MicroRNA discovery by similarity search to a database of RNA-seq profiles

Physiological RNA dynamics in RNA-Seq analysis

Physiological RNA dynamics cause problems in transcriptome analysis. Physiological RNA accumulation affects the analysis of RNA quantification, and physiological RNA degradation affects the analysis of the RNA sequence length, feature site and quantification. In the present article, we review the effects of physiological degradation and accumulation of RNA on analysing RNA sequencing data. Physiological RNA accumulation and degradation probably led to such phenomena as incorrect estimations of transcription quantification, differential expressions, co-expressions, RNA decay rates, alternative splicing, boundaries of transcription, novel genes, new single-nucleotide polymorphisms, small RNAs and gene fusion. Thus, the transcriptomic data obtained up to date warrant further scrutiny. New and improved techniques and bioinformatics software are needed to produce accurate data in transcriptome research.

MicroRNAs in Taenia solium Neurocysticercosis: Insights as Promising Agents in Host-Parasite Interaction and Their Potential as Biomarkers

MicroRNAs (miRNAs) are short, endogenous, non-coding, single-stranded RNAs involved in post-transcriptional gene regulation. Although, several miRNAs have been identified in parasitic helminths, there is little information about their identification and function in Taenia. Furthermore, the impact of miRNAs in neurocysticercosis, the brain infection caused by larvae of Taenia solium is still unknown. During chronic infection, T. solium may activate numerous mechanisms aimed to modulate host immune responses. Helminthic miRNAs might also have effects on host mRNA expression and thus play an important role regulating host-parasite interactions. Also, the diagnosis of this disease is difficult and it usually requires neuroimaging and confirmatory serology. Since miRNAs are stable when released, they can be detected in body fluids and therefore have potential to diagnose infection, determine parasite burden, and ascertain effectiveness of treatment or disease progression, for instance. This review discusses the potential roles of miRNAs in T. solium infection, including regulation of host-parasite relationships and their eventual use as diagnostic or disease biomarkers. Additionally, we summarize the bioinformatics resources available for identification of T. solium miRNAs and prediction of their targets.

PR2S2Clust: Patched RNA-seq read segments' structure-oriented clustering

RNA-seq, the next generation sequencing platform, enables researchers to explore deep into the transcriptome of organisms, such as identifying functional non-coding RNAs (ncRNAs), and quantify their expressions on tissues. The functions of ncRNAs are mostly related to their secondary structures. Thus by exploring the clustering in terms of structural profiles of the corresponding read-segments would be essential and this fuels in our motivation behind this research. In this manuscript we proposed PR2S2Clust, Patched RNA-seq Read Segments' Structure-oriented Clustering, which is an analysis platform to extract features to prepare the secondary structure profiles of the RNA-seq read segments. It provides a strategy to employ the profiles to annotate the segments into ncRNA classes using several clustering strategies. The system considers seven pairwise structural distance metrics by considering short-read mappings onto each structure, which we term as the "patched structure" while clustering the segments. In this regard, we show applications of both classical and ensemble clusterings of the partitional and hierarchical variations. Extensive real-world experiments over three publicly available RNA-seq datasets and a comparative analysis over four competitive systems confirm the effectiveness and superiority of the proposed system. The source codes and dataset of PR2S2Clust are available at the http://biomecis.uta.edu/~ashis/res/PR2S2Clust-suppl/ .

Strategies to antagonize miRNA functions in vitro and in vivo

miRNAs are a class of short noncoding RNAs that regulate gene expression post-transcriptionally. Diseased tissues have altered miRNA expression patterns, which could provide potential therapeutic targets. Introducing chemically engineered antisense oligonucleotides to cells can silence upregulated miRNAs. Successful miRNA inhibition can be assessed directly by quantitative reverse transcription PCR or northern blot, or indirectly by measuring de-repression of target genes or using reporter assays. In this review, we will discuss the design of chemically modified antisense oligonucleotides (anti-miRNA), in vivo delivery of anti-miRNA to inhibit disease-related miRNAs and the development of nanoparticle-based anti-miRNA delivery systems. In particular, we will focus on interfering nanoparticles that we designed for in vivo delivery of chemically modified anti-miRNA-122 in mice.

Emerging applications of read profiles towards the functional annotation of the genome

Functional annotation of the genome is important to understand the phenotypic complexity of various species. The road toward functional annotation involves several challenges ranging from experiments on individual molecules to large-scale analysis of high-throughput sequencing (HTS) data. HTS data is typically a result of the protocol designed to address specific research questions. The sequencing results in reads, which when mapped to a reference genome often leads to the formation of distinct patterns (read profiles). Interpretation of these read profiles is essential for their analysis in relation to the research question addressed. Several strategies have been employed at varying levels of abstraction ranging from a somewhat ad hoc to a more systematic analysis of read profiles. These include methods which can compare read profiles, e.g., from direct (non-sequence based) alignments to classification of patterns into functional groups. In this review, we highlight the emerging applications of read profiles for the annotation of non-coding RNA and cis-regulatory elements (CREs) such as enhancers and promoters. We also discuss the biological rationale behind their formation.

Computational Prediction of miRNA Genes from Small RNA Sequencing Data

Next-generation sequencing now for the first time allows researchers to gage the depth and variation of entire transcriptomes. However, now as rare transcripts can be detected that are present in cells at single copies, more advanced computational tools are needed to accurately annotate and profile them. microRNAs (miRNAs) are 22 nucleotide small RNAs (sRNAs) that post-transcriptionally reduce the output of protein coding genes. They have established roles in numerous biological processes, including cancers and other diseases. During miRNA biogenesis, the sRNAs are sequentially cleaved from precursor molecules that have a characteristic hairpin RNA structure. The vast majority of new miRNA genes that are discovered are mined from small RNA sequencing (sRNA-seq), which can detect more than a billion RNAs in a single run. However, given that many of the detected RNAs are degradation products from all types of transcripts, the accurate identification of miRNAs remain a non-trivial computational problem. Here, we review the tools available to predict animal miRNAs from sRNA sequencing data. We present tools for generalist and specialist use cases, including prediction from massively pooled data or in species without reference genome. We also present wet-lab methods used to validate predicted miRNAs, and approaches to computationally benchmark prediction accuracy. For each tool, we reference validation experiments and benchmarking efforts. Last, we discuss the future of the field.

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.