Transterm

Bioinformatic approaches to finding cis acting regulatory motifs in eukaryotic mRNAs - focusing on human 3' UTR mRNA analysis

This is a brief introduction related to the TransTerm databases (mRNA.otago.ac.nz). If you find this useful in your research. please cite our publication in the database issue of Nucleic Acids Research. Other methods are described in the references (1-5) and related web sites (6-12). We provide a selection of tools via the www interface to access the TransTerm data (13)

A list of related tools and resources for analyzing mRNA and UTRs is also available here.

Classification based on location in the mRNA

Many motifs are located in the 5' or 3' UTRs of mRNA sequences. They have been found less commonly in coding sequences.

What do motifs do? Classification based on function

Motifs in particular mRNAs and translated viral RNAs have been shown to be involved in mediating many functions and post-transcriptional controls in cells. These include (with recent selected references):

Detailed examples of some of these motifs can be found by using "Describe Transterm motifs" in the pull down menu, choosing a pattern.

A sequence and structural classification

Motifs can be classified into three broad classes based on structure

  1. Sequence alone
  2. Structure alone
  3. A combination of sequence and structure

How can I find these types of motifs computationally?

These classes of motifs reflect the different ways in which they interact with other RNAs, RNA-binding proteins or ribosomes. The different classes require different methods for computational recognition. Two types of questions are often asked: "How can I find known elements in my sequence?" or, "Given a group of related sequences how can I find common elements?"

1. Sequence alone

Motifs can vary greatly in size, although many are small ~4-8 bases long, and may repeat in the sequence e.g. ARE stability elements.

A single mRNA. These may be recognised by RNA binding proteins or by other RNAs. Known motifs can be identified computationally using consensus sequences, consensus matrices and statistical models of motifs. The first two are provided at this site. More sophisticated methods are available, but these will usually require implementing the programs at your site. Examples are the common AU Rich Elements (ARE, repeating core motifs of AUUUA), or rare Nanos Response Elements (NRE, repeating motifs of UUGU). Although superficially similar, these motifs are recognised by different classes of proteins. Furthermore the function of such motifs may be determined by the binding of secondary ligand(s). Thus a destabilising element in one cell may stabilise it in another.

Finding motifs using regular expressions. Scan for matches (Patscan) is provided here to search the Transterm datasets for known or putative motifs (60).

Finding motifs using BLAST. Blast is not well designed to finding these motifs (61). However, we provide an online BLAST search with parameters such that it is able to find longer degenerate motifs (e.g. AUUUAUUUAUUUA).

Aligned or unaligned related sequences. Methods involving local alignments are usually utilised to find small motifs or structures. Reviewed for DNA motifs, particularly transcription factor binding sites in (56,62-65). Methods that attempt to find global alignments, e.g. ClustalW or pileup are not so successful, although they will find longer motifs.

2. Structure alone

A single mRNA. Known motifs may be described and searched for at this site using user-defined base pairing rules. Methods involving energy minimisation, utilising thermodynamic parameters are available (66,67). However, the theoretically most stable structure may not the physiological motif, as other proteins, RNAs and complexes binding the mRNA will affect structure. Induced fit has been demonstrated in RNA-protein recognition (59,68). In some cases simplification of the structure may assist analysis (69,70).

It should also be recognised that unusual base-pairing may, in some cases contribute to unusual structures, for example A-G base pairs in the SECIS element (71). These unusual base pairs, and the more common U-U and G-G base pairs, will not be favoured by thermodynamic computational approaches. Unusual base pairs or pinched out bases may provide discrimination between similar structural motifs.

Aligned or unaligned related sequences. By definition, it is difficult to make a multiple alignment of sequences with only conservation in structure. However, new methods for recognition of structural motifs in unaligned sequences have recently become available {Bradley, 2008 #18638; Xu, 2007 #12944}.

3. A combination of sequence and structure

A single mRNA. Known motifs may be described and searched for at this site using user-defined base pairing rules and consensus methods. For example the well characterised Iron Responsive Element (IRE) {Leipuviene, 2007 #13526;Pavesi, 2004 #8650}.

Aligned or unaligned related sequences. Few methods currently exist to combine the approaches described above. Utilisation of both sequence and structural recognition elements may allow the discovery of such motifs (72,73).

How do I know if this match is significant?

This is perhaps the most difficult question. It is possible to apply statistical methods to determine how often a sequence motif is expected to occur by chance in a particular database. Small motifs will give many false positives. When ascertaining significance it is essential to take into account the expected composition of the bases in similar regions of the genome in question. Usually at least dinucleotide bias is taken into account.

In addition searching for motifs in regions of similar composition where they are known not to function can give an estimate of the false positive rate. For most patterns described in TransTerm we give an estimate of the number of hits in a typical mRNA database.

Motifs in coding sequences

Much of a mRNA sequence encodes protein and is thus constrained (59), motifs in the 5' or 3' UTRs have been easier to identify (74,75). However, coding region motifs have previously been discovered experimentally (76,77). Computational methods to discover regulatory elements within coding are now becoming feasible, utilising comparative genomics (42,45,78-80).

References and further reading

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