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1.
Nucleic Acids Res ; 42(Web Server issue): W182-6, 2014 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-24792159

RESUMO

Neuropeptides (NPs) are short secreted peptides produced in neurons. NPs act by activating signaling cascades governing broad functions such as metabolism, sensation and behavior throughout the animal kingdom. NPs are the products of multistep processing of longer proteins, the NP precursors (NPPs). We present NeuroPID (Neuropeptide Precursor Identifier), an online machine-learning tool that identifies metazoan NPPs. NeuroPID was trained on 1418 NPPs annotated as such by UniProtKB. A large number of sequence-based features were extracted for each sequence with the goal of capturing the biophysical and informational-statistical properties that distinguish NPPs from other proteins. Training several machine-learning models, including support vector machines and ensemble decision trees, led to high accuracy (89-94%) and precision (90-93%) in cross-validation tests. For inputs of thousands of unseen sequences, the tool provides a ranked list of high quality predictions based on the results of four machine-learning classifiers. The output reveals many uncharacterized NPPs and secreted cell modulators that are rich in potential cleavage sites. NeuroPID is a discovery and a prediction tool that can be used to identify NPPs from unannotated transcriptomes and mass spectrometry experiments. NeuroPID predicted sequences are attractive targets for investigating behavior, physiology and cell modulation. The NeuroPID web tool is available at http:// neuropid.cs.huji.ac.il.


Assuntos
Neuropeptídeos/classificação , Precursores de Proteínas/classificação , Software , Animais , Inteligência Artificial , Genômica , Humanos , Internet , Neuropeptídeos/química , Neuropeptídeos/genética , Precursores de Proteínas/química , Precursores de Proteínas/genética , Análise de Sequência de Proteína
2.
Nucleic Acids Res ; 40(Database issue): D313-20, 2012 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-22121228

RESUMO

ProtoNet 6.0 (http://www.protonet.cs.huji.ac.il) is a data structure of protein families that cover the protein sequence space. These families are generated through an unsupervised bottom-up clustering algorithm. This algorithm organizes large sets of proteins in a hierarchical tree that yields high-quality protein families. The 2012 ProtoNet (Version 6.0) tree includes over 9 million proteins of which 5.5% come from UniProtKB/SwissProt and the rest from UniProtKB/TrEMBL. The hierarchical tree structure is based on an all-against-all comparison of 2.5 million representatives of UniRef50. Rigorous annotation-based quality tests prune the tree to most informative 162,088 clusters. Every high-quality cluster is assigned a ProtoName that reflects the most significant annotations of its proteins. These annotations are dominated by GO terms, UniProt/Swiss-Prot keywords and InterPro. ProtoNet 6.0 operates in a default mode. When used in the advanced mode, this data structure offers the user a view of the family tree at any desired level of resolution. Systematic comparisons with previous versions of ProtoNet are carried out. They show how our view of protein families evolves, as larger parts of the sequence space become known. ProtoNet 6.0 provides numerous tools to navigate the hierarchy of clusters.


Assuntos
Bases de Dados de Proteínas , Proteínas/classificação , Análise de Sequência de Proteína , Algoritmos , Análise por Conglomerados , Internet , Metagenoma , Anotação de Sequência Molecular
3.
Artigo em Inglês | MEDLINE | ID: mdl-24907353

RESUMO

MicroRNAs (miRNAs) are short, non-coding RNAs that negatively regulate post-transcriptional mRNA levels. Recent data from cross-linking and immunoprecipitation technologies confirmed the combinatorial nature of the miRNA regulation. We present the miRror-Suite platform, developed to yield a robust and concise explanation for miRNA regulation from a large collection of differentially expressed transcripts and miRNAs. The miRror-Suite platform includes the miRror2.0 and Probability Supported Iterative miRror (PSI-miRror) tools. Researchers who performed large-scale transcriptomics or miRNA profiling experiments from cells and tissues will benefit from miRror-Suite. Our platform provides a concise, plausible explanation for the regulation of miRNAs in such complex settings. The input for miRror2.0 may include hundreds of differentially expressed genes or miRNAs. In the case of miRNAs as input, the algorithm seeks the statistically most likely set of genes regulated by this input. Alternatively, for a set of genes, the miRror algorithm seeks a collection of miRNAs that best explains their regulation. The miRror-Suite algorithm designates statistical criteria that were uniformly applied to a dozen miRNA-target prediction databases. Users select the preferred databases for predictions and numerous optional filters/parameters that restrict the search to the desired tissues, cell lines, level of expression and predictor scores. PSI-miRror is an advanced application for refining the input set by gradually enhancing the degree of pairing of the sets of miRNAs with the sets of targets. The iterations of PSI-miRror probe the interlinked nature of miRNAs and targets within cells. miRror-Suite serves experimentalists in facilitating the understanding of miRNA regulation through combinatorial- cooperative activity. The platform applies to human, mouse, rat, fly, worm and zebrafish. Database URL: http://www.mirrorsuite.cs.huji.ac.il.


Assuntos
Regulação da Expressão Gênica , MicroRNAs/genética , Animais , Bases de Dados Genéticas , Humanos , Camundongos , MicroRNAs/metabolismo , Ratos , Estatística como Assunto
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