CRAPome is a database of annotated negative controls contributed by the proteomics research community. It addresses the common problem of distinguishing real interactions from the non-specific background (also known as ‘contaminants’). The database and associated computational tools to score protein interactions are available online.
TRAP (Transcription factor Affinity Prediction) calculates the affinity of transcription factors for DNA sequences on the basis of a biophysical model. This method has proven to be useful for several applications, including for determining the putative target genes of a given factor. This protocol covers two other applications: (i) determining which transcription factors have the highest affinity in a set of sequences (illustrated with chromatin immunoprecipitation–sequencing (ChIP-seq) peaks), and (ii) finding which factor is the most affected by a regulatory single-nucleotide polymorphism.
mmCSM-NA is the first scalable method capable of quantitatively and accurately predicting the effects of multiple-point mutations on nucleic acid binding affinities.
CSM-AB is a machine learning method capable of predicting antibody-antigen binding affinity by modelling interaction interfaces as graph-based signatures.
mCSM-AB is a web server for predicting antibody-antigen affinity changes upon mutation with graph-based signatures
mCSM-AB2 is an updated and refined version approach, capable of accurately modelling the effects of mutations on antibody-antigen binding affinity, through the inclusion of evolutionary and energetic terms.
mmCSM-AB is a tool for analysing the effects of introducing multiple point mutations on antibody-antigen binding affinity.
MAPSD is a multi-omic signal diffusion algorithm designed to identify the disease susceptibility scores in unknown genes (or proteins) in complex and polygenic diseases such as schizophrenia.
APEG uses a biophysical model to analyze transcription (TF)-DNA binding data, such as ChIP-seq data by incorporating epigenomic modifications and genome sequence data. This model can learn synergistic and antagonistic interactions between specific TFs and epigenomic modifications from genome-wide TF binding and epigenomic data.
The apcluster package implements Frey’s and Dueck’s Affinity Propagation clustering in R. The algorithms are largely analogous to the Matlab code published by Frey and Dueck. The package further provides an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing clustering results
