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The critical role microRNAs play in modulating global functions is emerging, both in the maintenance of homeostatic mechanisms and in the adaptation to diverse environmental stresses. When stressed, cells must divert metabolic requirements toward immediate survival and eventual recovery and the unique features of miRNAs, such as their relatively ATP-inexpensive biogenesis costs, and the quick and reversible nature of their action, renders them excellent "master controllers" for rapid responses. Many animal survival strategies for dealing with extreme environmental pressures involve prolonged retreats into states of suspended animation to extend the time that they can survive on their limited internal fuel reserves until conditions improve. The ability to retreat into such hypometabolic states is only possible by coupling the global suppression of nonessential energy-expensive functions with an activation of prosurvival networks, a process in which miRNAs are now known to play a major role. In this chapter, we discuss the activation, expression, biogenesis, and unique attributes of miRNA regulation required to facilitate profound metabolic rate depression and implement stress-specific metabolic adaptations. We examine the role of miRNA in strategies of biochemical adaptation including mammalian hibernation, freeze tolerance, freeze avoidance, anoxia and hypoxia survival, estivation, and dehydration tolerance. By comparing these seemingly different adaptive programs in traditional and exotic animal models, we highlight both unique and conserved miRNA-meditated mechanisms for survival. Additional topics discussed include transcription factor networks, temperature dependent miRNA-targeting, and novel species-specific and stress-specific miRNAs.Breast cancer has five major immune types; luminal A, luminal B, HER2, Basal-like, and normal-like. Cells produce a family of protein called heat shock proteins (Hsps) in response to exposure to thermal and other proteotoxic stresses play essential roles in cancer metabolism and this large family shows a diverse set of Hsp involvement in different breast cancer immune types. Recently, Hsp members categorized according to their immune type roles. Hsp family consists of several subtypes formed by molecular weight; Hsp70, Hsp90, Hsp100, Hsp40, Hsp60, and small molecule Hsps. Cancer cells employ Hsps as survival factors since most of these proteins prevent apoptosis. Several studies monitored Hsp roles in breast cancer cells and reported Hsp27 involvement in drug resistance, Hsp70 in tumor cell transformation-progression, and interaction with p53. Furthermore, the association of Hsp90 with steroid receptors and signaling proteins in patients with breast cancer directed research to focus on Hsp-based treatments. miRNAs are known to play key roles in all types of cancer that are upregulated or downregulated in cancer which respectively referred to as oncogenes (oncomirs) or tumor suppressors. Expression profiles of miRNAs may be used to classify, diagnose, and predict different cancer types. It is clear that miRNAs play regulatory roles in gene expression and this work reveals miRNA correlation to Hsp depending on specific breast cancer immune types. Deregulation of specific Hsp genes in breast cancer subtypes allows for identification of new targets for drug design and cancer treatment. Here, we performed miRNA network analysis by recruiting Hsp genes detected in breast cancer subtypes and reviewed some of the miRNAs related to aforementioned Hsp genes.Exosomes, a type of extracellular vesicle, are small vesicles (30-100 nm) secreted into extracellular space from almost all types of cells. Exosomes mediate cell-to-cell communication carrying various biologically active molecules including microRNAs. Studies have shown that exosomal microRNAs play fundamental roles in healthy and pathological conditions such as immunity, cancer, and inflammation. In this chapter, we introduce the current knowledge on exosome biogenesis, techniques used in exosome research, and exosomal miRNA and their functions in biological and pathological processes.Since their first discovery more than 20 years ago, miRNAs have been subject to deliberate research and analysis for revealing their physiological or pathological involvement. Regulatory roles of miRNAs in signal transduction, gene expression, and cellular processes in development, differentiation, proliferation, apoptosis, and homeostasis also imply their critical role in disease pathogenesis. Their roles in cancer, neurodegenerative diseases, and other systemic diseases have been studied broadly. In these regulatory pathways, their mutations and target sequence variations play critical roles to determine their functional repertoire. In this chapter, we summarize studies that investigated the role of mutations, polymorphisms, and other variations of miRNAs in respect to pathological processes.Gene regulation is of utmost importance to cell homeostasis; thus, any dysregulation in it often leads to disease. MicroRNAs (miRNAs) are involved in posttranscriptional gene regulation and consequently, their dysregulation has been associated with many diseases.MiRBase version 21 contains microRNAs from about 200 species organized into about 70 clades. It has been shown that not all miRNAs collected in the database are likely to be real and, therefore, novel routes to delineate between correct and false miRNAs should be explored. We introduce a novel approach based on k-mer frequencies and machine learning that assigns an unknown/unlabeled miRNA to its most likely clade/species of origin. A simple way to filter new data would be to ensure that the novel miRNA categorizes closely to the species it is said to originate from. For that, an ensemble classifier of multiple two-class random forest classifiers was designed, where each random forest was trained on one species-clade pair. The approach was tested with different sampling methods on a dataset that was taken from miRBase version 21 and it was evaluated using a hierarchical F-measure. The approach predicted 81% to 94% of the test data correctly, depending on the sampling method. This is the first classifier that can classify miRNAs to their species of origin. This method will aid in the evaluation of miRNA database integrity and analysis of noisy miRNA samples.MicroRNAs are important regulators in many eukaryotic lineages. Typical miRNAs have a length of about 22nt and are processed from precursors that form a characteristic hairpin structure. Once they appear in a genome, miRNAs are among the best-conserved elements in both animal and plant genomes. Functionally, they play an important role in particular in development. In contrast to protein-coding genes, miRNAs frequently emerge de novo. The genomes of animals and plants harbor hundreds of mutually unrelated families of homologous miRNAs that tend to be persistent throughout evolution. The evolution of their genomic miRNA complement closely correlates with important morphological innovation. In addition, miRNAs have been used as valuable characters in phylogenetic studies. An accurate and comprehensive annotation of miRNAs is required as a basis to understand their impact on phenotypic evolution. Since experimental data on miRNA expression are limited to relatively few species and are subject to unavoidable ascertainment biases, it is inevitable to complement miRNA sequencing by homology based annotation methods. This chapter reviews the state of the art workflows for homology based miRNA annotation, with an emphasis on their limitations and open problems.MicroRNAs (miRNAs) are small noncoding RNAs that are recognized as posttranscriptional regulators of gene expression. These molecules have been shown to play important roles in several cellular processes. Selleck DBZ inhibitor MiRNAs act on their target by guiding the RISC complex and binding to the mRNA molecule. Thus, it is recognized that the function of a miRNA is determined by the function of its target (s). By using high-throughput methodologies, novel miRNAs are being identified, but their functions remain uncharted. Target validation is crucial to properly understand the specific role of a miRNA in a cellular pathway. However, molecular techniques for experimental validation of miRNA-target interaction are expensive, time-consuming, laborious, and can be not accurate in inferring true interactions. Thus, accurate miRNA target predictions are helpful to understand the functions of miRNAs. There are several algorithms proposed for target prediction and databases containing miRNA-target information. However, these available computational tools for prediction still generate a large number of false positives and fail to detect a considerable number of true targets, which indicates the necessity of highly confident approaches to identify bona fide miRNA-target interactions. This chapter focuses on tools and strategies used for miRNA target prediction, by providing practical insights and outlooks.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.MicroRNA (miRNA) studies have been one of the most popular research areas in recent years. Although thousands of miRNAs have been detected in several species, the majority remains unidentified. Thus, finding novel miRNAs is a vital element for investigating miRNA mediated posttranscriptional gene regulation machineries. Furthermore, experimental methods have challenging inadequacies in their capability to detect rare miRNAs, and are also limited to the state of the organism under examination (e.g., tissue type, developmental stage, stress-disease conditions). These issues have initiated the creation of high-level computational methodologies endeavoring to distinguish potential miRNAs in silico. On the other hand, most of these tools suffer from high numbers of false positives and/or false negatives and as a result they do not provide enough confidence for validating all their predictions experimentally. In this chapter, computational difficulties in detection of pre-miRNAs are discussed and a machine learning based approach that has been designed to address these issues is reviewed.In this era of big data, sets of methodologies and strategies are designed to extract knowledge from huge volumes of data. However, the cost of where and how to get this information accurately and quickly is extremely important, given the diversity of genomes and the different ways of representing that information. Among the huge set of information and relationships that the genome carries, there are sequences called miRNAs (microRNAs). These sequences were described in the 1990s and are mainly involved in mechanisms of regulation and gene expression. Having this in mind, this chapter focuses on exploring the available literature and providing useful and practical guidance on the miRNA database and tools topic. For that, we organized and present this text in two ways (a) the update reviews and articles, which best summarize and discuss the theme; and (b) our update investigation on miRNA literature and portals about databases and tools. Finally, we present the main challenge and a possible solution to improve resources and tools.