miRNA | the Non-Coding RNA Group

HCMV

Sat, 10 Oct 2020 00:00:00 +0000

HCMV is present in humans with seroprevalence ranging from 45 to 96% globally (about 96% in China). HCMV infection occurs throughout the body, is always mild and asymptomatic in healthy populations, but is symptomatic or even lethal in immunocompromised/immune-immature people, such as HIV-infected patients, transplant recipients and new-borns infected in utero. The virus can enter latency after primary infection, but can be re-activated at any time.

In the general population, the immune system restricts HCMV infection to latency and is asymptomatic. However, HCMV infection causes severe diseases in immune-immature and immune-compromised patients. This includes transplant recipients, who often suffer severe complications including pneumonia, hepatitis and gastrointestinal ulcerations after transplantation. This is a consequence of their impaired immune system which allows the latent HCMV infection to be reactivated. Consequently, transplant recipients always receive pre-emptive or prophylactic antiviral therapy but nevertheless have a high risk of developing drug resistance and HCMV reactivation.

To date, no effective vaccine is available for HCMV, and only four licensed drugs (ganciclovir/valganciclovir, cidofovir and foscarnet, which all target viral DNA replication) are available. Most recently, in the US in 2017, letermovir, which targets viral DNA packaging, was licenced for prophylaxis in bone marrow patients. However, all of these drugs can cause severe side effects and, in the long-term, drug resistance inevitably occurs due to accumulation of mutations in the HCMV genome. In spite of these problems, HCMV drug resistance has not been well characterized, leaving a huge gap in options for clinical therapy and treatment.

HCMV studies to date have primarily been experimental and have used “classical” approaches to identify key proteins and clarify their roles in the infection process. Computational methods can assist in genome characterization and is widely used in the study of other viral and bacterial genomes, but this approach has not been widely adopted in the investigation of the HCMV genome. We are developing computational methods to perform genome wide comparison of HCMV strains, including both coding and non-coding (miRNA) regions. In this work, we collaboratiing with the lab of Luo Minhua in Wuhan Institute of Virology and hospitals in Beijing and Changsha who have been collecting viral isolates from transplant patients. This work is supported by a Scientia Fellowship from UiO

Ethnicity

Wed, 17 Jun 2020 00:00:00 +0000

A 2016 report on the ethnic composition of WES and WGS studies found that 84% of studies involved Europeans, only 14% & 3% of samples originated from Asia and Africa respectively. The bias is even more pronounced for studies of the non-coding genome, with only a handful of reports on population SNVs. Even though the situation is improving, the majority of studies remain biased towards Caucasian populations. This lack of variation among populations can impact awareness of how efficacious a drug may be or how likely it is to cause adverse events. However, the advent of large-scale WGS with broader populations means it is now possible to consider population specific variation within NC genomic regions.

In this work we are using publicly available data from the 1000 genomes project and the African Variome Project and combining it with data from collaborators in Africa and China to identify variations in specific populations that occur within miRNA associated regions. This work is funded by a Research Council of Norway FRIPRO grant.

miRAW

Mon, 27 Apr 2020 00:00:00 +0000

miRAW is a miRNA target prediction based on Deep Learning (DL) which, rather than incorporating any knowledge (such as seed regions), investigates the entire miRNA and 3’UTR mRNA nucleotides to learn a uninhibited set of feature descriptors related to the targeting process.

The trained model is based on more than 150,000 experimentally validated homo sapiens miRNA:gene targets cross referenced with different CLIP-Seq, CLASH and iPAR-CLIP datasets to obtain ∼20,000 validated miRNA:gene exact target sites.

Using this data, we implemented and trained a deep neural network—composed of autoencoders and a feed-forward network—able to automatically learn features describing miRNA-mRNA interactions and assess functionality. Predictions were then refined using information such as site location or site accessibility energy. In a comparison using independent datasets, our DL approach consistently outperformed existing prediction methods, recognizing the seed region as a common feature in the targeting process, but also identifying the role of pairings outside this region. Thermodynamic analysis also suggests that site accessibility plays a role in targeting but that it cannot be used as a sole indicator for functionality.

miRBaseMiner

Mon, 27 Apr 2020 00:00:00 +0000

microRNAs are small non-coding RNA molecules playing a central role in gene regulation. miRBase is the standard reference source for analysis and interpretation of experimental studies. However, the richness and complexity of the annotation is often underappreciated by users. Moreover, even for experienced users, the size of the resource can make it difficult to explore annotation to determine features such as species coverage, the impact of specific characteristics and changes between successive releases. A further consideration is that each new miRBase release contains entries that have had limited review and which may subsequently be removed in a future release to ensure the quality of annotation. To aid the miRBase user, we developed a software tool, miRBaseMiner, for investigating miRBase annotation and generating custom annotation sets.

We apply the tool to characterize each release from v9.2 to v22 to examine how annotation has changed across releases and highlight some of the annotation features that users should keep in mind when using for miRBase for data analysis.

These include:

entries with identical or very similar sequences;
entries with multiple annotated genome locations;
hairpin precursor entries with extremely low-estimated minimum free energy;
entries possessing reverse complementary;
entries with 3ʹ poly(A) ends.

As each of these factors can impact the identification of dysregulated features and subsequent clinical or biological conclusions, miRBaseMiner is a valuable resource for any user using miRBase as a reference source.

miRNAs

Mon, 27 Apr 2020 00:00:00 +0000

There are more than 45 000 miRNA related publications in PubMed. While some studies investigate miRNA biogenesis, function and decay, the majority of work has focused on identifying specific miRNAs with roles in disease and developmental processes. In the most common approach, miRNA association studies are performed using microarray or Next Generation Sequencing (NGS) platforms to compare two conditions (e.g. healthy versus cancer) and identify miRNAs that have statistically significant differences in expression levels. The mRNA targets of these miRNAs are predicted using computational tools such as TargetScan3 and functionally interesting ones may be also experimentally verified.

However, these studies implicitly assume an oversimplistic model of miRNA function

Annotation: miRNA studies are dependent on annotation and the primary reference resource is miRBase. The quality of this resource is variable and different versions can return different results in miRNA expression studies (i.e. identification of differentially expressed miRNAs). A further complication is that miRBase includes highly similar or duplicate miRNAs, and miRNAs that have multiple copies.
isomiRs: In most miRNA expression studies it is assumed that miRNAs exist as a well-defined and stable entity, i.e., the single sequence specified in miRBase is the exact form in which a miRNA is expressed. In reality, a miRNA is expressed as a series of highly similar isoforms, or isomiRs, which have demonstrated functional roles18. Microarray based studies are unable to capture this variation, and most NGS Small RNA Sequencing (Small RNA Seq) studies generally fail to consider such deviations.
Ethnicity: This is rarely considered in miRNA studies. miRBase annotation is based on the standard reference genome, GrCh38.p13, but ethnicity can impact miRNA studies by (i) failing to map reads to features containing population specific SNVs, and (ii) failing to incorporate population specific variation in the 3’UTR targets.
Targeting: Due to cost and throughput issues, determining miRNA targets is heavily dependent on computational prediction tools. Many of these are rule based, i.e., they incorporate knowledge into the prediction process - in particular, they require the presence of seed region binding (nt2 to nt7/8 in a miRNA). Even machine learning based approaches used by tools such as TargetScan incorporate this information into their models. While this helps to improve model performance, it biases the model to identify targeting events based on existing knowledge, rather than providing new insight into the targeting process.

Jasmine: a Java pipeline for isomiR characterization in miRNA-seq Data

Sun, 01 Mar 2020 00:00:00 +0000

Jasmine

Wed, 27 Apr 2016 00:00:00 +0000

The existence of complex subpopulations of miRNA isoforms, or isomiRs, is well established. While many tools exist for investigating isomiR populations, they differ in how they characterize an isomiR, making it difficult to compare results across different tools. Thus, there is a need for a more comprehensive and systematic standard for defining isomiRs. Such a standard allows investigation of isomiR population structure in progressively more refined sub-populations, permitting the identification of more subtle changes between conditions and leading to an improved understanding of the processes that generate these differences.

Jasmine is a software tool that incorporates a hierarchal framework for characterizing isomiR populations. Jasmine is a Java application that can process raw read data in fastq/fasta format, or mapped reads in SAM format to produce a detailed characterization of isomiR populations. Thus, Jasmine can reveal structure not apparent in a standard miRNA-Seq analysis pipeline.

Predicting response to preoperative chemotherapy agents by identifying drug action on modeled microRNA regulation networks

Thu, 01 May 2014 00:00:00 +0000

MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences

Thu, 01 Dec 2011 00:00:00 +0000