bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accurate estimation of heritability in genome wide studies using random effects models
Authors: Golan David , Rosset Saharon 
Published: June 2011

AbstractMotivation: Random effects models have recently been introduced as an approach for analyzing genome wide association studies (GWASs), which allows estimation of overall heritability of traits without explicitly identifying the genetic loci responsible. Using this approach, Yang et al. (2010) have demonstrated that the heritability of height is much higher than the ~10% associated with identified genetic factors. However, Yang et al. (2010) relied on a heuristic for performing estimation in this model.Results: We adopt the model framework of Yang et al. (2010) and develop a method for maximum-likelihood (ML) estimation in this framework. Our method is based on Monte-Carlo expectation-maximization (MCEM; Wei et al., 1990), an expectation-maximization algorithm wherein a Markov chain Monte Carlo approach is used in the E-step. We demonstrate that this method leads to more stable and accurate heritability estimation compared to the approach of Yang et al. (2010), and it also allows us to find ML estimates of the portion of markers which are causal, indicating whether the heritability stems from a small number of powerful genetic factors or a large number of less powerful ones.Contact: saharon@post.tau.ac.il
Identification of metabolic network models from incomplete high-throughput datasets
Authors: Berthoumieux Sara , Brilli Matteo , de Jong Hidde , Kahn Daniel , Cinquemani Eugenio 
Published: June 2011

Abstract
Motivation: High-throughput measurement techniques for metabolism and gene expression provide a wealth of information for the identification of metabolic network models. Yet, missing observations scattered over the dataset restrict the number of effectively available datapoints and make classical regression techniques inaccurate or inapplicable. Thorough exploitation of the data by identification techniques that explicitly cope with missing observations is therefore of major importance.
Results: We develop a maximum-likelihood approach for the estimation of unknown parameters of metabolic network models that relies on the integration of statistical priors to compensate for the missing data. In the context of the linlog metabolic modeling framework, we implement the identification method by an Expectation-Maximization (EM) algorithm and by a simpler direct numerical optimization method. We evaluate performance of our methods by comparison to existing approaches, and show that our EM method provides the best results over a variety of simulated scenarios. We then apply the EM algorithm to a real problem, the identification of a model for the Escherichia coli central carbon metabolism, based on challenging experimental data from the literature. This leads to promising results and allows us to highlight critical identification issues.
Contact: sara.berthoumieux@inria.fr; eugenio.cinquemani@inria.fr
Supplementary information: Supplementary data are available at Bioinformatics online.
Ontology patterns for tabular representations of biomedical knowledge on neglected tropical diseases
Authors: Santana Filipe , Schober Daniel , Medeiros Zulma , Freitas Fred , Schulz Stefan 
Published: June 2011

Abstract
Motivation: Ontology-like domain knowledge is frequently published in a tabular format embedded in scientific publications. We explore the re-use of such tabular content in the process of building NTDO, an ontology of neglected tropical diseases (NTDs), where the representation of the interdependencies between hosts, pathogens and vectors plays a crucial role.
Results: As a proof of concept we analyzed a tabular compilation of knowledge about pathogens, vectors and geographic locations involved in the transmission of NTDs. After a thorough ontological analysis of the domain of interest, we formulated a comprehensive design pattern, rooted in the biomedical domain upper level ontology BioTop. This pattern was implemented in a VBA script which takes cell contents of an Excel spreadsheet and transforms them into OWL-DL. After minor manual post-processing, the correctness and completeness of the ontology was tested using pre-formulated competence questions as description logics (DL) queries. The expected results could be reproduced by the ontology. The proposed approach is recommended for optimizing the acquisition of ontological domain knowledge from tabular representations.
Availability and implementation: Domain examples, source code and ontology are freely available on the web at http://www.cin.ufpe.br/~ntdo.
Contact: fss3@cin.ufpe.br
From sets to graphs: towards a realistic enrichment analysis of transcriptomic systems
Authors: Geistlinger Ludwig , Csaba Gergely , Küffner Robert , Mulder Nicola , Zimmer Ralf 
Published: June 2011

Abstract
Motivation: Current gene set enrichment approaches do not take interactions and associations between set members into account. Mutual activation and inhibition causing positive and negative correlation among set members are thus neglected. As a consequence, inconsistent regulations and contextless expression changes are reported and, thus, the biological interpretation of the result is impeded.
Results: We analyzed established gene set enrichment methods and their result sets in a large-scale investigation of 1000 expression datasets. The reported statistically significant gene sets exhibit only average consistency between the observed patterns of differential expression and known regulatory interactions. We present Gene Graph Enrichment Analysis (GGEA) to detect consistently and coherently enriched gene sets, based on prior knowledge derived from directed gene regulatory networks. Firstly, GGEA improves the concordance of pairwise regulation with individual expression changes in respective pairs of regulating and regulated genes, compared with set enrichment methods. Secondly, GGEA yields result sets where a large fraction of relevant expression changes can be explained by nearby regulators, such as transcription factors, again improving on set-based methods. Thirdly, we demonstrate in additional case studies that GGEA can be applied to human regulatory pathways, where it sensitively detects very specific regulation processes, which are altered in tumors of the central nervous system. GGEA significantly increases the detection of gene sets where measured positively or negatively correlated expression patterns coincide with directed inducing or repressing relationships, thus facilitating further interpretation of gene expression data.
Availability: The method and accompanying visualization capabilities have been bundled into an R package and tied to a grahical user interface, the Galaxy workflow environment, that is running as a web server.
Contact: Ludwig.Geistlinger@bio.ifi.lmu.de; Ralf.Zimmer@bio.ifi.lmu.de
Sequence-based prediction of protein crystallization, purification and production propensity
Authors: Mizianty Marcin J. , Kurgan Lukasz 
Published: June 2011

Abstract
Motivation: X-ray crystallography-based protein structure determination, which accounts for majority of solved structures, is characterized by relatively low success rates. One solution is to build tools which support selection of targets that are more likely to crystallize. Several in silico methods that predict propensity of diffraction-quality crystallization from protein chains were developed. We show that the quality of their predictions drops when applied to more recent crystallization trails, which calls for new solutions. We propose a novel approach that alleviates drawbacks of the existing methods by using a recent dataset and improved protocol to annotate progress along the crystallization process, by predicting the success of the entire process and steps which result in the failed attempts, and by utilizing a compact and comprehensive set of sequence-derived inputs to generate accurate predictions.
Results: The proposed PPCpred (predictor of protein Production, Purification and Crystallization) predict propensity for production of diffraction-quality crystals, production of crystals, purification and production of the protein material. PPCpred utilizes comprehensive set of inputs based on energy and hydrophobicity indices, composition of certain amino acid types, predicted disorder, secondary structure and solvent accessibility, and content of certain buried and exposed residues. Our method significantly outperforms alignment-based predictions and several modern crystallization propensity predictors. Receiver operating characteristic (ROC) curves show that PPCpred is particularly useful for users who desire high true positive (TP) rates, i.e. low rate of mispredictions for solvable chains. Our model reveals several intuitive factors that influence the success of individual steps and the entire crystallization process, including the content of Cys, buried His and Ser, hydrophobic/hydrophilic segments and the number of predicted disordered segments.
Availability: http://biomine.ece.ualberta.ca/PPCpred/.
Contact: lkurgan@ece.ualberta.ca
Supplementary information: Supplementary data are available at Bioinformatics online.
Reconstruction of genealogical relationships with applications to Phase III of HapMap
Authors: Kyriazopoulou-Panagiotopoulou Sofia , Kashef Haghighi Dorna , Aerni Sarah J. , Sundquist Andreas , Bercovici Sivan , Batzoglou Serafim 
Published: June 2011

Abstract
Motivation: Accurate inference of genealogical relationships between pairs of individuals is paramount in association studies, forensics and evolutionary analyses of wildlife populations. Current methods for relationship inference consider only a small set of close relationships and have limited to no power to distinguish between relationships with the same number of meioses separating the individuals under consideration (e.g. aunt–niece versus niece–aunt or first cousins versus great aunt–niece).
Results: We present CARROT (ClAssification of Relationships with ROTations), a novel framework for relationship inference that leverages linkage information to differentiate between rotated relationships, that is, between relationships with the same number of common ancestors and the same number of meioses separating the individuals under consideration. We demonstrate that CARROT clearly outperforms existing methods on simulated data. We also applied CARROT on four populations from Phase III of the HapMap Project and detected previously unreported pairs of third- and fourth-degree relatives.
Availability: Source code for CARROT is freely available at http://carrot.stanford.edu.
Contact: sofiakp@stanford.edu
Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling
Authors: Łabaj Paweł P. , Leparc Germán G. , Linggi Bryan E. , Markillie Lye Meng , Wiley H. Steven , Kreil David P. 
Published: June 2011

Abstract
Motivation: Measurement precision determines the power of any analysis to reliably identify significant signals, such as in screens for differential expression, independent of whether the experimental design incorporates replicates or not. With the compilation of large-scale RNA-Seq datasets with technical replicate samples, however, we can now, for the first time, perform a systematic analysis of the precision of expression level estimates from massively parallel sequencing technology. This then allows considerations for its improvement by computational or experimental means.
Results: We report on a comprehensive study of target identification and measurement precision, including their dependence on transcript expression levels, read depth and other parameters. In particular, an impressive recall of 84% of the estimated true transcript population could be achieved with 331 million 50 bp reads, with diminishing returns from longer read lengths and even less gains from increased sequencing depths. Most of the measurement power (75%) is spent on only 7% of the known transcriptome, however, making less strongly expressed transcripts harder to measure. Consequently, &lt;30% of all transcripts could be quantified reliably with a relative error &lt;20%. Based on established tools, we then introduce a new approach for mapping and analysing sequencing reads that yields substantially improved performance in gene expression profiling, increasing the number of transcripts that can reliably be quantified to over 40%. Extrapolations to higher sequencing depths highlight the need for efficient complementary steps. In discussion we outline possible experimental and computational strategies for further improvements in quantification precision.
Contact: rnaseq10@boku.ac.at
Supplementary information: Supplementary data are available at Bioinformatics online.
HiTRACE: high-throughput robust analysis for capillary electrophoresis
Authors: Yoon Sungroh , Kim Jinkyu , Hum Justine , Kim Hanjoo , Park Seunghyun , Kladwang Wipapat , Das Rhiju 
Published: June 2011

Abstract
Motivation: Capillary electrophoresis (CE) of nucleic acids is a workhorse technology underlying high-throughput genome analysis and large-scale chemical mapping for nucleic acid structural inference. Despite the wide availability of CE-based instruments, there remain challenges in leveraging their full power for quantitative analysis of RNA and DNA structure, thermodynamics and kinetics. In particular, the slow rate and poor automation of available analysis tools have bottlenecked a new generation of studies involving hundreds of CE profiles per experiment.
Results: We propose a computational method called high-throughput robust analysis for capillary electrophoresis (HiTRACE) to automate the key tasks in large-scale nucleic acid CE analysis, including the profile alignment that has heretofore been a rate-limiting step in the highest throughput experiments. We illustrate the application of HiTRACE on 13 datasets representing 4 different RNAs, 3 chemical modification strategies and up to 480 single mutant variants; the largest datasets each include 87 360 bands. By applying a series of robust dynamic programming algorithms, HiTRACE outperforms prior tools in terms of alignment and fitting quality, as assessed by measures including the correlation between quantified band intensities between replicate datasets. Furthermore, while the smallest of these datasets required 7–10 h of manual intervention using prior approaches, HiTRACE quantitation of even the largest datasets herein was achieved in 3–12 min. The HiTRACE method, therefore, resolves a critical barrier to the efficient and accurate analysis of nucleic acid structure in experiments involving tens of thousands of electrophoretic bands.
Availability: HiTRACE is freely available for download at http://hitrace.stanford.edu.
Contact: sryoon@korea.ac.kr; rhiju@stanford.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Collaborative Analysis of Wheat Endosperm Compressive Material Properties
Authors: Morris Craig F. , Delwiche Stephen R. , Bettge Arthur D. , Mabille Frédéric , Abécassis Joël , Pitts Marvin J. , Dowell Floyd E. , Deroo Camille , Pearson Tom 
Published: June 2011

ABSTRACTThe objective measurement of cereal endosperm texture, for wheat (Triticum spp. L.) in particular, is relevant to the milling, processing, and utilization of grain. The objective of this study was to evaluate the interlaboratory results of compression failure testing of wheat endosperm specimens of defined geometry. Parallelepipeds (bricks) and cylinders were prepared from individual soft and hard near‐isogenic wheat kernels and compressed in two orientations (parallel and perpendicular to the long brush‐to‐germ axis). Compression curves were used to derive failure stress, failure strain, work density (area under the curve), and Young's modulus. In all five laboratories, the ability to delineate hard from soft wheat endosperm material properties was quite high. Four laboratories compressed endosperm bricks in the same orientation, on edge; texture class (soft vs. hard) was consistently the greatest source of variation in analysis of variance models (F‐values from 417 to 1401, Young's modulus and failure stress, respectively). Failure stress was found to be the best overall means of measuring the difference in what is known in the vernacular as wheat hardness. Across laboratories, the absolute measures of all four material properties ranged on the order of about two‐ to threefold from low to high, although within a laboratory, results were highly consistent. Laboratory by texture class interaction was deemed to be of minor importance. Brick size and moisture content within the ranges tested were not major sources of variation, and cylinders prepared from endosperm produced results similar to those obtained from bricks. The results suggested that wheat endosperm might express some level of anisotropic behavior, as specimens compressed in the kernel orientation parallel to the long axis failed at lower strain and stress values, with lower work density, when compared with kernel orientation perpendicular to the long axis. A key feature of interlaboratory variation was identified as being instrument rigidity, a subject of ongoing research. In conclusion, the preparation of endosperm specimens of defined size and shape, in combination with compression failure testing at low moisture content (<18%), is useful for objectively delineating the phenomenon known as hardness. The study presented here will advance our ability to objectively measure cereal grain texture and the material properties of endosperm.
Brain‐derived neurotrophic factor is increased in serum and skin levels of patients with chronic spontaneous urticaria
Authors: Rössing K. , Novak N. , Mommert S. , Pfab F. , Gehring M. , Wedi B. , Kapp A. , Raap U. 
Published: June 2011

Summary
Background Chronic spontaneous urticaria is triggered by many direct and indirect aggravating factors including autoreactive/autoimmune mechanisms, infections, non‐allergic and pseudoallergic intolerance reactions. However, the role of neuroimmune mechanisms in chronic spontaneous urticaria so far is unclear.
Objective Thus, we wanted to address the regulation of the neurotrophin brain‐derived neurotrophic factor (BDNF) in serum and inflammatory skin of patients with chronic spontaneous urticaria in comparison to subjects with healthy skin.
Methods Fifty adult patients with chronic spontaneous urticaria and 23 skin‐healthy subjects were studied. Chronic spontaneous urticaria was defined as recurrent weals for more than 6 weeks. Autologous serum skin test was performed in all patients with chronic spontaneous urticaria and BDNF serum levels were analysed by enzyme immunoassay in all subjects. Furthermore, skin biopsies were taken from weals of eight patients with chronic spontaneous urticaria as well as from healthy skin of eight controls to evaluate the expression of BDNF and its receptors including tyrosine kinase (trk) B and pan‐neurotrophin receptor p75NTR by immunohistochemistry.
Results BDNF serum levels were detectable in all subjects studied. However, BDNF levels were significantly higher in patients with chronic spontaneous urticaria compared to non‐atopic skin‐healthy controls (P<0.001). Furthermore, epidermal and dermal expression of BDNF and epidermal expression of p75NTR was significantly higher in patients with chronic spontaneous urticaria compared with controls (P<0.05–0.001). There was no difference with regard to the expression of trkB between chronic spontaneous urticaria and controls and no difference in BDNF serum levels between autologous serum skin test‐positive (n=23) and ‐negative (n=27) patients with chronic spontaneous urticaria.
Conclusions and Clinical Relevance This study shows that BDNF is increased in serum and diseased skin of patients with chronic spontaneous urticaria, suggesting a role for neurotrophins in the pathophysiology of this chronic inflammatory skin disease. Further studies are needed to address the functional role of BDNF on key target effector cells in chronic spontaneous urticaria to establish new therapeutic implications.
Cite this as: K. Rössing, N. Novak, S. Mommert, F. Pfab, M. Gehring, B. Wedi, A. Kapp and U. Raap, Clinical & Experimental Allergy, 2011 (41) 1392–1399.
Multiparameter phenotyping of T‐cell subsets in distinct subgroups of patients with pulmonary sarcoidosis
Authors: Wikén M. , Grunewald J. , Eklund A. , Wahlström J. 
Published: June 2011

Abstract.  Wikén M, Grunewald J, Eklund A, Wahlström J (Respiratory Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden). Multiparameter phenotyping of T‐cell subsets in distinct subgroups of patients with pulmonary sarcoidosis. J Intern Med 2012; 271: 90–103.Objectives.  Sarcoidosis is an inflammatory disorder in which elevated numbers of activated T cells are found in the lung. HLA‐DRB1*0301pos (DR3pos) patients are characterized by good prognosis and an accumulation of lung CD4pos T cells expressing the T‐cell receptor (TCR) gene segment AV2S3. Our aim was to phenotype lung and blood T‐cell subsets in distinct patient groups to better understand the function of these subsets.Design.  Bronchoalveolar lavage (BAL) fluid and whole blood were obtained from a total of 22 patients with sarcoidosis, of whom 11 were DR3pos. Using eight‐colour flow cytometry, phenotyping of T cells was performed with regard to CD3, CD4, CD8, CD25, CD27, CD45RO, CD57, CD69, CD103, FOXP3 and TCR AV2S3.Results.  DR3pos patients had fewer FOXP3pos (regulatory) CD45ROpos (memory) BAL T cells than DR3neg patients. Fewer AV2S3pos T cells were FOXP3pos, compared with AV2S3neg cells, thus indicating an effector function and not a regulatory role for this subset. Fewer lung and blood AV2S3pos T cells were CD25pos CD27pos, and more were CD25neg CD27neg and CD69pos, compared with AV2S3neg T cells, indicating a higher degree of differentiation and activation in both compartments.Conclusion.  Our main findings were a lower proportion of regulatory T cells in DR3pos patients, together with the accumulation of AV2S3pos T cells with a highly activated effector phenotype in the lungs of these patients. This may provide for efficient elimination of a harmful antigen in DR3pos patients and could thus help to explain the spontaneous recovery typically seen in these patients.