Essential Tools and Databases in Bioinformatics - Part 2

Bioinformatics is a constantly developing discipline allowing researchers to analyze huge biological data sets efficiently. In Part 1, we discussed key tools for sequence alignment, phylogenetics, gene annotation, protein structure prediction, and microbiome analysis.

In this second part, we discuss advanced bioinformatics tools applied in structural bioinformatics, pathway and network analysis, transcriptomics, molecular docking, and machine learning in bioinformatics.

1. Structural bioinformatics  

Structural bioinformatics is the prediction and analysis of the three-dimensional (3D) structure of biomolecules, which plays an important role in understanding protein function, molecular interactions, and drug design. A number of tools and databases can be used to aid in structure visualization, refinement, molecular docking, and comparative modeling. Below are five common tools utilized in structural bioinformatics:

A. PyMOL

PyMOL is a molecular visualization system commonly employed for the visualization of protein-ligand interactions, molecular structure, and high-resolution rendering for publication. the tool provides high-resolution molecular structure rendering and facilitates visualization of molecular docking and structure-based drug design. it also includes scripting for automation and mainly employed in research as well as educational environments for structural analysis.

B. UCSF Chimera

UCSF Chimera is a useful tool for comparative analysis, structure editing, and molecular visualization that offers an interactive environment for analyzing macromolecular structures.

Important Features:

• Advanced molecular visualization through high-quality graphics.
  
  
• Structure superposition and molecular dynamics simulations support.
  
  
• Supports atomic structure editing, including mutations and modeling.
  
  
• Offers integrated tools for sequence-structure comparison and analysis.   

C. ModRefiner

ModRefiner is an atomic-level structure refinement high-resolution tool used to refine atomic models with enhanced.

Important Features:

• Atomic model refinement for enhanced structural accuracy.              

• May be applied in homology models and low-resolution structural prediction.

• Energy minimization to enhance stereochemical quality.

• Available either as a standalone or incorporated into computational pipelines.

D. SwissDock

SwissDock is an online molecular docking program that predicts protein-ligand interactions using the CHARMM force field.

Key Features:

• Makes precise binding mode predictions.

• Has the SwissSidechain library integrated for ligand modifications.
  

• Automated docking process for convenience.

• Can be used for drug discovery and virtual screening research.
  
  

E. I-TASSER

I-TASSER (Iterative Threading ASSEmbly Refinement) is a popular protein structure prediction server that combines several methods, such as homology modeling and ab initio predictions, to produce high-quality 3D structures.

Key Features:

• Makes 3D protein structure predictions by combining template-based and ab initio modeling.
  

• Offers function annotations based on structural similarity.
  

• Has an energy refinement step for enhanced accuracy.
  

• Suitable for modeling new proteins with sparse experimental data.

2. Pathway and Network Analysis Tools

Pathway and network analysis tools assist in learning about molecular interactions, gene regulation networks, and biological pathways and offer insight into cellular function, disease mechanism, and drug development. 

A. KEGG (Kyoto Encyclopedia of Genes and Genomes)

KEGG is a large-scale database for elucidation of biological systems, which encompass metabolic pathways, regulatory networks, and disease pathways.

Key Features:

• Provides comprehensive pathway maps for metabolism, genetic information processing, and human diseases.

• Comprehensively integrates genomic, chemical, and systemic functional information.

• Suitable for annotation and enrichment analysis of omics data.

B. Reactome

Reactome is an open-source, curated biological pathways database for metabolism, signal transduction, and immune system function.

Key Features:

• Provides high-level pathway maps with interactive visualization.

• Facilitates enrichment analysis to determine affected pathways from omics data.

• Permits pathway curation and integration with other resources.

C. STRING (Search Tool for the Retrieval of Interacting Genes/Proteins)

STRING is a database that offers information on protein-protein interactions (PPIs) based on known and predicted interactions.

Key Features:

• Includes a large set of experimental and computational PPI data.

• Enables functional enrichment analysis for gene/protein networks.

• Provides a visualization interface for network interaction analysis.

D. BioGRID

BioGRID (Biological General Repository for Interaction Datasets) is a database that stores and shares genetic and protein interaction information across different organisms.

Key Features:

• Offers manually curated datasets of physical and genetic interactions.

• Combinations of data from high-throughput and low-throughput experiments.

• Helpful for the analysis of complex biological networks.

E. Pathway Commons

Pathway Commons is a repository of publicly available biological pathway data from several sources, which supports network-based data analysis.

Key Features:

• Aggregates information from several pathway resources, such as Reactome and KEGG.

• Offers network visualization and analysis tools.

• Facilitates searches for molecular interactions, signaling pathways, and regulations of genes.

3. Transcriptomics and RNA-seq Analysis

Transcriptomics involves analyzing RNA sequencing (RNA-seq) data to understand gene expression patterns.

1. STAR (Spliced Transcripts Alignment to a Reference)

STAR is a RNA-seq Read Alignment tool. The tool is a fast and accurate splice-aware aligner that can align RNA-seq reads to a reference genome. It is extensively used in transcriptomic analysis because it can process large-scale sequencing data at high speeds and accuracy. STAR is especially effective in identifying exon-intron boundaries and alternative splicing events and is hence a first choice for differential gene expression analysis and transcript reconstruction. It generates high-quality alignments in BAM/SAM format, which are compatible with many downstream analysis tools.

Key Features:

• High-speed, splice-aware RNA-seq aligner for large genomes.

• Itifies alternative splicing and exon-exon junctions.

• Handles single-end and paired-end sequencing data.

• Generates BAM/SAM output for downstream analysis.

• Memory-efficient indexing for large-scale data.

2. HISAT2 (Hierarchical Indexing for Spliced Alignment of Transcripts)

HISAT2 is a fast and memory-effective RNA-seq aligner based on a graph-indexing approach that supports accurate and efficient alignment of sequencing reads, even in large genomes. It is well suited to map reads from highly repetitive genomic regions and account for alternative splicing events, and therefore, is a high-priority tool for transcriptomic research. HISAT2 is also amenable to most downstream RNA-seq analysis pipelines, such as differential expression analysis and transcript assembly.

Key Features:

• Highly efficient RNA-seq aligner with minimal memory requirements.

• Applies graph-based indexing for quick mapping.

• Is capable of alternative splicing detection.

• Handles large and complex genomes.

• Produces aligned reads for subsequent transcriptomics analysis.

3. DESeq2

DESeq2 is a bioinformatic program used to evaluate RNA-seq count data for differential expression of genes (DEGs). DESeq2 utilizes shrinkage estimation procedures to better estimate fold-changes, with a guarantee for strong differential expression analysis even at low-count genes. DESeq2 also accommodates batch effect removal, critical in the case of datasets arising from various experimental conditions or platforms. DESeq2 is predominantly used in transcriptomics research across biomedical and agriculture.

Key Features:

• Detects differentially expressed genes with statistical significance.

• Applies shrinkage estimation to enhance fold-change accuracy.

• Removes batch effects in multi-sample data.

• Offers visualization tools including PCA plots, heatmaps, and volcano plots.

• Supports RNA-seq quantification packages such as Salmon and HTSeq.

4. Salmon

Salmon is a light and efficient program for quantifying transcript abundance from RNA-seq data. Unlike other alignment-based approaches, Salmon applies a quasi-mapping strategy, which supports quicker processing with high accuracy. It considers bias correction (e.g., GC-content and sequence-specific biases) to enhance quantification accuracy. Salmon is ideal for large transcriptomics projects, such as single-cell RNA-seq (scRNA-seq) and bulk RNA-seq studies.

Key Features:

• Fast, alignment-free transcript quantification.

• Uses quasi-mapping for fast read processing.

• Corrects for sequence bias and GC-content differences.

• Outputs TPM (Transcripts Per Million) and FPKM (Fragments Per Kilobase Million) values.

• Complements RNA-seq differential expression packages such as DESeq2 and edgeR.

5. Cufflinks

Cufflinks is a robust software tool for transcript assembly and quantification, allowing scientists to rebuild full-length transcripts from RNA-seq data. It calculates FPKM values (Fragments Per Kilobase Million) to quantify gene expression levels and discovers new transcript isoforms, and thus is useful for finding alternative splicing events. Cufflinks can usually be applied with Cuffdiff, where differential gene expression analysis between two or more conditions can be done.

Key Features:

• Reconstructs full-length transcripts from RNA-seq data.

• Estimates transcript abundance from FPKM values.

• Detects novel transcript isoforms and alternative splicing events.

• Serves as input to Cuffdiff for differential gene expression analysis.

• Produces transcript structures for subsequent functional annotation.

4. Molecular Docking and Dynamics Tools

Molecular docking and dynamics tools are critical in the study of biomolecular interactions, drug discovery, and the simulation of molecular motion in biological systems. They predict ligand-receptor binding, improve drug candidates, and model the dynamic behavior of biomolecules. Listed below are five popular tools in this field:

A. AutoDock

AutoDock is a popular molecular docking tool used to predict the  interaction between target macromolecules and small molecules, mostly proteins and nucleic acids.

Main Features:

• Automated small molecule docking to biomolecular targets.
  

• Genetic algorithms for flexible docking simulations.

• Both rigid and flexible docking methodologies are supported.

• AutoDockTools (ADT) integrated for analysis and preparation of structures.
  
  

B. GROMACS

GROMACS is a molecular dynamics (MD) simulation tool that is employed for simulating the motion of biomolecules like proteins, lipids, and nucleic acids over time.

Main Features:

• Delivers efficient MD simulations with support for parallel computing.
  

• Contains facilities for energy minimization, solvation, and analysis of trajectories.
  

• Is capable of supporting large biomolecular systems simulations. 

• Employed in drug research in analyzing drug-drug interactions and stability of biomolecules.

C. HADDOCK (High Ambiguity Driven protein-protein Docking)

HADDOCK is a versatile docking program that employs experimental data to drive molecular docking simulations, especially for protein-protein and protein-ligand interactions.

Key Features:

• Supports NMR, cryo-EM, and mutagenesis data for docking.
  

• Supports flexible and multi-body docking.
  

• Provides a web-based interface for convenience.
  

• Applied in structural biology for protein interaction research.

D. SwissDock

SwissDock is a web-based molecular docking server that predicts protein-ligand interactions based on the CHARMM force field.

Key Features:

• Makes precise binding mode predictions.
  

• Integrated with SwissSidechain library for ligand modifications.
  

• Automated docking process for convenience.
  

• Suitable for drug discovery and virtual screening research.

E. NAMD (Nanoscale Molecular Dynamics)

NAMD is a parallel molecular dynamics program for large-scale biomolecular simulations, allowing the study of intricate biological systems with high computational performance.

Key Features:

• It is highly scalable to support simulations using thousands of processors.
  

• Employ the CHARMM and AMBER force fields to do precise molecular modeling.
  

• Effective processing of large biomolecular structures, such as membrane proteins.
  

• Linked with visualization packages such as VMD (Visual Molecular Dynamics).

Conclusion

Overall, This article highlighted advanced bioinformatics tools used in structural bioinformatics, pathway analysis, transcriptomics, and molecular docking. These tools play essential roles in understanding biological functions, drug discovery, and computational modeling.

Comprehensive List of Links

For convenience, here is a compiled list of all the tools and databases mentioned above:

PyMOL: PyMOL

UCSF Chimera: UCSF Chimera

ModRefiner: ModRefiner

SwissDock: SwissDock

I-TASSER: I-TASSER

Reactome: Reactome

STRING: STRING

BioGRID: BioGRID

Pathway Commons: Pathway Commons

KEGG: KEGG

STAR: STAR

HISAT2: HISAT2

DESeq2: DESeq2

Salmon: Salmon

Cufflinks: Cufflinks

AutoDock: AutoDock

GROMACS: GROMACS

HADDOCK: HADDOCK

SwissDock: SwissDock

NAMD: NAMD

"Bioinformatics thrives on collaboration and shared knowledge. With so many tools available, we’d love to know—which one has been the most useful in your research? Have you discovered any underrated tools that deserve more attention? As technology advances, new bioinformatics tools are constantly emerging. Which one do you think will revolutionize the field in the coming years? Join the discussion below!"