categorization of species based on their micrornas ... ?· micrornas employing sequence motifs,...
Post on 06-Mar-2019
Embed Size (px)
RESEARCH Open Access
Categorization of species based on theirmicroRNAs employing sequence motifs,information-theoretic sequence featureextraction, and k-mersMalik Yousef1* , Dawit Nigatu2, Dalit Levy1, Jens Allmer3,4 and Werner Henkel2
Background: Diseases like cancer can manifest themselves through changes in protein abundance, and microRNAs(miRNAs) play a key role in the modulation of protein quantity. MicroRNAs are used throughout all kingdoms andhave been shown to be exploited by viruses to modulate their host environment. Since the experimental detectionof miRNAs is difficult, computational methods have been developed. Many such tools employ machine learning forpre-miRNA detection, and many features for miRNA parameterization have been proposed. To train machine learningmodels, negative data is of importance yet hard to come by; therefore, we recently started to employ pre-miRNAs fromone species as positive data versus another species pre-miRNAs as negative examples based on sequence motifs andk-mers. Here, we introduce the additional usage of information-theoretic (IT) features.
Results: Pre-miRNAs from one species were used as positive and another species pre-miRNAs as negative training datafor machine learning. The categorization capability of IT and k-mer features was investigated. Both feature sets and theircombinations yielded a very high accuracy, which is as good as the previously suggested sequence motif and k-merbased method. However, for obtaining a high performance, a sufficiently large phylogenetic distance between thespecies and sufficiently high number of pre-miRNAs in the training set is required. To examine the contribution of the ITand k-mer features, an information gain-based feature ranking was performed. Although the top 3 are IT features, 80%of the top 100 features are k-mers. The comparison of all three individual approaches (motifs, IT, and k-mers) shows thatthe distinction of species based on their pre-miRNAs k-mers are sufficient.
Conclusions: IT sequence feature extraction enables the distinction among species and is less computationally expensivethan motif calculations. However, since IT features need larger amounts of data to have enough statistics for producinghighly accurate results, future categorization into species can be effectively done using k-mers only. The biologicalreasoning for this is the existence of a codon bias between species which can, at least, be observed in exonic miRNAs.Future work in this direction will be the ab initio detection of pre-miRNA. In addition, prediction of pre-miRNAfrom RNA-seq can be done.
Keywords: MicroRNA, Sequence motifs, Pre-microRNA, Machine learning, Differentiate miRNAs among species,k-mer, miRNA categorization, Information theory
* Correspondence: firstname.lastname@example.orgCommunity Information Systems, Zefat Academic College, 13206 Zefat,IsraelFull list of author information is available at the end of the article
EURASIP Journal on Advancesin Signal Processing
The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made.
Yousef et al. EURASIP Journal on Advances in Signal Processing (2017) 2017:70 DOI 10.1186/s13634-017-0506-8
1 IntroductionProteins define a phenotype, and their dysregulationoften leads to a disease. Protein abundance is highlyregulated, and microRNAs are responsible for itspost-transcriptional modulation. Mature microRNAs(miRNAs), which act as recognition sequences fortheir target messenger RNAs, are produced from amolecular pathway which is different for plants andanimals . They have in common that pri-miRNAsare transcribed from the genome and that hairpins(pre-miRNAs) are excised from these transcripts. Eachpre-miRNA can have multiple mature miRNAs (1824nucleotides in length) which are incorporated into a pro-tein complex, responsible for modulating the translationefficiency of multiple targets. MicroRNAs have beenshown to exist in a variety of species ranging from viruses to plants . MicroRNAs need to be co-expressed withtheir targets  in order to be functional, and many tran-scripts in an organism are only produced in response tointernal or external stresses. Thus, it may not be possibleto experimentally determine all miRNAs, their targets,and their interactions. Computational approaches todetect miRNAs have been developed to overcome thelimitation, and most methods for pre-miRNA detectionare based on machine learning . With the exceptionof few approaches based on one class classification ,most methods rely on two class classification. While allparts contributing to model establishment are important, the selection of negative data is crucial since no goldstandard is available. Although other databases likemiRTarBase , TarBase , and MirGeneDB  areavailable, positive data is generally derived from miRBase. While negative data is of unknown quality, also positivedata from miRBase contains questionable entries [14, 16]and even MirGeneDB which filters miRBase entries is notfree from questionable examples .Parameterization of pre-miRNAs is important for ap-
plying machine learning algorithms, and numerous fea-tures have been proposed . Short sequences (k-mers)have been used early on for the machine learning-basedab initio detection of pre-miRNAs . Since miRNAgenesis depends on a pathway involving several proteincomplexes, structural features of pre-miRNAs have beenfound to be important . Additionally, we haverecently established the use of sequence motifs asfeatures enabling the detection of pre-miRNAs [21, 22].Many machine learning models for pre-miRNA detectionhave been established using a variety of learning algo-rithms and training schemes . All the establishedmodels suffer from the selection of arbitrary examples forthe negative class. Gao and colleagues , for example,reasoned that exons and other non-coding RNAs wouldbe useful as negative data, but miRNAs can be derivedfrom anywhere in the genome, including exons .
Due to the unknown quality of the negative data, ina previous work, we successfully used the one-classclassification approach for the detection of pre-miRNAs[29, 30]. However, we realized that using positive examplesto represent the negative class from different species holdsa number of promises . One of the promises is that itenables the categorization of pre-miRNAs into species.Hairpins can be structurally classified fairly well, andmany approaches are available despite data quality issues. Categorization of the identified pre-miRNAs intotheir species of origin or a very closely related one adds afurther line of evidence to their identification. Weestablished random forest machine learning models usingtwo-class classification with the positive class being pre-miRNAs from one species and the negative pre-miRNAsfrom a different species. Therefore, both positive andnegative classes for training and testing were derived fromknown pre-miRNAs, effectively removing the need forpseudo negative data. We have previously proposed thesame strategy  using sequence motifs and k-mers. Inthis study, we further introduced information-theoreticapproaches and important additional analyses. In ourprevious work, we showed that discrimination amongmiRNAs from different species is possible which is likelydue to alleged fast evolution for some miRNAs ,supporting the possibility to differentiate among evolu-tionary distant species based on miRNAs. We thenfocused on sequence motifs since structure is evolutionarilymore conserved than sequence. Due to the large impact ofk-mers on the categorization in our previous work, in anattempt to add more discriminating power, here, we addedinformation theory (IT)-based features. Apart from our pre-vious study , only Lopes and colleagues attempted touse pre-miRNAs to discriminate between species .However, they resorted to establishing ab initio pre-miRNAdetection models with the same bias on negative data asexisting pre-miRNA detection methods [26, 3942]; usingthe same training and testing strategies [32, 4244]. Fur-thermore, a large part of the features they used assessesstructural features of pre-miRNAs, which poses problemswhen analyzing closely related species since structure ismore conserved than sequence. In this work, we analyzedthe discriminative power of sequence motifs, information-theoretic quantities, and k-mers for the categorization ofpre-miRNAs into species. It became clear that k-mers alonecan separate between species that are not strongly related.We also showed that the number of examples is importantfor the establishment of suitable machine learning models.If enough examples are not available for a species, a modelcan be established for the next higher level (genus), whichmay even outperform all species-based models. Sincesequence motifs and IT features are computationally expen-sive compared to k-mers, it would be extremely difficult toestablish models for all pairs of species for automatic
Yousef et al. EURASIP Journal on Advances in Signal Processing (2017) 2017:70 Page 2 of 10
categorization. However, since we were able to showthat k-mers have enough discriminative power, auto-matic species categorization will become possible in thefuture. All in all, this work not only provides an imp