High Through-Put DNA Methylation Analysis of Lung Cancer: Plasma cfDNA for Bi...Kate Barlow
• Technology pipeline for methylation biomarker
development
• High throughput DNA methylation-qPCR workflows
• Liquid biopsy – cfDNA methylation testing
Introduction.
History of Protein Sequencing.
Determining Amino Acid Composition.
N-terminal amino acid analysis.
C-terminal amino acid analysis
The Edman degradation reaction.
Limitations of the Edman degradation.
Mass spectrometry
Importance
Conclusion
Reference
This document summarizes key concepts about nucleic acids and their interactions with proteins. It discusses how DNA can be denatured and reanneal through hybridization. It then describes the polymerase chain reaction (PCR) process which involves denaturation, annealing of primers, and extension to amplify specific DNA sequences. Other topics covered include the genetic code, messenger RNA, transfer RNA, ribosomal peptidyl transferase activity, non-canonical nucleic acid structures, and the different forces (electrostatic, hydrogen bonding, hydrophobic) involved in protein-nucleic acid interactions.
This presentation accompanies a webinar at: https://www1.gotomeeting.com/register/367952841
===
Hitachi Solutions has partnered with OpGen to offer MapIt® Optical Mapping Services to our customers. Trevor Wagner, Senior Applications Scientist Manager from OpGen will be our guest presenter. Trevor was part of the team that developed, tested, and released OpGen’s first major product, the Argus Optical Mapping System in 2010.
This webinar will describe:
1. How Optical Mapping technology will benefit you in the following application areas:
-Strain Typing
-Comparative Genomics
-Whole-genome Sequence Assembly
2. How the MapIt Service works.
This presentation is about Riboswitches and Riboswitches mediated regulation. Riboswitches are the small mRNA element that has tertiary structure and regulate the down stream genes in the same mRNA by interacting with small metabolites and metal ions.Various types of regulatory mechanism and structure and ligand binding of some important riboswitches are given here.Like TPP,PURINE AND FMN riboswitches. Also the role of some tandem and cooperative riboswitches are given here. Applications of Riboswitches are also given here like drug targets. Some future challenges are also given here.
1. What is post transcriptional Modification of RNA
2. How is Post Transcriptional Modification of RNA different in Prokaryotes and Eukaryotes
3. What are the Various Types of Post Transcriptional Modification of RNA
4. What is the Mechanism of 5' Capping of RNA
5. What is the Mechanism of 3' Polyadenylation of RNA
6. What is the Function of 5' Capping of RNA
7. What is Function of 3' Polyadenylation of RNA
8. What is Splicing ?
9. What is the Mechanism of Splicing ?
10. What is Spliceosomes ?
11. What is Sn RNA or Small Nuclear RNA ?
12. What is SnRNP Complex or SNURPs ?
13. Beta Thalessemia because of faulty Splicing ?
14. What is Methylation post transcriptional modification ?
15. What is Alternative Splicing?
16. What is Selective Splicing ?
17. What is Alternative polyadenylation ?
18. What is Alternative 5' donor Splicing ?
19. What is Alternative 3' Donor Splicing ?
20. What is the role of Alternative Splicing ?
21. What is RNA Editing ?
22. How is RNA Editing an Exception to Central Dogma ?
23. Example of Apolipoprotein B Gene for RNA Editing
24. Other Examples of RNA Editing
Brief introduction of post-translational modifications (PTMs)Creative Proteomics
PTMs are chemical alterations to protein structure, typically catalyzed by exceedingly substrate-specific enzymes, which themselves are under strict control by PTMs. They generate a large diversity of gene products because many types of PTMs are covalently attached to amino-acid residues in each protein. For protein post-translational modification analysis at Creative Proteomics, please visit https://www.creative-proteomics.com/services/protein-post-translational-modification-analysis.htm
Dna Methylation Analysis in a Single Day - Download the SlidesQIAGEN
This webinar introduces the new PyroMark Q48 Autoprep system. Combined with the latest EpiTect Fast bisulfite conversion technology, the new PyroMark Q48 Autoprep can now provide highly automated methylation analysis in a single day.
High Through-Put DNA Methylation Analysis of Lung Cancer: Plasma cfDNA for Bi...Kate Barlow
• Technology pipeline for methylation biomarker
development
• High throughput DNA methylation-qPCR workflows
• Liquid biopsy – cfDNA methylation testing
Introduction.
History of Protein Sequencing.
Determining Amino Acid Composition.
N-terminal amino acid analysis.
C-terminal amino acid analysis
The Edman degradation reaction.
Limitations of the Edman degradation.
Mass spectrometry
Importance
Conclusion
Reference
This document summarizes key concepts about nucleic acids and their interactions with proteins. It discusses how DNA can be denatured and reanneal through hybridization. It then describes the polymerase chain reaction (PCR) process which involves denaturation, annealing of primers, and extension to amplify specific DNA sequences. Other topics covered include the genetic code, messenger RNA, transfer RNA, ribosomal peptidyl transferase activity, non-canonical nucleic acid structures, and the different forces (electrostatic, hydrogen bonding, hydrophobic) involved in protein-nucleic acid interactions.
This presentation accompanies a webinar at: https://www1.gotomeeting.com/register/367952841
===
Hitachi Solutions has partnered with OpGen to offer MapIt® Optical Mapping Services to our customers. Trevor Wagner, Senior Applications Scientist Manager from OpGen will be our guest presenter. Trevor was part of the team that developed, tested, and released OpGen’s first major product, the Argus Optical Mapping System in 2010.
This webinar will describe:
1. How Optical Mapping technology will benefit you in the following application areas:
-Strain Typing
-Comparative Genomics
-Whole-genome Sequence Assembly
2. How the MapIt Service works.
This presentation is about Riboswitches and Riboswitches mediated regulation. Riboswitches are the small mRNA element that has tertiary structure and regulate the down stream genes in the same mRNA by interacting with small metabolites and metal ions.Various types of regulatory mechanism and structure and ligand binding of some important riboswitches are given here.Like TPP,PURINE AND FMN riboswitches. Also the role of some tandem and cooperative riboswitches are given here. Applications of Riboswitches are also given here like drug targets. Some future challenges are also given here.
1. What is post transcriptional Modification of RNA
2. How is Post Transcriptional Modification of RNA different in Prokaryotes and Eukaryotes
3. What are the Various Types of Post Transcriptional Modification of RNA
4. What is the Mechanism of 5' Capping of RNA
5. What is the Mechanism of 3' Polyadenylation of RNA
6. What is the Function of 5' Capping of RNA
7. What is Function of 3' Polyadenylation of RNA
8. What is Splicing ?
9. What is the Mechanism of Splicing ?
10. What is Spliceosomes ?
11. What is Sn RNA or Small Nuclear RNA ?
12. What is SnRNP Complex or SNURPs ?
13. Beta Thalessemia because of faulty Splicing ?
14. What is Methylation post transcriptional modification ?
15. What is Alternative Splicing?
16. What is Selective Splicing ?
17. What is Alternative polyadenylation ?
18. What is Alternative 5' donor Splicing ?
19. What is Alternative 3' Donor Splicing ?
20. What is the role of Alternative Splicing ?
21. What is RNA Editing ?
22. How is RNA Editing an Exception to Central Dogma ?
23. Example of Apolipoprotein B Gene for RNA Editing
24. Other Examples of RNA Editing
Brief introduction of post-translational modifications (PTMs)Creative Proteomics
PTMs are chemical alterations to protein structure, typically catalyzed by exceedingly substrate-specific enzymes, which themselves are under strict control by PTMs. They generate a large diversity of gene products because many types of PTMs are covalently attached to amino-acid residues in each protein. For protein post-translational modification analysis at Creative Proteomics, please visit https://www.creative-proteomics.com/services/protein-post-translational-modification-analysis.htm
Dna Methylation Analysis in a Single Day - Download the SlidesQIAGEN
This webinar introduces the new PyroMark Q48 Autoprep system. Combined with the latest EpiTect Fast bisulfite conversion technology, the new PyroMark Q48 Autoprep can now provide highly automated methylation analysis in a single day.
The information for the proteins found in a cell is encoded in genes of the genome of the cell. A protein- coding gene is expressed by the process of transcription to produce an mRNA, followed by translation of the mRNA. Translation involves the conversion of the base sequence of the mRNA into the amino acid sequence of a polypeptide.
This document discusses the stability of nucleic acids and how differential scanning calorimetry (DSC) can be used to characterize it. DSC directly measures the stability and unfolding of biomolecules like DNA and RNA as they are heated. It determines values like the transition midpoint temperature (Tm), enthalpy (ΔH), and heat capacity change (ΔCp) associated with unfolding. DSC data provides information on factors influencing nucleic acid stability, including sequence effects, environmental conditions, and structure formation.
MicroRNA and thier role in gene regulationIbad khan
MicroRNAs are small non-coding RNAs that regulate gene expression post-transcriptionally. They were first discovered in 1993 and their biogenesis involves two key steps - processing in the nucleus by the Drosha-DGCR8 complex into pre-miRNAs, followed by export to the cytoplasm and further processing by the Dicer enzyme into mature miRNA. The miRNA is then loaded into the RISC complex containing Argonaute proteins and guides it to target mRNAs to repress translation or promote degradation. MicroRNAs play important roles in various cellular functions and diseases by mediating gene silencing through nine different mechanisms.
The document summarizes the process of translation. It describes:
1) The machinery involved including mRNA, tRNA, ribosomes and other proteins.
2) The three main steps - initiation, elongation, and termination. Initiation involves binding of the ribosome and first tRNA. Elongation is the repetitive addition of amino acids by tRNA and peptide bond formation. Termination occurs when a stop codon is reached and release factors trigger the release of the complete protein.
3) Key processes within each step like activation of amino acids, charging of tRNA, translocation during elongation, and hydrolysis of the peptide bond during termination.
DNA is constantly damaged by radiation, chemicals, and other agents. There are multiple pathways for repairing DNA damage, including direct reversal, base excision repair, nucleotide excision repair, and mismatch repair. Base excision repair removes individual damaged bases. Nucleotide excision repair removes short fragments of 24-32 bases to repair more substantial damage like thymine dimers. Mismatch repair recognizes and fixes incorrect incorporations during DNA replication. Together, these pathways help maintain the integrity of DNA.
This document discusses the bioinformatics analysis of ChIP-seq data. It begins with an overview of ChIP-seq experiments and the major steps in processing and analyzing the sequencing data, including quality control, alignment, peak calling, and downstream analyses. Pipelines for automated analysis are described, such as Cluster Flow and Nextflow. The talk emphasizes that there is no single correct approach and the analysis depends on the biological question and experimental design.
This presentation explains DNA transcription and RNA Processing.
It gives details about prokaryotic DNA transcription and eukaryotic DNA transcription. it also explains post-transcriptional modification both in prokaryotes and eukaryotes.
This document discusses long non-coding RNAs (lncRNAs). It begins by describing the discovery of lncRNAs in the 1980s-2000s through cDNA sequencing. It then states that lncRNAs are the largest class of transcripts in mouse and human genomes. The document discusses that lncRNAs were once thought to be useless but are now known to have regulatory functions. It provides details on the characteristics, locations in the genome, functions, mechanisms of action, roles in human disease, and implications in human carcinomas of lncRNAs.
mRNA stability and localization.RNA is critical at many stages of gene expression. How frequently it will be translated, how long it is likely to survive, and where in the cell it will be translated. RNA cis-elements & associated proteins
This document provides an overview of RNA-seq and its applications. It discusses key aspects of RNA-seq including transcriptome profiling, alignment, quantification, differential expression analysis, clustering and visualization. It also covers experimental design considerations and highlights some commonly used tools and software. The document is a comprehensive guide that describes the RNA-seq workflow and analysis from start to finish.
This document discusses different DNA binding motifs that allow proteins to interact with DNA without disrupting the hydrogen bonds between the DNA bases. It describes several conserved structural motifs common to many DNA binding proteins, including the helix-turn-helix motif, zinc finger domains, and leucine zipper domains. The helix-turn-helix motif contains two short alpha helices separated by a beta turn. Zinc finger domains use cysteine or histidine residues to coordinate a zinc ion, stabilizing their structure. Leucine zipper domains contain repeated leucine residues that allow dimerization of regulatory proteins.
Comparative genomic hybridization (CGH) is a molecular cytogenetic technique that compares the DNA of a test sample to a reference sample to detect copy number variations without cell culturing. It involves labelling the tumor and normal DNA with different fluorescent dyes, mixing them, and hybridizing them to normal chromosomes to detect losses or gains of genetic material in the tumor DNA through fluorescence ratios. While CGH can detect events over 10-20 Mb, array CGH uses genomic fragments as targets and can detect changes as small as 5-10 kb, making it a faster and more sensitive technique. Array CGH is commonly used in cancer research and diagnosis of genetic disorders.
Non-coding RNAs (ncRNAs) are functional RNA molecules that are not translated into proteins. There are several types of ncRNAs that play important roles in biological processes. tRNAs help translate nucleotides into amino acids during protein synthesis. rRNA and snoRNAs are involved in ribosome and RNA structure/modification. MiRNAs regulate gene expression by binding to mRNA. LncRNAs regulate processes like chromatin structure and transcription. Mt-tRNAs specific to mitochondria are essential for oxidative phosphorylation. Mutations can cause diseases like myopathies.
Mitochondrial DNA (mtDNA) is located in mitochondria and contains genes that code for proteins in mitochondria. In humans, mtDNA contains 37 genes and is 16,600 base pairs. It is inherited solely from the mother in most species, including humans. The sequencing of mtDNA has helped scientists study evolutionary relationships between species and trace maternal lineages far back in time. MtDNA mutates more rapidly than nuclear DNA, making it useful for evolutionary studies.
Post translation modification in proteinKAUSHAL SAHU
This document discusses post-translational modifications in proteins. It begins with an introduction explaining that proteins undergo folding and modifications after translation to become functional. It then covers various types of post-translational modifications like the role of chaperones in protein folding, enzymes that catalyze folding like protein disulfide isomerase, and protein cleavage involved in maturation. Other modifications discussed are glycosylation, the addition of carbohydrates, and attachment of lipids. The document concludes that post-translational modifications are important for protein maturation and function.
TaqMan® MicroRNA Assays quantitate miRNAs with the specificity and sensitivity of TaqMan® Assay chemistry. A simple, two-step protocol requires only reverse transcription with a miRNA-specific primer, followed by real-time PCR with TaqMan® probes.
For more information visit:
http://www.invitrogen.com/site/us/en/home/Products-and-Services/Applications/epigenetics-noncoding-rna-research/miRNA-Profiling-/miRNA_qRT_PCR/TaqMan-MicroRNA-Assays-and-Arrays.html?CID=TaqmanMicroRNA-SS-12312
RNA transport
Multiple classes of RNA are exported from the nucleus
Transportation through nuclear pore complex.
Ribosomal subunits are assembled in the nucleolus and exported by exportin 1
tRNAs are exported by a dedicated exportin
Messenger RNAs are exported from the nucleus as RNA-protein complexes
Messenger RNAs are exported from the nucleus as RNA-protein complexes
hnRNPs move from sites of processing to NPCs
Precursors to microRNAs are exported from the nucleus and processed in the cytoplasm
Gene expression is the process by which the information from a gene is used in the synthesis of a functional gene product. It involves two main stages - transcription of DNA to mRNA and translation of mRNA to protein. In eukaryotes, gene expression requires several processing steps between transcription and translation including 5' capping, splicing, and 3' polyadenylation of mRNA. Protein synthesis occurs via three phases - initiation, elongation, and termination on ribosomes in the cytoplasm. Gene expression is regulated at multiple levels including transcription, RNA processing, translation and post-translation.
The document discusses quality control, filtering, and normalization procedures for Illumina 450k methylation array data. It describes initial quality control checks to identify failed samples and technical artifacts, such as color biases. A variety of normalization approaches are presented, including within-array normalization to correct for color bias and background noise, between-array normalization to remove technical variation across arrays, and data-driven approaches to evaluate different preprocessing methods. The goal of preprocessing is to improve concordance with independent validation data while retaining meaningful biological variation.
This document provides an overview of DNA methylation analysis. It begins with background on DNA methylation functions and diseases. It then discusses methods for measuring DNA methylation status, including bisulfite sequencing. The document reviews steps for DNA methylation data analysis using tools like methylKit in R. It presents a case study example of analyzing DNA methylation data from human stem cells and fibroblasts. Alignment, quality control, differential methylation analysis and visualization are discussed.
The information for the proteins found in a cell is encoded in genes of the genome of the cell. A protein- coding gene is expressed by the process of transcription to produce an mRNA, followed by translation of the mRNA. Translation involves the conversion of the base sequence of the mRNA into the amino acid sequence of a polypeptide.
This document discusses the stability of nucleic acids and how differential scanning calorimetry (DSC) can be used to characterize it. DSC directly measures the stability and unfolding of biomolecules like DNA and RNA as they are heated. It determines values like the transition midpoint temperature (Tm), enthalpy (ΔH), and heat capacity change (ΔCp) associated with unfolding. DSC data provides information on factors influencing nucleic acid stability, including sequence effects, environmental conditions, and structure formation.
MicroRNA and thier role in gene regulationIbad khan
MicroRNAs are small non-coding RNAs that regulate gene expression post-transcriptionally. They were first discovered in 1993 and their biogenesis involves two key steps - processing in the nucleus by the Drosha-DGCR8 complex into pre-miRNAs, followed by export to the cytoplasm and further processing by the Dicer enzyme into mature miRNA. The miRNA is then loaded into the RISC complex containing Argonaute proteins and guides it to target mRNAs to repress translation or promote degradation. MicroRNAs play important roles in various cellular functions and diseases by mediating gene silencing through nine different mechanisms.
The document summarizes the process of translation. It describes:
1) The machinery involved including mRNA, tRNA, ribosomes and other proteins.
2) The three main steps - initiation, elongation, and termination. Initiation involves binding of the ribosome and first tRNA. Elongation is the repetitive addition of amino acids by tRNA and peptide bond formation. Termination occurs when a stop codon is reached and release factors trigger the release of the complete protein.
3) Key processes within each step like activation of amino acids, charging of tRNA, translocation during elongation, and hydrolysis of the peptide bond during termination.
DNA is constantly damaged by radiation, chemicals, and other agents. There are multiple pathways for repairing DNA damage, including direct reversal, base excision repair, nucleotide excision repair, and mismatch repair. Base excision repair removes individual damaged bases. Nucleotide excision repair removes short fragments of 24-32 bases to repair more substantial damage like thymine dimers. Mismatch repair recognizes and fixes incorrect incorporations during DNA replication. Together, these pathways help maintain the integrity of DNA.
This document discusses the bioinformatics analysis of ChIP-seq data. It begins with an overview of ChIP-seq experiments and the major steps in processing and analyzing the sequencing data, including quality control, alignment, peak calling, and downstream analyses. Pipelines for automated analysis are described, such as Cluster Flow and Nextflow. The talk emphasizes that there is no single correct approach and the analysis depends on the biological question and experimental design.
This presentation explains DNA transcription and RNA Processing.
It gives details about prokaryotic DNA transcription and eukaryotic DNA transcription. it also explains post-transcriptional modification both in prokaryotes and eukaryotes.
This document discusses long non-coding RNAs (lncRNAs). It begins by describing the discovery of lncRNAs in the 1980s-2000s through cDNA sequencing. It then states that lncRNAs are the largest class of transcripts in mouse and human genomes. The document discusses that lncRNAs were once thought to be useless but are now known to have regulatory functions. It provides details on the characteristics, locations in the genome, functions, mechanisms of action, roles in human disease, and implications in human carcinomas of lncRNAs.
mRNA stability and localization.RNA is critical at many stages of gene expression. How frequently it will be translated, how long it is likely to survive, and where in the cell it will be translated. RNA cis-elements & associated proteins
This document provides an overview of RNA-seq and its applications. It discusses key aspects of RNA-seq including transcriptome profiling, alignment, quantification, differential expression analysis, clustering and visualization. It also covers experimental design considerations and highlights some commonly used tools and software. The document is a comprehensive guide that describes the RNA-seq workflow and analysis from start to finish.
This document discusses different DNA binding motifs that allow proteins to interact with DNA without disrupting the hydrogen bonds between the DNA bases. It describes several conserved structural motifs common to many DNA binding proteins, including the helix-turn-helix motif, zinc finger domains, and leucine zipper domains. The helix-turn-helix motif contains two short alpha helices separated by a beta turn. Zinc finger domains use cysteine or histidine residues to coordinate a zinc ion, stabilizing their structure. Leucine zipper domains contain repeated leucine residues that allow dimerization of regulatory proteins.
Comparative genomic hybridization (CGH) is a molecular cytogenetic technique that compares the DNA of a test sample to a reference sample to detect copy number variations without cell culturing. It involves labelling the tumor and normal DNA with different fluorescent dyes, mixing them, and hybridizing them to normal chromosomes to detect losses or gains of genetic material in the tumor DNA through fluorescence ratios. While CGH can detect events over 10-20 Mb, array CGH uses genomic fragments as targets and can detect changes as small as 5-10 kb, making it a faster and more sensitive technique. Array CGH is commonly used in cancer research and diagnosis of genetic disorders.
Non-coding RNAs (ncRNAs) are functional RNA molecules that are not translated into proteins. There are several types of ncRNAs that play important roles in biological processes. tRNAs help translate nucleotides into amino acids during protein synthesis. rRNA and snoRNAs are involved in ribosome and RNA structure/modification. MiRNAs regulate gene expression by binding to mRNA. LncRNAs regulate processes like chromatin structure and transcription. Mt-tRNAs specific to mitochondria are essential for oxidative phosphorylation. Mutations can cause diseases like myopathies.
Mitochondrial DNA (mtDNA) is located in mitochondria and contains genes that code for proteins in mitochondria. In humans, mtDNA contains 37 genes and is 16,600 base pairs. It is inherited solely from the mother in most species, including humans. The sequencing of mtDNA has helped scientists study evolutionary relationships between species and trace maternal lineages far back in time. MtDNA mutates more rapidly than nuclear DNA, making it useful for evolutionary studies.
Post translation modification in proteinKAUSHAL SAHU
This document discusses post-translational modifications in proteins. It begins with an introduction explaining that proteins undergo folding and modifications after translation to become functional. It then covers various types of post-translational modifications like the role of chaperones in protein folding, enzymes that catalyze folding like protein disulfide isomerase, and protein cleavage involved in maturation. Other modifications discussed are glycosylation, the addition of carbohydrates, and attachment of lipids. The document concludes that post-translational modifications are important for protein maturation and function.
TaqMan® MicroRNA Assays quantitate miRNAs with the specificity and sensitivity of TaqMan® Assay chemistry. A simple, two-step protocol requires only reverse transcription with a miRNA-specific primer, followed by real-time PCR with TaqMan® probes.
For more information visit:
http://www.invitrogen.com/site/us/en/home/Products-and-Services/Applications/epigenetics-noncoding-rna-research/miRNA-Profiling-/miRNA_qRT_PCR/TaqMan-MicroRNA-Assays-and-Arrays.html?CID=TaqmanMicroRNA-SS-12312
RNA transport
Multiple classes of RNA are exported from the nucleus
Transportation through nuclear pore complex.
Ribosomal subunits are assembled in the nucleolus and exported by exportin 1
tRNAs are exported by a dedicated exportin
Messenger RNAs are exported from the nucleus as RNA-protein complexes
Messenger RNAs are exported from the nucleus as RNA-protein complexes
hnRNPs move from sites of processing to NPCs
Precursors to microRNAs are exported from the nucleus and processed in the cytoplasm
Gene expression is the process by which the information from a gene is used in the synthesis of a functional gene product. It involves two main stages - transcription of DNA to mRNA and translation of mRNA to protein. In eukaryotes, gene expression requires several processing steps between transcription and translation including 5' capping, splicing, and 3' polyadenylation of mRNA. Protein synthesis occurs via three phases - initiation, elongation, and termination on ribosomes in the cytoplasm. Gene expression is regulated at multiple levels including transcription, RNA processing, translation and post-translation.
The document discusses quality control, filtering, and normalization procedures for Illumina 450k methylation array data. It describes initial quality control checks to identify failed samples and technical artifacts, such as color biases. A variety of normalization approaches are presented, including within-array normalization to correct for color bias and background noise, between-array normalization to remove technical variation across arrays, and data-driven approaches to evaluate different preprocessing methods. The goal of preprocessing is to improve concordance with independent validation data while retaining meaningful biological variation.
This document provides an overview of DNA methylation analysis. It begins with background on DNA methylation functions and diseases. It then discusses methods for measuring DNA methylation status, including bisulfite sequencing. The document reviews steps for DNA methylation data analysis using tools like methylKit in R. It presents a case study example of analyzing DNA methylation data from human stem cells and fibroblasts. Alignment, quality control, differential methylation analysis and visualization are discussed.
This document introduces analyzing methylation data from Reduced Representation Bisulfite Sequencing (RRBS) experiments using the R package methylKit. It begins with an overview of basic R operations and data structures. Next, it discusses relevant genomics packages in Bioconductor like GenomicRanges and IRanges that are useful for working with genomic intervals. Finally, it demonstrates how to use methylKit to analyze RRBS methylation data, including working with annotated methylation events.
DNA methylation is an epigenetic mechanism that involves the addition of a methyl group to cytosine residues in DNA. It is catalyzed by DNA methyltransferase enzymes and plays a key role in gene expression and cellular differentiation. Aberrant DNA methylation, including both hypermethylation and hypomethylation, has been associated with cancer development by disrupting gene expression. Detection of DNA methylation patterns can provide insights into cancer biology and may have applications as a diagnostic tool.
The document introduces analyzing methylation data from Reduced Representation Bisulfite Sequencing (RRBS) experiments using the R package methylKit. It begins with an introduction and outline, then covers downloading example RRBS data, basics of R including vectors, matrices, and data frames, genomics and R packages for working with genomic intervals, and using methylKit to analyze RRBS methylation data.
Ibica2014 p(8) visualizing and identifying the dna methylationAboul Ella Hassanien
DNA methylation is an epigenetic mechanism that cells use to control
gene expression. DNA methylation has become one of the hottest topics in cancer
research, especially for abnormally hypermethylated tumor suppressor genes
or hypomethylaed oncogenes research. The analysis of DNA methylation data
determines the differential hypermethlated or hypomethylated genes that are candidate
to be cancer biomarkers. Visualization the DNA methylation status may
lead to discover new relationships between hypomethylated and hypermethylated
genes, therefore this paper applied a mathematical modelling theory called formal
concept analysis for visualizing DNA methylation status.
Methylation and Expression data integrationsahirbhatnagar
1) The document describes analysis of methylation and expression data from cord blood and placenta tissue samples and their relationship to gestational diabetes and childhood obesity.
2) Significant differentially methylated sites were identified between samples exposed and unexposed to gestational diabetes after adjusting for cell mixtures.
3) Methylation sites were also identified that correlated with various body fat measures in childhood after adjusting for covariates.
4) Some gene expression was also found to correlate with body fat measures, though fewer significant associations were identified compared to methylation.
Analysis of DNA methylation and Gene expression to predict childhood obesitysahirbhatnagar
Recent advances in genomic technologies have made it feasible to measure, on the same individual, multiple types of genomic activity such as genotypes, gene expression, DNA copy number, methylation and microRNA expression. However, in order to benefit from the increasing amounts of heterogeneous data and to obtain a more complete view of genomic functions, there is a great need for statistical and computationally efficient methods that allow us to combine this information in an intelligent way. Challenges with prediction models in this setting arise from the high-dimensional non-linear nature of the data, the large number of measurements compared to the few samples for whom they are collected, and the presence of complex interactions between the different types of data. Methods such as sparse regression, hierarchical clustering and principal component analysis can address any one of these challenges, but can not do so simultaneously. Kernel methods, which use matrices measuring the similarity between two individuals, offer a powerful way of simultaneously addressing these challenges without significantly increasing the computational burden. In this work, we investigate the benefits and challenges that arise from using kernel methods in the context of integrating DNA methylation, gene expression and phenotypic data in a sample of mother-child pairs from a prospective birth cohort. The goal of this study is to identify epigenetic marks observed at birth that help predict childhood obesity.
Next generation sequencing techniques have revolutionized DNA sequencing by increasing throughput and decreasing costs compared to previous methods like Sanger sequencing. Some key next generation sequencing methods include 454 sequencing (pyrosequencing), ABI Solid sequencing (sequencing by ligation), Illumina/Solexa sequencing (sequencing by synthesis), and nanopore sequencing. These new techniques allow for faster and cheaper large-scale sequencing and have enabled applications like whole genome sequencing.
The document summarizes the whole genome sequencing of the Tulsi (Ocimum sanctum) plant conducted by researchers at CSIR-Central Institute of Medicinal & Aromatic Plants in Lucknow, India. They published the whole genome sequence in BMC Genomics in 2015. The sequencing found that Tulsi has the smallest nuclear genome in the Lamiaceae family at 386 Mb. Comparison to related plants showed Tulsi has more evolutionary closeness to Salvia miltiorrhiza. Over 74% of reads were mapped back to the assembled genome. The sequencing provides insights into genes involved in Tulsi's medicinal properties.
New insights into the human genome by encode 14.12.12Ranjani Reddy
ENCODE is a project that aims to identify all functional elements in the human genome. It has characterized elements such as transcribed regions, protein-coding genes, transcription factor binding sites, chromatin structure, and DNA methylation sites across many cell types using various methods like RNA-seq, ChIP-seq, and RRBS. While it has provided valuable information on the functional elements encoded in the human genome, it is limited by the small number of cell types and factors analyzed. Future goals include expanding the analysis to more cell types to further understanding of the human genome.
This document summarizes three main next generation sequencing technologies: Roche/454FLX pyrosequencing, Illumina/Solexa sequencing by synthesis, and Applied Biosystems SOLiD sequencing by ligation. Pyrosequencing works by detecting pyrophosphate released during DNA polymerization, producing light signals to determine the sequence. Roche/454FLX amplifies DNA fragments on beads in emulsions and sequences in picotiter plates. Illumina attaches DNA fragments to a flow cell for bridge amplification and sequencing by synthesis. Applied Biosystems SOLiD performs sequencing by ligation, determining sequences through sequential ligation of oligos.
Whole genome sequencing is a technique to sequence the entire genome of an organism. It involves breaking the genome into small fragments, copying the fragments, sequencing the fragments, and reassembling the sequence data into the full genome. Key steps include isolating DNA, fragmenting it, ligating fragments into plasmids, amplifying the plasmids, sequencing the fragments using Sanger sequencing, and assembling the sequence reads into the complete genome. Whole genome sequencing allows researchers to discover coding and non-coding regions, predict disease susceptibility, and perform evolutionary studies by comparing species.
The document discusses epigenetics and DNA methylation in oncology. It provides an introduction to epigenetics and how epigenetic modifications can regulate gene expression without changing DNA sequence. It then discusses using DNA methylation as an epigenetic biomarker for cancer, including prostate and bladder cancers. Specific methylated genes are highlighted as biomarkers for bladder cancer detection in urine samples from patients with hematuria. Validation study results show the biomarkers can accurately detect bladder cancer with high sensitivity and negative predictive value, reducing unnecessary cystoscopies.
Genome and exome sequencing can be used to identify genetic variants that cause rare diseases. Whole genome sequencing requires 30-50X coverage to sequence the entire genome, while exome sequencing only sequences the 1-2% of the genome that is the exome, or protein-coding regions. Read mapping is used to align sequencing reads to the reference genome and is computationally intensive. Variant detection methods like spaced seeds and Burrows-Wheeler transforms are used to identify SNPs and indels, while structural variation can be detected using tools like BreakDancer that analyze read pairs and soft-clipped reads.
This document is a presentation from Illumina about genomics and their company. It discusses how genome sequencing can provide health insights, their progress in clinical adoption and research use, and their mission to improve human health through genomics. It provides details on Illumina's background, products, markets served, and goals to accelerate genomic adoption and establish new businesses in cancer screening and consumer genomics.
DNA Methylation: An Essential Element in Epigenetics Facts and TechnologiesQIAGEN
The document discusses DNA methylation and related epigenetic mechanisms. It defines epigenetics as the reversible inheritance of gene expression patterns not involving changes to DNA sequences. The key epigenetic mechanisms are described as DNA methylation, histone modification, and microRNA activity. DNA methylation involves the addition of a methyl group to cytosine bases in CpG dinucleotides and is an important regulator of gene expression. Methods for analyzing DNA methylation patterns like bisulfite sequencing are also summarized.
The document describes the steps of Illumina sequencing. Genomic DNA is first fragmented and adapters are ligated to create single-stranded DNA fragments. These fragments are attached to a flow cell and undergo bridge amplification to create clusters of identical DNA fragments. Sequencing occurs through cycles of reversible terminator-based sequencing using fluorescently labeled dNTPs, imaging of the fluorescence, and cleavage of the label and terminator to allow the next cycle. After multiple cycles, the sequenced reads are aligned to the reference genome to determine the original sequence.
This document provides an overview of statistical analysis methods for metabolomics data, including data pre-treatment, univariate analysis, and multivariate analysis. It discusses normalization techniques, student's t-tests, volcano plots, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The goal of metabolomics data analysis is biomarker discovery and disease diagnosis by identifying significant metabolic features associated with conditions.
Validation of pediatric thyroid phantom using Single-Energy and Dual-Energy CTMOAYYAD ALSSABBAGH
1. The researcher fabricated a pediatric thyroid phantom and validated it using single-energy and dual-energy computed tomography (CT).
2. CT scans of the phantom in air and water were performed at various voltages. Mass attenuation coefficients calculated from the CT images matched closely with literature values from the National Institute of Standards and Technology.
3. Both single-energy and dual-energy CT can accurately determine the mass attenuation coefficients of materials in the thyroid phantom.
Exploiting technical replicate variance in omics data analysis (RepExplore)Enrico Glaab
High-throughput omics datasets often contain technical replicates included to account for technical sources of noise in the measurement process. Although summarizing these replicate measurements by using robust averages may help to reduce the influence of noise on downstream data analysis, the information on the variance across the replicate measurements is lost in the averaging process and therefore typically disregarded in subsequent statistical analyses.
We introduce RepExplore, a web-service dedicated to exploit the information captured in the technical replicate variance to provide more reliable and informative differential expression and abundance statistics for omics datasets. The software builds on previously published statistical methods, which have been applied successfully to biomedical omics data but are difficult to use without prior experience in programming or scripting. RepExplore facilitates the analysis by providing a fully automated data processing and interactive ranking tables, whisker plot, heat map and principal component analysis visualizations to interpret omics data and derived statistics.
Availability and implementation: Freely available at http://www.repexplore.tk
Journal publication: http://bioinformatics.oxfordjournals.org/content/31/13/2235.long (Glaab, E., & Schneider, R. (2015). RepExplore: Addressing technical replicate variance in proteomics and metabolomics data analysis. Bioinformatics, 31 (13): 2235-2237)
This presentation is based on an article titled "Knowledge-Primed Neural Networks Enable Biologically Interpretable Deep Learning on Single-Cell Sequencing Data" as an application of Artificial Neural Networks in Gene Regulatory Networks in System Biology.
Boost model accuracy of imbalanced covid 19 mortality predictionBindhuBhargaviTalasi
The document proposes using a GAN-based oversampling technique to boost the accuracy of an imbalanced COVID-19 mortality prediction model. It analyzes patient data to find correlations between features like gender and age with mortality. A GAN is used to generate synthetic minority class data to address data imbalance. The GAN-enhanced random forest model is evaluated using metrics like recall, precision and F1 score, and shows improved performance over the base model at predicting COVID-19 mortality.
This document discusses different types of data and statistical tests used in orthodontics. It outlines categorical (qualitative) and numerical (quantitative) data, including nominal, ordinal, discrete, and continuous variables. Appropriate statistical tests are described for each data type, such as chi square tests for categorical data and t-tests or ANOVA for numerical data. Key concepts in data summarization are also covered, including measures of central tendency, variability, normal distribution, correlation, and hypothesis testing. The importance of selecting the right analysis method based on data type is emphasized.
This document discusses methods for incorporating single-arm study data into network meta-analyses (NMAs). Currently, NMAs primarily use data from randomized controlled trials. The document presents two methods for including single-arm evidence: 1) Using single-arm results to create informative priors in Bayesian NMA models, and 2) Creating "virtual comparisons" based on patient characteristics to include single-arm studies directly in the NMA. It provides examples applying these methods in analyses of treatments for cryptococcal meningitis and hepatitis C. The results showed inclusion of single-arm evidence can improve model fit and precision of treatment effect estimates in NMAs.
This document discusses genomic meta-analysis and summarization techniques. It introduces MetaQC for quality control, MetaDE for detecting differentially expressed genes through meta-analysis, and MetaPCA for integrative visualization of multiple genomic studies. MetaQC uses quality measures to determine inclusion/exclusion of studies in meta-analysis. MetaDE detects biomarkers statistically significant across studies using Fisher's and adaptive weighting methods. MetaPCA integrates multiple genomic datasets by finding a common principal component space.
AI approaches in healthcare - targeting precise and personalized medicine DayOne
1) AI approaches show promise in precision medicine by mining medical records to design personalized treatments and power virtual healthcare assistants.
2) A document discusses the role of AI in healthcare, predicting it could save $150 billion annually and reduce treatment costs by 50% while improving outcomes by 30-40%.
3) The document then describes several IBM Research projects applying AI to healthcare, including reconstructing molecular networks from literature, integrating data to stratify patients, and using network-based models to predict drug sensitivity and identify biomarkers.
2D gel electrophoresis is a powerful technique that separates proteins based on two properties - their isoelectric point and molecular weight. In the first dimension, isoelectric focusing separates proteins based on isoelectric point, while SDS-PAGE in the second dimension separates them based on molecular weight. Each spot on the 2D gel corresponds to a single protein that can then be analyzed by mass spectrometry. This technique allows for the high-throughput separation and analysis of thousands of proteins from biological samples.
This document discusses accuracy, precision, and statistical validation in analytical chemistry. It defines accuracy as how close a measurement is to the true value, and precision as the agreement between multiple measurements. Greater accuracy means smaller errors, while precision refers to reproducibility independent of accuracy. Several statistical terms are also defined, including mean, median, standard deviation, and coefficient of variation, which are used to analyze data from multiple measurements. Statistical tests like the t-test, F-test, and analysis of variance are discussed for comparing analytical results.
Partitioning Heritability using GWAS Summary Statistics with LD Score Regressionbbuliksullivan
1) The document describes a new method for partitioning heritability of complex traits using summary statistics from large GWAS. It uses LD Score Regression to estimate the proportion of heritability associated with different functional annotations of the genome.
2) The method was validated in simulations, where it accurately estimated null and enriched heritability proportions.
3) The method was applied to real GWAS data for 10 complex traits, finding many functional elements enriched including conserved regions, enhancers, and cell-type specific H3K27ac regions, providing new insights into genetic architecture and disease biology.
The chi-square test is used to determine if an observed frequency distribution differs from an expected theoretical distribution. It can test goodness of fit, independence of attributes, and homogeneity. The test involves calculating chi-square by taking the sum of the squares of the differences between observed and expected frequencies divided by expected frequencies. For the test to be valid, certain conditions must be met regarding sample size, expected frequencies, independence, and randomness. The test has some limitations such as not measuring strength of association and being unreliable with small expected frequencies.
This document provides an overview of mathematical modeling of infectious diseases. It discusses how to build deterministic compartmental models using systems of differential equations. It also covers fitting models to data by estimating parameters, and analyzing uncertainty and sensitivity using techniques like Latin hypercube sampling, partial rank correlation coefficients, and Fourier amplitude sensitivity testing. The goal is to understand disease transmission dynamics and evaluate the impact of interventions through mathematical modeling.
Michael DeBrota et al. - Assessment of Computational Histopathology in Thorac...Michael DeBrota
Background and Hypothesis:
Thoracic aortic aneurysm (TAA) histopathology includes elastic fiber (EF) abnormalities, mucoid extracellular matrix (MECM) accumulation, and smooth muscle derangement in the aortic medial layer. While semi-quantitative grading of these characteristics is a standard practice, computational characterization of medial layer components may facilitate novel quantitative analyses at higher throughput. We hypothesized that computational results would correlate with results of semi-quantitative grading of aortic histopathology.
Experimental Design:
Formalin-fixed, paraffin-embedded human aortic tissue sections were stained with Movat’s pentachrome to characterize aortic microstructure. Sections were also immunostained for nitrotyrosine residues to assess oxidative stress. Samples were initially graded semi-quantitatively by two independent blinded readers. Next, computational histopathology software was used a) to quantify the proportions of EF, MECM, and cellular area in the medial layer of pentachrome-stained sections and b) to quantify the distribution and intensity of positive nitrotyrosine staining in immunostained sections. Association between semi-quantitative grading and computed values was tested with ANOVA.
Results:
The cohort included 74 participants who underwent prophylactic aortic replacement for TAA and 23 healthy controls. The mean age was 54±17 years. On average, EFs accounted for 49% (range 6-90%) of medial tissue area, whereas MECM accounted for 25% (1-73%). The overall semi-quantitative grade of medial degeneration severity was associated with decrease in EF fraction (p=0.02). The grade of EF thinning also strongly correlated with decrease in EF fraction (p=1x10-6). Meanwhile, grade for accumulation of MECM was associated with increase in MECM (p=0.004). Increased semi-quantitative grading for nitrotyrosine levels was associated with increased nuclear signal optical density (p=9x10-10) and greater percentage of cells labeled as strongly positive (p=8x10-10).
Conclusion and Potential Impact:
We observed significant correlations between computed quantitative values and semi-quantitative grading. This suggests that computational histopathology is a valid method for investigation of human TAA tissues.
tranSMART Community Meeting 5-7 Nov 13 - Session 3: tranSMART a Data Warehous...David Peyruc
tranSMART Community Meeting 5-7 Nov 13 - Session 3: tranSMART a Data Warehouse for Translational Medicine at Takeda Pharmaceuticals
International
Dave Marberg, Takeda
We have used the tranSMART platform to construct a warehouse containing data from several
Takeda clinical trials, proprietary preclinical drug activity studies, 1600 Gene Expression
Omnibus studies, and data from TCGA, CCLE, and other sources. All gene expression data has
been globally normalized. We extended the tranSMART platform with a set of R function calls
to enable cross-study queries and analysis via the rich toolset available in R. The utility of the
data warehouse is exemplified by a study in which we built a predictive model for drug
sensitivities. The model was trained on gene expression and IC50 data from cell lines and was
found to correctly predict drug activity in oncology indications.
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
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2. Related papers
• A beta-mixture quantile normalization method for correcting probe
design bias in Illumina Infinium 450k DNA methylation data: Andrew E.
Teschendorff, Francesco Marabita, Matthias Lechner, Thomas Bartlett,
Jesper Tegner, David Gomez-Cabrero, and Stephan Beck (2012)
• Evaluation of the infinium methylation 450k technology. Epigenomics,
3, 771–784: Dedeurwaerder,S. et al. (2011)
• Complete pipeline for infinium human methylation 450k beadchip data
processing using subset quantile normalization for accurate dna
methylation estimation. Epigenomics, 4, 325–341: Touleimat,N. and
Tost,J. (2012)
• Comparison of Beta-value and M-value methods for quantifying
methylation levels by microarray analysis. BMC Bioinformatics, 11, 587:
Du,P. et al. (2010)
• Applications of beta-mixture models in bioinformatics Yuan Ji1 et al.
(2005)
• SWAN: Subset quantile Within Array Normalization for Illumina
Infinium HumanMethylation 450k beadchips Maksimovic et al. 2012
• The minfi User's Guide Analyzing Illumina 450k Methylation Arrays
Hansen et al. (2011)
3. Background information
• DNA methylation – addition of a methyl group which affects gene expression.
• Beta Value (β): β = M/(M + U + α)
• Measure of methylation for each CpG
• Where M = Methylated intensity and U = Unmethylated intensity
• 27k array design (old)
• Infinium I assays only: M and U same color, different beads
• 450k array design (new)
• Hybrid of the Infinium I and Infinium II assays. Two different assays on
the same array
• Infinium II assays: M and U different color, same bead. Single probe pair
for each CpG site
• Allows (for 12 samples in parallel) assessment of the methylation status
of more than 480,000 cytosines distributed over the whole genome.
• Covers 99% of all RefSeq genes, average of 17 probes per gene.
4. Illumina Infinium 450k DNA methylation Beadchip
• Useful tool in EWAS studies
• Can provide more insight than 27k DNA methylation Beadchip.
• Problem: Two different designs causes the methylation vales derived from
these two designs to exhibit different distributions
• β-values obtained from Infinium II probes were less accurate and
reproducible than those obtained from Infinium I probes. Confirmed in at
least two papers
• “Evaluation of the Infinium Methylation 450K technology”
Dedeurwaeder et al., 2011
• “Complete pipeline for Infinium® Human Methylation 450K BeadChip
data processing using subset quantile normalization for accurate DNA
methylation estimation” Touleimat,N. and Tost,J. (2012)
• Inf1 probes can report for a wider range of β-values, reflecting all possible
methylation states even after adjustment for differences in biological
characteristics such as CpG density
• Because of this Inf2 probes may not be able to report with the same
sensitivity as Inf1probes as shown in the following graphs.
5. Infinium I vs Infinium II β Values
Dedeurwaeder et al., 2011
6. Infinium I vs Infinium II β Values
Touleimat,N. and Tost,J. (2012)
7. How to account for variation?
• Should account for extra source of variation between probe
type1 and probe type2 by normalizing each.
• Normalization means adjusting values measured on different
scales to a notionally common scale. In more complicated
cases, normalization may refer to more sophisticated
adjustments where the intention is to bring the entire probability
distributions of adjusted values into alignment.
• Several methods have been developed to normalize between
probe1 and probe2 data
• Peak-based-correction (PBC) – Adjust type2 probes based
on type1 probe peak values
• Subset Quantile Normalization (SQN) – Adjust type2 probes
quantile rank based on similar type1 probe‟s quantile rank
• Beta-MIxture Quantile dilation (BMIQ) – Adjust type2 probe
distribution based on type1 distribution
8. Normalization technique PBC
• Peak-based-correction (PBC) - Proposed by Dedeurwaeder et al., 2011, to
rescale the Infinium2 data on the basis of infinium1 density distribution
modes. 4 Steps to PBC:
1) Convert βvalues to Mvalues: Mvalue = log2(βvalue/(1 – βvalue)
2) Determine peaks from Infinium I and II independently using kernel
density estimation with Gaussian smoothing function and a band-width
= 0.5. Unmethylated peak summits were computed as SU = argmax
(density Mvalue) for negative Mvalues for both Infinium I and II.
Similarly, methylated peak summits were computed as SM = argmax
(density Mvalue) for positive Mvalues
9. Normalization technique PBC
3) Rescale raw Mvalues using peak summits as reference to get corrected
Mvalues
• The corrected Mvalues were then obtained by rescaling independently
negative and positive Mvalues using the distance between the peak
summits and zero.
• For negative Mvalues the corrected Mvalues were computed as follows:
corrected Mvalue = Mvalue/σU where σU is the distance between the
peak summit and zero (σU = 0 - SU).
• Corrected positive Mvalues were computed using the formula: corrected
Mvalue = Mvalue/σM with σM = SM - 0.
10. Normalization technique PBC
4) Rescale corrected M-values to match Infinium I range, then convert
back to β-values
• To convert back the corrected M-values to β-values, the M-values
were first rescaled to match Infinium I range. Negative M-values
were rescaled by the Infinium I σU (rescaled M-value = corrected M-
value. σU) and positive M-values by the Infinium I σM (rescaled M-
value = corrected M-value. σM). Finally, rescaled M-values were
converted to β-values by means of the relation β-value = 2M-
value/(2M-value + 1)
11. (A) Bar plots indicating the range of b‐values generated for HCT116 wild‐type (WT) sample (r3) with the Infinium I and Infinium II assays. (B)
Density plots of the beta‐values for the two Infinium assay types considered for HCT116 WT sample (r3). (C) Box plots of probe‐wise variance
between the three replicates of HCT116 WT (r1, r2 and r3) probes (outliers not drawn). On the left part of the figure, b‐values have undergone
no correction (raw data); on the right part, they have been subjected to the peak‐based correction.
Data: eight tumor samples,
eight normal breast tissue
samples
12. Normalization technique PBC
• PBC efficiently corrects for InfI/Inf2 shift and improves results.
• PBC implemented in R package Illumina Methylation Analyzer (IMA)
• However, two recent studies have exposed potential problems with PBC
• PBC depends on bimodal shape of methylation density profiles. It
breaks down when the methylation density distribution does not exhibit
well-defined peaks/modes (Maksimovic et al., 2012, Touleimat and
Tost, 2012)
• One proposed solution is Subset Quantile Normalization (SQN ) to
correct for this. (Touleimat and Tost, 2012)
• Another solutions is the technique is Beta-MIxture Quantile
Normalization (BMIQ) (Teschendorff et al. 2012)
13. Normalization technique SQN
• In general, β-values distributions should be normalized using standard
approaches, such as quantile normalization for inter-sample normalization.
However, three constraints prohibit such a straightforward approach for the
two different assays on the 450k beadchip:
1) The number of InfI (28%) and InfII (72%) probes differ and prevent from
computing a common set of reference quantiles
2) The population to „correct‟ (InfII) is the larger one and may therefore
bias the distribution of the other population (InfI)
3) There is a large imbalance in the proportions of Inf I and Inf II probes
covering the different CpG and gene-sequence regions
• A global standardization of methylation values distributions may lead to a
dramatic loss of information because the variation of the methylation status
may be specific for probes covering different subcategories of CpG
• SQN proposed to solve the two first issues by normalizing the gene-
expression signal by splitting between type1 and type2 and „anchor‟ type2
probes by the more stable and accurate type1 probes.
14. How does SQN work?
• Reference quantiles of a target set of features are estimated from the
smaller set of features used as „anchors‟ that are considered to be more
reliable and stable.
• Modifies the values of the target distribution based on rank equivalence
• Correct the data so that non-anchor and anchor probes of the same
percentiles will have the same value.
• Use InfI signals as the anchors to estimate a reference distribution of
quantiles and to use this reference to estimate a target distribution of
quantiles for InfII probes
• This should provide an accurate normalization of InfI/InfII probes and
correct for the shift
• Implemented two versions of SQN approach using provided Illumina
annotations.
1) Based on the „relation to CpG‟
2) Based on the „relation to gene sequence‟
17. Verification of SQN
• Touleimat,N. and Tost,J. (2012) verified their results using Pyrosequencing
• A technique which, according to their paper, provides high quantitative
precision and provides data with single-nucleotide resolution.
• Chose 13 probes for comparison which had to meet following criteria:
• Stable methylation values between samples of the same phenotype
(β SD < 0.1)
• differentially methylated (differential methylation > 20%) between
samples of different phenotypes
• Most importantly, large difference between median β-values
obtained with each variant of our preprocessing pipeline
• Their results, Table 1 on next slide, show SQN using the relation to CpG
annotations to identify category-related anchors provided the greatest
number of closest methylation values (n = 7) to those obtained by
pyrosequencing for the very same CpG.
• Note: With the exception of normalization method F, most performed
fairly well with G being best
19. Verification of SQN Cont.
• Their results, Table 2 on next slide, also show the SQN approach, together
with the peak-based correction approach, provided the smallest absolute
differences in the methylation values when compared with pyrosequencing-
based methylation values.
• Note: Most performed fairly well with G and E tied for best results
21. Subset Quantile Normalization (SQN) Results
• In general, SQN works well and avoids sensitivity issues to variations in the
shape of the methylation density curves seen by PBC.
• However, SQN requires a separate normalization to be performed on
selected subsets of probes that are matched for biological characteristics
(e.g. CpG density).
• SQN depends on a priori choices of which biological characteristics to use
when matching the type1 and type2 distribution
• Another model, BMIQ, is assumption-free, as it does not require a separate
normalization to be performed
22. Beta-MIxture Quantile dilation (BMIQ)
• New technique proposed aims to adjust the beta-values of type2 design
probes into a statistical distribution characteristic of type1 probes in order to
make their statistical distributions comparable. 3 steps:
1. Assign probes to methylation states
2. Transform probabilities into quantiles
3. Perform methylation dependent dilation transformation to preserve the
monotonicity and continuity of the data
23. Beta-MIxture Quantile dilation (BMIQ)
• Authors verified data by comparing results from tumor tissue samples to
other known methods. After assessment, BMIQ improves on „no
normalization‟ and compares favorably to other methods of normalization
with:
• Improved robustness of the normalization procedure
• Reduced technical variation and bias of type2 probe values
• Elimination of type1 enrichment bias cause by lower dynamic range of
type2 probes
• Code available at http://code.google.com/p/bmiq/downloads/list
24. BMIQ INPUT
• ### beta.v: vector consisting of beta-values for a given sample. NAs are not allowed Beta-values
that are exactly 0 or 1 will be replaced by the min positive or max value below 1, respectively.
• ### design.v: corresponding vector specifying probe design type (1=type1,2=type2). This must be
of the same length as beta.v and in the same order.
• ### doH: perform normalization for hemimethylated type2 probes. By default TRUE.
• ### nfit: number of probes of a given design to use for the fitting. Default is 50000. Smaller values
(~10000) will make BMIQ run faster at the expense of a small loss in accuracy. For most
applications, 10000 is ok.
• ### nL: number of states in beta mixture model. 3 by default. At present BMIQ only works for nL=3.
• ### th1.v: thresholds used for the initialization of the EM-algorithm, they should represent best
guesses for calling type1 probes hemi-methylated and methylated, and will be refined by the EM
algorithm. Default values work well in most cases.
• ### th2.v: thresholds used for the initialization of the EM-algorithm, they should represent best
guesses for calling type2 probes hemi-methylated and methylated, and will be refined by the EM
algorithm. By default this is null, and the thresholds are estimated based on th1.v and a modified
PBC correction method.
• ### niter: maximum number of EM iterations to do. By default 5.
• ### tol: tolerance threshold for EM algorithm. By default 0.001.
• ### plots: logical specifying whether to plot the fits and normalized profiles out. By default TRUE.
• ### sampleID: the ID of the sample being normalized.
25. Beta-MIxture Quantile dilation (BMIQ)
• ### OUTPUT
• ### A list with the following elements:
• ### nbeta: the normalized beta-profile for the sample
• ### class1: the assigned methylation state of type1 probes
• ### class2: the assigned methylation state of type2 probes
• ### av1: mean beta-values for the nL classes for type1 probes.
• ### av2: mean beta-values for the nL classes for type2 probes.
• ### hf: the "Hubble" dilation factor
• ### th1: estimated thresholds used for type1 probes
• ### th2: estimated thresholds used for type2 probes
26. BMIQ Paper used 10, 450k data sets
• Datasets 1 and 2: (BT) and (CL) subset of the dataset considered in
Dedeurwaerder et al. (2011). eight fresh frozen (FF) breast tumors and eight
normal breast tissue specimens [hereafter referred to as (BT)], as well as the
three replicates from the HCT116 WT cell-line [hereafter referred to as (CL)].
For these cell-lines, matched bisulphite pyrosequencing (BPS) data were
available for nine type2 probes.
• Datasets 3 and 4: (FFPE) and (FF) consists of 32 formalin-fixed paraffin-
embedded (FFPE) head and neck cancers HNCs), of which 18 were HPV+
and 14 HPV-, as well as five fresh frozen HNCs (FF), of which 2 were HPV+
and 3 HPV-. Available from GEO under accession number GSE38271.
• Dataset 5: (GBM) consists of 81 glioblastoma multiformes (GBMs) (Turcan
et al., 2012), 49 of which were categorized as CpG island methylator positive
(CIMP+) and 32 as CIMP-.
• Datasets 6–10: TCGA, LIV, LC, BLDC, HCC samples are all from the
TCGA: Dataset6 (TCGA) consists of 10 samples as provided in the
Bioconductor data package TCGAmethylation 450k, Dataset7 (LIV) consists
of nine normal liver tissue samples from Batch203 in the TCGA data portal,
Dataset8 (LC) consists of 22 lung cancer samples from Batch196, Dataset9
(BLDC) consists of 12 bladder cancer samples from Batch86 and Dataset10
(HCC) consists of 10 hepatocellular carcinoma samples from Batch153.
27. BMIQ normalization criteria
i. Must allow for the different biological characteristics of type1 and type2
probes
• Type1 probes are significantly more likely to map to CpG islands than
type2 probes, and hence the relative proportion of methylated and
unmethylated probes will vary between the two designs. In the case of
the type2 probes, this means that these proportions must be invariant
under the normalization transformation.
ii. The transformation of the type2 probe values should reduce the bias
• which amounts to matching of the density distributions of the two
design types, specially at the unmethylated and methylated extremes.
iii. The transformation must be monotonic
• Relative ranking of beta values of the type2 probes must be invariant
under the transformation.
28. BMIQ normalization strategy
• Fit a three state beta mixture model (unmethylated-U, hemimethylated-
H, fully methylated-M) to type1 and type2 probes separately using three
steps
• Note: Let {(aI
U,bI
U),(aI
H,bI
H),(aI
M,bI
M)} denote the parameters of the three
beta distributions for the type1 probes, and similarly let
{(aII
U,bII
U),(aII
H,bII
H),(aII
M,bII
M)} describe the estimated parameters the
three beta components for the type2 probes. State membership of
individual probes is determined by the maximum probability criterion.
29. Beta Distribution
• Family of continuous probability distributions defined on the interval [0, 1]
parametrized by two positive shape parameters, denoted by α and β, that
appear as exponents of the random variable and control the shape of the
distribution.
http://en.wikipedia.org/wiki/Beta_distribution
30. BMIQ normalization strategy 3 steps cont.
1. For those type2 probes assigned to the U-state, transform their probabilities
of belonging to the U-state to quantiles using the inverse of the cumulative
beta distribution with beta parameters (aI
U,bI
U) estimated from the type1 U
component. Let nu
II denote the normalized values of the type2 U-probes.
2. For those type2 probes assigned to the M-state, transform their probabilities
of belonging to the M-state to quantiles using the inverse of the cumulative
beta distribution with beta parameters (aI
M,bI
M) estimated from the type1 M
component. Let nM
II denote the normalized values of the type2 M-probes.
3. For the type2 probes assigned to the H-state, we perform a dilation (scale)
transformation to „fit‟ the data into the „gap‟ with endpoints defined by
ma{nu
II} and min{nM
II}
32. Aside: Expectation Maximization using Beta Mixture
• EM – Uses Beta-mixture model. From Ji et al. 2005
• The beta-mixture model deals with a vector of correlation coefficients of
gene-expression levels. Correlation coefficients are assumed to come from
multiple underlying probability distributions, in our case, beta distributions. To
fit the beta distribution, for each correlation coefficient xi , we apply a linear
transformation yi = (xi +1)/2, so that the range of the transformed values is
between 0 and 1. The index i represents the gene with respect to which the
correlation coefficient y is calculated. Let {yi }, i = 1, . . . , n, denote the
transformed correlation coefficients (where n is the total number of
observations and L is the number of components in the mixture) under a
mixture of beta distributions,
Denotes the density of the beta-distribution:
34. Aside: Expectation Maximization using Beta Mixture
Use expectation maximization algorithm (Dempster et al., 1977) to iteratively
maximize the log-likelihood and update the conditional probability that yi comes
from the l-th component, which is defined as
Consists of 4 steps. Repeat the first three until Repeat M-step and E-step until
the change in the value of the log-likelihood in Equation (1) is negligible.
Ji et al. 2005
The EM algorithm yields the final estimated posterior probability z∗ , the value
of which represents the il posterior probability that correlation coefficient yi
comes from component l.
36. BMIQ normalization procedure
• Results from EM algorithm are two-tailed so need to subdivide Beta values
into those values which fall left or right of the mean. Unmethylated being to
the left and Methylated to the right.
• Use these to normalize U and M beta values
• Now need to normalize H beta values
• Normalized beta-values for the H-probes is given by the conformal
(shift+dilation) transformation based on max{M} and min{U} values
• This conformal transformation involves a non-uniform rescaling of the H
probe beta values since it depends on the beta-value of the probe. This
is absolutely key in order to avoid gaps or holes from emerging in the
normalized distribution
• It is important to match normalize with respect to which tail the beta value
falls in because the left tail end of the methylated type2 distribution is
generally not well described by a beta-distribution, presumably a result of die
bias. Similar for unmethylated and the right tail.
39. BMIQ normalization procedure
• Resulting thresholds would normally fall within the ranges 0.2–0.3 and 0.60–
0.8, respectively. Having thus identified reasonable initial estimates for the
weights {πU
II,πH
II,πM
(II)} the algorithm will then automatically determine the
unmethylated, hemi- methylated and methylated fractions for each sample
individually.
40. Improved robustness of BMIQ
• BMIQ does not use the type1 modes to adjust the type2 data, and hence
BMIQ normalization of the type2 probes generated a much smoother density
distribution, suggestive of an improved normalization framework (Fig. 1B)
41. BMIQ reduces technical variation (ERROR)
• BMIQ not only led to a significant improvement, but was also marginally
better than PBC (Fig. 2B)
Manhattan distance – distance between two points in a grid based on a strictly horizontal and/or vertical path
42. BMIQ reduces bias of type2 methylation values
• BMIQ significantly reduced the bias of type2 values (Fig. 3), although
there was no improvement over PBC itself
43. BMIQ eliminates the type1 enrichment bias
• To assess any potential bias towards type1 probes, computed for a given
number of top ranked probes the odds ratio (OR) of relative enrichment of
type1 over type2 probes. BMIQ successfully avoided any type1/type2
enrichment bias in all three datasets, indicative of an improved normalization
of type2 values
44. Reduced technical variability within probe clusters
• Defined probe clusters as contiguous regions containing at least seven
probes with no two adjacent probes separated by >300bp.
• Within these probe clusters, paper posited that pairs of adjacent probes,
one from each design and within 200 bp of each other, should have
similar methylation values.
• To evaluate normalization algorithms evaluated which one minimizes the
absolute difference in methylation between such closely adjacent type1-
type2 pairs
47. BMIQ robustly identifies features associated with HPV status
• Paper attempted to verify that a reduction in technical variation obtained
with BMIQ is not at the expense of reduced biological signal.
• Used a training test set strategy to identify features in a training set and
calling them true positives if validated in a test set.
• Allows for a comparison of sensitivity and positive predictive value (PPV)
between the different normalization methods.
• BMIQ identified more differentially methylated features than PBC or
SWAN, not at the expense of a smaller PPV, and so, overall, BMIQ
identified more true positives
48. Results
• Because of the different nature of type1 and type2 probes on the Illumina
450k Methylation Beadchip a different kind of normalization is necessary
then what was used on 27k data
• There are several methods to do this, each is better then performing
quantile normalization without discriminating probe types.
• Normalization with regard to probe type improved robustness, Reduced
technical variation, Reduced bias of type2 methylation values,
Elimination of type1 enrichment bias