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. 2017 Jan 20;33(10):1554–1560. doi: 10.1093/bioinformatics/btx019

miRmine: a database of human miRNA expression profiles

Bharat Panwar 1,✉, Gilbert S Omenn 1,2,3, Yuanfang Guan 1,2,4
Editor: Ivo Hofacker
PMCID: PMC6044334  PMID: 28108447

Abstract

Motivation

MicroRNAs (miRNAs) are small non-coding RNAs that are involved in post-transcriptional regulation of gene expression. In this high-throughput sequencing era, a tremendous amount of RNA-seq data is accumulating, and full utilization of publicly available miRNA data is an important challenge. These data are useful to determine expression values for each miRNA, but quantification pipelines are in a primitive stage and still evolving; there are many factors that affect expression values significantly.

Results

We used 304 high-quality microRNA sequencing (miRNA-seq) datasets from NCBI-SRA and calculated expression profiles for different tissues and cell-lines. In each miRNA-seq dataset, we found an average of more than 500 miRNAs with higher than 5x coverage, and we explored the top five highly expressed miRNAs in each tissue and cell-line. This user-friendly miRmine database has options to retrieve expression profiles of single or multiple miRNAs for a specific tissue or cell-line, either normal or with disease information. Results can be displayed in multiple interactive, graphical and downloadable formats.

Availability and Implementation

http://guanlab.ccmb.med.umich.edu/mirmine

Supplementary information

Supplementary data are available at Bioinformatics online.

1 Introduction

MicroRNAs (miRNAs) are small non-coding RNAs that target specific mRNAs through the RNA interference (RNAi) mechanism, and regulate gene expression and mRNA degradation (Davis-Dusenbery and Hata, 2010; Fire et al., 1998; Ha and Kim, 2014; Siomi and Siomi, 2010). The average length of mature miRNA is ∼22 nucleotides, generated from an approximately 70–100 nucleotides long hairpin RNA, called the precursor miRNA, or pre-miRNA (Pritchard et al., 2012; Zeng, 2006). These mature miRNAs control expression from most human genes; it was estimated that transcripts of more than 60% of human genes carry at least one conserved miRNA-binding site (Friedman et al., 2009), and many non-canonical binding sites were also reported in the human miRNA interactome (Helwak et al., 2013). Moreover, a single miRNA can target multiple mRNAs; likewise, a single mRNA can be targeted by more than one miRNA (Hsu et al., 2014). Indirectly, miRNAs can also affect the expression level of multiple mRNAs by targeting a transcription factor (Arora et al., 2013; Wang et al., 2015). There is a cumulative effect of miRNA expression level on many mRNAs that ultimately results in diverse cellular functions because final protein output is defined by miRNA expression level as well as the availability of mRNA targets (Wang and Wang, 2006). Therefore, a change in the expression level of a particular miRNA sometimes leads to severe pathological conditions (Abdellatif, 2012; Sheedy, 2015). These miRNA expression profiles are also useful to classify different tumors and assist cancer diagnosis (Berindan-Neagoe et al., 2014; Lu et al., 2005).

The current miRBase release (v21, June 2014) contains more than 2500 mature human miRNAs, and these entries are continuously increasing, especially from high-throughput studies (Kozomara and Griffiths-Jones, 2014). The rapid advancement of small RNA sequencing technologies facilitated the quantification of all miRNAs in a particular condition with high-level sensitivity and accuracy (Guo et al., 2014a; Kang and Friedländer, 2015). A major challenge is to process publicly available heterogeneous microRNA sequencing (miRNA-seq) data with a robust pipeline and measure the abundance of all miRNAs in different experiments. Some miRNAs are expressed preferentially or exclusively in certain tissues; those miRNAs are generally associated with tissue identity, differentiation and function (Guo et al., 2014b). Therefore, it is important to explore and quantify expression values of these tissue-specific miRNAs for better understanding their biological roles. There are many extracellular circulating miRNAs expressed in blood, plasma or serum may be promising noninvasive biomarker candidates for many diseases, including diagnosis, prognosis and treatment of cancers (Allegra et al., 2012; Turchinovich et al., 2012).

In the past, some databases such as microRNA.org (Betel et al., 2008), miRGator (Nam et al., 2008), YM500 (Cheng et al., 2013, 2015), HMED (Gong et al., 2014), deepBase v2.0 (Zheng et al., 2016), miRbase (Kozomara and Griffiths-Jones, 2014) and DASHR (Leung et al., 2016) used small-RNAseq data and calculated expression values of miRNAs. Different library preparation protocols can affect the miRNA expression profiles (Baran-Gale et al., 2015). It is more appropriate to use miRNA-seq data exclusively because sometimes miRNAs are underrepresented in the small-RNAseq (Raabe et al., 2014). There are several other factors that affect the quantitative value of miRNA expression. For example, a simple adapter trimming step can change miRNA expression values drastically because public data contain different adapter sequences from diverse studies; therefore, manual detection and removal of those adapter sequences are necessary. There is a need to process public data with a robust pipeline and develop a comprehensive resource for human miRNA expression profiles.

In the present study, we have developed the miRmine database of miRNA expression profiles from publicly available human miRNA-seq data. This database provides a global view of tissue and cell-line based expression profiles and relative abundance of different human miRNAs. We processed the miRNA-seq data with a robust pipeline and measured the expression values. All expression profiles were integrated into this user-interactive web-resource.

2 Materials and methods

2.1 Data source

We retrieved raw reads of 349 publicly available experiments of human miRNA-seq from NCBI-SRA by specifying LibraryStrategy search as miRNA-Seq (Leinonen et al., 2011). All these experiments have been performed on an Illumina platform; 360 total runs were available for these 349 experiments (dated 18 August 2014). Our workflow is provided in Figure 1.

Fig. 1.

Fig. 1

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A workflow of the data source and processing of miRNA-seq data (Color version of this figure is available at Bioinformatics online.)

2.2 Data pre-processing

The recently published pipeline CAP-miRSeq (Sun et al., 2014) has been applied for processing of miRNA-seq data. It was important to check the quality of public data before using them for any purpose (Su et al., 2014). Therefore, we used a repetitive 3-step strategy: first, the FastQC (v0.10.1) tool was applied for assessing reads quality; second, Cutadapt (v1.6) was used for removing adapter sequences; finally, FastQC was employed again for assessing reads quality after trimming. The public data contain diverse adapter sequences; therefore, we repeated these 3-steps with different adapter sequences until we got high-quality assurance from FastQC. We have used ‘CUTADAPT_PARAMS=-b AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -b GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT -b GATCTACACGTTCAGAGTTCTACAGTCCGACGATC -b GATCGTCGGACTGTAGAACTCTGAACGTGTAGATC -O 3 -m 17 -f fastq’ to process the data. Ultimately, we retained only 304 high-quality experiments for further use in building the miRmine database.

2.3 Reads alignment

A miRDeep2 (Friedländer et al., 2012) tool has been used for alignment of high-quality reads from the 304 experiments. Initially, we used miRDeep2 mapper for mapping of trimmed reads with the reference human genome (GRCh38 release). From that point forward, the miRDeep2 module has been applied for mapping of reads with predefined miRNA annotations based on miRBase v21 (Kozomara and Griffiths-Jones, 2014). We used different parameters of MAPPER =-e -h -q -m -r 5 -u -v -o 4, MIRDEEP2 =-P -t Human, QUANTIFIER =-P –W and BOWTIE =-p 1 -S -q -n 1 -e 80 -l 30 -a -m 5 –best –strata, to process the data.

2.4 miRNA quantification

All reads mapped to miRNA coordinates are used to calculate expression profiles by miRDeep2. Although there were 2588 unique mature miRNAs, some mature miRNAs are derived from more than one precursor miRNA, so we calculated separate expression values for all 2822 mature miRNAs (Kozomara and Griffiths-Jones, 2014). We used the normalized reads count as reads per million (RPM) for all known miRNAs. Some experiments have more than one sample run, so we used the average of RPM values in our database.

2.5 miRNA-seq data of different tissues and cell-lines

The 304 high-quality experiments contain data from 16 different types of human tissues and bio fluids: Bladder (10), Blood (18), Brain (10), Breast (4), Hair follicle (11), Liver (1), Lung (2), Nasopharynx (5), Pancreas (6), Placenta (8), Plasma (37), Saliva (3), Semen (2), Serum (13), Sperm (2) and Testis (3), and 24 different types of cell-lines: 143B (24), 293T (6), A549 (11), Akata_LCLd3 (1), AU565 (1), BEAS-2B (1), C8166 (3), HEK-293 (1), HeLa-S3 (9), HepG2 (2), HKCI-4 (1), HKCI-8 (1), Hs-578Bst (1), IBL_LCLd3 (1), iMSC (4), LCL (1), MCF7 (34), MIHA (1), NPC-CNE-2 (2), SH-SY5Y (17), SKBR3 (1), SUP-T1 (26), THP1 (18) and TZM-bl (2). We calculated average RPM values for each tissue and cell-line separately and compared the expression profiles of miRNAs in different tissues by generating a heatmap using MeV software (Howe et al., 2011). We used hierarchical clustering option using Pearson correlation as distance metric. The average linkage clustering method was used for generating clusters where both sample and miRNA are used in the tree selection.

3 Results and discussion

3.1 Comprehensive analysis of miRNA expression profiles

We processed publicly available high-quality miRNA-seq data and analyzed the overall expression profiles of the different miRNAs in the human. We found an average of ∼532 miRNAs expressed in these experiments with  > =5x coverage, with the highest, 1300 miRNAs, expressed in an experiment on neuroblastoma cells (SRX334996). The top five expressed miRNAs in public data are hsa-miR-21-5p, hsa-miR-191-5p, hsa-miR-451a, hsa-miR-92a-3p and hsa-miR-27b-3p, with average 75844, 39785, 25708, 25229 and 25051 RPM values, respectively. Where miRNAs hsa-miR-22-3p, hsa-miR-10b-5p, hsa-miR-181a-5p, hsa-miR-10a-5p and hsa-miR-486-5p are also highly expressed. We used online Wordle software (http://www.wordle.net) to create a word cloud for the different miRNAs (Supplementary Fig. S1).

3.2 Comparison of miRmine with existing databases

There are many databases such as deepBase v2.0 (Zheng et al., 2016), HMED (Gong et al., 2014), miRbase (Kozomara and Griffiths-Jones, 2014), DASHR (Leung et al., 2016) are available for miRNA expression profiles; therefore it is important to compare existing databases with miRmine (Table 1).

Table 1.

Comparison of existing miRNA expression databases with miRmine

Features deepBase v2.0 HMED miRbase DASHR miRmine
Functional website Not working Yes Yes Yes Yes
Sequencing data Small RNA-seq and RNA-seq Small RNA-seq Small RNA-seq Small RNA-seq miRNA-seq
Type of small RNAs Multiple small RNAs miRNAs miRNAs Multiple small RNAs miRNAs
Species Multi-species Human Multi-species Human Human
Single miRNA expression profile – Yes Yes Yes Yes
Multiple (selected) miRNAs comparison – No No No Yes
Downloadable expression profile data – Yes No Yes Yes
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Different datasets and data processing pipelines make it difficult to directly compare miRmine with existing databases. The only approach of HMED is somewhat similar to miRmine because that is specific to human and miRNAs only. HMED previously reported six highly expressed miRNAs and hsa-miR-21-5p (average 219057 RPM) as the top expressed miRNA in humans (Gong et al., 2014). Only two (hsa-miR-21-5p & hsa-miR-191-5p) of the top six expressed miRNAs are shared between HMED and miRmine database. The difference may be due to our different methodology; while processing these data we found that manual removal of adapter sequence is important for heterogeneous public data. Comparatively, HMED found 70% of the known miRNAs as low or not expressed (RPM < 1) whereas we found that only 51% of known miRNAs are low or not expressed. We found 12% of known miRNAs as highly expressed (>100 RPM) whereas HMED reported 9% of known miRNAs as highly expressed (Table 2).

Table 2.

Comparison of miRmine with HMED databse

Features HMED miRmine
Dataset (NCBI-SRA) 410 small RNA-seq 304 miRNA-seq
% of known miRNAs as highly expressed (>100 RPM) 9% 12%
% of known miRNAs as low or not expressed (RPM < 1) 70% 51%
% of known miRNAs not expressed in all samples 30% 7%
Top six highly expressed miRNAs hsa-miR-21-5p, hsa-let-7f-5p, hsa-let-7a-5p, hsa-miR-26a-5p, hsa-miR-103a-3p, hsa-miR-191-5p hsa-miR-21-5p, hsa-miR-191-5p, hsa-miR-451a, hsa-miR-92a-3p, hsa-miR-27b-3p, hsa-miR-22-3p
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We used CAP-miRSeq pipeline because they compared CAP-miRSeq with different omiRAS, miRTools2 and Novoalign pipelines, and reported better performance in terms of total number of detected mature miRNAs as well as the computational memory (Sun et al., 2014). There are some other pipelines such as ShortStack also available to use in future (Johnson et al., 2016).

3.3 Highly expressed miRNAs in different tissues and cell-lines

The finding of tissue-specific miRNAs is important for understanding underlying function (Kowarsch et al., 2011). These data of global miRNA expression profiles provided us an excellent opportunity to explore tissue-specificity of miRNAs. We calculated the average RPM value for each mature miRNA in every tissue separately. As shown in Figure 2, there is tissue and cell-line specific patterns present for different miRNAs based on their expression values. The miRNA expression profile in the hair follicle is entirely different from other tissues; only a few miRNAs are expressed. The highest numbers of miRNAs are expressed in breast tissue and SH-SY5Y cell-line. Experimental availability, nature of study, and normal or disease condition for all tissues differ, which may create some biases within miRNA-based human tissue-specificity. Supplementary Table S1 shows the top 5 highly expressed miRNAs in each human tissue and cell-line. The highly expressed miRNAs in different tissues are supported by published literature for example miR-375 in pancreas (Avnit-Sagi et al., 2009). The expression of miR-21 is also important in placenta (Lasabová et al., 2015; Maccani et al., 2011) and Nasopharynx (Miao et al., 2015).

Fig. 2.

Fig. 2

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Tissue and cell-line specific patterns of all mature miRNAs based on the average expression values (RPM). The hierarchical clustering of both different tissues and cell-lines, and miRNAs are based on the distance metric of Pearson correlation values. In upper panel, all tissues are highlighted with green color whereas cell-lines are highlighted with red color (Color version of this figure is available at Bioinformatics online.)

3.4 Web-interface

All expression profiles from the 304 experimental datasets have been implemented in the form of a web-resource called miRmine (http://guanlab.ccmb.med.umich.edu/mirmine). Different modules are integrated into miRmine for query search and visualization purposes (Fig. 3). We intend to update this resource with new release of miRBase.

Fig. 3.

Fig. 3

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A schematic representation of different applications of the miRmine database. All these search options are provided in user-friendly manner (please see section 3.3 for details) (Color version of this figure is available at Bioinformatics online.)

3.4.1. miRNA-based search

Users have the option to search single or multiple (comma separated) human miRNAs using standard accession IDs (e.g. hsa-miR-21-5p). An auto-completion option is provided so the user can enter any sub-part of an accession ID (e.g. miR-21 or 21), and it will automatically provide all the corresponding entries from our database.

3.4.2. Tissue or cell-line based search

There is an option to select any tissue or cell-line of 15 tissues and 24 cell-lines in the database. The hair follicle tissue has been excluded because so few miRNAs are expressed, which may create a balancing problem in graphs. Multiple tissues can be selected using control + select option. By default, the database will give expression values from all tissues. The user can combine both miRNA and tissue or cell-line based search for retrieving expression values.

3.4.3. User interactive and downloadable graphs

All graphs are provided in the user-interactive mode for better visualization and customization, as developed previously for proteomics data (Panwar et al., 2015). A user can select or hide a particular miRNA from the graph by using an option at the bottom of the column graph. In the box-plot, there will be five different log2 values (maximum, upper quartile, median, lower quartile and minimum) of average RPM for each tissue type from different experiments (Fig. 4a). We also provided a link on the top for plotting separate box-plot of normal vs diseased samples. These graphs are downloadable in various formats (png, jpeg, pdf, svg); users can also print using an option at the right top.

Fig. 4.

Fig. 4

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Different examples of graphical results of the tissue-based search; (a) a box-plot graph for a single miRNA (has-miR-21-5p) query, (b) a column graph for comparing tissue-expression values of four-specific miRNAs, (c) detailed results of experimental information, published literature and expression profiles and (d) downloadable expression profiles of all miRNAs in all tissues (Color version of this figure is available at Bioinformatics online.)

3.4.4. Comparison of multiple miRNAs

In multiple-miRNAs searches, a column graph will be generated for comparing expression values of different miRNAs. The graph is based on the log2 value of an average RPM value from corresponding experiments (Fig. 4b).

3.4.5. Link to other databases and curate literature

Each miRNA result page is further linked to miRBase (Kozomara and Griffiths-Jones, 2011, 2014) and miRTarBase (Hsu et al., 2014) databases. There is a detailed result in the table for each miRNA query. It includes a link to NCBI-SRA experiments, tissue or cell-line type, description, disease, sex, PubMed ID for published study and RPM values in all different experiments (Fig. 4c). Samples are labeled with such human disease conditions as cancers, tuberculosis, diabetes, osteopetrosis, chronic fatigue syndrome and liver cirrhosis. We performed manual curation of published literature for public data because this information is not readily available for many experiments.

3.4.6. Download miRmine

We have provided an option to download the whole miRmine database by just clicking on Run miRmine without doing anything else. It will give a table, which the user can download in any format (copy, excel, csv, pdf and print) (Fig. 4d). If users are interested in any particular type of tissue or cell-line, they can select accordingly before clicking Run miRmine. Three major table files (miRmine-info.txt, miRmine-tissues.xlsx and miRmine-cell-lines.xlsx) are provided in the downloadable file (miRmine.zip).

3.4.7. Database architecture

The miRmine is built on Apache HTTP server version 2.2.15 (Unix) with MySQL 5.1.73 at the back end, and the PHP 5.3.3, HTML and JavaScript at the front end. We preferred Apache, MySQL and PHP because these are open-source software and platform independent.

4 Conclusion and future perspectives

We have developed the database miRmine for the global research community using miRNAs, especially in studies of the regulation of gene expression and translation of mRNAs and their splice isoforms. This web-resource will assist researchers comparing expression profiles of different miRNAs and exploring highly expressed miRNAs in tissues or cell-lines and human diseases. The relative abundance of a cohort of miRNAs is helpful to examine miRNA-based biomarkers. Integration of miRNA expression with mRNA abundance and miRNA-interactome will be useful for system-level studies.

Supplementary Material

Supplementary Data
Click here for additional data file. (434.8KB, docx)

Funding

This work was supported by NSF 1452656 (Y.G.) and National Institute of Health grant U54ES017885 (G.S.O.). The European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement number 305608.

Conflict of Interest: none declared.

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Supplementary Materials

Supplementary Data
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