BIOINFORMATICS • COMPUTATIONAL BIOLOGY

Explore the Science of Bioinformatics

Learn how biological data is transformed into meaningful scientific insights.

BioinfSurvey provides accessible educational content covering bioinformatics, genomics, sequencing, transcriptomics, proteomics, single-cell analysis, and computational biology.

BIOINFORMATICS FIELDS

Explore Bioinformatics

Explore major fields of bioinformatics and discover how computational approaches are used to study biological data.

GENOMICS

Genomics

Genomics is the study of the complete genetic material of an organism, including the structure, organization, variation, and function of its genome. Bioinformatics provides computational methods for processing and interpreting large-scale genomic datasets.

Common genomic analyses include genome assembly, sequence alignment, genome annotation, variant identification, comparative genomics, and functional interpretation of genomic regions.

Genome Assembly Variant Analysis Genome Annotation Comparative Genomics
BIOINFORMATICS WORKFLOW

From Biological Data to Scientific Insight

Understand how computational methods transform complex biological datasets into information that can be explored, interpreted, and used to answer scientific questions.

01
DATA

Biological Data

Biological research generates diverse datasets from DNA and RNA sequencing, protein analysis, single-cell experiments, and other molecular technologies.

02
QC

Data Processing

Raw datasets are assessed and prepared through quality control, preprocessing, filtering, and other steps required for reliable downstream analysis.

03
ANALYZE

Computational Analysis

Computational methods are applied to identify patterns and biological features through sequence alignment, genome assembly, quantification, variant analysis, and other approaches.

04
INTERPRET

Biological Interpretation

Analytical results are examined through annotation, functional analysis, statistical approaches, and biological context to understand their potential significance.

05
INSIGHT

Scientific Insight

The final results can contribute to a deeper understanding of biological systems, molecular mechanisms, genetic variation, cellular processes, and other research questions.

CORE BIOINFORMATICS

Core Concepts in Bioinformatics

Build a foundation for understanding how biological data is analyzed, compared, interpreted, and connected to biological knowledge.

01
ALIGN

Sequence Alignment

Sequence alignment compares DNA, RNA, or protein sequences to identify regions of similarity, conserved patterns, and possible evolutionary or functional relationships.

DNA RNA Protein FASTA
02
ASSEMBLY

Genome Assembly

Genome assembly reconstructs longer genomic sequences from sequencing reads, producing contigs and scaffolds that represent portions of a genome.

Reads Contigs Scaffolds Genome
03
VARIANT

Variant Calling

Variant calling identifies differences between sequencing data and a reference sequence, including single-nucleotide variants, insertions, deletions, and other genomic changes.

SNV Indel VCF Reference Genome
04
EXPR

Gene Expression

Gene expression analysis examines the activity of genes by measuring transcript abundance and identifying differences between biological conditions or cellular populations.

RNA-seq Transcripts Expression DEGs
05
ANNOT

Functional Annotation

Functional annotation connects genes, proteins, or genomic regions with biological functions using computational analysis and existing biological knowledge.

Gene Ontology Pathways Function Annotation
06
PHYLO

Phylogenetics

Phylogenetic analysis uses molecular and computational data to study evolutionary relationships among organisms, genes, or protein sequences.

Evolution Phylogeny Clades Homology
07
STRUCT

Structural Bioinformatics

Structural bioinformatics applies computational approaches to study the three-dimensional structures, interactions, and properties of proteins and other biological macromolecules.

Proteins 3D Structure Domains Interactions
08
DB

Biological Databases

Biological databases organize and provide access to genomic, proteomic, structural, functional, and other scientific datasets used in biological research.

GenBank NCBI UniProt PDB
COMPUTATIONAL ANALYSIS

Explore Bioinformatics Analysis

Discover computational approaches used to process, compare, quantify, and interpret biological datasets across different molecular levels.

SEQUENCE ANALYSIS

Sequence Analysis

Computational analysis of DNA, RNA, and protein sequences to identify similarities, conserved regions, motifs, and other sequence features. Sequence analysis is fundamental to many bioinformatics workflows, including sequence comparison and functional investigation.

Alignment Motifs Homology
TOOLS & TECHNOLOGIES

Explore Bioinformatics Tools

Explore computational tools commonly used to process, analyze, compare, and interpret biological data across different stages of bioinformatics workflows.

01
QUALITY CONTROL

Quality Control

Assessment of sequencing data quality and identification of technical issues before downstream analysis.

FastQC MultiQC
02
SEQUENCE ANALYSIS

Sequence Alignment

Comparison of biological sequences against reference sequences or databases to identify similarities and genomic relationships.

BLAST BWA Bowtie
03
TRANSCRIPTOMICS

RNA-seq Analysis

Processing and analysis of RNA sequencing data to quantify transcripts and investigate gene expression patterns.

STAR HISAT2 DESeq2
04
VARIANT ANALYSIS

Variant Calling

Detection and characterization of genomic variants from sequencing data, including single nucleotide variants and small insertions or deletions.

GATK SAMtools FreeBayes
05
GENOME ASSEMBLY

Genome Assembly

Reconstruction of genomic sequences from sequencing reads to generate continuous genomic representations.

SPAdes MEGAHIT
06
FUNCTIONAL ANALYSIS

Functional Annotation

Assignment of biological functions to genes and proteins using sequence similarity, domains, ontologies, and curated biological resources.

InterPro Gene Ontology BLAST
07
EVOLUTIONARY ANALYSIS

Phylogenetics

Computational reconstruction of evolutionary relationships among biological sequences or organisms.

MAFFT MUSCLE IQ-TREE
08
STRUCTURAL BIOINFORMATICS

Structural Bioinformatics

Computational study of biological macromolecular structures to investigate their organization, interactions, and potential functions.

PDB AlphaFold Molecular Modeling
BIOLOGICAL DATA

Understand Bioinformatics Data

Bioinformatics integrates diverse biological datasets to investigate molecular systems, biological variation, cellular states, and relationships between biological processes.

BIOLOGICAL DATA ANALYSIS
01
GENOMICS

DNA Sequences

Genomes, exomes, contigs, and genomic variants used to investigate genetic composition and variation.

Genome Exome Variants
02
TRANSCRIPTOMICS

RNA Data

Transcriptomic datasets used to study RNA abundance, gene expression, and transcriptional activity.

RNA-seq mRNA Expression
03
PROTEOMICS

Protein Data

Protein sequences, structures, domains, and interactions used to investigate molecular functions.

Sequences Structures Domains
04
SINGLE-CELL

Single-Cell Data

High-resolution molecular profiles used to characterize cell populations and cellular heterogeneity.

scRNA-seq Cell Types Clusters
05
EPIGENOMICS

Epigenomic Data

Molecular measurements describing DNA methylation, chromatin states, and regulatory landscapes.

Methylation ChIP-seq Chromatin
06
METAGENOMICS

Metagenomic Data

Sequence data from microbial communities used to study community composition and functional potential.

Microbiome Taxonomy Microbial Genes
07
MULTI-OMICS

Multi-Omics Data

Integration of multiple molecular data layers to provide a broader view of biological systems and processes.

Genomics Transcriptomics Proteomics
ANALYTICAL WORKFLOW

From Biological Data to Interpretation

Bioinformatics workflows transform raw biological datasets into structured information that can be analyzed, annotated, compared, and interpreted in a biological context.

01
INPUT

Raw Data

Biological sequencing or molecular datasets are collected for computational analysis.

FASTQ FASTA Reads
02
QUALITY

Quality Control

Sequencing quality, read characteristics, and technical artifacts are assessed before downstream processing.

Quality Scores Adapters Filtering
03
PROCESSING

Preprocessing

Biological datasets are prepared for analysis through operations such as trimming, filtering, and normalization.

Trimming Filtering Normalization
04
COMPUTATION

Computational Analysis

Processed data are analyzed using computational methods appropriate for the biological question and dataset.

Alignment Assembly Variant Calling
05
ANNOTATION

Functional Annotation

Genes, variants, proteins, and other biological features are connected with functional information and databases.

Genes Variants Pathways
06
INTERPRETATION

Biological Interpretation

Computational results are examined in their biological context to identify patterns, relationships, and molecular insights.

Functions Processes Insights
RAW BIOLOGICAL DATA COMPUTATIONAL ANALYSIS BIOLOGICAL INTERPRETATION
SCIENTIFIC RESOURCES

Explore Bioinformatics Resources

Discover established biological databases, genome resources, protein repositories, functional annotation systems, and scientific literature platforms used across bioinformatics.

External resources open in a new browser tab. BioinfSurvey does not operate or modify these databases.

DATA REPRESENTATION

Essential Bioinformatics File Formats

Bioinformatics workflows rely on specialized file formats to represent biological sequences, sequencing reads, genomic coordinates, genetic variants, annotations, and molecular structures.

.FASTQ SEQUENCING DATA

FASTQ — Sequencing Reads

FASTQ is commonly used to store nucleotide sequencing reads together with their associated quality scores. It is a fundamental input format for many next-generation sequencing workflows.

NGS Reads Quality Scores Sequencing
Typical workflow position Raw Data → Quality Control
DATA FORMAT COMPUTATIONAL PROCESSING BIOLOGICAL INTERPRETATION
COMPUTATIONAL METHODS

Core Bioinformatics Methods

Computational approaches used to transform biological data into interpretable sequence, genomic, transcriptomic, structural, and systems-level information.

01
SEQUENCE ANALYSIS

Sequence Alignment

Computational comparison of DNA, RNA, or protein sequences to identify similarities, conserved regions, sequence differences, motifs, and potential evolutionary relationships.

DNA RNA Protein Similarity Conservation
BIOLOGICAL DATA COMPUTATIONAL METHODS BIOLOGICAL INSIGHT
COMPUTATIONAL RESOURCES

Bioinformatics Tools & Resources

Explore computational tools and platforms used for sequence analysis, genomics, transcriptomics, proteomics, structural biology, and biological data interpretation.

01 SEQUENCE

BLAST

Searches for regions of similarity between nucleotide or protein sequences and biological sequence databases.

DNA RNA Protein
Official resource ↗
02 WORKFLOWS

Galaxy

An open platform for accessible, reproducible, and transparent computational analysis of biological data.

NGS Workflows Data Analysis
Official resource ↗
03 R / BIOLOGY

Bioconductor

An ecosystem of R-based software packages and resources for analysis and comprehension of biological data.

R RNA-seq Genomics
Official resource ↗
04 PHYLOGENY

MEGA

A software environment used for molecular evolutionary analysis, sequence comparison, and phylogenetic studies.

Evolution Sequences Trees
Official resource ↗
05 PROTEOMICS

InterPro

Provides integrated information for identifying protein families, domains, and functional sites.

Proteins Domains Function
Official resource ↗
06 GENOMICS

UCSC Genome Browser

An interactive genomic visualization resource for exploring genome assemblies, annotations, variation, and other biological datasets.

Genome Annotation Visualization
Official resource ↗
07 STRUCTURE

PyMOL

Molecular visualization software used to examine and represent three-dimensional structures of biological macromolecules.

Protein 3D Molecular Structure
Official resource ↗
08 GENOMICS

Ensembl

A genomic resource providing genome annotations, genes, transcripts, variants, comparative genomics, and related biological information.

Genes Transcripts Variants
Official resource ↗
i

External resources are linked to their official websites. BioinfSurvey provides educational context and does not represent or operate these external platforms.

LEARNING PATHWAY

Explore the Bioinformatics Learning Path

Follow a structured progression from fundamental biological concepts to advanced computational analysis of genomic, transcriptomic, proteomic, single-cell, and multi-omics data.

01
FOUNDATION

Bioinformatics Fundamentals

Introduces the biological and computational concepts needed to understand sequence data, biological databases, genomic coordinates, molecular information, and computational approaches used in modern life science research.

DNA RNA Proteins Databases
FUNDAMENTALS SPECIALIZED ANALYSIS INTEGRATED BIOLOGY
SCIENTIFIC KNOWLEDGE

Bioinformatics Questions & Concepts

Explore concise explanations of fundamental bioinformatics concepts, technologies, biological data types, and computational analysis approaches.

Understanding
Biological Data

Bioinformatics combines biology, computational science, statistics, and data analysis to study biological information at different molecular and cellular scales.

EXPLORE THE CONCEPTS