Non Targeted Analysis
Non Targeted Analysis - Related Videos
08:38
Targeted DNA Methylation Analysis by Next-generation Sequencing
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Cited by 89 •
2015
Bisulfite amplicon sequencing (BSAS) is a method for quantifying cytosine methylation in targeted genomic regions of interest. This method uses bisulfite conversion paired with PCR amplification of target regions prior to next-generation sequencing to produce absolute quantitation of DNA methylation at a base-specific level.
08:16
Comparative Lesions Analysis Through a Targeted Sequencing Approach
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2019
This article describes a method to identify clonal and subclonal alterations among different specimens from a given patient. Although the experiments described here focus on a specific tumor type, the approach is broadly applicable to other solid tumors.
11:52
Analysis of Targeted Viral Protein Nanoparticles Delivered to HER2+ Tumors
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Cited by 1 •
2013
This article details the procedures for optical imaging analysis of the tumor-targeted nanoparticle, HerDox. In particular, detailed use of the multimode imaging device for detecting tumor targeting and assessing tumor penetration is described here.
07:35
Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries
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Cited by 2 •
2023
Here, we present a protocol demonstrating the installation and use of a bioinformatics pipeline to analyze chimeric RNA sequencing data used in the study of in vivo RNA:RNA interactions.
11:19
Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets
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Cited by 13 •
2010
Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.
FAQs
These three evidence types strengthen the interpretation of molecular features. Accurate mass narrows possible chemical formulas, isotopic patterns provide additional molecular clues, and fragmentation data reveal structural information from the measured signal. Because the resulting assignments are tentative, researchers can use this combined evidence to recognize previously unrecognized contaminants or transformation products before confirmatory testing.
Chromatography separates chemical constituents before measurement, while high-resolution mass spectrometry records signals across a wide mass range. Combining these capabilities helps distinguish and characterize many features within complex water, soil, sediment, or biota samples. The broad measurement space supports discovery beyond compounds selected in advance, while separation provides information that would be harder to interpret from an unresolved mixture.
A molecular feature is a detected signal that computational workflows can locate, group, and compare across environmental samples. Its interpretation may draw on accurate mass, isotopic patterns, and fragmentation data, but detection does not automatically establish a definitive chemical identity. This distinction allows researchers to catalog chemical differences first and then prioritize selected features for further confirmation.
The workflow can reveal all three categories within environmental samples, but the measured signal alone does not necessarily provide a final classification. Researchers interpret features using their mass, isotopic patterns, and fragmentation information, then examine their occurrence in relevant sample types and contexts. This broad view helps prevent monitoring from being limited only to already recognized contaminants.
A typical workflow begins with collecting an environmental sample such as water, soil, sediment, or biota. The sample is separated by chromatography and measured with high-resolution mass spectrometry across a wide mass range. Computational processing then detects and groups molecular features, evaluates accurate mass, isotopic, and fragmentation information, and produces tentative identifications for interpretation or confirmation.
It is particularly useful when researchers need to survey chemical constituents without restricting the investigation to a predefined compound list. The approach can expose expected contaminants alongside previously unrecognized chemicals, supporting broader environmental monitoring and pollution-source assessment. Comparing detected feature patterns across samples can also help identify chemical signatures relevant to contamination investigations.
Broad chemical profiles can identify features occurring in environmental media or biota that may merit closer investigation during exposure evaluation. Researchers can use the resulting evidence to prioritize compounds for confirmatory testing rather than treating every detected signal as equally important. This creates a practical path from wide chemical screening to more focused identification and assessment.