Bioinformatics discovery of non-coding RNAs

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Non-coding RNAs have been discovered using both experimental and bioinformatic approaches. Bioinformatic approaches can be divided into three main categories. The first involves homology search, although these techniques are by definition unable to find new classes of ncRNAs. The second category includes algorithms designed to discover specific types of ncRNAs that have similar properties. Finally, some discovery methods are based on very general properties of RNA, and are thus able to discover entirely new kinds of ncRNAs.

Contents

Homology search refers to the process of searching a sequence database for RNAs that are similar to already known RNA sequences. Any algorithm that is designed for homology search of nucleic acid sequences can be used, e.g., BLAST. [1] However, such algorithms typically are not as sensitive or accurate as algorithms specifically designed for RNA.

Of particular importance for RNA is its conservation of a secondary structure, which can be modeled to achieve additional accuracy in searches. For example, Covariance models [2] can be viewed as an extension to a profile hidden Markov model that also reflects conserved secondary structure. Covariance models are implemented in the Infernal software package. [3]

Discovery of specific types of ncRNAs

Some types of RNAs have shared properties that algorithms can exploit. For example, tRNAscan-SE [4] is specialized to finding tRNAs. The heart of this program is a tRNA homology search based on covariance models, but other tRNA-specific search programs are used to accelerate searches.

The properties of snoRNAs have enabled the development of programs to detect new examples of snoRNAs, including those that might be only distantly related to previously known examples. Computer programs implementing such approaches include snoscan [5] and snoReport. [6]

Similarly, several algorithms have been developed to detect microRNAs. Examples include miRNAFold [7] and miRNAminer. [8]

Discovery by general properties

Some properties are shared by multiple unrelated classes of ncRNA, and these properties can be targeted to discover new classes. Chief among them is the conservation of an RNA secondary structure. To measure conservation of secondary structure, it is necessary to somehow find homologous sequences that might exhibit a common structure. Strategies to do this have included the use of BLAST between two sequences [9] or multiple sequences, [10] exploited synteny via orthologous genes [11] [12] or used locality sensitive hashing in combination with sequence and structural features. [13]

Mutations that change the nucleotide sequence, but preserve secondary structure are called covariation, and can provide evidence of conservation. Other statistics and probabilistic models can be used to measure such conservation. The first ncRNA discovery method to use structural conservation was QRNA, [9] which compared the probabilities of an alignment of two sequences based on either an RNA model or a model in which only the primary sequence conserved. Work in this direction has allowed for more than two sequences and included phylogenetic models, e.g., with EvoFold. [14] An approach taken in RNAz [15] involved computing statistics on an input multiple-sequence alignment. Some of these statistics relate to structural conservation, while others measure general properties of the alignment that could affect the expected ranges of the structural statistics. These statistics were combined using a support vector machine.

Other properties include the appearance of a promoter to transcribe the RNA. ncRNAs are also often followed by a Rho-independent transcription terminator.

Using a combination of these approaches, multiple studies have enumerated candidate RNAs, e.g., [9] [12] Some studies have proceeded to manual analysis of the predictions to find a details structural and functional prediction. [11] [16] [17]

See also

Related Research Articles

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In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Sequence alignments are also used for non-biological sequences, such as calculating the distance cost between strings in a natural language or in financial data.

Grammar theory to model symbol strings originated from work in computational linguistics aiming to understand the structure of natural languages. Probabilistic context free grammars (PCFGs) have been applied in probabilistic modeling of RNA structures almost 40 years after they were introduced in computational linguistics.

<span class="mw-page-title-main">Nucleic acid sequence</span> Succession of nucleotides in a nucleic acid

A nucleic acid sequence is a succession of bases within the nucleotides forming alleles within a DNA or RNA (GACU) molecule. This succession is denoted by a series of a set of five different letters that indicate the order of the nucleotides. By convention, sequences are usually presented from the 5' end to the 3' end. For DNA, with its double helix, there are two possible directions for the notated sequence; of these two, the sense strand is used. Because nucleic acids are normally linear (unbranched) polymers, specifying the sequence is equivalent to defining the covalent structure of the entire molecule. For this reason, the nucleic acid sequence is also termed the primary structure.

<span class="mw-page-title-main">Structural alignment</span> Aligning molecular sequences using sequence and structural information

Structural alignment attempts to establish homology between two or more polymer structures based on their shape and three-dimensional conformation. This process is usually applied to protein tertiary structures but can also be used for large RNA molecules. In contrast to simple structural superposition, where at least some equivalent residues of the two structures are known, structural alignment requires no a priori knowledge of equivalent positions. Structural alignment is a valuable tool for the comparison of proteins with low sequence similarity, where evolutionary relationships between proteins cannot be easily detected by standard sequence alignment techniques. Structural alignment can therefore be used to imply evolutionary relationships between proteins that share very little common sequence. However, caution should be used in using the results as evidence for shared evolutionary ancestry because of the possible confounding effects of convergent evolution by which multiple unrelated amino acid sequences converge on a common tertiary structure.

<span class="mw-page-title-main">Pseudoknot</span> Nucleic acid secondary structure

A pseudoknot is a nucleic acid secondary structure containing at least two stem-loop structures in which half of one stem is intercalated between the two halves of another stem. The pseudoknot was first recognized in the turnip yellow mosaic virus in 1982. Pseudoknots fold into knot-shaped three-dimensional conformations but are not true topological knots. These structures are categorized as cross (X) topology within the circuit topology framework, which, in contrast to knot theory, is a contact-based approach.

<span class="mw-page-title-main">Pfam</span> Database of protein families

Pfam is a database of protein families that includes their annotations and multiple sequence alignments generated using hidden Markov models. The most recent version, Pfam 35.0, was released in November 2021 and contains 19,632 families.

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<span class="mw-page-title-main">DNA annotation</span> The process of describing the structure and function of a genome

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αr7 is a family of bacterial small non-coding RNAs with representatives in a broad group of Alphaproteobacterial species from the order Hyphomicrobiales. The first member of this family was found in a Sinorhizobium meliloti 1021 locus located in the chromosome (C). Further homology and structure conservation analysis identified full-length homologs in several nitrogen-fixing symbiotic rhizobia, in the plant pathogens belonging to Agrobacterium species as well as in a broad spectrum of Brucella species. αr7 RNA species are 134-159 nucleotides (nt) long and share a well defined common secondary structure. αr7 transcripts can be catalogued as trans-acting sRNAs expressed from well-defined promoter regions of independent transcription units within intergenic regions (IGRs) of the Alphaproteobacterial genomes.

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<span class="mw-page-title-main">RAGATH RNA motifs</span> Bioinformatics strategy

RNAs Associated with Genes Associated with Twister and Hammerhead ribozymes (RAGATH) refers to a bioinformatics strategy that was devised to find self-cleaving ribozymes in bacteria. It also refers to candidate RNAs, or RAGATH RNA motifs, discovered using this strategy.

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<span class="mw-page-title-main">Protein tandem repeats</span>

An array of protein tandem repeats is defined as several adjacent copies having the same or similar sequence motifs. These periodic sequences are generated by internal duplications in both coding and non-coding genomic sequences. Repetitive units of protein tandem repeats are considerably diverse, ranging from the repetition of a single amino acid to domains of 100 or more residues.

DIMPL is a bioinformatic pipeline that enables the extraction and selection of bacterial GC-rich intergenic regions (IGRs) that are enriched for structured non-coding RNAs (ncRNAs). The method of enriching bacterial IGRs for ncRNA motif discovery was first reported for a study in "Genome-wide discovery of structured noncoding RNAs in bacteria".

An RNA motif is a description of a group of RNAs that have a related structure. RNA motifs consist of a pattern of features within the primary sequence and secondary structure of related RNAs. Thus, it extends the concept of a sequence motif to include RNA secondary structure. The term "RNA motif" can refer both to the pattern and to the RNA sequences that match it.

References

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