Can MiRBase Provide Positive Data for Machine Learning for the Detection of MiRNA Hairpins?

Saçar, Müşerref Duygu; Hamzeiy, Hamid; Allmer, Jens

doi:10.2390/biecoll-jib-2013-215

Cited by 18 publications

References 33 publications

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Differential Expression of Toxoplasma gondii MicroRNAs in Murine and Human Hosts

Demirci

Bağcı

Allmer

2016

Non-Coding RNAs and Inter-Kingdom Communication

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MicroRNAs are short RNA sequences involved in post-transcriptional gene regulation. MicroRNAs are known for a wide variety of species ranging from bacteria to plants. It has become clear that some cross-kingdom regulation is possible especially between viruses and their hosts. We hypothesized that intracellular parasites, like Toxoplasma gondii, similar to viruses would be able to modulate their host's gene expression. We were able to show that T. gondii produces many putative pre-miRNAs which are actually transcribed. Furthermore, some of these expressed pre-miRNAs have a striking resemblance to host mature miRNAs. Previous studies indicated that T. gondii infection coincides with increased abundance of some miRNAs. Here we were able to show that many of these miRNAs have close relatives in T. gondii which may not be distinguishable using PCR. Taken together, the similarity to host miRNAs, their confirmed expression, and their upregulation during infection, it suggests that T. gondii actively transfers miRNAs to regulate its host. We conclude, that this type of cross-kingdom regulation may be possible, but that targeted analysis is necessary to consolidate our computational findings.

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Differential Expression of Toxoplasma gondii MicroRNAs in Murine and Human Hosts

Demirci

Bağcı

Allmer

2016

Non-Coding RNAs and Inter-Kingdom Communication

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Computational Prediction of MicroRNAs from Toxoplasma Gondii Potentially Regulating the Hosts’ Gene Expression

Saçar

Bağcı

Allmer

2014

Genomics, Proteomics &Amp; Bioinformatics

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MicroRNAs (miRNAs) were discovered two decades ago, yet there is still a great need for further studies elucidating their genesis and targeting in different phyla. Since experimental discovery and validation of miRNAs is difficult, computational predictions are indispensable and today most computational approaches employ machine learning. Toxoplasma gondii, a parasite residing within the cells of its hosts like human, uses miRNAs for its post-transcriptional gene regulation. It may also regulate its hosts’ gene expression, which has been shown in brain cancer. Since previous studies have shown that overexpressed miRNAs within the host are causal for disease onset, we hypothesized that T. gondii could export miRNAs into its host cell. We computationally predicted all hairpins from the genome of T. gondii and used mouse and human models to filter possible candidates. These were then further compared to known miRNAs in human and rodents and their expression was examined for T. gondii grown in mouse and human hosts, respectively. We found that among the millions of potential hairpins in T. gondii, only a few thousand pass filtering using a human or mouse model and that even fewer of those are expressed. Since they are expressed and differentially expressed in rodents and human, we suggest that there is a chance that T. gondii may export miRNAs into its hosts for direct regulation.

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On the performance of pre-microRNA detection algorithms

2017

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MicroRNAs are crucial for post-transcriptional gene regulation, and their dysregulation has been associated with diseases like cancer and, therefore, their analysis has become popular. The experimental discovery of miRNAs is cumbersome and, thus, many computational tools have been proposed. Here we assess 13 ab initio pre-miRNA detection approaches using all relevant, published, and novel data sets while judging algorithm performance based on ten intrinsic performance measures. We present an extensible framework, izMiR, which allows for the unbiased comparison of existing algorithms, adding new ones, and combining multiple approaches into ensemble methods. In an exhaustive attempt, we condense the results of millions of computations and show that no method is clearly superior; however, we provide a guideline for biomedical researchers to select a tool. Finally, we demonstrate that combining all of the methods into one ensemble approach, for the first time, allows reliable purely computational pre-miRNA detection in large eukaryotic genomes.

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Can MiRBase Provide Positive Data for Machine Learning for the Detection of MiRNA Hairpins?

Cited by 18 publications

References 33 publications

Differential Expression of Toxoplasma gondii MicroRNAs in Murine and Human Hosts

Differential Expression of Toxoplasma gondii MicroRNAs in Murine and Human Hosts

Computational Prediction of MicroRNAs from Toxoplasma Gondii Potentially Regulating the Hosts’ Gene Expression

On the performance of pre-microRNA detection algorithms

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