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miRNA Prediction

microRPM

Submitted by ChenLiang on Tue, 01/09/2018 - 19:29

MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine learning-based approaches have been developed to identify novel miRNAs from next generation sequencing (NGS) data. Typically, precursor/genomic sequences are required as references for most methods. However, the non-availability of genomic sequences is often a limitation in miRNA discovery in non-model plants.

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4
Average: 3.5 (2 votes)

plantMirP

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs are a predominant type of small non-coding RNAs approximately 21 nucleotides in length that play an essential role at the post-transcriptional level by either RNA degradation, translational repression or both through an RNA-induced silencing complex. Identification of these molecules can aid the dissecting of their regulatory functions. The secondary structures of plant pre-miRNAs are much more complex than those of animal pre-miRNAs. In contrast to prediction tools for animal pre-miRNAs, much less effort has been contributed to plant pre-miRNAs.

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Average: 5 (1 vote)

miRSeqNovel

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

We present miRSeqNovel, an R based workflow for miRNA sequencing data analysis. miRSeqNovel can process both colorspace (SOLiD) and basespace (Illumina/Solexa) data by different mapping algorithms. It finds differentially expressed miRNAs and gives conservative prediction of novel miRNA candidates with customized parameters. miRSeqNovel is freely available at http://sourceforge.net/projects/mirseq/files.[1]

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Average: 5 (1 vote)

miRQuest

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

This report describes the miRQuest - a novel middleware available in a Web server that allows the end user to do the miRNA research in a user-friendly way. It is known that there are many prediction tools for microRNA (miRNA) identification that use different programming languages and methods to realize this task. It is difficult to understand each tool and apply it to diverse datasets and organisms available for miRNA analysis. miRQuest can easily be used by biologists and researchers with limited experience with bioinformatics.

Rating: 
5
Average: 4.5 (2 votes)

mirnaDetect

Submitted by ChenLiang on Thu, 04/06/2017 - 19:28

MicroRNA (miRNA) plays an important role as a regulator in biological processes. Identification of (pre-)miRNAs helps in understanding regulatory processes. Machine learning methods have been designed for pre-miRNA identification. However, most of them cannot provide reliable predictive performances on independent testing datasets. We assumed this is because the training sets, especially the negative training sets, are not sufficiently representative.

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Average: 5 (1 vote)

YamiPred

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs (miRNAs) are small non-coding RNAs, which play a significant role in gene regulation. Predicting miRNA genes is a challenging bioinformatics problem and existing experimental and computational methods fail to deal with it effectively. We developed YamiPred, an embedded classification method that combines the efficiency and robustness of support vector machines (SVM) with genetic algorithms (GA) for feature selection and parameters optimization.

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Average: 5 (1 vote)

BioSeq-Analysis

Submitted by ChenLiang on Tue, 01/09/2018 - 17:37

With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems is how to computationally analyze their structures and functions. Machine learning techniques are playing key roles in this field. Typically, predictors based on machine learning techniques contain three main steps: feature extraction, predictor construction and performance evaluation. Although several Web servers and stand-alone tools have been developed to facilitate the biological sequence analysis, they only focus on individual step.

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Average: 5 (1 vote)

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