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Updated: Aug 5, 2026
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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 provides guidance on phylogenetic analysis tools and workflows for next-generation sequencing (NGS) omics data. It compares methods for sequence alignment and tree reconstruction to aid researchers in making informed, scenario-driven decisions for biological insights.
Area of Science:
- Bioinformatics and Computational Biology
- Genomics and Phylogenetics
- Next-Generation Sequencing (NGS) Data Analysis
Background:
- Practitioners in phylogenetic analysis using high-throughput sequencing (NGS) face fragmented, tool-centric guidance.
- The field spans single genes to long-read pangenomes and metagenomes, necessitating clear decision-making frameworks.
- Existing resources often lack comparative performance data and scenario-specific recommendations.
Purpose of the Study:
- To provide a comprehensive review of algorithms, tools, and workflows for sequence and phylogenetic analysis in the NGS omics era.
- To focus on comparative performance and scenario-driven decision-making for practitioners.
- To offer actionable guidance through a decision framework, checklists, and comparative tables.
Main Methods:
- Organized classical tree reconstruction methods (distance, parsimony, ML, Bayesian) by criteria like consistency, efficiency, 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, considering accuracy, scalability, and uncertainty.
Main Results:
- Discussed trade-offs in accuracy, memory, scalability, and uncertainty support for various phylogenetic tools.
- Highlighted the impact of GPU-enabled implementations on feasible design space.
- Addressed current trends including long-read assemblies, data quality issues, metagenomics, and real-time pathogen surveillance.
Conclusions:
- Emphasized reproducible pipelines using workflow management and containerization (Snakemake, Nextflow, Docker/Singularity) as essential.
- Provided a methodological checklist and decision framework to map input data to recommended strategies.
- Demonstrated practical applications in infectious disease genomics, oncology, and microbiome research for biological and clinical insight.
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