide curated functional annotations and task-specific insights, using ensemble database, Gene Ontology, and InterPro, plus Reactome and KEGG pathways.

Learn how primary databases store original data and raw sequences, while scanned databases offer curated annotations, and drive databases provide computed, integrated data for pathway analysis in bioinformatics.

Explore bioinformatics tools for sequence alignment, genome annotation, and data visualization, and learn how coding languages like Python, R, and C++ power scalable, reproducible analyses.

Explore pairwise sequence alignment of DNA, RNA, and proteins to identify matches, mismatches, and gaps, and apply tools like blast and emboss for evolutionary insights.

Perform pairwise sequence alignment using emboss needle and emboss water on protein sequences from the NCBI gene page, interpreting identity, similarity, gaps, scores, and global versus local alignment.

Learn how multiple sequence alignment reveals conserved regions and evolutionary relationships across DNA, RNA, and proteins, using tools like Clustal Omega and fast Fourier transform methods.

Perform practical multiple sequence alignment by compiling protein sequences and using tools like Mega, Clustal W, Blast, and Map Fit; identify consensus regions and motifs across sequences.

Explore homology searching with blast, learning sequence alignment, query versus database comparisons, and how to identify evolutionary and functional relationships among DNA, RNA, and protein sequences.

Explore protein and nucleotide blast tools, select databases like non-redundant, RefSeq, and UniProt, and interpret results to identify homologous sequences and conserved domains.

Explore functional genomics through sequencing technologies, genome assembly, variant calling, annotation, and gene expression analysis to understand genome structure and regulation.

Learn gene prediction analysis using softberry tools, comparing ab initio and similarity-based methods to identify gene models in eukaryotes and analyze exon-intron structures.

Learn how to predict and visualize gene structures using the gene structure display server, uploading genomic and mRNA sequences, viewing exons and introns, and generating publication-ready results.

Annotate and visualize a genome using NCBI genome data and the Pro CE tool, highlighting transfer RNA, ribosomal RNA, and coding sequences on circular and linear maps for publication-ready figures.

Learn to map gene locations on chromosomes using the M2C tool, extract start and end coordinates from the NCBI gene page, and visualize the HB gene on chromosome 11.

Learn to perform gene ontology enrichment and pathway analysis with the shiny go tool, selecting species and gene IDs, and visualizing enriched pathways and GO terms.

Explore proteomics, from primary amino acid sequences to three-dimensional protein structures and interactions. Learn mass spectrometry workflows for protein identification and sequencing, plus network analysis with Cytoscape and UniProt.

Learn to identify protein motifs—short conserved sequences with specific roles—using motif discovery tools like MEME, analyzing sequences from BLAST, and interpreting motif logos and functions.

Learn how protein domains serve as structural and functional units, identify them with NCBI CDD batch search, and visualize domain patterns using TB tools for plant proteins.

Explore protein protein interactions by building a functional protein association network in STRING, search and upload protein sequences, and visualize or export the results for downstream analysis with Cytoscape.

Learn how to compute protein physical parameters from a sequence using ProtParam, including molecular weight, theoretical pI, amino acid composition, instability index, gravy, and subcellular localization predictions with Broadcom.

Explore evolutionary bioinformatics and phylogenetics, learn sequence alignment and phylogenetic tree reconstruction, and visualize evolutionary relationships across species using sequencing methods.

Construct and visualize a phylogenetic tree from bzip protein sequences across Arabidopsis species, using mega, iq-tree, and idol, with sequence retrieval, alignment, maximum likelihood, and bootstrap.

Explore Linux basics for bioinformatics, including Ubuntu usage, shell scripting, and package managers, and apply tools like blast and Hisat2 with workflow automation on HPC.

Learn to set up Windows Subsystem for Linux on Windows, install WSL with Ubuntu, configure terminals, update packages, and enable GUI apps with X11 for bioinformatics work.

Learn to navigate the Linux file system, understand absolute and relative paths, and organize bioinformatics data by creating structured directories using commands like ls, pwd, and mkdir -p.

Learn basic linux commands for bioinformatics to manage files and directories, copy, move, and delete data. Organize sequencing data and view files with head and tail using practical, efficient workflows.

Explore installing bioinformatics tools on Linux with apt, yum, and conda, including fastqc and blast, building from source, and using containers; learn to troubleshoot dependencies and manage environments.

Learn how to write and run bash scripts for bioinformatics, automating file manipulation, sequence data processing, and end-to-end workflows with practical command examples.

Explore transcriptomics and RNA sequencing to analyze gene expression and cellular activity. Learn pre-processing, alignment, quantification, differential expression, and single-cell RNA-seq analysis with tools like STAR, HISAT2, and DESeq2.

Analyze geo microarray data to study gene expression profiles using the geo database. Learn to query, download, and perform differential expression analysis with R.

Learn how to perform RNA-Seq data analysis from raw fastq reads to gene expression quantification, including quality control, alignment to a reference genome, and differential expression with Deseq in R.

Retrieve RNA-seq data by downloading fastq files from NCBI, obtaining Drosophila melanogaster genome and GTF annotation, and outlining preprocessing to feature counting in a complete RNA-seq workflow.

Learn how to preprocess RNA-seq data by performing quality control with fastqc, trim with fastp, and retain high-quality reads using phred scores above 30, with qc reports.

Learn to align reads to a reference genome, index the genome, and convert sam to bam while handling gtf/gff annotations, using bwa or bowtie and sam/bam tools.

Quantify RNA-Seq reads by counting aligned reads with feature counts on bam files using a gtf annotation, producing a counts file and enabling deseq2 differential expression analysis in R.