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Publish over 230 plus peer-reviewed open access journals. These free-to-view online journals cover all major disciplines of science, technology, medicine and social sciences. Bentham OPEN offers its ''Complimentary Membership'' to International R & D organizations, institutes and universities. This opportunity will entitle authors from different member institutes to a special discount of 30% in the open access publication fee for submission of articles to Bentham OPEN journals. Additionally, input and contributions from associate institutes would also be recognized and a link to their respective Website would be displayed on the Bentham OPEN membership page. The member institution''s logo will also be published on the same page. Bentham Open Membership provides the following advantages: * Possibility to explore 73 distinct disciplines by means of publishing in 239 open access journals. * Free access to all provides prospects of higher citations. * Author(s) own the copyrights to their published articles. * High standard criteria for peer-review. * Unbound right to read, download or print open access articles. * Access to a range of articles in printed form such as short communications, full length research articles, reviews or conference proceedings. * Simple steps from submission to publication, leading to fast turn-around. * Possibility of archiving published articles. The complimentary membership is valid for a span of one year and upon completion of the prescribed period, it is renewed by mutual interest and agreement. If you find the above mentioned details relevant, then kindly contact us via e-mail at membership_at_benthamscience.org or oa_at_benthamscience.org.

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• SciCrunch
• 14 years ago - by Anonymous

An ontology consisting of two main components, an ontology of behavioral processes and an ontology of behavioral phenotypes. The behavioral process branch of NBO contains a classification of behavior processes complementing and extending the GO process ontology. The behavior phenotype branch of NBO consists of a classification of both normal and abnormal behavioral characteristics of organisms. The prime application of NBO is to provide the vocabulary that is required to integrate behavior observations within and across species. It is currently being applied by several model organism communities as well as in the description of human behavior-related disease phenotypes. The main ontology is available in both the OBO Flatfile Format and the Web Ontology Language (OWL).

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• SciCrunch
• 14 years ago - by Anonymous

A blog on the neural organization of language moderated by Greg Hickok and David Poeppel. Essays, comments on new papers, and job openings. Greg Hickok is Professor of Cognitive Sciences, and Director of the Center for Cognitive Neuroscience at UC Irvine. David Poeppel, after several years as Professor of Linguistics and Biology at the University of Maryland, College Park, is now Professor of Psychology at NYU. Hickok and Poeppel first crossed paths in 1991 at MIT in the McDonnell-Pew Center for Cognitive Neuroscience where Hickok was a post doc, and Poeppel a grad student. Meeting up again a few years later at a Cognitive Neuroscience Society Meeting in San Francisco, they began a collaboration aimed at developing an integrated model of the functional anatomy of language. Research in both the Hickok and Poeppel labs is supported by NIDCD.

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• SciCrunch
• 14 years ago - by Anonymous

A web tool for the prediction of transmembrane segments in alpha-helical membrane proteins. A sliding window of 20 residues is used in order to calculate an average residue hydrophobicity profile, using a hydrophobicity scale. Discrete Wavelet Transform is applied on the average residue hydrophobicity signal and the different frequency coefficients produced are adaptively thresholded so that a denoised signal is reconstructed. A dynamic programming algorithm processes the denoised signal to provide the optimal model for the number, the length and the location of membrane-spanning segments. The end points of the predicted segments are extended to include flanking hydrophobic residues. Topology prediction can also be obtained in conjunction with OrienTM (Liakopoulos et al, 2001). Analysis of a non-redundant test set, provides a ~95% per segment accuracy and ~90% per residue accuracy. Now, you can: * Run waveTM on a sequence * Browse the results obtained with the algorithm * View additional material concerning the hydrophobicity scale

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• SciCrunch
• 14 years ago - by Anonymous

A web tool used in the discrimination of beta-barrel outer membrane proteins with a Markov chain model. MCMBB is a fast algorithm, which discriminates beta-barrel outer membrane proteins from globular proteins and from alpha-helical membrane proteins. The algorithm is based on a 1st order Markov Chain model, which captures the alternating pattern of hydrophilic-hydrophobic residues occurring in the membrane-spanning beta-strands of beta-barrel outer membrane proteins. The model achieves high accuracy in discriminating outer membrane proteins, since it can discriminate beta-barrel outer membrane with a correct classification rate of 90.08% and the globular proteins with a correct classification rate of 92.67%. When submitting alpha-helical membrane proteins, the method shows an accuracy of 100%. A score greater than zero, indicates that the protein is more likely to be a beta-barrel outer membrane protein, whereas a result lower than zero, indicates that the protein is probable not a beta-barrel. You may enter up to 1000 sequences in Fasta format.

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• SciCrunch
• 14 years ago - by Anonymous

Guide by the Australian National Data Service (ANDS) regarding data citation for eResearch infrastructure support providers and researchers with the aim of enabling more researchers to re-use research data more often. It is not so much a guide to how to cite data, but a guide to the issues around it, and activities underway to change the culture around data citation in order to support improved data management and sharing. ANDS is engaged in activities that will make it easier to share data, to recognize the importance of making data available and to make data citation a standard procedure.

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• SciCrunch
• 16 years ago - by Anonymous

A prediction tool for GPCR Family Classification from sequence alone based on a probabilistic method that uses family-specific profile Hidden Markov Models. The PRED-GPCR system is based on a probabilistic method that uses family specific profile HMMs in order to determine to which GPCR family a query sequence belongs or resembles. The approach proposed in this method exploits the descriptive power of profile HMMs along with an exhaustive discrimination assessment method to select only highly selective and sensitive profiles, for each family. The collection of these profiles constitutes a signature library, which is scanned, for significant matches with a given query sequence. The output report for a query sequence consists of two sections: * A ranked list of the profile HMM matches, below the selected individual motif E-value cutoff, along with their corresponding family. * A ranked list of the Combined P-values, E-values as well as the number of profiles matched for each family. To cross-evaluate your results you can browse through Swiss-Prot, Trembl, Pfam and Prosite family related entries.

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• SciCrunch
• 14 years ago - by Anonymous

A fold change ranks ordering statistics based software for detecting differentially expressed genes.

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• SciCrunch
• 13 years ago - by Anonymous

A web tool for the Consensus Prediction of TransMembrane Beta-Barrel Proteins. Prediction of the transmembrane strands and topology of beta-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of beta-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 beta-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane beta-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies.

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• SciCrunch
• 14 years ago - by Anonymous

A tool that predicts the coupling specificity of G-protein coupled receptors to G-proteins. We present a method that combines hidden Markov models and a feed-forward artificial neural network to overcome these limitations, while producing the most accurate predictions currently available. Using an up-to-date curated dataset, our method yields a 94% correct classification rate in a 5-fold cross-validation test. The method predicts also promiscuous coupling preferences, including coupling to G12/13, whereas unlike other methods avoids overpredictions (false positives) when non-GPCR sequences are encountered. * The PRED-COUPLE 2.00 system predicts coupling specificty of GPCRs to all families of G-proteins (including G12/13). * Coupling to more than one G-protein families can also be predicted with this tool. * No membrane topology information is required. Furthermore, no membrane topology prediction is executed by this method. * The method is based on a refined library of highly-discriminative Hidden Markov Models. Hits from individual profiles are combined by a feed-forward Artificial Neural Network to produce the final output. * Seven (7) transmembrane receptor signatures from the Pfam database version 17.00 are also applied in order to verify a true GPCR sequence. When a query sequence is not recognized as a 7 transmembrane receptor a message is shown.

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• SciCrunch
• 14 years ago - by Anonymous

A per-based software to estimate multiplicity of infection (MOI) in parasite genomic sequence data. It is primarily developed to address the limitations of current laboratory (PCR) based estimates of multiplicity using high throughput sequence data. It requires a BAM (alignment output of short reads to the reference genome), VCF (a file with information on variant calls) and FASTA (reference genome) files. # Short reads are aligned to a reference genome using BWA, BOWTIE, SMALT or other short read aligners to generate a BAM file. # Single Nucleotide Polymorphisms (SNPs) are then identified using SAMTools/BCFtools and stored in the VCF format. # The reference FASTA file is expected to be indexed using ''samtools faidx'' to generate a *.fai file. estMOI generates files containing MOI estimates for each SNP combinations (file with name *.log) and a summary for all chromosomes (file with name *.txt).

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• SciCrunch
• 13 years ago - by Anonymous

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• SciCrunch
• 17 years ago - submitted by Stephen Larson

A web tool, based on a Hidden Markov Model, capable of predicting the transmembrane beta-strands of the gram-negative bacteria outer membrane proteins, and of discriminating such proteins from water-soluble ones when screening large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of the correct prediction rather than the likelihood of the sequences. The training is performed on a non-redundant database consisting of 16 outer membrane proteins (OMP''s) with their structures known at atomic resolution. We show that we can achieve predictions at least as good comparing with other existing methods, using as input only the amino-acid sequence, without the need of evolutionary information included in multiple alignments. The method is also powerful when used for discrimination purposes, as it can discriminate with a high accuracy the outer membrane proteins from water soluble in large datasets, making it a quite reliable solution for screening entire genomes. This web-server can help you run a discriminating process on any amino-acid sequence and thereafter localize the transmembrane strands and find the topology of the loops.

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• SciCrunch
• 14 years ago - by Anonymous

Web-based tool to create claims, hypotheses, or statements, and relate these to other claims using an open set of relationships. It is usable for science, but also for structuring online debates on other topics. The visual tool allows users to create, connect and share Ideas. Back them up with websites. Support or challenge them. Embed them to spread virally. Discover who - literally - connects with your thinking.

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• SciCrunch
• 16 years ago - by Anonymous

A web tool for the prediction of Cell Wall-Anchored Proteins in Gram+ Bacteria. Gram-positive bacteria have surface proteins that are often implicated in virulence. A group of extracellular proteins attached to the cell wall contains an LPXTG-like motif that is target for cleavage and covalent coupling to peptidoglycan by sortase enzymes. A new Hidden Markov Model (HMM), an extension to the HMM model from Litou et al., http://www.ncbi.nlm.nih.gov/pubmed/18464329, was developed for predicting the LPXTG and LPXTG-like cell-wall proteins of Gram-positive bacteria. An analysis of 177 completely sequenced genomes has been performed as well. We identified in total 1456 cell-wall proteins, from which 1283 have the LPXTG motif, 39 the NPXTG motif, 53 have the LPXTA and 81 the LAXTG motif.

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• SciCrunch
• 14 years ago - by Anonymous

A web tool using the Hidden Markov Model method for the prediction of lipoprotein signal peptides of Gram-positive bacteria, trained on a set of 67 experimentally verified lipoproteins. The method outperforms LipoP and the methods based on regular expression patterns, in various data sets containing experimentally characterized lipoproteins, secretory proteins, proteins with an N-terminal TM segment and cytoplasmic proteins. The method is also very sensitive and specific in the detection of secretory signal peptides and in terms of overall accuracy outperforms even SignalP, which is the top-scoring method for the prediction of signal peptides.

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• SciCrunch
• 14 years ago - by Anonymous

A web tool using the Hidden Markov Model method for the topology prediction of alpha-helical membrane proteins that incorporates experimentally derived topological information. Hidden Markov Models (HMMs) have been extensively used in computational molecular biology, for modelling protein and nucleic acid sequences. In many applications, such as transmembrane protein topology prediction, the incorporation of limited amount of information regarding the topology, arising from biochemical experiments, has been proved a very useful strategy that increased remarkably the performance of even the top-scoring methods. However, no clear and formal explanation of the algorithms that retains the probabilistic interpretation of the models has been presented so far in the literature. We present here, a simple method that allows incorporation of prior topological information concerning the sequences at hand, while at the same time the HMMs retain their full probabilistic interpretation in terms of conditional probabilities. We present modifications to the standard Forward and Backward algorithms of HMMs and we also show explicitly, how reliable predictions may arise by these modifications, using all the algorithms currently available for decoding HMMs. A similar procedure may be used in the training procedure, aiming at optimizing the labels of the HMM''s classes, especially in cases such as transmembrane proteins where the labels of the membrane-spanning segments are inherently misplaced. We present an application of this approach developing a method to predict the transmembrane regions of alpha-helical membrane proteins, trained on crystallographically solved data. We show that this method compares well against already established algorithms presented in the literature, and it is extremely useful in practical applications.

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• SciCrunch
• 14 years ago - by Anonymous

A web tool using the consensus prediction method for identifying possible amyloidogenic regions in protein sequences. This tool uses an assortment of different methods that have been found or specifically developed to predict features related to the formation of amyloid fibrils. The consensus of these methods is defined as the the hit overlap of at least two out of five methods and it is the primary output of the program. However, the individual predictions of these methods are also made available in the form of a text file, maintained on the server for 1 (one) day. Consequently, the tool predicts probable amyloidogenic determinants for a given amino acid sequence of a peptide or protein.

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• SciCrunch
• 14 years ago - by Anonymous

Created in 1997, Training Programs in Epidemiology and Public Health Interventions Network (TEPHINET) is a professional network of field epidemiology training programs (FETPs) located in 53 countries around the world. TEPHINET aims to strengthen international public health capacity by training field epidemiologists through an applied apprenticeship program. Trainees of FETPs master a set of core competencies that are vital to the practice of public health, while providing a valuable public health service to their countries and regions.

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• SciCrunch
• 15 years ago - by Anonymous

A Parkinson's research foundation dedicated to finding a cure for Parkinson's disease and to ensuring the development of improved therapies. Pipeline Programs fund investigator-initiated proposals focused on the following critical points along the translational pathway to new therapies for Parkinson's disease.

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• SciCrunch
• 15 years ago - by Anonymous