Dataset Information

Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change.

ABSTRACT: BACKGROUND: Non-coding RNAs (ncRNAs) have a multitude of roles in the cell, many of which remain to be discovered. However, it is difficult to detect novel ncRNAs in biochemical screens. To advance biological knowledge, computational methods that can accurately detect ncRNAs in sequenced genomes are therefore desirable. The increasing number of genomic sequences provides a rich dataset for computational comparative sequence analysis and detection of novel ncRNAs. RESULTS: Here, Dynalign, a program for predicting secondary structures common to two RNA sequences on the basis of minimizing folding free energy change, is utilized as a computational ncRNA detection tool. The Dynalign-computed optimal total free energy change, which scores the structural alignment and the free energy change of folding into a common structure for two RNA sequences, is shown to be an effective measure for distinguishing ncRNA from randomized sequences. To make the classification as a ncRNA, the total free energy change of an input sequence pair can either be compared with the total free energy changes of a set of control sequence pairs, or be used in combination with sequence length and nucleotide frequencies as input to a classification support vector machine. The latter method is much faster, but slightly less sensitive at a given specificity. Additionally, the classification support vector machine method is shown to be sensitive and specific on genomic ncRNA screens of two different Escherichia coli and Salmonella typhi genome alignments, in which many ncRNAs are known. The Dynalign computational experiments are also compared with two other ncRNA detection programs, RNAz and QRNA. CONCLUSION: The Dynalign-based support vector machine method is more sensitive for known ncRNAs in the test genomic screens than RNAz and QRNA. Additionally, both Dynalign-based methods are more sensitive than RNAz and QRNA at low sequence pair identities. Dynalign can be used as a comparable or more accurate tool than RNAz or QRNA in genomic screens, especially for low-identity regions. Dynalign provides a method for discovering ncRNAs in sequenced genomes that other methods may not identify. Significant improvements in Dynalign runtime have also been achieved.

SUBMITTER: Uzilov AV

PROVIDER: S-EPMC1570369 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

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Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change.

Uzilov Andrew V AV Keegan Joshua M JM Mathews David H DH

BMC bioinformatics 20060327

<h4>Background</h4>Non-coding RNAs (ncRNAs) have a multitude of roles in the cell, many of which remain to be discovered. However, it is difficult to detect novel ncRNAs in biochemical screens. To advance biological knowledge, computational methods that can accurately detect ncRNAs in sequenced genomes are therefore desirable. The increasing number of genomic sequences provides a rich dataset for computational comparative sequence analysis and detection of novel ncRNAs.<h4>Results</h4>Here, Dyna ...[more]

PMID: 16566836

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Project description:BACKGROUND: Non-coding RNAs (ncRNAs) play a crucial role in the intricate regulation of bacterial gene expression, allowing bacteria to quickly adapt to changing environments. In the past few years, a growing number of regulatory RNA elements have been predicted by computational methods, mostly in well-studied gamma-proteobacteria but lately in several alpha-proteobacteria as well. Here, we have compared an extensive compilation of these non-coding RNA predictions to intergenic expression data of a whole-genome high-resolution tiling array in the soil-dwelling alpha-proteobacterium Rhizobium etli. RESULTS: Expression of 89 candidate ncRNAs was detected, both on the chromosome and on the six megaplasmids encompassing the R. etli genome. Of these, 11 correspond to functionally well characterized ncRNAs, 12 were previously identified in other alpha-proteobacteria but are as yet uncharacterized and 66 were computationally predicted earlier but had not been experimentally identified and were therefore classified as novel ncRNAs. The latter comprise 17 putative sRNAs and 49 putative cis-regulatory ncRNAs. A selection of these candidate ncRNAs was validated by RT-qPCR, Northern blotting and 5' RACE, confirming the existence of 4 ncRNAs. Interestingly, individual transcript levels of numerous ncRNAs varied during free-living growth and during interaction with the eukaryotic host plant, pointing to possible ncRNA-dependent regulation of these specialized processes. CONCLUSIONS: Our data support the practical value of previous ncRNA prediction algorithms and significantly expand the list of candidate ncRNAs encoded in the intergenic regions of R. etli and, by extension, of alpha-proteobacteria. Moreover, we show high-resolution tiling arrays to be suitable tools for studying intergenic ncRNA transcription profiles across the genome. The differential expression levels of some of these ncRNAs may indicate a role in adaptation to changing environmental conditions.

Project description:Insect population dynamics are closely related to 'human' ecological and economic environments, and a central focus of research is outbreaks. However, the lack of molecular-based investigations restricts our understanding of the intrinsic mechanisms responsible for insect outbreaks. In this context, the moth Dendrolimus punctatus Walker can serve as an ideal model species for insect population dynamics research because it undergoes periodic outbreaks. Here, high-throughput whole-transcriptome sequencing was performed using D. punctatus, sampled during latent and outbreak periods, to systemically explore the molecular basis of insect outbreaks and to identify the involved non-coding RNA (ncRNA) regulators, namely microRNAs, long non-coding RNAs, and circular RNAs. Differentially expressed mRNAs of D. punctatus from different outbreak periods were involved in developmental, reproductive, immune, and chemosensory processes; results that were consistent with the physiological differences in D. punctatus during differing outbreak periods. Targets analysis of the non-coding RNAs indicated that long non-coding RNAs could be the primary ncRNA regulators of D. punctatus outbreaks, while circular RNAs mainly regulated synapses and cell junctions. The target genes of differentially expressed microRNAs mainly regulated the metabolic and reproductive pathways during the D. punctatus outbreaks. Developmental, multi-organismal, and reproductive processes, as well as biological adhesion, characterized the competing endogenous RNA network. Chemosensory and immune genes closely related to the outbreak of D. punctatus were further analyzed in detail: from their ncRNA regulators' analysis, we deduce that both lncRNA and miRNA may play significant roles. This is the first report to examine the molecular basis of coding and non-coding RNAs' roles in insect outbreaks. The results provide potential biomarkers for control targets in forest insect management, as well as fresh insights into underlying outbreak-related mechanisms, which could be used for improving insect control strategies in the future.

Project description:BACKGROUND:Long non-coding RNAs (lncRNAs) are increasingly recognized as regulators of tissue-specific cellular functions and have been shown to regulate transcriptional and translational processes, acting as signals, decoys, guides, and scaffolds. It has been suggested that some lncRNAs act in cis to regulate the expression of neighboring protein-coding genes (PCGs) in a mechanism that fine-tunes gene expression. Gut microbiome is increasingly recognized as a regulator of development, inflammation, host metabolic processes, and xenobiotic metabolism. However, there is little known regarding whether the gut microbiome modulates lncRNA gene expression in various host metabolic organs. The goals of this study were to 1) characterize the tissue-specific expression of lncRNAs and 2) identify and annotate lncRNAs differentially regulated in the absence of gut microbiome. RESULTS:Total RNA was isolated from various tissues (liver, duodenum, jejunum, ileum, colon, brown adipose tissue, white adipose tissue, and skeletal muscle) from adult male conventional and germ-free mice (n =?3 per group). RNA-Seq was conducted and reads were mapped to the mouse reference genome (mm10) using HISAT. Transcript abundance and differential expression was determined with Cufflinks using the reference databases NONCODE 2016 for lncRNAs and UCSC mm10 for PCGs. Although the constitutive expression of lncRNAs was ubiquitous within the enterohepatic (liver and intestine) and the peripheral metabolic tissues (fat and muscle) in conventional mice, differential expression of lncRNAs by lack of gut microbiota was highly tissue specific. Interestingly, the majority of gut microbiota-regulated lncRNAs were in jejunum. Most lncRNAs were co-regulated with neighboring PCGs. STRING analysis showed that differentially expressed PCGs in proximity to lncRNAs form tissue-specific networks, suggesting that lncRNAs may interact with gut microbiota/microbial metabolites to regulate tissue-specific functions. CONCLUSIONS:This study is among the first to demonstrate that gut microbiota critically regulates the expression of lncRNAs not only locally in intestine but also remotely in other metabolic organs, suggesting that common transcriptional machinery may be shared to transcribe lncRNA-PCG pairs, and lncRNAs may interact with PCGs to regulate tissue-specific pathways.

Dataset Information

Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change.

Publications

Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change.

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