Finding a Common Motif of RNA Sequences Using Genetic Programming: The GeRNAMo System

Michal, S.; Ivry, Tor; Schalit-Cohen, O.; Sipper, Moshe; Barash, Danny

doi:10.1109/tcbb.2007.1045

Cited by 12 publications

(8 citation statements)

References 43 publications

(67 reference statements)

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“…The literature has also seen algorithms based on genetic programming, a methodology motivated by biological evolution. We discuss two methods GPRM [ 57 , 58 ] and GeRNAMo [ 59 ] under this approach. The general style of genetic programming algorithms is as follows.…”

Section: Methodsmentioning

confidence: 99%

RNA motif discovery: a computational overview

Achar

Sætrom

2015

Biol Direct

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Genomic studies have greatly expanded our knowledge of structural non-coding RNAs (ncRNAs). These RNAs fold into characteristic secondary structures and perform specific-structure dependent biological functions. Hence RNA secondary structure prediction is one of the most well studied problems in computational RNA biology. Comparative sequence analysis is one of the more reliable RNA structure prediction approaches as it exploits information of multiple related sequences to infer the consensus secondary structure. This class of methods essentially learns a global secondary structure from the input sequences. In this paper, we consider the more general problem of unearthing common local secondary structure based patterns from a set of related sequences. The input sequences for example could correspond to 3′ or 5′ untranslated regions of a set of orthologous genes and the unearthed local patterns could correspond to regulatory motifs found in these regions. These sequences could also correspond to in vitro selected RNA, genomic segments housing ncRNA genes from the same family and so on. Here, we give a detailed review of the various computational techniques proposed in literature attempting to solve this general motif discovery problem. We also give empirical comparisons of some of the current state of the art methods and point out future directions of research.ReviewersThis article was reviewed by Dr. Erez Levanon, Dr. Sebastian Maurer-Stroh and Dr. Weixiong Zhang.

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Section: Methodsmentioning

confidence: 99%

RNA motif discovery: a computational overview

Achar

Sætrom

2015

Biol Direct

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“…Our goal was to try all methods that could be applicable on sets of coregulated nonaligned transcripts (for discussion of limitations, see the Introduction). We considered GPRM (Hu 2003), GeRNAMo (Michal et al 2007), CMfinder (Yao et al 2006), comRNA (Ji et al 2004), MEME (Bailey et al 2006), MEMERIS (Hiller et al 2006), and RNA Sampler (Xu et al 2007). Among these, our computational resources (Linux cluster allowing up to 90 GB of RAM) permitted us to use CMfinder, MEME, and MEMERIS (although we could not use MEMERIS on all possible element lengths due to computation time restrictions) (see Materials and Methods).…”

Section: Performance Of Complementary Methodsmentioning

confidence: 99%

“…There are several algorithms that take folded RNA sequence as input and perform multiple sequence alignment (e.g., RNAforester [Hochsmann et al 2003], MARNA [Siebert and Backofen 2005], MXSCARNA [Tabei et al 2008], MASTR [Lindgreen et al 2007]). Some software such as comRNA (Ji et al 2004), RNA Sampler (Xu et al 2007), GPRM (Hu 2003), and GeRNAMo (Ji et al 2004;Michal et al 2007) do not use alignment. However, almost of all of these software packages were designed to identify RNA elements in a relatively small number of sequences with limited length.…”

Section: Introductionmentioning

confidence: 99%

Regulatory element identification in subsets of transcripts: Comparison and integration of current computational methods

Fan

Bitterman

Larsson

2009

RNA

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Regulatory elements in mRNA play an often pivotal role in post-transcriptional regulation of gene expression. However, a systematic approach to efficiently identify putative regulatory elements from sets of post-transcriptionally coregulated genes is lacking, hampering studies of coregulation mechanisms. Although there are several analytical methods that can be used to detect conserved mRNA regulatory elements in a set of transcripts, there has been no systematic study of how well any of these methods perform individually or as a group. We therefore compared how well three algorithms, each based on a different principle (enumeration, optimization, or structure/sequence profiles), can identify elements in unaligned untranslated sequence regions. Two algorithms were originally designed to detect transcription factor binding sites, Weeder and BioProspector; and one was designed to detect RNA elements conserved in structure, RNAProfile. Three types of elements were examined: (1) elements conserved in both primary sequence and secondary structure; (2) elements conserved only in primary sequence; and (3) microRNA targets. Our results indicate that all methods can uniquely identify certain known RNA elements, and therefore, integrating the output from all algorithms leads to the most complete identification of elements. We therefore developed an approach to integrate results and guide selection of candidate elements from several algorithms presented as a web service (https://dbw.msi.umn.edu:8443/recit). These findings together with the approach for integration can be used to identify candidate elements from genome-wide post-transcriptional profiling data sets.

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“…There are also algorithms that use evolutionary algorithms such as in RNAGA, 23 GPRM, 24 and GeRNAMo. 25 In addition, there are other heuristics speci¯cally designed to tackle the motif discovery problem such as RNAPro¯le. 26 Motifs are scored using many objective functions including: thermodynamic stability, alignment score, and probabilistic measures.…”

Section: Structural Motifs Discovery Algorithmsmentioning

confidence: 99%

IncMD: Incremental trie-based structural motif discovery algorithm

Badr

Al-Turaiki

Turcotte

et al. 2014

J. Bioinform. Comput. Biol.

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The discovery of common RNA secondary structure motifs is an important problem in bioinformatics. The presence of such motifs is usually associated with key biological functions. However, the identi¯cation of structural motifs is far from easy. Unlike motifs in sequences, which have conserved bases, structural motifs have common structure arrangements even if the underlying sequences are di®erent. Over the past few years, hundreds of algorithms have been published for the discovery of sequential motifs, while less work has been done for the structural motifs case. Current structural motif discovery algorithms are limited in terms of accuracy and scalability. In this paper, we present an incremental and scalable algorithm for discovering RNA secondary structure motifs, namely IncMD. We consider the structural motif discovery as a frequent pattern mining problem and tackle it using a modi¯ed a priori algorithm. IncMD uses data structures, trie-based linked lists of pre¯xes (LLP), to accelerate the search and retrieval of patterns, support counting, and candidate generation. We modify the candidate generation step in order to adapt it to the RNA secondary structure representation. IncMD constructs the frequent patterns incrementally from RNA secondary structure basic elements, using nesting and joining operations. The notion of a motif group is introduced in order to simulate an alignment of motifs that only di®er in the number of unpaired bases. In addition, we use a cluster beam approach to select motifs that will survive to the next iterations of the search. Results indicate that IncMD can perform better than some of the available structural motif Journal of Bioinformatics and Computational Biology Vol. 12, No. 5 (2014) discovery algorithms in terms of sensitivity (Sn), positive predictive value (PPV), and specicity (Sp). The empirical results also show that the algorithm is scalable and runs faster than all of the compared algorithms.

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Finding a Common Motif of RNA Sequences Using Genetic Programming: The GeRNAMo System

Cited by 12 publications

References 43 publications

RNA motif discovery: a computational overview

RNA motif discovery: a computational overview

Regulatory element identification in subsets of transcripts: Comparison and integration of current computational methods

IncMD: Incremental trie-based structural motif discovery algorithm

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