The DiNAMO software implements an exhaustive algorithm to detect over-represented IUPAC motifs in a set of DNA sequences. It has two modes: scanning mode, where all windows are parsed, or fixed-position mode, where only motifs occurring at a specific position in the sequences are taken into account.
The OMiMa (the Optimized Mixture Markov model) System is a computational tool for identifying functional motifs in DNA or protein sequences. OMiMa System is based on the Optimized Mixture of Markov models that are able to incorporate most dependencies within a motif. Most important, OMiMa is capable to adjust model complexity according to motif dependency structures, so it can minimize model complexity without compromising prediction accuracy. OMiMa uses our fast Markov chain optimization method, the Directed Neighbor-Joining (DNJ), which makes OMiMa more computationally efficent.
Chromatin Cutter simulates the distribution of DNA in a gel electrophoresis study of Yeast chromatin cut by enzymes. It lets you specify the fraction of nucleosomes that are unwrapped (wrapped ones have inaccessible DNA). It lets you specify the variance in the distribution of nucleosomes around their mean distance. It lets you specify the number of cuts per unit length (enzyme concentration). It plots the effects of these on the log number of base pairs, which matches the gel electrophoresis mass distribution.
CISMM (Computer Integrated Systems for Microscopy and Manipulation)
HelixMC is a software package for Monte-Carlo (MC) simulations of DNA/RNA helices using the base-pair level model. It provides a powerful tool to understand the flexibility of DNA/RNA helices through numerical simualtions.
TfReg implements the calculation of Peyrard-Bishop style Hamiltonians to obtain some physical properties of DNA and RNA duplexes. The method uses the transfer matrix technique for the calculation of the classical partition function. Also, TfReg calculates the regression of experimental versus predicted melting temperatures using the equivalent melting index.
The web-server iDNA-Methyl is according to its genetic codes by combining its trinucleotide composition (TNC) and the pseudo amino acid components (PseAAC) of the protein translated from the DNA sample. And by means of the approach of optimizing training datasets for predicting DNA methylation sites. Rigorous cross-validations on a set of experiment-confirmed datasets have indicated that these new predictors remarkably outperformed their counterparts in the existing prediction methods
Multi-VORFFIP is a structure-based, machine learning, computational method designed to predict protein-protein, protein-peptide, protein-DNA and protein-RNA binding sites. M-VORFFIP integrates a wide and heterogeneous set of residue- and environment-based information using a two-step Random Forest ensemble classifier.
VORFFIP (Voronoi Random Forest Feedback Interface Predictor) is structure-based computational method for prediction of protein binding sites.