Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Computational identification of functional RNA homologs in metagenomic data.

ABSTRACT: A key step toward understanding a metagenomics data set is the identification of functional sequence elements within it, such as protein coding genes and structural RNAs. Relative to protein coding genes, structural RNAs are more difficult to identify because of their reduced alphabet size, lack of open reading frames, and short length. Infernal is a software package that implements "covariance models" (CMs) for RNA homology search, which harness both sequence and structural conservation when searching for RNA homologs. Thanks to the added statistical signal inherent in the secondary structure conservation of many RNA families, Infernal is more powerful than sequence-only based methods such as BLAST and profile HMMs. Together with the Rfam database of CMs, Infernal is a useful tool for identifying RNAs in metagenomics data sets.

SUBMITTER: Nawrocki EP

PROVIDER: S-EPMC3849165 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Computational identification of functional RNA homologs in metagenomic data.

Nawrocki Eric P EP Eddy Sean R SR

RNA biology 20130520 7

A key step toward understanding a metagenomics data set is the identification of functional sequence elements within it, such as protein coding genes and structural RNAs. Relative to protein coding genes, structural RNAs are more difficult to identify because of their reduced alphabet size, lack of open reading frames, and short length. Infernal is a software package that implements "covariance models" (CMs) for RNA homology search, which harness both sequence and structural conservation when se ...[more]

PMID: 23722291

Similar Datasets

Computational discovery and functional validation of novel fluoroquinolone resistance genes in public metagenomic data sets.

Project description:BackgroundFluoroquinolones are broad-spectrum antibiotics used to prevent and treat a wide range of bacterial infections. Plasmid-mediated qnr genes provide resistance to fluoroquinolones in many bacterial species and are increasingly encountered in clinical settings. Over the last decade, several families of qnr genes have been discovered and characterized, but their true prevalence and diversity still remain unclear. In particular, environmental and host-associated bacterial communities have been hypothesized to maintain a large and unknown collection of qnr genes that could be mobilized into pathogens.ResultsIn this study we used computational methods to screen genomes and metagenomes for novel qnr genes. In contrast to previous studies, we analyzed an almost 20-fold larger dataset comprising almost 13 terabases of sequence data. In total, 362,843 potential qnr gene fragments were identified, from which 611 putative qnr genes were reconstructed. These gene sequences included all previously described plasmid-mediated qnr gene families. Fifty-two of the 611 identified qnr genes were reconstructed from metagenomes, and 20 of these were previously undescribed. All of the novel qnr genes were assembled from metagenomes associated with aquatic environments. Nine of the novel genes were selected for validation, and six of the tested genes conferred consistently decreased susceptibility to ciprofloxacin when expressed in Escherichia coli.ConclusionsThe results presented in this study provide additional evidence for the ubiquitous presence of qnr genes in environmental microbial communities, expand the number of known qnr gene variants and further elucidate the diversity of this class of resistance genes. This study also strengthens the hypothesis that environmental bacterial communities act as sources of previously uncharacterized qnr genes.

| S-EPMC5581476 | biostudies-literature

Genome-wide computational identification of functional RNA elements in Trypanosoma brucei.

Project description:BackgroundPost-transcriptional regulation of gene expression is the dominant regulatory mechanism in trypanosomatids as their mRNAs are transcribed from polycistronic units. A few cis-acting RNA elements in 3'-untranslated regions of mRNAs have been identified in trypanosomatids, which affect the mRNA stability or translation rate in different life stages of these parasites. Other functional RNAs (fRNAs) also play essential roles in these organisms. However, there has been no genome-wide analysis for identification of fRNAs in trypanosomatids.ResultsFunctional RNAs, including non-coding RNAs (ncRNAs) and cis-acting RNA elements involved in post-transcriptional gene regulation, were predicted based on two independent computational analyses of the genome of Trypanosoma brucei. In the first analysis, the predicted candidate ncRNAs were identified based on conservation with the related trypanosomatid Leishmania braziliensis. This prediction had a substantially low estimated false discovery rate, and a considerable number of the predicted ncRNAs represented novel classes with unknown functions. In the second analysis, we identified a number of function-specific regulatory motifs, based on which we devised a classifier that can be used for homology-independent function prediction in T. brucei.ConclusionThis first genome-wide analysis of fRNAs in trypanosomatids restricts the search space of experimental approaches and, thus, can significantly expedite the process of characterization of these elements. Our classifier for function prediction based on cis-acting regulatory elements can also, in combination with other methods, provide the means for homology-independent annotation of trypanosomatid genomes.

| S-EPMC2907701 | biostudies-literature

Computational identification and functional predictions of long noncoding RNA in Zea mays.

Project description:BackgroundComputational analysis of cDNA sequences from multiple organisms suggests that a large portion of transcribed DNA does not code for a functional protein. In mammals, noncoding transcription is abundant, and often results in functional RNA molecules that do not appear to encode proteins. Many long noncoding RNAs (lncRNAs) appear to have epigenetic regulatory function in humans, including HOTAIR and XIST. While epigenetic gene regulation is clearly an essential mechanism in plants, relatively little is known about the presence or function of lncRNAs in plants.Methodology/principal findingsTo explore the connection between lncRNA and epigenetic regulation of gene expression in plants, a computational pipeline using the programming language Python has been developed and applied to maize full length cDNA sequences to identify, classify, and localize potential lncRNAs. The pipeline was used in parallel with an SVM tool for identifying ncRNAs to identify the maximal number of ncRNAs in the dataset. Although the available library of sequences was small and potentially biased toward protein coding transcripts, 15% of the sequences were predicted to be noncoding. Approximately 60% of these sequences appear to act as precursors for small RNA molecules and may function to regulate gene expression via a small RNA dependent mechanism. ncRNAs were predicted to originate from both genic and intergenic loci. Of the lncRNAs that originated from genic loci, ∼20% were antisense to the host gene loci.Conclusions/significanceConsistent with similar studies in other organisms, noncoding transcription appears to be widespread in the maize genome. Computational predictions indicate that maize lncRNAs may function to regulate expression of other genes through multiple RNA mediated mechanisms.

| S-EPMC3420876 | biostudies-literature

Identification of RNA polymerase III-transcribed Alu loci by computational screening of RNA-Seq data.

Project description:Of the ∼ 1.3 million Alu elements in the human genome, only a tiny number are estimated to be active in transcription by RNA polymerase (Pol) III. Tracing the individual loci from which Alu transcripts originate is complicated by their highly repetitive nature. By exploiting RNA-Seq data sets and unique Alu DNA sequences, we devised a bioinformatic pipeline allowing us to identify Pol III-dependent transcripts of individual Alu elements. When applied to ENCODE transcriptomes of seven human cell lines, this search strategy identified ∼ 1300 Alu loci corresponding to detectable transcripts, with ∼ 120 of them expressed in at least three cell lines. In vitro transcription of selected Alus did not reflect their in vivo expression properties, and required the native 5'-flanking region in addition to internal promoter. We also identified a cluster of expressed AluYa5-derived transcription units, juxtaposed to snaR genes on chromosome 19, formed by a promoter-containing left monomer fused to an Alu-unrelated downstream moiety. Autonomous Pol III transcription was also revealed for Alus nested within Pol II-transcribed genes. The ability to investigate Alu transcriptomes at single-locus resolution will facilitate both the identification of novel biologically relevant Alu RNAs and the assessment of Alu expression alteration under pathological conditions.

| S-EPMC4333407 | biostudies-literature

Computational identification of RNA functional determinants by three-dimensional quantitative structure-activity relationships.

Project description:Anti-infection drugs target vital functions of infectious agents, including their ribosome and other essential non-coding RNAs. One of the reasons infectious agents become resistant to drugs is due to mutations that eliminate drug-binding affinity while maintaining vital elements. Identifying these elements is based on the determination of viable and lethal mutants and associated structures. However, determining the structure of enough mutants at high resolution is not always possible. Here, we introduce a new computational method, MC-3DQSAR, to determine the vital elements of target RNA structure from mutagenesis and available high-resolution data. We applied the method to further characterize the structural determinants of the bacterial 23S ribosomal RNA sarcin-ricin loop (SRL), as well as those of the lead-activated and hammerhead ribozymes. The method was accurate in confirming experimentally determined essential structural elements and predicting the viability of new SRL variants, which were either observed in bacteria or validated in bacterial growth assays. Our results indicate that MC-3DQSAR could be used systematically to evaluate the drug-target potentials of any RNA sites using current high-resolution structural data.

| S-EPMC4176186 | biostudies-literature

Reconstruction of ribosomal RNA genes from metagenomic data.

Project description:Direct sequencing of environmental DNA (metagenomics) has a great potential for describing the 16S rRNA gene diversity of microbial communities. However current approaches using this 16S rRNA gene information to describe community diversity suffer from low taxonomic resolution or chimera problems. Here we describe a new strategy that involves stringent assembly and data filtering to reconstruct full-length 16S rRNA genes from metagenomicpyrosequencing data. Simulations showed that reconstructed 16S rRNA genes provided a true picture of the community diversity, had minimal rates of chimera formation and gave taxonomic resolution down to genus level. The strategy was furthermore compared to PCR-based methods to determine the microbial diversity in two marine sponges. This showed that about 30% of the abundant phylotypes reconstructed from metagenomic data failed to be amplified by PCR. Our approach is readily applicable to existing metagenomic datasets and is expected to lead to the discovery of new microbial phylotypes.

| S-EPMC3384625 | biostudies-literature

Identification of ribosomal RNA genes in metagenomic fragments.

Project description:MotivationIdentification of genes coding for ribosomal RNA (rRNA) is considered an important goal in the analysis of data from metagenomics projects. Here, we report the development of a software program designed for the identification of rRNA genes from metagenomic fragments based on hidden Markov models (HMMs). This program provides rRNA gene predictions with high sensitivity and specificity on artificially fragmented genomic DNAs.AvailabilitySupplementary files, scripts and sample data are available at http://tools.camera.calit2.net/camera/meta_rna.

| S-EPMC2677747 | biostudies-literature

Computational identification of functional introns: high positional conservation of introns that harbor RNA genes.

Project description:An appreciable fraction of introns is thought to have some function, but there is no obvious way to predict which specific intron is likely to be functional. We hypothesize that functional introns experience a different selection regime than non-functional ones and will therefore show distinct evolutionary histories. In particular, we expect functional introns to be more resistant to loss, and that this would be reflected in high conservation of their position with respect to the coding sequence. To test this hypothesis, we focused on introns whose function comes about from microRNAs and snoRNAs that are embedded within their sequence. We built a data set of orthologous genes across 28 eukaryotic species, reconstructed the evolutionary histories of their introns and compared functional introns with the rest of the introns. We found that, indeed, the position of microRNA- and snoRNA-bearing introns is significantly more conserved. In addition, we found that both families of RNA genes settled within introns early during metazoan evolution. We identified several easily computable intronic properties that can be used to detect functional introns in general, thereby suggesting a new strategy to pinpoint non-coding cellular functions.

| S-EPMC3675471 | biostudies-literature

Functional data analysis for computational biology.

Project description:Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC6736445 | biostudies-literature

VirBot: an RNA viral contig detector for metagenomic data.

Project description:SummaryWithout relying on cultivation, metagenomic sequencing greatly accelerated the novel RNA virus detection. However, it is not trivial to accurately identify RNA viral contigs from a mixture of species. The low content of RNA viruses in metagenomic data requires a highly specific detector, while new RNA viruses can exhibit high genetic diversity, posing a challenge for alignment-based tools. In this work, we developed VirBot, a simple yet effective RNA virus identification tool based on the protein families and the corresponding adaptive score cutoffs. We benchmarked it with seven popular tools for virus identification on both simulated and real sequencing data. VirBot shows its high specificity in metagenomic datasets and superior sensitivity in detecting novel RNA viruses.Availability and implementationhttps://github.com/GreyGuoweiChen/RNA_virus_detector.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC9978584 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data