miRNA target enrichment analysis reveals directly active miRNAs in health and disease

Decoupled degradation and translation enables noise modulation by poly(A) tails

Poly(A) tails are crucial for mRNA translation and degradation, but the exact relationship between tail length and mRNA kinetics remains unclear. Here, we employ a small library of identical mRNAs that differ only in their poly(A)-tail length to examine their behavior in human embryonic kidney cells. We find that tail length strongly correlates with mRNA degradation rates but is decoupled from translation. Interestingly, an optimal tail length of ∼100 nt displays the highest translation rate, which is identical to the average endogenous tail length measured by nanopore sequencing. Furthermore, poly(A)-tail length variability-a feature of endogenous mRNAs-impacts translation efficiency but not mRNA degradation rates. Stochastic modeling combined with single-cell tracking reveals that poly(A) tails provide cells with an independent handle to tune gene expression fluctuations by decoupling mRNA degradation and translation. Together, this work contributes to the basic understanding of gene expression regulation and has potential applications in nucleic acid therapeutics.

PanomiR: a systems biology framework for analysis of multi-pathway targeting by miRNAs

Charting microRNA (miRNA) regulation across pathways is key to characterizing their function. Yet, no method currently exists that can quantify how miRNAs regulate multiple interconnected pathways or prioritize them for their ability to regulate coordinate transcriptional programs. Existing methods primarily infer one-to-one relationships between miRNAs and pathways using differentially expressed genes. We introduce PanomiR, an in silico framework for studying the interplay of miRNAs and disease functions. PanomiR integrates gene expression, mRNA-miRNA interactions and known biological pathways to reveal coordinated multi-pathway targeting by miRNAs. PanomiR utilizes pathway-activity profiling approaches, a pathway co-expression network and network clustering algorithms to prioritize miRNAs that target broad-scale transcriptional disease phenotypes. It directly resolves differential regulation of pathways, irrespective of their differential gene expression, and captures co-activity to establish functional pathway groupings and the miRNAs that may regulate them. PanomiR uses a systems biology approach to provide broad but precise insights into miRNA-regulated functional programs. It is available at https://bioconductor.org/packages/PanomiR.

miRNAs as Biomarkers in Diabetes: Moving towards Precision Medicine

Diabetes mellitus (DM) is a complex metabolic disease with many specifically related complications. Early diagnosis of this disease could prevent the progression to overt disease and its related complications. There are several limitations to using existing biomarkers, and between 24% and 62% of people with diabetes remain undiagnosed and untreated, suggesting a large gap in current diagnostic practices. Early detection of the percentage of insulin-producing cells preceding loss of function would allow for effective therapeutic interventions that could delay or slow down the onset of diabetes. MicroRNAs (miRNAs) could be used for early diagnosis, as well as for following the progression and the severity of the disease, due to the fact of their pancreatic specific expression and stability in various body fluids. Thus, many studies have focused on the identification and validation of such groups or "signatures of miRNAs" that may prove useful in diagnosing or treating patients. Here, we summarize the findings on miRNAs as biomarkers in diabetes and those associated with direct cellular reprogramming strategies, as well as the relevance of miRNAs that act as a bidirectional switch for cell therapy of damaged pancreatic tissue and the studies that have measured and tracked miRNAs as biomarkers in insulin resistance are addressed.

enrichMiR predicts functionally relevant microRNAs based on target collections

MicroRNAs (miRNAs) are small non-coding RNAs that are among the main post-transcriptional regulators of gene expression. A number of data collections and prediction tools have gathered putative or confirmed targets of these regulators. It is often useful, for discovery and validation, to harness such collections to perform target enrichment analysis in given transcriptional signatures or gene-sets in order to predict involved miRNAs. While several methods have been proposed to this end, a flexible and user-friendly interface for such analyses using various approaches and collections is lacking. enrichMiR (https://ethz-ins.org/enrichMiR/) addresses this gap by enabling users to perform a series of enrichment tests, based on several target collections, to rank miRNAs according to their likely involvement in the control of a given transcriptional signature or gene-set. enrichMiR results can furthermore be visualised through interactive and publication-ready plots. To guide the choice of the appropriate analysis method, we benchmarked various tests across a panel of experiments involving the perturbation of known miRNAs. Finally, we showcase enrichMiR functionalities in a pair of use cases.

Differential and Common Signatures of miRNA Expression and Methylation in Childhood Central Nervous System Malignancies: An Experimental and Computational Approach

Epigenetic modifications are considered of utmost significance for tumor ontogenesis and progression. Especially, it has been found that miRNA expression, as well as DNA methylation plays a significant role in central nervous system tumors during childhood. A total of 49 resected brain tumors from children were used for further analysis. DNA methylation was identified with methylation-specific MLPA and, in particular, for the tumor suppressor genes CASP8, RASSF1, MGMT, MSH6, GATA5, ATM1, TP53, and CADM1. miRNAs were identified with microarray screening, as well as selected samples, were tested for their mRNA expression levels. CASP8, RASSF1 were the most frequently methylated genes in all tumor samples. Simultaneous methylation of genes manifested significant results with respect to tumor staging, tumor type, and the differentiation of tumor and control samples. There was no significant dependence observed with the methylation of one gene promoter, rather with the simultaneous presence of all detected methylated genes' promoters. miRNA expression was found to be correlated to gene methylation. Epigenetic regulation appears to be of major importance in tumor progression and pathophysiology, making it an imperative field of study.

miRNA activity inferred from single cell mRNA expression

High throughput single-cell RNA sequencing (scRNAseq) can provide mRNA expression profiles for thousands of cells. However, miRNAs cannot currently be studied at the same scale. By exploiting that miRNAs bind well-defined sequence motifs and typically down-regulate target genes, we show that motif enrichment analysis can be used to derive miRNA activity estimates from scRNAseq data. Motif enrichment analyses have traditionally been used to derive binding motifs for regulatory factors, such as miRNAs or transcription factors, that have an effect on gene expression. Here we reverse its use. By starting from the miRNA seed site, we derive a measure of activity for miRNAs in single cells. We first establish the approach on a comprehensive set of bulk TCGA cancer samples (n = 9679), with paired mRNA and miRNA expression profiles, where many miRNAs show a strong correlation with measured expression. By downsampling we show that the method can be used to estimate miRNA activity in sparse data comparable to scRNAseq experiments. We then analyze a human and a mouse scRNAseq data set, and show that for several miRNA candidates, including liver specific miR-122 and muscle specific miR-1 and miR-133a, we obtain activity measures supported by the literature. The methods are implemented and made available in the miReact software. Our results demonstrate that miRNA activities can be estimated at the single cell level. This allows insights into the dynamics of miRNA activity across a range of fields where scRNAseq is applied.

miRNA normalization enables joint analysis of several datasets to increase sensitivity and to reveal novel miRNAs differentially expressed in breast cancer

Different miRNA profiling protocols and technologies introduce differences in the resulting quantitative expression profiles. These include differences in the presence (and measurability) of certain miRNAs. We present and examine a method based on quantile normalization, Adjusted Quantile Normalization (AQuN), to combine miRNA expression data from multiple studies in breast cancer into a single joint dataset for integrative analysis. By pooling multiple datasets, we obtain increased statistical power, surfacing patterns that do not emerge as statistically significant when separately analyzing these datasets. To merge several datasets, as we do here, one needs to overcome both technical and batch differences between these datasets. We compare several approaches for merging and jointly analyzing miRNA datasets. We investigate the statistical confidence for known results and highlight potential new findings that resulted from the joint analysis using AQuN. In particular, we detect several miRNAs to be differentially expressed in estrogen receptor (ER) positive versus ER negative samples. In addition, we identify new potential biomarkers and therapeutic targets for both clinical groups. As a specific example, using the AQuN-derived dataset we detect hsa-miR-193b-5p to have a statistically significant over-expression in the ER positive group, a phenomenon that was not previously reported. Furthermore, as demonstrated by functional assays in breast cancer cell lines, overexpression of hsa-miR-193b-5p in breast cancer cell lines resulted in decreased cell viability in addition to inducing apoptosis. Together, these observations suggest a novel functional role for this miRNA in breast cancer. Packages implementing AQuN are provided for Python and Matlab: https://github.com/YakhiniGroup/PyAQN.

This work demonstrates a practical approach to the joint-analysis of multiple miRNA expression profiling datasets acquired with different measurement technologies. The use of different platforms in miRNA profiling can lead to major differences in results. In particular, some miRNA species are less amenable to detection and quantification by certain platforms or designs. Our approach, termed AQuN, combines quantile normalization with special attention to missing entities, to normalize miRNA expression across datasets, technologies, designs and platforms. As we show, our proposed approach uncovers patterns of interest that would not have emerged as statistically significant when analyzing the datasets individually or with other standard-practice normalization methods.

Amongst our findings, we noted a previously undocumented miRNA that is significantly over-expressed in samples from estrogen-receptor positive breast cancer patients as compared to samples from estrogen-receptor negative patients. We further investigated this miRNA, hsa-miR-193b-5p, and experimentally show, in cell lines, that its expression level impacts the viability of tumor cells. AQuN is available to the community in the form of Python and Matlab packages. The joint-processed data is also made available for further investigation.

MiR-29 coordinates age-dependent plasticity brakes in the adult visual cortex

Visual cortical circuits show profound plasticity during early life and are later stabilized by molecular "brakes" limiting excessive rewiring beyond a critical period. The mechanisms coordinating the expression of these factors during the transition from development to adulthood remain unknown. We found that miR-29a expression in the visual cortex dramatically increases with age, but it is not experience-dependent. Precocious high levels of miR-29a blocked ocular dominance plasticity and caused an early appearance of perineuronal nets. Conversely, inhibition of miR-29a in adult mice using LNA antagomirs activated ocular dominance plasticity, reduced perineuronal nets, and restored their juvenile chemical composition. Activated adult plasticity had the typical functional and proteomic signature of critical period plasticity. Transcriptomic and proteomic studies indicated that miR-29a manipulation regulates the expression of plasticity brakes in specific cortical circuits. These data indicate that miR-29a is a regulator of the plasticity brakes promoting age-dependent stabilization of visual cortical connections.

Robust partitioning of microRNA targets from downstream regulatory changes

The biological impact of microRNAs (miRNAs) is determined by their targets, and robustly identifying direct miRNA targets remains challenging. Existing methods suffer from high false-positive rates and are unable to effectively differentiate direct miRNA targets from downstream regulatory changes. Here, we present an experimental and computational framework to deconvolute post-transcriptional and transcriptional changes using a combination of RNA-seq and PRO-seq. This novel approach allows us to systematically profile the regulatory impact of a miRNA. We refer to this approach as CARP: Combined Analysis of RNA-seq and PRO-seq. We apply CARP to multiple miRNAs and show that it robustly distinguishes direct targets from downstream changes, while greatly reducing false positives. We validate our approach using Argonaute eCLIP-seq and ribosome profiling, demonstrating that CARP defines a comprehensive repertoire of targets. Using this approach, we identify miRNA-specific activity of target sites within the open reading frame. Additionally, we show that CARP facilitates the dissection of complex changes in gene regulatory networks triggered by miRNAs and identification of transcription factors that mediate downstream regulatory changes. Given the robustness of the approach, CARP would be particularly suitable for dissecting miRNA regulatory networks in vivo.

Placental microRNAs: Responders to environmental chemicals and mediators of pathophysiology of the human placenta

MicroRNAs (miRNAs) are epigenetic modifiers that play an important role in the regulation of the expression of genes across the genome. miRNAs are expressed in the placenta as well as other organs, and are involved in several biological processes including the regulation of trophoblast differentiation, migration, invasion, proliferation, apoptosis, angiogenesis and cellular metabolism. Related to their role in disease process, miRNAs have been shown to be differentially expressed between normal placentas and placentas obtained from women with pregnancy/health complications such as preeclampsia, gestational diabetes mellitus, and obesity. This dysregulation indicates that miRNAs in the placenta likely play important roles in the pathogenesis of diseases during pregnancy. Furthermore, miRNAs in the placenta are susceptible to altered expression in relation to exposure to environmental toxicants. With relevance to the placenta, the dysregulation of miRNAs in both placenta and blood has been associated with maternal exposures to several toxicants. In this review, we provide a summary of miRNAs that have been assessed in the context of human pregnancy-related diseases and in relation to exposure to environmental toxicants in the placenta. Where data are available, miRNAs are discussed in their context as biomarkers of exposure and/or disease, with comparisons made across-tissue types, and conservation across studies detailed.

maTE: discovering expressed interactions between microRNAs and their targets

MOTIVATION

Disease is often manifested via changes in transcript and protein abundance. MicroRNAs (miRNAs) are instrumental in regulating protein abundance and may measurably influence transcript levels. miRNAs often target more than one mRNA (for humans, the average is three), and mRNAs are often targeted by more than one miRNA (for the genes considered in this study, the average is also three). Therefore, it is difficult to determine the miRNAs that may cause the observed differential gene expression. We present a novel approach, maTE, which is based on machine learning, that integrates information about miRNA target genes with gene expression data. maTE depends on the availability of a sufficient amount of patient and control samples. The samples are used to train classifiers to accurately classify the samples on a per miRNA basis. Multiple high scoring miRNAs are used to build a final classifier to improve separation.

RESULTS

The aim of the study is to find a set of miRNAs causing the regulation of their target genes that best explains the difference between groups (e.g. cancer versus control). maTE provides a list of significant groups of genes where each group is targeted by a specific miRNA. For the datasets used in this study, maTE generally achieves an accuracy well above 80%. Also, the results show that when the accuracy is much lower (e.g. ∼50%), the set of miRNAs provided is likely not causative of the difference in expression. This new approach of integrating miRNA regulation with expression data yields powerful results and is independent of external labels and training data. Thereby, this approach allows new avenues for exploring miRNA regulation and may enable the development of miRNA-based biomarkers and drugs.

AVAILABILITY AND IMPLEMENTATION

The KNIME workflow, implementing maTE, is available at Bioinformatics online.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MIENTURNET: an interactive web tool for microRNA-target enrichment and network-based analysis

BACKGROUND

miRNAs regulate the expression of several genes with one miRNA able to target multiple genes and with one gene able to be simultaneously targeted by more than one miRNA. Therefore, it has become indispensable to shorten the long list of miRNA-target interactions to put in the spotlight in order to gain insight into understanding the regulatory mechanism orchestrated by miRNAs in various cellular processes. A reasonable solution is certainly to prioritize miRNA-target interactions to maximize the effectiveness of the downstream analysis.

RESULTS

We propose a new and easy-to-use web tool MIENTURNET (MicroRNA ENrichment TURned NETwork) that receives in input a list of miRNAs or mRNAs and tackles the problem of prioritizing miRNA-target interactions by performing a statistical analysis followed by a fully featured network-based visualization and analysis. The statistics is used to assess the significance of an over-representation of miRNA-target interactions and then MIENTURNET filters based on the statistical significance associated with each miRNA-target interaction. In addition, the holistic approach of the network theory is used to infer possible evidences of miRNA regulation by capturing emergent properties of the miRNA-target regulatory network that would be not evident through a pairwise analysis of the individual components.

CONCLUSION

MIENTURNET offers the possibility to consistently perform both statistical and network-based analyses by using only a single tool leading to a more effective prioritization of the miRNA-target interactions. This has the potential to avoid researchers without computational and informatics skills to navigate multiple websites and thus to independently investigate miRNA activity in every cellular process of interest in an easy and at the same time exhaustive way thanks to the intuitive web interface. The web application along with a well-documented and comprehensive user guide are freely available at http://userver.bio.uniroma1.it/apps/mienturnet/ without any login requirement.

Gene set analysis methods for the functional interpretation of non-mRNA data—Genomic range and ncRNA data

Gene set analysis (GSA) is one of the methods of choice for analyzing the results of current omics studies; however, it has been mainly developed to analyze mRNA (microarray, RNA-Seq) data. The following review includes an update regarding general methods and resources for GSA and then emphasizes GSA methods and tools for non-mRNA omics datasets, specifically genomic range data (ChIP-Seq, SNP and methylation) and ncRNA data (miRNAs, lncRNAs and others). In the end, the state of the GSA field for non-mRNA datasets is discussed, and some current challenges and trends are highlighted, especially the use of network approaches to face complexity issues.

MicroRNA-10a-5p regulates macrophage polarization and promotes therapeutic adipose tissue remodeling

Objective

This study investigated the role of microRNAs generated from adipose tissue macrophages (ATMs) during adipose tissue remodeling induced by pharmacological and nutritional stimuli.

Methods

Macrophage-specific Dicer knockout (KO) mice were used to determine the roles of microRNA generated in macrophages in adipose tissue remodeling induced by the β3-adrenergic receptor agonist CL316,243 (CL). RNA-seq was performed to characterize microRNA and mRNA expression profiles in isolated macrophages and PDGFRα+ adipocyte stem cells (ASCs). The role of miR-10a-5p was further investigated in cell culture, and in adipose tissue remodeling induced by CL treatment and high fat feeding.

Results

Macrophage-specific deletion of Dicer elevated pro-inflammatory gene expression and prevented CL-induced de novo beige adipogenesis in gonadal white adipose tissue (gWAT). Co-culture of ASCs with ATMs of wild type mice promoted brown adipocyte gene expression upon differentiation, but co-culture with ATMs of Dicer KO mice did not. Bioinformatic analysis of RNA expression profiles identified miR-10a-5p as a potential regulator of inflammation and differentiation in ATMs and ASCs, respectively. CL treatment increased levels of miR-10a-5p in ATMs and ASCs in gWAT. Interestingly, CL treatment elevated levels of pre-mir-10a in ATMs but not in ASCs, suggesting possible transfer from ATMs to ASCs. Elevating miR-10a-5p levels inhibited proinflammatory gene expression in cultured RAW 264.7 macrophages and promoted the differentiation of C3H10T1/2 cells into brown adipocytes. Furthermore, treatment with a miR-10a-5p mimic in vivo rescued CL-induced beige adipogenesis in Dicer KO mice. High fat feeding reduced miR-10a-5p levels in ATMs of gWAT, and treatment of mice with a miR-10a-5p mimic suppressed pro-inflammatory responses, promoted the appearance of new white adipocytes in gWAT, and improved systemic glucose tolerance.

Conclusions

These results demonstrate an important role of macrophage-generated microRNAs in adipogenic niches and identify miR-10a-5p as a key regulator that reduces adipose tissue inflammation and promotes therapeutic adipogenesis.

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Macrophage-specific Dicer KO prevented CL-induced beige adipogenesis in gWAT.

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Bioinfomatic analysis of RNAseq identified miR-10a-5p as a key player in AT remodeling.

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CL treatment upregulated miR-10a-5p in AT macrophages and in activated ASCs.

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miR-10a-5p promoted beige adipogenesis in vivo and in vitro, in part by targeting Rora.

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miR-10a-5p treatment reduced HFD-induced inflammation and improved glucose tolerance.

SMARTIV: combined sequence and structure de-novo motif discovery for in-vivo RNA binding data

Gene expression regulation is highly dependent on binding of RNA-binding proteins (RBPs) to their RNA targets. Growing evidence supports the notion that both RNA primary sequence and its local secondary structure play a role in specific Protein-RNA recognition and binding. Despite the great advance in high-throughput experimental methods for identifying sequence targets of RBPs, predicting the specific sequence and structure binding preferences of RBPs remains a major challenge. We present a novel webserver, SMARTIV, designed for discovering and visualizing combined RNA sequence and structure motifs from high-throughput RNA-binding data, generated from in-vivo experiments. The uniqueness of SMARTIV is that it predicts motifs from enriched k-mers that combine information from ranked RNA sequences and their predicted secondary structure, obtained using various folding methods. Consequently, SMARTIV generates Position Weight Matrices (PWMs) in a combined sequence and structure alphabet with assigned P-values. SMARTIV concisely represents the sequence and structure motif content as a single graphical logo, which is informative and easy for visual perception. SMARTIV was examined extensively on a variety of high-throughput binding experiments for RBPs from different families, generated from different technologies, showing consistent and accurate results. Finally, SMARTIV is a user-friendly webserver, highly efficient in run-time and freely accessible via http://smartiv.technion.ac.il/.

Bioinformatic analysis of miRNA–mRNA interaction associated with LMP2A gene in nasopharyngeal carcinoma

Aim: The purpose of the study was to construct miRNA–mRNA network associated with LMP2A in nasopharyngeal carcinoma (NPC). Materials & methods: The dataset GSE53914, GSE12452 and GSE26596 were downloaded from Gene Expression Omnibus and differentially expressed genes (DEGs) identified by GEO2R. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed by ClusterProfiler R package. Protein–protein interaction network and mRNA–miRNA network associated with LMP2A were constructed. Hub genes were identified by Cytoscape. Results: The 135 DEGs associated with LMP2A were identified in NPC. Gene ontology function analysis showed DEGs were significantly enriched in cell–cell adhesion and NF-κB pathway. The hub genes were related to cell cycle. miRNA–mRNA network associated with LMP2A was constructed. Conclusion: The network may provide a way to explore the function of LMP2A in NPC by miRNA.

Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments

Background

Motif analysis methods have long been central for studying biological function of nucleotide sequences. Functional genomics experiments extend their potential. They typically generate sequence lists ranked by an experimentally acquired functional property such as gene expression or protein binding affinity. Current motif discovery tools suffer from limitations in searching large motif spaces, and thus more complex motifs may not be included. There is thus a need for motif analysis methods that are tailored for analyzing specific complex motifs motivated by biological questions and hypotheses rather than acting as a screen based motif finding tool.

Methods

We present Regmex (REGular expression Motif EXplorer), which offers several methods to identify overrepresented motifs in ranked lists of sequences. Regmex uses regular expressions to define motifs or families of motifs and embedded Markov models to calculate exact p-values for motif observations in sequences. Biases in motif distributions across ranked sequence lists are evaluated using random walks, Brownian bridges, or modified rank based statistics. A modular setup and fast analytic p value evaluations make Regmex applicable to diverse and potentially large-scale motif analysis problems.

Results

We demonstrate use cases of combined motifs on simulated data and on expression data from micro RNA transfection experiments. We confirm previously obtained results and demonstrate the usability of Regmex to test a specific hypothesis about the relative location of microRNA seed sites and U-rich motifs. We further compare the tool with an existing motif discovery tool and show increased sensitivity.

Conclusions

Regmex is a useful and flexible tool to analyze motif hypotheses that relates to large data sets in functional genomics. The method is available as an R package (https://github.com/muhligs/regmex).

Electronic supplementary material

The online version of this article (10.1186/s13015-018-0135-2) contains supplementary material, which is available to authorized users.

An Enrichment Analysis for Cardiometabolic Traits Suggests Non-Random Assignment of Genes to microRNAs

MicroRNAs (miRNAs) regulate the expression of the majority of genes. However, it is not known whether they regulate genes in random or are organized according to their function. To this end, we chose cardiometabolic disorders as an example and investigated whether genes associated with cardiometabolic disorders are regulated by a random set of miRNAs or a limited number of them. Single-nucleotide polymorphisms (SNPs) reaching genome-wide level significance were retrieved from most recent genome-wide association studies on cardiometabolic traits, which were cross-referenced with Ensembl to identify related genes and combined with miRNA target prediction databases (TargetScan, miRTarBase, or miRecords) to identify miRNAs that regulate them. We retrieved 520 SNPs, of which 355 were intragenic, corresponding to 304 genes. While we found a higher proportion of genes reported from all GWAS that were predicted targets for miRNAs in comparison to all protein-coding genes (75.1%), the proportion was even higher for cardiometabolic genes (80.6%). Enrichment analysis was performed within each database. We found that cardiometabolic genes were over-represented in target genes for 29 miRNAs (based on TargetScan) and 3 miRNAs (miR-181a, miR-302d and miR-372) (based on miRecords) after Benjamini-Hochberg correction for multiple testing. Our work provides evidence for non-random assignment of genes to miRNAs and supports the idea that miRNAs regulate sets of genes that are functionally related.

Genome-wide analysis of fitness data and its application to improve metabolic models

Background

Synthetic biology and related techniques enable genome scale high-throughput investigation of the effect on organism fitness of different gene knock-downs/outs and of other modifications of genomic sequence.

Results

We develop statistical and computational pipelines and frameworks for analyzing high throughput fitness data over a genome scale set of sequence variants. Analyzing data from a high-throughput knock-down/knock-out bacterial study, we investigate differences and determinants of the effect on fitness in different conditions. Comparing fitness vectors of genes, across tens of conditions, we observe that fitness consequences strongly depend on genomic location and more weakly depend on gene sequence similarity and on functional relationships. In analyzing promoter sequences, we identified motifs associated with conditions studied in bacterial media such as Casaminos, D-glucose, Sucrose, and other sugars and amino-acid sources.

We also use fitness data to infer genes associated with orphan metabolic reactions in the iJO1366 E. coli metabolic model. To do this, we developed a new computational method that integrates gene fitness and gene expression profiles within a given reaction network neighborhood to associate this reaction with a set of genes that potentially encode the catalyzing proteins. We then apply this approach to predict candidate genes for 107 orphan reactions in iJO1366. Furthermore - we validate our methodology with known reactions using a leave-one-out approach. Specifically, using top-20 candidates selected based on combined fitness and expression datasets, we correctly reconstruct 39.7% of the reactions, as compared to 33% based on fitness and to 26% based on expression separately, and to 4.02% as a random baseline. Our model improvement results include a novel association of a gene to an orphan cytosine nucleosidation reaction.

Conclusion

Our pipeline for metabolic modeling shows a clear benefit of using fitness data for predicting genes of orphan reactions. Along with the analysis pipelines we developed, it can be used to analyze similar high-throughput data.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2341-9) contains supplementary material, which is available to authorized users.

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

BACKGROUND

MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model.

RESULTS

Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations.

CONCLUSIONS

Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks.

miR-27b-3p suppresses cell proliferation, migration and invasion by targeting LIMK1 in colorectal cancer

Accumulating evidence has indicated that microRNAs (miRNAs) can act as candidate tumor suppressors. However, the mechanism of miR-27b-3p in colorectal cancer (CRC) is still unknown. In this study, the relative expression levels of miR-27b-3p and LIMK1 were detected by quantitative real-time PCR (qRT-PCR) in CRC and adjacent normal tissues as well as in CRC cell lines. The proliferation, cell cycle and migratory and invasive abilities of SW620 cells were analyzed by CCK-8, flow cytometry, wound healing assays, and Transwell assays, respectively. The regulatory relationships between LIMK1 and miR-27b-3p were detected by qRT-PCR, luciferase reporter gene and Western blot assays. The results showed that the expression levels of miR-27b-3p were downregulated in CRC samples compared with those in adjacent nontumor tissues. Moreover, we found that miR-27b-3p suppressed CRC cell proliferation, cycle and migration and invasion. Additionally, there was an inverse correlation between LIMK1 expression and miR-27b-3p expression in CRC tissues. LIMK1 was identified as a direct target of miR-27b-3p. Our findings indicated that miR-27b-3p inhibited CRC cell proliferation, migration and invasion by targeting LIMK1.

Mutual enrichment in aggregated ranked lists with applications to gene expression regulation

MOTIVATION

It is often the case in biological measurement data that results are given as a ranked list of quantities-for example, differential expression (DE) of genes as inferred from microarrays or RNA-seq. Recent years brought considerable progress in statistical tools for enrichment analysis in ranked lists. Several tools are now available that allow users to break the fixed set paradigm in assessing statistical enrichment of sets of genes. Continuing with the example, these tools identify factors that may be associated with measured differential expression. A drawback of existing tools is their focus on identifying single factors associated with the observed or measured ranks, failing to address relationships between these factors. For example, a scenario in which genes targeted by multiple miRNAs play a central role in the DE signal but the effect of each single miRNA is too subtle to be detected, as shown in our results.

RESULTS

We propose statistical and algorithmic approaches for selecting a sub-collection of factors that can be aggregated into one ranked list that is heuristically most associated with an input ranked list (pivot). We examine performance on simulated data and apply our approach to cancer datasets. We find small sub-collections of miRNA that are statistically associated with gene DE in several types of cancer, suggesting miRNA cooperativity in driving disease related processes. Many of our findings are consistent with known roles of miRNAs in cancer, while others suggest previously unknown roles for certain miRNAs.

AVAILABILITY AND IMPLEMENTATION

Code and instructions for our algorithmic framework, MULSEA, are in: https://github.com/YakhiniGroup/MULSEAContact:dalia.cohn@gmail.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The biological functions and mechanism of miR‑212 in prostate cancer proliferation, migration and invasion via targeting Engrailed-2

Accumulating evidence indicates that Engrailed-2 (EN-2), which is a homeobox-containing transcription factor, act as a candidate oncogene in prostate cancer (PCa). Even though there are some treatments targeting EN-2, however, it is limited because the mechanism of EN-2 upregulation in PCa cells is still unknown. In this study, we investigate the role of miR‑212 on EN-2 expression and explored the mechanism of prostate cancer survival and metastasis. The relative expression levels of miR‑212 and EN-2 in PCa samples and adjacent normal tissues as well as in PCa cell lines were detected by using quantitative real-time PCR. CCK-8, TUNEL and Transwell assays were used to analyze cell proliferation, apoptosis and invasion, respectively. EN-2 was identified as a direct target of miR‑212 via luciferase reporter and western blot assays. Results showed that the expression level of miR‑212 was downregulated in both PCa samples and PCa cell lines when compared with prostate epithelial cells and the adjacent no tumor tissues. Moreover, we found that overexpression of miR‑212 suppressed PCa cell proliferation and invasion, promoted PCa cell apoptosis. EN-2 was identified as a direct target gene of miR‑212 by using luciferase reporter and western blot assays. Also, the expression of EN-2 and miR‑212 in the PCa cells had an opposite correlation. The critical role of miR‑212 in inhibiting prostate tumor growth was verified in xenograft models of prostate cancer. These findings highlighted the role of miR‑212 in PCa progression. More importantly, we speculate that EN-2 is a direct target gene of miR‑212.

Genome-Wide Scleral Micro- and Messenger-RNA Regulation During Myopia Development in the Mouse

Purpose

MicroRNA (miRNAs) have been previously implicated in scleral remodeling in normal eye growth. They have the potential to be therapeutic targets for prevention/retardation of exaggerated eye growth in myopia by modulating scleral matrix remodeling. To explore this potential, genome-wide miRNA and messenger RNA (mRNA) scleral profiles in myopic and control eyes from mice were studied.

Methods

C57BL/6J mice (n = 7; P28) reared under a 12L:12D cycle were form-deprived (FD) unilaterally for 2 weeks. Refractive error and axial length changes were measured using photorefraction and 1310-nm spectral-domain optical coherence tomography, respectively. Scleral RNA samples from FD and fellow control eyes were processed for microarray assay. Statistical analyses were performed using National Institute of Aging array analysis tool; group comparisons were made using ANOVA, and gene ontologies were identified using software available on the Web. Findings were confirmed using quantitative PCR in a separate group of mice (n = 7).

Results

Form-deprived eyes showed myopic shifts in refractive error (−2.02 ± 0.47 D; P < 0.01). Comparison of the scleral RNA profiles of test eyes with those of control eyes revealed 54 differentially expressed miRNAs and 261 mRNAs fold-change >1.25 (maximum fold change = 1.63 and 2.7 for miRNAs and mRNAs, respectively) (P < 0.05; minimum, P = 0.0001). Significant ontologies showing gene over-representation (P < 0.05) included intermediate filament organization, scaffold protein binding, detection of stimuli, calcium ion, G protein, and phototransduction. Significant differential expression of Let-7a and miR-16-2, and Smok4a, Prph2, and Gnat1 were confirmed.

Conclusions

Scleral mi- and mRNAs showed differential expression linked to myopia, supporting the involvement of miRNAs in eye growth regulation. The observed general trend of relatively small fold-changes suggests a tightly controlled, regulatory mechanism for scleral gene expression.

Impact of MicroRNAs in the Cellular Response to Hypoxia

In mammalian cells, hypoxia, or inadequate oxygen availability, regulates the expression of a specific set of MicroRNAs (MiRNAs), termed "hypoxamiRs." Over the past 10 years, the appreciation of the importance of hypoxamiRs in regulating the cellular adaptation to hypoxia has grown dramatically. At the cellular level, each hypoxamiR, including the master hypoxamiR MiR-210, can simultaneously regulate expression of multiple target genes in order to fine-tune the adaptive response of cells to hypoxia. This review addresses the complex molecular regulation of MiRNAs in both physiological and pathological conditions of low oxygen adaptation and the multiple functions of hypoxamiRs in various hypoxia-associated biological processes, including apoptosis, survival, proliferation, angiogenesis, inflammation, and metabolism. From a clinical perspective, we also discuss the potential use of hypoxamiRs as new biomarkers and/or therapeutic targets in cancer and aging-associated diseases including cardiovascular and fibroproliferative disorders.

A combined sequence and structure based method for discovering enriched motifs in RNA from in vivo binding data

RNA binding proteins (RBPs) play an important role in regulating many processes in the cell. RBPs often recognize their RNA targets in a specific manner. In addition to the RNA primary sequence, the structure of the RNA has been shown to play a central role in RNA recognition by RBPs. In recent years, many experimental approaches, both in vitro and in vivo, were developed and employed to identify and characterize RBP targets and extract their binding specificities. In vivo binding techniques, such as CrossLinking and ImmunoPrecipitation (CLIP)-based methods, enable the characterization of protein binding sites on RNA targets. However, these methods do not provide information regarding the structural preferences of the protein. While methods to obtain the structure of RNA are available, inferring both the sequence and the structure preferences of RBPs remains a challenge. Here we present SMARTIV, a novel computational tool for discovering combined sequence and structure binding motifs from in vivo RNA binding data relying on the sequences of the target sites, the ranking of their binding scores and their predicted secondary structure. The combined motifs are provided in a unified representation that is informative and easy for visual perception. We tested the method on CLIP-seq data from different platforms for a variety of RBPs. Overall, we show that our results are highly consistent with known binding motifs of RBPs, offering additional information on their structural preferences.

Tools for Sequence-Based miRNA Target Prediction: What to Choose?

MicroRNAs (miRNAs) are defined as small non-coding RNAs ~22 nt in length. They regulate gene expression at a post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and therefore blocking translation. In the last decade, the dysfunction of miRNAs has been related to the development and progression of many diseases. Currently, researchers need a method to identify precisely the miRNA targets, prior to applying experimental approaches that allow a better functional characterization of miRNAs in biological processes and can thus predict their effects. Computational prediction tools provide a rapid method to identify putative miRNA targets. However, since a large number of tools for the prediction of miRNA:mRNA interactions have been developed, all with different algorithms, the biological researcher sometimes does not know which is the best choice for his study and many times does not understand the bioinformatic basis of these tools. This review describes the biological fundamentals of these prediction tools, characterizes the main sequence-based algorithms, and offers some insights into their uses by biologists.

Identification of miRNAs Potentially Involved in Bronchiolitis Obliterans Syndrome: A Computational Study

The pathogenesis of Bronchiolitis Obliterans Syndrome (BOS), the main clinical phenotype of chronic lung allograft dysfunction, is poorly understood. Recent studies suggest that epigenetic regulation of microRNAs might play a role in its development. In this paper we present the application of a complex computational pipeline to perform enrichment analysis of miRNAs in pathways applied to the study of BOS. The analysis considered the full set of miRNAs annotated in miRBase (version 21), and applied a sequence of filtering approaches and statistical analyses to reduce this set and to score the candidate miRNAs according to their potential involvement in BOS development. Dysregulation of two of the selected candidate miRNAs–miR-34a and miR-21 –was clearly shown in in-situ hybridization (ISH) on five explanted human BOS lungs and on a rat model of acute and chronic lung rejection, thus definitely identifying miR-34a and miR-21 as pathogenic factors in BOS and confirming the effectiveness of the computational pipeline.

miEAA: microRNA enrichment analysis and annotation

Similar to the development of gene set enrichment and gene regulatory network analysis tools over a decade ago, microRNA enrichment tools are currently gaining importance. Building on our experience with the gene set analysis toolkit GeneTrail, we implemented the miRNA Enrichment Analysis and Annotation tool (miEAA). MiEAA is a web-based application that offers a variety of commonly applied statistical tests such as over-representation analysis and miRNA set enrichment analysis, which is similar to Gene Set Enrichment Analysis. Besides the different statistical tests, miEAA also provides rich functionality in terms of miRNA categories. Altogether, over 14 000 miRNA sets have been added, including pathways, diseases, organs and target genes. Importantly, our tool can be applied for miRNA precursors as well as mature miRNAs. To make the tool as useful as possible we additionally implemented supporting tools such as converters between different miRBase versions and converters from miRNA names to precursor names. We evaluated the performance of miEAA on two sets of miRNAs that are affected in lung adenocarcinomas and have been detected by array analysis. The web-based application is freely accessible at: http://www.ccb.uni-saarland.de/mieaa_tool/.

miR-3646 promotes cell proliferation, migration, and invasion via regulating G2/M transition in human breast cancer cells

MicroRNAs (miRNAs) are small non-coding RNAs that are often located in genomic breakpoint regions and play a critical role in regulating a variety of the cellular processes in human cancer. miR-3646 has been reported to take part in tumorigenic progression in breast and bladder cancer, but its potential functions and exact mechanistic roles in breast cancer are still unclear. The objective of this study was to investigate the role of miR-3646 in breast cancer growth and metastasis using both bioinformatic and experimental approaches. Before starting the bench work, we conducted a bioinformatic study to predict the target genes regulated by miR-3646 using a panel of different algorithms. The results showed that miR-3646 might regulate a large number of genes that are related to cell growth, proliferation, metabolis, transport, and apoptosis and some were cancer-related genes. We found that the expression level of miR-3646 was significantly upregulated in breast cancer cells and tissues compared with normal breast cells and no tumor tissues. Subsequently, the MTT and colony formation assay results showed that up-regulation of miR-3646 promoted the cell viability and proliferation. Our results also showed that down-regulation of miR-3646 arrested the cells in G2/M phase in MCF7 and MDA-MB-231 cells which was accompanied by the down-regulation of CDK1/CDC2 and cyclin B1 and upregulation of p21Waf1/Cip1, p27 Kip1, and p53, suggesting that down-regulation of miR-3646 induces G2/M arrest through activation of the p53/p21/CDC2/cyclin B1 pathway. In addition, overexpression of miR-3646 promoted migration and invasion of MCF7 and MDA-MB-231 cells. Taken together, miR-3646 is a potential oncogene in breast cancer and it may represent a new niomarker in the diagnosis and prediction of prognosis and therapeutic response.

Publisher’s Note:Abstraction for data integration:Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction

With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data.

mBISON: Finding miRNA target over-representation in gene lists from ChIP-sequencing data

Background

Over-representation of predicted miRNA targets in sets of genes regulated by a given transcription factor (e.g. as defined by ChIP-sequencing experiments) helps to identify biologically relevant miRNA targets and is useful to get insight into post-transcriptional regulation.

Findings

To facilitate the application of this approach we have created the mBISON web-application. mBISON calculates the significance of over-representation of miRNA targets in a given non-ranked gene set. The gene set can be specified either by a list of genes or by one or more ChIP-seq datasets followed by a user-defined peak-gene association procedure. mBISON is based on predictions from TargetScan and uses a randomization step to calculate False-Discovery-Rates for each miRNA, including a correction for gene set specific properties such as 3’UTR length. The tool can be accessed from the following web-resource: http://cbdm.mdc-berlin.de/~mgebhardt/cgi-bin/mbison/home.

Conclusion

mBISON is a web-application that helps to extract functional information about miRNAs from gene lists, which is in contrast to comparable applications easy to use by everyone and can be applied on ChIP-seq data directly.

Candidate pathways and genes for nasopharyngeal carcinoma based on bioinformatics study

PURPOSE

To reveal the potential microRNAs (miRNAs), genes, pathways and regulatory network involved in the process of nasopharyngeal carcinoma (NPC) by using the method of bioinformatics.

METHODS

Gene expression profiles GSE12452 (31 NPC and 10 normal samples) and GSE53819 (18 NPC and 18 normal samples), as well as miRNA expression profiles GSE32960 (312 NPC and 18 normal samples) and GSE36682 (62 NPC and 6 normal samples) were obtained from Gene Expression Omnibus database. The differentially expressed genes (DEGs) and miRNAs (DEmiRNAs) between NPC and normal samples were identified by using t-test based on MATLAB software (FDR < 0.01), followed by pathway enrichment analysis based on DAVID software (P-value < 0.1). Then, DEmiRNA-DEG regulatory network was constructed.

RESULTS

A total of 1254 DEGs and 107 DEmiRNAs were identified, respectively. Then, 16 pathways (including cell cycle) and 32 pathways (including pathways in cancer) were enriched by DEGs and target genes of DEmiRNAs, respectively. Furthermore, DEmiRNA-DEG regulatory network was constructed, containing 12 DEmiRNAs (including has-miR-615-3P) and 180 DEGs (including MCM4 and CCNE2).

CONCLUSION

has-miR-615-3p might take part in the pathogenetic process of NPC through regulating MCM4 which is enriched in cell cycle. The DEmiRNAs identified in the present study might serve as new biomarkers for NPC.

Epidermal growth factor signaling in transformed cells

Members of the epidermal growth factor receptor (EGFR/ErbB) family play a critical role in normal cell growth and development. However, many ErbB family members, especially EGFR, are aberrantly expressed or deregulated in tumors and are thought to play crucial roles in cancer development and metastatic progression. In this chapter, we provide an overview of key mechanisms contributing to aberrant EGFR/ErbB signaling in transformed cells, which results in many phenotypic changes associated with the earliest stages of tumor formation, including several hallmarks of epithelial-mesenchymal transition (EMT). These changes often occur through interaction with other major signaling pathways important to tumor progression, causing a multitude of transcriptional changes that ultimately impact cell morphology, proliferation, and adhesion, all of which are crucial for tumor progression. The resulting mesh of signaling networks will need to be taken into account as new regimens are designed for targeting EGFR for therapeutic intervention. As new insights are gained into the molecular mechanisms of cross talk between EGFR signaling and other signaling pathways, including their roles in therapeutic resistance to anti-EGFR therapies, a continual reassessment of clinical therapeutic regimes and strategies will be required. Understanding the consequences and complexity of EGF signaling and how it relates to tumor progression is critical for the development of clinical compounds and establishing clinical protocols for the treatment of cancer.

miRNAs and their application in drug-induced liver injury

The complex miRNA regulatory network plays an important role in diverse biological activities of physiopathological processes. In addition to the discovery of and research on the extracellular miRNAs detected in multiple biofluids, the properties of tissue specificity and high stability underlie the great potential of these small miRNAs to serve as translational biomarkers for various diseases in the clinical setting, including in drug-induced liver injury. In this review, we describe the major technologies currently used and challenges in miRNA measurement and provide information on major bioinformatics resources available for current miRNA research. We also discuss novel findings in liver disease and highlight the potential of miRNAs for clinical and basic research as translational biomarkers for drug-induced liver injury.

Inferring condition-specific miRNA activity from matched miRNA and mRNA expression data

MOTIVATION

MicroRNAs (miRNAs) play crucial roles in complex cellular networks by binding to the messenger RNAs (mRNAs) of protein coding genes. It has been found that miRNA regulation is often condition-specific. A number of computational approaches have been developed to identify miRNA activity specific to a condition of interest using gene expression data. However, most of the methods only use the data in a single condition, and thus, the activity discovered may not be unique to the condition of interest. Additionally, these methods are based on statistical associations between the gene expression levels of miRNAs and mRNAs, so they may not be able to reveal real gene regulatory relationships, which are causal relationships.

RESULTS

We propose a novel method to infer condition-specific miRNA activity by considering (i) the difference between the regulatory behavior that an miRNA has in the condition of interest and its behavior in the other conditions; (ii) the causal semantics of miRNA-mRNA relationships. The method is applied to the epithelial-mesenchymal transition (EMT) and multi-class cancer (MCC) datasets. The validation by the results of transfection experiments shows that our approach is effective in discovering significant miRNA-mRNA interactions. Functional and pathway analysis and literature validation indicate that the identified active miRNAs are closely associated with the specific biological processes, diseases and pathways. More detailed analysis of the activity of the active miRNAs implies that some active miRNAs show different regulation types in different conditions, but some have the same regulation types and their activity only differs in different conditions in the strengths of regulation.

AVAILABILITY AND IMPLEMENTATION

The R and Matlab scripts are in the Supplementary materials.

Computational prediction of disease microRNAs in domestic animals

BACKGROUND

The most important means of identifying diseases before symptoms appear is through the discovery of disease-associated biomarkers. Recently, microRNAs (miRNAs) have become highly useful biomarkers of infectious, genetic and metabolic diseases in human but they have not been well studied in domestic animals. It is probable that many of the animal homologs of human disease-associated miRNAs may be involved in domestic animal diseases. Here we describe a computational biology study in which human disease miRNAs were utilized to predict orthologous miRNAs in cow, chicken, pig, horse, and dog.

RESULTS

We identified 287 human disease-associated miRNAs which had at least one 100% identical animal homolog. The 287 miRNAs were associated with 359 human diseases referenced in 2,863 Pubmed articles. Multiple sequence analysis indicated that over 60% of known horse mature miRNAs found perfect matches in human disease-associated miRNAs, followed by dog (50%). As expected, chicken had the least number of perfect matches (5%). Phylogenetic analysis of miRNA precursors indicated that 85% of human disease pre-miRNAs were highly conserved in animals, showing less than 5% nucleotide substitution rates over evolutionary time. As an example we demonstrated conservation of human hsa-miR-143-3p which is associated with type 2 diabetes and targets AKT1 gene which is highly conserved in pig, horse and dog. Functional analysis of AKT1 gene using Gene Ontology (GO) showed that it is involved in glucose homeostasis, positive regulation of glucose import, positive regulation of glycogen biosynthetic process, glucose transport and response to food.

CONCLUSIONS

This data provides the animal and veterinary research community with a resource to assist in generating hypothesis-driven research for discovering animal disease-related miRNA from their datasets and expedite development of prophylactic and disease-treatment strategies and also influence research efforts to identify novel disease models in large animals. Integrated data is available for download at http://agbase.hpc.msstate.edu/cgi-bin/animal_mirna.cgi.

Mutual enrichment in ranked lists and the statistical assessment of position weight matrix motifs

Background

Statistics in ranked lists is useful in analysing molecular biology measurement data, such as differential expression, resulting in ranked lists of genes, or ChIP-Seq, which yields ranked lists of genomic sequences. State of the art methods study fixed motifs in ranked lists of sequences. More flexible models such as position weight matrix (PWM) motifs are more challenging in this context, partially because it is not clear how to avoid the use of arbitrary thresholds.

Results

To assess the enrichment of a PWM motif in a ranked list we use a second ranking on the same set of elements induced by the PWM. Possible orders of one ranked list relative to another can be modelled as permutations. Due to sample space complexity, it is difficult to accurately characterize tail distributions in the group of permutations. In this paper we develop tight upper bounds on tail distributions of the size of the intersection of the top parts of two uniformly and independently drawn permutations. We further demonstrate advantages of this approach using our software implementation, mmHG-Finder, which is publicly available, to study PWM motifs in several datasets. In addition to validating known motifs, we found GC-rich strings to be enriched amongst the promoter sequences of long non-coding RNAs that are specifically expressed in thyroid and prostate tissue samples and observed a statistical association with tissue specific CpG hypo-methylation.

Conclusions

We develop tight bounds that can be calculated in polynomial time. We demonstrate utility of mutual enrichment in motif search and assess performance for synthetic and biological datasets. We suggest that thyroid and prostate-specific long non-coding RNAs are regulated by transcription factors that bind GC-rich sequences, such as EGR1, SP1 and E2F3. We further suggest that this regulation is associated with DNA hypo-methylation.