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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...

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Related Experiment Video

Updated: Jan 4, 2026

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence

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Published on: March 9, 2015

Forensic STR allele extraction using a machine learning paradigm.

Yao-Yuan Liu¹, David Welch², Ryan England³

¹Forensic Science Program, School of Chemical Sciences, University of Auckland, 38 Princes Street, Auckland 1010, New Zealand.

Forensic Science International. Genetics

|November 8, 2019

Summary

This summary is machine-generated.

Fragsifier, a machine learning tool, accurately detects short tandem repeat (STR) sequences in next-generation sequencing data. Its results align with traditional capillary electrophoresis methods, offering a new approach for genetic analysis.

Keywords:

Bioinformatics Machine learning Massively parallel sequencing STR extraction

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Published on: July 15, 2011

Area of Science:

Bioinformatics
Genetics
Machine Learning

Background:

Short tandem repeats (STRs) are crucial genetic markers.
Accurate STR detection from next-generation sequencing (NGS) data is challenging.

Purpose of the Study:

Introduce Fragsifier, a novel machine learning approach for STR detection and extraction from NGS data.
Evaluate Fragsifier's performance against established methods.

Main Methods:

Fragsifier utilizes machine learning sequence models with k-mers for locus prediction.
It identifies longest repeat stretches and aligns flanking sequences for precise STR boundary determination.
The method is scriptable for flexible data analysis and model experimentation.

Main Results:

Fragsifier achieves high concordance with capillary electrophoresis (CE) genotyping results.
Performance is comparable to existing tools like STRait Razor and ForenSeq UAS.
The approach demonstrates robust STR sequence detection and extraction capabilities.

Conclusions:

Fragsifier offers a reliable and accurate machine learning-based method for STR analysis in NGS data.
Its flexibility allows for adaptation to various datasets and research questions.
This tool advances genetic analysis by improving STR detection from complex sequencing data.