Get isoTar

isoTar is a high performance web-based containerized application for Consensus miRNA Targeting Prediction and Functional Enrichment Analysis. It leverages on Docker, a virtual environment manager, independent of the host Operating System (OS). Through a friendly web-based User Interface (UI), users can explore the impact of Human miRNA variants on the Human Transcriptome.

Disclaimer: This program is for educational and research purposes only.

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

A copy of the license text can be obtained from https://www.gnu.org/licenses/gpl-3.0.en.html

Available for

  • Ubuntu 16.04+

  • macOS 10.12+

  • Windows 10 Pro

Download latest version (image size ~10 GB)

MD5: c54f10e21e591fedcdbb4c71d88b9e1e

SHA1: c71c96c1cb94a866ac3d4777aad6cca2e955508c

SHA256: c67a12f905d45ecdcf7b64997a4b90275a74be6cfc075e44dfcedaf43b8bf18a


How to cite

Distefano R., Nigita G., Veneziano D., Romano G., Croce CM., Acunzo M. "isoTar: Consensus Target Prediction with Enrichment Analysis for MicroRNAs Harboring Editing Sites and Other Variations." Methods Mol Biol. (2019)

PubMed

Overview

Technologies

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Resources

Resources included in isoTar

Prediction tools included in isoTar

Installation

isoTar requires Docker installed on your PC. Please follow the instructions associated to your Operating System ( Ubuntu (Linux), Mac, or Windows). You can find instructions for additional Platforms/Operating Systems here.

Once you get Docker, you can proceed with the isoTar installation as specified below:

Instructions for Linux/Mac/Windows Systems

1. Download isoTar (click on the button above).

2. Open Terminal (Linux/Mac), Powershell (Windows).

3. Install isoTar (image name: isotar_v1.tar) using the following command:

docker load -i /ABSOLUTE_PATH_TO_THE_TAR_FILE/isotar_v1.tar

The installation process will generate an output similar to the one reported below:

34394faab172: Loading layer [==================================================>]  17.92kB/17.92kB
fbf57c7a838c: Loading layer [==================================================>]  11.78kB/11.78kB
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097adadfb283: Loading layer [==================================================>]  7.429GB/7.429GB
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e518cf78e956: Loading layer [==================================================>]  77.04MB/77.04MB
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8085b1d466a4: Loading layer [==================================================>]  84.26MB/84.26MB
42d84f02a1ed: Loading layer [==================================================>]  1.778MB/1.778MB
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fff38d1ba0f3: Loading layer [==================================================>]  6.212MB/6.212MB
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0dc1195a50cd: Loading layer [==================================================>]  156.6MB/156.6MB
17f94a00c15c: Loading layer [==================================================>]  337.4MB/337.4MB
f1699f58bd58: Loading layer [==================================================>]  2.048kB/2.048kB
dcc74bbe2463: Loading layer [==================================================>]  3.072kB/3.072kB
9eb14f476dd0: Loading layer [==================================================>]  3.584kB/3.584kB
30ff6d340466: Loading layer [==================================================>]  3.072kB/3.072kB
aa61ab025cdc: Loading layer [==================================================>]  4.096kB/4.096kB
8494dd294d5f: Loading layer [==================================================>]  4.096kB/4.096kB
0dc19cc08570: Loading layer [==================================================>]  3.584kB/3.584kB
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ec4f7b5e60cb: Loading layer [==================================================>]   2.56kB/2.56kB
21adbf10d522: Loading layer [==================================================>]   2.56kB/2.56kB
46acdfccfc0a: Loading layer [==================================================>]  4.096kB/4.096kB
Loaded image: isotar:rc1

The previous command will install the isoTar image on your PC. By running

docker images

you will list Docker images available on your PC. You can verify if the installation process has been executed correctly by checking if the isotar image is showed as reported below:

REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
isotar              v1                  15e0f7fa60fe        44 minutes ago      10.4GB

Keep in mind that the IMAGE ID showed here represents an example, since you will see a different one on your PC.

4. Run isoTar using the following command:

$ docker run --rm --name isotar -d -p 80:8080 isotar:v1

The previous command will run isoTar as a Web Service on your PC. In particular, it will bind the 80 port of your PC with the isoTar Web Service 8080 port. You can specify a different port for your PC if it is already used by other services. For example, you could use port 8080 or similar instead of 80 (docker run --rm --name isotar -d -p 8080:8080 isotar:v1).
Note This command will create an instance of the isoTar image previously installed on your PC. As consequence, you will be required to run such command every time you will run/reboot your PC, whenever you need to use isoTar.

5. Open your web browser and write the URL localhost, if you used port 80 on your PC. If you specified a different port, for example 8080 port, use the URL localhost:8080


How to

Consensus Prediction + Functional Analysis, as well as Functional Enrichment Analysis only input setups are available through the corresponding button.

Input setup



STEP 1 miRNA(s) setup


There are three ways to load a list of miRNA(s) and their variation(s):

  1. Using the "Search and customize a miRNA" form ([Option 1])
  2. Using the "Known Variations Databases" button ([Option 2])
  3. Pasting them directly into the "textarea" ([Option 3])

These options are shown in the following figure:

Prediction input module

Each option requires specific steps in order to generate the correct input format for the analysis. Whatever option is chosen, each miRNA specification must be compliant with the isoTar format (see below).

Input format
>hsa-miR-XXX hsa-[mir/let]-XXX <variations list>
mature miRNA sequence
Input format example
>hsa-let-7c-5p hsa-let-7c 17:A|G;+1|-2
UGAGGUAGUAGGUUGUAUGGUU

hsa-let-7c-5p, hsa-let-7c, 17:A|G;+1|-2, and UGAGGUAGUAGGUUGUAUGGUU represent, respectively, the mature miRNA name (Human only), the pre-miRNA name (Human only), the variations list (in this example, a nucleotide and an isomiR modification), and the mature miRNA reference sequence (Human only).

[Option 1] Search and customize a miRNA
Using this module, it is possible to search for a specific mature miRNA. It shows a list of miRNA(s) and their pre-miRNA(s).

Search and customize form

Once a miRNA has been selected, click on the Search button in order to open the customization module (figure below).


Customization module

Two distinct variations are shown in the following figure:

  • A single nucleotide variation at position 6 that changes the nucleotide from A (Ref) to G (Mod)
  • A single isomiR variation that adds one nucleotide at 5p end, resulting in an extension of the miRNA sequence. Added nucleotides are retrieved from the pre-miRNA which the selected miRNA originates from.
An example of vatiations

Once finished, press the <Add customized miRNA> button to add all variations to the textarea. The textarea will look like this:

miRNA's variations

[Option 2] Known Variations Databases
An alternative way to specify miRNA variations is to select them using one or more of the databases provided with isoTar. These databases can be selected using the following dropdown menu (figure below).

isoTar Known Variation Databases

Each database can be consulted through a user-friendly interface which lists variations in a tabular form.

Variation List


STEP 2 Tools selection and Minimum Consensus


Before performing any prediction analysis, it is necessary to select which tools should be executed and, possibly, their combination by specifying the AND/OR option (if one or more tools must/can identify the same predicted target). In addition, a Minimum Consensus has to be provided as well. It corresponds to the minimum number of tools that must or can identify the same predicted target. The following figure shows these notions:

isoTar databases


STEP 3 CPU cores selection


It is possible to perform all the analyses using a single core CPU or taking advantage of a multiple CPU cores. In this latter case, tools and the analysis are executed in parallel in order to optimize execution time, hiding the underlying complexity to the user.
The following figure shows a hypothetical system equipped with 8 CPU cores, as well as a dropdown menu reporting all selectable CPU cores. In this example, it is possible to select from 1 to 8 cores.

CPU cores selection


Consensus Prediction input submission & analysis execution


Click on the Submit button to start input verification (the button is located below the STEP 3 box). Then, isoTar will show you a summary of all submitted miRNA(s) and their variation(s). Results are grouped according to the specified variation types, such as single nucleotide variation, isomiR, and/or wild type miRNA (no variation specified). The figure below shows variations grouped for Single nucleotide variation and isomiR.

Consensus prediction input check

Once you reviewed your input, click on the Run button to start the Prediction Analysis. isoTar will show you a box reporting the analysis status, as well as the stopping button to shut down its execution (both shown below).

Running & stopping statuses


STEP 1 P-value specifics


Results generated through the Functional Analysis are filtered according to the user-specified P-value:

  • Type, which represents the type of the P-value that must be taken into account in the filtering. A user can choose to filter on: (Raw P-value)-based filter or (Adjusted P-value)-based filter (Benjamini and Hochberg).
  • Threshold, which represents the upper bound limit value for the set of P-values the user wishes to display.

The figure below shows the module which allows to set the P-value type and threshold.

P-value specifics module


STEP 2 Resources selection


The Functional Analysis can be performed using different resources:

  • Link Gene Ontology (GO terms)
  • Link KEGG (Pathways)
  • Link Reactome (Pathways)

Select the resources in the Functional Analysis to be considered, as shown in the following figure:

P-value specifics module


STEP 3 CPU cores selection


It is possible to perform all the analyses using a single core CPU or taking advantage of a multiple CPU cores. In this latter case, tools and the analysis are executed in parallel in order to optimize execution time, hiding the underlying complexity to the user.
The following figure shows a hypothetical system equipped with 8 CPU cores, as well as a dropdown menu reporting all selectable CPU cores. In this example, it is possible to select from 1 to 8 cores.

CPU cores selection


Analysis execution


Click on the Submit button to start the Functional Analysis. isoTar will show you a box reporting the analysis status, as well as the stopping button to shut down its execution (both shown below).

Running & stopping statuses



STEP 1 Gene list(s)


This module (the figure below) allows to specify whether the Functional Enrichment Analysis must be performed on one or two lists of genes, by specifying their official gene name (e.g.: PTEN) and/or refseq ID (e.g.: NM_000314). If two lists are specified, the analysis is performed by comparing both lists. As an example, a user could be interested in comparing two lists of genes, each one associated with a specific biological condition (e.g.: Wild Type vs. Knockout).
Both the first label and the list of genes are mandatory. However, it is not necessary to specify the second set of label and genes list if no comparison must be performed.

Gene list(s) module


STEP 2 P-value specifics


Results generated through the Functional Analysis are filtered according to the user-specified P-value:

  • Type, which represents the type of the P-value that must be taken into account in the filtering. A user can choose to filter on: (Raw P-value)-based filter or (Adjusted P-value)-based filter (Benjamini and Hochberg).
  • Threshold, which represents the upper bound limit value for the set of P-values the user wishes to display.

The figure below shows the module which allows to set the P-value type and threshold.

P-value specifics module


STEP 3 Resources selection


The Functional Analysis can be performed using different resources:

  • Link Gene Ontology (GO terms)
  • Link KEGG (Pathways)
  • Link Reactome (Pathways)

Select the resources in the Functional Analysis to be considered, as shown in the following figure:

P-value specifics module


STEP 4 CPU cores selection


It is possible to perform all the analyses using a single core CPU or taking advantage of a multiple CPU cores. In this latter case, tools and the analysis are executed in parallel in order to optimize execution time, hiding the underlying complexity to the user.
The following figure shows a hypothetical system equipped with 8 CPU cores, as well as a dropdown menu reporting all selectable CPU cores. In this example, it is possible to select from 1 to 8 cores.

CPU cores selection

Results explanation



After the conclusion of the analysis, prediction results will be shown in a new tab panel (named Results), as shown below:

Prediction results

As shown above, isoTar adopts the same criterion for results visualization. In fact, prediction results are grouped per variation type, and results for each selected prediction tool are displayed. Each specified variation is associated with:

  • Variation details, such as its type (i.e., Substitution or isomiR), position (for Substitution), and so on;
  • The number of targets (named Targets), in which predicted targets are reported according to the specified minimum consensus, (0 in case there are none);
  • The number of targets conclusion by each selected prediction tool (0 in case there are none);
  • A Venn diagram reporting the targets intersection among all selected prediction tools.

Each number (colored in blue) represents the number of targets for a specific variation (i.e., Substitution or isomiR) and prediction tool/consensus. By clicking on it, the system will show you a modal box (figure below) reporting the targets list supplied with additional information. Results can be downloaded in different data formats, such as Excel, PDF, TSV, and so forth.

miRanda predicted targets

A Venn diagram is also provided, reporting the targets intersection among selected prediction tools, together with the histogram representing the targets distribution among such tools.

Venn diagram

After the conclusion of the analysis (based on predicted targets), results are grouped according to each selected resource (i.e., GO term, KEGG, Reactome), and details are displayed by variation type (i.e., Substitution, isomiR).
For each resource, the Functional Analysis is performed considering wild type, variation, and wild type vs. variation (comparison). Depending on which resource is selected, isoTar provides different details. Below, we report results for the three main resources here available.

GO term

The GO term-based Functional Analysis is performed for each pair wild type-variation, specified as input for the Prediction Analysis. Then, results are grouped per Ontology category:

  • Biological Process (BP);
  • Cellular Component (CC);
  • Molecular Function (MF).

A typical analysis provides information according to:

  • Wild Type
    A GO term list for BP, CC, and MF, respectively, is supplied with additional details. Such GO terms are identified considering all the consensus predicted targets associated to the wild type miRNA;

  • Variation
    A GO term list for BP, CC, and MF, respectively, is supplied with additional details. Such GO terms are identified considering all the consensus predicted targets associated to the variation miRNA;

  • Wild Type vs. Variation
    • A GO term list for each of the GO category (BP, CC, and MF) is supplied with additional details. GO terms are identified considering those consensus predicted targets that are associated with each of the following:

      • Wild type only
      • Variation only
      • Wild type-variation intersection
(GO term)-based Functional Analysis results
(GO term)-based Functional Analysis results

Results are displayed adopting the same format employed when displaying prediction results. Results are grouped per variation type and ontology category (i.e., BP, CC, MF). Each specified variation is associated with:

  • Variation details, such as type (i.e., Substitution or isomiR), position (for Substitution), and so on;
  • The number of identified GO terms (filtered per P-value type and threshold) for each ontology category, supplied with GO term trees and histograms;
  • The number of GO terms (per ontology category) considering only those predicted targets associated to the wild type, variation, and their intersection, respectively. GO term trees and histograms are provided as well;

Each number (colored in blue) represents the number of GO terms for a specific wild type or variation (i.e., Substitution or isomiR) and ontology category (i.e., BP, CC, MF). By clicking on it, the system will show you a modal box (figure below) reporting the GO terms list supplied with additional information. Results can be downloaded in different data formats, such as Excel, PDF, TSV, and so forth.

GO terms modal box

Histograms are available for each ontology category. They report:


  • Comparison between wild type and variation. As an example, we report (figure below) a histogram associated with the wild type and the variation at position 6 according to the ontology category CC (wild type in blue and variation in red);

    Histogram wild type-variation according to CC
  • Comparison between wild type only, variation only, and their intersection. As an example, we report (figure below) a histogram associated with the comparison between the wild type and the variation (wild type only, variation only, and their intersection) at position 6 according to the ontology category CC (wild type in blue, variation in red, and intersection in green);

    Histogram comparison between wild type variation according to CC

Histograms can be downloaded in different formats, such as PDF, PNG, JPEG, and SVG.


GO term trees are available for each ontology type. They are associated with:

  • Each wild type and their variation(s). As an example, we report (figure below) two GO term trees associated with the wild type and the variation at position 6, respectively, according to the ontology type CC (in green, the GO term nodes for which the P-value is also reported);

    Wild Type
    Wild type GO term tree according to CC

    Variation
    Variation GO term tree according to CC

  • Comparison between wild type only, variation only, and their intersection. As an example, we report (figure below) the GO term tree associated with the comparison between the wild type and the variation (wild type only, variation only, and their intersection) at position 6 according to the ontology type CC;

    Wild Type vs. Variation
    Wild type vs. variation GO term tree according to CC

    Colors are used to highlight GO term nodes, according to all possible combinations among all possible intersection subsets. A table reporting the adopted colors is provided as well (figure below).

    GO term tree's node colors legend

GO term trees can be downloaded in PDF format.

KEGG & Reactome

The Pathway-based Functional Analysis is performed for each pair wild type-variation, specified as input for the Prediction Analysis. A typical analysis provides information according to:
  • Wild Type
    A pathways list is supplied with additional details. Pathways are identified considering all consensus predicted targets associated to the wild type;

  • Variation
    A pathways list is supplied with additional details. Pathways are identified considering all consensus predicted targets associated to the variation;

  • Wild Type vs. Variation
    • A pathways list is supplied with additional details. Pathways are identified considering those consensus predicted targets that are respectively associated only to:

      • Wild type
      • Variation
      • Wild type-variation intersection
Pathway-based Functional Analysis results

Results are displayed adopting the same format employed when displaying prediction results (grouped per variation type). Each specified variation is associated with:

  • Variation details, such as type (i.e., Substitution or isomiR), position (Substitution only), and so on;
  • The number of identified pathways (filtered by P-value type and threshold), supplied with histograms;
  • The number of pathways considering only those predicted targets associated with the wild type, variation, and their intersection, respectively. Histograms are provided as well;

Each number (colored in blue) represents the number of pathways for a specific variation (i.e., Substitution or isomiR). By clicking on it, the system will show you a modal box (figure below) reporting the pathways list supplied with additional information. Results can be downloaded in different data formats, such as Excel, PDF, TSV, and so forth.

Pathways modal box


Histograms report:

  • Comparison between wild type and variation. As an example, we report (figure below) a histogram associated with the wild type and the variation at position 6 according to KEGG (wild type in blue and variation in red);

    Histogram wild type-variation according to KEGG
  • Comparison between wild type only, variation only, and their intersection. As an example, we report (figure below) a histogram associated with the comparison between the wild type and the variation (wild type only, variation only, and their intersection) at position 6 according to KEGG (wild type in blue, variation in red, and intersection in green);

    Histogram comparison between wild type variation according to KEGG

Histograms can be downloaded in different formats, such as PDF, PNG, JPEG, and SVG.

What is described above is valid for Reactome results al well.

When performing Functional Analysis without leveraging on consensus predicted targets, one or two user-specified lists of targets may be provided. In this section, we describe results for two lists of targets. For clarity, we named the first list wild_type (List_1) and the second one variation (List_2). After the conclusion of the analysis, results are grouped according to each selected resource (i.e., GO term, KEGG, Reactome).
For each resource, the Functional Analysis is performed considering List_1, List_2, and List_1 vs. List_2 (comparison). Depending on which resource is selected, isoTar provides different details. Below, we report results for the three main resources here available.

GO term

The GO term-based Functional Analysis is performed for List_1 and List_2. Then, the results are grouped per Ontology category:

  • Biological Process (BP);
  • Cellular Component (CC);
  • Molecular Function (MF).

A typical analysis provides information according to:

  • List_1
    A GO terms list for BP, CC, and MF, respectively, is supplied with additional details. Such GO terms are identified considering all List_1 targets;

  • List_2
    A GO terms list for BP, CC, and MF, respectively, is supplied with additional details. Such GO terms are identified considering all List_2 targets;

  • List_1 vs. List_2
    • A GO term list for each of the GO category (BP, CC, MF) is supplied with additional details. GO terms are identified considering those consensus predicted targets that are associated with each of the following:

      • List_1
      • List_2
      • List_1-List_2 intersection
(GO term)-based Functional Analysis results

Results are displayed adopting the same format employed when displaying prediction results. Results are grouped per ontology category (i.e., BP, CC, MF). List_2 is associated with:

  • The number of identified GO terms (filtered per P-value type and threshold) for each ontology category, supplied with GO term trees and histograms;
  • The number of GO terms (per ontology category) considering only those targets associated to the List_1, List_2, and their intersection, respectively. GO term trees and histograms are provided as well.

Each number (colored in blue) represents the number of GO terms for List_1 or List_2 and ontology category (i.e., BP, CC, MF). By clicking on it, the system will show you a modal box (figure below) reporting the GO terms list supplied with additional information. Results can be downloaded in different data formats, such as Excel, PDF, TSV, and so forth.

GO terms modal box


Histograms are available for each ontology category. They report:

  • Comparison between List_1 and List_2. As an example, we report (figure below) a histogram associated with List_1 and List_2 according to the ontology category CC (wild type in blue and variation in red);

    Histogram List_1-List_2 according to CC
  • Comparison between List_1 only, List_2 only, and their intersection. As an example, we report (figure below) a histogram associated with the comparison between the List_1 and the List_2 (wild type only, variation only, and their intersection) according to the ontology category CC (wild type in blue, variation in red, and intersection in green);

    Histogram comparison between List_1 and List_2 according to CC

Histograms can be downloaded in different formats, such as PDF, PNG, JPEG, and SVG.


GO term trees are available for each ontology category. They are associated with:

  • List_1 and List_2. As an example, we report (figure below) two GO term trees associated with List_1 and List_2, respectively, according to the ontology category CC (in green, the GO term nodes for which the P-value is also reported);

    List_1
    List_1 GO term tree according to CC

    List_2
    List_2 GO term tree according to CC

  • Comparison between List_1 only, List_1 only, and their intersection. As an example, we report (figure below) the GO term tree associated with the comparison between List_1 and List_2 (List_1 only, List_2 only, and their intersection) according to the ontology category CC;

    List_1 vs. List_2
    List_1 vs. List_2 GO term tree according to CC

    Colors are used to highlight GO term nodes, according to all possible combinations among all possible intersection subsets. A table reporting the adopted colors is provided as well (figure below).

    GO term tree's node colors legend

GO term trees can be downloaded in PDF format.

KEGG & Reactome

The Pathway-based Functional Analysis is performed considering the pair List_1-List_2. A typical analysis provides information according to:

  • List_1
    A pathways list is supplied with additional details. Pathways are identified considering all List_1 targets;

  • List_2
    A pathways list is supplied with additional details. Pathways are identified considering all List_2 targets;

  • List_1 vs. List_2
    • A pathways list is supplied with additional details. Pathways are identified considering those consensus predicted targets that are respectively associated only to:

      • List_1
      • List_2
      • List_1-List_2 intersection
Pathway-based Functional Analysis results

List_2 is associated with:

  • The number of identified pathways (filtered per P-value type and threshold), supplied with histograms;
  • The number of pathways considering only those targets associated with List_1, List_2, and their intersection. Histograms are provided as well;

Each number (colored in blue) represents the number of pathways. By clicking on it, the system will show you a modal box (figure below) reporting the pathways list supplied with additional information. Results can be downloaded in different data formats, such as Excel, PDF, TSV, and so forth.

Pathways modal box


Histograms report:

  • Comparison between List_1 and List_2. As an example, we report (figure below) a histogram associated with List_1 and List_2 according to KEGG (List_1 in blue and List_2 in red);

    Histogram List_1-List_2 according to KEGG
  • Comparison between List_1 only, List_2 only, and their intersection. As an example, we report (figure below) a histogram associated with the comparison between List_1 and List_2 (List_1 only, List_2 only, and their intersection) according to KEGG (List_1 in blue, List_2 in red, and intersection in green);

    Histogram comparison between List_1 and List_2 according to KEGG

Histograms can be downloaded in different formats, such as PDF, PNG, JPEG, and SVG.

What is described above is valid for Reactome results as well.


Changelog

		# Changelog
		All notable changes to this project will be documented in this section.
		
		The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
		and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
		
		## [Released]
		
		## [1.0.0] - 2019-04-05
		### Added
		- Initial release