Jie Zheng at the #ICG12 GigaScience Prize Track: PhenoSpD: an atlas of phenotypic correlations and a multiple testing correction for the human phenome. ICG12, Shenzhen, 26th October 2017
Numerical Prediction of Microbubble Attachment in Biological Flows (2)Joshua Gosney
This document presents a numerical model to estimate hydrodynamic forces on microbubbles in biological flows. The model is validated by comparing predictions of microbubble attachment to experimental measurements under controlled flow conditions. The model finds stable microbubble attachment can be expected up to average fluid velocities of 0.025 cm/s near the microbubble, corresponding to particle Reynolds numbers around 0.001. Both computational fluid dynamics simulations and simplified analytical models are used to predict forces on microbubbles in capillaries, venules and veins.
This study developed a computational approach combining gene expression ranking and co-expression network analysis to identify novel stress regulatory genes in Arabidopsis thaliana. The researchers ranked genes based on their expression response to multiple abiotic stresses and analyzed co-expression networks to select candidate stress regulatory genes. Screening 62 mutants defective in candidate genes yielded a remarkably high gene discovery rate of up to 62% for stress phenotypes, far greater than typical screens. Additionally screening 64 other mutants based only on expression ranking yielded a lower but still improved rate of 36%, showing the power of combining methods. This systems approach can enhance gene discovery for other processes given suitable transcriptome data.
CIIProCluster: Developing Read-Across Predictive Toxicity Models Using Big DataDaniel Russo
This document summarizes the CIIProCluster approach for developing predictive toxicity models using large biological and chemical data. It clusters bioassays based on the chemical substructures that activate them to identify mechanisms of toxicity. For oral acute toxicity data, it extracts biological assay profiles for compounds and clusters 1,948 assays based on relevant fingerprints. Evaluation of a cluster of cytotoxicity assays shows their potential for predicting oral toxicity when restricting predictions to those with high confidence based on active responses between compounds. The approach demonstrates how leveraging large biological data through clustering can provide insight into toxicity mechanisms and develop predictive models.
Searching for predictors of male fecundityChirag Patel
This document summarizes a presentation on searching for semen phenotypes that are predictive of impaired male fecundity. The study analyzed data from 501 couples in the Longitudinal Investigation of Fertility and the Environment study. It found that several semen morphology measures, including abnormal tail shape and head shape, were weakly associated with longer time to pregnancy of over 6 months after adjusting for factors like age, smoking, and previous pregnancies. However, semen phenotypes provided only modest predictive value beyond traditional risk factors. Larger studies are needed to better understand variations in male fecundity.
This document summarizes research on developing a human cancer coessentiality network using data from pooled shRNA screens across 107 cancer cell lines. Key points:
- A network of 866 genes and 1877 edges was constructed based on correlations in essentiality profiles across cell lines.
- Network clustering identified groups of genes essential for similar cell line subtypes (e.g. breast, ovarian, pancreatic cancers).
- One cluster involved in oxidative phosphorylation was particularly essential for luminal/HER2 breast cancers.
- The network provides a functional genomics resource, though opportunities exist to improve coverage and accuracy.
Atomic force spectroscopy, a technique derived from Atomic Force Microscopy (AFM), allowed us to distinguish nonspecific and specific interactions between the acetolactate synthase enzyme (ALS) and anti-atrazine antibody biomolecules and the herbicides imazaquin, metsulfuron-methyl and atrazine. The presence of specific interactions increased the adhesion force (Fadh) between the AFM tip and the herbicides, which made the modified tip a powerful biosensor. Increases of approximately 132% and 145% in the Fadh values were observed when a tip functionalized with ALS was used to detect imazaquin and metsulfuron-methyl, respectively. The presence of specific interactions between the atrazine and the anti-atrazine antibody also caused an increase in the Fadh values (approximately 175%) compared to those observed when using an unfunctionalized tip. The molecular modeling results obtained with the ALS enzyme suggest that the orientation of the biomolecule on the tip surface could be suitable for allowing interaction with the herbicides imazaquin and metsulfuron-methyl
This document summarizes research on analyzing differential miRNA-mRNA co-expression networks in colorectal cancer. The researchers analyzed paired expression data from cancer and normal tissues to identify changes in interactions. They found that cancer networks have decreased connectivity and identified differentially connected genes, including known cancer genes. Pathway analysis revealed an alteration in colorectal cancer tissues in the interplay between miRNAs and the eukaryotic translation initiation factor 3 complex, which is important for translation. Certain miRNAs were also identified as having many differentially co-expressed target mRNAs.
Association mapping, GWAS, Mapping, natural population mappingMahesh Biradar
This document discusses association mapping for crop improvement. It explains that association mapping exploits historical recombination events in populations to map quantitative trait loci with greater precision than family-based linkage analysis. Association mapping can be applied to diverse populations and detect more alleles than bi-parental mapping. Genome-wide association studies allow for high-resolution mapping of traits down to the sequence level by leveraging linkage disequilibrium. Statistical methods must account for population structure and kinship to avoid false positives in association analyses.
Numerical Prediction of Microbubble Attachment in Biological Flows (2)Joshua Gosney
This document presents a numerical model to estimate hydrodynamic forces on microbubbles in biological flows. The model is validated by comparing predictions of microbubble attachment to experimental measurements under controlled flow conditions. The model finds stable microbubble attachment can be expected up to average fluid velocities of 0.025 cm/s near the microbubble, corresponding to particle Reynolds numbers around 0.001. Both computational fluid dynamics simulations and simplified analytical models are used to predict forces on microbubbles in capillaries, venules and veins.
This study developed a computational approach combining gene expression ranking and co-expression network analysis to identify novel stress regulatory genes in Arabidopsis thaliana. The researchers ranked genes based on their expression response to multiple abiotic stresses and analyzed co-expression networks to select candidate stress regulatory genes. Screening 62 mutants defective in candidate genes yielded a remarkably high gene discovery rate of up to 62% for stress phenotypes, far greater than typical screens. Additionally screening 64 other mutants based only on expression ranking yielded a lower but still improved rate of 36%, showing the power of combining methods. This systems approach can enhance gene discovery for other processes given suitable transcriptome data.
CIIProCluster: Developing Read-Across Predictive Toxicity Models Using Big DataDaniel Russo
This document summarizes the CIIProCluster approach for developing predictive toxicity models using large biological and chemical data. It clusters bioassays based on the chemical substructures that activate them to identify mechanisms of toxicity. For oral acute toxicity data, it extracts biological assay profiles for compounds and clusters 1,948 assays based on relevant fingerprints. Evaluation of a cluster of cytotoxicity assays shows their potential for predicting oral toxicity when restricting predictions to those with high confidence based on active responses between compounds. The approach demonstrates how leveraging large biological data through clustering can provide insight into toxicity mechanisms and develop predictive models.
Searching for predictors of male fecundityChirag Patel
This document summarizes a presentation on searching for semen phenotypes that are predictive of impaired male fecundity. The study analyzed data from 501 couples in the Longitudinal Investigation of Fertility and the Environment study. It found that several semen morphology measures, including abnormal tail shape and head shape, were weakly associated with longer time to pregnancy of over 6 months after adjusting for factors like age, smoking, and previous pregnancies. However, semen phenotypes provided only modest predictive value beyond traditional risk factors. Larger studies are needed to better understand variations in male fecundity.
This document summarizes research on developing a human cancer coessentiality network using data from pooled shRNA screens across 107 cancer cell lines. Key points:
- A network of 866 genes and 1877 edges was constructed based on correlations in essentiality profiles across cell lines.
- Network clustering identified groups of genes essential for similar cell line subtypes (e.g. breast, ovarian, pancreatic cancers).
- One cluster involved in oxidative phosphorylation was particularly essential for luminal/HER2 breast cancers.
- The network provides a functional genomics resource, though opportunities exist to improve coverage and accuracy.
Atomic force spectroscopy, a technique derived from Atomic Force Microscopy (AFM), allowed us to distinguish nonspecific and specific interactions between the acetolactate synthase enzyme (ALS) and anti-atrazine antibody biomolecules and the herbicides imazaquin, metsulfuron-methyl and atrazine. The presence of specific interactions increased the adhesion force (Fadh) between the AFM tip and the herbicides, which made the modified tip a powerful biosensor. Increases of approximately 132% and 145% in the Fadh values were observed when a tip functionalized with ALS was used to detect imazaquin and metsulfuron-methyl, respectively. The presence of specific interactions between the atrazine and the anti-atrazine antibody also caused an increase in the Fadh values (approximately 175%) compared to those observed when using an unfunctionalized tip. The molecular modeling results obtained with the ALS enzyme suggest that the orientation of the biomolecule on the tip surface could be suitable for allowing interaction with the herbicides imazaquin and metsulfuron-methyl
This document summarizes research on analyzing differential miRNA-mRNA co-expression networks in colorectal cancer. The researchers analyzed paired expression data from cancer and normal tissues to identify changes in interactions. They found that cancer networks have decreased connectivity and identified differentially connected genes, including known cancer genes. Pathway analysis revealed an alteration in colorectal cancer tissues in the interplay between miRNAs and the eukaryotic translation initiation factor 3 complex, which is important for translation. Certain miRNAs were also identified as having many differentially co-expressed target mRNAs.
Association mapping, GWAS, Mapping, natural population mappingMahesh Biradar
This document discusses association mapping for crop improvement. It explains that association mapping exploits historical recombination events in populations to map quantitative trait loci with greater precision than family-based linkage analysis. Association mapping can be applied to diverse populations and detect more alleles than bi-parental mapping. Genome-wide association studies allow for high-resolution mapping of traits down to the sequence level by leveraging linkage disequilibrium. Statistical methods must account for population structure and kinship to avoid false positives in association analyses.
Bioinformatics Strategies for Exposome 100416Chirag Patel
This document discusses the challenges of using big data from the exposome for robust biomedical discovery. It notes that the exposome generates a huge number of potential exposure-phenotype hypotheses but observational data is susceptible to biases. It proposes bioinformatics-inspired guidelines to enhance discovery, including systematically testing hypotheses while addressing multiplicity, replicating findings, developing databases to disseminate results, and practicing reproducible research. Specific examples are given of searching large exposure-phenotype spaces and developing phenotype-exposure association maps to systematically explore connections in a big data context.
Detection of novel metabolites and enzyme functions though in silico expansio...James Jeffryes
This document discusses methods for expanding metabolic models to detect novel metabolites and enzyme functions. It describes how metabolic models called MINEs are generated by applying chemical reaction rules to known compounds. These MINEs contain many more predicted compounds and reactions than the source databases. MINEs have been shown to annotate more mass features from metabolomics studies while maintaining accuracy. They have also been integrated into workflows to discover novel metabolites through mass spectrometry. The MINEs and methods described provide a way to explore currently unknown areas of metabolism.
This document provides an overview of association mapping as a tool for dissecting phenotypic variation and mapping quantitative trait loci (QTLs). It discusses the differences between traditional QTL mapping using biparental mapping populations versus association mapping using natural populations. Association mapping offers higher mapping resolution, uses more diverse germplasm, and is less time-consuming and costly than traditional QTL mapping. It then describes linkage disequilibrium-based association mapping and factors that influence linkage disequilibrium. The document also discusses different approaches to association mapping, including candidate gene association studies, genome-wide association studies, and localized association studies.
This document discusses quantitative trait loci (QTL) analysis and mapping. It begins with a brief history of genetics and quantitative traits. QTL analysis uses phenotypic and genotypic data to link complex trait variation to genetics. There are several approaches for QTL analysis, including single marker analysis, interval mapping, and association mapping. Interval mapping uses flanking markers and likelihood estimates to more precisely map QTL locations compared to single marker analysis. Composite interval mapping further refines this by using additional markers as cofactors. The accuracy of QTL mapping is influenced by genetic and environmental factors as well as population size and experimental error. QTLs can be confirmed through multiple methods such as stability across environments or using near-isogenic lines.
1) Association mapping is a method that relies on linkage disequilibrium to study the relationship between genetic markers and phenotypic traits in natural populations. It aims to find statistical associations between genes or markers and complex traits.
2) The key advantage of association mapping over traditional QTL mapping is that it has higher resolution because it utilizes more historical recombinations, does not require developing new mapping populations, and allows evaluation of many alleles simultaneously.
3) Linkage disequilibrium, which is the non-random association of alleles at different loci, is required for association mapping to be effective. Factors like population structure, relatedness, and recombination rates impact the extent of linkage disequilibrium.
Crimson Publishers-Potential Application of Raman Micro-Spectroscopy as an In...CrimsonPublishersMAPP
Potential Application of Raman Micro-Spectroscopy as an In vitro Drug Screening and Companion Diagnostic Tool for Clinical Application: Chemotherapeutic Drug Mechanism of Action, Cellular Effects and Resistance by Z Farhane in Modern Applications in Pharmacy & Pharmacology
Personalized medicine via molecular interrogation, data mining and systems bi...Gerald Lushington
One of the major problems in our medical system is the prescription of medicines that, although well validated over a general group of clinical trial patients for specific ailments, may produce unhelpful or even harmful results in some individuals. A major emerging goal in the pharmaceutical and biomedical industries is the ability to tailor medicines to the individual. This can be achieved, but in practice still requires careful analysis of an extensive array of data and thus has not yet entered the mainstream medical practice.
My presentation at the 2014 MetaCenter symposium in Eugene, OR entitled "High-throughput annotation of metagenomes reveals community physiological variation in the mammalian microbiome"
1) The document discusses interfaces between nanoelectronic and biological systems, including applications of nanotechnology in medicine (nanomedicine) and molecular biomimetics.
2) It describes techniques like phage display and cell surface display that use libraries of random peptides to select sequences that bind to inorganic surfaces for constructing hybrid biomaterials.
3) The applications discussed include bio-nanowire devices for ultrasensitive detection, nanowire-based sensors for cancer markers, and interfaces between nanoelectronic devices and cells.
Looking Back at Mycobacterium tuberculosis Mouse Efficacy Testing To Move Ne...Sean Ekins
1) Tuberculosis kills over 1.6 million people per year and 1/3 of the world's population is infected. However, only one new drug has been approved in the past 40 years.
2) The authors have compiled a database of over 1,500 molecules tested in murine models of tuberculosis infection, along with their activities and molecular properties.
3) Machine learning models were able to retrospectively predict the activities of compounds in the murine models with up to 72.7% accuracy, suggesting these models may help prioritize compounds for further testing and identify new treatment leads.
Correlation globes of the exposome 2016Chirag Patel
This document discusses developing exposome correlation globes to map associations between exposures and phenotypes. It summarizes work analyzing replicated correlations between over 250 quantitative exposures measured in NHANES participants to create a globe visualization. The analysis found that while the exposome correlations were dense, with around 3% of pair-wise correlations replicated between cohorts, the correlations were modest in absolute size. The exposome globes could help contextualize exposome-wide association studies and identify co-occurring exposures.
This team aims to identify drugs that can disrupt vital virulence pathways in Mycobacterium tuberculosis (Mtb) by inhibiting specific protein-protein interactions. They will use the mycobacterial protein fragment complementation (M-PFC) assay to screen a library of 446 approved drugs for inhibitors of interactions important for Mtb pathogenesis. Hits from the screen will be further analyzed for toxicity against human cells and potential to treat tuberculosis. Identifying repurposed drugs that target Mtb virulence could advance new treatment options without the need for extensive clinical testing.
This document summarizes a presentation on multi-trait modeling in polygenic scores. It discusses sparse regression models like LASSO to build polygenic risk scores (PRS) from large genetic datasets. It introduces multi-PRS models that combine disease PRS with biomarker PRS to improve disease prediction. It also presents genetic component-based PRS models called DeGAs-PRS that group genetic variants into components for enhanced interpretation. The presentation evaluates these multi-trait modeling approaches using large-scale UK Biobank data.
Loss of Connectivity in Cancer Co-Expression Networks - PLoS ONE 9(1): e87075...Roberto Anglani
This document summarizes a study that analyzed gene expression data from five types of cancer (colorectal, lung, gastric, pancreatic, cervical) to identify genes with significantly different connectivity patterns between normal and tumor tissues. The study found that a loss of connectivity is a common feature of cancer networks compared to normal networks. Analyzing differentially connected genes can reveal new candidate cancer genes not identified by differential expression analysis alone. Integrating differential expression and differential connectivity data improves pathway enrichment analysis and provides insights into novel cancer-related pathways.
This document provides an overview of genome-wide association studies (GWAS). It defines key terms related to GWAS such as linkage disequilibrium, minor allele frequency, and odds ratio. It compares linkage mapping and association mapping. It describes the methodology of GWAS including identifying population structure, selecting case and control subjects, genotyping samples, and determining associated SNPs. It discusses challenges such as multiple hypothesis testing and population structure. It provides examples of successful GWAS in crops like maize and Arabidopsis. Overall, the document provides a comprehensive introduction and overview of GWAS.
A miniaturized sandwich immunoassay platformQing Chen
This document describes a new miniaturized sandwich immunoassay platform (MSIP) for detecting protein-protein interactions (PPIs) in a high-throughput manner. The MSIP combines antibody microarray technology with co-immunoprecipitation methods to allow simple, rapid, and large-scale PPI detection using small amounts of cell lysate. Evaluation of the MSIP showed it could accurately identify both known interacting and non-interacting protein pairs. Compared to traditional resin-based co-immunoprecipitation, the MSIP has higher sensitivity and throughput while being simpler and more cost-effective. The MSIP is presented as an effective method for validating PPIs identified by other techniques like yeast two-hybrid screening
Effect of Food Source on Enzymatic Activity in C. maculatus [draft 2]Dylan Easterday
1. The study investigated the effect of food source (mung beans vs cowpeas) on enzymatic activity in Callosobruchus maculatus beetles, which are agricultural pests.
2. Experiments measured alpha-naphthyl acetate esterase (ANAE) and beta-naphthyl acetate esterase (BNAE) activity in beetles fed each food source.
3. Statistical analysis using t-tests found no significant differences in ANAE or BNAE activity between the food sources, failing to support the hypothesis that food source affects enzymatic activity.
Jiankang Wang. Principle of QTL mapping and inclusive composite interval mapp...FOODCROPS
This document discusses quantitative trait locus (QTL) mapping and the inclusive composite interval mapping (ICIM) method. It begins with an overview of quantitative traits, QTL mapping, and different mapping populations. It then describes problems with previous interval mapping methods and introduces the theoretical basis and methodology of ICIM, which can detect additive and interacting QTL while avoiding biased estimates. The document highlights several publications that have used ICIM in crops like rice, wheat, soybean, and maize. It concludes with an overview of the biparental population (BIP) functionality in the QTL IciMapping software, which implements six different QTL mapping methods including various interval mapping approaches and single marker analysis.
Bioengineered 3D Co culture Lung In Vitro Models: Platforms to Integrate Cell...Ken Rogan
Cian O'Leary and his lab are developing 3D bioengineered in vitro models of the lung and other tissues using scaffolds.
[1] They have created bilayered collagen-hyaluronate scaffolds that support a mucociliary epithelial phenotype in lung cell culture models.
[2] The lab is also working on 3D hydrogel models of pancreatic cancer to study cell-matrix interactions and cancer progression.
[3] Future work includes developing dynamically stiffening hydrogel models and applying these platforms to study lung cancer and the pre-metastatic niche.
This document summarizes Christopher Mason's presentation on epigenetics quality control and single-cell RNA-seq variant calling using samples from the Genome in a Bottle project. It discusses generating reference epigenetics datasets, including whole genome bisulfite sequencing data, Illumina 450K methylation array data, and targeted bisulfite sequencing data for several GIAB samples. Parameters for variant calling from single-cell RNA-seq data are evaluated, finding best sensitivity and specificity at 97% and 80% respectively using certain settings. The work aims to establish high quality epigenetics and variant calling references to help benchmark computational methods for personalized medicine.
Bioinformatic Analysis of Synthetic Lethality in Breast CancerTom Kelly
This document summarizes a bioinformatic analysis of synthetic lethal genetic interactions in breast cancer. It describes how the researchers used gene expression data from breast cancer samples to predict potential synthetic lethal gene pairs through statistical testing. Many statistically significant interactions were found, including known synthetic lethal partners. The researchers validated some predictions and discuss applications for targeted cancer therapies and chemoprevention. High performance computing resources were crucial for analyzing large genome-scale datasets.
Bioinformatics Strategies for Exposome 100416Chirag Patel
This document discusses the challenges of using big data from the exposome for robust biomedical discovery. It notes that the exposome generates a huge number of potential exposure-phenotype hypotheses but observational data is susceptible to biases. It proposes bioinformatics-inspired guidelines to enhance discovery, including systematically testing hypotheses while addressing multiplicity, replicating findings, developing databases to disseminate results, and practicing reproducible research. Specific examples are given of searching large exposure-phenotype spaces and developing phenotype-exposure association maps to systematically explore connections in a big data context.
Detection of novel metabolites and enzyme functions though in silico expansio...James Jeffryes
This document discusses methods for expanding metabolic models to detect novel metabolites and enzyme functions. It describes how metabolic models called MINEs are generated by applying chemical reaction rules to known compounds. These MINEs contain many more predicted compounds and reactions than the source databases. MINEs have been shown to annotate more mass features from metabolomics studies while maintaining accuracy. They have also been integrated into workflows to discover novel metabolites through mass spectrometry. The MINEs and methods described provide a way to explore currently unknown areas of metabolism.
This document provides an overview of association mapping as a tool for dissecting phenotypic variation and mapping quantitative trait loci (QTLs). It discusses the differences between traditional QTL mapping using biparental mapping populations versus association mapping using natural populations. Association mapping offers higher mapping resolution, uses more diverse germplasm, and is less time-consuming and costly than traditional QTL mapping. It then describes linkage disequilibrium-based association mapping and factors that influence linkage disequilibrium. The document also discusses different approaches to association mapping, including candidate gene association studies, genome-wide association studies, and localized association studies.
This document discusses quantitative trait loci (QTL) analysis and mapping. It begins with a brief history of genetics and quantitative traits. QTL analysis uses phenotypic and genotypic data to link complex trait variation to genetics. There are several approaches for QTL analysis, including single marker analysis, interval mapping, and association mapping. Interval mapping uses flanking markers and likelihood estimates to more precisely map QTL locations compared to single marker analysis. Composite interval mapping further refines this by using additional markers as cofactors. The accuracy of QTL mapping is influenced by genetic and environmental factors as well as population size and experimental error. QTLs can be confirmed through multiple methods such as stability across environments or using near-isogenic lines.
1) Association mapping is a method that relies on linkage disequilibrium to study the relationship between genetic markers and phenotypic traits in natural populations. It aims to find statistical associations between genes or markers and complex traits.
2) The key advantage of association mapping over traditional QTL mapping is that it has higher resolution because it utilizes more historical recombinations, does not require developing new mapping populations, and allows evaluation of many alleles simultaneously.
3) Linkage disequilibrium, which is the non-random association of alleles at different loci, is required for association mapping to be effective. Factors like population structure, relatedness, and recombination rates impact the extent of linkage disequilibrium.
Crimson Publishers-Potential Application of Raman Micro-Spectroscopy as an In...CrimsonPublishersMAPP
Potential Application of Raman Micro-Spectroscopy as an In vitro Drug Screening and Companion Diagnostic Tool for Clinical Application: Chemotherapeutic Drug Mechanism of Action, Cellular Effects and Resistance by Z Farhane in Modern Applications in Pharmacy & Pharmacology
Personalized medicine via molecular interrogation, data mining and systems bi...Gerald Lushington
One of the major problems in our medical system is the prescription of medicines that, although well validated over a general group of clinical trial patients for specific ailments, may produce unhelpful or even harmful results in some individuals. A major emerging goal in the pharmaceutical and biomedical industries is the ability to tailor medicines to the individual. This can be achieved, but in practice still requires careful analysis of an extensive array of data and thus has not yet entered the mainstream medical practice.
My presentation at the 2014 MetaCenter symposium in Eugene, OR entitled "High-throughput annotation of metagenomes reveals community physiological variation in the mammalian microbiome"
1) The document discusses interfaces between nanoelectronic and biological systems, including applications of nanotechnology in medicine (nanomedicine) and molecular biomimetics.
2) It describes techniques like phage display and cell surface display that use libraries of random peptides to select sequences that bind to inorganic surfaces for constructing hybrid biomaterials.
3) The applications discussed include bio-nanowire devices for ultrasensitive detection, nanowire-based sensors for cancer markers, and interfaces between nanoelectronic devices and cells.
Looking Back at Mycobacterium tuberculosis Mouse Efficacy Testing To Move Ne...Sean Ekins
1) Tuberculosis kills over 1.6 million people per year and 1/3 of the world's population is infected. However, only one new drug has been approved in the past 40 years.
2) The authors have compiled a database of over 1,500 molecules tested in murine models of tuberculosis infection, along with their activities and molecular properties.
3) Machine learning models were able to retrospectively predict the activities of compounds in the murine models with up to 72.7% accuracy, suggesting these models may help prioritize compounds for further testing and identify new treatment leads.
Correlation globes of the exposome 2016Chirag Patel
This document discusses developing exposome correlation globes to map associations between exposures and phenotypes. It summarizes work analyzing replicated correlations between over 250 quantitative exposures measured in NHANES participants to create a globe visualization. The analysis found that while the exposome correlations were dense, with around 3% of pair-wise correlations replicated between cohorts, the correlations were modest in absolute size. The exposome globes could help contextualize exposome-wide association studies and identify co-occurring exposures.
This team aims to identify drugs that can disrupt vital virulence pathways in Mycobacterium tuberculosis (Mtb) by inhibiting specific protein-protein interactions. They will use the mycobacterial protein fragment complementation (M-PFC) assay to screen a library of 446 approved drugs for inhibitors of interactions important for Mtb pathogenesis. Hits from the screen will be further analyzed for toxicity against human cells and potential to treat tuberculosis. Identifying repurposed drugs that target Mtb virulence could advance new treatment options without the need for extensive clinical testing.
This document summarizes a presentation on multi-trait modeling in polygenic scores. It discusses sparse regression models like LASSO to build polygenic risk scores (PRS) from large genetic datasets. It introduces multi-PRS models that combine disease PRS with biomarker PRS to improve disease prediction. It also presents genetic component-based PRS models called DeGAs-PRS that group genetic variants into components for enhanced interpretation. The presentation evaluates these multi-trait modeling approaches using large-scale UK Biobank data.
Loss of Connectivity in Cancer Co-Expression Networks - PLoS ONE 9(1): e87075...Roberto Anglani
This document summarizes a study that analyzed gene expression data from five types of cancer (colorectal, lung, gastric, pancreatic, cervical) to identify genes with significantly different connectivity patterns between normal and tumor tissues. The study found that a loss of connectivity is a common feature of cancer networks compared to normal networks. Analyzing differentially connected genes can reveal new candidate cancer genes not identified by differential expression analysis alone. Integrating differential expression and differential connectivity data improves pathway enrichment analysis and provides insights into novel cancer-related pathways.
This document provides an overview of genome-wide association studies (GWAS). It defines key terms related to GWAS such as linkage disequilibrium, minor allele frequency, and odds ratio. It compares linkage mapping and association mapping. It describes the methodology of GWAS including identifying population structure, selecting case and control subjects, genotyping samples, and determining associated SNPs. It discusses challenges such as multiple hypothesis testing and population structure. It provides examples of successful GWAS in crops like maize and Arabidopsis. Overall, the document provides a comprehensive introduction and overview of GWAS.
A miniaturized sandwich immunoassay platformQing Chen
This document describes a new miniaturized sandwich immunoassay platform (MSIP) for detecting protein-protein interactions (PPIs) in a high-throughput manner. The MSIP combines antibody microarray technology with co-immunoprecipitation methods to allow simple, rapid, and large-scale PPI detection using small amounts of cell lysate. Evaluation of the MSIP showed it could accurately identify both known interacting and non-interacting protein pairs. Compared to traditional resin-based co-immunoprecipitation, the MSIP has higher sensitivity and throughput while being simpler and more cost-effective. The MSIP is presented as an effective method for validating PPIs identified by other techniques like yeast two-hybrid screening
Effect of Food Source on Enzymatic Activity in C. maculatus [draft 2]Dylan Easterday
1. The study investigated the effect of food source (mung beans vs cowpeas) on enzymatic activity in Callosobruchus maculatus beetles, which are agricultural pests.
2. Experiments measured alpha-naphthyl acetate esterase (ANAE) and beta-naphthyl acetate esterase (BNAE) activity in beetles fed each food source.
3. Statistical analysis using t-tests found no significant differences in ANAE or BNAE activity between the food sources, failing to support the hypothesis that food source affects enzymatic activity.
Jiankang Wang. Principle of QTL mapping and inclusive composite interval mapp...FOODCROPS
This document discusses quantitative trait locus (QTL) mapping and the inclusive composite interval mapping (ICIM) method. It begins with an overview of quantitative traits, QTL mapping, and different mapping populations. It then describes problems with previous interval mapping methods and introduces the theoretical basis and methodology of ICIM, which can detect additive and interacting QTL while avoiding biased estimates. The document highlights several publications that have used ICIM in crops like rice, wheat, soybean, and maize. It concludes with an overview of the biparental population (BIP) functionality in the QTL IciMapping software, which implements six different QTL mapping methods including various interval mapping approaches and single marker analysis.
Bioengineered 3D Co culture Lung In Vitro Models: Platforms to Integrate Cell...Ken Rogan
Cian O'Leary and his lab are developing 3D bioengineered in vitro models of the lung and other tissues using scaffolds.
[1] They have created bilayered collagen-hyaluronate scaffolds that support a mucociliary epithelial phenotype in lung cell culture models.
[2] The lab is also working on 3D hydrogel models of pancreatic cancer to study cell-matrix interactions and cancer progression.
[3] Future work includes developing dynamically stiffening hydrogel models and applying these platforms to study lung cancer and the pre-metastatic niche.
This document summarizes Christopher Mason's presentation on epigenetics quality control and single-cell RNA-seq variant calling using samples from the Genome in a Bottle project. It discusses generating reference epigenetics datasets, including whole genome bisulfite sequencing data, Illumina 450K methylation array data, and targeted bisulfite sequencing data for several GIAB samples. Parameters for variant calling from single-cell RNA-seq data are evaluated, finding best sensitivity and specificity at 97% and 80% respectively using certain settings. The work aims to establish high quality epigenetics and variant calling references to help benchmark computational methods for personalized medicine.
Bioinformatic Analysis of Synthetic Lethality in Breast CancerTom Kelly
This document summarizes a bioinformatic analysis of synthetic lethal genetic interactions in breast cancer. It describes how the researchers used gene expression data from breast cancer samples to predict potential synthetic lethal gene pairs through statistical testing. Many statistically significant interactions were found, including known synthetic lethal partners. The researchers validated some predictions and discuss applications for targeted cancer therapies and chemoprevention. High performance computing resources were crucial for analyzing large genome-scale datasets.
Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...Elia Brodsky
This workshop will address critical issues related to Transcriptomics data:
Processing raw Next Generation Sequencing (NGS) data:
1. Next Generation Sequencing data preprocessing:
Trimming technical sequences
Removing PCR duplicates
2. RNA-seq based quantification of expression levels:
Conventional pipelines (looking at known transcripts)
Identification of novel isoforms
Analysis of Expression Data Using Machine Learning:
3. Unsupervised analysis of expression data:
Principal Component Analysis
Clustering
4. Supervised analysis:
Differential expression analysis
Classification, gene signature construction
5. Gene set enrichment analysis
The workshop will include hands-on exercises utilizing public domain datasets:
breast cancer cell lines transcriptomic profiles (https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-10-r110),
patient-derived xenograft (PDX) mouse model of tumor and stroma transcriptomic profiles (http://www.oncotarget.com/index.php?journal=oncotarget&page=article&op=view&path[]=8014&path[]=23533), and
processed data from The Cancer Genome Atlas samples (https://cancergenome.nih.gov/).
Team: The workshops are designed by the researchers at the Tauber Bioinformatics Research Center at University of Haifa, Israel in collaboration with academic centers across the US. Technical support for the workshops is provided by the Pine Biotech team. https://edu.t-bio.info/a-critical-approach-to-transcriptomic-data-analysis/
STRING - Prediction of a functional association network for the yeast mitocho...Lars Juhl Jensen
The document discusses predicting functional associations between proteins in the yeast mitochondrial system using the STRING database. It summarizes how STRING integrates genomic context, experimental data, and evidence from other species to infer functional links. It then describes applying these methods to predict mitochondrial proteins in yeast and build an association network for the yeast mitochondrial system, identifying functional modules within it.
This document summarizes a lecture about analyzing genetic variations like SNPs and haplotypes. It discusses:
1) Why we study genetic variations - they underlie phenotypic differences, cause inherited diseases, and allow tracking human history.
2) How variations are discovered by looking for mismatches across sequences and using quality values to distinguish true variations from errors.
3) Computational methods like PolyBayes use a reference genome and quality values to cluster sequences and detect SNPs while removing paralogs and errors.
4) Models of genetic drift, mutation, recombination and demography can help understand patterns of variation within and between populations.
The document discusses a new technology for analyzing the three-dimensional conformational structure of monoclonal antibodies (mAbs). It describes how the technology was developed using an antibody array to measure epitope exposure on mAbs. Case studies show the technology can detect minor conformational differences between biosimilar and reference mAbs, and distinguish between mAbs that were clinical successes versus failures. The technology provides a unique conformational signature for each mAb.
Exploiting bigger data and collaborative tools for predictive drug discovery Sean Ekins
This document summarizes Sean Ekins' work exploiting big data and collaborative tools for predictive drug discovery. Some key points:
- CDD has screened over 250,000 molecules through Bayesian models to identify hits for tuberculosis. Around 750 molecules were tested in vitro, identifying 198 active molecules.
- Machine learning models have been over 20% accurate in prospective tests at identifying active molecules. Models have shown 3-10 fold enrichment in retrospective tests.
- There is a lack of data on compounds tested in vivo for tuberculosis. Only a small fraction of compounds tested in vitro are also tested in vivo. Building a mouse tuberculosis database could help prioritize further testing.
- Open source implementations of fingerprints and machine learning methods
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A talk given at the International Congress "Contrasts in Pharmacology 2.0" held in Turin, May 14-16 2015
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More presentations at http://www.swinecast.com/2018-leman-swine-conference-material
The aim of the study is to establish new accurate
Turbidometrical measurement of Sickle Hemoglobin using
spectrophotometer instead of using naked eyes. Moreover, the
study aimed to find out the most suitable filter and reducing
reagent, which gives best result to improve the outcome of
Solubility test. The study also intended to find out correlation
between readings and previous transfusions as well as Jaundice.
The study was carried out in Khartoum state among patients
with sickle cell trait who were attending Khartoum educational
hospital, Gafer Ibn Aouf Clinic and STAC International Centre
Laboratory.
Forty, 26 female and 14 male patients were recruited for the
study. Of them, 34 were children and 6 were adults over 20 years
old. There were also 30 normal persons recruited as control
group for comparison.
Results showed that 600 Nanometer is the best filter, which
yielded highest light absorbance with significant statistical
difference, and Na Meta-bisulphite is the best reducing agent
because it produced turbidity more intense than Na Dithionate
reagent. There is no significant correlation between reading and
previous transfusion and jaundice. Therefore, the study
recommend to use Na Meta-bisulphite for processing blood
samples in Solubility test and to read the final reaction
(Turbidity) by spectrophotometer using 600 Nanometer filter.
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Global Gene Expression Profiles from Breast Tumor Samples using the Ion Ampli...Thermo Fisher Scientific
Thousands of genes are expressed in a controlled fashion in each eukaryotic cell
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the entire gene expression pattern of a given sample is critical in understanding the
natural homeostatic state of a healthy tissue, as well as providing useful information
when a system is altered due to environmental queues or potentially disease state.
Many technologies have been utilized to measure the entire gene expression profile of a
RNA test sample. DNA microarrays have become a key method to acquire a
comparative snapshot of the gene expression profile from test samples in a high
throughput manner. Quantitative PCR and newer sequencing techniques are popular
alternatives offering highly accurate gene expression measurements, but with limitations
due to cost and complex analysis needs.
To address the challenges of current sequencing based methods of global gene
expression profiling and take advantage of the simplicity of analysis that comes with
defined expression profiling content from technologies such as microarrays, we have
tested the Ion AmpliSeq™ Transcriptome Human Gene Expression Kit using RNA
isolated from invasive ductal tumor samples. This novel approach allows profiling the
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Torrent sequencing platform. The results show detection of more genes than popular
microarray platforms with comparable differential gene expression measurements to
quantitative PCR (r = 0.96) and RNA-Seq methods (r = 0.94).
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all technical replicates. We saw >64% of the over 22,800 genes in the single pool panel
detected for all libraries. The most highly expressed genes include genes expected to
be over-expressed in breast tumor samples. The Ion AmpliSeq™ Transcriptome Human
Gene Expression Kit is a novel method to measure global gene expression profiles from
human RNA samples in a timely, cost effective, and high throughput manner resulting in
sensitive and accurate gene expression measurements.
Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D ...Rajarshi Guha
The document describes efforts to screen drug combinations in high throughput settings beyond traditional one-dimensional metrics. It discusses the infrastructure and workflows used to screen compound combinations against a library of over 2000 small molecules with diverse mechanisms of action. Quality control of combination screening experiments poses challenges due to the multi-dimensional nature of the data. The researchers are exploring various metrics and analytical approaches to characterize synergistic, additive and antagonistic combination responses across different cell lines and combinations.
This document describes an experiment investigating the use of paired short oligonucleotide probes (mixed probes) on microarrays to improve hybridization sensitivity compared to single probes. The researchers designed microarray spots containing mixtures of two 30-nucleotide probes targeting non-adjacent sites on the same RNA sequence. They found that spots with mixed probes showed significantly greater hybridization than spots with single 30-mer probes alone, and approached the hybridization levels of spots containing longer 60-mer or 70-mer probes. Using paired short probes provides higher synthesis yields compared to long probes, while maintaining greater sensitivity than single short probes. This strategy offers new options for microarray probe design.
1) Discovery: Over 1 million structural variant calls were discovered across 30 sequence-resolved callsets from 4 technologies for an AJ Trio. After clustering, over 128,000 sequence-resolved calls remained.
2) Discovery Support: Over 30,000 structural variants had support from 2+ technologies or 5+ callers in the trio.
3) Evaluate/genotype: Nearly 20,000 structural variants had a consensus variant genotype predicted for the son from analyzing the trio.
Choosing the Right Microbial Typing Method: A Quantitative ApproachJoão André Carriço
This document discusses different methods for microbial typing and comparing the results of different typing methods. It introduces Simpson's Index of Diversity, Adjusted Rand coefficient, and Adjusted Wallace coefficient as quantitative methods for comparing partitions or groupings obtained from different typing methods. These coefficients, along with their confidence intervals, provide a standardized way to determine which methods produce similar or concordant results and which typing markers or combinations of markers best discriminate between microbial strains.
dual-event machine learning models to accelerate drug discoverySean Ekins
This document describes using dual-event machine learning models to accelerate drug discovery for tuberculosis (TB). Bayesian machine learning models were generated using molecular fingerprints and descriptors from large TB screening datasets. The models were able to retrospectively and prospectively predict active compounds from both internal and external datasets with good enrichment. A prospective virtual screen of an antimalarial library identified a hit with good efficacy and selectivity in vitro and in vivo. Overall, the dual-event models integrating phenotypic screening data with cytotoxicity were able to identify several novel TB drug leads and represent an approach to accelerate drug discovery.
The document summarizes the Genome in a Bottle (GIAB) project, which aims to develop reference materials and benchmarks for evaluating human genome sequencing. GIAB has characterized 7 human genomes to high accuracy using multiple sequencing technologies and bioinformatics analyses. The characterized genomes and variant calls are made publicly available to benchmark sequencing performance. Recently, GIAB has incorporated linked and long read sequencing to expand reference benchmarks to more difficult genomic regions and develop benchmarks for structural variants.
Next-generation sequencing has enabled clinicians and researchers alike to identify novel genetic variants associated with rare Mendelian Diseases across the human genome. To help enable researchers and clinicians understand the role of CNVs in human health and disease, Golden Helix has integrated a specialized NGS-based CNV caller capable of detecting deletion and duplication events as small as single-exons and as large as whole chromosome aneuploidy events. In this webcast, we will present our workflows that integrates the NGS-based CNV caller into SVS.
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Jie Zheng at #ICG12: PhenoSpD: an atlas of phenotypic correlations and a multiple testing correction for the human phenome
1. PhenoSpD: an integrated toolkit for phenotypic
correlation estimation and multiple testing
correction using GWAS summary statistics
Jie Zheng
The 12th International Conference on Genomics
27th Oct 2017
4. Phenome wide association study (PheWAS)
• PheWAS analyzes many phenotypes
compared to a single or multiple
genetic variant(s).
• PheWAS is common place, e.g.
• MR-PheWAS. Millard et al, Sci Rep,
2015
• Haycock et al, JAMA Oncology, 2017
It is likely that longer telomeres increase risk for
several cancers but reduce risk for some non-
neoplastic diseases, including cardiovascular
diseases.
5. Post GWAS era: a database of harmonized GWAS summary
data in MRC Integrative Epidemiology Unit in Bristol
6. The network of post GWAS analysis software
Centralized
Database
PhenoSpD MR-Base
LD Hub
13. Scope of MR-Base
MR-Base
SNP lookups
12 two-sample
MR
methodologies
MR-Base
R- package
Database
~2000 GWAS
(1100 with full data)
14. PhenoSpD: why we need it?
• Molecular phenotypes such as
metabolites are highly correlated.
• Multiple testing correction is a
headache problem: Bonferroni
correction is definitely over killed.
• When individual-level phenotype
data is available, phenotypic
correlation matrix can be calculated
easily.
• However, in real world, phenotype
data is normally not available.
• In MR-Base / LD Hub, we only have
GWAS summary statistics.
• We need a magic hand to correct
multiple testing!
Wurtz et al, J Am Coll Cardiol. 2013
15. PhenoSpD: how it works
1. Harmonize GWAS summary statistics
2. Estimate phenotypic correlation matrix
using metaCCA / LD score regression
3. Apply Spectral decomposition (SpD) to
estimate the equivalent number of
independent variables in the
phenotypic correlation matrix
16. MetaCCA
• Summary statistics-based multivariate association
testing using canonical correlation analysis –
Cichonska et al Bioinformatics 2016
• As a sub-product, it provides a way to estimate
phenotypic correlation matrix 𝑌𝑌, which is equal
to the Pearson correlation between regression
coefficients (betas) of two GWASs
• The assumption is, both traits are from the same
samples
• PS: 1000 Genomes is not the best option to
estimate LD matrix between SNPs. See Benner et
al AJHG 2017, and LDstore
17. LD score regression
• Method to estimate SNP heritability and
genetic correlations -- Bulik-Sullivan et al NG
2014, 2015
• It is also provides a way to estimate phenotypic
correlations between two traits, which is the
intercept term of the bi-variate LD score
regression.
• Compare to metaCCA, it adjusted for sample
overlap automatically
• Both genetic and phenotypic correlation
matrixes can be found in LD Hub
18. SNPSpD and MatSpD
• SNPSpD: A simple correction for multiple testing for SNPs in LD using
spectral decomposition (SpD). Nyholt 2004 AJHG
• MatSpD: MatrixSpD, estimate the equivalent number of independent
variables in a correlation (r) matrix
• The same method can be used to estimate the number of
independent variables in a phenotypic correlation matrix
19. Simulation
• How accurate is the phenotypic correlation estimation using GWAS results?
• Is there any parameters strongly affecting such estimation?
21. Accuracy tests using real data
The estimated phenotypic correlations have
good agreement with observed phenotypic
correlations
The exceptions are traits with limited sample size
(therefore limited sample overlap).
• Shin et al provided the observed phenotypic correlation matrix for 452 metabolites, which can be used as a
test dataset
• So we compared the observed phenotypic correlation with the estimated phenotypic correlation using
PhenoSpD.
22. Growth importance of PhenoSpD
• PhenoSpD is particularly useful for multiple GWASs from the same
samples, e.g. complex molecular traits such as metabolites and
cytokines
• It can also be applied to all traits in MR-Base / LD Hub, which we can
split traits into groups, e.g. all traits in GIANT consortium are highly
possible to be correlated and majority of them are from the same
sample
23. Real case application in MR-Base and LD Hub
Consortium / First
author
Category N_traits N_SNPs N_correlations N_independent_traits
Kettunen Blood metabolites 123 9826292 7503 44.9
Shin Metaoblites 451 2482345 101475 324.4
Roederer Immune system
phenotypes
151 1585187 11325 94.2
CARDIOGRAM 2 335391 1 1
TRICL 4 335391 6 3
TAG 4 1449634 6 3.98
SSGAC 7 1449634 21 6
PGC 4 335391 6 3.644
Leptin 2 1449634 1 1
MAGIC 16 1449634 120 11.098
IIBDGC 3 335391 3 2
Hrgene 8 1449634 28 7
HaemGen 6 1449634 15 5
GPC 6 1449634 15 5
GLGC 4 1449634 6 3
GIANT 15 1449634 105 10.1097
GEFOS 3 1449634 3 3
CKDGen 9 335391 36 8
EGG 4 1449634 6 4
GIS 2 2029112 1 1
GUGC 2 2449580 1 1
ENIGMA 7 7237736 21 6
UK Biobank 5 9440243 9 5
Others 24 / / 24
All 862 / 120713 577.3317
Number of independent traits in MR-Base
Consortium /
First author
Category N_traits N_SNPs N_correlations N_independent_traits
All traits All traits 221 / 24310 134.1167
Number of independent traits in LD Hub
24. Growth importance of PhenoSpD
• There is a great potential to apply PhenoSpD to multiple traits in large
scale biobanks and cohorts such as UK Biobank, China Kadoorie
Biobank, HUNT study (all traits in one sample)
25. UK Biobank release from Ben Neale’s group
• RAPID GWAS OF THOUSANDS OF
PHENOTYPES FOR 337,000 SAMPLES IN
THE UK BIOBANK
(http://www.nealelab.is/blog/2017/7/
19/rapid-gwas-of-thousands-of-
phenotypes-for-337000-samples-in-
the-uk-biobank)
• GWAS summary statistics of 337,000
European samples are available for
over 2,400 human traits, everyone can
access and download the results.
• ~600 traits are heritable, which are the
most valuable data
26. PhenoSpD application
• Assess the potential causal relationship between genetic variation, DNA methylation and 139
complex traits.
• PhenoSpD:
139 outcomes 62 independent outcomes
Hypothesis free MR of DNA methylation on 139 human traits
27. Links for PhenoSpD
• PhenoSpD Paper is on bioRxiv:
https://www.biorxiv.org/content/early/2017/07/25/148627
• R scripts of PhenoSpD can be found on MRC-IEU github:
https://github.com/MRCIEU/PhenoSpD
• LD Hub: http://ldsc.broadinstitute.org/ldhub/
• MR-Base: www.mrbase.org
28. Acknowledgements
• LD Hub team
• Jie Zheng
• David M Evans
• Benjamin Neale
• MR-Base team
• Gibran Hemani
• Jie Zheng
• George Davey Smith
• Tom Gaunt
• Philip Haycock
• PhenoSpD team
• Jie Zheng
• Tom Richardson
• Louise Millard
• Gibran Hemani
• Chris Raistrick
• Bjarni Vilhjalmsson
• Philip Haycock
• Tom Gaunt