MicroTrout: A comprehensive, genome-wide miRNA target prediction framework for rainbow trout, Oncorhynchus mykiss

Sugarcane Bacilliform Guadeloupe A Virus (SCBGAV, genus Badnavirus, family Caulimoviridae) is an emerging, deleterious pathogen of sugarcane which presents a substantial barrier to producing high sugarcane earnings. The circular, double-stranded (ds) DNA genome of SCBGAV (7.4 Kb) is composed of three open reading frames (ORF) that replicate by a reverse transcriptase. In the current study, we used miRNA target prediction algorithms to identify and comprehensively analyze the genome-wide sugarcane (Saccharum officinarum L.)-encoded microRNA (miRNA) targets against the SCBGAV. A total of 28 potential mature target miRNAs were retrieved from the miRBase database and were further analyzed for hybridization to the SCBGAV genome. Multiple computational approaches—including miRNA-target seed pairing, multiple target positions, minimum free energy, target site accessibility, maximum complementarity, pattern recognition and minimum folding energy for attachments— were considered by all algorithms. Only 4 sugarcane miRNAs are selected for SCBGAV silencing. Among those 4, sof-miR396 was identified as the top effective candidate, capable of targeting the vital ORF3 which encodes polyprotein of the SCBGAV genome. miRanda, RNA22 and RNAhybrid algorithms predicted hybridization of sof-miR396 at common locus position 3394. A Circos plot was created to study the network visualization of sugarcane-encoded miRNAs with SCBGAV genes determines detailed evidence for any ideal targets of SCBGAV ORFs by precise miRNAs. The present study concludes a comprehensive report towards the creation of SCBGAV-resistant sugarcane through the expression analysis of the identified miRNAs.

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A novel computational approach to the silencing of Sugarcane Bacilliform Guadeloupe A Virus determines potential host-derived MicroRNAs in sugarcane (Saccharum officinarum L.)

10.7287/peerj.preprints.27842v1 ◽

2019 ◽

Author(s):

Fakiha Ashraf ◽

Muhammad Aleem Ashraf ◽

Xiaowen Hu ◽

Shuzhen Zhang

Keyword(s):

Mirna Target ◽

Target Prediction ◽

Saccharum Officinarum ◽

Minimum Free Energy ◽

Open Reading Frames ◽

Mirna Target Prediction ◽

Folding Energy ◽

Multiple Target ◽

Genome Wide ◽

Reading Frames

Sugarcane Bacilliform Guadeloupe A Virus (SCBGAV, genus Badnavirus, family Caulimoviridae) is an emerging, deleterious pathogen of sugarcane which presents a substantial barrier to producing high sugarcane earnings. The circular, double-stranded (ds) DNA genome of SCBGAV (7.4 Kb) is composed of three open reading frames (ORF) that replicate by a reverse transcriptase. In the current study, we used miRNA target prediction algorithms to identify and comprehensively analyze the genome-wide sugarcane (Saccharum officinarum L.)-encoded microRNA (miRNA) targets against the SCBGAV. A total of 28 potential mature target miRNAs were retrieved from the miRBase database and were further analyzed for hybridization to the SCBGAV genome. Multiple computational approaches—including miRNA-target seed pairing, multiple target positions, minimum free energy, target site accessibility, maximum complementarity, pattern recognition and minimum folding energy for attachments— were considered by all algorithms. Only 4 sugarcane miRNAs are selected for SCBGAV silencing. Among those 4, sof-miR396 was identified as the top effective candidate, capable of targeting the vital ORF3 which encodes polyprotein of the SCBGAV genome. miRanda, RNA22 and RNAhybrid algorithms predicted hybridization of sof-miR396 at common locus position 3394. A Circos plot was created to study the network visualization of sugarcane-encoded miRNAs with SCBGAV genes determines detailed evidence for any ideal targets of SCBGAV ORFs by precise miRNAs. The present study concludes a comprehensive report towards the creation of SCBGAV-resistant sugarcane through the expression analysis of the identified miRNAs.

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Detection of anoxia-responsive genes in cultured cells of the rainbow trout Oncorhynchus mykiss (Walbaum), using an optimized, genome-wide oligoarray

Journal of Fish Biology ◽

10.1111/j.1095-8649.2008.01877.x ◽

2008 ◽

Vol 72 (9) ◽

pp. 2170-2186 ◽

Cited By ~ 8

Author(s):

L. A. Olohan ◽

W. Li ◽

T. Wulff ◽

H. Jarmer ◽

A. Y. Gracey ◽

...

Keyword(s):

Rainbow Trout ◽

Oncorhynchus Mykiss ◽

Cultured Cells ◽

Rainbow Trout Oncorhynchus Mykiss ◽

Genome Wide

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A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction

BMC Genomics ◽

10.1186/1471-2164-15-348 ◽

2014 ◽

Vol 15 (1) ◽

pp. 348 ◽

Cited By ~ 82

Author(s):

Prashant K Srivastava ◽

Taraka Moturu ◽

Priyanka Pandey ◽

Ian T Baldwin ◽

Shree P Pandey

Keyword(s):

Mirna Target ◽

Target Prediction ◽

Mirna Target Prediction ◽

Prediction Tools ◽

Genome Wide ◽

Plant Mirna

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A Scalable Genetic Programming Approach to Integrate miRNA-Target Predictions: Comparing Different Parallel Implementations of M3GP

Complexity ◽

10.1155/2018/4963139 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13

Author(s):

Stefano Beretta ◽

Mauro Castelli ◽

Luis Muñoz ◽

Leonardo Trujillo ◽

Yuliana Martínez ◽

...

Keyword(s):

Genetic Programming ◽

Mirna Target ◽

Target Genes ◽

Parallel Implementation ◽

Target Prediction ◽

Programming Approach ◽

Learning Approaches ◽

Mirna Target Prediction ◽

Parallel Implementations ◽

Genome Wide

There are many molecular biology approaches to the analysis of microRNA (miRNA) and target interactions, but the experiments are complex and expensive. For this reason, in silico computational approaches able to model these molecular interactions are highly desirable. Although several computational methods have been developed for predicting the interactions between miRNA and target genes, there are substantial differences in the results achieved since most algorithms provide a large number of false positives. Accordingly, machine learning approaches are widely used to integrate predictions obtained from different tools. In this work, we adopt a method called multidimensional multiclass GP with multidimensional populations (M3GP), which relies on a genetic programming approach, to integrate and classify results from different miRNA-target prediction tools. The results are compared with those obtained with other classifiers, showing competitive accuracy. Since we aim to provide genome-wide predictions with M3GP and, considering the high number of miRNA-target interactions to test (also in different species), a parallel implementation of this algorithm is recommended. In this paper, we discuss the theoretical aspects of this algorithm and propose three different parallel implementations. We show that M3GP is highly parallelizable, it can be used to achieve genome-wide predictions, and its adoption provides great advantages when handling big datasets.

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