STAGE 01
Biological question
Define a research problem, hypothesis or objective, such as identifying genetic variants associated with a biological trait.
BIOINFORMATICS GUIDE
BIOLOGY × DATA × DISCOVERY
A NEW PERSPECTIVE ON BIOLOGICAL DATA
Bioinformatics,
from questions
to discoveries.
Explore the science behind biological data through accessible bioinformatics guides, computational methods, genomics and sequencing analysis, research tools, scientific articles and products. Discover how computational biology transforms complex data into meaningful biological insight.
FIG 01 / BIOLOGICAL SEQUENCE MATRIX
Patterns in life.
CONCEPTUAL
SEQUENCE VISUALIZATION
SEQUENCE VISUALIZATION
RESEARCH / 001
Explore biological complexity.
ANALYSIS / SYSTEMS
Connected science.
DATA NETWORKS
FIG 02 / DATA PATTERNS
Find the signal.
BIOINFORMATICS / INSIGHT
Where biological questions meet computational thinking.
Discover key disciplines
02 / THE KNOWLEDGE ATLAS
One science.
Many ways to
understand life.
From genome sequences and gene expression to protein function and biological networks, explore the research disciplines that make bioinformatics essential to modern biology and data-driven scientific discovery.
BIOGUID / MAP OF SCIENTIFIC FIELDS
CONCEPTUAL RESEARCH MAP
SELECT A FIELD TO EXPLORE Explore connections ↗
SELECT A FIELD TO EXPLORE Explore connections ↗
Scientific disciplines
Explore / 01—06
Selected discipline / 01
Genomics
Genomics uses computational and experimental approaches to study genome sequences, organization, genetic variation and function.
Related methods and concepts
Genome assembly
Variant calling
Genome annotation
Comparative genomics
Continue exploring
Discover related resources
These disciplines overlap and frequently share algorithms, statistical approaches and analytical tools. Explore their individual methods to understand how they contribute to biological research.
Biology is interconnected.
BIOGUID
FIELD NOTES
03.
FIELD NOTES
03.
03 / Understanding Bioinformatics
What is bioinformatics,
What is bioinformatics,
and why does it matter?
Modern biological research generates vast amounts of information, from DNA sequences and RNA expression profiles to protein structures and microbial communities. Bioinformatics provides the computational approaches needed to organize, examine and interpret this information.
The essential definition
Bioinformatics is an interdisciplinary field combining biology, computer science, mathematics and statistics to collect, manage, analyze and interpret biological data.
How biological data becomes knowledge.
A conceptual five-stage workflow. Real analytical pipelines vary according to the biological question, experimental technology and type of data.
STAGE 02
Data generation
Obtain biological measurements from sequencing, mass spectrometry, imaging, experiments or carefully curated public databases.
STAGE 03
Quality control
Evaluate data quality, detect technical artifacts, assess potential biases and prepare suitable inputs for downstream analysis.
STAGE 04
Computational analysis
Apply appropriate algorithms and statistical methods to identify patterns, compare samples and test research hypotheses.
STAGE 05
Biological interpretation
Connect analytical findings to biological mechanisms, existing evidence, uncertainty and potential follow-up experiments.
More than computational processing
Reliable interpretation begins with a meaningful question.
Bioinformatics is not simply the application of software to large datasets. Effective research depends on appropriate study design, data quality, reproducible analytical methods, statistical reasoning and biological context. From disease genomics to microbial ecology and molecular evolution, these approaches help scientists investigate complex biological systems.
BIOGUID / BIOINFORMATICS EXPLAINED
CONCEPTUAL WORKFLOW — NOT EXPERIMENTAL DATA
04 / RESEARCH RESOURCES
The right tools for
every scientific question.
Bioinformatics connects computational methods with experimental research. Explore the tools, technologies and scientific resources that support genomic discovery, biological data processing and reproducible analysis.
RESEARCH / VISUAL INDEX
RESOURCE 01 / 06
RESOURCE CLASS / 01
Bioinformatics Software
Computational tools help researchers process, compare, visualize and interpret biological datasets, using methods appropriate to specific research questions.
Sequence alignment
NGS pipelines
Data visualization
Connect scientific knowledge with practical research.
Explore technologies in the context of their real applications. Our resource directory brings together educational explanations and relevant product categories to support informed scientific decisions.
BIOGUID / SCIENTIFIC RESOURCE DISCOVERY
EDUCATION × RESEARCH × TECHNOLOGY
05 / THE DISCOVERY PROCESS
Every dataset tells a story.
Science helps us understand it.
Biological datasets are more than collections of measurements. With rigorous experimental design, appropriate computational methods and careful interpretation, they can reveal patterns that help researchers explore biological processes.
From observation to scientific insight
Four analytical stages / Conceptual model
01 / INPUT
DATA ORIGIN
Biological data
Experimental and reference datasets may contain nucleotide sequences, RNA counts, protein measurements or other molecular observations.
02 / PREPARE
QUALITY CONTROL
Data preparation
Researchers assess data quality, identify technical artifacts and apply suitable preprocessing, filtering or normalization procedures.
03 / ANALYZE
COMPUTATION
Computational discovery
Statistical models and computational algorithms help identify differential patterns, associations and candidate biological signals.
04 / INTERPRET
BIOLOGICAL INSIGHT
Scientific interpretation
Results are assessed against biological knowledge, study limitations and independent evidence before drawing conclusions or planning validation.
Research example / RNA sequencing
From RNA-seq reads to
From RNA-seq reads to
gene-expression insights.
RNA sequencing can help researchers investigate how transcript abundance differs between biological conditions. After sequence quality control, reads or transcripts are quantified using a suitable reference and analytical workflow.
Statistical analysis can identify transcripts or genes with evidence of differential expression, while downstream functional analysis explores potential biological significance.
Interpretation must account for biological replicates, experimental design, normalization, multiple testing and uncertainty. Differential expression alone does not establish a causal biological mechanism.
Explore Bioinformatics Methods
Figure / RNA-seq analysis concept
Illustrative data — not experimental results
Conceptual volcano plot illustrating how estimated effect sizes and statistical evidence may be displayed in differential-expression analysis. The plotted points are synthetic and do not represent a scientific experiment. For real RNA-seq analysis, use appropriate multiple-testing corrections and statistical methods.
Beyond one dataset
Different questions.
Different questions.
Connected methods.
Investigate genetic variation and molecular patterns associated with disease mechanisms.
Characterize microbial diversity and explore functional potential across environments.
Integrate molecular measurements to investigate biological heterogeneity and research hypotheses.
06 / Scientific Journal & Research Articles
BIOINFORMATICS GUIDE / KNOWLEDGE EDITION
Ideas, methods
and discoveries
worth exploring.
Explore the concepts shaping modern computational biology. Our scientific reading collection connects fundamental ideas with practical methods in genomics, transcriptomics, proteomics and biological data analysis.
Figure 06.1 / Conceptual scientific illustration
Foundations of Computational Biology
What Is Bioinformatics? Understanding Biology Through Data
Bioinformatics combines biological knowledge, computing, mathematics and statistics to investigate complex biological information. Explore how researchers move from DNA sequences and molecular measurements to hypotheses, computational analysis and scientific interpretation.
Explore Scientific Reading
From the research index
TOPICS / 002—004
Transcriptomics / RNA Sequencing
RNA Sequencing: From Raw Reads to Gene Expression
Understand RNA-seq workflows, sequencing-read quality, transcript quantification and differential-expression analysis.
Explore Related Articles ↗
Genomics / DNA Analysis
Genomic Data Analysis: From DNA Sequence to Variation
Explore how computational methods help researchers examine sequence information, align reads, identify genomic variation and interpret genetic evidence.
Explore Related Articles ↗
Proteomics / Molecular Biology
Proteomics Explained: From Proteins to Biological Function
Learn how protein identification, quantitative proteomics, functional annotation and computational interpretation support the study of cellular systems.
Explore Related Articles ↗
THE RESEARCH READING INDEX
Learn the concepts. Understand the methods. Explore scientific explanations that connect foundational biology with modern computational research.
Continue your scientific exploration.
Explore the Scientific Blog
Discover educational research content about bioinformatics, genomics, molecular biology, sequencing technologies and computational methods.
BIOGUID / SCIENTIFIC ARTICLES & RESEARCH KNOWLEDGE
BIOLOGY × DATA × COMPUTATIONAL DISCOVERY
07 / BIOINFORMATICS METHODS & TUTORIALS
Learn the methods
behind the discoveries.
Explore the computational methods behind modern biological research. Discover how sequence alignment, sequencing quality control, RNA-seq analysis, genetic variant detection and data visualization support reliable scientific investigation.
01 BLAST Sequence Alignment Sequence similarity and database searching
METHOD 01 / SEQUENCE ANALYSIS
The Basic Local Alignment Search Tool (BLAST) compares nucleotide or protein sequences against sequence databases to identify regions of local similarity. Researchers use BLAST to investigate possible homologous sequences, sequence relationships and functional clues.
WORKFLOW / CONCEPTUAL OVERVIEW
STEP 01Prepare query sequence
STEP 02Select database and program
STEP 03Run similarity search
STEP 04Evaluate matching sequences
Expected analytical output
Ranked sequence matches, local alignments, sequence identity, query coverage, E-values and alignment statistics.
Scientific consideration
Sequence similarity does not independently establish identical biological function. Evaluate statistical evidence, alignment coverage and biological context.
OFFICIAL RESOURCE / NCBI BLAST
Open Official Resource ↗
02 FASTQ Quality Control Sequencing data quality assessment
METHOD 02 / SEQUENCING QUALITY
FASTQ quality control assesses sequencing-read properties before downstream analysis. Tools such as FastQC help researchers examine base quality, GC composition, duplicated sequences and potential adapter contamination.
WORKFLOW / CONCEPTUAL OVERVIEW
STEP 01Import FASTQ reads
STEP 02Assess quality metrics
STEP 03Identify potential artifacts
STEP 04Plan preprocessing
Expected analytical output
Base-quality summaries, sequence composition plots, duplication assessments and diagnostic quality reports.
Scientific consideration
Quality warnings must be interpreted in the context of library preparation, assay design and sequencing technology.
OFFICIAL RESOURCE / BABRAHAM FASTQC
Open Official Resource ↗
03 RNA-seq Analysis Transcriptomics and gene expression
METHOD 03 / TRANSCRIPTOMICS
RNA sequencing enables researchers to study transcript abundance and gene-expression changes across biological conditions. Computational workflows support sequence quality assessment, transcript quantification, statistical testing and biological interpretation.
WORKFLOW / CONCEPTUAL OVERVIEW
STEP 01Evaluate sequencing quality
STEP 02Align or quantify reads
STEP 03Analyze expression differences
STEP 04Interpret biological results
Expected analytical output
Gene or transcript abundance estimates, differential-expression results, expression plots and possible functional interpretations.
Scientific consideration
Reliable conclusions require suitable experimental design, biological replication, normalization and multiple-testing correction.
OFFICIAL RESOURCE / GALAXY TRANSCRIPTOMICS
Open Official Resource ↗
04 Variant Calling Genomic variation and annotation
METHOD 04 / GENOMIC VARIATION
Variant calling analyzes sequencing evidence to identify candidate genetic differences relative to a reference genome, including single-nucleotide variants and small insertions or deletions. Downstream annotation helps researchers evaluate their possible significance.
WORKFLOW / CONCEPTUAL OVERVIEW
STEP 01Assess sequencing data
STEP 02Map reads to a reference
STEP 03Call and filter variants
STEP 04Annotate and interpret calls
Expected analytical output
Variant call files (VCF), genotype information, quality metrics and variant annotations.
Scientific consideration
Variant accuracy depends on sequencing coverage, mapping quality, caller assumptions and validation procedures.
OFFICIAL RESOURCE / GALAXY VARIANT ANALYSIS
Open Official Resource ↗
05 Biological Data Visualization Visual exploration and scientific interpretation
METHOD 05 / DATA VISUALIZATION
Biological data visualization helps researchers identify meaningful distributions, sample relationships, expression patterns and molecular network structures. Effective visualization combines appropriate graphical methods with accurate scientific interpretation.
WORKFLOW / CONCEPTUAL OVERVIEW
STEP 01Define analytical question
STEP 02Select appropriate chart
STEP 03Review scales and labels
STEP 04Communicate findings
Expected analytical output
Heatmaps, PCA plots, distribution charts, biological network diagrams and other analytical visualizations.
Scientific consideration
Scientific figures should communicate real observations faithfully, with appropriate scales, uncertainty and interpretation.
OFFICIAL RESOURCE / GALAXY VISUALIZATION
Open Official Resource ↗
08 / RESEARCH SERVICES & SOLUTIONS
Research questions
deserve intelligent
analytical solutions.
Explore computational approaches used in modern bioinformatics research. From next-generation sequencing to gene expression and genomic variation, understand how biological questions guide the selection of data-analysis methods.
FIGURE 08.1 / COMPUTATIONAL BIOLOGY
RESEARCH INTELLIGENCE MODEL
RESEARCH INTELLIGENCE MODEL
Scientific analytical domains
01 / 04
NGS Data Analysis
Explore analytical workflows for sequencing-read quality assessment, reference alignment, genomic data processing and downstream interpretation.
Transcriptomics & RNA-seq
Investigate RNA abundance, differential gene expression and functional interpretation using appropriate computational and statistical methods.
Genomic Variant Analysis
Understand computational techniques for variant detection, quality filtering and annotation, including analysis of sequencing-derived genomic differences.
Custom Bioinformatics Workflows
Explore reproducible computational pipelines, data integration, statistical modeling and biological visualization across different research applications.
Analytical framework / 001
From research questions to interpretable evidence.
01 / QUESTION
Define
Identify the biological objective and the evidence needed to investigate it.
02 / DATA
Evaluate
Assess dataset characteristics, quality, study design and relevant limitations.
03 / METHODS
Analyze
Select computational methods and statistical approaches suited to the research question.
04 / EVIDENCE
Interpret
Examine analytical findings within their scientific and biological context.
BIOGUID / SCIENTIFIC SOLUTIONS
The next discovery begins with the right question.
Explore computational biology, analytical methodologies and bioinformatics research resources through our scientific platform.
Discover the methods and solutions behind
modern biological research.
Explore Research Services
BIOGUID / BIOINFORMATICS RESEARCH
EDUCATION × COMPUTATION × SCIENTIFIC DISCOVERY
09 / BIOINFORMATICS IN THE REAL WORLD
One computational language.
Countless scientific
possibilities.
Bioinformatics helps researchers investigate biological systems across medicine, biotechnology, agriculture, ecology and evolutionary biology. Discover how computational analysis connects molecular information with important scientific questions.
DOMAIN / 01
Human Health
GENOMICS / DISEASE
DOMAIN / 02
Biotechnology
MOLECULES / DISCOVERY
DOMAIN / 03
Agriculture
PLANT GENOMICS / TRAITS
DOMAIN / 04
Environment
MICROBIOMES / ECOLOGY
DOMAIN / 05
Evolution
PHYLOGENETICS / DIVERSITY
01 Human Health & Disease Research
Bioinformatics supports human health research by helping investigators analyze genomic variants, gene-expression patterns and molecular pathways. These approaches contribute to studying disease mechanisms, identifying candidate biomarkers and generating hypotheses for further experimental or clinical validation.
Cancer genomics
Rare disease research
Biomarker discovery
Precision medicine research
Explore NHGRI research areas ↗
02 Biotechnology & Drug Discovery
Computational approaches help researchers examine protein sequences, molecular structures, functional annotations and biological interactions. Combined with experimental evidence, these methods can support target investigation, protein engineering and the exploration of molecular mechanisms relevant to biotechnology and drug discovery.
Protein structure analysis
Target research
Molecular modeling
Protein engineering
Explore EMBL-EBI scientific training ↗
03 Agriculture & Plant Genomics
Plant bioinformatics enables researchers to compare genomes, investigate genetic diversity and explore molecular traits associated with growth, yield, disease resistance and environmental stress. Computational genomics and transcriptomics can support crop improvement research when combined with appropriate field and laboratory evidence.
Crop genomics
Genetic diversity
Stress-response genes
Plant transcriptomics
Explore Ensembl Plants ↗
04 Microbiomes & Environmental Science
Metagenomics and related computational methods allow scientists to investigate microorganisms in soil, oceans, freshwater, human-associated environments and other ecosystems. Sequence analysis helps characterize community composition and potential biological functions, although detected genes do not necessarily establish actual metabolic activity.
Metagenomics
Microbial diversity
Environmental DNA
Functional annotation
Explore MGnify ↗
05 Evolution & Biodiversity
Comparative genomics, phylogenetic inference and molecular sequence analysis help researchers explore evolutionary relationships among organisms. These approaches contribute to understanding sequence divergence, evolutionary history, species relationships and patterns of biodiversity. Phylogenetic conclusions depend on the selected data, models and analytical assumptions.
Phylogenetics
Comparative genomics
Sequence evolution
Biodiversity research
Explore European Nucleotide Archive ↗
BIOGUID / THE BIGGER PICTURE
Data does not replace scientific reasoning. It expands what researchers can investigate.
Across disciplines, meaningful discoveries depend on reliable measurements, suitable analytical methods, reproducibility and careful interpretation. Bioinformatics connects these elements and helps translate complex biological information into testable scientific understanding.
10 / THE BIOINFORMATICS DISCOVERY GATEWAY
THE OPEN RESEARCH INDEX
The next discovery
begins with a
better question.
Bioinformatics transforms biological questions into opportunities for scientific investigation. Continue exploring computational biology through accessible educational resources, scientific articles, analytical methods, research products and bioinformatics services.
BIOGUID / THE OPEN RESEARCH INDEX
FIG 10.1 — INTERCONNECTED KNOWLEDGE
FIG 10.1 — INTERCONNECTED KNOWLEDGE
Knowledge connects.
FIVE PATHWAYS
ONE SCIENTIFIC PLATFORM
ONE SCIENTIFIC PLATFORM
BIOGUID / CONTINUE YOUR EXPLORATION
Begin Exploring
Explore the science. Understand the methods. Connect the discoveries.
Whether you are beginning your journey in bioinformatics or exploring advanced computational approaches, Bioinformatics Guide brings scientific knowledge, analytical methods and research resources together.
BIOINFORMATICS GUIDE / SCIENCE THROUGH DATA
THE END OF THE HOMEPAGE · THE BEGINNING OF DISCOVERY