Structured RNAs and synteny regions in the pig genome

Background

Annotating mammalian genomes for noncoding RNAs (ncRNAs) is nontrivial since far from all ncRNAs are known and the computational models are resource demanding. Currently, the human genome holds the best mammalian ncRNA annotation, a result of numerous efforts by several groups. However, a more direct strategy is desired for the increasing number of sequenced mammalian genomes of which some, such as the pig, are relevant as disease models and production animals.

Results

We present a comprehensive annotation of structured RNAs in the pig genome. Combining sequence and structure similarity search as well as class specific methods, we obtained a conservative set with a total of 3,391 structured RNA loci of which 1,011 and 2,314, respectively, hold strong sequence and structure similarity to structured RNAs in existing databases. The RNA loci cover 139 cis-regulatory element loci, 58 lncRNA loci, 11 conflicts of annotation, and 3,183 ncRNA genes. The ncRNA genes comprise 359 miRNAs, 8 ribozymes, 185 rRNAs, 638 snoRNAs, 1,030 snRNAs, 810 tRNAs and 153 ncRNA genes not belonging to the here fore mentioned classes. When running the pipeline on a local shuffled version of the genome, we obtained no matches at the highest confidence level. Additional analysis of RNA-seq data from a pooled library from 10 different pig tissues added another 165 miRNA loci, yielding an overall annotation of 3,556 structured RNA loci. This annotation represents our best effort at making an automated annotation. To further enhance the reliability, 571 of the 3,556 structured RNAs were manually curated by methods depending on the RNA class while 1,581 were declared as pseudogenes. We further created a multiple alignment of pig against 20 representative vertebrates, from which RNAz predicted 83,859 de novo RNA loci with conserved RNA structures. 528 of the RNAz predictions overlapped with the homology based annotation or novel miRNAs. We further present a substantial synteny analysis which includes 1,004 lineage specific de novo RNA loci and 4 ncRNA loci in the known annotation specific for Laurasiatheria (pig, cow, dolphin, horse, cat, dog, hedgehog).

Conclusions

We have obtained one of the most comprehensive annotations for structured ncRNAs of a mammalian genome, which is likely to play central roles in both health modelling and production. The core annotation is available in Ensembl 70 and the complete annotation is available at http://rth.dk/resources/rnannotator/susscr102/version1.02.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-459) contains supplementary material, which is available to authorized users.     

LocARNA-P: accurate boundary prediction and improved detection of structural RNAs

Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing methods struggle with these goals because they rely on sequence-based multiple sequence alignments, which regularly misalign RNA structure and therefore do not support identification of structural similarities. To overcome this limitation, we compute columnwise and global reliabilities of alignments based on sequence and structure similarity; we refer to these structure-based alignment reliabilities as STARs. The columnwise STARs of alignments, or STAR profiles, provide a versatile tool for the manual and automatic analysis of ncRNAs. In particular, we improve the boundary prediction of the widely used ncRNA gene finder RNAz by a factor of 3 from a median deviation of 47 to 13 nt. Post-processing RNAz predictions, LocARNA-P's STAR score allows much stronger discrimination between true- and false-positive predictions than RNAz's own evaluation. The improved accuracy, in this scenario increased from AUC 0.71 to AUC 0.87, significantly reduces the cost of successive analysis steps. The ready-to-use software tool LocARNA-P produces structure-based multiple RNA alignments with associated columnwise STARs and predicts ncRNA boundaries. We provide additional results, a web server for LocARNA/LocARNA-P, and the software package, including documentation and a pipeline for refining screens for structural ncRNA, at http://www.bioinf.uni-freiburg.de/Supplements/LocARNA-P/.     

smyRNA: A Novel Ab Initio ncRNA Gene Finder

Background

Non-coding RNAs (ncRNAs) have important functional roles in the cell: for example, they regulate gene expression by means of establishing stable joint structures with target mRNAs via complementary sequence motifs. Sequence motifs are also important determinants of the structure of ncRNAs. Although ncRNAs are abundant, discovering novel ncRNAs on genome sequences has proven to be a hard task; in particular past attempts for ab initio ncRNA search mostly failed with the exception of tools that can identify micro RNAs.

Methodology/Principal Findings

We present a very general ab initio ncRNA gene finder that exploits differential distributions of sequence motifs between ncRNAs and background genome sequences.

Conclusions/Significance

Our method, once trained on a set of ncRNAs from a given species, can be applied to a genome sequences of other organisms to find not only ncRNAs homologous to those in the training set but also others that potentially belong to novel (and perhaps unknown) ncRNA families. Availability: http://compbio.cs.sfu.ca/taverna/smyrna     

Identification of non-coding RNAs with a new composite feature in the Hybrid Random Forest Ensemble algorithm

To identify non-coding RNA (ncRNA) signals within genomic regions, a classification tool was developed based on a hybrid random forest (RF) with a logistic regression model to efficiently discriminate short ncRNA sequences as well as long complex ncRNA sequences. This RF-based classifier was trained on a well-balanced dataset with a discriminative set of features and achieved an accuracy, sensitivity and specificity of 92.11%, 90.7% and 93.5%, respectively. The selected feature set includes a new proposed feature, SCORE. This feature is generated based on a logistic regression function that combines five significant features—structure, sequence, modularity, structural robustness and coding potential—to enable improved characterization of long ncRNA (lncRNA) elements. The use of SCORE improved the performance of the RF-based classifier in the identification of Rfam lncRNA families. A genome-wide ncRNA classification framework was applied to a wide variety of organisms, with an emphasis on those of economic, social, public health, environmental and agricultural significance, such as various bacteria genomes, the Arthrospira (Spirulina) genome, and rice and human genomic regions. Our framework was able to identify known ncRNAs with sensitivities of greater than 90% and 77.7% for prokaryotic and eukaryotic sequences, respectively. Our classifier is available at http://ncrna-pred.com/HLRF.htm.     

NSIT: Novel Sequence Identification Tool

Novel sequences are DNA sequences present in an individual''s genome but absent in the human reference assembly. They are predicted to be biologically important, both individual and population specific, and consistent with the known human migration paths. Recent works have shown that an average person harbors 2–5 Mb of such sequences and estimated that the human pan-genome contains as high as 19–40 Mb of novel sequences. To identify them in a de novo genome assembly, some existing sequence aligners have been used but no computational method has been specifically proposed for this task. In this work, we developed NSIT (Novel Sequence Identification Tool), a software that can accurately and efficiently identify novel sequences in an individual''s de novo whole genome assembly. We identified and characterized 1.1 Mb, 1.2 Mb, and 1.0 Mb of novel sequences in NA18507 (African), YH (Asian), and NA12878 (European) de novo genome assemblies, respectively. Our results show very high concordance with the previous work using the respective reference assembly. In addition, our results using the latest human reference assembly suggest that the amount of novel sequences per individual may not be as high as previously reported. We additionally developed a graphical viewer for comparisons of novel sequence contents. The viewer also helped in identifying sequence contamination; we found 130 kb of Epstein-Barr virus sequence in the previously published NA18507 novel sequences as well as 287 kb of zebrafish repeats in NA12878 de novo assembly. NSIT requires 2GB of RAM and 1.5–2 hrs on a commodity desktop. The program is applicable to input assemblies with varying contig/scaffold sizes, ranging from 100 bp to as high as 50 Mb. It works in both 32-bit and 64-bit systems and outperforms, by large margins, other fast sequence aligners previously applied to this task. To our knowledge, NSIT is the first software designed specifically for novel sequence identification in a de novo human genome assembly.     

Plant noncoding RNA gene discovery by "single-genome comparative genomics"

Plant genomes have undergone multiple rounds of duplications that contributed massively to the growth of gene families. The structure of resulting families has been studied in depth for protein-coding genes. However, little is known about the impact of duplications on noncoding RNA (ncRNA) genes. Here we perform a systematic analysis of duplicated regions in the rice genome in search of such ncRNA repeats. We observe that, just like their protein counterparts, most ncRNA genes have undergone multiple duplications that left visible sequence conservation footprints. The extent of ncRNA gene duplication in plants is such that these sequence footprints can be exploited for the discovery of novel ncRNA gene families on a large scale. We developed an SVM model that is able to retrieve likely ncRNA candidates among the 100,000+ repeat families in the rice genome, with a reasonably low false-positive discovery rate. Among the nearly 4000 ncRNA families predicted by this means, only 90 correspond to putative snoRNA or miRNA families. About half of the remaining families are classified as structured RNAs. New candidate ncRNAs are particularly enriched in UTR and intronic regions. Interestingly, 89% of the putative ncRNA families do not produce a detectable signal when their sequences are compared to another grass genome such as maize. Our results show that a large fraction of rice ncRNA genes are present in multiple copies and are species-specific or of recent origin. Intragenome comparison is a unique and potent source for the computational annotation of this major class of ncRNA.     

Empirical comparison of cross-platform normalization methods for gene expression data

Background

The prediction of secondary structure, i.e. the set of canonical base pairs between nucleotides, is a first step in developing an understanding of the function of an RNA sequence. The most accurate computational methods predict conserved structures for a set of homologous RNA sequences. These methods usually suffer from high computational complexity. In this paper, TurboFold, a novel and efficient method for secondary structure prediction for multiple RNA sequences, is presented.

Results

TurboFold takes, as input, a set of homologous RNA sequences and outputs estimates of the base pairing probabilities for each sequence. The base pairing probabilities for a sequence are estimated by combining intrinsic information, derived from the sequence itself via the nearest neighbor thermodynamic model, with extrinsic information, derived from the other sequences in the input set. For a given sequence, the extrinsic information is computed by using pairwise-sequence-alignment-based probabilities for co-incidence with each of the other sequences, along with estimated base pairing probabilities, from the previous iteration, for the other sequences. The extrinsic information is introduced as free energy modifications for base pairing in a partition function computation based on the nearest neighbor thermodynamic model. This process yields updated estimates of base pairing probability. The updated base pairing probabilities in turn are used to recompute extrinsic information, resulting in the overall iterative estimation procedure that defines TurboFold. TurboFold is benchmarked on a number of ncRNA datasets and compared against alternative secondary structure prediction methods. The iterative procedure in TurboFold is shown to improve estimates of base pairing probability with each iteration, though only small gains are obtained beyond three iterations. Secondary structures composed of base pairs with estimated probabilities higher than a significance threshold are shown to be more accurate for TurboFold than for alternative methods that estimate base pairing probabilities. TurboFold-MEA, which uses base pairing probabilities from TurboFold in a maximum expected accuracy algorithm for secondary structure prediction, has accuracy comparable to the best performing secondary structure prediction methods. The computational and memory requirements for TurboFold are modest and, in terms of sequence length and number of sequences, scale much more favorably than joint alignment and folding algorithms.

Conclusions

TurboFold is an iterative probabilistic method for predicting secondary structures for multiple RNA sequences that efficiently and accurately combines the information from the comparative analysis between sequences with the thermodynamic folding model. Unlike most other multi-sequence structure prediction methods, TurboFold does not enforce strict commonality of structures and is therefore useful for predicting structures for homologous sequences that have diverged significantly. TurboFold can be downloaded as part of the RNAstructure package at http://rna.urmc.rochester.edu.     

Automatic prediction of non-coding RNA genes in prokaryotes based on compositional statistics

Although non-coding RNA (ncRNA) genes do not encode proteins, they play vital roles in cells by producing functionally important RNAs. In this paper, we present a novel method for predicting ncRNA genes based on compositional features extracted directly from gene sequences. Our method consists of two Support Vector Machine (SVM) models--Codon model which uses codon usage features derived from ncRNA genes and protein-coding genes and Kmer model which utilizes features of nucleotide and dinucleotide frequency extracted respectively from ncRNA genes and randomly chosen genome sequences. The 10-fold cross-validation accuracy for the two models is found to be 92% and 91%, respectively. Thus, we could make an automatic prediction of ncRNA genes in one genome without manual filtration of protein-coding genes. After applying our method in Sulfolobus solfataricus genome, 25 prediction results have been generated according to 25 cut-off pairs. We have also applied the approach in E. coli and found our results comparable to those of previous studies. In general, our method enables automatic identification of ncRNA genes in newly sequenced prokaryotic genomes.     

Computational RNomics: Structure identification and functional prediction of non-coding RNAs <Emphasis Type="Italic">in silico</Emphasis>

The eukaryotic genome contains varying numbers of non-coding RNA(ncRNA) genes.Computational RNomics takes a multidisciplinary approach,like information science,to resolve the structure and function of ncRNAs.Here,we review the main issues in Computational RNomics of data storage and management,ncRNA gene identification and characterization,ncRNA target identification and functional prediction,and we summarize the main methods and current content of computational RNomics.     

Pair hidden Markov models on tree structures

MOTIVATION: Computationally identifying non-coding RNA regions on the genome has much scope for investigation and is essentially harder than gene-finding problems for protein-coding regions. Since comparative sequence analysis is effective for non-coding RNA detection, efficient computational methods are expected for structural alignments of RNA sequences. On the other hand, Hidden Markov Models (HMMs) have played important roles for modeling and analysing biological sequences. Especially, the concept of Pair HMMs (PHMMs) have been examined extensively as mathematical models for alignments and gene finding. RESULTS: We propose the pair HMMs on tree structures (PHMMTSs), which is an extension of PHMMs defined on alignments of trees and provides a unifying framework and an automata-theoretic model for alignments of trees, structural alignments and pair stochastic context-free grammars. By structural alignment, we mean a pairwise alignment to align an unfolded RNA sequence into an RNA sequence of known secondary structure. First, we extend the notion of PHMMs defined on alignments of 'linear' sequences to pair stochastic tree automata, called PHMMTSs, defined on alignments of 'trees'. The PHMMTSs provide various types of alignments of trees such as affine-gap alignments of trees and an automata-theoretic model for alignment of trees. Second, based on the observation that a secondary structure of RNA can be represented by a tree, we apply PHMMTSs to the problem of structural alignments of RNAs. We modify PHMMTSs so that it takes as input a pair of a 'linear' sequence and a 'tree' representing a secondary structure of RNA to produce a structural alignment. Further, the PHMMTSs with input of a pair of two linear sequences is mathematically equal to the pair stochastic context-free grammars. We demonstrate some computational experiments to show the effectiveness of our method for structural alignments, and discuss a complexity issue of PHMMTSs.     

RNAstrand: reading direction of structured RNAs in multiple sequence alignments

Motivation

Genome-wide screens for structured ncRNA genes in mammals, urochordates, and nematodes have predicted thousands of putative ncRNA genes and other structured RNA motifs. A prerequisite for their functional annotation is to determine the reading direction with high precision.

Results

While folding energies of an RNA and its reverse complement are similar, the differences are sufficient at least in conjunction with substitution patterns to discriminate between structured RNAs and their complements. We present here a support vector machine that reliably classifies the reading direction of a structured RNA from a multiple sequence alignment and provides a considerable improvement in classification accuracy over previous approaches.

Software

RNAstrand is freely available as a stand-alone tool from http://www.bioinf.uni-leipzig.de/Software/RNAstrand and is also included in the latest release of RNAz, a part of the Vienna RNA Package.     

Computational RNomics:Structure identification and functional prediction of non-coding RNAs in silico

The eukaryotic genome contains varying numbers of non-coding RNA(ncRNA) genes."Computational RNomics" takes a multidisciplinary approach,like information science,to resolve the structure and function of ncRNAs.Here,we review the main issues in "Computational RNomics" of data storage and management,ncRNA gene identification and characterization,ncRNA target identification and functional prediction,and we summarize the main methods and current content of "computational RNomics".     

RNA 3D Modules in Genome-Wide Predictions of RNA 2D Structure

Recent experimental and computational progress has revealed a large potential for RNA structure in the genome. This has been driven by computational strategies that exploit multiple genomes of related organisms to identify common sequences and secondary structures. However, these computational approaches have two main challenges: they are computationally expensive and they have a relatively high false discovery rate (FDR). Simultaneously, RNA 3D structure analysis has revealed modules composed of non-canonical base pairs which occur in non-homologous positions, apparently by independent evolution. These modules can, for example, occur inside structural elements which in RNA 2D predictions appear as internal loops. Hence one question is if the use of such RNA 3D information can improve the prediction accuracy of RNA secondary structure at a genome-wide level. Here, we use RNAz in combination with 3D module prediction tools and apply them on a 13-way vertebrate sequence-based alignment. We find that RNA 3D modules predicted by metaRNAmodules and JAR3D are significantly enriched in the screened windows compared to their shuffled counterparts. The initially estimated FDR of 47.0% is lowered to below 25% when certain 3D module predictions are present in the window of the 2D prediction. We discuss the implications and prospects for further development of computational strategies for detection of RNA 2D structure in genomic sequence.     

Genome-wide searching with base-pairing kernel functions for noncoding RNAs: computational and expression analysis of snoRNA families in Caenorhabditis elegans

Despite the accumulating research on noncoding RNAs (ncRNAs), it is likely that we are seeing only the tip of the iceberg regarding our understanding of the functions and the regulatory roles served by ncRNAs in cellular metabolism, pathogenesis and host-pathogen interactions. Therefore, more powerful computational and experimental tools for analyzing ncRNAs need to be developed. To this end, we propose novel kernel functions, called base-pairing profile local alignment (BPLA) kernels, for analyzing functional ncRNA sequences using support vector machines (SVMs). We extend the local alignment kernels for amino acid sequences in order to handle RNA sequences by using STRAL's; scoring function, which takes into account sequence similarities as well as upstream and downstream base-pairing probabilities, thus enabling us to model secondary structures of RNA sequences. As a test of the performance of BPLA kernels, we applied our kernels to the problem of discriminating members of an RNA family from nonmembers using SVMs. The results indicated that the discrimination ability of our kernels is stronger than that of other existing methods. Furthermore, we demonstrated the applicability of our kernels to the problem of genome-wide search of snoRNA families in the Caenorhabditis elegans genome, and confirmed that the expression is valid in 14 out of 48 of our predicted candidates by using qRT-PCR. Finally, highly expressed six candidates were identified as the original target regions by DNA sequencing.     

Non-referenced genome assembly from epigenomic short-read data

Current computational methods used to analyze changes in DNA methylation and chromatin modification rely on sequenced genomes. Here we describe a pipeline for the detection of these changes from short-read sequence data that does not require a reference genome. Open source software packages were used for sequence assembly, alignment, and measurement of differential enrichment. The method was evaluated by comparing results with reference-based results showing a strong correlation between chromatin modification and gene expression. We then used our de novo sequence assembly to build the DNA methylation profile for the non-referenced Psammomys obesus genome. The pipeline described uses open source software for fast annotation and visualization of unreferenced genomic regions from short-read data.     

Barcode Server: A Visualization-Based Genome Analysis System

We have previously developed a computational method for representing a genome as a barcode image, which makes various genomic features visually apparent. We have demonstrated that this visual capability has made some challenging genome analysis problems relatively easy to solve. We have applied this capability to a number of challenging problems, including (a) identification of horizontally transferred genes, (b) identification of genomic islands with special properties and (c) binning of metagenomic sequences, and achieved highly encouraging results. These application results inspired us to develop this barcode-based genome analysis server for public service, which supports the following capabilities: (a) calculation of the k-mer based barcode image for a provided DNA sequence; (b) detection of sequence fragments in a given genome with distinct barcodes from those of the majority of the genome, (c) clustering of provided DNA sequences into groups having similar barcodes; and (d) homology-based search using Blast against a genome database for any selected genomic regions deemed to have interesting barcodes. The barcode server provides a job management capability, allowing processing of a large number of analysis jobs for barcode-based comparative genome analyses. The barcode server is accessible at http://csbl1.bmb.uga.edu/Barcode.