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A comprehensive encyclopedia of genomic functional elements in the model organisms C. elegans and D. melanogaster. modENCODE is run as a Research Network and the consortium is formed by 11 primary projects, divided between worm and fly, spanning the domains of gene structure, mRNA and ncRNA expression profiling, transcription factor binding sites, histone modifications and replacement, chromatin structure, DNA replication initiation and timing, and copy number variation. The raw and interpreted data from this project is vetted by a data coordinating center (DCC) to ensure consistency and completeness. The entire modENCODE data corpus is now available on the Amazon Web Services EC2 cloud. What this means is that virtual machines and virtual compute clusters that you run within the EC2 cloud can mount the modENCODE data set in whole or in part. Your software can run analyses against the data files directly without experiencing the long waits and logistics associated with copying the datasets over to your local hardware. You may also view the data using GBrowse, Dataset Search, or download the data via FTP, as well as download pre-release datasets.

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• SciCrunch
• 14 years ago - by Anonymous

A web server that classifies proteins into two classes from their sequences alone: the membrane protein class and the non-membrane protein class. This may be important in the functional assignment and analysis of open reading frames (ORF''s) identified in complete genomes and, especially, those ORF''s that correspond to proteins with unknown function. The network has a simple hierarchical feed-forward topology and a limited number of neurons which makes it very fast. By using only information contained in 11 protein sequences, the method was able to identify, with 100% accuracy, all membrane proteins with reliable topologies collected from several papers in the literature. Applied to a test set of 995 globular, water-soluble proteins, the neural network classified falsely 23 of them in the membrane protein class (97.7% of correct assignment). The method was also applied to the complete SWISS-PROT database with considerable success and on ORF''s of several complete genomes. The neural network developed was associated with the PRED-TMR algorithm (Pasquier,C., Promponas,V.J., Palaios,G.A., Hamodrakas,J.S. and Hamodrakas,S.J., 1999) in a new application package called PRED-TMR2.

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• SciCrunch
• 14 years ago - by Anonymous

A package for neuroinformatics and neuroimaging analysis mostly programmed in Matlab with a few additional programs in Python and Perl. It allows coordinate-based meta-analysis and visualization, neuroimaging analysis of voxel or regional data - not the original data but rather the summary images (e.g., statistical parametric images) and location data in stereotactic space. Among the algorithms implemented are kernel density estimation (for coordinate-based meta-analysis), independent component analysis, non-negative matrix factorization, k-means clustering, singular value decomposition, partial correlation analysis with permutation testing and partial canonical correlation analysis. Visualization of coordinate, surfaces and volumes are possible in 2D and 3D. Generation of HTML for results are possible and algorithms can be accessed from the command line or via a flexible graphical interface. With the Brede Toolbox comes the Brede Database with a small coordinate database from published neuroimaging studies, and ontologies for, e.g., brain function and brain regions.

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• SciCrunch
• 17 years ago - by Anonymous

A web server that predicts transmembrane domains in proteins using solely information contained in the sequence itself. The algorithm refines a standard hydrophobicity analysis with a detection of potential termini (edges, starts and ends) of transmembrane regions. This allows both to discard highly hydrophobic regions not delimited by clear start and end configurations and to confirm putative transmembrane segments not distinguishable by their hydrophobic composition. The accuracy obtained on a test set of 101 non homologous transmembranes proteins with reliable topologies compares well with that of other popular existing methods. Only a slight decrease in prediction accuracy was observed when the algorithm was applied to all transmembrane proteins of the SwissProt database (release 35).

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• SciCrunch
• 14 years ago - by Anonymous

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