Motif discovery software tools | CLIP sequencing data analysis
High-throughput protein-RNA interaction data generated by CLIP-seq has provided an unprecedented depth of access to the activities of RNA-binding proteins (RBPs), the key players in co- and post-transcriptional regulation of gene expression. Motif discovery forms part of the necessary follow-up data analysis for CLIP-seq, both to refine the exact locations of RBP binding sites, and to characterize them. The specific properties of RBP binding sites, and the CLIP-seq methods, provide additional information not usually present in the classic motif discovery problem: the binding site structure, and cross-linking induced events in reads.
Discovers the position-specific affinity matrices for unknown RNAbinding factors and infers their condition-specific activities. MatrixREDUCE uses genome-wide occupancy data for a transcription factor and associated nucleotide sequences to discover the sequence specific binding affinity of the transcription factor.
Permits the analysis of Nrd1-Nab3-RNA interactions. pyCRAC combines many popular cross-linking and immunoprecipitation (CLIP)/ cross-linking and cDNA analysis (CRAC) analysis methods. It can be used to remove duplicate reads. The tool is designed to tackle directional libraries and reports sense and anti-sense hits. It offers a user-friendly and coherent set of tools tailored more specifically to handle CRAC/CLIP data.
Infers protein-RNA preferences from RNAcompete experimental data. RCK is a software which uses a k-mer based model for sequence preferences and specific structural context preferences for each k-mer based on probability profiles. The software can incorporate either predicted or experimentally measured RNA structure probabilities to improve in vivo binding prediction.
A computational method for searching sequence motifs in a set of RNA sequences and simultaneously integrating information about secondary structures. MEMERIS precomputes values that characterize the single-strandedness of all putative motif occurrences. These values are then used to guide the motif search towards single-stranded regions. Authors conclude that MEMERIS preferably selects single stranded motif occurrences and that it is able to identify a weaker over a stronger motif if the average single strandedness is sufficiently higher.
Enables genome-wide prediction of bacterial intrinsic terminators. RNIE consists of a probabilistic method that aims to determine Rho-independent terminators (RITs). It can be used for exemple, for the detection of terminators across a broad range of bacterial genomes outside of Escherichia coli and Bacillus subtilis genomes and without requiring gene annotation information.