Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Accelign: a GPU-based library for accelerating pairwise sequence alignment.

BMC bioinformatics·2026

Same author

gpuPairHMM: High-Speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs.

IEEE transactions on computational biology and bioinformatics·2026

Same author

RMapAlign3N: fast mapping of 3N-Reads.

Bioinformatics advances·2025

Same author

GPU-accelerated homology search with MMseqs2.

Nature methods·2025

Same author

Corrigendum to Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data.

Studies in health technology and informatics·2025

Same author

RabbitTrim: An Efficient and Versatile Trimmer on Multi-Core Platforms.

IEEE transactions on computational biology and bioinformatics·2025

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026

Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026

Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 17, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters.

Haidong Lan¹, Yuandong Chan¹, Kai Xu¹

¹School of Computer Science and Technology, Shandong University, Shunhua Road 1500, Jinan, Shandong, China.

BMC Bioinformatics

|July 26, 2016

Summary

This summary is machine-generated.

This study introduces a novel parallelization strategy for biological sequence alignment on Xeon Phi clusters, achieving high performance for database scanning and multiple sequence alignment. The method offers competitive speedups and scalability for computational molecular biology tasks.

Keywords:

Dynamic programming Multiple sequence alignment Pairwise sequence alignment Smith-Waterman Xeon Phi clusters

More Related Videos

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Related Experiment Videos

Last Updated: Mar 17, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Area of Science:

Computational molecular biology
Bioinformatics
High-performance computing

Background:

Sequence alignment is a fundamental operation in computational molecular biology.
Growing biological sequence databases necessitate efficient parallel implementations on modern accelerators.

Purpose of the Study:

To develop high-performance parallel approaches for biological sequence database scanning using the Smith-Waterman algorithm.
To optimize the first stage of progressive multiple sequence alignment based on the ClustalW heuristic on Xeon Phi compute clusters.

Main Methods:

Implementation of a three-level parallelization scheme: cluster-level data parallelism, thread-level coarse-grained parallelism, and vector-level fine-grained parallelism.
Reorganization of sequence datasets and utilization of Xeon Phi shuffle operations for improved I/O efficiency.

Main Results:

Achieved a peak performance of 220 GCUPS for scanning real protein sequence databanks on a single node.
Demonstrated good scalability with respect to sequence length, database size, and number of compute nodes.
Exhibited highly competitive performance compared to optimized Xeon Phi and GPU implementations.

Conclusions:

The proposed method significantly enhances the efficiency of biological sequence alignment on Xeon Phi architectures.
The approach is scalable and offers competitive performance, making it suitable for large-scale bioinformatics analyses.
The implementation is publicly available for further research and application.