bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reliability and utility of single nucleotide polymorphisms for genetic association studies
Authors: Pearce C. Leigh , Hirschhorn Joel N. 
Published: November 2009

Abstract
Single‐nucleotide polymorphisms (SNPs) are the most common type of genetic variation in the human genome. Significant efforts have been made to attempt to understand their association to common diseases and complex traits. Genetic association studies, however, have been plagued by inconsistent results. The reliability of SNP‐based association studies is reviewed in the light of their limited success. The validity, comprehensiveness, and usefulness of the public SNP database (dbSNP) is evaluated, followed by a detailed discussion of why these studies have proven so difficult to replicate. Potential approaches to minimize the inconsistency across studies are considered. We show that while the coverage of the public SNP database is adequate to conduct comprehensive and meaningful association studies, methodological concerns must be taken into account.
Using oligonucleotide arrays
Authors: Mitchell Lindsay W. , Hoffman Eric P. 
Published: November 2009

Abstract

Nucleic acid sequence microarrays have become one of the first highly parallel, genome‐wide technologies to mature into a tool used routinely by biologists in many fields of research. The principle, covered in more detail elsewhere in this volume, is to hybridize a complex solution of labeled mRNA transcripts from cells or tissues onto a solid support containing thousands or millions of segments of unique DNA. The extent of hybridization of each mRNA sequence in the solution to the target “feature” is proportional to the concentration of that specific mRNA in the solution. This “extent of hybridization” to each feature is quantified (signal) by detection tools, which then calculate the relative concentration of the mRNA in the original solution. Affymetrix arrays are factory manufactured microarrays of 25‐mer oligonucleotides. Construction of the arrays is accomplished by in situ photochemistry, allowing about 1 million distinct features to be synthesized on 1.2 cM
2
quartz supports. The bioinformatics‐driven design and production of Affymetrix arrays has made them increasingly popular in both academic and pharmaceutical arenas, and has allowed the development of quality control and standard operating procedures (QC/SOP). This specialist review discusses the design and production of Affymetrix arrays, preparation of target tissue and hybridization, methods of quantifying resulting hybridization data (probe set algorithms), experimental design considerations, and emerging public access databases.
Integrating genotypic, molecular profiling, and clinical data to elucidate common human diseases
Authors: Schadt Eric E. , Sachs Alan 
Published: November 2009

Abstract
The identification of genes and, more generally, gene networks underlying common human diseases remain one of the primary objectives in the biomedical and life sciences. Success in reconstructing gene networks underlying complex physiological traits and identifying the key drivers of these traits will largely rest on integrative approaches that combine data from multiple different sources. Key to this integration will be the development and application of sophisticated algorithms to mine the diversity of data. Here we review an integrative genomics approach that enhances the utility of genetics approaches commonly used to dissect complex traits. This integrative genomics approach takes into account genotypic, molecular profiling, and clinical data in segregating populations to identify the key drivers of disease and reconstruct disease‐specific gene networks.
The use of external controls in microarray experiments
Authors: van Helvoort Joop M.L.M. , van Leenen Dik , Holstege Frank C.P. 
Published: November 2009

Abstract
Accuracy in reporting expression levels or differences therein is an important issue for microarray technology. Here we discuss the reasons for using external RNA controls for the optimization of nearly every aspect of microarray technology. The main conclusion is that external RNA controls offer an extremely versatile system that does not suffer from the disadvantages of other approaches and also lend themselves for assessing accuracy within every subsequent microarray experiment after optimization. We describe what external controls are, how they can contribute toward systematic analysis of accuracy, and how such controls should be implemented. Other uses of external controls such as normalization, determining sensitivity, and reporting absolute mRNA copies per cell are also discussed.
Quadrupole mass analyzers: theoretical and practical considerations
Authors: Kero Frank A. , Pedder Randall E. , Yost Richard A. 
Published: November 2009

AbstractMass spectrometry is perhaps the key core methodology for proteomics, and the quadrupole mass analyzer is currently the most widely used mass analyzer in proteomic applications of mass spectrometry. The theory of quadrupole mass analyzers (and of the related quadrupole and multipole devices used to confine and transmit beams of ions into and between mass analyzers) can lend valuable insight into practical considerations for effective use of mass spectrometry.
Basic techniques for the use of reverse phase protein microarrays for signal pathway profiling
Authors: Espina Virginia , Calvert Valerie S. , Wulfkuhle Julia , Liotta Lance A. , Petricoin Emanuel F. 
Published: November 2009

Abstract
Ultimately, disease‐related genetic defects are manifested on a protein level, involving derangements in protein function and the information flow within diseased cells and the surrounding tissue microenvironment. New classes of protein microarrays have been recently developed using highly sensitive, specific technology, and can be used to generate a “wiring diagram” of information flow mediated through posttranslational modification driven networks. Through the combined use of laser capture microdissection of pure populations of tumor cells obtained from patient biopsies, these arrays can provide the foundation for the development of individualized combinatorial therapies of molecular inhibitors to target tumor‐specific deranged pathways regulating key biologic processes including proliferation, differentiation, apoptosis, immunity, and metastasis. In the future, therapies can be tailored to the specific deranged molecular circuitry of an individual patient's disease. The successful transition of these technologies from research tools to integrated clinical diagnostic platforms will require ongoing continued development, development of reference standards, and optimization with rigorous standardization development and quality‐control procedures.
Computational methods for the prediction of protein interaction partners
Authors: Valencia Alfonso , Juan David 
Published: November 2009

AbstractSeveral computational methods have been recently developed for the prediction of protein interactions. The first class of these methods exclusively uses sequence information for the predictions, including three methods rooted in comparative genomics (phylogenetic profiles,conserved gene neighborhood, andgene fusion), and two other methods that use multiple sequence alignments as input information (in silico two‐hybridandMirrorTreemethods). Interestingly, these two methods can be extended beyond the prediction of interaction partners to the detection of regions of interactions and key functional residues, as demonstrated in various experimental systems. The second type of computational methods exploits the information of protein structures by analyzing the observed combinatorial of protein domains or by extrapolating the structural information of protein complexes to compatible sequences.Several studies have addressed the interesting possibilities of combining predicted and experimentally derived interactions. The current view is that the overlap between the interactions predicted by different methods is relatively small, and the various experimental and computational methods seem to be able to detect specific types of protein interactions.A number of computational methods for predicting functional and/or physical relations between proteins have been developed in the last 4 years. The computational methods are strongly rooted in the traces left by the evolution in the organization and composition of bacterial genomes (seeMicrobial genomes). The methods developed can be divided into three categories: those methods that only use the information from genomes and sequences for the prediction of interaction partners, those methods that use the information of protein complexes of known structures (seeFundamentals of protein structure and function,Large complexes by X‐ray methods, andLarge complexes and molecular machines by electron microscopy), and those methods that use the puzzle composition of proteins in domains (seeClassification of proteins into families,Pfam: the protein families database, andCOGs) to predict the probability of interaction between the corresponding proteins.
GPIanchors
Authors: Hooper Nigel M. 
Published: November 2009

AbstractMany proteins are attached to the outer surface of the membrane by a glycosyl‐phosphatidylinositol (GPI) anchor covalently linked to the C‐terminal amino acid. All GPI anchors have the core structure “ethanolamine‐PO4‐6Manα1‐2Manα1‐6Manα1‐4GlcNH2α1‐6myo‐inositol‐1‐PO4‐lipid” but vary in the nature of the lipid moiety and in side‐chain modifications to the core glycan (seeGlycosylphosphatidylinositol anchors‐ a structural perspective). The GPI anchor is built up by sequential addition of sugars and ethanolamine phosphate to phosphatidylinositol and then transferred by a transamidase enzyme to the protein. Proteins destined to be GPI anchored have an N‐terminal signal peptide for translocation into the lumen of the endoplasmic reticulum and a C‐terminal signal that directs addition of the GPI anchor. Experimentally, the presence of a GPI anchor can be determined by digestion with bacterial phosphatidylinositol‐specific phospholipase C, detection of the cross‐reacting determinant, metabolic labeling with components of the anchor, or relative detergent insolubility. In addition to the stable association of a protein with the membrane, GPI anchors convey other properties on proteins including susceptibility to cleavage by phospholipases, association with detergent‐insoluble lipid rafts, intracellular targeting, transmembrane signaling, endocytosis via caveolae, and intercellular transfer.
Classification of proteins by sequence signatures
Authors: Hofmann Kay 
Published: November 2009

Abstract
The prediction of a protein's function from its amino acid sequence is one of the most important tasks in bioinformatics. The traditional procedure of searching databases for related sequences and inferring the function from the best matches has several shortcomings and pitfalls. Alternatively, the sequence under study can be scrutinized for the occurrence of particular sequence signatures that can be associated with certain protein functionalities. Useful sequence signatures not only include short motifs such as protein modification sites or specific binding motifs but also encompass larger protein regions, such as homology domains. There exist a number of fundamentally different bioinformatical data structures, which can be used to store information about sequence signatures, thus making them available for the purpose of protein classification.
Metabolic dynamics in cells viewed as multilayered, distributed, mass‐energy‐information networks
Authors: Aon Miguel A. , Cortassa Sonia 
Published: November 2009

AbstractThe role of mathematical models in integrating available information on metabolic networks is highlighted. General modeling and other quantitative methodologies currently available are critically reviewed and the potential contributions of proteomics complemented by theoretical concepts derived from these modeling frameworks are assessed. The multilayered, distributed, view of cells increases the necessity of a more fundamental understanding of the dynamics of complex reaction networks as well as cytoplasmic organization, on cell physiology. The future of mathematical modeling will be largely determined by the trade‐off betweencompletenessandunderstandability.
Statistical signals
Authors: Gentles Andrew J. , Karlin Samuel 
Published: November 2009

Abstract

Statistical signals play a central role in a wide range of bioinformatics tools and analyses. The basic theory underlying general scoring schemes, particularly maximal aggregate scoring segments, is reviewed briefly. Applications include homology detection using BLAST, alignment algorithms that incorporate global normalization, analysis of distributions of sequence markers (
r
‐scans), and characterization of frequent words and repeat abundances in DNA and protein sequences.
Polymorphism and sequence assembly
Authors: Rana Brinda K. , Smith Douglas W. , Schork Nicholas J. 
Published: November 2009

Abstract
DNA sequence information is being deposited into public databases at a rapid rate, largely through worldwide genomic efforts to sequence the human genome as well as the genomes of other species. This information has enabled the identification of over a million of naturally occurring genetic variations in humans, in particular, single‐nucleotide polymorphisms (SNPs). SNPs have become a focal point in the study of the genetic basis of multifactorial diseases and traits and human evolutionary history. In this article, we will outline bioinformatic and molecular approaches for using available sequence information and technology to identify SNPs in the human population. These approaches may be extended to study SNPs in other organisms as well as other types of polymorphisms.
In silico
approaches to functional analysis of proteins
Authors: Aravind L. 
Published: November 2009

Abstract
Inference of protein function from sequence data by means of computational techniques is one of the essential requirements for biological investigations in the postgenomic era. While the basic principles of functional inference are derived from standard ideas in structural and evolutionary biology, their practical application is far from trivial. In this introductory article, we provide an overview of these principles and some definitions for frequently encountered concepts pertaining to the sequence analysis of proteins, such as conserved domains, domain architectures, nonglobular segments, and the trends in sequence conservation. We also discuss various extraneous sources of information that emerge from different contextual associations of proteins and their utility in function prediction. The remaining articles in this section delve into the specific aspects of the various themes introduced here.
The domains of life and their evolutionary implications
Authors: Woese Carl R. 
Published: November 2009

AbstractAfter almost a century in virtual stasis, Darwin's world, the world of biology as fundamentally an expression of the evolutionary process, is once again emerging at the forefront of the discipline of biology. This has followed a century in which biology was perceived in mechanistic, reductionist terms, and the gene was effectively taken as the be‐all and end‐all of biology. The evolutionary renaissance is the result of intra‐ and interdisciplinary factors. On the one hand, the molecular paradigm, which so successfully guided biology during the twentieth century, is being recognized for what it was; an incomplete, superficial (and now failed) conception of the living world. In addition to this, the advent of genome sequencing (in the last decade of the 1990s) has provided huge amounts of information that require evolutionary explanation. On the other hand, developments in the physical sciences and mathematics over the past 50 years have shown us that classical physics (which served as the basis for the twentieth century view of biology), is far from a complete world view, and with the aid of computers, we have come to appreciate and begin to understand the vast “nonlinear” side of reality. The fact that evolution is fundamentally a nonlinear (complex dynamic) process explains why: (1) it was neglected (not recognized as important) during the twentieth century and (2) it is now reemerging as the essence of biology.
Phylogenomic approaches to bacterial phylogeny
Authors: Daubin Vincent 
Published: November 2009

AbstractReconstructing the history of bacterial species is one of the most challenging question molecular phylogeneticists have to answer. Both the antiquity of bacteria and their mode of evolution constitute barriers to the elucidation of the relationships among phyla. Time, on one side, has eroded the phylogenetic signal that would allow reconstructing the deep branches of this several billion years old tree. In addition, because of frequent gene exchanges among distantly related bacteria, even an accurate gene phylogeny may no longer represent the species tree. Methods aimed at using complete genomes for phylogenetic inference may however help resolve this issue.
Mass spectrometry and computational proteomics
Authors: Bafna Vineet , Reinert Knut 
Published: November 2009

AbstractMass Spectrometry is the tool of choice for proteomics, with applications to peptide sequencing, protein structure prediction, protein–protein interactions, and many others. Continued improvements in instrumentation and computational technologies will only help accelerate this trend. A short overview of algorithms for interpreting mass spectrometry (MS) data is provided. This overview is not intended as an introduction to the technology itself or to proteomics. Instead, an abstract overview of MS data is presented in order to describe key algorithmic ideas required for its interpretation. Three proteomic applications are considered:protein identification,protein interactions, anddifferential analysis of protein expression.
Integrating statistical approaches in experimental design and data analysis
Authors: Wit Ernst , Khanin Raya 
Published: November 2009

Abstract
In complex experiments such as microarray studies, the quantity of interest is measured via a long path of intermediate technologies. It is not surprising that the final signal is partially corrupted by the measurement process itself. Statistical design is aimed at assigning the replicates in an optimal way to the conditions of interest. It should be seamlessly integrated with the statistical methods to recover the signal, so that it is possible to evaluate the scientific question of interest most efficiently.
Algorithms for gene expression analysis
Authors: Brazma Alvis , Culhane Aedín C. 
Published: November 2009

AbstractA new genre of algorithms, which blends bioinformatics, statistics, and machine learning, is emerging to tackle the challenges of gene expression data analysis. Microarrays enable genome‐scale high‐throughout measurement of gene expression, and have produced unprecedented amounts of gene expression data. In this chapter, we present an overview of some of the many algorithms that have been employed in the analysis of these data. These include many well‐known algorithms and new algorithms developed to address data‐specific issues. We describe various clustering algorithms, including hierarchical andk‐means, and ordination methods, such as principal component analysis, that can be applied to unsupervised exploration of data. We also provide an introduction to a number of supervised, meta‐analysis, and gene selection methods. Where possible, we mention software tools that implement these algorithms. Finally, we stress the importance of a standard data format and data repositories for sharing of microarray gene expression data.
Differential expression with the Bioconductor Project
Authors: von Heydebreck Anja , Huber Wolfgang , Gentleman Robert 
Published: November 2009

Abstract
A basic, yet challenging task in the analysis of microarray gene expression data is the identification of changes in gene expression that are associated with particular biological conditions. We discuss different approaches to this task and illustrate how they can be applied using software from the Bioconductor Project. A central problem is the high dimensionality of gene expression space, which prohibits a comprehensive statistical analysis without focusing on particular aspects of the joint distribution of the genes' expression levels. Possible strategies are to do univariate gene‐by‐gene analysis, and to perform data‐driven nonspecific filtering of genes before the actual statistical analysis. However, more focused strategies that make use of biologically relevant knowledge are more likely to increase our understanding of the data.
Extracting networks from expression data
Authors: Hatzimanikatis Vassily , Papoutsakis Eleftherios T. 
Published: November 2009

Abstract
We discuss the properties of some of the model‐based identification methods used for the development of genetic networks from gene expression data. The performance of these methods is evaluated with respect to their ability to deal with different type of data, the dimensionality of the data, the combinatorial nature of gene expression regulation, and the mathematical representation of the genetic network. We identify several key issues that must be resolved in order to improve our ability to extract networks from expression data.
A comparison of existing tools for ontological analysis of gene expression data
Authors: Khatri Purvesh , Draghici Sorin 
Published: November 2009

AbstractIndependent of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of differentially expressed genes. An automatic ontological analysis approach has been recently proposed to help with the biological interpretation of such results. Currently, this approach is the de facto standard for the secondary analysis of high‐throughput experiments, and a large number of tools have been developed for this purpose. We present a detailed comparison of 14 such tools using the following criteria: scope of the analysis, visualization capabilities, statistical model(s) used, correction for multiple comparisons, reference microarrays available, installation issues, and sources of annotation data. This detailed analysis of the capabilities of these tools will help researchers choose the most appropriate tool for a given type of analysis.
Large error models for microarray intensities
Authors: Huber Wolfgang , von Heydebreck Anja , Vingron Martin 
Published: November 2009

AbstractWe derive the additive‐multiplicative error model for microarray intensities, and describe two applications. For the detection of differentially expressed genes, we obtain a statistic whose variance is approximately independent of the mean intensity. For the post hoc calibration (“normalization”) of data with respect to experimental factors, we describe a method for parameter estimation.
Protein structure analysis and prediction
Authors: Taylor William R. 
Published: November 2009

Abstract
The comparative analysis of protein sequence and structure has provided great insights into the function and evolution of proteins. This approach relies on both sequence comparison and structure comparison with the latter, giving the deepest view on distant relationships, especially when just the overall fold of the proteins is compared. The establishment of correlation between sequence features and structural features can allow structure to be predicted from sequence. This can range from isolated fragments of structure (sometimes called motifs) to the overall fold of the proteins. With the progress in the determination of genomes and many new structures from structural genomics programs, there is an increasing wealth of data for comparative analysis.