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JASSA / Joined Advanced Sumoylation Site and Sim Analyser
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Uses a scoring system based on a position frequency matrix derived from the alignment of experimental SUMOylation sites or SUMO-interacting motifs (SIMs). Compared to existing web-tools, JASSA displays on par or better performances. Novel features were implemented towards a better evaluation of the prediction, including identification of database hits matching the query sequence and representation of candidate sites within the secondary structural elements and/or the 3D fold of the protein of interest, retrievable from deposited PDB files.
Predicts sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. Rigorous cross-validations indicated that pSumo-CD remarkably outperformed the existing state-of-the-art prediction method for the same purpose. For the convenience of most experimental scientists, a user-friendly web-server for pSumo-CD has been established, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.
A homology-based pipeline that allows identification of potential modification sites for most of the proteomes lacking post-translational modifications (PTMs) data. PTMProber provides a unique functionality for constructing customized models (such as organism-specific and modification-specific models) from user-provided data sets. Cross-promotion E-value (CPE) as stringent benchmark has been used in PTMProber to evaluate homology to known modification sites. Independent-validation tests show that PTMProber achieves over 58.8% recall with high precision by CPE benchmark. Comparisons with other machine-learning tools show that PTMProber pipeline performs better on general predictions.
This service can predict the SUMOAMVR types and sumoylation states. Here, we defined the SUMOAMVR as sumoylation related amino acid variations that affect sumoylation sites or enzymes involved in the process of connectivity, and categorized four types of potential SUMOAMVR. For each query protein, this web server uses the constructed lysine sumoylation prediction model (SumoPred) to assign a score for each lysine site. The SUMOAMVR could be identified when the sumoylation sites were altered between the original and variant sequence.
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