BIOLOGY / DATA / COMPUTATION

Read biology through data.

Bioinformatics Guides is an educational platform for understanding how biological questions become computational analyses — from sequencing reads and genomes to transcripts, proteins, and complex biological systems.

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FROM BIOLOGICAL QUESTION TO COMPUTATIONAL INSIGHT

ANALYSIS VIEW

GENOMIC DATA

CHR 07 REFERENCE GENOME

55,231,842 — 55,244,103

ATG CGT ACG ATC GAT CGT AAG TCC GCA CTA GGA TGC

READ ALIGNMENT

VARIANTS

READS 24,812 SEQUENCED

ALIGNMENT 97.4% REFERENCE

VARIANTS 18 DETECTED

01 FOUNDATIONS

02 DATA ANALYSIS

03 COMPUTATIONAL BIOLOGY

SCROLL TO EXPLORE ↓

02 / FOUNDATIONS

THE FIELD

What is bioinformatics?

Bioinformatics brings together biological knowledge, computational methods, mathematics, and statistics to organize, analyze, and interpret biological data.

A BRIDGE BETWEEN BIOLOGY & DATA

Modern experiments can generate millions of biological measurements. Bioinformatics provides the computational frameworks needed to transform these measurements into patterns, relationships, and testable biological insights.

BIO
DATA

01 BIO

Biology

Understanding genes, genomes, cells, organisms, molecular pathways, and biological processes.

02 CODE

Computing

Using algorithms, programming, pipelines, and computing systems to process complex biological datasets.

03 STAT

Statistics

Measuring variation, identifying meaningful patterns, testing hypotheses, and evaluating biological evidence.

04 DATA

Data

Working with sequences, expression profiles, variants, structures, networks, and other biological datasets.

SELECTED FOUNDATION

Biology

Biological knowledge gives computational analysis its context. Understanding what a gene, transcript, protein, pathway, or biological process represents is essential for interpreting analytical results correctly.

BIOINFORMATICS DOMAINS

Explore the Major Bioinformatics Domains.

Bioinformatics integrates computational approaches with biological data to investigate genomes, transcripts, proteins, microbial communities, individual cells, and complex biological systems.

GENOMICS

Genomics

Genomics focuses on the study of complete genomes, including their sequence, structure, variation, organization, and biological function.

TRANSCRIPTOMICS

Transcriptomics

Transcriptomics examines the complete collection of RNA molecules produced by cells or tissues, providing insight into gene expression, regulation, and cellular activity.

PROTEOMICS

Proteomics

Proteomics investigates the composition, abundance, structure, functions, and interactions of proteins within biological systems.

METAGENOMICS

Metagenomics

Metagenomics analyzes genetic material obtained directly from microbial communities to investigate their composition, diversity, and functional potential.

SINGLE-CELL

Single-Cell

Single-cell bioinformatics analyzes molecular measurements from individual cells to reveal cellular diversity, cell states, and relationships between different cell populations.

SYSTEMS BIOLOGY

Systems Biology

Systems biology integrates multiple layers of biological information to investigate pathways, molecular interactions, networks, and complex biological behavior.

BUILD YOUR KNOWLEDGE

Explore the bioinformatics toolkit.

From biological sequences to complex molecular networks, bioinformatics provides a diverse set of computational concepts for understanding biological data.

ATGC

Sequence Analysis

Computational analysis of DNA, RNA, and protein sequences helps identify patterns, similarities, functional regions, and evolutionary relationships.

DNA RNA Protein

NGS

NGS Data Analysis

Next-generation sequencing generates large volumes of sequence data that require quality control, processing, alignment or assembly, and downstream interpretation.

FASTQ QC Alignment

Genome Annotation

Annotation connects genomic sequences with biological information by identifying genes, genomic features, predicted functions, and relationships to known biological resources.

Genes Features Function

RNA

Gene Expression

Expression analysis compares RNA abundance across biological samples to investigate which genes are active, altered, or associated with particular cellular conditions.

RNA-seq Expression DEGs

SNP

Variant Analysis

Variant analysis identifies differences between biological sequences and helps researchers evaluate their genomic location, frequency, potential impact, and biological relevance.

SNPs Indels Variants

NET

Biological Networks

Network-based approaches integrate relationships between genes, proteins, pathways, and other biological entities to investigate how components interact within complex systems.

Pathways Interactions Networks

LEARNING PATH

Start with the biological question.

The right computational approach depends on the biological question, the data available, and the level of resolution required.

UNDERSTANDING BIOLOGICAL DATA

What does a bioinformatician actually analyse?

Biological experiments produce different types of data. Each data type has its own structure, challenges, computational methods, and biological meaning.

DATA BIOLOGICAL FROM MOLECULE
TO SYSTEM

A T G C

DNA Sequences

DNA sequences encode genetic information and can be analysed to study genes, genomic variation, genome organization, and evolutionary relationships.

Sequence Genome Variants

RNA

RNA & Expression Data

RNA measurements provide information about gene activity and can reveal changes in transcription across tissues, cell types, developmental stages, or experimental conditions.

RNA-seq Expression Transcripts

PRO

Protein Data

Protein data can describe abundance, sequence, structure, modifications, and interactions, allowing researchers to investigate molecular mechanisms beyond the genome and transcriptome.

Proteins Structure Interactions

SNP

Genetic Variants

Variants represent differences between biological sequences. Computational analysis can characterize substitutions, insertions, deletions, and other forms of genomic variation.

SNPs Indels Genotypes

CELL

Single-Cell Data

Single-cell datasets measure molecular features at the level of individual cells, making it possible to investigate cellular heterogeneity and identify distinct populations and states.

scRNA-seq Clusters Cell States

NET

Biological Networks

Networks represent relationships between biological entities such as genes, proteins, metabolites, and pathways, helping researchers investigate interactions within complex biological systems.

Pathways Interactions Networks

MOLECULAR CELLULAR SYSTEM

ANALYSIS IN PRACTICE

From raw data to biological evidence.

A bioinformatics analysis is usually a sequence of connected computational steps. Each stage contributes to the quality, reliability, and interpretation of the final result.

INPUT

Raw Data

Sequencing instruments and other experimental technologies generate raw biological data that must first be organized and assessed.

FASTQ / FASTA / BAM

QUALITY

Quality Control

Quality assessment identifies sequencing errors, low-quality regions, adapter contamination, and other technical characteristics that may influence downstream analysis.

QC REPORTS

PROCESSING

Data Processing

Depending on the experiment, reads may be trimmed, filtered, aligned to a reference, assembled, or transformed into another representation suitable for analysis.

CLEAN / ALIGNED DATA

ANALYSIS

Statistical Analysis

Computational and statistical methods are applied to identify patterns, differences, relationships, variants, expression changes, or other features relevant to the biological question.

RESULTS / TABLES

INTERPRETATION

Biological Meaning

Results are connected to genes, pathways, functions, biological processes, or other relevant knowledge resources to help answer the original research question.

PATHWAYS / FUNCTIONS

REPRODUCIBILITY

Documented Results

A robust analysis should preserve the computational steps, software versions, parameters, data sources, and relevant metadata so that the workflow can be evaluated and reproduced.

WORKFLOW / REPORT

THINK BEFORE YOU ANALYSE

Start with the biological question.

There is no single bioinformatics workflow for every experiment. The biological objective, experimental design, data type, and research context all influence the appropriate analytical strategy.

WHAT DO YOU WANT TO KNOW?

01 Which genes are changing? →

RNA / EXPRESSION

Gene Expression Analysis

If the goal is to understand how gene activity differs between biological conditions, transcriptomic data such as RNA sequencing can provide a basis for measuring and comparing transcript abundance.

DATA RNA-seq → Expression Matrix → Differential Analysis

02 What genetic variants are present? →

DNA / VARIATION

Variant Analysis

When the research question concerns genetic differences, sequencing data can be analysed to identify variants and characterize their genomic locations and potential biological relevance.

DATA Sequencing → Variant Calling → Annotation

03 Which microorganisms are present? →

MICROBIAL / COMMUNITY

Metagenomic Analysis

If the objective is to characterize microbial communities, metagenomic approaches can investigate the organisms and genetic content represented within a biological sample.

DATA Metagenomic Reads → Classification → Community Profile

04 Which cell populations are present? →

SINGLE-CELL

Single-Cell Analysis

Questions about cellular heterogeneity can be explored using single-cell datasets, allowing molecular profiles to be examined across individual cells or cellular populations.

DATA scRNA-seq → Clustering → Cell States

05 Which biological functions are involved? →

FUNCTION / PATHWAYS

Functional Interpretation

When the objective is to understand the biological processes represented by a set of genes or proteins, functional and pathway-based analyses can connect molecular observations to biological concepts.

DATA Gene / Protein List → Pathway Analysis → Biological Meaning

INTEGRATING BIOLOGICAL INFORMATION

Biology works across multiple layers.

Genomic, transcriptomic, proteomic, and phenotypic measurements describe different aspects of biology. Integrating these layers can provide a broader view of biological systems.

A T G C A G

GENOMICS

What could happen?

The genome contains the underlying genetic information of an organism, including genes, regulatory regions, and genetic variation.

RNA RNA RNA

TRANSCRIPTOMICS

What is being expressed?

Transcriptomic measurements provide information about RNA molecules and gene activity under particular biological conditions.

PROTEOMICS

What is being produced?

Proteomic data describes proteins and their abundance, modifications, structures, or interactions within biological systems.

● ● ●

PHENOTYPE

What do we observe?

Phenotypic observations represent measurable biological characteristics that can ultimately be related back to molecular and cellular changes.

INTEGRATION Connecting molecular observations to biological systems.

THE BIOGUID APPROACH

Good analysis begins with good questions.

Bioinformatics is more than software, pipelines, and datasets. Reliable analysis depends on asking the right question, understanding the data, and interpreting results in their biological context.

DEFINE

Start with the question

Clearly define what you want to understand before selecting data, software, or analytical methods.

◇

EVALUATE

Understand the data

Consider the biological source, experimental design, data quality, limitations, and metadata before interpreting results.

→

ANALYSE

Choose the right approach

Analytical methods should be selected according to the biological objective and the characteristics of the dataset.

∞

REPRODUCE

Make the workflow traceable

Document software, parameters, datasets, and analytical decisions so that computational work can be evaluated and reproduced.

BIOINFORMATICS

From biological data to meaningful insight.

Explore the concepts, methods, technologies, and analytical strategies that transform complex biological datasets into interpretable scientific knowledge.

BIOGUID

EDUCATIONAL RESOURCE BIOINFORMATICS • DATA • BIOLOGY 2026