miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences

Serum-Exosome-Derived miRNAs Serve as Promising Biomarkers for HCC Diagnosis

BACKGROUND

Serum exosomes are emerging as key liquid biopsy biomarkers for the early diagnosis of cancer. However, the proportion and distribution of small RNA (sRNA) species from serum exosomes of hepatocellular carcinoma (HCC) patients remain unclear. Effective and reliable biomarkers for HCC diagnosis should be explored.

METHODS

In this study, we aimed to use sRNA sequencing to profile the sRNAs of serum exosomes in HCC and non-tumor donors. The serum exosomes of 124 HCC patients and 46 non-tumor donors were enrolled for detecting the values of the potential biomarkers for the diagnosis of HCC.

RESULTS

We found that miRNAs accounted for the maximal percentage of all types of sRNAs both in the serum exosomes of HCC patients and non-tumor donors. This indicated that the serum-exosome-derived microRNAs (miRNAs) were the most valuable as potential biomarkers in HCC diagnosis. Then, miRNAs were set as research candidates. In our Chinese cohorts, three serum-exosome-derived miRNAs (miR-122-5p, let-7d-5p, and miR-425-5p) could be promising biomarkers for distinguishing HCC patients from non-tumor donors. In addition, they were preferred for the early diagnosis of HCC. We also presented the base distribution of some novel serum-exosome-derived miRNAs and described the potential values as biomarkers.

CONCLUSIONS

The results suggested that the serum-exosome-derived miRNAs were the most crucial sRNA species and they highlighted the potential of serum-exosome-derived miRNAs as promising biomarkers for HCC diagnosis.

The Multiverse of Plant Small RNAs: How Can We Explore It?

Plant small RNAs (sRNAs) are a heterogeneous group of noncoding RNAs with a length of 20–24 nucleotides that are widely studied due to their importance as major regulators in various biological processes. sRNAs are divided into two main classes—microRNAs (miRNAs) and small interfering RNAs (siRNAs)—which differ in their biogenesis and functional pathways. Their identification and enrichment with new structural variants would not be possible without the use of various high-throughput sequencing (NGS) techniques, allowing for the detection of the total population of sRNAs in plants. Classifying sRNAs and predicting their functional role based on such high-performance datasets is a nontrivial bioinformatics task, as plants can generate millions of sRNAs from a variety of biosynthetic pathways. Over the years, many computing tools have been developed to meet this challenge. Here, we review more than 35 tools developed specifically for plant sRNAs over the past few years and explore some of their basic algorithms for performing tasks related to predicting, identifying, categorizing, and quantifying individual sRNAs in plant samples, as well as visualizing the results of these analyzes. We believe that this review will be practical for biologists who want to analyze their plant sRNA datasets but are overwhelmed by the number of tools available, thus answering the basic question of how to choose the right one for a particular study.

Computational Methods for Predicting Mature microRNAs

Tiny single-stranded noncoding RNAs with size 19-27 nucleotides serve as microRNAs (miRNAs), which have emerged as key gene regulators in the last two decades. miRNAs serve as one of the hallmarks in regulatory pathways with critical roles in human diseases. Ever since the discovery of miRNAs, researchers have focused on how mature miRNAs are produced from precursor mRNAs. Experimental methods are faced with notorious challenges in terms of experimental design, since it is time consuming and not cost-effective. Hence, different computational methods have been employed for the identification of miRNA sequences where most of them labeled as miRNA predictors are in fact pre-miRNA predictors and provide no information about the putative miRNA location within the pre-miRNA. This chapter provides an update and the current state of the art in this area covering various methods and 15 software suites used for prediction of mature miRNA.