1385 citations
6 runs

fimo

By Bailey T., Elkan C., Last update 1494698998
All tools Run this tool

fimo description

FIMO stands for 'Find Individual Motif Occurences.' The program searches a database of DNA or protein sequences for occurrences of known motifs, treating each motif independently. The program uses a dynamic programming algorithm to convert log-odds scores (in bits) into p-values, assuming a zero-order background model. By default the program reports all motif occurrences with a p-value less than 1e-4. The threshold can be set using the --thresh option. The p-values for each motif occurence are converted to q-values following the method of Benjamini and Hochberg ('q-value' is defined as the minimal false discovery rate at which a given motif occurrence is deemed significant). The --qv-thresh option directs the program to use q-values rather than p-values for the threshold. If a motif has the strand feature set to + or - (rather than +), then fimo will search both strands for occurrences. The parameter --max-stored-scores sets the maximum number of motif occurrences that will be retained in memory. It defaults to 100,000. If the number of matches found reaches the maximum value allowed, FIMO will discard 50% of the least significant matches, and new matches falling below the significance level of the retained matches will also be discarded. FIMO can make use of position specific priors (PSP) to improve its identification of true motif occurrences. To take advantage of PSP in FIMO you use must provide two command line options. The --psp option is used to set the name of a MEME PSP file, and the --prior-dist option is used to set the name of a file containing the binned distribution of priors.


Parent program: meme

MEME is a tool for discovering motifs in a group of related DNA or protein sequences. MEME takes as input a group of DNA or protein sequences and outputs as many motifs as requested up to a user-specified statistical confidence threshold. MEME uses statistical modeling techniques to automatically choose the best width, number of occurrences, and description for each motif.