BIOINFORMATIX LEARNING PATH
Learn how to transform raw RNA sequencing reads into differential expression results, visualizations and biological interpretation.
PATH ORIENTATION
Build practical bulk RNA-Seq analysis skills before moving into advanced transcriptomics.
FROM RAW READS TO BIOLOGICAL INSIGHT
Each step changes the representation of the data and answers a different analytical question.
CORE BULK RNA-SEQ PATH
Ten connected stages turn transcriptomics concepts into a complete, reproducible expression analysis.
RNA-Seq measures a dynamic collection of transcripts. Learn what is being counted before deciding how to analyze it.
Good computational analysis cannot rescue a fundamentally poor experimental design. Define the comparison and sources of variation before sequencing.
Measure biological variation, not just technical repetition.
Make the intended comparison interpretable.
Technical structure can resemble biology.
A factor must not perfectly overlap the condition.
FASTQ records contain RNA-derived sequencing reads and per-base quality information. Paired-end data provides two reads for each sequenced fragment.
@sample_A_read_01
GATCGTACGTTGCA
+
IIHHHFFFGGGGEEFastQC helps diagnose possible issues before alignment or quantification. Review patterns across all samples rather than judging one metric in isolation.
Both approaches estimate expression, but they represent reads and transcripts differently. The appropriate strategy depends on the question.
Creates genomic alignments, then assigns reads to annotated features.
Estimates transcript abundance without producing conventional genomic alignments.
Genes are rows, samples are columns and counts are measurements. Raw counts cannot be compared directly without considering sequencing depth and composition.
| Gene | A1 | A2 | A3 | B1 | B2 | B3 |
|---|---|---|---|---|---|---|
| TP53 | 120 | 128 | 119 | 684 | 701 | 659 |
| MYC | 902 | 875 | 911 | 240 | 263 | 251 |
| GAPDH | 530 | 548 | 521 | 560 | 553 | 571 |
| IL6 | 18 | 25 | 21 | 740 | 795 | 762 |
Normalization accounts for technical differences such as library size and composition effects. DESeq2 conceptually estimates sample-specific size factors rather than simply dividing every count by total reads.
Differential expression combines effect size and statistical evidence. Interpret both, along with study design and biological context.
RNA-Seq visualizations are diagnostic and explanatory tools. Choose each plot for the question it answers.
Do samples separate by condition or another source of variation?
Which samples have similar global expression profiles?
How do selected genes vary across samples?
Which genes combine large changes with strong evidence?
Does expression change depend on average abundance?
How does one gene vary across conditions and replicates?
A DEG list is not the end of an RNA-Seq analysis. Functional analysis asks whether genes share biological processes, pathways or coordinated functions.
AFTER THE CORE PATH
These are optional directions after bulk RNA-Seq fundamentals, not requirements for a beginner analyst.
Long non-coding transcript analysis
Small RNA expression and targeting
Isoform and exon usage changes
Percent spliced-in analysis
Expression at cellular resolution
WORKFLOW TOOLBOX
Learn why a tool is used and what it produces. You do not need every available tool.
Files, commands and reproducible execution.
Quality profiles and read diagnostics.
Splice-aware mapping to a genome.
Assign aligned reads to annotated genes.
Normalization and differential expression.
Clear statistical and expression graphics.
RNA-Seq data structures and methods.
GO, pathways and gene-set context.
PROJECT PROGRESSION
Each project adds a new data transformation until you can complete an end-to-end public dataset analysis.
Assess FASTQ quality across samples and write justified recommendations.
Follow alignment and counting to produce a sample-by-gene table.
Normalize counts, fit a comparison and interpret DEG statistics.
Create a coherent visual story from sample structure to gene changes.
Connect gene-level results with processes and pathways.
Frame a biological question and deliver a reproducible analysis with interpretation.
LEARN WITH BIOINFORMATIX
Use structured courses to support practical analysis and the next appropriate transcriptomics direction.
SELF-ASSESSMENT
Mark skills you can explain or perform without step-by-step guidance. Your progress is stored only in this browser.
FROM READS TO MEANING
Start with a clear biological question, respect the experimental design and carry the evidence through every step to interpretation.
Bioinformatix provides hands-on training in bioinformatics, genomics, AI, machine learning, and sequencing data analysis. Learn through real-world projects, practical workflows, and industry-focused courses designed for students, researchers, and professionals.