dataesr/scientific-paragraphs-categorization huggingface dataset:
Graph theory allows a more detailed evaluation of high-throughput sequencing datasets Beyond just being able to relay the number of OTUs, sequence similarity networks and Swarm provided additional underlying information for each of their OTUs in the form of network topologies. As pointed out by  Forster et al. (2015) , these network topologies can reveal additional within-OTU connections among environmental and reference amplicons by using shortest path analyses. In sequence similarity network OTUs containing both types of amplicons, 239,472 of the 253,965 environmental amplicons were directly connected to reference amplicons (Fig. 3 ), while the remaining 14,493 environmental amplicons were indirectly connected to reference amplicons. These latter environmental amplicons represent potentially novel genetic variation on top of the 47,116 amplicons placed into sequence similarity network OTUs which contained no reference amplicons.
In Swarm OTUs that contained both types of amplicons, only 5,757 of the 142,946 environmental amplicons were directly connected to reference amplicons. The 137,189 environmental amplicons with indirect connections also represent novel genetic variation along side of the 81,073 amplicons in exclusively environmental OTUs. This large number of indirectly connected amplicons in Swarm OTUs may be an overestimation because current reference databases do not yet cover intraspecific sequence variation (see above). However, our analyses are a first indication that shortest path analyses are a promising way to explore Swarm OTUs. By analyzing paths within an OTU one could, for example, investigate whether amplicons  A distance of '1' edge means that the environmental amplicon was directly connected to a reference.
CONCLUSIONS Each of the three clustering approaches provided different perspectives on microbial diversity, while also showing individual weaknesses. Our results corroborate previous observations of inaccuracy in heuristic clustering approaches and highlight how this inaccuracy also affects the detection of novel diversity. Despite their weaknesses, we argue that the combination of high stringency clustering methods and sequence similarity networks, and the implementation of additional tools based on graph theory principles will be beneficial for the evaluation of high-throughput sequencing datasets. Such tools will uncover underlying patterns from microbial high-throughput sequencing data, which hold important information about environmental microbial communities.
The detection and characterization of emerging infectious agents has been a continuing public health concern. High Throughput Sequencing (HTS) or Next-Generation Sequencing (NGS) technologies have proven to be promising approaches for efficient and unbiased detection of pathogens in complex biological samples, providing access to comprehensive analyses. As NGS approaches typically yield millions of putatively representative reads per sample, efficient data management and visualization resources have become mandatory. Most usually, those resources are implemented through a dedicated Laboratory Information Management System (LIMS), solely to provide perspective regarding the available information. We developed an easily deployable web-interface, facilitating management and bioinformatics analysis of metagenomics data-samples.
It was engineered to run associated and dedicated Galaxy workflows for the detection and eventually classification of pathogens. The web application allows easy interaction with existing Galaxy metagenomic workflows, facilitates the organization, exploration and aggregation of the most relevant sample-specific sequences among millions of genomic sequences, allowing them to determine their relative abundance, and associate them to the most closely related organism or pathogen. The user-friendly Django-Based interface, associates the users' input data and its metadata through a bio-IT provided set of resources (a Galaxy instance, and both sufficient storage and grid computing power). Galaxy is used to handle and analyze the user's input data from loading, indexing, mapping, assembly and DB-searches.
In addition, NGS projects usually represent such a huge amount of relevant sample-specific sequences that efficient data management and visualization resources have become mandatory. Those challenges accompanying HTS technologies have raised the following fundamental questions: (1) How do we best manage the enormous amount of sequencing data? (2) What are the most appropriate choices among the available computational methods and analysis tools? The issue concerning the growing amount of data can be managed through a dedicated Laboratory Information Management System (LIMS), solely to organize and plan their analysis, and to provide perspective. The question regarding the lack of adapted intertwining among the wide spectrum of available tools was in part filled by workflow management systems, even though it still requires fairly advanced knowledge of the tools available at hand.
Indeed, today, hundreds of bioinformatics tools are available, each demanding specific parameterization  1. NGS data analysts have been designing workflows in order to automate complex processing pipelines, both possibly using existing codes, avoiding rewriting software, and supporting parallel, distributed computations. These features make the workflows relatively simple to construct, make them easily reusable, hence aid reproducibility. Modern workflow managers, such as Galaxy  [2] [3] [4]  , offer the possibility of sharing them with others. As such, Galaxy as a data analysis and workflow management system, provides biologists with a hands-on toolbox to build multi-step computational workflows for data-processing, quality control, and analytic results aggregation, while additionally ensuring analysis reproducibility.
As a prerequisite to a system for composing pipelines for large-scale analyses, there is a need for an adapted and up-to-scale computational infrastructure capable of doing the processing and data storage. We therefore developed MetaGenSense, a bioinformatics webapplication framework whose principle purpose is to ease the scientists' work in management of NGS-project related data and analysis results. MetaGenSense is built on three fundamental components, two of which are specific to the project: a dedicated LIMS and a Django-based web user-interface. The third component is Galaxy, which is the main bioinformatics workflow management system. In the following paragraphs, we describe the interface's implementation and display how communication between the different parts takes place, in a smooth and user-friendly, managing web-user interface.
MetaGenSense global description MetaGenSense is a managing and analytical bioinformatics framework that is engineered to run dedicated Galaxy workflows for the detection and eventually classification of pathogens. It aims to facilitate large-scale genomic analysis for experts in sequencing among project partners. The web application was built and can be deployed in order to facilitate access to high throughput sequencing analysis tools, acting as an information resource for the project and interacting research partners. With its user-friendly interface, it was designed to take advantage of bio-IT provider resources (a local Galaxy instance, sufficient storage and grid computing power), for analysis of input data and its metadata. Also in MetaGenSense, a dedicated LIMS (postgreSQL-based) was implemented to ensure data coherence.
The web interface design is based on the Django web framework (http://www. djangoproject. com). Moreover, the communication with Galaxy is covered by the Bioblend library  5  which provides a high-level interface for interacting with the Galaxy application, promoting faster interaction, and facilitating reuse and sharing of scripts. The use of the available Galaxy tools and workflows is automated and seamless with MetaGenSense. Galaxy, as a pipeline management software, lets you define workflows and "pushes" the data through that pipeline. The pipeline manager ensures that all the tools in the pipeline run successfully, generally spreading the workload over a computational cluster. For example, MetaGenSense is used at the Pasteur Institute to do the bulk of the data processing for a large number of HTS projects, and can be adapted to launch any set of instructions stitched together in a dedicated workflow available in the Galaxy workflow designer interface.
A dedicated LIMS A LIMS can be described as a software-based laboratory that offers a set of key features that support modern laboratory operations. Those systems have become mandatory to manage the quantity of metadata related to both raw data and their analysis results, obtained through bioinformatic tools. In MetaGenSense, the LIMS is based on a postgresql database. It was designed and structured with expert knowledge from biologists and bioinformaticians. Its main feature is that it was designed to store note-worthy and shareable information resulting from analysis, as well as the applied workflowrelated parametrization. The database's schema is available in the Supplementary Figure  1.
An intuitive Django-based web user interface Django is a high-level Python Web-framework. It provides rapid development, as well as clean pragmatic design, and serves as data management and displaying backbone for a large number of websites where interaction is important. Moreover, the python language (https://www. python. org/) has become a reference programming language for a huge number of scientific applications. MetaGenSense's UI is divided in 4 modules: 1) User authentication management, 2) LIMS, 3) Workflow, 4) Analysis. Each has a specific function, and the task-partitioning has been designed to allow independent evolution of each part according to the user's needs. 1. User authentication management: can be done either by communication with an LDAP user authentication database, or through a user management database.
2. LIMS: ensures the organization of the data according to the project at hand. The LIMS can be provided with sample metadata, which can be shared with selected project-members, and ensures sample traceability, which is an important component of any present-day core resource laboratory management system. 3. Workflow: manages (a) the connection with the Galaxy instance, (b) execution tracking (the Galaxy "user histories") (c) the data from Galaxy "libraries" (the datasets), (d) association and import of data from a data-library to a Galaxy user-history instance. (e) Execution of the selected Galaxy workflow. This module handles data storage and links the samples to the selected workflow.
• User Authentication with LDAP or any authentication system defined by admin sys. • Creation of a new project, with a name, a context, a short description and (most importantly) the other persons involved in the project. Please note that to include a person, they need to have logged at least once on the application. • Supplying the LIMS database with metadata including sample information as the library sequencing protocol, supplementary run details and the path to raw data files. • At this point, the user needs to copy their input data files to their Galaxy transfer directory. MetaGenSense detects new files that are copied within the exchange Galaxy project directory. Those data files need to be copied manually. This solution was chosen such that the right files would be deposited deliberately in the right target Galaxy directory, with correct user/owner's file permissions (typically on a UNIX file-system).
• Using a new Galaxy "history", an appropriately selected workflow, and the set of workflow input data-files, an analysis can be launched. As usual in Galaxy, the workflow status can be monitored. • Intermediate and final results can be exported for download (using the native Galaxy tools), or they can be saved in the LIMS i. e. be tagged as potentially interesting and shared with project members. • Further exploration of workflow results can be done using, for example in a metagenomics data analysis context, Krona  6  , that enables taxonomic information to be explored.
Conclusions/Discussion The technology evolution in molecular biology, especially in NGS, has moved biology into the big data era (consisting of handling data, computation requirements, efficient workflow design, and knowledge extraction). With this trend, the challenges faced by life scientists have been shifted from data acquisition to data management, processing, and knowledge extraction. While many studies have recognized the big data challenge, few systematically present approaches to tackle it. New findings in biological sciences usually come out of multi-step data pipelines (workflows). Galaxy is a workflow-management tool which can deal with big data. However, it is still necessary to globally optimize the data flow in an overall multi-step workflow in order to eliminate unnecessary data movement and redundant computation.
On the other hand, data information traceability has become an inevitable requirement in a present-day laboratory setup, foreseeing that knowledge-embedded data and workflows are expected to be an integral part of future scientific publications. We therefore, engineered MetaGenSense, a Django-based web interface which helps biologists, who in particular are unfamiliar with the design of Galaxy workflows, to quickly obtain analysis results from HTS sequencing projects. It uses Galaxy as a workflow management software and the BioBlend API to remotely manage data upload, workflow execution as well as analysis of results. MetaGenSense covers data processing up to presentation of data and results in a human-readable data format. Its main advantages encompass data handling through its incorporated LIMS, user and project handling in a cooperative context, it enables data sharing without compromising data confidentiality, it features automated workflow execution, resulting altogether in decreasing the data and analysis delivery time.
Open Peer Review Current Referee Status: The installation instructions were much improved, and I was able to set up MetaGenSense without any major difficulty. The availability of a Virtual Machine was especially convenient for quick testing/assessing of the application, and I was able to run the example pipeline on the provided test data easily. The manuscript itself was also much improved and I am now happy to approve this manuscript. I think this is a nice example of how one can integrate a LIMS/project management system with Galaxy at the back-end for analysis.
Minor (optional) remarks: Unless there is a compelling reason not to, please consider adding the requirements. txt file to the github repo, it was a little bit confusing that I had to create this file myself. Your installation instructions refer to the command "manage. py migrate" but this appears not to be implemented until Django 1. 7, yet your documentation lists the requirement "Django==1. 6. 2". Consider changing the requirement version for Django from 1. 6. 2 to 1. 7 (I had no problem running the rest of the setup using Django 1. 7) or changing this part of the installation instructions to fit with F1000Research 2. 3.
F1000Research In the installation notes of the README. md, the first step points to virtualenv, virtualenvwrapper. I would have appreciated if authors could provide in there how I can install virtualenv. It is just need to take a few instructions from https://virtualenv. pypa. io/en/stable/installation/. I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. No competing interests were disclosed.
2. 1. parts of the application, but since no information is provided on how to install the various components (LIMS/Django/KRONA/BioBlend), and since the Galaxy server used in the code was not accessible to me, it was not fully functional, and because documentation was lacking, it was unclear to me how to proceed with the setup. To make this work more valuable to the readers, the following additions would be helpful: Installation instructions for the code on GitHub. The readme file is empty at the moment. How to install the various components (LIMS, Django UI, KRONA, BioBlend)? And how to connect the different components together? How to configure the webserver correctly (apache/nginx/other)? Which parts of the code are specific to the authors' local setup and need to be adapted when readers install their own MetaGenSense instance? A description of the Galaxy workflows used by the authors would also be very interesting, which tools are used? are they available from the Galaxy tool shed? Either create a demo server with an example project or add screenshots of the application to the manuscript.
Yes, the README file is now complete. It contains installation information. Please tell us if you feel that it gathers all useful information to install and configure MetaGenSense. Along with the version 2 of MetaGenSense publication, we built a release of the software. It is available at the URL ( https://github. com/pgp-pasteur-fr/MetaGenSense/releases/tag/v1. 0) https://github. com/pgp-pasteur-fr/MetaGenSense/releases/tag/v1. 0) How to install the various components (LIMS, Django UI, KRONA, BioBlend)? And how to connect the different components together? How to configure the webserver correctly (apache/nginx/other)? Which parts of the code are specific to the authors' local setup and need to be adapted when readers install their own MetaGenSense instance? Concerning the LIMS, Django, BioBlend, everything is in the application itself so the connection between the components is natively implemented.
The figure is the available at this url: https://github. com/pgp-pasteur-fr/MetaGenSense/blob/master/doc/images/metagensense_complete as well as each of the steps (in bigger picture). This figure is now added to the publication. The case study section is very technical, and would be enhanced by showing the use-case in terms of a real biological example, add screenshots of a real-world analysis to the various steps in this section. The introduction to the article is perhaps too long (almost half of the article). There are sections that are not necessarily related to the research presented here, e. g. , the paragraph focusing on the assembly problem of next generation sequencing reads. It would be useful, however, that the authors give a more comprehensive introduction into metagenomics as this topic is only covered very briefly at the beginning of the introduction.
Please tell us if The section on the software tool itself is very technical. I have trouble identifying a clear train of thought. Also it could be shorter and more precise. The case study does not really seem to be a case study on how the application can be used to actually find pathogen information in metagenomic data but is more like a step by step protocol on how to use the application. I suggest that this kind of information should be moved to the documentation and that instead a concrete biological example is demonstrated in the article. Moreover, the title says that MetaGenSense can visualise its results. However, this is not shown in the article. Therefore I would advise the authors to consider replacing the current figure with a figure demonstrating the results of a concrete biological use case.
Reviewer Comment: The section on the software tool itself is very technical. I have trouble identifying a clear train of thought. Also it could be shorter and more precise. The case study does not really seem to be a case study on how the application can be used to actually find pathogen information in metagenomic data but is more like a step by step protocol on how to use the application. I suggest that this kind of information should be moved to the documentation and that instead a concrete biological example is demonstrated in the article. Moreover, the title says that MetaGenSense can visualise its results. However, this is not shown in the article. Therefore I would advise the authors to consider replacing the current figure with a figure demonstrating the results of a concrete biological use case.
At the moment, I have no idea what the GUI for MetaGenSense looks like since there is also no example instance of MetaGenSense available on the Web which would have been useful for reviewing this paper. In the final paragraph, it is stated that MetaGenSense can be easily deployed but looking at the source code in , there appears to be a lack of documentation https://github. com/F1000Research/MetaGenSense to enable me to do this. For example, which version of Django should I use with MetaGenSense? How do I get the MetaGenSense source code integrated with the Django framework? The LIMS for MetaGenSense uses PostgreSQL; how do I set up this database and get it linked to MetaGenSense? How do I provide MetaGenSense with access to a Galaxy server? I think the authors need to provide this type of information to help readers install MetaGenSense for the authors' source code to be more useful.
5256/f1000research. 11103. r18134 Saskia Hiltemann Department of Bioinformatics, Erasmus University Medical Center, Rotterdam, Netherlands I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. No competing interests were disclosed. Competing Interests: Version 2 22 November 2016 Referee Report doi:10. 5256/f1000research. 10150. r15805 Saskia Hiltemann Department of Bioinformatics, Erasmus University Medical Center, Rotterdam, Netherlands With this revision, the authors have successfully addressed all my previous concerns.
Yes we agree with you, we omitted to add screenshots in the first version of the article. We added a new figure that resumes all MetaGenSense steps and functionalities. It is available at: https://github. com/pgp-pasteur-fr/MetaGenSense/blob/master/doc/images/metagensense_complete. jpeg. It is now the 2nd figure of the article. Moreover all the small figures (steps) are also available in the doc directory of our GitHub repository.
Acknowledgements The authors are grateful to Pathoquest and the  CEA/DSV/IG/ Genoscope  for their contribution in the initiation of the project. They are also grateful to  Fabien Mareuil  ( C3BI ,  Institut Pasteur)  for his technical advices, to  Corinne Maufrais  ( C3BI ,  Institut Pasteur)  and  Véronique Legrand  ( DSI, Institut Pasteur)  to have made publicly available their software in order for us to include them in the MetaGenSense test workflow on the virtual machine. And at last, to  Hervé Menager  ( C3BI ,  Institut Pasteur)  for his advices on the MetaGenSense documentation.
It is very well documented on this url: https://docs. djangoproject. com/en/1. 8/howto/deployment/ For KRONA, the javascript which enables the taxonomy distribution exploration is actually generated by a tool, installed in Galaxy. At the Institut Pasteur, it is part of an in-house package which gathers several tools for taxonomy analyses. Those tools are available on GitHub now (it was not a year ago) at the URL: https://github. com/C3BI-pasteur-fr/taxo_pack. To test those tools, we implemented a Virtual Machine image pre-configured to test MetaGenSense directly on a web browser. Instructions are also available in the GitHub README file.
Thank you for this interesting remark; the use-case part of the article was modified to be more precise. Moreover, a user can now use MetaGenSense on a concrete biological dataset. We created a virtual machine image preconfigured to directly use MetaGenSense with a web browser. A fastq file, as well as a light version of our metagenomic Galaxy workflow is included. The user can connect to the framework, launch an analysis, explore the framework, as well as the results. F1000Research Moreover, as the screenshots are small, they are also available on our github repository in the doc directory. Competing Interests: No competing interests were disclosed. Referee Report 19 June 2015 doi:10. 5256/f1000research. 6578. r8901 Peter Li GigaScience, Hong Kong SAR, China.