Optimization of miRNA-seq data preprocessing

mmannot: How to improve small-RNA annotation?

High-throughput sequencing makes it possible to provide the genome-wide distribution of small non coding RNAs in a single experiment, and contributed greatly to the identification and understanding of these RNAs in the last decade. Small non coding RNAs gather a wide collection of classes, such as microRNAs, tRNA-derived fragments, small nucleolar RNAs and small nuclear RNAs, to name a few. As usual in RNA-seq studies, the sequencing step is followed by a feature quantification step: when a genome is available, the reads are aligned to the genome, their genomic positions are compared to the already available annotations, and the corresponding features are quantified. However, problem arises when many reads map at several positions and while different strategies exist to circumvent this problem, all of them are biased. In this article, we present a new strategy that compares all the reads that map at several positions, and their annotations when available. In many cases, all the hits co-localize with the same feature annotation (a duplicated miRNA or a duplicated gene, for instance). When different annotations exist for a given read, we propose to merge existing features and provide the counts for the merged features. This new strategy has been implemented in a tool, mmannot, freely available at https://github.com/mzytnicki/mmannot.

Multi-Omic Regulation of the PAM50 Gene Signature in Breast Cancer Molecular Subtypes

Breast cancer is a disease that exhibits heterogeneity that goes from the genomic to the clinical levels. This heterogeneity is thought to be captured (at least partially) by the so-called breast cancer molecular subtypes. These molecular subtypes were initially defined based on the unsupervised clustering of gene expression and its correlate with histological, morphological, phenotypic and clinical features already known. Later, a 50-gene signature, PAM50, was defined in order to identify the biological subtype of a given sample within the clinical setting. The PAM50 signature was obtained by the use of unsupervised statistical methods, and therefore no limitation was set on the biological relevance (or lack of) of the selected genes beyond its predictive capacity. An open question that remains is what are the regulatory elements that drive the various expression behaviors of this set of genes in the different molecular subtypes. This question becomes more relevant as the measurement of more biological layers of regulation becomes accessible. In this work, we analyzed the gene expression regulation of the 50 genes in the PAM50 signature, in terms of (a) gene co-expression, (b) transcription factors, (c) micro-RNAs, and (d) methylation. Using data from the Cancer Genome Atlas (TCGA) for the Luminal A and B, Basal, and HER2-enriched molecular subtypes as well as normal tumor adjacent tissue, we identified predictors for gene expression through the use of an elastic net model. We compare and contrast the sets of identified regulators for the gene signature in each molecular subtype, and systematically compare them to current literature. We also identified a unique set of predictors for the expression of genes in the PAM50 signature associated with each of the molecular subtypes. Most selected predictors are exclusive for a PAM50 gene and predictors are not shared across subtypes. There are only 13 coding transcripts and 2 miRNAs selected for the four subtypes. MiR-21 and miR-10b connect almost all the PAM50 genes in all the subtypes and normal tissue, but do it in an exclusive manner, suggesting a cancer switch from miR-10b coordination in normal tissue to miR-21. The PAM50 gene sets of selected predictors that enrich for a function across subtypes, support that different regulatory molecular mechanisms are taking place. With this study we aim to a wider understanding of the regulatory mechanisms that differentiate the expression of the PAM50 signature, which in turn could perhaps help understand the molecular basis of the differences between the molecular subtypes.

Sensitive detection of EBV microRNAs across cancer spectrum reveals association with decreased survival in adult acute myelocytic leukemia

Epstein Barr virus (EBV) is the etiologic agent involved in numerous human cancers. After infecting the host, EBV establishes a latent infection, with low levels of messenger RNA (mRNA) and protein expression, evolved to evade immune recognition. Conversely, EBV microRNAs (miRNA) are expressed ubiquitously and abundantly within infected cells. Their role in tumor biology and clinical outcomes across the spectrum of cancer is not fully explained. Here, we applied our bioinformatics pipeline for quantitative EBV miRNA detection to examine sequencing data of 8,955 individual tumor samples across 27 tumor types representing the breadth of cancer. We uncover an association of intermediate levels of viral miRNA with decreased survival in adult acute myeloid leukemia (AML) patients (P = 0.00013). Prognostic modeling of this association suggests that increased EBV miRNA levels represent an independent risk factor for poor patient outcomes. Furthermore, we explore differences in expression between elevated and absent viral miRNA loads in adult AML tumors finding that EBV positivity was associated with proinflammatory signals. Together, given no associations were found for pediatric AML, our analyses suggests EBV positivity has the potential for being a prognostic biomarker and might represent a surrogate measure related to immune impairment in adult patients.

Epigenetic changes in healthy human skeletal muscle following exercise- a systematic review

Exercise training is continually challenging whole-body homeostasis, leading to improvements in performance and health. Adaptations to exercise training are complex and are influenced by both environmental and genetic factors. Epigenetic factors regulate gene expression in a tissue-specific manner and constitute a link between the genotype and the environment. Moreover, epigenetic factors are emerging as potential biomarkers that could predict the response to exercise training. This systematic review aimed to identify epigenetic changes that have been reported in skeletal muscle following exercise training in healthy populations. A literature search of five databases (PUBMED, MEDLINE, CINHAL, SCOPUS and SportDiscuss) was conducted in November 2018. Articles were included if they examined epigenetic modifications (DNA methylation, histone modifications and non-coding RNAs) in skeletal muscle, following either an acute bout of exercise, an exercise intervention in a pre/post design, or a case/control type of study. Twenty-two studies met the inclusion criteria. Several epigenetic markers including DNA methylation of genes known to be differentially expressed after exercise and myomiRs were reported to be modified after exercise. Several epigenetic marks were identified to be altered in response to exercise, with potential influence on skeletal muscle metabolism. However, whether these epigenetic marks play a role in the physiological impact of exercise is unclear. Exercise epigenetics is still a very young research field, and it is expected that in the future the causality of such changes will be elucidated via the utilization of emerging experimental models able to target the epigenome.

Plasma MicroRNAs Are Altered Early and Consistently in a Mouse Model of Tauopathy

Pathological accumulation of tau protein in brain cells is the hallmark of a group of neurodegenerative diseases called tauopathies. Accumulation of tau protein begins years before the onset of symptoms, which include deficits in cognition, behavior and movement. The pre-symptomatic phase of tauopathy may be the best time to deliver disease-modifying treatments, but this is only possible if prognostic, pre-symptomatic biomarkers are identified. Here we describe the profiling of blood plasma microRNAs in a mouse model of tauopathy, in order to identify biomarkers of pre-symptomatic tauopathy. Circulating RNAs were isolated from blood plasma of 16-week-old and 53-week-old hTau mice and age-matched wild type controls (n = 28). Global microRNA profiling was performed using small RNA sequencing (Illumina) and selected microRNAs were validated using individual TaqMan RT-qPCR. The area under the receiver operating characteristic curve (AUC) was used to evaluate discriminative accuracy. We identified three microRNAs (miR-150-5p, miR-155-5p, miR-375-3p) that were down-regulated in 16-week-old hTau mice, which do not yet exhibit a behavioral phenotype and therefore represent pre-symptomatic tauopathy. The discriminative accuracy was AUC 0.98, 0.95 and 1, respectively. Down-regulation of these microRNAs persisted at 53 weeks of age, when hTau mice exhibit cognitive deficits and advanced neuropathology. Bioinformatic analysis showed that these three microRNAs converge on pathways associated with neuronal signaling and phosphorylation of tau. Thus, these circulating microRNAs appear to reflect neuropathological change and are promising candidates in the development of biomarkers of pre-symptomatic tauopathy.

Transcriptional consequences of impaired immune cell responses induced by cystic fibrosis plasma characterized via dual RNA sequencing

Background

In cystic fibrosis (CF), impaired immune cell responses, driven by the dysfunctional CF transmembrane conductance regulator (CFTR) gene, may determine the disease severity but clinical heterogeneity remains a major therapeutic challenge. The characterization of molecular mechanisms underlying impaired immune responses in CF may reveal novel targets with therapeutic potential. Therefore, we utilized simultaneous RNA sequencing targeted at identifying differentially expressed genes, transcripts, and miRNAs that characterize impaired immune responses triggered by CF and its phenotypes.

Methods

Peripheral blood mononuclear cells (PBMCs) extracted from a healthy donor were stimulated with plasma from CF patients (n = 9) and healthy controls (n = 3). The PBMCs were cultured (1 × 10⁵ cells/well) for 9 h at 37 ° C in 5% CO₂. After culture, total RNA was extracted from each sample and used for simultaneous total RNA and miRNA sequencing.

Results

Analysis of expression signatures from peripheral blood mononuclear cells induced by plasma of CF patients and healthy controls identified 151 genes, 154 individual transcripts, and 41 miRNAs differentially expressed in CF compared to HC while the expression signatures of 285 genes, 241 individual transcripts, and seven miRNAs differed due to CF phenotypes. Top immune pathways influenced by CF included agranulocyte adhesion, diapedesis signaling, and IL17 signaling, while those influenced by CF phenotypes included natural killer cell signaling and PI3K signaling in B lymphocytes. Upstream regulator analysis indicated dysregulation of CCL5, NF-κB and IL1A due to CF while dysregulation of TREM1 and TP53 regulators were associated with CF phenotype. Five miRNAs showed inverse expression patterns with three target genes relevant in CF-associated impaired immune pathways while two miRNAs showed inverse expression patterns with two target genes relevant to a dysregulated immune pathway associated with CF phenotypes.

Conclusions

Our results indicate that miRNAs and individual transcript variants are relevant molecular targets contributing to impaired immune cell responses in CF.

Electronic supplementary material

The online version of this article (10.1186/s12920-019-0529-0) contains supplementary material, which is available to authorized users.

Differentially Expressed miRNAs Influence Metabolic Processes in Pituitary Oncocytoma

Spindle cell oncocytomas (SCO) of the pituitary are rare tumors accounting for 0.1–0.4% of all sellar tumors. Due to their rarity, little information is available regarding their pathogenesis. Our aim was to investigate miRNA expression profile of pituitary oncocytomas. Total RNA was extracted from 9 formalin-fixed paraffin embedded pituitary samples (4 primary, 3 recurrent oncocytomas and 2 normal tissues). Next-generation sequencing was performed for miRNA profiling. Transcriptome data of additional 6 samples’ were obtained from NBCI GEO database for gene expression reanalysis and tissue-specific target prediction. Bioinformatical analysis, in vitro miRNA mimics transfection, luciferase reporter system and AlamarBlue assay were applied to characterize miRNA’s function. 54 differentially expressed miRNAs and 485 genes in pituitary SCO vs. normal tissue and 8 miRNAs in recurrent vs. primary SCO were determined. Global miRNA downregulation and decreased level of DROSHA were detected in SCO samples vs. normal tissue. Transcriptome analysis revealed cell cycle alterations while miRNAs influenced mainly metabolic processes (tricarboxylic acid cycle-TCA, carbohydrate, lipid metabolism). Through miRNA-target interaction network the overexpressed Aconitase 2 potentially targeted by two downregulated miRNAs (miR-744-5p, miR-127-3p) was revealed. ACO2 and miR-744-5p interaction was validated by luciferase assay. MiR-127-3p and miR-744-5p significantly decreased cell proliferation in vitro. Our study firstly reported miRNA profile of pituitary oncocytoma. Our results suggest that tumor suppressor miRNAs may have an essential role in the pathogenesis of pituitary oncocytoma. Earlier reports showed downregulated TCA cycle in SCO which is extended by our results adding the role of miR-744-5p targeting ACO2.

Mining Magnaporthe oryzae sRNAs With Potential Transboundary Regulation of Rice Genes Associated With Growth and Defense Through Expression Profile Analysis of the Pathogen-Infected Rice

In recent years, studies have shown that phytopathogenic fungi possess the ability of cross-kingdom regulation of host plants through small RNAs (sRNAs). Magnaporthe oryzae, a causative agent of rice blast, introduces disease by penetrating the rice tissues through appressoria. However, little is known about the transboundary regulation of M. oryzae sRNAs during the interaction of the pathogen with its host rice. Therefore, investigation of the regulation of M. oryzae through sRNAs in the infected rice plants has important theoretical and practical significance for disease control and production improvement. Based on the high-throughput data of M. oryzae sRNAs and the mixed sRNAs during infection, the differential expressions of sRNAs in M. oryzae before and during infection were compared, it was found that expression levels of 366 M. oryzae sRNAs were upregulated significantly during infection. We trained a SVM model which can be used to predict differentially expressed sRNAs, which has reference significance for the prediction of differentially expressed sRNAs of M. oryzae homologous species, and can facilitate the research of M. oryzae in the future. Furthermore, fifty core targets were selected from the predicted target genes on rice for functional enrichment analysis, the analysis reveals that there are nine biological processes and one KEGG pathway associated with rice growth and disease defense. These functions correspond to thirteen rice genes. A total of fourteen M. oryzae sRNAs targeting the rice genes were identified by data analysis, and their authenticity was verified in the database of M. oryzae sRNAs. The 14 M. oryzae sRNAs may participate in the transboundary regulation process and act as sRNA effectors to manipulate the rice blast process.

Hypomorphic mutation of the mouse Huntington's disease gene orthologue

Rare individuals with inactivating mutations in the Huntington's disease gene (HTT) exhibit variable abnormalities that imply essential HTT roles during organ development. Here we report phenotypes produced when increasingly severe hypomorphic mutations in the murine HTT orthologue Htt, (HdhneoQ20, HdhneoQ50, HdhneoQ111), were placed over a null allele (Hdhex4/5). The most severe hypomorphic allele failed to rescue null lethality at gastrulation, while the intermediate, though still severe, alleles yielded recessive perinatal lethality and a variety of fetal abnormalities affecting body size, skin, skeletal and ear formation, and transient defects in hematopoiesis. Comparative molecular analysis of wild-type and Htt-null retinoic acid-differentiated cells revealed gene network dysregulation associated with organ development that nominate polycomb repressive complexes and miRNAs as molecular mediators. Together these findings demonstrate that Htt is required both pre- and post-gastrulation to support normal development.

Statistical Methods for Identifying Biomarkers from miRNA Profiles of Cancers

Biomarkers play important roles in early diagnosis and treatment plan for cancer patients and the importance is growing. With advances in high-throughput molecular profiling technology for various types of molecules such as DNA, RNA, proteins, or metabolites, it is now possible to perform massive profiling analysis that allows accelerating discovery of novel biomolecules. Because no single marker is sufficiently accurate for clinical use, the cancer biomarker is developed in the form of multiple biomarker panels. No single marker is sufficiently accurate for clinical use, and thus cancer biomarkers are developed in the form of multiple biomarker panels. Of various types of molecular biomarkers, microRNA (miRNA) has emerged as a class of promising cancer biomarker recently. MiRNAs are small noncoding RNAs that regulate gene expression. The chapter overviews the process of identification of biomarker panels from miRNA profiles focusing on statistical methods. Introduction to molecular cancer biomarkers is touched first. From sample design to miRNA profiling process is reviewed in the method section.Statistical methods for biomarker development are introduced according to three typical purposes of molecular biomarkers: tumor subtype classification, early detection, and prediction of treatment response or prognosis of patients. Example codes for R program are provided as well for selected methods.

The transcription factor POU3F2 regulates a gene coexpression network in brain tissue from patients with psychiatric disorders

Schizophrenia and bipolar disorder are complex psychiatric diseases with risks contributed by multiple genes. Dysregulation of gene expression has been implicated in these disorders, but little is known about such dysregulation in the human brain. We analyzed three transcriptome datasets from 394 postmortem brain tissue samples from patients with schizophrenia or bipolar disorder or from healthy control individuals without a known history of psychiatric disease. We built genome-wide coexpression networks that included microRNAs (miRNAs). We identified a coexpression network module that was differentially expressed in the brain tissue from patients compared to healthy control individuals. This module contained genes that were principally involved in glial and neural cell genesis and glial cell differentiation, and included schizophrenia risk genes carrying rare variants. This module included five miRNAs and 545 mRNAs, with six transcription factors serving as hub genes in this module. We found that the most connected transcription factor gene POU3F2, also identified on a genome-wide association study for bipolar disorder, could regulate the miRNA hsa-miR-320e and other putative target mRNAs. These regulatory relationships were replicated using PsychENCODE/BrainGVEX datasets and validated by knockdown and overexpression experiments in SH-SY5Y cells and human neural progenitor cells in vitro. Thus, we identified a brain gene expression module that was enriched for rare coding variants in genes associated with schizophrenia and that contained the putative bipolar disorder risk gene POU3F2 The transcription factor POU3F2 may be a key regulator of gene expression in this disease-associated gene coexpression module.

RATEmiRs: the rat atlas of tissue-specific and enriched miRNAs database

Background

MicroRNAs (miRNAs) regulate gene expression and have been targeted as indicators of environmental/toxicologic stressors. Using the data from our deep sequencing of miRNAs in an extensive sampling of rat tissues, we developed a database called RATEmiRs for the Rat Atlas of Tissue-specific and Enriched miRNAs to allow users to dynamically determine mature-, iso- and pre-miR expression abundance, enrichment and specificity in rat tissues and organs.

Results

Illumina sequencing count data from mapped reads and meta data from the miRNA body atlas consisting of 21 and 23 tissues (14 organs) of toxicologic interest from 12 to 13 week old male and female Sprague Dawley rats respectively, were managed in a relational database with a user-friendly query interface. Data-driven pipelines are available to tailor the identification of tissue-enriched (TE) and tissue-specific (TS) miRNAs. Data-driven organ-specific (OS) pipelines reveal miRNAs that are expressed predominately in a given organ. A user-driven approach is also available to assess the tissue expression of user-specified miRNAs. Using one tissue vs other tissues and tissue(s) of an organ vs other organs, we illustrate the utility of RATEmiRs to facilitate the identification of candidate miRNAs. As a use case example, RATEmiRs revealed two TS miRNAs in the liver: rno-miR-122-3p and rno-miR-122-5p. When liver is compared to just the brain tissues for example, rno-miR-192-5p, rno-miR-193-3p, rno-miR-203b-3p, rno-miR-3559-5p, rno-miR-802-3p and rno-miR-802-5p are also detected as abundantly expressed in liver. As another example, 55 miRNAs from the RATEmiRs query of ileum vs brain tissues overlapped with miRNAs identified from the same comparison of tissues in an independent, publicly available dataset of 10 week old male rat microarray data suggesting that these miRNAs are likely not age-specific, platform-specific nor pipeline-dependent. Lastly, we identified 10 miRNAs that have conserved tissue/organ-specific expression between the rat and human species.

Conclusions

RATEmiRs provides a new platform for identification of TE, TS and OS miRNAs in a broad array of rat tissues. RATEmiRs is available at: https://www.niehs.nih.gov/ratemirs

SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data

Quality control (QC) and preprocessing are essential steps for sequencing data analysis to ensure the accuracy of results. However, existing tools cannot provide a satisfying solution with integrated comprehensive functions, proper architectures, and highly scalable acceleration. In this article, we demonstrate SOAPnuke as a tool with abundant functions for a “QC-Preprocess-QC” workflow and MapReduce acceleration framework. Four modules with different preprocessing functions are designed for processing datasets from genomic, small RNA, Digital Gene Expression, and metagenomic experiments, respectively. As a workflow-like tool, SOAPnuke centralizes processing functions into 1 executable and predefines their order to avoid the necessity of reformatting different files when switching tools. Furthermore, the MapReduce framework enables large scalability to distribute all the processing works to an entire compute cluster.

We conducted a benchmarking where SOAPnuke and other tools are used to preprocess a ∼30× NA12878 dataset published by GIAB. The standalone operation of SOAPnuke struck a balance between resource occupancy and performance. When accelerated on 16 working nodes with MapReduce, SOAPnuke achieved ∼5.7 times the fastest speed of other tools.

Dengue virus potentially promotes migratory responses on endothelial cells by enhancing pro-migratory soluble factors and miRNAs

The most life-threatening effect of the Dengue virus (DENV) infection is an acute destabilization of the microvascular endothelial cell (MEC) barrier leading to plasma leakage, hypovolemic shock and haemorrhage. However, the underlying cellular mechanisms responsible for the dysfunction of MECs are not well understood. To identify potential cellular processes altered during DENV infection of MECs, expression profiles of cytokines/growth factors and microRNAs were measured by Luminex assay and next generation sequencing, respectively. Synchronously DENV2-infected MECs increase the secretion of IL-6, IL-8, FGF-2, GM-CSF, G-CSF, TGF-α, GRO, RANTES, MCP-1 and MCP-3. Conditioned media of infected MECs increased the migration of non-infected MECs. Furthermore, six miRNAs deregulated at 24 hpi were predicted to regulate host genes involved in cell migration and vascular developmental processes such as angiogenesis. These in silico analyses provide insights that support that DENV promotes an acute migratory phenotype in MECs that contributes to the vascular destabilization observed in severe dengue cases.

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common abnormal miRNA expression patterns and their potential roles in cancer have not yet been evaluated. To account for individual differences between patients, we retrieved miRNA sequencing data for 575 patients with both tumor and adjacent non-tumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA). We then performed differential expression analysis using DESeq2 and edgeR. Results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns between tumor and non-tumor samples. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) in a single cancer. We also found 21 key SDEmiRNAs (nine over-expressed and 12 under-expressed) associated with at least eight cancers each and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effects of these 21 SDEmiRNAs on cellular function were evaluated through enrichment and pathway analysis of 7186 protein-coding gene targets mined from literature reports of differential expression of miRNAs in cancer. This analysis enables identification of SDEmiRNA functional similarity in cell proliferation control across a wide range of cancers, and assembly of common regulatory networks over cancer-related pathways. These findings were validated by construction of a regulatory network in the PI3K pathway. This study provides evidence for the value of further analysis of SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

Identifying and characterizing functional 3' nucleotide addition in the miRNA pathway

Over the past decade, modifications to microRNAs (miRNAs) via 3' end nucleotide addition have gone from a deep-sequencing curiosity to experimentally confirmed drivers of a range of regulatory activities. Here we overview the methods that have been deployed by researchers seeking to untangle these diverse functional roles and include characterizing not only the nucleotidyl transferases catalyzing the additions but also the nucleotides being added, and the timing of their addition during the miRNA pathway. These methods and their further development are key to clarifying the diverse and sometimes contradictory functional findings presently attributed to these nucleotide additions.

Identification of novel ΔNp63α-regulated miRNAs using an optimized small RNA-Seq analysis pipeline

Advances in high-throughput sequencing have enabled profiling of microRNAs (miRNAs), however, a consensus pipeline for sequencing of small RNAs has not been established. We built and optimized an analysis pipeline using Partek Flow, circumventing the need for analyzing data via scripting languages. Our analysis assessed the effect of alignment reference, normalization method, and statistical model choice on biological data. The pipeline was evaluated using sequencing data from HaCaT cells transfected with either a non-silencing control or siRNA against ΔNp63α, a p53 family member protein which is highly expressed in non-melanoma skin cancer and shown to regulate a number of miRNAs. We posit that 1) alignment and quantification to the miRBase reference provides the most robust quantitation of miRNAs, 2) normalizing sample reads via Trimmed Mean of M-values is the most robust method for accurate downstream analyses, and 3) use of the lognormal with shrinkage statistical model effectively identifies differentially expressed miRNAs. Using our pipeline, we identified previously unrecognized regulation of miRs-149-5p, 18a-5p, 19b-1-5p, 20a-5p, 590-5p, 744-5p and 93-5p by ΔNp63α. Regulation of these miRNAs was validated by RT-qPCR, substantiating our small RNA-Seq pipeline. Further analysis of these miRNAs may provide insight into ΔNp63α's role in cancer progression. By defining the optimal alignment reference, normalization method, and statistical model for analysis of miRNA sequencing data, we have established an analysis pipeline that may be carried out in Partek Flow or at the command line. In this manner, our pipeline circumvents some of the major hurdles encountered during small RNA-Seq analysis.

Genome-wide microRNA screening in Nile tilapia reveals pervasive isomiRs' transcription, sex-biased arm switching and increasing complexity of expression throughout development

MicroRNAs (miRNAs) are key regulators of gene expression in multicellular organisms. The elucidation of miRNA function and evolution depends on the identification and characterization of miRNA repertoire of strategic organisms, as the fast-evolving cichlid fishes. Using RNA-seq and comparative genomics we carried out an in-depth report of miRNAs in Nile tilapia (Oreochromis niloticus), an emergent model organism to investigate evo-devo mechanisms. Five hundred known miRNAs and almost one hundred putative novel vertebrate miRNAs have been identified, many of which seem to be teleost-specific, cichlid-specific or tilapia-specific. Abundant miRNA isoforms (isomiRs) were identified with modifications in both 5p and 3p miRNA transcripts. Changes in arm usage (arm switching) of nine miRNAs were detected in early development, adult stage and even between male and female samples. We found an increasing complexity of miRNA expression during ontogenetic development, revealing a remarkable synchronism between the rate of new miRNAs recruitment and morphological changes. Overall, our results enlarge vertebrate miRNA collection and reveal a notable differential ratio of miRNA arms and isoforms influenced by sex and developmental life stage, providing a better picture of the evolutionary and spatiotemporal dynamics of miRNAs.

Threshold-seq: a tool for determining the threshold in short RNA-seq datasets

Summary

We present ‘Threshold-seq,’ a new approach for determining thresholds in deep-sequencing datasets of short RNA transcripts. Threshold-seq addresses the critical question of how many reads need to support a short RNA molecule in a given dataset before it can be considered different from ‘background.’ The proposed scheme is easy to implement and incorporate into existing pipelines.

Availability and Implementation

Source code of Threshold-seq is freely available as an R package at: http://cm.jefferson.edu/threshold-seq/

Supplementary information

Supplementary data are available at Bioinformatics online.

Interrogating the Functions of PRDM9 Domains in Meiosis

Homologous recombination is required for proper segregation of homologous chromosomes during meiosis. It occurs predominantly at recombination hotspots that are defined by the DNA binding specificity of the PRDM9 protein. PRDM9 contains three conserved domains typically involved in regulation of transcription; yet, the role of PRDM9 in gene expression control is not clear. Here, we analyze the germline transcriptome of Prdm9^−/− male mice in comparison to Prdm9^+/+ males and find no apparent differences in the mRNA and miRNA profiles. We further explore the role of PRDM9 in meiosis by analyzing the effect of the KRAB, SSXRD, and post-SET zinc finger deletions in a cell culture expression system and the KRAB domain deletion in mice. We found that although the post-SET zinc finger and the KRAB domains are not essential for the methyltransferase activity of PRDM9 in cell culture, the KRAB domain mutant mice show only residual PRDM9 methyltransferase activity and undergo meiotic arrest. In aggregate, our data indicate that domains typically involved in regulation of gene expression do not serve that role in PRDM9, but are likely involved in setting the proper chromatin environment for initiation and completion of homologous recombination.

microRNA profiles in urine by next-generation sequencing can stratify bladder cancer subtypes

Bladder cancer (BC) is the most frequent malignancy of the urinary tract with a high incidence in men and smokers. Currently, there are no non-invasive markers useful for BC diagnosis and subtypes classification that could overcome invasive procedures such as cystoscopy. Dysregulated miRNA profiles have been associated with numerous cancers, including BC. Cell-free miRNAs are abundantly present in a variety of biofluids including urine and make them promising candidates in cancer biomarker discovery.

In the present study, the identification of miRNA fingerprints associated with different BC status was performed by next-generation sequencing on urine samples from 66 BC and 48 controls. Three signatures based on dysregulated miRNAs have been identified by regression models, assessing the power to discriminate different BC subtypes. Altered miRNAs according to invasiveness and grade were validated by qPCR on 112 cases and 65 controls (among which 46 cases and 16 controls were an independent group of subjects while the rest were replica samples).

The area under the curve (AUC) computed including three miRNAs (miR-30a-5p, let-7c-5p and miR-486-5p) altered in all BC subtypes showed a significantly increased accuracy in the discrimination of cases and controls (AUC model = 0.70; p-value = 0.01).

In conclusions, the non-invasive detection in urine of a selected number of miRNAs altered in different BC subtypes could lead to an accurate early diagnosis of cancer and stratification of patients.

Circulating miRNAs, isomiRs and small RNA clusters in human plasma and breast milk

Circulating small RNAs, including miRNAs but also isomiRs and other RNA species, have the potential to be used as non-invasive biomarkers for communicable and non-communicable diseases. This study aims to characterize and compare small RNA profiles in human biofluids. For this purpose, RNA was extracted from plasma and breast milk samples from 15 healthy postpartum mothers. Small RNA libraries were prepared with the NEBNext® small RNA library preparation kit and sequenced in an Illumina HiSeq2000 platform. miRNAs, isomiRs and clusters of small RNAs were annotated using seqBuster/seqCluster framework in 5 plasma and 10 milk samples that passed the initial quality control. The RNA yield was 81 ng/mL [standard deviation (SD): 41] and 3985 ng/mL (SD: 3767) for plasma and breast milk, respectively. Mean number of good quality reads was 4.04 million (M) (40.01% of the reads) in plasma and 12.5M (89.6%) in breast milk. One thousand one hundred eighty two miRNAs, 12,084 isomiRs and 1,053 small RNA clusters that included piwi-interfering RNAs (piRNAs), tRNAs, small nucleolar RNAs (snoRNA) and small nuclear RNAs (snRNAs) were detected. Samples grouped by biofluid, with 308 miRNAs, 1,790 isomiRs and 778 small RNA clusters differentially detected. In summary, plasma and milk showed a different small RNA profile. In both, miRNAs, piRNAs, tRNAs, snRNAs, and snoRNAs were identified, confirming the presence of non-miRNA species in plasma, and describing them for the first time in milk.

Finite-size effects in transcript sequencing count distribution: its power-law correction necessarily precedes downstream normalization and comparative analysis

BACKGROUND

Though earlier works on modelling transcript abundance from vertebrates to lower eukaroytes have specifically singled out the Zip's law, the observed distributions often deviate from a single power-law slope. In hindsight, while power-laws of critical phenomena are derived asymptotically under the conditions of infinite observations, real world observations are finite where the finite-size effects will set in to force a power-law distribution into an exponential decay and consequently, manifests as a curvature (i.e., varying exponent values) in a log-log plot. If transcript abundance is truly power-law distributed, the varying exponent signifies changing mathematical moments (e.g., mean, variance) and creates heteroskedasticity which compromises statistical rigor in analysis. The impact of this deviation from the asymptotic power-law on sequencing count data has never truly been examined and quantified.

RESULTS

The anecdotal description of transcript abundance being almost Zipf's law-like distributed can be conceptualized as the imperfect mathematical rendition of the Pareto power-law distribution when subjected to the finite-size effects in the real world; This is regardless of the advancement in sequencing technology since sampling is finite in practice. Our conceptualization agrees well with our empirical analysis of two modern day NGS (Next-generation sequencing) datasets: an in-house generated dilution miRNA study of two gastric cancer cell lines (NUGC3 and AGS) and a publicly available spike-in miRNA data; Firstly, the finite-size effects causes the deviations of sequencing count data from Zipf's law and issues of reproducibility in sequencing experiments. Secondly, it manifests as heteroskedasticity among experimental replicates to bring about statistical woes. Surprisingly, a straightforward power-law correction that restores the distribution distortion to a single exponent value can dramatically reduce data heteroskedasticity to invoke an instant increase in signal-to-noise ratio by 50% and the statistical/detection sensitivity by as high as 30% regardless of the downstream mapping and normalization methods. Most importantly, the power-law correction improves concordance in significant calls among different normalization methods of a data series averagely by 22%. When presented with a higher sequence depth (4 times difference), the improvement in concordance is asymmetrical (32% for the higher sequencing depth instance versus 13% for the lower instance) and demonstrates that the simple power-law correction can increase significant detection with higher sequencing depths. Finally, the correction dramatically enhances the statistical conclusions and eludes the metastasis potential of the NUGC3 cell line against AGS of our dilution analysis.

CONCLUSIONS

The finite-size effects due to undersampling generally plagues transcript count data with reproducibility issues but can be minimized through a simple power-law correction of the count distribution. This distribution correction has direct implication on the biological interpretation of the study and the rigor of the scientific findings.

REVIEWERS

This article was reviewed by Oliviero Carugo, Thomas Dandekar and Sandor Pongor.

iSmaRT: a toolkit for a comprehensive analysis of small RNA-Seq data

Summary

The interest in investigating the biological roles of small non-coding RNAs (sncRNAs) is increasing, due to the pleiotropic effects of these molecules exert in many biological contexts. While several methods and tools are available to study microRNAs (miRNAs), only few focus on novel classes of sncRNAs, in particular PIWI-interacting RNAs (piRNAs). To overcome these limitations, we implemented iSmaRT ( i ntegrative Sm all R NA T ool-kit), an automated pipeline to analyze smallRNA-Seq data.

Availability and Implementation

iSmaRT is a collection of bioinformatics tools and own algorithms, interconnected through a Graphical User Interface (GUI). In addition to performing comprehensive analyses on miRNAs, it implements specific computational modules to analyze piRNAs, predicting novel ones and identifying their RNA targets. A smallRNA-Seq dataset generated from brain samples of Huntington's Disease patients was used here to illustrate iSmaRT performances, demonstrating how the pipeline can provide, in a rapid and user friendly way, a comprehensive analysis of different classes of sncRNAs. iSmaRT is freely available on the web at ftp://labmedmolge-1.unisa.it (User: iSmart - Password: password).

Contact

aweisz@unisa.it or ggiurato@unisa.it.

Supplementary information

Supplementary data are available at Bioinformatics online.

Impact of Dietary Interventions on Noncoding RNA Networks and mRNAs Encoding Chromatin-Related Factors

Dietary interventions dramatically affect metabolic disease and lifespan in various aging models. Here, we profiled liver microRNA (miRNA), coding, and long non-coding RNA (lncRNA) expression by high-throughput deep sequencing in mice across multiple energy intake and expenditure interventions. Strikingly, three dietary intervention network design patterns were uncovered: (1) lifespan-extending interventions largely repressed the expression of miRNAs, lncRNAs, and transposable elements; (2) protein-coding mRNAs with expression positively correlated with long lifespan are highly targeted by miRNAs; and (3) miRNA-targeting interactions mainly target chromatin-related functions. We experimentally validated miR-34a, miR-107, and miR-212-3p targeting of the chromatin remodeler Chd1 and further demonstrate that Chd1 knockdown mimics high-fat diet and aging-induced gene expression changes and activation of transposons. Our findings demonstrate lifespan-extending diets repress miRNA-chromatin remodeler interactions and safeguard against deregulated transcription induced by aging and lifespan shortening diets, events linked by microRNA, chromatin, and ncRNA crosstalk.

Network analysis of EMT and MET micro-RNA regulation in breast cancer

Over the last years, microRNAs (miRs) have shown to be crucial for breast tumour establishment and progression. To understand the influence that miRs have over transcriptional regulation in breast cancer, we constructed mutual information networks from 86 TCGA matched breast invasive carcinoma and control tissue RNA-Seq and miRNA-Seq sequencing data. We show that miRs are determinant for tumour and control data network structure. In tumour data network, miR-200, miR-199 and neighbour miRs seem to cooperate on the regulation of the acquisition of epithelial and mesenchymal traits by the biological processes: Epithelial-Mesenchymal Transition (EMT) and Mesenchymal to Epithelial Transition (MET). Despite structural differences between tumour and control networks, we found a conserved set of associations between miR-200 family members and genes such as VIM, ZEB-1/2 and TWIST-1/2. Further, a large number of miRs observed in tumour network mapped to a specific chromosomal location in DLK1-DIO3 (Chr14q32); some of those miRs have also been associated with EMT and MET regulation. Pathways related to EMT and TGF-beta reinforce the relevance of miR-200, miR-199 and DLK1-DIO3 cluster in breast cancer. With this approach, we stress that miR inclusion in gene regulatory network construction improves our understanding of the regulatory mechanisms underlying breast cancer biology.

miRNA Profile in Three Different Normal Human Ocular Tissues by miRNA-Seq

Purpose

Because microRNAs (miRNAs) have been associated with eye diseases, our study aims to profile ocular miRNA expression in normal human ciliary body (CB), cornea, and trabecular meshwork (TM) using miRNA-Seq to provide a foundation for better understanding of miRNA function and disease involvement in these tissues.

Methods

Total RNAs were extracted from seven normal human CB, seven cornea, and seven TM samples using mirVana total RNA isolation kit. miRNA-Seq was done with Illumina MiSeq. Bowtie software was used to trim and align generated sequence reads, and only exact matches to mature miRNAs from miRBase were included. The miRTarBase database was used to analyze miRNA target interactions, and the expression of five selected miRNAs was validated using droplet digital PCR (ddPCR).

Results

Using the miRNA extracted from 21 human samples, we found 378 miRNAs collectively expressed, of which the 11 most abundant miRNAs represented 80% of the total normalized reads. We also identified uniquely expressed miRNAs, of which five share 18 highly validated gene targets, and created a profile of miRNAs known to target genes associated with keratoconus and glaucoma. Using ddPCR, we validated the expression profile of five miRNAs from miRNA-Seq.

Conclusions

For the first time, we profiled miRNA expression in three human ocular tissues using miRNA-Seq, identifying many miRNAs that had not been previously reported in ocular tissue. Defining the relative expression of miRNAs in nondiseased eye tissues could help uncover changes in miRNA expression that accompany diseases such as glaucoma and keratoconus.

The Importance of Standardization on Analyzing Circulating RNA

Circulating RNAs, especially microRNAs (miRNAs), have recently emerged as non-invasive disease biomarkers. Despite enthusiasm and numerous reports on disease-associated circulating miRNAs, currently there is no circulating miRNA-based diagnostic in use. In addition, there are many contradictory reports on the concentration changes of specific miRNA in circulation. Here we review the impact of various technical and non-technical factors related to circulating miRNA measurement and elucidate the importance of having a general guideline for sample preparation and concentration measurement in studying circulating RNA.

Genome-Wide Analyses of MicroRNA Profiling in Interleukin-27 Treated Monocyte-Derived Human Dendritic Cells Using Deep Sequencing: A Pilot Study

MicroRNAs (miRNAs) regulate gene expression and thereby influence cell fate and function. Recent studies suggest that an abundant class of miRNAs play important roles in immune cells, such as T cells, natural killer (NK) cells, B cells, and dendritic cells (DCs). Interleukin (IL)-27 is a member of the IL-12 family of cytokines with broad anti-viral effects. It is a potent inhibitor of HIV-1 infection in CD4+ T cells and macrophages, as well as monocyte-derived immature dendritic cells (iDCs). This pilot study compared miRNA profiles between iDCs and IL-27-treated iDCs (27DCs) using deep sequencing methods and identified 46 known miRNAs that were significantly differentially expressed in 27DCs: 36 were upregulated and 10 downregulated by IL-27. Many of the potential target genes of these miRNAs are involved in IL-27 associated pathways, such as JAK/STAT, MAPKs, and PI3K and several were also previously reported to be involved in the regulation of human DC function. This study found that these miRNAs also potentially target several viral genomes and therefore may have antiviral effects. Four of these differential miRNAs (miR-99a-5p, miR-222-3p, miR-138-5p, and miR-125b-5p) were validated using quantitative reverse transcription PCR (RT-qPCR). Twenty-two novel miRNAs were discovered from deep sequencing and confirmed using RT-qPCR. This study furthers the understanding of the role of IL-27 in immunity and lays a foundation for future characterization of the role of specific miRNAs in DCs.

Circulating microRNAs as Potential Biomarkers of Infectious Disease

microRNAs (miRNAs) are a class of small non-coding endogenous RNA molecules that regulate a wide range of biological processes by post-transcriptionally regulating gene expression. Thousands of these molecules have been discovered to date, and multiple miRNAs have been shown to coordinately fine-tune cellular processes key to organismal development, homeostasis, neurobiology, immunobiology, and control of infection. The fundamental regulatory role of miRNAs in a variety of biological processes suggests that differential expression of these transcripts may be exploited as a novel source of molecular biomarkers for many different disease pathologies or abnormalities. This has been emphasized by the recent discovery of remarkably stable miRNAs in mammalian biofluids, which may originate from intracellular processes elsewhere in the body. The potential of circulating miRNAs as biomarkers of disease has mainly been demonstrated for various types of cancer. More recently, however, attention has focused on the use of circulating miRNAs as diagnostic/prognostic biomarkers of infectious disease; for example, human tuberculosis caused by infection with Mycobacterium tuberculosis, sepsis caused by multiple infectious agents, and viral hepatitis. Here, we review these developments and discuss prospects and challenges for translating circulating miRNA into novel diagnostics for infectious disease.

miRNAs differentially expressed by next-generation sequencing in cord blood buffy coat samples of boys and girls

AIM

Differences in children's development and susceptibility to diseases and exposures have been observed by sex, yet human studies of sex differences in miRNAs are limited.

MATERIALS & METHODS

The genome-wide miRNA expression was characterized by sequencing-based EdgeSeq assay in cord blood buffy coats from 89 newborns, and 564 miRNAs were further analyzed.

RESULTS

Differential expression of most miRNAs was higher in boys. Neurodevelopment, RNA metabolism and metabolic ontology terms were enriched among miRNA targets. The majority of upregulated miRNAs (86%) validated by nCounter maintained positive-fold change values; however, only 21% reached statistical significance by false discovery rate.

CONCLUSION

Accounting for host factors like sex may improve the sensitivity of epigenetic analyses for epidemiological studies in early childhood.

Identification and Expression Profiling of miRNAome in Goat longissimus dorsi Muscle from Prenatal Stages to a Neonatal Stage

Skeletal muscle development is a complex biological process regulated by numerous genes and non-coding RNAs, such as microRNAs (miRNAs). In the current study, we made use of the deep sequencing data from Jianzhou Da’er goat longissimus dorsi sampled on days 45, 60, and 105 of gestation, as well as day three after birth to identify miRNAs that regulate goat skeletal myogenesis, and examine their temporal expression profiles. A total of 410 known goat miRNAs, 752 miRNA homologs and 88 novel miRNAs were identified across four stages. Besides three myomiRs, the abundance of 17 miRNAs, including chi-miR-424, chi-miR-542-3p and chi-miR-136-5p was more than 10,000 reads per million mapped reads (RPM), on average. Furthermore, 50 miRNAs with more than 100 RPM clustered at the imprinted DLK1–DIO3 locus on chromosome 21 and showed similar expression patterns, indicating that these miRNAs played important roles in skeletal myogenesis of goats. Based on pairwise comparisons, 221 differentially expressed (DE), known miRNAs were identified across four stages. GO and KEGG analyses of the genes targeted by the DE miRNAs revealed the significantly enriched processes and pathways to be consistent with temporal changes of skeletal muscle development across all sampled stages. However, follow-up experimental studies were required to explore functions of these miRNAs and targets underlying skeletal myogenesis.

Novel equine tissue miRNAs and breed-related miRNA expressed in serum

BACKGROUND

MiRNAs regulate multiple genes at the post-transcriptional level and therefore play an important role in many biological processes. It has been suggested that miRNA exported outside the cells contribute to inter-cellular communication. Consequently, circulating miRNAs are of particular interest and are promising biomarkers for many diseases. The number of miRNAs annotated in the horse genome is much lower compared to model organisms like human and mouse. We therefore aimed to identify novel equine miRNAs for tissue types and breed in serum.

RESULTS

We analysed 71 small RNA-seq libraries derived from nine tissues (gluteus medius, platysma, masseter muscle, heart, liver, cartilage, bone, total blood and serum) using miRDeep2 and miRdentify tools. Known miRNAs represented between 2.3 and 62.9 % of the reads in 71 libraries. A total of 683 novel miRNAs were identified. Breed and tissue type affected the number of miRNAs detected and interestingly, affected its average intensity. A total of 50 miRNAs in serum proved to be potential biomarkers to differentiate specific breed types, of which miR-122, miR-200, miR-483 were over-expressed and miR-328 was under-expressed in ponies compared to Warmbloods. The different miRNAs profiles, as well as the differences in their expression levels provide a foundation for more hypotheses based on the novel miRNAs discovered.

CONCLUSIONS

We identified 683 novel equine miRNAs expressed in seven solid tissues, blood and serum. Additionally, our approach evidenced that such data supported identification of specific miRNAs as markers of functions related to breeds or disease tissues.

microRNA-guided diagnostics in clinical samples

miRNA-guided diagnostics is a powerful molecular approach for evaluating clinical samples through miRNA detection and/or visualization. To date, this approach has been successfully used to diagnose, manage, and/or monitor a wide range of neoplastic and non-neoplastic diseases. Despite the promise of miRNA-guided diagnostics, particularly in the field of minimally invasive biomarkers, several knowledge and practical issues confound or hinder translation into routine clinical practice including: miRNA sequence database errors, suboptimal RNA extraction methods, detection assay variability, a vast array of online resources for bioinformatic analyses, and non-standardized statistical analyses for miRNA clinical testing. In this review, we raise awareness of these issues and recommend research directions to help specialists in endocrinology and metabolism integrate miRNA testing into clinical decision-making.

Evaluation of microRNA alignment techniques

Genomic alignment of small RNA (smRNA) sequences such as microRNAs poses considerable challenges due to their short length (∼21 nucleotides [nt]) as well as the large size and complexity of plant and animal genomes. While several tools have been developed for high-throughput mapping of longer mRNA-seq reads (>30 nt), there are few that are specifically designed for mapping of smRNA reads including microRNAs. The accuracy of these mappers has not been systematically determined in the case of smRNA-seq. In addition, it is unknown whether these aligners accurately map smRNA reads containing sequence errors and polymorphisms. By using simulated read sets, we determine the alignment sensitivity and accuracy of 16 short-read mappers and quantify their robustness to mismatches, indels, and nontemplated nucleotide additions. These were explored in the context of a plant genome (Oryza sativa, ∼500 Mbp) and a mammalian genome (Homo sapiens, ∼3.1 Gbp). Analysis of simulated and real smRNA-seq data demonstrates that mapper selection impacts differential expression results and interpretation. These results will inform on best practice for smRNA mapping and enable more accurate smRNA detection and quantification of expression and RNA editing.

Metatranscriptomic analysis of diverse microbial communities reveals core metabolic pathways and microbiome-specific functionality

BACKGROUND

Metatranscriptomics is emerging as a powerful technology for the functional characterization of complex microbial communities (microbiomes). Use of unbiased RNA-sequencing can reveal both the taxonomic composition and active biochemical functions of a complex microbial community. However, the lack of established reference genomes, computational tools and pipelines make analysis and interpretation of these datasets challenging. Systematic studies that compare data across microbiomes are needed to demonstrate the ability of such pipelines to deliver biologically meaningful insights on microbiome function.

RESULTS

Here, we apply a standardized analytical pipeline to perform a comparative analysis of metatranscriptomic data from diverse microbial communities derived from mouse large intestine, cow rumen, kimchi culture, deep-sea thermal vent and permafrost. Sequence similarity searches allowed annotation of 19 to 76% of putative messenger RNA (mRNA) reads, with the highest frequency in the kimchi dataset due to its relatively low complexity and availability of closely related reference genomes. Metatranscriptomic datasets exhibited distinct taxonomic and functional signatures. From a metabolic perspective, we identified a common core of enzymes involved in amino acid, energy and nucleotide metabolism and also identified microbiome-specific pathways such as phosphonate metabolism (deep sea) and glycan degradation pathways (cow rumen). Integrating taxonomic and functional annotations within a novel visualization framework revealed the contribution of different taxa to metabolic pathways, allowing the identification of taxa that contribute unique functions.

CONCLUSIONS

The application of a single, standard pipeline confirms that the rich taxonomic and functional diversity observed across microbiomes is not simply an artefact of different analysis pipelines but instead reflects distinct environmental influences. At the same time, our findings show how microbiome complexity and availability of reference genomes can impact comprehensive annotation of metatranscriptomes. Consequently, beyond the application of standardized pipelines, additional caution must be taken when interpreting their output and performing downstream, microbiome-specific, analyses. The pipeline used in these analyses along with a tutorial has been made freely available for download from our project website: http://www.compsysbio.org/microbiome .

A Comprehensive Prescription for Plant miRNA Identification

microRNAs (miRNAs) are tiny ribo-regulatory molecules involved in various essential pathways for persistence of cellular life, such as development, environmental adaptation, and stress response. In recent years, miRNAs have become a major focus in molecular biology because of their functional and diagnostic importance. This interest in miRNA research has resulted in the development of many specific software and pipelines for the identification of miRNAs and their specific targets, which is the key for the elucidation of miRNA-modulated gene expression. While the well-recognized importance of miRNAs in clinical research pushed the emergence of many useful computational identification approaches in animals, available software and pipelines are fewer for plants. Additionally, existing approaches suffers from mis-identification and annotation of plant miRNAs since the miRNA mining process for plants is highly prone to false-positives, particularly in cereals which have a highly repetitive genome. Our group developed a homology-based in silico miRNA identification approach for plants, which utilizes two Perl scripts "SUmirFind" and "SUmirFold" and since then, this method helped identify many miRNAs particularly from crop species such as Triticum or Aegliops. Herein, we describe a comprehensive updated guideline by the implementation of two new scripts, "SUmirPredictor" and "SUmirLocator," and refinements to our previous method in order to identify genuine miRNAs with increased sensitivity in consideration of miRNA identification problems in plants. Recent updates enable our method to provide more reliable and precise results in an automated fashion in addition to solutions for elimination of most false-positive predictions, miRNA naming and miRNA mis-annotation. It also provides a comprehensive view to genome/transcriptome-wide location of miRNA precursors as well as their association with transposable elements. The "SUmirPredictor" and "SUmirLocator" scripts are freely available together with a reference high-confidence plant miRNA list.

iSRAP - a one-touch research tool for rapid profiling of small RNA-seq data

Small non-coding RNAs have been significantly recognized as the key modulators in many biological processes, and are emerging as promising biomarkers for several diseases. These RNA species are transcribed in cells and can be packaged in extracellular vesicles, which are small vesicles released from many biotypes, and are involved in intercellular communication. Currently, the advent of next-generation sequencing (NGS) technology for high-throughput profiling has further advanced the biological insights of non-coding RNA on a genome-wide scale and has become the preferred approach for the discovery and quantification of non-coding RNA species. Despite the routine practice of NGS, the processing of large data sets poses difficulty for analysis before conducting downstream experiments. Often, the current analysis tools are designed for specific RNA species, such as microRNA, and are limited in flexibility for modifying parameters for optimization. An analysis tool that allows for maximum control of different software is essential for drawing concrete conclusions for differentially expressed transcripts. Here, we developed a one-touch integrated small RNA analysis pipeline (iSRAP) research tool that is composed of widely used tools for rapid profiling of small RNAs. The performance test of iSRAP using publicly and in-house available data sets shows its ability of comprehensive profiling of small RNAs of various classes, and analysis of differentially expressed small RNAs. iSRAP offers comprehensive analysis of small RNA sequencing data that leverage informed decisions on the downstream analyses of small RNA studies, including extracellular vesicles such as exosomes.

microRNAs as pharmacogenomic biomarkers for drug efficacy and drug safety assessment

Much evidence has documented that microRNAs (miRNAs) play an important role in the modulation of interindividual variability in the production of drug metabolizing enzymes and transporters (DMETs) and nuclear receptors (NRs) through multidirectional interactions involving environmental stimuli/stressors, the expression of miRNA molecules and genetic polymorphisms. MiRNA expression has been reported to be affected by drugs and miRNAs themselves may affect drug metabolism and toxicity. In cancer research, miRNA biomarkers have been identified to mediate intrinsic and acquired resistance to cancer therapies. In drug safety assessment, miRNAs have been found associated with cardiotoxicity, hepatotoxicity and nephrotoxicity. This review article summarizes published studies to show that miRNAs can serve as early biomarkers for the evaluation of drug efficacy and drug safety.

Diagnostic biases in translational bioinformatics

BACKGROUND

With the surge of translational medicine and computational omics research, complex disease diagnosis is more and more relying on massive omics data-driven molecular signature detection. However, how to detect and prevent possible diagnostic biases in translational bioinformatics remains an unsolved problem despite its importance in the coming era of personalized medicine.

METHODS

In this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines for different model selection methods. We further categorize the diagnostic biases into different types by conducting rigorous kernel matrix analysis and provide effective machine learning methods to conquer the diagnostic biases.

RESULTS

In this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines. We have found that the diagnostic biases happen for data with different distributions and SVM with different kernels. Moreover, we identify total three types of diagnostic biases: overfitting bias, label skewness bias, and underfitting bias in SVM diagnostics, and present corresponding reasons through rigorous analysis. Compared with the overfitting and underfitting biases, the label skewness bias is more challenging to detect and conquer because it can be easily confused as a normal diagnostic case from its deceptive accuracy. To tackle this problem, we propose a derivative component analysis based support vector machines to conquer the label skewness bias by achieving the rivaling clinical diagnostic results.

CONCLUSIONS

Our studies demonstrate that the diagnostic biases are mainly caused by the three major factors, i.e. kernel selection, signal amplification mechanism in high-throughput profiling, and training data label distribution. Moreover, the proposed DCA-SVM diagnosis provides a generic solution for the label skewness bias overcome due to the powerful feature extraction capability from derivative component analysis. Our work identifies and solves an important but less addressed problem in translational research. It also has a positive impact on machine learning for adding new results to kernel-based learning for omics data.