BlastR--fast and accurate database searches for non-coding RNAs

Comparative RNA Genomics

Over the last quarter of a century it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large-scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible non-coding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of non-coding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.

Genome-wide analysis of mRNA and lncRNA expression and mitochondrial genome sequencing provide insights into the mechanisms underlying a novel cytoplasmic male sterility system, BVRC-CMS96, in Brassicarapa

Characterization of a novel and valuable CMS system in Brassicarapa. Cytoplasmic male sterility (CMS) is extensively used to produce F₁ hybrid seeds in a variety of crops. However, it has not been successfully used in Chinese cabbage (Brassicarapa L. ssp. pekinensis) because of degeneration or temperature sensitivity. Here, we characterize a novel CMS system, BVRC-CMS96, which originated in B.napus cybrid obtained from INRAE, France and transferred by us to B.rapa. Floral morphology and agronomic characteristics indicate that BVRC-CMS96 plants are 100% male sterile and show no degeneration in the BC₇ generation, confirming its suitability for commercial use. We also sequenced the BVRC-CMS96 and maintainer line 18BCM mitochondrial genomes. Genomic analyses showed the presence of syntenic blocks and distinct structures between BVRC-CMS96 and 18BCM and the other known CMS systems. We found that BVRC-CMS96 has one orf222 from 'Nap'-type CMS and two copies of orf138 from 'Ogu'-type CMS. We analyzed expression of orf222, orf138, orf261b, and the mitochondrial energy genes (atp6, atp9, and cox1) in flower bud developmental stages S1-S5 and in four floral organs. orf138 and orf222 were both highly expressed in S4, S5-stage buds, calyx, and the stamen. RNA-seq identified differentially expressed mRNAs and lncRNAs (long non-coding RNAs) that were significantly enriched in pollen wall assembly, pollen development, and pollen coat. Our findings suggest that an energy supply disorder caused by orf222/orf138/orf261b may inhibit a series of nuclear pollen development-related genes. Our study shows that BVRC-CMS96 is a valuable CMS system, and our detailed molecular analysis will facilitate its application in Chinese cabbage breeding.

PlncRNADB: A Repository of Plant lncRNAs and lncRNA-RBP Protein Interactions

Background:

Long noncoding RNAs (lncRNAs) are endogenous noncoding RNAs, arbitrarily longer than 200 nucleotides, that play critical roles in diverse biological processes. LncRNAs exist in different genomes ranging from animals to plants.

Objective:

PlncRNADB is a searchable database of lncRNA sequences and annotation in plants.

Methods:

We built a pipeline for lncRNA prediction in plants, providing a convenient utility for users to quickly distinguish potential noncoding RNAs from protein-coding transcripts.

Results:

More than five thousand lncRNAs are collected from four plant species (Arabidopsis thaliana, Arabidopsis lyrata, Populus trichocarpa and Zea mays) in PlncRNADB. Moreover, our database provides the relationship between lncRNAs and various RNA-binding proteins (RBPs), which can be displayed through a user-friendly web interface.

Conclusion:

PlncRNADB can serve as a reference database to investigate the lncRNAs and their interaction with RNA-binding proteins in plants. The PlncRNADB is freely available at http://bis.zju.edu.cn/PlncRNADB/.

Detection of long non-coding RNA homology, a comparative study on alignment and alignment-free metrics

BACKGROUND

Long non-coding RNAs (lncRNAs) represent a novel class of non-coding RNAs having a crucial role in many biological processes. The identification of long non-coding homologs among different species is essential to investigate such roles in model organisms as homologous genes tend to retain similar molecular and biological functions. Alignment-based metrics are able to effectively capture the conservation of transcribed coding sequences and then the homology of protein coding genes. However, unlike protein coding genes the poor sequence conservation of long non-coding genes makes the identification of their homologs a challenging task.

RESULTS

In this study we compare alignment-based and alignment-free string similarity metrics and look at promoter regions as a possible source of conserved information. We show that promoter regions encode relevant information for the conservation of long non-coding genes across species and that such information is better captured by alignment-free metrics. We perform a genome wide test of this hypothesis in human, mouse, and zebrafish.

CONCLUSIONS

The obtained results persuaded us to postulate the new hypothesis that, unlike protein coding genes, long non-coding genes tend to preserve their regulatory machinery rather than their transcribed sequence. All datasets, scripts, and the prediction tools adopted in this study are available at https://github.com/bioinformatics-sannio/lncrna-homologs .

Comparative RNA Genomics

Over the last two decades it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible noncoding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of noncoding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.

High-throughput annotation of full-length long noncoding RNAs with capture long-read sequencing

Accurate annotation of genes and their transcripts is a foundation of genomics, but currently no annotation technique combines throughput and accuracy. As a result, reference gene collections remain incomplete-many gene models are fragmentary, and thousands more remain uncataloged, particularly for long noncoding RNAs (lncRNAs). To accelerate lncRNA annotation, the GENCODE consortium has developed RNA Capture Long Seq (CLS), which combines targeted RNA capture with third-generation long-read sequencing. Here we present an experimental reannotation of the GENCODE intergenic lncRNA populations in matched human and mouse tissues that resulted in novel transcript models for 3,574 and 561 gene loci, respectively. CLS approximately doubled the annotated complexity of targeted loci, outperforming existing short-read techniques. Full-length transcript models produced by CLS enabled us to definitively characterize the genomic features of lncRNAs, including promoter and gene structure, and protein-coding potential. Thus, CLS removes a long-standing bottleneck in transcriptome annotation and generates manual-quality full-length transcript models at high-throughput scales.

piRNAs and Their Functions in the Brain

Piwi-interacting RNAs (piRNAs) are the non-coding RNAs with 24-32 nucleotides (nt). They exhibit stark differences in length, expression pattern, abundance, and genomic organization when compared to micro-RNAs (miRNAs). There are hundreds of thousands unique piRNA sequences in each species. Numerous piRNAs have been identified and deposited in public databases. Since the piRNAs were originally discovered and well-studied in the germline, a few other studies have reported the presence of piRNAs in somatic cells including neurons. This paper reviewed the common features, biogenesis, functions, and distributions of piRNAs and summarized their specific functions in the brain. This review may provide new insights and research direction for brain disorders.

FEELnc: a tool for long non-coding RNA annotation and its application to the dog transcriptome

Whole transcriptome sequencing (RNA-seq) has become a standard for cataloguing and monitoring RNA populations. One of the main bottlenecks, however, is to correctly identify the different classes of RNAs among the plethora of reconstructed transcripts, particularly those that will be translated (mRNAs) from the class of long non-coding RNAs (lncRNAs). Here, we present FEELnc (FlExible Extraction of LncRNAs), an alignment-free program that accurately annotates lncRNAs based on a Random Forest model trained with general features such as multi k-mer frequencies and relaxed open reading frames. Benchmarking versus five state-of-the-art tools shows that FEELnc achieves similar or better classification performance on GENCODE and NONCODE data sets. The program also provides specific modules that enable the user to fine-tune classification accuracy, to formalize the annotation of lncRNA classes and to identify lncRNAs even in the absence of a training set of non-coding RNAs. We used FEELnc on a real data set comprising 20 canine RNA-seq samples produced by the European LUPA consortium to substantially expand the canine genome annotation to include 10 374 novel lncRNAs and 58 640 mRNA transcripts. FEELnc moves beyond conventional coding potential classifiers by providing a standardized and complete solution for annotating lncRNAs and is freely available at https://github.com/tderrien/FEELnc.

Multiple sequence alignment modeling: methods and applications

This review provides an overview on the development of Multiple sequence alignment (MSA) methods and their main applications. It is focused on progress made over the past decade. The three first sections review recent algorithmic developments for protein, RNA/DNA and genomic alignments. The fourth section deals with benchmarks and explores the relationship between empirical and simulated data, along with the impact on method developments. The last part of the review gives an overview on available MSA local reliability estimators and their dependence on various algorithmic properties of available methods.

In silico discovery and modeling of non-coding RNA structure in viruses

This review covers several computational methods for discovering structured non-coding RNAs in viruses and modeling their putative secondary structures. Here we will use examples from two target viruses to highlight these approaches: influenza A virus-a relatively small, segmented RNA virus; and Epstein-Barr virus-a relatively large DNA virus with a complex transcriptome. Each system has unique challenges to overcome and unique characteristics to exploit. From these particular cases, generically useful approaches can be derived for the study of additional viral targets.

Plastid-LCGbase: a collection of evolutionarily conserved plastid-associated gene pairs

Plastids carry their own genetic material that encodes a variable set of genes that are limited in number but functionally important. Aside from orthology, the lineage-specific order and orientation of these genes are also relevant. Here, we develop a database, Plastid-LCGbase (http://lcgbase.big.ac.cn/plastid-LCGbase/), which focuses on organizational variability of plastid genes and genomes from diverse taxonomic groups. The current Plastid-LCGbase contains information from 470 plastid genomes and exhibits several unique features. First, through a genome-overview page generated from OrganellarGenomeDRAW, it displays general arrangement of all plastid genes (circular or linear). Second, it shows patterns and modes of all paired plastid genes and their physical distances across user-defined lineages, which are facilitated by a step-wise stratification of taxonomic groups. Third, it divides the paired genes into three categories (co-directionally-paired genes or CDPGs, convergently-paired genes or CPGs and divergently-paired genes or DPGs) and three patterns (separation, overlap and inclusion) and provides basic statistics for each species. Fourth, the gene pairing scheme is expandable, where neighboring genes can also be included in species-/lineage-specific comparisons. We hope that Plastid-LCGbase facilitates gene variation (insertion-deletion, translocation and rearrangement) and transcription-level studies of plastid genomes.

StemSearch: RNA search tool based on stem identification and indexing

The discovery and functional analysis of noncoding RNA (ncRNA) systems in different organisms motivates the development of tools for aiding ncRNA research. Several tools exist that search for occurrences of a given RNA structural profile in genomic sequences. Yet, there is a need for an "RNA BLAST" tool, i.e., a tool that takes a putative functional RNA sequence as input, and efficiently searches for similar sequences in genomic databases, taking into consideration potential secondary structure features of the input query sequence. This work aims at providing such a tool. Our tool, denoted StemSearch, is based on a structural representation of an RNA sequence by its potential stems. Potential stems in genomic sequences are identified in a preprocessing stage, and indexed. A user-provided query sequence is likewise processed, and stems from the target genomes that are similar to the query stems are retrieved from the index. Then, relevant genomic regions are identified and ranked according to their similarity to the query stem-set while enforcing conservation of cross-stem topology. Experiments using RFAM families show significantly improved recall for StemSearch over BLAST, with small loss of precision. We further demonstrate our system's capability to handle eukaryotic genomes by successfully searching for members of the 7SK family in chromosome 2 of the human genome. StemSearch is freely available on the web at: http://www.cs.bgu.ac.il/∼negevcb/StemSearch.

Acceleration of short and long DNA read mapping without loss of accuracy using suffix array

HPG Aligner applies suffix arrays for DNA read mapping. This implementation produces a highly sensitive and extremely fast mapping of DNA reads that scales up almost linearly with read length. The approach presented here is faster (over 20× for long reads) and more sensitive (over 98% in a wide range of read lengths) than the current state-of-the-art mappers. HPG Aligner is not only an optimal alternative for current sequencers but also the only solution available to cope with longer reads and growing throughputs produced by forthcoming sequencing technologies.

AVAILABILITY AND IMPLEMENTATION

https://github.com/opencb/hpg-aligner.

ITS2, 18S, 16S or any other RNA - simply aligning sequences and their individual secondary structures simultaneously by an automatic approach

Secondary structures of RNA sequences are increasingly being used as additional information in reconstructing phylogenies and/or in distinguishing species by compensatory base change (CBC) analyses. However, in most cases just one secondary structure is used in manually correcting an automatically generated multiple sequence alignment and/or just one secondary structure is used in guiding a sequence alignment still completely generated by hand. With the advent of databases and tools offering individual RNA secondary structures, here we re-introduce a twelve letter code already implemented in 4SALE - a tool for synchronous sequence and secondary structure alignment and editing - that enables one to align RNA sequences and their individual secondary structures synchronously and fully automatic, while dramatically increasing the phylogenetic information content. We further introduce a scaled down non-GUI version of 4SALE particularly designed for big data analysis, and available at: http://4sale.bioapps.biozentrum.uni-wuerzburg.de.

Bioinformatics of prokaryotic RNAs

The genome of most prokaryotes gives rise to surprisingly complex transcriptomes, comprising not only protein-coding mRNAs, often organized as operons, but also harbors dozens or even hundreds of highly structured small regulatory RNAs and unexpectedly large levels of anti-sense transcripts. Comprehensive surveys of prokaryotic transcriptomes and the need to characterize also their non-coding components is heavily dependent on computational methods and workflows, many of which have been developed or at least adapted specifically for the use with bacterial and archaeal data. This review provides an overview on the state-of-the-art of RNA bioinformatics focusing on applications to prokaryotes.

Computational analysis of conserved RNA secondary structure in transcriptomes and genomes

Transcriptomics experiments and computational predictions both enable systematic discovery of new functional RNAs. However, many putative noncoding transcripts arise instead from artifacts and biological noise, and current computational prediction methods have high false positive rates. I discuss prospects for improving computational methods for analyzing and identifying functional RNAs, with a focus on detecting signatures of conserved RNA secondary structure. An interesting new front is the application of chemical and enzymatic experiments that probe RNA structure on a transcriptome-wide scale. I review several proposed approaches for incorporating structure probing data into the computational prediction of RNA secondary structure. Using probabilistic inference formalisms, I show how all these approaches can be unified in a well-principled framework, which in turn allows RNA probing data to be easily integrated into a wide range of analyses that depend on RNA secondary structure inference. Such analyses include homology search and genome-wide detection of new structural RNAs.

lincRNAs: genomics, evolution, and mechanisms

Long intervening noncoding RNAs (lincRNAs) are transcribed from thousands of loci in mammalian genomes and might play widespread roles in gene regulation and other cellular processes. This Review outlines the emerging understanding of lincRNAs in vertebrate animals, with emphases on how they are being identified and current conclusions and questions regarding their genomics, evolution and mechanisms of action.

Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts

It is a challenge to classify protein-coding or non-coding transcripts, especially those re-constructed from high-throughput sequencing data of poorly annotated species. This study developed and evaluated a powerful signature tool, Coding-Non-Coding Index (CNCI), by profiling adjoining nucleotide triplets to effectively distinguish protein-coding and non-coding sequences independent of known annotations. CNCI is effective for classifying incomplete transcripts and sense-antisense pairs. The implementation of CNCI offered highly accurate classification of transcripts assembled from whole-transcriptome sequencing data in a cross-species manner, that demonstrated gene evolutionary divergence between vertebrates, and invertebrates, or between plants, and provided a long non-coding RNA catalog of orangutan. CNCI software is available at http://www.bioinfo.org/software/cnci.

Detecting and comparing non-coding RNAs in the high-throughput era

In recent years there has been a growing interest in the field of non-coding RNA. This surge is a direct consequence of the discovery of a huge number of new non-coding genes and of the finding that many of these transcripts are involved in key cellular functions. In this context, accurately detecting and comparing RNA sequences has become important. Aligning nucleotide sequences is a key requisite when searching for homologous genes. Accurate alignments reveal evolutionary relationships, conserved regions and more generally any biologically relevant pattern. Comparing RNA molecules is, however, a challenging task. The nucleotide alphabet is simpler and therefore less informative than that of amino-acids. Moreover for many non-coding RNAs, evolution is likely to be mostly constrained at the structural level and not at the sequence level. This results in very poor sequence conservation impeding comparison of these molecules. These difficulties define a context where new methods are urgently needed in order to exploit experimental results to their full potential. This review focuses on the comparative genomics of non-coding RNAs in the context of new sequencing technologies and especially dealing with two extremely important and timely research aspects: the development of new methods to align RNAs and the analysis of high-throughput data.

LocARNAscan: Incorporating thermodynamic stability in sequence and structure-based RNA homology search

Background

The search for distant homologs has become an import issue in genome annotation. A particular difficulty is posed by divergent homologs that have lost recognizable sequence similarity. This same problem also arises in the recognition of novel members of large classes of RNAs such as snoRNAs or microRNAs that consist of families unrelated by common descent. Current homology search tools for structured RNAs are either based entirely on sequence similarity (such as blast or hmmer) or combine sequence and secondary structure. The most prominent example of the latter class of tools is Infernal. Alternatives are descriptor-based methods. In most practical applications published to-date, however, the information contained in covariance models or manually prescribed search patterns is dominated by sequence information. Here we ask two related questions: (1) Is secondary structure alone informative for homology search and the detection of novel members of RNA classes? (2) To what extent is the thermodynamic propensity of the target sequence to fold into the correct secondary structure helpful for this task?

Results

Sequence-structure alignment can be used as an alternative search strategy. In this scenario, the query consists of a base pairing probability matrix, which can be derived either from a single sequence or from a multiple alignment representing a set of known representatives. Sequence information can be optionally added to the query. The target sequence is pre-processed to obtain local base pairing probabilities. As a search engine we devised a semi-global scanning variant of LocARNA’s algorithm for sequence-structure alignment. The LocARNAscan tool is optimized for speed and low memory consumption. In benchmarking experiments on artificial data we observe that the inclusion of thermodynamic stability is helpful, albeit only in a regime of extremely low sequence information in the query. We observe, furthermore, that the sensitivity is bounded in particular by the limited accuracy of the predicted local structures of the target sequence.

Conclusions

Although we demonstrate that a purely structure-based homology search is feasible in principle, it is unlikely to outperform tools such as Infernal in most application scenarios, where a substantial amount of sequence information is typically available. The LocARNAscan approach will profit, however, from high throughput methods to determine RNA secondary structure. In transcriptome-wide applications, such methods will provide accurate structure annotations on the target side.

Availability

Source code of the free software LocARNAscan 1.0 and supplementary data are available at http://www.bioinf.uni-leipzig.de/Software/LocARNAscan.

Using tertiary structure for the computation of highly accurate multiple RNA alignments with the SARA-Coffee package

MOTIVATION

Aligning RNAs is useful to search for homologous genes, study evolutionary relationships, detect conserved regions and identify any patterns that may be of biological relevance. Poor levels of conservation among homologs, however, make it difficult to compare RNA sequences, even when considering closely evolutionary related sequences.

RESULTS

We describe SARA-Coffee, a tertiary structure-based multiple RNA aligner, which has been validated using BRAliDARTS, a new benchmark framework designed for evaluating tertiary structure-based multiple RNA aligners. We provide two methods to measure the capacity of alignments to match corresponding secondary and tertiary structure features. On this benchmark, SARA-Coffee outperforms both regular aligners and those using secondary structure information. Furthermore, we show that on sequences in which <60% of the nucleotides form base pairs, primary sequence methods usually perform better than secondary-structure aware aligners.

AVAILABILITY AND IMPLEMENTATION

The package and the datasets are available from http://www.tcoffee.org/Projects/saracoffee and http://structure.biofold.org/sara/.

Sequencing and analysis of full-length cDNAs, 5'-ESTs and 3'-ESTs from a cartilaginous fish, the elephant shark (Callorhinchus milii)

Cartilaginous fishes are the most ancient group of living jawed vertebrates (gnathostomes) and are, therefore, an important reference group for understanding the evolution of vertebrates. The elephant shark (Callorhinchus milii), a holocephalan cartilaginous fish, has been identified as a model cartilaginous fish genome because of its compact genome (∼910 Mb) and a genome project has been initiated to obtain its whole genome sequence. In this study, we have generated and sequenced full-length enriched cDNA libraries of the elephant shark using the 'oligo-capping' method and Sanger sequencing. A total of 6,778 full-length protein-coding cDNA and 10,701 full-length noncoding cDNA were sequenced from six tissues (gills, intestine, kidney, liver, spleen, and testis) of the elephant shark. Analysis of their polyadenylation signals showed that polyadenylation usage in elephant shark is similar to that in mammals. Furthermore, both coding and noncoding transcripts of the elephant shark use the same proportion of canonical polyadenylation sites. Besides BLASTX searches, protein-coding transcripts were annotated by Gene Ontology, InterPro domain, and KEGG pathway analyses. By comparing elephant shark genes to bony vertebrate genes, we identified several ancient genes present in elephant shark but differentially lost in tetrapods or teleosts. Only ∼6% of elephant shark noncoding cDNA showed similarity to known noncoding RNAs (ncRNAs). The rest are either highly divergent ncRNAs or novel ncRNAs. In addition to full-length transcripts, 30,375 5'-ESTs and 41,317 3'-ESTs were sequenced and annotated. The clones and transcripts generated in this study are valuable resources for annotating transcription start sites, exon-intron boundaries, and UTRs of genes in the elephant shark genome, and for the functional characterization of protein sequences. These resources will also be useful for annotating genes in other cartilaginous fishes whose genomes have been targeted for whole genome sequencing.

Computational approaches to discovering noncoding RNA

New developments are being brought to the field of molecular biology with the mounting evidence that RNA transcripts not translated into protein (noncoding RNAs, ncRNAs) hold a variety of biological functions. Computational discovery of ncRNAs is one of these developments, fueled not only by the urge to characterize these sequences but also by necessity to prioritize ones with the most relevant functions for experimental verification. The heterogeneity in size and mode of activity of ncRNAs is reflected in the corresponding diversity of computational methods for their study. Sequence and structural analysis, conservation across species, and relative position to other genomic elements are being used for ncRNA detection. In addition, the recent development of techniques that allow deep sequencing of cell transcripts either globally or from isolated ncRNA-related material is leading the field toward increased use of such high-throughput data. We expect that imminent breakthroughs will include the classification of newer types of ncRNA and new insights into miRNA and piRNA biology, eventually leading toward the completion of a catalog of all human ncRNAs.

Use of ChIP-Seq data for the design of a multiple promoter-alignment method

We address the challenge of regulatory sequence alignment with a new method, Pro-Coffee, a multiple aligner specifically designed for homologous promoter regions. Pro-Coffee uses a dinucleotide substitution matrix estimated on alignments of functional binding sites from TRANSFAC. We designed a validation framework using several thousand families of orthologous promoters. This dataset was used to evaluate the accuracy for predicting true human orthologs among their paralogs. We found that whereas other methods achieve on average 73.5% accuracy, and 77.6% when trained on that same dataset, the figure goes up to 80.4% for Pro-Coffee. We then applied a novel validation procedure based on multi-species ChIP-seq data. Trained and untrained methods were tested for their capacity to correctly align experimentally detected binding sites. Whereas the average number of correctly aligned sites for two transcription factors is 284 for default methods and 316 for trained methods, Pro-Coffee achieves 331, 16.5% above the default average. We find a high correlation between a method's performance when classifying orthologs and its ability to correctly align proven binding sites. Not only has this interesting biological consequences, it also allows us to conclude that any method that is trained on the ortholog data set will result in functionally more informative alignments.