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1.  A Probabilistic Framework for Peptide and Protein Quantification from Data-Dependent and Data-Independent LC-MS Proteomics Experiments 
A probability-based quantification framework is presented for the calculation of relative peptide and protein abundance in label-free and label-dependent LC-MS proteomics data. The results are accompanied by credible intervals and regulation probabilities. The algorithm takes into account data uncertainties via Poisson statistics modified by a noise contribution that is determined automatically during an initial normalization stage. Protein quantification relies on assignments of component peptides to the acquired data. These assignments are generally of variable reliability and may not be present across all of the experiments comprising an analysis. It is also possible for a peptide to be identified to more than one protein in a given mixture. For these reasons the algorithm accepts a prior probability of peptide assignment for each intensity measurement. The model is constructed in such a way that outliers of any type can be automatically reweighted. Two discrete normalization methods can be employed. The first method is based on a user-defined subset of peptides, while the second method relies on the presence of a dominant background of endogenous peptides for which the concentration is assumed to be unaffected. Normalization is performed using the same computational and statistical procedures employed by the main quantification algorithm. The performance of the algorithm will be illustrated on example data sets, and its utility demonstrated for typical proteomics applications. The quantification algorithm supports relative protein quantification based on precursor and product ion intensities acquired by means of data-dependent methods, originating from all common isotopically-labeled approaches, as well as label-free ion intensity-based data-independent methods.
doi:10.1089/omi.2012.0019
PMCID: PMC3721451  PMID: 22871168
2.  Biomarkers in the Age of Omics: Time for a Systems Biology Approach 
Abstract
Limitations to biomarker discovery are not only technical or bioinformatic but conceptual as well. In our attempt to offer a solution, we are highlighting three issues that we think are limiting progress in biomarkers discovery. First, the confusion stemming from the imposition of a pathology-type immunohistochemical marker (IHCM) concept on omics data without fully understanding the characteristics and limitations of IHCMs as applied in clinical pathology. Second, the lack of serious consideration for the scope of disease heterogeneity. Third, the refusal of the biomedical community to borrow from other biological disciplines their well established methods for dealing with heterogeneity. Therefore, real progress in biomarker discovery will be attained when we recognize that an omics biomarker cannot be assigned and validated without a priori data modeling and subtyping of the disease itself to reveal the extent of its heterogeneity, and its omics' clonal aberrations (drivers) underlying its subtypes and pathways' diversity. To further support our viewpoints, we are contributing a novel a systems biology method such as parsimony phylogenetic approach for disease modeling prior to biomarker circumscription. As an analytical approach that has been successfully used for a half of a century in other biological disciplines, parsimony phylogenetics simultaneously achieves several objectives: it provides disease modeling in a hierarchical phylogenetic classification, identifies biomarkers as the shared derived expressions or mutations—synapomorphies, constructs the omics profiles of specimens based on the most parsimonious arrangement of their heterogeneous data, and permits network profiling of affected signaling pathways as the biosignature of disease classes.
doi:10.1089/omi.2010.0023
PMCID: PMC3060038  PMID: 21319991
3.  Design and Initial Characterization of the SC-200 Proteomics Standard Mixture 
Abstract
High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate = 1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels.
doi:10.1089/omi.2010.0118
PMCID: PMC3110723  PMID: 21250827
4.  Taking the First Steps towards a Standard for Reporting on Phylogenies: Minimal Information about a Phylogenetic Analysis (MIAPA) 
In the eight years since phylogenomics was introduced as the intersection of genomics and phylogenetics, the field has provided fundamental insights into gene function, genome history and organismal relationships. The utility of phylogenomics is growing with the increase in the number and diversity of taxa for which whole genome and large transcriptome sequence sets are being generated. We assert that the synergy between genomic and phylogenetic perspectives in comparative biology would be enhanced by the development and refinement of minimal reporting standards for phylogenetic analyses. Encouraged by the development of the Minimum Information About a Microarray Experiment (MIAME) standard, we propose a similar roadmap for the development of a Minimal Information About a Phylogenetic Analysis (MIAPA) standard. Key in the successful development and implementation of such a standard will be broad participation by developers of phylogenetic analysis software, phylogenetic database developers, practitioners of phylogenomics, and journal editors.
doi:10.1089/omi.2006.10.231
PMCID: PMC3167193  PMID: 16901231
5.  Design and Initial Characterization of the SC-200 Proteomics Standard Mixture 
High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate = 1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels.
doi:10.1089/omi.2010.0118
PMCID: PMC3110723  PMID: 21250827
6.  Biomarkers in the Age of Omics: Time for a Systems Biology Approach 
Limitations to biomarker discovery are not only technical or bioinformatic but conceptual as well. In our attempt to offer a solution, we are highlighting three issues that we think are limiting progress in biomarkers discovery. First, the confusion stemming from the imposition of a pathology-type immunohistochemical marker (IHCM) concept on omics data without fully understanding the characteristics and limitations of IHCMs as applied in clinical pathology. Second, the lack of serious consideration for the scope of disease heterogeneity. Third, the refusal of the biomedical community to borrow from other biological disciplines their well established methods for dealing with heterogeneity. Therefore, real progress in biomarker discovery will be attained when we recognize that an omics biomarker cannot be assigned and validated without a priori data modeling and subtyping of the disease itself to reveal the extent of its heterogeneity, and its omics’ clonal aberrations (drivers) underlying its subtypes and pathways’ diversity. To further support our viewpoints, we are contributing a novel a systems biology method such as parsimony phylogenetic approach for disease modeling prior to biomarker circumscription. As an analytical approach that has been successfully used for a half of a century in other biological disciplines, parsimony phylogenetics simultaneously achieves several objectives: it provides disease modeling in a hierarchical phylogenetic classification, identifies biomarkers as the shared derived expressions or mutations—synapomorphies, constructs the omics profiles of specimens based on the most parsimonious arrangement of their heterogeneous data, and permits network profiling of affected signaling pathways as the biosignature of disease classes.
doi:10.1089/omi.2010.0023
PMCID: PMC3060038  PMID: 21319991
7.  Ginkgo Biloba Extract Induces Gene Expression Changes in Xenobiotics Metabolism and the Myc-Centered Network 
Abstract
The use of herbal dietary supplements in the United States is rapidly growing, and it is crucial that the quality and safety of these preparations be ensured. To date, it is still a challenge to determine the mechanisms of toxicity induced by mixtures containing many chemical components, such as herbal dietary supplements. We previously proposed that analyses of the gene expression profiles using microarrays in the livers of rodents treated with herbal dietary supplements is a potentially practical approach for understanding the mechanism of toxicity. In this study, we utilized microarrays to analyze gene expression changes in the livers of male B6C3F1 mice administered Ginkgo biloba leaf extract (GBE) by gavage for 2 years, and to determine pathways and mechanisms associated with GBE treatments. Analysis of 31,802 genes revealed that there were 129, 289, and 2,011 genes significantly changed in the 200, 600, and 2,000 mg/kg treatment groups, respectively, when compared with control animals. Drug metabolizing genes were significantly altered in response to GBE treatments. Pathway and network analyses were applied to investigate the gene relationships, functional clustering, and mechanisms involved in GBE exposure. These analyses indicate alteration in the expression of genes coding for drug metabolizing enzymes, the NRF2-mediated oxidative stress response pathway, and the Myc gene-centered network named “cell cycle, cellular movement, and cancer” were found. These results indicate that Ginkgo biloba-related drug metabolizing enzymes may cause herb–drug interactions and contribute to hepatotoxicity. In addition, the outcomes of pathway and network analysis may be used to elucidate the toxic mechanisms of Ginkgo biloba.
doi:10.1089/omi.2009.0115
PMCID: PMC2912699  PMID: 20141330
8.  Ginkgo Biloba Extract Induces Gene Expression Changes in Xenobiotics Metabolism and the Myc-centered Network 
The use of herbal dietary supplements in the United States is rapidly growing and it is crucial that the quality and safety of these preparations be ensured. To date, it is still a challenge to determine the mechanisms of toxicity induced by mixtures containing many chemical components, such as herbal dietary supplements. We previously proposed that analyses of the gene expression profiles using microarrays in the livers of rodents treated with herbal dietary supplements is a potentially practical approach for understanding the mechanism of toxicity. In this study, we utilized microarrays to analyze gene expression changes in the livers of male B6C3F1 mice administered Ginkgo biloba leaf extract (GBE) by gavage for two years, and to determine pathways and mechanisms associated with GBE treatments. Analysis of 31,802 genes revealed that there were 129, 289, and 2011 genes significantly changed in the 300, 600, and 2,000 mg/kg treatment groups, respectively, when compared with control animals. Drug metabolizing genes were significantly altered in response to GBE treatments. Pathway and network analyses were applied to investigate the gene relationships, functional clustering, mechanisms involved in GBE exposure. These analyses indicate alteration in the expression of genes coding for drug metabolizing enzymes, the NRF2- mediated oxidative stress response pathway, and the Myc gene-centered network named “cell cycle, cellular movement and cancer” were found. These results indicate that Ginkgo biloba related drug metabolizing enzymes may cause herb-drug interactions and contribute to hepatotoxicity. In addition, the outcomes of pathway and network analysis may be used to elucidate the toxic mechanisms of Ginkgo biloba.
doi:10.1089/omi.2009.0115
PMCID: PMC2912699  PMID: 20141330
Ginkgo biloba extract; microarray; gene expression; drug metabolizing gene; pathway analysis; network analysis
9.  The STEDMAN Project: Biophysical, Biochemical and Metabolic Effects of a Behavioral Weight Loss Intervention during Weight Loss, Maintenance, and Regain 
Abstract
The Study of the Effects of Diet on Metabolism and Nutrition (STEDMAN) Project uses comprehensive metabolic profiling to probe biochemical mechanisms of weight loss in humans. Measurements at baseline, 2 and 4 weeks, 6 and 12 months included diet, body composition, metabolic rate, hormones, and 80 intermediary metabolites measured by mass spectrometry. In 27 obese adults in a behavioral weight loss intervention, median weight decreased 13.9 lb over the first 6 months, then reverted towards baseline by 12 months. Insulin resistance (HOMA) was partially ameliorated in the first 6 months and showed sustained improvement at 12 months despite weight regain. Ghrelin increased with weight loss and reverted to baseline, whereas leptin and PYY fell at 6 months and remained persistently low. NPY levels did not change. Factors possibly contributing to sustained improvement in insulin sensitivity despite weight regain include adiponectin (increased by 12 months), IGF-1 (increased during weight loss and continued to increase during weight regain), and visceral fat (fell at 6 months but did not change thereafter). We observed a persistent reduction in free fatty acids, branched chain amino acids, and related metabolites that may contribute to improved insulin action. These findings provide evidence for sustained benefits of weight loss in obese humans and insights into mechanisms.
doi:10.1089/omi.2008.0035
PMCID: PMC2678563  PMID: 19290809
10.  Risk Assessment and Communication Tools for Genotype Associations with Multifactorial Phenotypes: The Concept of “Edge Effect” and Cultivating an Ethical Bridge between Omics Innovations and Society 
Abstract
Applications of omics technologies in the postgenomics era swiftly expanded from rare monogenic disorders to multifactorial common complex diseases, pharmacogenomics, and personalized medicine. Already, there are signposts indicative of further omics technology investment in nutritional sciences (nutrigenomics), environmental health/ecology (ecogenomics), and agriculture (agrigenomics). Genotype–phenotype association studies are a centerpiece of translational research in omics science. Yet scientific and ethical standards and ways to assess and communicate risk information obtained from association studies have been neglected to date. This is a significant gap because association studies decisively influence which genetic loci become genetic tests in the clinic or products in the genetic test marketplace. A growing challenge concerns the interpretation of large overlap typically observed in distribution of quantitative traits in a genetic association study with a polygenic/multifactorial phenotype. To remedy the shortage of risk assessment and communication tools for association studies, this paper presents the concept of edge effect. That is, the shift in population edges of a multifactorial quantitative phenotype is a more sensitive measure (than population averages) to gauge the population level impact and by extension, policy significance of an omics marker. Empirical application of the edge effect concept is illustrated using an original analysis of warfarin pharmacogenomics and the VKORC1 genetic variation in a Brazilian population sample. These edge effect analyses are examined in relation to regulatory guidance development for association studies. We explain that omics science transcends the conventional laboratory bench space and includes a highly heterogeneous cast of stakeholders in society who have a plurality of interests that are often in conflict. Hence, communication of risk information in diagnostic medicine also demands attention to processes involved in production of knowledge and human values embedded in scientific practice, for example, why, how, by whom, and to what ends association studies are conducted, and standards are developed (or not). To ensure sustainability of omics innovations and forecast their trajectory, we need interventions to bridge the gap between omics laboratory and society. Appreciation of scholarship in history of omics science is one remedy to responsibly learn from the past to ensure a sustainable future in omics fields, both emerging (nutrigenomics, ecogenomics), and those that are more established (pharmacogenomics). Another measure to build public trust and sustainability of omics fields could be legislative initiatives to create a multidisciplinary oversight body, at arm's length from conflict of interests, to carry out independent, impartial, and transparent innovation analyses and prospective technology assessment.
doi:10.1089/omi.2009.0011
PMCID: PMC2727354  PMID: 19290811
11.  Development of FuGO: An Ontology for Functional Genomics Investigations 
The development of the Functional Genomics Investigation Ontology (FuGO) is a collaborative, international effort that will provide a resource for annotating functional genomics investigations, including the study design, protocols and instrumentation used, the data generated and the types of analysis performed on the data. FuGO will contain both terms that are universal to all functional genomics investigations and those that are domain specific. In this way, the ontology will serve as the “semantic glue” to provide a common understanding of data from across these disparate data sources. In addition, FuGO will reference out to existing mature ontologies to avoid the need to duplicate these resources, and will do so in such a way as to enable their ease of use in annotation. This project is in the early stages of development; the paper will describe efforts to initiate the project, the scope and organization of the project, the work accomplished to date, and the challenges encountered, as well as future plans.
doi:10.1089/omi.2006.10.199
PMCID: PMC2783628  PMID: 16901226
12.  Data Standards for Flow Cytometry 
Flow cytometry (FCM) is an analytical tool widely used for cancer and HIV/AIDS research, and treatment, stem cell manipulation and detecting microorganisms in environmental samples. Current data standards do not capture the full scope of FCM experiments and there is a demand for software tools that can assist in the exploration and analysis of large FCM datasets. We are implementing a standardized approach to capturing, analyzing, and disseminating FCM data that will facilitate both more complex analyses and analysis of datasets that could not previously be efficiently studied. Initial work has focused on developing a community-based guideline for recording and reporting the details of FCM experiments. Open source software tools that implement this standard are being created, with an emphasis on facilitating reproducible and extensible data analyses. As well, tools for electronic collaboration will assist the integrated access and comprehension of experiments to empower users to collaborate on FCM analyses. This coordinated, joint development of bioinformatics standards and software tools for FCM data analysis has the potential to greatly facilitate both basic and clinical research—impacting a notably diverse range of medical and environmental research areas.
doi:10.1089/omi.2006.10.209
PMCID: PMC2768474  PMID: 16901228
13.  Phylogenetic Modeling of Heterogeneous Gene-Expression Microarray Data from Cancerous Specimens 
Abstract
The qualitative dimension of gene expression data and its heterogeneous nature in cancerous specimens can be accounted for by phylogenetic modeling that incorporates the directionality of altered gene expressions, complex patterns of expressions among a group of specimens, and data-based rather than specimen-based gene linkage. Our phylogenetic modeling approach is a double algorithmic technique that includes polarity assessment that brings out the qualitative value of the data, followed by maximum parsimony analysis that is most suitable for the data heterogeneity of cancer gene expression. We demonstrate that polarity assessment of expression values into derived and ancestral states, via outgroup comparison, reduces experimental noise; reveals dichotomously expressed asynchronous genes; and allows data pooling as well as comparability of intra- and interplatforms. Parsimony phylogenetic analysis of the polarized values produces a multidimensional classification of specimens into clades that reveal shared derived gene expressions (the synapomorphies); provides better assessment of ontogenic pathways and phyletic relatedness of specimens; efficiently utilizes dichotomously expressed genes; produces highly predictive class recognition; illustrates gene linkage and multiple developmental pathways; provides higher concordance between gene lists; and projects the direction of change among specimens. Further implication of this phylogenetic approach is that it may transform microarray into diagnostic, prognostic, and predictive tool.
doi:10.1089/omi.2008.0010
PMCID: PMC2583934  PMID: 18699725
14.  Risk Assessment and Communication Tools for Genotype Associations with Multifactorial Phenotypes: The Concept of “Edge Effect” and Cultivating an Ethical Bridge between Omics Innovations and Society 
Applications of omics technologies in the postgenomics era swiftly expanded from rare monogenic disorders to multifactorial common complex diseases, pharmacogenomics, and personalized medicine. Already, there are signposts indicative of further omics technology investment in nutritional sciences (nutrigenomics), environmental health/ecology (ecogenomics), and agriculture (agrigenomics). Genotype–phenotype association studies are a centerpiece of translational research in omics science. Yet scientific and ethical standards and ways to assess and communicate risk information obtained from association studies have been neglected to date. This is a significant gap because association studies decisively influence which genetic loci become genetic tests in the clinic or products in the genetic test marketplace. A growing challenge concerns the interpretation of large overlap typically observed in distribution of quantitative traits in a genetic association study with a polygenic/multifactorial phenotype. To remedy the shortage of risk assessment and communication tools for association studies, this paper presents the concept of edge effect. That is, the shift in population edges of a multi-factorial quantitative phenotype is a more sensitive measure (than population averages) to gauge the population level impact and by extension, policy significance of an omics marker. Empirical application of the edge effect concept is illustrated using an original analysis of warfarin pharmacogenomics and the VKORC1 genetic variation in a Brazilian population sample. These edge effect analyses are examined in relation to regulatory guidance development for association studies. We explain that omics science transcends the conventional laboratory bench space and includes a highly heterogeneous cast of stakeholders in society who have a plurality of interests that are often in conflict. Hence, communication of risk information in diagnostic medicine also demands attention to processes involved in production of knowledge and human values embedded in scientific practice, for example, why, how, by whom, and to what ends association studies are conducted, and standards are developed (or not). To ensure sustainability of omics innovations and forecast their trajectory, we need interventions to bridge the gap between omics laboratory and society. Appreciation of scholarship in history of omics science is one remedy to responsibly learn from the past to ensure a sustainable future in omics fields, both emerging (nutrigenomics, ecogenomics), and those that are more established (pharmacogenomics). Another measure to build public trust and sustainability of omics fields could be legislative initiatives to create a multidisciplinary oversight body, at arm's length from conflict of interests, to carry out independent, impartial, and transparent innovation analyses and prospective technology assessment.
doi:10.1089/omi.2009.0011
PMCID: PMC2727354  PMID: 19290811
15.  The STEDMAN Project: Biophysical, Biochemical and Metabolic Effects of a Behavioral Weight Loss Intervention during Weight Loss, Maintenance, and Regain 
The Study of the Effects of Diet on Metabolism and Nutrition (STEDMAN) Project uses comprehensive metabolic profiling to probe biochemical mechanisms of weight loss in humans. Measurements at baseline, 2 and 4 weeks, 6 and 12 months included diet, body composition, metabolic rate, hormones, and 80 intermediary metabolites measured by mass spectrometry. In 27 obese adults in a behavioral weight loss intervention, median weight decreased 13.9 lb over the first 6 months, then reverted towards baseline by 12 months. Insulin resistance (HOMA) was partially ameliorated in the first 6 months and showed sustained improvement at 12 months despite weight regain. Ghrelin increased with weight loss and reverted to baseline, whereas leptin and PYY fell at 6 months and remained persistently low. NPY levels did not change. Factors possibly contributing to sustained improvement in insulin sensitivity despite weight regain include adiponectin (increased by 12 months), IGF-1 (increased during weight loss and continued to increase during weight regain), and visceral fat (fell at 6 months but did not change thereafter). We observed a persistent reduction in free fatty acids, branched chain amino acids, and related metabolites that may contribute to improved insulin action. These findings provide evidence for sustained benefits of weight loss in obese humans and insights into mechanisms.
doi:10.1089/omi.2008.0035
PMCID: PMC2678563  PMID: 19290809
16.  Phylogenetic Modeling of Heterogeneous Gene-Expression Microarray Data from Cancerous Specimens 
The qualitative dimension of gene expression data and its heterogeneous nature in cancerous specimens can be accounted for by phylogenetic modeling that incorporates the directionality of altered gene expressions, complex patterns of expressions among a group of specimens, and data-based rather than specimen-based gene linkage. Our phylogenetic modeling approach is a double algorithmic technique that includes polarity assessment that brings out the qualitative value of the data, followed by maximum parsimony analysis that is most suitable for the data heterogeneity of cancer gene expression. We demonstrate that polarity assessment of expression values into derived and ancestral states, via outgroup comparison, reduces experimental noise; reveals dichotomously expressed asynchronous genes; and allows data pooling as well as comparability of intra- and interplatforms. Parsimony phylogenetic analysis of the polarized values produces a multidimensional classification of specimens into clades that reveal shared derived gene expressions (the synapomorphies); provides better assessment of ontogenic pathways and phyletic relatedness of specimens; efficiently utilizes dichotomously expressed genes; produces highly predictive class recognition; illustrates gene linkage and multiple developmental pathways; provides higher concordance between gene lists; and projects the direction of change among specimens. Further implication of this phylogenetic approach is that it may transform microarray into diagnostic, prognostic, and predictive tool.
doi:10.1089/omi.2008.0010
PMCID: PMC2583934  PMID: 18699725

Results 1-16 (16)