bluuebunny/crossref_metadata_2025_split huggingface dataset:
A chronology of the Little Ice Age in the tropical Andes of Bolivia (16°S) and its implications for climate reconstruction
Authors: Rabatel Antoine , Francou Bernard , Jomelli Vincent , Naveau Philippe , Grancher Delphine 
Published: August 2008

AbstractDating moraines by lichenometry enabled us to reconstruct glacier recession in the Bolivian Andes since the Little Ice Age maximum. On the 15 proglacial margins studied, we identified a system of ten principal moraines that marks the successive positions of glaciers over the last four centuries. Moraines were dated by performing statistical analysis of lichen measurements based on the extreme values theory. Like glaciers in many mid-latitude mountain areas, Bolivian glaciers reached their maximal extent during the second half of the 17th century. This glacier maximum coincides with the Maunder minimum of solar irradiance. By reconstructing the equilibrium-line altitude and changes in mass-balance, we think the glacier maximum may be due to a 20 to 30% increase in precipitation and a 1.1 to 1.2 °C decrease in temperature compared with present conditions. In the early 18th century, glaciers started to retreat at varying rates until the late 19th to early 20th century; this trend was generally associated with decreasing accumulation rates. By contrast, glacier recession in the 20th century was mainly the consequence of an increase in temperature and humidity. These results are consistent with observations made in the study region based on other proxies.
Detection of 3D atomic similarities and their use in the discrimination of small molecule protein-binding sites
Authors: Najmanovich Rafael , Kurbatova Natalja , Thornton Janet 
Published: August 2008

Abstract
Motivation: Current computational methods for the prediction of function from structure are restricted to the detection of similarities and subsequent transfer of functional annotation. In a significant minority of cases, global sequence or structural (fold) similarities do not provide clues about protein function. In these cases, one alternative is to detect local binding site similarities. These may still reflect more distant evolutionary relationships as well as unique physico-chemical constraints necessary for binding similar ligands, thus helping pinpoint the function. In the present work, we ask the following question: is it possible to discriminate within a dataset of non-homologous proteins those that bind similar ligands based on their binding site similarities?
Methods: We implement a graph-matching-based method for the detection of 3D atomic similarities introducing some simplifications that allow us to extend its applicability to the analysis of large allatom binding site models. This method, called IsoCleft, does not require atoms to be connected either in sequence or space. We apply the method to a cognate-ligand bound dataset of non-homologous proteins. We define a family of binding site models with decreasing knowledge about the identity of the ligand-interacting atoms to uncouple the questions of predicting the location of the binding site and detecting binding site similarities. Furthermore, we calculate the individual contributions of binding site size, chemical composition and geometry to prediction performance.
Results: We find that it is possible to discriminate between different ligand-binding sites. In other words, there is a certain uniqueness in the set of atoms that are in contact to specific ligand scaffolds. This uniqueness is restricted to the atoms in close proximity of the ligand in which case, size and chemical composition alone are sufficient to discriminate binding sites. Discrimination ability decreases with decreasing knowledge about the identity of the ligand-interacting binding site atoms. The decrease is quite abrupt when considering size and chemical composition alone, but much slower when including geometry. We also observe that certain ligands are easier to discriminate. Interestingly, the subset of binding site atoms belonging to highly conserved residues is not sufficient to discriminate binding sites, implying that convergently evolved binding sites arrived at dissimilar solutions.
Availability: IsoCleft can be obtained from the authors.
Contact: rafael.najmanovich@ebi.ac.uk
Logical analysis of survival data: prognostic survival models by detecting high-degree interactions in right-censored data
Authors: Kronek Louis-Philippe , Reddy Anupama 
Published: August 2008

Abstract
Motivation: Survival analysis involves predicting the time to event for patients in a dataset, based on a set of recorded attributes. In this study we focus on right-censored survival problems. Detecting high-degree interactions for the estimation of survival probability is a challenging problem in survival analysis from the statistical perspective.
Results: We propose a new methodology, Logical Analysis of Survival Data (LASD), to identify interactions between variables (survival patterns) without any prior hypotheses. Using these set of patterns, we predict survival distributions for each observation. To evaluate LASD we select two publicly available datasets: a lung adenocarcinoma dataset (gene-expression profiles) and the other a breast cancer dataset (clinical profiles). The performance of LASD when compared with survival decision trees improves the cross-validation accuracy by 18% for the gene-expression dataset, and by 2% for the clinical dataset.
Availability: Executable codes will be provided upon request.
Contact: louis-philippe.kronek@g-scop.fr; areddy@rutcor.rutgers.edu
Logical modelling of the role of the Hh pathway in the patterning of the Drosophila wing disc
Authors: González Aitor , Chaouiya Claudine , Thieffry Denis 
Published: August 2008

Abstract
Motivations: The development of most tissues and organs relies on a limited number of signal transduction pathways enabling the coordination of cellular differentiation. A proper understanding of the roles of signal transduction pathways requires the definition of formal models capturing the main qualitative features of these patterning processes. This is a challenging task because the underlying processes, diffusion, regulatory modifications, reception and sequestration of signalling molecules, transcriptional regulation of target genes, etc. are only partly characterized. In this context, qualitative models can be more readily proposed on the basis of available (molecular) genetic data. But this requires novel computational tools and proper qualitative representations of phenomena such as diffusion or sequestration.
To assess the power and limits of a logical formalism in this context, we propose a multi-level model of the multi-cellular network involved in the definition of the anterior–posterior boundary during the development of the wing disc of Drosophila melanogaster. The morphogen Hedgehog (Hh) is the inter-cellular signal coordinating this process. It diffuses from the posterior compartment of the disc to activate its pathway in cells immediately anterior to the boundary. In these boundary cells, the Hh gradient induces target genes in distinct domains as a function of the Hh concentration. One target of Hh signalling is the gene coding for the receptor Patched (Ptc), which sequesters Hh and impedes further diffusion, thereby refining the boundary.
Results: We have delineated a logical model of the patterning process defining the cellular anterior–posterior boundary in the developing imaginal disc of Drosophila melanogaster. This model qualitatively accounts for the formation of a gradient of Hh, as well as for the transduction of this signal through a balance between the activatory (CiA) and inhibitory (CiR) products of the gene cubitus interruptus (ci). Wild-type and mutant simulations have been carried out to assess the coherence of the model with experimental data. Interestingly, our computational analysis provides novel insights into poorly understood processes such as the regulation of Ptc by CiR, the formation of a functional gradient of CiA across boundary cells, or yet functional En differences between anterior and posterior cells.
In conclusion, our model analysis demonstrates the flexibility of the logical formalism, enabling consistent qualitative representation of diffusion, sequestration and post-transcriptional regulatory processes within and between neighbouring cells.
Availability: An XML file containing the proposed model together with annotations can be downloaded from our website (http://gin.univ-mrs.fr/GINsim/), along with GINsim, a logical modelling and simulation software freely available to academic groups.
Contact: thieffry@tagc.univ-mrs.fr
Gain and loss of phosphorylation sites in human cancer
Authors: Radivojac Predrag , Baenziger Peter H. , Kann Maricel G. , Mort Matthew E. , Hahn Matthew W. , Mooney Sean D. 
Published: August 2008

Abstract
Motivation: Coding-region mutations in human genes are responsible for a diverse spectrum of diseases and phenotypes. Among lesions that have been studied extensively, there are insights into several of the biochemical functions disrupted by disease-causing mutations. Currently, there are more than 60 000 coding-region mutations associated with inherited disease catalogued in the Human Gene Mutation Database (HGMD, August 2007) and more than 70 000 polymorphic amino acid substitutions recorded in dbSNP (dbSNP, build 127). Understanding the mechanism and contribution these variants make to a clinical phenotype is a formidable problem.
Results: In this study, we investigate the role of phosphorylation in somatic cancer mutations and inherited diseases. Somatic cancer mutation datasets were shown to have a significant enrichment for mutations that cause gain or loss of phosphorylation when compared to our control datasets (putatively neutral nsSNPs and random amino acid substitutions). Of the somatic cancer mutations, those in kinase genes represent the most enriched set of mutations that disrupt phosphorylation sites, suggesting phosphorylation target site mutation is an active cause of phosphorylation deregulation. Overall, this evidence suggests both gain and loss of a phosphorylation site in a target protein may be important features for predicting cancercausing mutations and may represent a molecular cause of disease for a number of inherited and somatic mutations.
Contact: sdmooney@iupui.edu
Comprehensive in silico mutagenesis highlights functionally important residues in proteins
Authors: Bromberg Yana , Rost Burkhard 
Published: August 2008

Abstract
Motivation: Mutating residues into alanine (alanine scanning) is one of the fastest experimental means of probing hypotheses about protein function. Alanine scans can reveal functional hot spots, i.e. residues that alter function upon mutation. In vitro mutagenesis is cumbersome and costly: probing all residues in a protein is typically as impossible as substituting by all non-native amino acids. In contrast, such exhaustive mutagenesis is feasible in silico.
Results: Previously, we developed SNAP to predict functional changes due to non-synonymous single nucleotide polymorphisms. Here, we applied SNAP to all experimental mutations in the ASEdb database of alanine scans; we identified 70% of the hot spots (≥1 kCal/mol change in binding energy); more severe changes were predicted more accurately. Encouraged, we carried out a complete all-against-all in silico mutagenesis for human glucokinase. Many of the residues predicted as functionally important have indeed been confirmed in the literature, others await experimental verification, and our method is ready to aid in the design of in vitro mutagenesis.
Availability: ASEdb and glucokinase scores are available at http://www.rostlab.org/services/SNAP. For submissions of large/whole proteins for processing please contact the author.
Contact: yb2009@columbia.edu
PhyloDetect: a likelihood-based strategy for detecting microorganisms with diagnostic microarrays
Authors: Rehrauer Hubert , Schönmann Susan , Eberl Leo , Schlapbach Ralph 
Published: August 2008

Abstract
Motivation: Detection and identification of microbes using diagnostic arrays is still subject of ongoing research. Existing significance-based algorithms consider an organism detected even if a significant number of the microarray probes that match the organism are called absent in a hybridization. Further, they do generate redundant results if the target organisms show high sequence similarity and the microarray probes cannot discriminate all of them.
Results: We propose a new analysis strategy that considers organism similarities and calls organisms only present if the probes that match the organism but are absent in a hybridization can be explained by random events. In our strategy, we first identify the groups of target organisms that are actually distinguishable by the array. Subsequently, these organism groups are placed in a hierarchical tree such that groups matching only less specific probes are closer to the tree root, and groups that are discriminated only by few probes are close to each other. Finally, we compute for each group a likelihood score that is based on a hypothesis test with the null hypothesis that the group was actually present in the hybridized sample. We have validated our strategy using datasets from two different array types and implemented it as an easy-to-use web application.
Availability: http://www.fgcz.ethz.ch/PhyloDetect
Contact: Hubert.Rehrauer@fgcz.uzh.ch
Supplementary information: Example data is available at
http://www.fgcz.ethz.ch/PhyloDetect
Towards de novo identification of metabolites by analyzing tandem mass spectra
Authors: Böcker Sebastian , Rasche Florian 
Published: August 2008

Abstract
Motivation: Mass spectrometry is among the most widely used technologies in proteomics and metabolomics. Being a highthroughput method, it produces large amounts of data that necessitates an automated analysis of the spectra. Clearly, database search methods for protein analysis can easily be adopted to analyze metabolite mass spectra. But for metabolites, de novo interpretation of spectra is even more important than for protein data, because metabolite spectra databases cover only a small fraction of naturally occurring metabolites: even the model plant Arabidopsis thaliana has a large number of enzymes whose substrates and products remain unknown. The field of bio-prospection searches biologically diverse areas for metabolites which might serve as pharmaceuticals. De novo identification of metabolite mass spectra requires new concepts and methods since, unlike proteins, metabolites possess a non-linear molecular structure.
Results: In this work, we introduce a method for fully automated de novo identification of metabolites from tandem mass spectra. Mass spectrometry data is usually assumed to be insufficient for identification of molecular structures, so we want to estimate the molecular formula of the unknown metabolite, a crucial step for its identification. The method first calculates all molecular formulas that explain the parent peak mass. Then, a graph is build where vertices correspond to molecular formulas of all peaks in the fragmentation mass spectra, whereas edges correspond to hypothetical fragmentation steps. Our algorithm afterwards calculates the maximum scoring subtree of this graph: each peak in the spectra must be scored at most once, so the subtree shall contain only one explanation per peak. Unfortunately, finding this subtree is NP-hard. We suggest three exact algorithms (including one fixedparameter tractable algorithm) as well as two heuristics to solve the problem. Tests on real mass spectra show that the FPT algorithm and the heuristics solve the problem suitably fast and provide excellent results: for all 32 test compounds the correct solution was among the top five suggestions, for 26 compounds the first suggestion of the exact algorithm was correct.
Availability: http://www.bio.inf.uni-jena.de/tandemms
Contact: florian.rasche@minet.uni-jena.de
Protein structure alignment considering phenotypic plasticity
Authors: Csaba Gergely , Birzele Fabian , Zimmer Ralf 
Published: August 2008

Abstract
Motivation: Protein structure comparison exhibits differences and similarities of proteins and protein families and may help to elucidate protein sequence and structure evolution. Despite many methods to score protein structure similarity with and without flexibility and to align proteins accurately based on their structures, a meaningful evolutionary distance measure and alignment method which models the cost of mutations, insertions and deletions occurring in protein sequences on the structure level is still missing.
Results: Here, we introduce a new measure for protein structure similarity and propose a novel method called phenotypic plasticity method (PPM) which explicitly tries to model the evolutionary distance of two proteins on the structure level by measuring the cost of ‘morphing’ one structure into the other one. PPM aligns protein structures taking variations naturally observed in groups of structures (‘phenotypic plasticity’) into account while preserving the overall topological arrangement of the structures. The performance of PPM in detecting similarities between protein structures is evaluated against well-known structure classification methods on two benchmark sets. The larger set consists of more than 3.6 million structure pairs from the SCOP database which are also consistently classified in CATH. In the current parameterization, PPM already performs comparable or better than other methods such as TM-Align and Vorolign on those two sets according to various evaluation criteria showing that the method is able to reliably classify known protein structures, to detect their similarities and to compute accurate alignments despite phenotypic plasticity.
Availability: Executables are available upon request. Datasets and supplementary data (datasets and superpositions) can be accessed on http://www.bio.ifi.lmu.de/PPM
Contact: ralf.zimmer@bio.ifi.lmu.de
SIRENE: supervised inference of regulatory networks
Authors: Mordelet Fantine , Vert Jean-Philippe 
Published: August 2008

Abstract
Motivation: Living cells are the product of gene expression programs that involve the regulated transcription of thousands of genes. The elucidation of transcriptional regulatory networks is thus needed to understand the cell's working mechanism, and can for example, be useful for the discovery of novel therapeutic targets. Although several methods have been proposed to infer gene regulatory networks from gene expression data, a recent comparison on a large-scale benchmark experiment revealed that most current methods only predict a limited number of known regulations at a reasonable precision level.
Results: We propose SIRENE (Supervised Inference of Regulatory Networks), a new method for the inference of gene regulatory networks from a compendium of expression data. The method decomposes the problem of gene regulatory network inference into a large number of local binary classification problems, that focus on separating target genes from non-targets for each transcription factor. SIRENE is thus conceptually simple and computationally efficient. We test it on a benchmark experiment aimed at predicting regulations in Escherichia coli, and show that it retrieves of the order of 6 times more known regulations than other state-of-the-art inference methods.
Availability: All data and programs are freely available at http://cbio.ensmp.fr/sirene.
Contact: Fantine.Mordelet@ensmp.fr
Organization and integration of biomedical knowledge with concept maps for key peroxisomal pathways
Authors: Willemsen A.M. , Jansen G.A. , Komen J.C. , van Hooff S. , Waterham H.R. , Brites P.M.T. , Wanders R.J.A. , van Kampen A.H.C. 
Published: August 2008

Abstract
Motivation: One important area of clinical genomics research involves the elucidation of molecular mechanisms underlying (complex) disorders which eventually may lead to new diagnostic or drug targets. To further advance this area of clinical genomics one of the main challenges is the acquisition and integration of data, information and expert knowledge for specific biomedical domains and diseases. Currently the required information is not very well organized but scattered over biological and biomedical databases, basic text books, scientific literature and experts’ minds and may be highly specific, heterogeneous, complex and voluminous.
Results: We present a new framework to construct knowledge bases with concept maps for presentation of information and the web ontology language OWL for the representation of information. We demonstrate this framework through the construction of a peroxisomal knowledge base, which focuses on four key peroxisomal pathways and several related genetic disorders. All 155 concept maps in our knowledge base are linked to at least one other concept map, which allows the visualization of one big network of related pieces of information.
Availability: The peroxisome knowledge base is available from www.bioinformaticslaboratory.nl (Support → Web applications).
Contact: a.h.vankampen@amc.uva.nl
Temporal logic patterns for querying dynamic models of cellular interaction networks
Authors: Monteiro Pedro T. , Ropers Delphine , Mateescu Radu , Freitas Ana T. , de Jong Hidde 
Published: August 2008

Abstract
Motivation: Models of the dynamics of cellular interaction networks have become increasingly larger in recent years. Formal verification based on model checking provides a powerful technology to keep up with this increase in scale and complexity. The application of modelchecking approaches is hampered, however, by the difficulty for nonexpert users to formulate appropriate questions in temporal logic.
Results: In order to deal with this problem, we propose the use of patterns, that is, high-level query templates that capture recurring biological questions and can be automatically translated into temporal logic. The applicability of the developed set of patterns has been investigated by the analysis of an extended model of the network of global regulators controlling the carbon starvation response in Escherichia coli.
Availability: GNA and the model of the carbon starvation response network are available at http://www-helix.inrialpes.fr/gna
Contact: Hidde.de-Jong@inrialpes.fr
Annotation of metagenome short reads using proxygenes
Authors: Dalevi Daniel , Ivanova Natalia N. , Mavromatis Konstantinos , Hooper Sean D. , Szeto Ernest , Hugenholtz Philip , Kyrpides Nikos C. , Markowitz Victor M. 
Published: August 2008

Abstract
Motivation: A typical metagenome dataset generated using a 454 pyrosequencing platform consists of short reads sampled from the collective genome of a microbial community. The amount of sequence in such datasets is usually insufficient for assembly, and traditional gene prediction cannot be applied to unassembled short reads. As a result, analysis of such datasets usually involves comparisons in terms of relative abundances of various protein families. The latter requires assignment of individual reads to protein families, which is hindered by the fact that short reads contain only a fragment, usually small, of a protein.
Results: We have considered the assignment of pyrosequencing reads to protein families directly using RPS-BLAST against COG and Pfam databases and indirectly via proxygenes that are identified using BLASTx searches against protein sequence databases. Using simulated metagenome datasets as benchmarks, we show that the proxygene method is more accurate than the direct assignment. We introduce a clustering method which significantly reduces the size of a metagenome dataset while maintaining a faithful representation of its functional and taxonomic content.
Contact: vmmarkowitz@lbl.gov