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Updated: Aug 5, 2026
12:00
A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
Revisiting Algorithms, Tools, and Applications for Sequence and Phylogenetic Analyses in the NGS-Based Omics Era
Abhishek Kumar1,2, Tikam Chand Dakal3, Kayenat Parveen4
1Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India. abhishek@ibioinformatics.org.
Biochemical Genetics
|July 24, 2026
Summary
This review guides Next-Generation Sequencing (NGS) omics practitioners through phylogenetic analysis tools and workflows. It compares methods for sequence alignment and tree reconstruction, aiding scenario-driven decision-making for biological insights.
Area of Science:
- Bioinformatics and Computational Biology
- Genomics and Phylogenetics
- Data Science in Life Sciences
Background:
- Next-Generation Sequencing (NGS) has revolutionized omics research, enabling large-scale phylogenetic analyses.
- Practitioners often face fragmented guidance, hindering optimal tool and workflow selection.
- A comprehensive overview of current algorithms, tools, and workflows is crucial for effective phylogenetic analysis.
Purpose of the Study:
- To provide a consolidated review of algorithms, tools, and workflows for sequence and phylogenetic analysis in the NGS omics era.
- To focus on comparative performance and facilitate scenario-driven decision-making for researchers.
- To address current trends and challenges influencing method choice in phylogenetic analysis.
Main Methods:
- Organized classical tree reconstruction methods (distance, parsimony, ML, Bayesian) by criteria like consistency, efficiency, robustness, and cost.
- Examined multiple sequence alignment strategies (progressive, consistency-based, structure-aware, segment-based, alignment-free).
- Compared inference engines for large alignments, ML frameworks, and Bayesian platforms for phylodynamics, considering GPU acceleration.
Main Results:
- Detailed comparison of algorithmic principles, best use cases, strengths, limitations, scalability, and uncertainty support for various tools.
- Discussion of trade-offs in accuracy, memory, scalability, and uncertainty, highlighting the impact of GPU implementations.
- Integration of current trends like long-read assemblies, pangenomes, data quality, metagenomics, and real-time surveillance into method selection.
Conclusions:
- Provided actionable guidance through a methodological checklist, decision framework, and comparative table for tool selection.
- Emphasized reproducible pipelines using workflow management systems (Snakemake, Nextflow) and containerization (Docker, Singularity).
- Demonstrated practical applications in infectious disease genomics, oncology, and microbiome research, translating method choices into biological and clinical insights.
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