Freiburg RNA Tools
Frequently Asked Questions

Frequently Asked Questions

If your question is not listed, please send it to us!


? How long are computed results available and stored?

All jobs computed on the Freiburg Tools webserver are stored for 30 days. Afterwards they are automatically removed. In order to preserve your job results you might want to use the job zip-file that is offered for each job on the according result page. This file contains all input information, call details and output files generated.

? What publications to cite when using the server?

Each tool offered by this web server comes with a specific list of publications. Please cite them when using the web server.

? Why do I get a warning saying that my browser has incompatibility issues with the web server?

The Freiburg Tools web server is known to have serious compatibility issues with Internet Explorer. If you are using this browser to access it, we highly recommend switching to Mozilla Firefox so that you don't experience lack of functionality.
The web server was also tested under Google Chrome, Opera, Safari and Konqueror and they are all known to work fine.

If you are not using Internet Explorer and you still get the warning message, then browser mimicry could be the reason. Please let us know and we will correct the problem as soon as possible.

? Who to contact if I have further trouble or encounter problems not listed here?

Please contact us as soon as you encounter any problems or difficulties. If you have problems with a specific tool please send as much detail as possible. In case your problems are related to a certain jobs, please provide the job ID etc. Thanks for your help and feedback!

? I have created a cool new RNA Bioinformatics tool, is it possible to integrate it into the web server?

The Freiburg Tools web server is a very flexible and generic platform to integrate new tools. So please contact us and we will happily discuss the possibilities of an integration of YOUR TOOL into our web server.

General questions

? How are RNA structures modelled?

RNA structures are modelled as secondary structures, ie. each nucleotide/base can participate in at most one base pairing. Only Watson-Crick (G-C,A-U) and wobble (G-U) base pairs are considered. A structure is typically represented by dot-bracket-string encodings, where an opening bracket denotes a pairing with a nucleotide successive in the sequences and a closing bracket the contrary. For crossing, pseudoknot structures, different bracket symbols are needed.

? Why are mRNAs assumed to fold locally and not globally?

mRNAs are usually loaded with translating ribosomes, which are able to disrupt structures in the mRNA coding sequence due to their helicase activity (Takyar et al., 2005). Furthermore, long range base pairs in large RNA molecules are kinetically unfavored (Flamm et al., 2000), and long range base pairs predicted by energy minimization are very inaccurate (Doshi et al., 2004).


? Other tools for whole genome sRNA target prediction are much faster and do not require previous assembly of homologs. Why should I use CopraRNA?

Truthfully, the runtime of CopraRNA is not excellent and sequence assembly can be tedious. However, the quality of the results outcompetes all other state of the art sRNA target prediction algorithms. Our results show that CopraRNA is even very competetive when compared with the insights gained from micro array analyses. The cost of additional runtime and previous data assembly, is justified by the results being several orders of magnitude better than those computed by other algorithms. Furthermore, CopraRNA is free and fast when compared with microarrays. In some cases (i.e. GcvB) it allows a complete in silico characterization of a certain sRNA's function within the organism.

? Why are only organisms supported that are part of the RefSeq database?

In order to guarantee easy usability, CopraRNA requires a certain degree of consitency within the files that it accesses. RefSeq is in most cases a very reliable and cosistent database, that meets sensible consitency terms. Find all CopraRNA compatible organisms in this list. Already more than 2000 organisms are CopraRNA compatible.

? Why does the target on rank 1 have a p-value = 0 ?

In some cases one of the putative target sequences is encoded on the complementary strand at the same genomic location as the sRNA. In these cases, the complementarity is perfect, which leads to extremely low IntaRNA energy scores and consequently to a p-value of 0. Usually this can be discarded as an artifact. However in some cases it has been shown that sRNAs not only act on trans but also have cis regulatory effects, in which case a putative target with a p-value of 0 should not be disregarded.

? What are the fdr values and how to interpret them?

The fdr (false discovery rate) values are most easily explained with an example. Assume a fdr cutoff of 0.5. Statistically speaking, 50% of all predicted targets in the list up to this cutoff are assumed to be false positives. The fdr gives you an impression of how many incorrect predictions to expect up to a certain threshold. The fdr values are computed using the R-function p.adjust and the method by (Benjamini&Hochberg, 1995).

? When are sRNAs homologous? or Are the sequences I am inputing feasible for CopraRNA?

This is not a trivial question and subject to reasearch in itsself. Usually if you find similar sequences of similar lengths with a BLAST search, it is highly likely that the sequences you found are homologous. Yet, if you don't find anything with BLAST this doesn't mean there is nothing to find. In these cases we suggest that you resort to more sophisticated methods to find sRNA homologs, such as GotohScan. Nevertheless, there are cases in which no sRNA homologs exist. In these cases we suggest you resort to an IntaRNA whole genome target prediction.

? What are additional homologs?

Sometimes the clustering of homologous genes, assigns several genes from one organism to the same cluster. In this case the analysis is only executed on the candidate with the best IntaRNA energy score. In order to prevent losing the other putative targets, they are added at the end as additional homologs.

? Are the predictions always good?

Eventhough we could show that CopraRNA predictions are mostly reliable for Enterobacteria, it is still an in silico method and not flawless. You should look at, and think about the output and try to make sense of it, instead of blindly trusting the top list (p-value <=0.01).

? Which putative targets should I take a closer look at?

Basically all putative targets with a CopraRNA p-value <=0.01 are statistically speaking interesting. Furthermore putative targets that belong to a certain enriched term are interesting.

? Do single outlier organisms affect my results?

Even though we have included a root function, in order to prevent overly strong effects of outlier organisms, in the prediction it is advisable not to use sets of organisms in which single organisms are very distant from all other organisms participating in the prediciton.

? My prediction list contains a putative target gene more than once. Why?

During the process of clustering homologous genes, it sometimes happens that extremely similar clusters are generated which may only differ in one gene which is not part of your organism of interest. This may look like a duplication when only considering the organism of interest, while it really isn't a duplication. Having a closer look at the "" file from the results archive should clarify when and how this happens.

? Does CopraRNA work for all bacterial and archaeal phyla?

Extensive testing of CopraRNA predictions has so far only been done for enteric bacteria. However, the basic idea is not limited to this branch of microorganisms. It is highly likely that CopraRNA can produce predictions of the same quality for other phyla but it has not yet been experimentally proven.

? Is CopraRNA deterministic? It appears your precalculated results are not identical to the results presented in the publication. Why?

Due to the p-value sampling for clusters that do not contain genes from each participating organism, CopraRNA is not a deterministic algorithm. However, usually only slight differences between distinct analyses are to be expected.

? The putative targets are sorted in the reverse order in the regions plot when compared to the main result table. Which sorting should I trust?

The reverse sorting in the regions plots is due to our plotting script. This means that you should trust the initial sorting of the main result table.

? Can I download CopraRNA to run batch jobs on my local machine?

The source code for CopraRNA is available from our Software page.


? How does the mRNA length influence the energy score reported by IntaRNA?

IntaRNA is based on minimization of an energy score that incorporates the hybridization energy and the accessibility of the interaction sites in both RNAs. The hybridization energy alone depends only the length of the interaction. For computation of accessibilities in the mRNA, it is assumed the mRNAs are folded locally allowing only base pairs with the given maximal span. The accessibilitiy for the interaction site in the mRNA is averaged over all windows of the given size that contain the interaction site.

? How is accessibility defined?

The accessibility of the interaction site is the free energy that is required to make it single stranded. It is defined as the difference between the free energy of the ensemble of all RNA secondary structures and the free energy of the ensemble of RNA secondary structures, where the interaction site is single stranded.

? How are accessibilities calculated?

The calculation of accessibilities is based on ensemble free energies. Ensemble free energies are calculated using a partition function approach (McCaskill, 1990) assuming global folding of the ncRNA and local folding of the mRNA. For this purpose, RNAplfold and RNAup are integrated into IntaRNA via the Vienna RNA library (Hofacker et al., 1994; Bernhart et al., 2006, M├╝ckstein et al., 2008).

? What are the fdr values and how to interpret them?

The fdr (false discovery rate) values are most easily explained with an example. Assume a fdr cutoff of 0.5. Statistically speaking, 50% of all predicted targets in the list up to this cutoff are assumed to be false positives. The fdr gives you an impression of how many incorrect predictions to expect up to a certain threshold. The fdr values are computed using the R-function p.adjust and the method by (Benjamini&Hochberg, 1995).

? Where can I see/download the used target RNAs derived for my NCBI RefSeq ID?

The target sequences downloaded from NCBI for the given RefSeq ID are available for download in FASTA format in the 'Input Parameter' section of the result page. The FASTA file is linked for parameter 'Target RNA in FASTA'.

? The putative targets are sorted in the reverse order in the regions plot when compared to the main result table. Which sorting should I trust?

The reverse sorting in the regions plots is due to our plotting script. This means that you should trust the initial sorting of the main result table.


? ClustalW (or my favourite sequence alignment tool) is faster, why should I use LocARNA for aligning RNAs?

Sequence alignment programs like ClustalW, T-Coffee, MUSCLE, or MAFFT compare RNAs only by sequence similarity. However, for many RNAs the structure information is strongly conserved. Thus, sequence similarity can be weak even for closely related RNAs of the same family. Consequently, only sequence-structure alignment programs that consider sequence and structure similarity, such as LocARNA, can reveal their true similarity. Pure sequence alignment programs will usually completely fail if sequence identity drops below 60%. Unfortunately, fully taking structure information into account is computationally expensive. Nevertheless, LocARNA achieves very good performance for this class of algorithms.

? Why should I use LocARNA compared to the RNA alignment program XY?

LocARNA is one of the fastest programs that do true sequence-structure alignment of RNAs and therefore produce highly accurate alignments. The LocARNA server is nice and offers a rich interface. You can even specify anchor constraints and structure constraints for global and local alignment; also probabilistic multiple alignment with alignment reliabilities is unique to LocARNA. Honestly, there are other very good programs out there; try them and compare!

? How does LocARNA achieve its speed and low space consumption? Does this compromise accuracy?

LocARNA uses different heuristics for improving speed and lowering space requirements. Most importantly, it filters base pairs by their probability in the RNA structure ensemble and considers only 'significant' base pairs that pass a certain probability threshold. This reduces complexity from O(n^6) time and O(n^4) space to O(n^4) time and O(n^2) space, respectively. Furthermore, one can control which base pairs are compared at all by setting a maximal length difference. Finally, as reasonable for global alignment, one can control which residues are compared by LocARNA by a maximal difference of sequence positions. All those heuristics are optional and can be controlled in the advanced section of the server. However, the heuristics with default settings were shown to preserve alignment accuracy on the comprehensive Bralibase 2.1 benchmark. It's therefore likely that default settings won't compromise the alignment accuracy for your RNAs.

? I am using LocARNA in local alignment mode; why does LocARNA return an empty multiple alignment (or only a very small one)?

Generally, LocARNA constructs multiple alignments by progressively merging (previously computed) alignments of fewer sequences. In the case of local alignment, LocARNA merges local subalignments from previous progressive alignment steps. In this way, the alignments can become shorter and shorter with every merge, since each progressive step retains only the sufficiently similar parts of the subsequences. Thus, if LocARNA cannot not find sufficiently similar common subsequences in all of your input sequences, it will produce a very small or even empty alignment.

Even in the latter case, LocARNA still computes the relations between all of your sequences in the form of a guide tree; furthermore it computes the alignments of the sequence subsets corresponding to the inner nodes of this tree. In many cases, one is rather interested in those alignments than in one single alignment of all sequences.

? I want to align more sequences than allowed. What can I do?

Often it is a good idea to first look for high sequence similarity in such a set of sequences. If one can identify sequence pairs with e.g. more than 80% or 90% identity, usually it does not pay off to align them based on structure similarity. Please check this by running a multiple sequence alignment first.

If this is the case with your set of sequences, you could reasonably reduce the number of sequences that you feed to our web server by omitting extremely similar sequences.

? I want to align longer sequences than allowed. What can I do?

An idea that might work: split the long sequence into (e.g. three) overlapping "windows", i.e. subsequences, and use the web server.

This is especially useful, if your are aligning short sequences with one (or few) long sequence(s) looking for a conserved motif.


? Can ExpaRNA compare multiple RNAs?

Currently, only pairwise comparison is supported. Maybe in future a multiple comparison is possible.

? What is the difference between the scoring 'default' and 'prefer larger substructures'?

With the default scheme (t=1) ExpaRNA tries to find a set of common substructures with maximal number of nucleotides. With the alternative scheme (t=2), the score of each substructure is quadratic to its size in nucleotides. For example, a base pair (2 nucleoides) has score 4, two base pairs have score 16 and so on. This prefers larger substructures in the result.

? Can ExpaRNA find substructures with mismatches?

No, ExpaRNA only finds exact matching substructures. Both sequence and structure of all nucleotides have to be exactly the same. If you are interested in a pure structural comparison you can change all nucleotides to 'N' and use the structure of the original sequence.

? Can ExpaRNA do a structural comparison only?

Yes, to some extend. As a trick you can change all nucleotides to 'N' in combination with the structure of the original sequence.


? Where can I download the orginal CRISPRmap REPEATS data that was used for the clustering?

The original REPEATS data set in a single fasta file and the species acronyms used for the IDs can be downloaded here: