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Peptide Identification Using Tandem Mass Spectrometry

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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Updated: Jun 22, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Fast multiple sequence alignment via multi-armed bandits.

Kayvon Mazooji¹, Ilan Shomorony¹

¹Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.

Bioinformatics (Oxford, England)

|June 28, 2024

Summary

This summary is machine-generated.

This study introduces a faster method for multiple sequence alignment using adaptive score estimation, reducing computation time without sacrificing accuracy. The approach accelerates sequence-to-model assignments in bioinformatics tools like UPP.

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Area of Science:

Computational Biology
Bioinformatics
Machine Learning

Background:

Multiple sequence alignment is crucial for understanding protein evolution and function.
Accurate alignment of large biological datasets is computationally intensive.
Existing methods like UPP rely on computationally expensive hidden Markov model (HMM) scoring.

Purpose of the Study:

To accelerate the sequence-to-HMM assignment step in multiple sequence alignment.
To maintain alignment accuracy while significantly reducing computation time.
To develop an adaptive approach for efficient score estimation.

Main Methods:

Replacing precise HMM probability scores with efficiently estimated alternative scores.
Utilizing a multi-armed bandit algorithm for adaptive score estimation.
Applying the method to large datasets, focusing on long sequences.

Main Results:

Achieved similar alignment accuracy compared to the established UPP software.
Demonstrated a significant reduction in computation time.
The speed-up is particularly notable for datasets containing long sequences.

Conclusions:

Adaptive score estimation offers a computationally efficient alternative for multiple sequence alignment.
The proposed method enhances the scalability of bioinformatics tools for large-scale sequence analysis.
This approach can be broadly applied to accelerate other HMM-based computational biology tasks.