OnTheFly2.0

Automated document annotation and biological information extraction


OnTheFly2.0 is a web application to aid users collecting biological information from documents. With OnTheFly2.0 one is able to:
  • Extract bioentities from individual articles in formats such as plain text, Microsoft Word, Excel and PDF files.
  • Scan images and identify terms by using Optical Character Recognition (OCR).
  • Handle multiple files simultaneously.
  • Isolate proteins, chemical compounds, organisms, tissues, diseases/phenotypes and gene ontology terms.
  • Extract selected terms along with their identifiers in databases.
  • Perform functional enrichment analysis on a selected group of terms.
  • Identify co-occurring proteins in the scientific literature and in protein domain databases
  • Generate and visualize protein-protein and protein-chemical interaction networks.

If you find OnTheFly2.0 useful in your work please cite:

  • Baltoumas, F.A., Zafeiropoulou, S., Karatzas, E., Paragkamian, S., Thanati, F., Iliopoulos, I., Eliopoulos, A.G., Schneider, R., Jensen, L.J., Pafilis, E., Pavlopoulos, G.A. (2021) OnTheFly2.0: a text-mining web application for automated biomedical entity recognition, document annotation, network and functional enrichment analysis. NAR Genomics and Bioinformatics, 2021, Vol. 3, No. 4. doi: 10.1093/nargab/lqab090
  • Pavlopoulos, G.A., Jensen, L.J., Pafilis, E., Kuhn, M., Hooper, S.D., Schneider, R. (2009) OnTheFly: a tool for automated document-based text annotation, data linking and network generation. Bioinformatics, Apr 1;25(7):977-8. doi: 10.1093/bioinformatics/btp081.

Annotate Documents

Create Dataset for Analysis

Functional Enrichment Analysis: g:Profiler

Functional Enrichment Analysis: aGOtool

Publication Enrichment

Protein-Protein interaction Network (STRING)

Protein-Chemical interaction Network (STITCH)

Help


1. Overview

OnTheFly2.0 offers a number of tools and functions, accessible through the menu on the left side of the page. The options offered are the following:

  1. Home: The web service's Home page.
  2. Annotate Files: Upload documents and annotate them using Named Entity Recognition (NER).
  3. Create Dataset: Use the bioentities obtained from annotated documents to prepare datasets for analysis.
  4. Functional Enrichment: Perform Functional Enrichment Analysis on your dataset. Two different sub-options are given:
    1. Ontologies & Pathways (g:Profiler): Search Gene Ontology, Pathway databases, Tissue Expression and Phenotype Onotology databases with g:Profiler.
    2. Domains & Diseases (aGOtool): Search Pfam, InterPro, UniProt and the DISEASES database with aGOtool.
  5. Literature Search: Perform scored searches against the literature.
  6. Protein Domain Search: Perform scored searches against protein classification databases.
  7. Interaction Networks: Create and visualize biological interaction networks. Two different sub-options are given:
    1. Protein - Protein (STRING): Create protein-protein interaction networks by retrieving interactions from STRING.
    2. Protein - Chemical (STITCH): Create protein-small molecule interaction networks by retrieving interactions from STITCH.
  8. Help: This Help page.
  9. About: Developer credits, contanct information and additional details.




2. How to use this help page

Topics in this manual are divided in separate tabs, accessible through header buttons at the top of the page, as shown in the figure below. Click on any of these header buttons to navigate to its respective tab:

In each tab, content is divided into collapsed sections, indicated by colored title bars, as shown in the figure above. Click on each title to expand or collapse its content.


The Annotation tab consists the principal feature of OnTheFly2.0, enabling bioentities extraction and isolation from multiple files in many different formats, as well as mapping of selected terms to their corresponding databases.


Figure 1: The File Upload form

OnTheFly2.0 provides the option to select and upload multiple files simultaneously and/or write in a text area field (Figure 1).

1. File Upload

Click the Browse button of the upload form to select and upload one or multiple files. Acceptable file formats currently include:

  • PDF (.pdf)
  • Rich Text Format (rtf)
  • Microsoft Word (.doc and .docx)
  • OpenOffice Writer(.odt)
  • Microsoft Excel (.xls and .xlsx)
  • OpenOffice Calc (.ods)
  • Flat text (.txt, .tsv, .csv)
  • Images (.bmp, .png, .jpg, .tif)
  • PostScript (.ps, .eps)
  • XML files (.xml)
Notes:
  1. The size of each file cannot exceed 10 MB.
  2. Images should have a resolution / pixel density of at least 150 ppi/dpi.
  3. A maximum of 10 files can be uploaded for each session.


2. Text area field

The text area field forms a basic text input area, enabling the creation of custom text by writing or pasting a section of a text. By pressing the ADD button the formed text can be added to the list of files for further analysis, whereas the CLEAR button can be used to discard the unwanted text area input. Clicking the LOAD EXAMPLE button will generate an example text in the form.


Figure 2. File handling: select, rename and delete.

1. Select / Rename / Delete

After file submission, a checkbox list will appear, containing all uploaded files and/or submitted texts (Figure 2). Any additional uploaded or created files are appended to the selection list.
Files can be selected and manipulated by clicking the checkboxes next to their names. One or multiple files can be deleted by selecting them and clicking the Delete button. Files can be renamed, by selecting them and clicking the Rename button. In both cases an dialog box will appear, asking you to rename or delete the selected file.


2. File display

Upon selection of one or more files from the checkbox list, a reactive tab panel will appear, containing each choice in a separate tab. Every tab in the tab panel is divided into two sub-panels: File and Entities (Figure 3). Selected documents are displayed in the File sub-panel

You can select or deselect a file from the checkbox list and the corresponding tab will be dynamically inserted or removed accordingly.


Figure 3: Display selected files

1. Select Annotation parameters

OnTheFly2.0 uses the EXTRACT Named Entity Recognition (NER) service to perform the biological annotation of documents, by highlighting terms of interest and extract identified bioentities:

  1. Select or deselect at least one out of 14 different entity type(s) from the Select entity type(s) selection list. By default, all entity types are selected.
    Available entity types currently include: Chemical compound, Organism, Protein, Biological process, Cellular component, Molecular function, Tissue, Disease, ENVO environment, APO phenotype, FYPO phenotype, MPheno phenotype, NBO behavior, Mammalian phenotype.
  2. In order for proteins to be identified, you must select an organism. The proteins of the choosen organism(s) will be highlighted in the text. OnTheFly2.0 currently supports 197 organisms, a list of which can be seen by clicking on the View available organisms link.
  3. Click the Annotate button to begin NER, or the Reset button to clear any previous annotations and reset the input form to its default values.


Figure 4: Annotation Parameters


Figure 5: Annotation results in the document viewer

2. Annotation Results - Graphical View

Upon pressing the Annotate button, the entire displayed document will be tagged and identified bioentities will be highlighted according to the selected parameters.
A legend, color-coding each entity term category, is shown above the document viewer.

By hovering the mouse cursor over highlighted terms a pop-up will appear assigning each word to the corresponding type, name and identifier (Figure 5).

A table with the parameters used during annotation is shown below the document viewer.


3. Annotation Results - Extracted Entities

Extracted bioentities are shown in an interactive table, that can be accessed through the Entities tab of each document (Figure 6).

The table shows the names, the entity types and the database identifiers of each extracted term. Identifiers for each term are retrieved from the following databases:

  • Proteins and genes: ENSEMBL
  • Chemical compounds: NCBI PubChem
  • Organisms: NCBI Taxonomy browser
  • Ontology terms (Biol. Process, Mol. Function, Cell Component): EMBL-EBI's QuickGO browser for Gene Ontology
  • Tissues: BRENDA Tissue Ontology
  • Diseases: Disease Ontology (DOID)
  • ENVO, APO, FYPO etc. phenotypes: EMBL-EBI Phenotype Ontology
Each identifier is a hyperlink, opening a new browser tab to its page in the relevant database.

The results can be filtered by entity type, using the selection list above the table, or by text search. The entire table, as well as filtered results, can be downloaded in CSV format.

Figure 6: Table of extracted bioentities

Create Dataset tab, as the name indicates, enables the creation of a dataset for analysis, containing selected bioentities terms of interest, originated from one or multiple previously annotated files.

Select entities and add them to a dataset

The Create Dataset page contains the following sections (Figure 7):

  • A side bar panel, containing the list of annotated documents, accompanied by short instructions.
  • A main panel tab called Annotated Documents. The annotated terms of each document will appear there.
  • A main panel tab called Dataset. The dataset created by will appear there.

Figure 7: Entity Selection for the creation of a dataset

Annotated documents appear in a checkbox list in the side bar panel of the page. The annotated terms of each selected document appear in the Annotated Documents section of the main portion of the page, under a tab named after the document.

  • Use the selection menu above to select the documents you wish to analyze.
  • For each document, select one or more proteins and/or chemicals by clicking on them. You can also click the Select All checkbox to select all entities in the table. When you are ready, click Add to Dataset to add them to a dataset for analysis.
  • Your selected terms will appear in a new panel, marked Dataset.
  • You can do the above for multiple documents, by repeating the same procedure. The produced dataset can include terms from multiple documents.

Manage the dataset

All the selected entities are collected and displayed in a table in the Dataset panel (Figure 8):

Figure 8: The created dataset table

The table displays the identifier, name, entity type and document origin of each term. The latter refers to the document from which each term was extracted. You can filter the table by entity type, using the drop down selection above, or by text search.
Single entities can be deleted from the table by hovering the mouse over their row (the row will be colored red and a trash icon will appear) and left-clicking. You can also delete all lines (and empty the dataset) by clicking the Delete All button.
The dataset csn be downloaded in CSV, Excel or PDF format by clicking the Download button.
To submit your dataset for analysis, click on Functional Enrichment Analysis, Literature Search, Protein Domain Search, Protein-Protein Network or Protein-Chemical Network to add your selected terms to a dataset for Functional Enrichment Analysis, Literature Search, Protein Domain Search, or to create Protein-Protein or Protein-Chemical interaction networks, respectively. When you are ready, click one of the options on the left-side menu to select an analysis method:

  • Enrichment: g:Profiler performs functional enrichment analysis with g:Profiler.
  • Enrichment: aGOtool performs functional enrichment analysis with aGOtool.
  • Literature Search searches your selected proteins against the scientific literature.
  • Interaction Network creates protein-protein or protein-chemical interaction networks. Two choices are given: Protein-Protein for protein-protein networks and Protein-Chemical for protein chemical networks.


This tab consists of two sub-tabs: (i) Input and (ii) Results and is used to perform functional enrichment analysis with g:Profiler on a selected dataset of extracted terms.

Prepare and run functional enrichment analysis

Note: In order to run functional enrichment analysis, you first need to create an input dataset through the Create Dataset menu.

Functional enrichment analysis involves the following options:

  1. Select organism: select organism for analysis. A choice among 197 species is given.
  2. Select data sources: select databases for enrichments. Available choices are:
    • Gene Ontology: Biological Process, Molecular Function and Cellular Component
    • Metabolic Pathways: KEGG, Reactome, WikiPathways
    • Regulatory Motifs: TransFac, miRTarBase
    • Protein Databases: Human Protein Atlas (for H. sapiens only!), CORUM
    • Phenotypes: Human Phenotype Ontology (for H. sapiens only!)
  3. Significance Options: define threshold type and cut-off value:
    • Threshold Type: Define the type of evaluation threshold (i.e. the correction method for p-value). Three options are given: g:SCS (the default g:Profiler p-value), Bonferroni and False Discovery Rate (FDR)
    • P-value cut-off: Set the p-value cut-off. Default value is 0.05 (5%)
  4. Select Protein ID type for analysis: define the ID type that will be used in the analysis, as well as in the output. Although extracted terms have ENSEMBL IDs, these can be converted to other database types depending on your needs. By default, Entrez gene names are used.
Figure 9: The Functional Enrichment Analysis input form.

To perform functional enrichment analysis on your dataset, set the aforementioned input options to the values that best suit you. Default selected values are Homo sapiens (Human) for species, All Gene Ontology (Biol. Process, Cell Component, Mol. Function) and Metabolic Pathway (KEGG, Reactome, WikiPathways) Databases, the g:SCS p-value type and a 0.05 cut-off, while protein ENSEMBL IDs will be translated to their Entrez gene name equivalents.
Click the Analyze Data button to begin. To reset your dataset, click the Delete All button.

Enrichment Results: Table

Enrichment results will appear in three sub-panels of the Results tab, Table, Manhattan Plot and Bar plot. In the Table sub-panel, results are shown in table format, both for all enrichment terms, and for each category separately.

Figure 10: Enrichment analysis results in table format.

Each results table contains the following columns:

  • Term ID: The unique term identifier. In the table, Term ID is a hyperlink that points to the correspoding data source of the term
  • Term Name: the short name of the function
  • P-value: hypergeometric p-value after correction for multiple testing
  • Term size: number of genes that are annotated to the term
  • Query size: number of genes that were included in the query
  • No. of Prositive Hits: the number of genes in the input query that are annotated to the corresponding term
  • Positive Hits: a comma separated list of genes from the query that are annotated to the corresponding term
You can filter your results using the text search field, as well as download the table to CSV, Excel and PDF file.


Enrichment Results: Manhattan plot

In addition to the tables, functional enrichment results are also visualized with an interactive Manhattan Plot, in Manhattan Plot sub-panel of the Results tab, graphically depicting the annotated functional terms. The grouping and color-coding of the terms is made according to data sources that are represented in the x-axis. The y-axis shows the adjusted p-values in negative log10 scale (-log10(P-value). The size of each colored circle, which corresponds to one term, depends on the size of this specific term, i.e larger terms have larger circles.

A wide range of actions, concerning the visualization of the plot, are available, including saving the plot as an image, selecting a single or multiple nodes with the mouse, zoom in/out, pan, selection of a specific area, autoscale etc. These can be accessed by the icons in the menu appearing at the top right of the plot

You can select a single node in the plot by left-clicking it. Alternatively, you can use Box Select or Lasso Select from the plot tools (top right of the graph) to select multiple terms.
In either case, selecting one or more nodes in the plot will show their details (ID, Name, P-value etc) in a table at the bottom of the plot.

Figure 11: Manhattan plot of Enrichment analysis results.

Enrichment Results: Bar plot

Enrichment results can also be shown in an interactive bar plot, through the Bar Plot sub-panel. In the plot, the x-axis represents the enrichment metric function (either -log10(P-value) or an enrichment score, defined as the % ratio of observed over expected terms). The y-axis shows the terms themselves.

Figure 12: Bar plot of Enrichment analysis results.

The components of the plot are defined from the plot controls above it. Three control options are given:

  1. Database: select which database(s) to plot. Multiple selections are available; in this case, each database type is colored differently, with a color index shown at the bottom left of the plot.
  2. Enrichment metric: select the metric for the bar lengths. Available options are -log10(P-value) or Enrichment Score (the % ratio of observed over expected terms).
  3. Number of terms in plot: a slider through which you can choose the number of terms (bars) to appear in the plot. Changing the number of terms will increase or decrease the plot height.

The terms depicted in the plot will also appear in table format below the graph. The number of terms in the table will be the same number of terms as in the graph. Both in the graph and in the table, the terms will appear sorted with regards to the chosen metric, in decreasing order.
The plot is interactive; hovering your mouse over a bar will display its title and metric score. A wide range of actions, concerning the visualization of the plot, are available, including saving the plot as an image, selecting a single or multiple nodes with the mouse, zoom in/out, pan, selection of a specific area, autoscale etc. These can be accessed by the icons in the menu appearing at the top right of the plot.
The results can also be downloaded through the table below the plot, in CSV, Excel or PDF format.


This tab consists of two sub-tabs: (i) Input and (ii) Results and is used to perform Publication Enrichment on a selected dataset of proteins and genes.

Prepare and run Literature Search

Note: In order to run functional enrichment analysis, you first need to create an input dataset through the Create Dataset menu.

Literature Search involves the following options:

  1. Select organism: select organism for analysis. A choice among 197 species is given.
  2. Significance Options: Define cut-off values for p-value and its False Discovery Rate (FDR) correction.
  3. Select Protein ID type: define the ID type that will be used in the analysis, as well as in the output. Although extracted terms have ENSEMBL IDs, these can be converted to other database types depending on your needs. By default, Entrez gene names are used.
Figure 13: The Literature Search input form.

To perform Liteature search analysis on your dataset, set the aforementioned input options to the values that best suit you.
Click the Analyze Data button to begin. To reset your dataset, click the Delete All button.

Search Results: Table

Search results will appear in two sub-panels of the Results tab, Table and Bar plot. In the Table sub-panel, results are shown in table format, both for all enrichment terms, and for each category separately.

Figure 14: Search results in table format.

Each results table contains the following columns:

  • Term ID: The unique term identifier. In the table, Term ID is a hyperlink that points to the correspoding data source of the term
  • Term Name: the short name of the function
  • P-value:The p-value
  • FDR: The FDR correction of the p-balue
  • Term size: number of genes that are annotated to the term
  • Query size: number of genes that were included in the query
  • No. of Prositive Hits: the number of genes in the input query that are annotated to the corresponding term
  • Positive Hits: a comma separated list of genes from the query that are annotated to the corresponding term
You can filter your results using the text search field, as well as download the table to CSV, Excel and PDF file.


Search Results: Bar plot

Search results can also be shown in an interactive bar plot, through the Bar Plot sub-panel. In the plot, the x-axis represents the enrichment metric function (-log10(FDR), -log10(P-value) or an enrichment score, defined as the % ratio of observed over expected terms). The y-axis shows the terms themselves.

Figure 15: Bar plot of search results.

The components of the plot are defined from the plot controls above it. Two control options are given:

  1. Enrichment metric: select the metric for the bar lengths. Available options are -log10(FDR), -log10(P-value) or Enrichment Score (the % ratio of observed over expected terms).
  2. Number of terms in plot: a slider through which you can choose the number of terms (bars) to appear in the plot. Changing the number of terms will increase or decrease the plot height.

The terms depicted in the plot will also appear in table format below the graph. The number of terms in the table will be the same number of terms as in the graph. Both in the graph and in the table, the terms will appear sorted with regards to the chosen metric, in decreasing order.
The plot is interactive; hovering your mouse over a bar will display its title and metric score. A wide range of actions, concerning the visualization of the plot, are available, including saving the plot as an image, selecting a single or multiple nodes with the mouse, zoom in/out, pan, selection of a specific area, autoscale etc. These can be accessed by the icons in the menu appearing at the top right of the plot.
The results can also be downloaded through the table below the plot, in CSV, Excel or PDF format.


This tab consists of two sub-tabs: (i) Input and (ii) Results and is used to perform Functional Enrichment Analysis on a selected dataset of proteins and genes.

Prepare and run Enrichment Analysis

Note: In order to run enrichment analysis, you first need to create an input dataset through the Create Dataset menu.

Literature Search involves the following options:

  1. Select organism: select organism for analysis. A choice among 197 species is given.
  2. Select data sources: select databases for search. Available choices are Pfam, InterProUniProt Keywords and the DISEASES database (only for H. sapiens).
  3. Significance Options: Define cut-off values for p-value and its False Discovery Rate (FDR) correction.
Figure 16: The input form.

To perform enrichment analysis on your dataset, set the aforementioned input options to the values that best suit you.
Click the Analyze Data button to begin. To reset your dataset, click the Delete All button.

Search Results: Table

Search results will appear in two sub-panels of the Results tab, Table and Bar plot. In the Table sub-panel, results are shown in table format, both for all enrichment terms, and for each category separately.

Figure 17: Enrichment results in table format.

Each results table contains the following columns:

  • Term ID: The unique term identifier. In the table, Term ID is a hyperlink that points to the correspoding data source of the term
  • Term Name: the short name of the function
  • P-value:The p-value
  • FDR: The FDR correction of the p-balue
  • Term size: number of genes that are annotated to the term
  • Query size: number of genes that were included in the query
  • No. of Prositive Hits: the number of genes in the input query that are annotated to the corresponding term
  • Positive Hits: a comma separated list of genes from the query that are annotated to the corresponding term
You can filter your results using the text search field, as well as download the table to CSV, Excel and PDF file.


Search Results: Bar plot

Search results can also be shown in an interactive bar plot, through the Bar Plot sub-panel. In the plot, the x-axis represents the enrichment metric function (-log10(FDR), -log10(P-value) or an enrichment score, defined as the % ratio of observed over expected terms). The y-axis shows the terms themselves.

Figure 18: Bar plot of enrichment results.

The components of the plot are defined from the plot controls above it. Three control options are given:

  1. Database: select which database(s) to plot. Multiple selections are available; in this case, each database type is colored differently, with a color index shown at the bottom left of the plot.
  2. Enrichment metric: select the metric for the bar lengths. Available options are -log10(FDR), -log10(P-value) or Enrichment Score (the % ratio of observed over expected terms).
  3. Number of terms in plot: a slider through which you can choose the number of terms (bars) to appear in the plot. Changing the number of terms will increase or decrease the plot height.

The terms depicted in the plot will also appear in table format below the graph. The number of terms in the table will be the same number of terms as in the graph. Both in the graph and in the table, the terms will appear sorted with regards to the chosen metric, in decreasing order.
The plot is interactive; hovering your mouse over a bar will display its title and metric score. A wide range of actions, concerning the visualization of the plot, are available, including saving the plot as an image, selecting a single or multiple nodes with the mouse, zoom in/out, pan, selection of a specific area, autoscale etc. These can be accessed by the icons in the menu appearing at the top right of the plot.
The results can also be downloaded through the table below the plot, in CSV, Excel or PDF format.


Interaction Networks tab offers a dynamic Protein-Protein and Protein-Chemical network visualization by using the APIs of STRING and STITCH respectively.

Network settings

Figure 19: Input options.

All the selected entities for Protein-Protein network analysis, originated from the Create dataset tab, are displayed in a datatable in Input sub-tab. The deletion of a specific row by clicking on it, as well as the deletion of the entire dataset by pressing the Delete All button, are both possible.

To create a network, you must choose an organism, define the properties of the network and press the Create Network button.


The Interaction Network Viewer

Figure 20: The Network Viewer.

The Network Viewer sub-tab is dedicated to the visualization of Protein-Protein associations networks. The network nodes, which are the selected group of proteins, are connected with undirected edges, representing the functional or physical associations.

You can download in TSV file the Protein-Protein interactions of the displayed network, export the network as an image or redirect the network to STRING database for further analysis.

Network settings

Figure 21: Input options.

All the selected entities for Protein-Chemical network analysis, originated from the Create dataset tab, are displayed in a datatable in Input sub-tab. The deletion of a specific row by clicking on it, as well as the deletion of the entire dataset by pressing the Delete All button, are both possible.

To create a network, you must choose an organism, define the properties of the network and press the Create Network button.


The Interaction Network Viewer

Figure 22: The Network Viewer.

The Network Viewer sub-tab is dedicated to the visualization of Protein-Protein associations networks. The network nodes, which are the selected group of proteins, are connected with undirected edges, representing the functional or physical associations.

You can download in TSV file the Protein-Chemical interactions of the displayed network, export the network as an image or redirect the network to STITCH database for further analysis.


List of supported organisms



Version History

2021-10-07:
-The OnTheFly2.0 paper has been published in Nucleic Acids Research Genomics and Bioinformatics!!!!!  The reference to the paper has been added to the OnTheFly2.0 GUI.
-Fixed the title and text in the STITCH "Connection Error" message to properly reference STITCH, instead of STRING.

2021-09-15:
-KEGG recently changed its API in the visualization of pathways, with regards to how genes/proteins are highlighted and colored. We have updated OnTheFly to reflect these changes.

2021-09-08:
-Added a new tab in the "Help" panel, called "Version History". This tab presents a change log (this text), detailing all changes and updates implemented in OnTheFly2.0.
-Added support for XML documents. XML files are now read natively, and parsed successfully. However, their visualization is rather plain, without any syntax highlighting for the XML tags.
-Publication enrichment now returns the full reference for each paper (Authors, year, title, journal, volume, issue and pages information).
-As part of the above, OnTheFly2.0 now also requires the installation and use of the "jsonlite" library for local use. The library has been added in the "install_libraries.R" script and in the GitHub README.md file.
-In addition, the Publication enrichment results table can now be sorted based on publication year, through a new column named "Publication year". 
-Improved the status checks in all HTTP requests, to fix crashes caused by complications in the user's DNS settings (exceeding timeouts, failure to resolve domain names etc).


2021-07-22:
-Added status checks in all HTTP requests (g:Profiler, aGOtool, STRING and STITCH). Now, if a web service is unavailable or returns errors, an informative message appears and the tool no longer crashes.
-Added code to set the default repository in "install_libraries.R". This way, the script can be run "as-is" through Rscript without having to manually set the CRAN mirror.
-Cleaned up ui.R, server.R and global.R from obsolete (commented out) code.
-Reverted g:Profiler to its default Ensembl version, as the "missing" GO associations for mouse, rat etc re-emerged in the latest Ensembl update.
-Corrected a few typos in README.md.

2021-05-31:
-Re-wrote the API requests used in the STRING and STITCH networks
-Corrected the name of one of the authors in README.md, ui.R and about.R
-Configured g:Profiler to use Ensembl version 102, until version 104 is released (Ensembl 103 has some errors, and it is missing GO data for mouse, rat, and a number of other organisms)

2021-05-13:
-OnTheFly2.0 is LIVE! A downloadable version can be found in https://github.com/PavlopoulosLab/OnTheFly.  The web server is accessible through http://bib.fleming.gr:3838/OnTheFly/ or http://onthefly.pavlopouloslab.info.

More information can be found in OnTheFly's GitHub repository.


About OnTheFly2.0


OnTheFly2.0 is actively developed and maintained by the Bioinformatics and Integrative Biology Lab

Developers

  • Fotis A. Baltoumas, baltoumas[at]fleming[dot]gr
  • Sofia Zafeiropoulou, sofzafeiropoulou[at]gmail[dot]com
  • Evangelos Karatzas, karatzas[at]fleming[dot]gr
  • Georgios A. Pavlopoulos, pavlopoulos[at]fleming[dot]gr

Code Availability

The source code for OnTheFly2.0 can be found in this repository.

Related Software

Cite OnTheFly2.0

If you find OnTheFly2.0 useful in your work please cite:

  • Baltoumas, F.A., Zafeiropoulou, S., Karatzas, E., Paragkamian, S., Thanati, F., Iliopoulos, I., Eliopoulos, A.G., Schneider, R., Jensen, L.J., Pafilis, E., Pavlopoulos, G.A. (2021) OnTheFly2.0: a text-mining web application for automated biomedical entity recognition, document annotation, network and functional enrichment analysis. NAR Genomics and Bioinformatics, 2021, Vol. 3, No. 4. doi: 10.1093/nargab/lqab090
  • Pavlopoulos, G.A., Pafilis, E., Kuhn, M., Hooper, S.D., Schneider, R. (2009) OnTheFly: a tool for automated document-based text annotation, data linking and network generation. Bioinformatics, Apr 1;25(7):977-8. doi: 10.1093/bioinformatics/btp081.

OnTheFly2.0 Privacy Policy


The administrators of OnTheFly2.0, in accordance with Regulation (EU) 2016/679 and the relevant national legislation on the protection of natural persons with regard to the processing of personal data, provide the following privacy notice to explain what personal data is collected, for what purposes, how it is processed and how we keep it secure.

1. What is the lawful basis for data collection?

Data are collected to help monitor website functionality, resolve issues, improve the allocated resources and provide services to you adequately.

2. What personal data is collected from users?

The personal data collected by the website’s services are as follows:

  1. Date and time of a visit to the service
  2. Amount of data transmitted

The aforementioned data are used for the following processes:
  1. To provide the user access to the service
  2. To conduct and monitor data protection activities
  3. To conduct and monitor website security
  4. To better understand the needs of the users and guide future improvements of the service
  5. To communicate with users and answer their questions

3. Who has access to your data?

Any collected data is solely accessed and controlled by the website’s administrators. No other person has access to the data.

4. Will your personal data be transferred to other organisations?

Any personal data directly collected are handled by the administrators of OnTheFly2.0 exclusively. There are no transfers to any other organisations whatsoever for these data.

Please note that OnTheFly2.0 utilizes a number of third-party resources to provide you with the best possible experience. These include jQuery, FontAwesome, Google Fonts and Bootstrap. Some of these resources store cookies and may record some data to function. The administrators of OnTheFly2.0 are not responsible for the treatment of any data by these services. You are advised to consult the Privacy Policies of each of these services through their respective web pages.

5. How long is your personal data kept?

Any personal data directly obtained from you will be retained for the minimum amount of time possible to ensure legal compliance and to facilitate internal and external audits if they arise.

6. Cookies Policy

OnTheFly2.0 uses cookies to achieve functionality and provide you with the best possible experience. Specifically, we use cookies for the following purposes:

  1. Functionality: we use cookies for the proper functionality of the web services offered by OnTheFly2.0.
  2. Security: we use cookies as part of the security measures aimedat protecting your privacy, and our website and services generally.
  3. Cookie consent: we use cookies to store your preferences in relation to the use of cookies more generally.
Most browsers allow you to refuse to accept cookies and to delete cookies. The methods for doing so vary from browser to browser, and from version to version. You can however obtain up-to-date information about blocking and deleting cookies via these links:
  1. Google Chrome
  2. Mozilla Firefox
  3. Opera
  4. Microsoft Internet Explorer
  5. Apple Safari
  6. Microsoft Edge
Blocking all cookies will have a negative impact upon the usability of OnTheFly2.0.

7. The website’s data controllers provide the following rights regarding your personal data

You have the right to:

  1. Not be subject to decisions based solely on an automated processing of data (i.e. without human intervention) without you having your views taken into consideration.
  2. Request at reasonable intervals and without excessive delay or expense, information about the personal data processed about you. Under your request we will inform you in writing about, for example, the origin of the personal data or the preservation period.
  3. Request information to understand data processing activities when the results of these activities are applied to you.
  4. Object at any time to the processing of your personal data unless we can demonstrate that we have legitimate reasons to process your personal data.
  5. Request free of charge and without excessive delay rectification or erasure of your personal data if we have not been processing it respecting the data protection policies of the respective controllers.

It must be clarified that rights 4 and 5 are only available whenever the processing of your personal data is not necessary to:
  1. Comply with a legal obligation.
  2. Perform a task carried out in the public interest.
  3. Exercise authority as a data controller.
  4. Archive for purposes in the public interest, or for historical research purposes, or for statistical purposes.
  5. Establish, exercise or defend legal claims.
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