bisulfite-based microarray methods Search Results


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Pyrosequencing Inc bisulfite-based pyrosequencing
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Microarray Based Illumina Arrays, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Illumina Inc hybridization-based microarrays illumina humanmethylation450 (hm450k)
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NextGen Sciences nextgen bisulfite sequencing
<t>Sequence-Dependent</t> ASM as a Tool for Extracting Maximum Information from GWAS
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Illumina Inc whole-genome bisulfite sequencing
The workflow illustrates the developed algorithm for determining methylation-expression relationships in A. thaliana and O. sativa using whole-genome bisulfite <t>sequencing</t> <t>(WGBS)</t> and RNA-seq data. For each experimental condition, gene-specific promoter sequences are processed by extracting methylation patterns and expression levels, which are then transformed into one-hot encoded representations. These data serve as input to our ResNet-9 deep learning model, which identifies critical cytosines influencing gene expression. Following model development, we characterize these regulatory cytosines using proprietary feature extraction methods and Gradient-weighted Class Activation Mapping (Grad-CAM) to determine their relative importance and positional biases within promoter regions. This integrated approach enables systematic identification of functional cis-regulatory elements across species and conditions.
Whole Genome Bisulfite Sequencing, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Sequenom maldi/tof/sequenom
The workflow illustrates the developed algorithm for determining methylation-expression relationships in A. thaliana and O. sativa using whole-genome bisulfite <t>sequencing</t> <t>(WGBS)</t> and RNA-seq data. For each experimental condition, gene-specific promoter sequences are processed by extracting methylation patterns and expression levels, which are then transformed into one-hot encoded representations. These data serve as input to our ResNet-9 deep learning model, which identifies critical cytosines influencing gene expression. Following model development, we characterize these regulatory cytosines using proprietary feature extraction methods and Gradient-weighted Class Activation Mapping (Grad-CAM) to determine their relative importance and positional biases within promoter regions. This integrated approach enables systematic identification of functional cis-regulatory elements across species and conditions.
Maldi/Tof/Sequenom, supplied by Sequenom, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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NextGen Sciences nextgen sequencing
The workflow illustrates the developed algorithm for determining methylation-expression relationships in A. thaliana and O. sativa using whole-genome bisulfite <t>sequencing</t> <t>(WGBS)</t> and RNA-seq data. For each experimental condition, gene-specific promoter sequences are processed by extracting methylation patterns and expression levels, which are then transformed into one-hot encoded representations. These data serve as input to our ResNet-9 deep learning model, which identifies critical cytosines influencing gene expression. Following model development, we characterize these regulatory cytosines using proprietary feature extraction methods and Gradient-weighted Class Activation Mapping (Grad-CAM) to determine their relative importance and positional biases within promoter regions. This integrated approach enables systematic identification of functional cis-regulatory elements across species and conditions.
Nextgen Sequencing, supplied by NextGen Sciences, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


Sequence-Dependent ASM as a Tool for Extracting Maximum Information from GWAS

Journal:

Article Title: Mapping Allele-Specific DNA Methylation: A New Tool for Maximizing Information from GWAS

doi: 10.1016/j.ajhg.2010.01.021

Figure Lengend Snippet: Sequence-Dependent ASM as a Tool for Extracting Maximum Information from GWAS

Article Snippet: Also, microarray-based methods are limited by the annoying fact that only a subset of all SNPs is “chipable.” Microarrays, particularly those with custom designs, will still be very useful for high sample throughput, but Nextgen bisulfite sequencing will inevitably become the way to go for analyzing fewer samples at definitive single-base-pair resolution, ultimately eliminating false negatives from incomplete genomic coverage.

Techniques: Sequencing

The workflow illustrates the developed algorithm for determining methylation-expression relationships in A. thaliana and O. sativa using whole-genome bisulfite sequencing (WGBS) and RNA-seq data. For each experimental condition, gene-specific promoter sequences are processed by extracting methylation patterns and expression levels, which are then transformed into one-hot encoded representations. These data serve as input to our ResNet-9 deep learning model, which identifies critical cytosines influencing gene expression. Following model development, we characterize these regulatory cytosines using proprietary feature extraction methods and Gradient-weighted Class Activation Mapping (Grad-CAM) to determine their relative importance and positional biases within promoter regions. This integrated approach enables systematic identification of functional cis-regulatory elements across species and conditions.

Journal: bioRxiv

Article Title: Deep-Learning on condition specific expression profiles reveal critical cytosines in gene regulation

doi: 10.1101/2025.06.01.657178

Figure Lengend Snippet: The workflow illustrates the developed algorithm for determining methylation-expression relationships in A. thaliana and O. sativa using whole-genome bisulfite sequencing (WGBS) and RNA-seq data. For each experimental condition, gene-specific promoter sequences are processed by extracting methylation patterns and expression levels, which are then transformed into one-hot encoded representations. These data serve as input to our ResNet-9 deep learning model, which identifies critical cytosines influencing gene expression. Following model development, we characterize these regulatory cytosines using proprietary feature extraction methods and Gradient-weighted Class Activation Mapping (Grad-CAM) to determine their relative importance and positional biases within promoter regions. This integrated approach enables systematic identification of functional cis-regulatory elements across species and conditions.

Article Snippet: Following bisulfite conversion, one can assess the overall DNA methylation status using microarray-based hybridization techniques such as Illumina HumanMethylation450 (HM450K) and advanced next-generation sequencing (NGS) methods such as whole-genome bisulfite sequencing (WGBS) ( ) [ – ] .

Techniques: Methylation, Expressing, Methylation Sequencing, RNA Sequencing, Transformation Assay, Gene Expression, Extraction, Activation Assay, Functional Assay