Another issue that hampers such analyses is the effect of biological variability and random noise in the context of expression data. Jackson AL, et al. The output is a landscape plot that tracks occurrence biases using hypergeometric P-values for all words across the gene ranking. It simply requires the user to submit a sorted list of genes from an expression experiment. Search panel top right: Both the command-line application and a simple JAVA graphical interface are provided. This resource should allow researchers from a broad area of expertise to perform fast and accurate detection of small RNA signatures in their gene-expression datasets.


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We developed an algorithm, Sylamer, to provide functionality similar to GSEA for nucleotide patterns in sequences instead of annotations. The publisher’s final edited version of this article is available at Nat Methods.

Extensive low-affinity transcriptional interactions in the yeast genome. Find articles by Anton J Enright. Above this threshold, a list of genes whose sequences contain appropriate word matches to a specific miRNA or siRNA is produced as a set of candidate targets supported by expression data.

Sylamer download

Tompa M, et al. BioinformaticsVolume 26, Issue 22, 15 NovemberPages —, https: Similarly, RNA interference RNAi experiments can be assessed to determine whether the resulting gene expression changes are likely due to the intended knock-down or secondary, miRNA-like, off-target effects 78. Sylamer enrichment landscape plots for a 6nt, b 7nt and c 8nt words. Receive exclusive offers and updates sjlamer Oxford Academic.


Detecting microRNA binding and siRNA off-target effects from expression data

Over- and underrepresentation are plotted on the positive sylameer negative y -axis, respectively. Analysis of overrepresented features in lists of genes is a powerful tool for associating function with biological effects.

The results again show Supplementary Fig. Sylamer takes these issues into account when assessing the effects of small-RNA binding on expression data. A typical observation in the case of miRNA knockout data is a steep incline in overrepresentation in the leading subsets, involving hundreds of genes Fig. User can also submit his own premade set of curated FASTA sequences to the web server for the further analysis.


Individual miRNAs can be highlighted sylaker their behavior cross-referenced across different seed lengths. Each possible word matching the siRNA is shown in colour. The full list of gene sets can be found as a supplementary material on the website.

It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Chart panel top left: Human RNAi off-target analysis Sylamer enrichment plots for: Birmingham A, et al. If a clear enrichment peak is found near the beginning of the ranked genelist, results for hexamers, heptamers and octamers should be compared and the shape of the curves and peak locations should approximately agree.


Once analysis is complete the results are passed to the Java-based Analysis Toolkit. Subramanian A, et al.


The starting point for analysis is a sorted gene list obtained by the user from a gene-expression experiment. Huber for useful discussions. Related articles in Web of Science Google Scholar.

Detecting microRNA binding and siRNA off-target effects from expression data

Both the command-line application and a simple JAVA graphical interface are provided. Nenad Bartonicek, Anton J. Support Center Support Center. Grey lines show the profiles of words unrelated to the seed region of miR, while colored lines represent words complementary to the seed-region. In each case, the x -axis represents the sorted genelist from most upregulated left to most downregulated right.

Homo sapiensMus musculusRattus sylamfrXenopus dylamerDanio rerioDrosophila melanogaster and Caenorhabditis elegans.


The peak closest to the start of the ranking can be chosen as a conservative threshold Supplementary Discussion. Sylamer is freely available at: