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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

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Related Experiment Video

Updated: Jun 16, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Decoding HMMs using the k best paths: algorithms and applications.

Daniel G Brown¹, Daniil Golod

¹Cheriton School of Computer Science, University of Waterloo, 200 University Avenue W, Waterloo, Ontario, Canada N2L 3G1. browndg@uwaterloo.ca

BMC Bioinformatics

|February 4, 2010

Summary

This summary is machine-generated.

This study introduces a new method for hidden Markov model (HMM) decoding that computes the k highest probability paths, offering better sequence explanations and reducing memory usage compared to traditional algorithms.

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Area of Science:

Computational Biology
Bioinformatics
Machine Learning

Background:

Traditional hidden Markov model (HMM) decoding algorithms offer limited solutions by providing only a single optimal path.
These conventional methods often yield suboptimal results in practical applications, particularly in biological sequence analysis.

Purpose of the Study:

To develop an alternative HMM decoding approach for enhanced sequence explanation.
To improve the efficiency and interpretability of HMM-based sequence analysis.

Main Methods:

Efficiently compute the k highest probability paths for sequence explanation.
Employ an online pruning technique to minimize primary memory consumption.
Explore alternative sequence explanations by analyzing multiple high-probability paths.

Main Results:

The proposed algorithm significantly reduces memory requirements compared to naive approaches.
Simple path combination methods provide effective explanations for membrane protein sequences.
The k-best paths offer insights into multiple valid sequence explanations, especially for proteins with dual topologies.

Conclusions:

The novel HMM decoding algorithm provides more comprehensive and confident sequence explanations.
This method enhances understanding of protein sequences, particularly those with complex structures.
The approach addresses limitations of traditional algorithms by offering multiple explanatory paths.