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MEMERIS specifications


Unique identifier OMICS_13258
Alternative name Multiple Em for Motif Elucidation in Rna’s Including secondary Structures
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Perl
Computer skills Advanced
Version 1.0
Stability Stable
Source code URL
Maintained Yes


No version available


  • person_outline Rolf Backofen

Publication for Multiple Em for Motif Elucidation in Rna’s Including secondary Structures

MEMERIS citations


Guardian of Genetic Messenger RNA Binding Proteins

PMCID: 4808798
PMID: 26751491
DOI: 10.3390/biom6010004

[…] al analyses []. PARalyzer is another popular peak calling algorithm for PAR-CLIP datasets only. Finally, motif recognition algorithms are used to identify motifs recognized by RBP. Algorithms such as MEMEris, PhyloGibbs, RNAcontext, and RNAmotifs identify motifs at the RNA level, while MEME, cERMIT, GLAM2, and MatrixREDUCE were developed to identify motif at the DNA level. Lastly, the identified R […]


RNA motif discovery: a computational overview

Biol Direct
PMCID: 4600295
PMID: 26453353
DOI: 10.1186/s13062-015-0090-5

[…] condary structure motifs embedded in longer RNAs (such as mRNAs), in spite of the presence of a few entirely spurious, unrelated RNAs among the input sequences.Of the stochastic algorithms discussed, MEMERIS can only infer linear motifs in single stranded regions and cannot discover secondary structures explicitly. Hence we consider CMfinder and RNApromo for benchmarking in this category. Yao et a […]


Revealing protein–lncRNA interaction

Brief Bioinform
PMCID: 4719072
PMID: 26041786
DOI: 10.1093/bib/bbv031

[…] Motif finding algorithms such as MEME [] or GLAM2 [] are often used to identify primary sequence preferences of binding. Other methods take advantage of structural properties of the identified RNAs. MEMERIS [] is an extension of the MEME algorithm that incorporates RNA structure predictions to identify single-stranded motifs, and it has been successfully applied to SELEX data. Similarly, Aptamoti […]


High throughput characterization of protein–RNA interactions

Brief Funct Genomics
PMCID: 4303715
PMID: 25504152
DOI: 10.1093/bfgp/elu047

[…] sed methods. Specialized methods that incorporate RNA secondary structure generally break down into two camps: the first is based on determining the linear structural context around a sequence motif. MEMERIS is an extension of the popular MEME algorithm that uses RNA accessibility as a prior probability to guide motif finding to single-stranded regions []. Similarly, Li and colleagues applied acce […]


Finding the target sites of RNA binding proteins

PMCID: 4253089
PMID: 24217996
DOI: 10.1002/wrna.1201

[…] The first structural context-based method, MEMERIS, incorporates the Hackermüller-Stadler model into the popular DNA motif finding program, MEME, by annotating nucleotides according to their predicted RNA secondary structure. MEMERIS precomput […]


Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1

BMC Genomics
PMCID: 3287504
PMID: 22369183
DOI: 10.1186/1471-2164-12-S5-S8

[…] ring flanking sequences. This strategy is different from RNAContext, which uses predicted RNA secondary structures as input such as 'Paired', 'Hairpin Loop', 'Unstructured' or 'Miscellaneous'. Unlike MEMERIS, RNAMotifModeler uses the base-pairing probability for each nucleotide rather than the entire binding site. For each binding instance, RNAMotifModeler defines a score that evaluates the consen […]


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MEMERIS institution(s)
Institute of Computer Science, Chair for Bioinformatics, Albert-Ludwigs-University Freiburg, Georges-Koehler-AlleeFreiburg, Germany
MEMERIS funding source(s)
This work was supported by the German Ministryof Education and Research (grant number 0312704K).

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