ABCC : Applications Web Page

Applications Web Page Welcome to the ABCC Accessible Application Web Page.

From this page, you can get information about our scientific applications and databases. To get more information about any topic, or to run an application, click on any of the menu items on the left-hand side. Program with a tag "Run" is runnable. This site continues to grow, so come back often to check out the many accessible applications available here at the Advanced Biomedical Computing Center (ABCC).

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Protein Analysis
Dipro2.0
Prediction of disulfide bonds from protein sequences
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TMHMM
TMHMM is a method for prediction transmembrane helices based on a hidden Markov model .
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ASSP
ASSP is a program to predict the expected best accuracy for a secondary structure prediction based on a multiple sequence alignment.
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Core-find
The core-find package is a collection of tools that facilitates calculation and analysis of the cores of families of structurally similar proteins.
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SeqSpace
Sequence Space implements a method of clustering aligned protein sequences based upon the algorithm developed by G. Casari, C. Sander and A. Valencia. Full details of the method can be found in the paper: A method to predict functional residues in proteins, Georg Casari, Chris Sander and Alfonso Valencia. Structural Biology volume 2, no. 2, February 1995. Each sequence is converted into a vector in "Sequence Space", projection of which onto a lower dimensional space allows 2- or 3-dimensional viewing of the resulting clusters. By projecting the sequence residues into the same space, groups of residues specific for the clusters are revealed, allowing the identification of residues that are predicted to be directly involved in protein function. There are two programs available, the main sequence space program and a set of Java viewers combining sequence space views with more conventional protein displays.
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SIGNALP
SIGNALP predicts the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms: Gram-positive bacteria, Gram-negative bacteria, and eukaryotes. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks. It also incorporates predictions done by a hidden Markov model specifically designed to distinguish between signal peptides, non-secretory proteins, and (for eukaryotes only) uncleaved signal anchors.
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