1 - 16 of 16 results

seer / Sequence Element EnRichment

Identifies sequence elements. seer can detect associations with antibiotic resistance caused by both presence of a gene and by single-nucleotide polymorphism (SNP) in coding regions, as well as discover novel invasiveness factors. This tool implements and combines three key insights: a scan of all possible k-mers with a distributed string mining algorithm, an appropriate alignment-free correction for clonal population structure, and a fast association analysis of all counted k-mers.

i-cisTarget

An integrative genomics method for the prediction of regulatory features and cis-regulatory modules in Human, Mouse, and Fly. ii-cisTarget enables: (i) to detect transcription factor motifs in a set of peaks (e.g. differentially active peaks based on H3K27ac ChIP-seq between 2 conditions) or co-expressed genes, (ii) to detect overrepresented in vivo features (histone modifications, TF ChIP-seq, DHS, Faire) for gene signatures or peaks. These regulatory features help to improve motif discovery and candidate target gene prediction, (iii) to dissect a set of co-expressed genes into direct target genes of different transcription factor motifs or ChIP-seq tracks. Some of the key features of i-cisTarget are: (i) over-represented motifs are predicted in the set of co-expressed genes, using entire intergenic and intronic sequences, (ii) 10 vertebrate species are used for motif scoring in Human and Mouse version, 12 Drosophila species are used in Drosophila version.

JFreq / Java Word Frequencies

Provides a front end to Schbath’s R’MES. JFreq finds the expected and actual frequencies of short nucleotide sequences, or words, using a Markov model to control for the effects of base composition and the frequencies of shorter words. Unusually frequent or infrequent occurrences of certain words may indicated biological relevance. R’MES front end can also determine whether words occur in overlapping positions unusually often or unusually rarely, and the expected and actual frequencies of user-supplied words.

GOMo / Gene Ontology for Motifs

Scans all promoters using nucleotide motifs you provide to determine if any motif is significantly associated with genes linked to one or more genome ontology (GO) terms. The significant GO terms can suggest the biological roles of the motifs. GOMO's prediction accuracy proves to be relatively insensitive to how promoters are defined. Because GOMO uses a threshold-free form of gene set analysis, there are no free parameters to tune. GOMo is part of the MEME Suite online platform.

R'MES

Detects which motifs of a given length occur with an exceptional frequency in a given DNA sequence. R’MES finds the expected and actual frequencies of short nucleotide sequences, or words, using a Markov model to control for the effects of base composition and the frequencies of shorter words. Unusually frequent or infrequent occurrences of certain words may indicate biological relevance. R’MES can also determine whether words occur in overlapping positions unusually often or unusually rarely, and the expected and actual frequencies of user-supplied words. R’MES has a companion tool, RMESPlot which provides a graphical user interface for the visualization of R’MES generated results.