Exploring the ncRNA–ncRNA patterns based on bridging rules

ncRNAs play an important role in the regulation of gene expression. However, many of their functions have not yet been fully discovered. There are complicated relationships between ncRNAs in different categories. Finding these relationships can contribute to identify ncRNAs’ functions and properties. We extend the association rule to represent the relationship between two ncRNAs. Based on this rule, we can speculate the ncRNA’s function when it interacts with other ncRNAs. We propose two measures to explore the relationships between ncRNAs in different categories. Entropy theory is to calculate how close two ncRNAs are. Association rule is to represent the interactions between ncRNAs. We use three datasets from miRBase and RNAdb. Two from miRBase are designed for finding relationships between miRNAs; the other from RNAdb is designed for relationships among miRNA, snoRNA and piRNA. We evaluate our measures from both biological significance and performance perspectives. All the cross-species patterns regarding miRNA that we found are proven correct using miRNAMap 2.0. In addition, we find novel cross-genomes patterns such as (hsa-mir-190b → hsa-mir-153-2). According to the patterns we find, we can (1) explore one ncRNA’s function from another with known function and (2) speculate the functions of both of them based on the relationship even we do no understand either of them. Our methods’ merits also include: (1) they are suitable for any ncRNA datasets and (2) they are not sensitive to the parameters.     

De novo search for non-coding RNA genes in the AT-rich genome of Dictyostelium discoideum: performance of Markov-dependent genome feature scoring

Genome data are increasingly important in the computational identification of novel regulatory non-coding RNAs (ncRNAs). However, most ncRNA gene-finders are either specialized to well-characterized ncRNA gene families or require comparisons of closely related genomes. We developed a method for de novo screening for ncRNA genes with a nucleotide composition that stands out against the background genome based on a partial sum process. We compared the performance when assuming independent and first-order Markov-dependent nucleotides, respectively, and used Karlin-Altschul and Karlin-Dembo statistics to evaluate the significance of hits. We hypothesized that a first-order Markov-dependent process might have better power to detect ncRNA genes since nearest-neighbor models have been shown to be successful in predicting RNA structures. A model based on a first-order partial sum process (analyzing overlapping dinucleotides) had better sensitivity and specificity than a zeroth-order model when applied to the AT-rich genome of the amoeba Dictyostelium discoideum. In this genome, we detected 94% of previously known ncRNA genes (at this sensitivity, the false positive rate was estimated to be 25% in a simulated background). The predictions were further refined by clustering candidate genes according to sequence similarity and/or searching for an ncRNA-associated upstream element. We experimentally verified six out of 10 tested ncRNA gene predictions. We conclude that higher-order models, in combination with other information, are useful for identification of novel ncRNA gene families in single-genome analysis of D. discoideum. Our generalizable approach extends the range of genomic data that can be searched for novel ncRNA genes using well-grounded statistical methods.     

Distinct Patterns of Genetic Variations in Potential Functional Elements in Long Noncoding RNAs

Non‐protein‐coding RNAs have increasingly been shown to be an important class of regulatory RNAs having significant roles in regulation of gene expression. The long noncoding RNA (lncRNA) gene family presently constitutes a large number of noncoding RNA (ncRNA) loci almost equaling the number of protein‐coding genes. Nevertheless, the biological roles and mechanisms of the majority of lncRNAs are poorly understood, with exceptions of a very few well‐studied candidates. The availability of genome‐scale variation datasets, and increasing number of variant loci from genome‐wide association studies falling in lncRNA loci have motivated us to understand the patterns of genomic variations in lncRNA loci, their potential functional correlates, and selection in populations. In the present study, we have performed a comprehensive analysis of genomic variations in lncRNA loci. We analyzed for patterns and distributions of genomic variations with respect to potential functional domains in lncRNAs. The analysis reveals a distinct distribution of variations in subclasses of long ncRNAs and in potential functional domains of lncRNAs. We further examined signals of selections and allele frequencies of these prioritized set of lncRNAs. To the best of our knowledge, this is the first and comprehensive large‐scale analysis of genetic variations in long ncRNAs.     

Viral noncoding RNAs: more surprises

Eukaryotic cells produce several classes of long and small noncoding RNA (ncRNA). Many DNA and RNA viruses synthesize their own ncRNAs. Like their host counterparts, viral ncRNAs associate with proteins that are essential for their stability, function, or both. Diverse biological roles—including the regulation of viral replication, viral persistence, host immune evasion, and cellular transformation—have been ascribed to viral ncRNAs. In this review, we focus on the multitude of functions played by ncRNAs produced by animal viruses. We also discuss their biogenesis and mechanisms of action.     

Non-coding RNA transcripts: Sensors of neuronal stress,modulators of synaptic plasticity,and agents of change in the onset of Alzheimer's disease

Non-protein-coding RNAs (ncRNAs) play critical roles on many levels of cellular information processing and pervasive expression of ncRNAs in the nervous system could help explain brain complexity. NcRNAs are enriched in the central nervous system and are associated with specific neuroanatomical regions. Additionally, several recent publications have revealed an important role for deregulation of ncRNAs in various human neuropathologies, such as Alzheimer's disease, Parkinson's disease and Fragile X mental retardation. Herein, we summarize reports on functional ncRNA molecules involved in cellular stress response, particularly related to Alzheimer's disease. We conclude that ncRNAs have a prominent role in maintaining precise physiological levels of gene products directly implicated in Alzheimer's disease pathology.     

BIP, a BRAM-interacting protein involved in TGF-beta signalling, regulates body length in Caenorhabditis elegans

BACKGROUND: The TGF-beta superfamily has diverse biological activities and is involved in the early development of animals. We previously identified a novel family member, BMP receptor associated molecule (BRAM), which binds to the intracellular domain of BMP type IA receptor and is involved in the BMP signalling pathway. RESULTS: To identify novel molecules involved in TGF-beta signalling pathways, we performed yeast two-hybrid screening using BRAM as bait. From a Xenopus cDNA library, we cloned a cDNA encoding 693 amino acids and containing the motif for an oxysterol binding protein (OSBP), which we designated BRAM interacting protein (BIP). We then isolated a BIP homologue from the Caenorhabditis elegans that encodes 733 amino acids and also contains the OSBP-like motif. Immunoprecipitation and Western blotting studies revealed that C. elegans BIP could interact with the C. elegans BRAM homologues BRA-1 and BRA-2. C. elegans BIP was expressed in pharyngeal muscle, hypodermis and several neuronal cells, an expression pattern overlaps with those of BRA-1 and BRA-2. Finally, we found that inhibition of BIP expression in C. elegans by double stranded RNA interference produces a Sma phenotype. CONCLUSIONS: BIP was isolated using the yeast two-hybrid systems. BIP may function in the TGF-beta pathway and regulate body length in C. elegans.