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New sequencing technologies allow unprecedented views into changes occurring in virus-infected cells, including comprehensive and largely unbiased measurements of different types of RNA. In this study, researchers from the University of Washington used RNA-Seq to profile dynamic changes in cellular microRNAs occurring in HIV-infected cells. The sensitivity afforded by sequencing allowed them to detect changes in microRNA expression early in infection, before the onset of viral replication. A phased pattern of expression was evident among these microRNAs, and many that were initially suppressed were later overexpressed at the height of infection, providing unique signatures of infection. By integrating additional mRNA data with the microRNA data, they identified a role for microRNAs in transcriptional regulation during infection and specifically a network of microRNAs involved in the expression of a known HIV cofactor. Finally, as a distinct benefit of sequencing, they identified candidate nonannotated microRNAs, including one whose downregulation may allow HIV-1 replication to proceed fully.

• Chang ST, Thomas MJ, Sova P, Green RR, Palermo RE, Katze MG. (2013) Next-generation sequencing of small RNAs from HIV-infected cells identifies phased microrna expression patterns and candidate novel microRNAs differentially expressed upon infection. MBio 4(1), e00549-12. [article]

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MicroRNAs (miRNAs) are a class of small RNAs that post-transcriptionally regulate gene expression in animals and plants. The recent rapid advancement in miRNA biology, including high-throughput sequencing of small RNA libraries, inspired the development of a bioinformatics software, miRAuto, which predicts putative miRNAs in model plant genomes computationally. Furthermore, miRAuto enables users to identify miRNAs in non-model plant species whose genomes have yet to be fully sequenced. miRAuto analyzes the expression of the 5′-end position of mapped small RNAs in reference sequences to prevent the possibility of mRNA fragments being included as candidate miRNAs.

Researchers at Seoul National University validated the utility of miRAuto on a small RNA dataset, and the results were compared to other publicly available miRNA prediction programs. In conclusion, miRAuto is a fully automated user-friendly tool for predicting miRNAs from small RNA sequencing data in both model and non-model plant species.

Availability – miRAuto is available at http://nature.snu.ac.kr/software/miRAuto.htm .

• Lee J, Kim DI, Park JH, Choi IY, Shin C. (2013) miRAuto: An automated user-friendly MicroRNA prediction tool utilizing plant small RNA sequencing data. Mol Cells 35(4), 342-7. [abstract]

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MicroRNAs (miRNAs) are small non-coding RNAs that play critical roles in regulating post transcriptional gene expression. Gall midges encompass a large group of insects that are of economic importance and also possess fascinating biological traits. The gall midge Mayetiola destructor, commonly known as the Hessian fly, is a destructive pest of wheat and model organism for studying gall midge biology and insect – host plant interactions.

In this study, researchers from the Department of Entomology, Kansas State University systematically analyzed miRNAs from the Hessian fly. Deep-sequencing a Hessian fly larval transcriptome led to the identification of 89 miRNA species that are either identical or very similar to known miRNAs from other insects, and 184 novel miRNAs that have not been reported from other species. A genome-wide search through a draft Hessian fly genome sequence identified a total of 611 putative miRNA-encoding genes based on sequence similarity and the existence of a stem-loop structure for miRNA precursors. Analysis of the 611 putative genes revealed a striking feature: the dramatic expansion of several miRNA gene families. The largest family contained 91 genes that encoded 20 different miRNAs. Microarray analyses revealed the expression of miRNA genes was strictly regulated during Hessian fly larval development and abundance of many miRNA genes were affected by host genotypes.

The identification of a large number of miRNAs for the first time from a gall midge provides a foundation for further studies of miRNA functions in gall midge biology and behavior. The dramatic expansion of identical or similar miRNAs provides a unique system to study functional relations among miRNA iso-genes as well as changes in sequence specificity due to small changes in miRNAs and in their mRNA targets. These results may also facilitate the identification of miRNA genes for potential pest control through transgenic approaches.

• Khajuria C, Williams CE, El Bouhssini M, Whitworth RJ, Richards S, Stuart JJ, Chen MS. (2013) Deep sequencing and genome-wide analysis reveals the expansion of MicroRNA genes in the gall midge Mayetiola destructor. BMC Genomics 14:187. [article]

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In silico generated search for microRNAs (miRNAs) have been driven by methods compiling structural features of the miRNA precursor hairpin as well as to some degree combining this with analysis of RNA-seq profiles for which the miRNA typically leave the drosha/dicer fingerprint of 1-2 ~22nt blocks of reads corresponding to the mature and star miRNA.

In complement to the previous methods, researchers at the University of Copenhagen, Denmark present a study where they systematically exploit these pattern of read profiles. They created databases of 2,540 miRNA read profiles using short RNA-seq data from miRBase and 4,795 read profiles from ENCODE (after preprocessing). Of the 4,795 ENCODE profiles, 1,361 are annotated as noncoding RNAs (ncRNAs) and of which 285 are further annotated as miRNAs. Using \prog{deepBlockAlign} (dba), they align ENCODE ncRNA profiles against the miRBase profiles (cleaned for “self-matches”) and are able to separate ENCODE miRNAs from the other ncRNAs by a Matthews correlation coefficient of 0.8 and then obtain the area under the curve of 0.93. Using the derived separation dba score cut-off, they predict 523 novel miRNA candidates. Further analysis reveal that these are located in genomic regions with (UCSC) MAF block fragmentation and poor sequence conservation, which in part might explain why they have been overlooked in previous efforts.

The researchers further analyzed known miRNAs from human and mouse and found two distinct classes containing two block or $>2$ block respectively, where the latter class hold profiles having less well defined arrangement of reads. They further compared the read profiles specific for plant and animals respectively, in terms of both length and distribution of reads within the profiles. They observed that some read profiles were specific for the two kingdoms respectively.

Availability: All data as well as a server to search miRBase profiles by uploading a BED file is available at http://rth.dk/resources/dba/mirna.

• Pundhir S, Gorodkin J. (2013) MicroRNA discovery by similarity search to a database of RNA-seq profiles. Frontiers in Bioinform & Comp Biol [Epub ahead of print]. [abstract]

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• Pundhir S Gorodkin J (2013) MicroRNA discovery by similarity search to a database of RNA-seq profiles Frontiers in Bioinform & Comp Biol [Epub ahead of print] [abstract]
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MicroRNAs (miRNAs) are a class of non-coding RNAs of ∼22 nucleotides in length, and constitute a novel class of gene regulators by imperfect base-pairing to the 3′UTR of protein encoding messenger RNAs. Growing evidence indicates that miRNAs are implicated in several pathological processes in myocardial disease. The past years, we have witnessed several profiling attempts using high-density oligonucleotide array-based approaches to identify the complete miRNA content (miRNOME) in the healthy and diseased mammalian heart. These efforts have demonstrated that the failing heart displays differential expression of several dozens of miRNAs. While the total number of experimentally validated human miRNAs is roughly two thousand, the number of expressed miRNAs in the human myocardium remains elusive.

With the objective of performing an unbiased assay to identify the miRNOME of the human heart, both under physiological and pathophysiological conditions, a team led by researchers at Maastricht University, The Netherlands used deep sequencing and bioinformatics to annotate and quantify microRNA expression in healthy and diseased human heart (heart failure secondary to hypertrophic or dilated cardiomyopathy). Their results indicate that the human heart expresses >800 miRNAs, the majority of which not being annotated nor described so far and some of which being unique to primate species. Furthermore, >250 miRNAs show differential and etiology-dependent expression in human dilated cardiomyopathy (DCM) or hypertrophic cardiomyopathy (HCM). The human cardiac miRNOME still possesses a large number of miRNAs that remain virtually unexplored. The current study provides a starting point for a more comprehensive understanding of the role of miRNAs in regulating human heart disease.

• Leptidis S, El Azzouzi H, Lok SI, de Weger R, Olieslagers S, Kisters N, Silva GJ, Heymans S, Cuppen E, Berezikov E, De Windt LJ, da Costa Martins P. (2013) A Deep Sequencing Approach to Uncover the miRNOME in the Human Heart. PLoS One 8(2), e57800. [article]

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MicroRNAs (miRNAs) can group together along the human genome to form stable secondary structures made of several hairpins hosting miRNAs in their stems. The few known examples of such structures are all involved in cancer development. A large scale computational analysis of human chromosomes crossing sequence analysis and deep sequencing data revealed the presence of >400 structural clusters of miRNAs in the human genome. An a posteriori analysis validates predictions as bona fide miRNAs. A functional analysis of structural clusters position along the chromosomes co-localizes them with genes involved in several key cellular processes like immune systems, sensory systems, signal transduction and development. Immune systems diseases, infectious diseases and neurodegenerative diseases are characterized by genes that are especially well organized around structural clusters of miRNAs. Target genes functional analysis strongly supports a regulatory role of most predicted miRNAs and, notably, a strong involvement of predicted miRNAs in the regulation of cancer pathways. This analysis provides new fundamental insights on the genomic organization of miRNAs in human chromosomes.

Availability: The program, called MIReStruC (standing for ‘miRNA Structural Cluster’), has been implemented in bash, C, Awk and Python. It is available at the address http://www.ihes.fr/∼carbone/data9/.

• Mathelier A, Carbone A. (2013) Large scale chromosomal mapping of human microRNA structural clusters. Nucleic Acids Res [Epub ahead of print]. [article]

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Degradome sequencing for identification of miRNA targets in plants

MicroRNAs (miRNAs) are endogenous regulators of a broad range of physiological processes and act by either degrading mRNA or blocking its translation. Mature miRNAs function within large complexes to negatively regulate specific target mRNAs. Plant miRNAs generally interact with their targets through perfect or near-perfect complementarity and direct mRNA target degradation.

In plants, miRNAs not only post-transcriptionally regulate their own targets but also interact with each other in regulatory networks to affect many aspects of development, such as growth, development and responses to biotic and abiotic stresses. Hundreds of miRNAs have been identified in higher plants by direct cloning or more recently by next-gen sequencing. To determine the function of these miRNAs we must first identify their targets.

Originally, plant miRNA targets have been studied via computational prediction, which is based on either perfect or near-perfect sequence complementarity between miRNA and the target mRNA or sequence conservation among different species. However, target prediction is very challenging, especially when a high level of mismatches exists in miRNA:target pairing.

Recently, a new method called degradome sequencing, which combines high-throughput RNA sequencing with bioinformatic tools, has-been successfully established to screen for miRNA targets in plants. Using degradome sequencing, many of the previously validated and predicted targets of miRNAs have been verified indicating that it is an efficient strategy to identify smRNA targets on a large scale in plants.

Degradome sequencing reveals miRNA targets by globally identifying the remnants of small RNA-directed target cleavage by sequencing the 5′ ends of uncapped RNAs. Sequencing reads are mapped to mRNAs and the 5′ terminal nucleotide of miRNA-cleaved mRNA fragments corresponds to the nucleotide that is complementary to the 10th nucleotide of the miRNA. Therefore, the cleaved RNA targets have distinct peaks in the degradome sequence reads at the predicted cleavage site relative to other regions of the transcript. Confirmed miRNA targets are presented in the form of target plots (t-plots).

Read more

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Biogenesis and molecular function are two key subjects in the field of microRNA (miRNA) research. Deep sequencing has become the principal technique in cataloging of miRNA repertoire and generating expression profiles in an unbiased manner.

A team led by researchers at Ewha Womans University, Korea have updated miRGator to version v3.0. miRGator compiles the deep sequencing miRNA data available in public and the team has implemented several novel tools to facilitate exploration of massive data. The miR-seq browser supports users to examine short read alignment with the secondary structure and read count information available in concurrent windows. Features such as sequence editing, sorting, ordering, import and export of user data would be of great utility for studying iso-miRs, miRNA editing and modifications. miRNA-target relation is essential for understanding miRNA function. Coexpression analysis of miRNA and target mRNAs, based on miRNA-Seq and RNA-Seq data from the same sample, is visualized in the heat-map and network views where users can investigate the inverse correlation of gene expression and target relations, compiled from various databases of predicted and validated targets. By keeping datasets and analytic tools up-to-date, miRGator should continue to serve as an integrated resource for biogenesis and functional investigation of miRNAs.

Availability – miRGator v3.0 update is available at: http://mirgator.kobic.re.kr

Cho S, Jang I, Jun Y, Yoon S, Ko M, Kwon Y, Choi I, Jang H, Ryu D, Lee B, Kim VN, Kim W, Lee S. (2012) miRGator v3.0: a microRNA portal for deep sequencing, expression profiling and mRNA targeting. Nucleic Acids Res [Epub ahead of print]. [article]

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from the Houston Chronicle

Hangzhou, China (PRWEB) May 29, 2012 – In a new study published online in Nature Communications, researchers from Sichuan Agricultural University and LC Sciences report the miRNAome in porcine adipose and muscle tissues. The report provides a valuable epigenomic source for obesity prediction and prevention and furthers the development of pig as a model organism for human obesity research.

Scientists now know that the genetic code alone isn’t responsible for adult phenotype or even the offspring of these adults. Epigenetics refers to changes in gene expression affecting phenotype that don’t involve changes to the DNA nucleotide sequence itself, and yet are heritable. DNA methylation, histone modification and microRNA (miRNA) expression are examples of epigenetic mechanisms that have recently been identified as important regulators of gene expression in many biological systems.

Obesity is a huge problem worldwide. Recently, the World Health Organization reported that obesity levels doubled in every region of the world between 1980 and 2008, spurring rates of non-communicable diseases such as diabetes and cancer that now account for almost two out of three deaths globally. It has become evident that epigenetic factors, such as DNA methylation and miRNA expression, have essential roles in obesity development. (read more…)

• Li, M. et al. (2012)An atlas of DNA methylomes in porcine adipose and muscle tissues. Nat Commun[Epub ahead of print]. [abstract]

 

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Researchers in the Department of Bioengineering, UCSD evaluated seven commonly used normalization methods:

Global normalization
Lowess normalization
Trimmed Mean Method (TMM)
Quantile normalization
Scaling normalization
Variance stabilization
Invariant method

They assessed these methods on two individual experimental data sets with the empirical statistical metrics of mean square error (MSE) and Kolmogorov-Smirnov (K-S) statistic.

The results consistently show that Lowess normalization and quantile normalization perform the best, whereas TMM, a method applied to the RNA-Sequencing normalization, performs the worst.

The poor performance of TMM normalization is further evidenced by abnormal results from the test of differential expression (DE) of microRNA-Seq data. Comparing with the models used for DE, the choice of normalization method is the primary factor that affects the results of DE. In summary, Lowess normalization and quantile normalization are recommended for normalizing microRNA-Seq data, whereas the TMM method should be used with caution.

• Garmire LX, Subramaniam S. (2012)v Evaluation of normalization methods in mammalian microRNA-Seq data. RNA [Epub ahead of print]. [abstract]

On the subject of microRNA-Seq… check out the latest poll in the left sidebar and cast your vote.

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Despite accumulating data on animal and plant microRNAs and their functions, existing public miRNA resources usually collect miRNAs from a very limited number of species. A lot of microRNAs, including those from model organisms, remain undiscovered. As a result there is a continuous need to search for new microRNAs.

Izabela Makałowska’s Laboratory of Evolutionary Genomics at The Adam Mickiewicz University in Poznan just published miRNEST, a comprehensive database of animal, plant and virus microRNAs. The core part of the database is built from the author’s miRNA predictions conducted on Expressed Sequence Tags of 225 animal and 202 plant species. The miRNA search was performed based on sequence similarity and as many as 10 004 miRNA candidates in 221 animal and 199 plant species were discovered. Out of them only 299 have already been deposited in miRBase. Additionally, miRNEST has been integrated with external miRNA data from literature and 13 databases, which includes miRNA sequences, small RNA sequencing data, expression, polymorphisms and targets data as well as links to external miRNA resources, whenever applicable. All this makes miRNEST a considerable miRNA resource in a sense of number of species (544) that integrates a scattered miRNA data into a uniform format with a user-friendly web interface.

miRNEST: http://mirnest.amu.edu.pl

Szczesniak MW, Deorowicz S, Gapski J, Kaczynski L, Makalowska I. (2011) miRNEST database: an integrative approach in microRNA search and annotation. Nucleic Acids Res [Epub ahead of print]. [article]

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RNA-Seq reveals plant microRNAs regulating expression in mammals

from The Scientist

Chen-Yu Zhang, a molecular biologist at Nanjing University in China, hypothesized that exogenous microRNAs, such as those ingested through the consumption of milk, could also be found circulating in the serum of mammals. To test this idea, Zhang and his team of researchers sequenced the blood microRNAs of 31 healthy human subjects and searched for the presence of plant microRNAs. Because plant microRNAs are structurally different from those of mammals, they react differently to oxidizing agents, and the researchers were able to differentiate the two by treating them with sodium periodate, which oxidizes mammal but not plant microRNAs.

To their surprise, they found about 40 types of plant microRNAs circulating in the subjects’ blood—some of which were found in concentrations that were comparable to major endogenous human microRNAs—and that these exogenous plant microRNAs are primarily acquired orally, through food intake.

(Read more…)

L. Zhang, et. al. (2011) Exogenous plant MIR168a specifically targets mammalian LDLRAP1: evidence of cross-kingdom regulation by microRNA. Cell Research [Epub ahead of print]. [abstract]

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MicroRNAs (miRNAs), small non-coding RNAs of 19 to 25 nt, play important roles in gene regulation in both animals and plants.

Expression profiling by microarray is one high-throughput and robust method for detecting miRNA expression, however, the approach is restricted to detecting the expression of known miRNAs. RNA-Seq is a promising new method with high sensitivity and specificity and can be used not only to measure the abundance of small-RNA sequences in a sample but also to discover novel miRNAs.

miRExpress is a stand-alone software package is implemented for generating miRNA expression profiles from high-throughput sequencing of RNA without the need for sequenced genomes. The software is also a database-supported, efficient and flexible tool for investigating miRNA regulation.

The software is freely available at: http://mirexpress.mbc.nctu.edu.tw/Download.php

• Wang, W.C., et al., (2009) miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression. BMC Bioinformatics 10(1), 328. [article]

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• SEQanswers – RNA Sequencing

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• Biostar – RNA-Seq

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    I am currently using STAR to map several Hi-SEQ mRNA runs. I'm having trouble getting a decent amount of reads to map, but I don't really understand why. I'm hoping you can shed some light :) In the final log, only about 50% (or less) of the reads map to the reference. I'm using a GTF in addition to the genome. The unmapped bin that most […]
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