bluuebunny/crossref_metadata_2025_split huggingface dataset:
A geometric approach for the alignment of liquid chromatography—mass spectrometry data
Authors: Lange Eva , Gröpl Clemens , Schulz-Trieglaff Ole , Leinenbach Andreas , Huber Christian , Reinert Knut 
Published: July 2007

Abstract
Motivation: Liquid chromatography coupled to mass spectrometry (LC-MS) and combined with tandem mass spectrometry (LC-MS/MS) have become a prominent tool for the analysis of complex proteomic samples. An important step in a typical workflow is the combination of results from multiple LC-MS experiments to improve confidence in the obtained measurements or to compare results from different samples. To do so, a suitable mapping or alignment between the data sets needs to be estimated. The alignment has to correct for variations in mass and elution time which are present in all mass spectrometry experiments.
Results: We propose a novel algorithm to align LC-MS samples and to match corresponding ion species across samples. Our algorithm matches landmark signals between two data sets using a geometric technique based on pose clustering. Variations in mass and retention time are corrected by an affine dewarping function estimated from matched landmarks. We use the pairwise dewarping in an algorithm for aligning multiple samples. We show that our pose clustering approach is fast and reliable as compared to previous approaches. It is robust in the presence of noise and able to accurately align samples with only few common ion species. In addition, we can easily handle different kinds of LC-MS data and adopt our algorithm to new mass spectrometry technologies.
Availability: This algorithm is implemented as part of the OpenMS software library for shotgun proteomics and available under the Lesser GNU Public License (LGPL) at www.openms.de
Contact: lange@inf.fu-berlin.de
A statistical method for alignment-free comparison of regulatory sequences
Authors: Kantorovitz Miriam R. , Robinson Gene E. , Sinha Saurabh 
Published: July 2007

Abstract
Motivation: The similarity of two biological sequences has traditionally been assessed within the well-established framework of alignment. Here we focus on the task of identifying functional relationships between cis-regulatory sequences that are non-orthologous or greatly diverged. ‘Alignment-free’ measures of sequence similarity are required in this regime.
Results: We investigate the use of a new score for alignment-free sequence comparison, called the score. It is based on comparing the frequencies of all fixed-length words in the two sequences. An important, novel feature of the score is that it is comparable across sequence pairs drawn from arbitrary background distributions. We present a method that gives quadratic improvement in the time complexity of calculating the score, over the naïve method. We then evaluate the score on several tissue-specific families of cis-regulatory modules (in Drosophila and human). The new score is highly successful in discriminating functionally related regulatory sequences from unrelated sequence pairs. The performance of the score is compared to five other alignment-free similarity measures, and shown to be consistently superior to all of these measures.
Availability: Our implementation of the score will be made freely available as source code, upon publication of this article, at: http://veda.cs.uiuc.edu/d2z/
Contact: sinhas@cs.uiuc.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Using genome-context data to identify specific types of functional associations in pathway/genome databases
Authors: Green Michelle L. , Karp Peter D. 
Published: July 2007

Abstract
Background: Hundreds of genes lacking homology to any protein of known function are sequenced every day. Genome-context methods have proved useful in providing clues about functional annotations for many proteins. However, genome-context methods detect many biological types of functional associations, and do not identify which type of functional association they have found.
Results: We have developed two new genome-context-based algorithms. Algorithm 1 extends our previous algorithm for identifying missing enzymes in predicted metabolic pathways (pathway holes) to use genome-context features. The new algorithm has significantly improved scope because it can now be applied to pathway reactions to which sequence similarity methods cannot be applied due to an absence of known sequences for enzymes catalyzing the reaction in other organisms. The new method identifies at least one known enzyme in the top ten hits for 58% of EcoCyc reactions that lack enzyme sequences in other organisms. Surprisingly, the addition of genome-context features does not improve the accuracy of the algorithm when sequences for the enzyme do exist in other organisms. Algorithm 2 uses genome-context methods to predict three distinct types of functional relationships between pairs of proteins: pairs that occur in the same protein complex, the same pathway, or the same operon. This algorithm performs with varying degrees of accuracy on each type of relationship, and performs best in predicting pathway and protein complex relationships.
Contact: pkarp@ai.sri.com
Inferring protein–DNA dependencies using motif alignments and mutual information
Authors: Mahony Shaun , Auron Philip E. , Benos Panayiotis V. 
Published: July 2007

Abstract
Motivation: Mutual information can be used to explore covarying positions in biological sequences. In the past, it has been successfully used to infer RNA secondary structure conformations from multiple sequence alignments. In this study, we show that the same principles allow the discovery of transcription factor amino acids that are coevolving with nucleotides in their DNA-binding targets.
Results: Given an alignment of transcription factor binding domains, and a separate alignment of their DNA target motifs, we demonstrate that mutually covarying base-amino acid positions may indicate possible protein–DNA contacts. Examples explored in this study include C2H2 zinc finger, homeodomain and bHLH DNA-binding motif families, where a number of known base-amino acid contacting positions are identified. Mutual information analyses may aid the prediction of base-amino acid contacting pairs for particular transcription factor families, thereby yielding structural insights from sequence information alone. Such inference of protein–DNA contacting positions may guide future experimental studies of DNA recognition.
Contact: shaun.mahony@ccbb.pitt.edu or benos@pitt.edu
Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations
Authors: Huang Jim C. , Kannan Anitha , Winn John 
Published: July 2007

AbstractMotivation: With the recent availability of large-scale data sets profiling single nucleotide polymorphisms (SNPs) and quantitative traits data across different human subpopulations, there has been much attention directed towards discovering patterns of genetic variation and their connection to gene regulation and the onset/progression of disease. While previous work has focused primarily on correlating individual SNP markers with gene expression and disease, it has been suggested that using haplotype blocks instead of individual markers can significantly increase statistical power.Results: We present BlockMapper, a probabilistic generative model for genotype data and quantitative traits data, such as gene expression or phenotype measurements. BlockMapper discovers the block structure of genotype data and associates these inferred blocks to patterns of variation in quantitative traits data, whilst accounting for non-genetic factors. Our model achieves high accuracy for predicting Crohn's disease phenotype in Chromosome 5q31 and reveals novel cis-associations between two haplotype blocks in the ENm006 genomic region and GDI1, a gene implicated in X-linked mental retardation. Our results underscore the importance of accounting for the influence of large sets of SNPs on patterns of regulatory/phenotypic variation and represent a step towards an understanding of human genetic variation.Contact: jwinn@microsoft.com
Continuous hidden process model for time series expression experiments
Authors: Shi Yanxin , Klustein Michael , Simon Itamar , Mitchell Tom , Bar-Joseph Ziv 
Published: July 2007

AbstractMotivation: When analyzing expression experiments, researchers are often interested in identifying the set of biological processes that are up-or down-regulated under the experimental condition studied. Current approaches, including clustering expression profiles and averaging the expression profiles of genes known to participate in specific processes, fail to provide an accurate estimate of the activity levels of many biological processes.Results: We introduce a probabilistic continuous hidden process Model (CHPM) for time series expression data. CHPM can simultaneously determine the most probable assignment of genes to processes and the level of activation of these processes over time. To estimate model parameters, CHPM uses multiple time series datasets and incorporates prior biological knowledge. Applying CHPM to yeast expression data, we show that our algorithm produces more accurate functional assignments for genes compared to other expression analysis methods. The inferred process activity levels can be used to study the relationships between biological processes. We also report new biological experiments confirming some of the process activity levels predicted by CHPM.Availability: A Java implementation is available at http:\\www.cs.cmu.edu\~yanxins\chpmContact: zivbj@cs.cmu.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Modeling recurrent DNA copy number alterations in array CGH data
Authors: Shah Sohrab P. , Lam Wan L. , Ng Raymond T. , Murphy Kevin P. 
Published: July 2007

Abstract
Motivation: Recurrent DNA copy number alterations (CNA) measured with array comparative genomic hybridization (aCGH) reveal important molecular features of human genetics and disease. Studying aCGH profiles from a phenotypic group of individuals can determine important recurrent CNA patterns that suggest a strong correlation to the phenotype. Computational approaches to detecting recurrent CNAs from a set of aCGH experiments have typically relied on discretizing the noisy log ratios and subsequently inferring patterns. We demonstrate that this can have the effect of filtering out important signals present in the raw data. In this article we develop statistical models that jointly infer CNA patterns and the discrete labels by borrowing statistical strength across samples.
Results: We propose extending single sample aCGH HMMs to the multiple sample case in order to infer shared CNAs. We model recurrent CNAs as a profile encoded by a master sequence of states that generates the samples. We show how to improve on two basic models by performing joint inference of the discrete labels and providing sparsity in the output. We demonstrate on synthetic ground truth data and real data from lung cancer cell lines how these two important features of our model improve results over baseline models. We include standard quantitative metrics and a qualitative assessment on which to base our conclusions.
Availability: http://www.cs.ubc.ca/~sshah/acgh
Contact: sshah@cs.ubc.ca
Systematic discovery of functional modules and context-specific functional annotation of human genome
Authors: Huang Yu , Li Haifeng , Hu Haiyan , Yan Xifeng , Waterman Michael S. , Huang Haiyan , Zhou Xianghong Jasmine 
Published: July 2007

Abstract
Motivation: The rapid accumulation of microarray datasets provides unique opportunities to perform systematic functional characterization of the human genome. We designed a graph-based approach to integrate cross-platform microarray data, and extract recurrent expression patterns. A series of microarray datasets can be modeled as a series of co-expression networks, in which we search for frequently occurring network patterns. The integrative approach provides three major advantages over the commonly used microarray analysis methods: (1) enhance signal to noise separation (2) identify functionally related genes without co-expression and (3) provide a way to predict gene functions in a context-specific way.
Results: We integrate 65 human microarray datasets, comprising 1105 experiments and over 11 million expression measurements. We develop a data mining procedure based on frequent itemset mining and biclustering to systematically discover network patterns that recur in at least five datasets. This resulted in 143 401 potential functional modules. Subsequently, we design a network topology statistic based on graph random walk that effectively captures characteristics of a gene's local functional environment. Function annotations based on this statistic are then subject to the assessment using the random forest method, combining six other attributes of the network modules. We assign 1126 functions to 895 genes, 779 known and 116 unknown, with a validation accuracy of 70%. Among our assignments, 20% genes are assigned with multiple functions based on different network environments.
Availability: http://zhoulab.usc.edu/ContextAnnotation
Contact: xjzhou@usc.edu
Efficient parameter estimation for RNA secondary structure prediction
Authors: Andronescu Mirela , Condon Anne , Hoos Holger H. , Mathews David H. , Murphy Kevin P. 
Published: July 2007

Abstract
Motivation: Accurate prediction of RNA secondary structure from the base sequence is an unsolved computational challenge. The accuracy of predictions made by free energy minimization is limited by the quality of the energy parameters in the underlying free energy model. The most widely used model, the Turner99 model, has hundreds of parameters, and so a robust parameter estimation scheme should efficiently handle large data sets with thousands of structures. Moreover, the estimation scheme should also be trained using available experimental free energy data in addition to structural data.
Results: In this work, we present constraint generation (CG), the first computational approach to RNA free energy parameter estimation that can be efficiently trained on large sets of structural as well as thermodynamic data. Our CG approach employs a novel iterative scheme, whereby the energy values are first computed as the solution to a constrained optimization problem. Then the newly computed energy parameters are used to update the constraints on the optimization function, so as to better optimize the energy parameters in the next iteration. Using our method on biologically sound data, we obtain revised parameters for the Turner99 energy model. We show that by using our new parameters, we obtain significant improvements in prediction accuracy over current state of-the-art methods.
Availability: Our CG implementation is available at http://www.rnasoft.ca/CG/
Contact: andrones@cs.ubc.ca
Multiple alignment by aligning alignments
Authors: Wheeler Travis J. , Kececioglu John D. 
Published: July 2007

Abstract
Motivation: Multiple sequence alignment is a fundamental task in bioinformatics. Current tools typically form an initial alignment by merging subalignments, and then polish this alignment by repeated splitting and merging of subalignments to obtain an improved final alignment. In general this form-and-polish strategy consists of several stages, and a profusion of methods have been tried at every stage. We carefully investigate: (1) how to utilize a new algorithm for aligning alignments that optimally solves the common subproblem of merging subalignments, and (2) what is the best choice of method for each stage to obtain the highest quality alignment.
Results: We study six stages in the form-and-polish strategy for multiple alignment: parameter choice, distance estimation, merge-tree construction, sequence-pair weighting, alignment merging, and polishing. For each stage, we consider novel approaches as well as standard ones. Interestingly, the greatest gains in alignment quality come from (i) estimating distances by a new approach using normalized alignment costs, and (ii) polishing by a new approach using 3-cuts. Experiments with a parameter-value oracle suggest large gains in quality may be possible through an input-dependent choice of alignment parameters, and we present a promising approach for building such an oracle. Combining the best approaches to each stage yields a new tool we call Opal that on benchmark alignments matches the quality of the top tools, without employing alignment consistency or hydrophobic gap penalties.
Availability: Opal, a multiple alignment tool that implements the best methods in our study, is freely available at http://opal.cs.arizona.edu
Contact: twheeler@cs.arizona.edu
Connecting quantitative regulatory-network models to the genome
Authors: Pan Yue , Durfee Tim , Bockhorst Joseph , Craven Mark 
Published: July 2007

Abstract
Motivation: An important task in computational biology is to infer, using background knowledge and high-throughput data sources, models of cellular processes such as gene regulation. Nachman et al. have developed an approach to inferring gene-regulatory networks that represents quantitative transcription rates, and simultaneously estimates both the kinetic parameters that govern these rates and the activity levels of unobserved regulators that control them. This approach is appealing in that it provides a more detailed and realistic description of how a gene's regulators influence its level of expression than alternative methods. We have developed an extension to this approach that involves representing and learning the key kinetic parameters as functions of features in the genomic sequence. The primary motivation for our approach is that it provides a more mechanistic representation of the regulatory relationships being modeled.
Results: We evaluate our approach using two Escherichia coli gene-expression data sets, with a particular focus on modeling the networks that are involved in controlling how E.coli regulates its response to the carbon source(s) available to it. Our results indicate that our sequence-based models provide predictive accuracy that is better than similar models without sequence-based parameters, and substantially better than a simple baseline. Moreover, our approach results in models that offer more explanatory power and biological insight than models without sequence-based parameters.
Contact: ypan@cs.wisc.edu
Manual curation is not sufficient for annotation of genomic databases
Authors: Baumgartner William A. , Cohen K. Bretonnel , Fox Lynne M. , Acquaah-Mensah George , Hunter Lawrence 
Published: July 2007

Abstract
Motivation: Knowledge base construction has been an area of intense activity and great importance in the growth of computational biology. However, there is little or no history of work on the subject of evaluation of knowledge bases, either with respect to their contents or with respect to the processes by which they are constructed. This article proposes the application of a metric from software engineering known as the found/fixed graph to the problem of evaluating the processes by which genomic knowledge bases are built, as well as the completeness of their contents.
Results: Well-understood patterns of change in the found/fixed graph are found to occur in two large publicly available knowledge bases. These patterns suggest that the current manual curation processes will take far too long to complete the annotations of even just the most important model organisms, and that at their current rate of production, they will never be sufficient for completing the annotation of all currently available proteomes.
Contact: larry.hunter@uchsc.edu
Swallowing Outcomes After Microvascular Head and Neck Reconstruction: A Prospective Review of 191 Cases
Authors: Khariwala Samir S. , Vivek Prashant P. , Lorenz Robert R. , Esclamado Ramon M. , Wood Benjamin , Strome Marshall , Alam Daniel S. 
Published: July 2007

AbstractBackground: The use of microvascular free tissue flaps tailored specifically to the ablative surgical defects has allowed precise anatomic reconstructions to be performed and, in turn, has improved patient outcomes. We report here the postoperative swallowing outcomes of patients undergoing microvascular reconstructions for a range of head and neck defects at the Cleveland Clinic.Methods: The study includes 191 consecutive reconstructions for varied defects. All patients were reconstructed with four specific microvascular flaps based on their surgical defect, and postoperative swallowing outcomes were evaluated and recorded on a prospectively maintained database. Pre‐ and postoperative swallowing was graded on an ordinal scale. Data were simultaneously collected on the precise anatomic ablative defect in each patient, subdividing the head and neck into 16 subsites. The data were analyzed using a multivariate analysis accounting for comorbid factors, type of flap used, and subsite of defect.Results: The findings are summarized as follows. There were no flap failures. The percent of patients who were able to swallow and maintain an exclusively oral diet postoperatively was 78.5%. Only 16.8% were unable to have an oral diet (NPO) and dependent on a gastric tube (G‐tube) for feeding. The factors that predicted an inability to swallow include tongue resection, preoperative radiation therapy, and hypopharyngeal defects. In contrast, floor of mouth, mandibular, and pharyngeal defects, regardless of size, had excellent long‐term swallowing outcomes. Most patients with these defects were able to tolerate at least a soft solid diet.Conclusions: In summary, we report excellent postoperative swallowing outcomes after microvascularreconstructions at our institution that compare favorably with outcomes with pedicled flaps and historic controls. The type of flap used and the size of defect had minimal effects on swallowing outcomes. The most difficult subsites to reconstruct were tongue defects, which strongly correlated with poor swallowing outcomes. The other factor that strongly impacted outcomes was preoperative radiation treatment. We believe these results highlight the utility of free flaps in recreating the precise anatomy required to maintain swallowing function. These data will hopefully support numerous previous studies that have established the use of microvascular reconstruction as standard of care for ablative surgical defects in the head and neck.
Estimating Subglottal Pressure via Airflow Redirection
Authors: Baggott Christopher D. , Yuen Alexander K. , Hoffman Matthew R. , Zhou Liang , Jiang Jack J. 
Published: July 2007

AbstractSubglottal pressure (SGP) is a valuable parameter in the research and clinical assessment of laryngeal function. The lungs serve as a constant pressure source during sustained phonation, and that pressure, SGP, can be used to determine the efficiency with which the larynx converts aerodynamic power to acoustic power. As the larynx serves as an aerodynamic transducer, the vocal efficiency (Ve) coefficient, defined as acoustic power (dB) divided by aerodynamic power (SGP × glottal airflow) has been shown to reliably reflect vocal health. However, current SGP measurement techniques are hesitantly used because of either an invasive nature or the requirement of intensive patient training. This study tests a novel device that has been designed to noninvasively estimate SGP through mechanical airflow redirection, producing a numeric output on completion of the trial, which lasts only a few seconds. The novelty of this design lies in the ease of use for both the patient and the clinician. Multiple mechanical airflow redirections occlude the airway for only 135 ms, which is predicted to limit the effect of confounding laryngeal reflexes that may occur during the trials. Additionally, the airflow redirection into a retention device allows for the pneumatic in‐trial comparison of the estimated SGP with the pressure achieved by the patient, providing a numeric output to the clinician on completion.