dataesr/scientific-paragraphs-categorization huggingface dataset:
Most well-acknowledged computational bottlenecks for those short-read assemblers concern memory footprints and difficulties in correctly handling repetitive sequences. Assembly very often results in discontinuous sequence contigs and hence insufficient genome coverage. Currently, de novo assembly yields better coverage for small genomes (i. e. bacterial and/or viral species), though assembly of genomes in a metagenomics setup is nowadays considered as complicated and very challenging. Concerning species with no reference in public databases, pre-processing steps are required to increase genome coverage. For example, the use of paired-end sequence data using different insert size libraries is a well established technique to increase assembly scaffold sizes. Genotype calling from low coverage data may require extra steps of imputation, filling the gaps that remain due to lack of coverage, and results in more accurate genotypes.
Bioinformatics and HTS projects Current NGS platforms including Illumina, Ion Torrent/Life Technologies, Pacific Bioscience and Nanopore can generate reads of 100-10,000 bases long allowing better coverage of the genome at lower cost. However, these platforms also generate huge amounts of raw data. For example, the raw data produced by an Illumina HiSeq-2500 platform adds up to 1TB per run. Sequencing reads are recorded as FastQ formatted files along with the corresponding quality score for each nucleotide. In addition to those sequence files, it has become important to also consider and store associated sample related metadata (collection date, location, etc. ). Thus, NGS projects usually represent such a huge amount of relevant sample-specific sequences that efficient data management and visualization resources have become mandatory.
Galaxy  [1] [2] [3]  , is a scientific workflow management system, which provides means to build multi-step computational data-processing, quality control, and analytic results aggregation, while additionally ensuring analysis reproducibility. In addition to a system for composing pipelines, there is a need for an adapted computational infrastructure capable of doing the processing and data storage in a scalable manner. 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 integrate the capacity for large-scale genomic analysis and technical expertise in sequencing and genotyping technology among project partners. The web application was produced in order to facilitate access to high throughput sequencing analysis tools, acting as an information resource for the project and interacting research partners.
This user-friendly interface has been designed to associate bio-IT provider resources (a local Galaxy instance, sufficient storage and grid computing power), with the input data to analyse and its metadata. The use of the available Galaxy tools is automated 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 get to run successfully, typically spreading the workload over a computational cluster. MetaGenSense is used at the Pasteur Institute to do the bulk of the data processing for a number of HTS projects, and can be adapted to launch any of the software packages available in the Galaxy workflow designer interface. A dedicated LIMS (postgreSQL-based) was developed to ensure data coherence.
MetaGenSense global description MetaGenSense is a bioinformatics application that is geared to ease the scientists' work in management of NGS project-related data and results. MetaGenSense is built upon three major 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 bioinformatics workflow management system. In the following paragraphs, we describe the interface's implementation and discuss how communication between the different parts takes place, in a smooth and user-friendly, managing web-user 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 this project, the LIMS is based on a postgresql database. It was designed and structured with expert knowledge from biologists and bioinformaticians with sequencing competence, in order to answer their specific needs ensued by the sample management. Its main feature is that it was also designed to store interesting and worth sharing information obtained by the analysis, as well as the information about the type of workflow that was used to perform the bioinformatics treatment. The database's schema is available in the Supplementary Figure  1. We provide here an excerpt of the existing tables (divided in three categories): (1) experimental data (LIBRARY_PREPARATION, SAMPLE, TECHNOL-OGY, RUN, GEOGRAPHIC_LOCATION, GPS_COORDS), (2) bioinformatic metadata (RAW_DATA, FILE_INFORMATION, WORKFLOW_DATA, RUN_WORKFLOW, WORKFLOW), and (3) user and project data (PROJECT, PROJECT_SUBSCRIBERS, AUTH_USER).
user_management: manages user authentication. Examples of implementations comprehend communication with an LDAP user authentication database, but it can be used as a user management database. 2. lims: ensures the organization and the data partitioning according to the selected project. A project contains sample metadata, and enables to share them only with selected. This part of the application handles sample traceability, an important component of any present-day core resource laboratory. 3. worflow_remote: is in charge of the communication with Galaxy. It manages: (a) the instance connection, (b) the user histories (c) the data from galaxy libraries, (d) import of data from a data library to a Galaxy user-history instance. (e) Execution of the selected galaxy workflow. This application handles data storage and links the samples to the selected workflow.
Communication with Galaxy The following paragraph discusses communication between MetaGenSense and Galaxy. Scientists and data-managers use Galaxy to facilitate bioinformatics analysis. A large number of XML formatted tool-configuration files have already been integrated, facilitating the execution of e. g. a mapping tool like BWA 4 through galaxy instead of executing it using the command line. For programming purposes and in order to interact with Galaxy using command line, the Galaxy team initially implemented a Galaxy-API (which allowed, for example, retrieval of the user list of a Galaxy instance, to create a library for a specific user, etc. ,). However, this project was rapidly replaced by a highly dedicated and specific python library called BioBlend  5. This API gives access to most Galaxy functionalities through scripts and command lines.
Case study -use example We exemplify a use-case of MetaGenSense's use through the analysis of a batch of biological samples for a dedicated project. The necessary steps to obtain a running MetaGenSense instance, with management of project data and analysis using workflows, are the following: 0/ Log onto MetaGenSense. 1/ Creation of a new project, with a name, a context, a short description and (most importantly) the other persons involved in the project. 2/ Start filling the LIMS database. Enter: a. the sample information b. the library sequencing protocol, c. the run details and d. the raw data file list. The raw data will be subjected to bioinformatic analysis. 3/ At this step, the user needs to use a terminal (or a FileZilla-like tool) to connect to its transfer directory. Create a subdirectory named after the project, and copy the raw data in that directory.
On the other hand, data information traceability has become an inevitable requirement in a present-day laboratory setup. In the meantime, 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 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 genome browser compatible 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.
Grant information Damien Correia and Olivia Doppelt-Azeroual were financed by the "COMMISSARIAT A L'ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES" in the scope of a national antiterrorism fight NRBC project. I confirm that the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The Django framework is very nice, and I think the integration of Galaxy with a LIMS system is very useful and something many readers will be interested in. However, many aspects of this application are tailored specifically to the authors' local setup. I installed 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? 1. 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? 2. Either create a demo server with an example project or add screenshots of the application to the manuscript. The UI looks quite nice, show it to the readers.
I confirm that I have read this submission and 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. 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.
2. Either create a demo server with an example project or add screenshots of the application to the manuscript. The UI looks quite nice, show it to the readers. Thank you for that. Yes, another reviewer suggested that we put screenshots of the UI. We added a figure that resumes all the windows and their use for each step. The figure is the available at this url: https://github. com/pgp-pasteurfr/MetaGenSense/blob/master/doc/images/metagensense_complete. jpeg as well as each of the steps (in bigger picture). This figure is now added to the publication.
Manuel Corpas Future Business Centre, Cambridge, UK Benedikt Rauscher The Genome Analysis Center, Norwich, UK MetaGenSense is intended to help find pathogen data in metagenomic data created through next generation sequencing. Measured data including the sequencing reads and metadata are fed into a Laboratory Information Management System (LIMS). The application can fetch that information and pipe it into predefined Galaxy workflows, run them and visualise the output via a framework called KRONA. 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.
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. The discussion and conclusion seem to be a summary rather than a discussion.
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. Moreover, we also would like to change the title of the publication for it to be more adapted to our approach; replacing visualisation by the word exploration which is really the goal of an application like MetaGenSense. The title of the second version of the article is: "MetaGenSense : A web application for analysis and exploration of high throughput sequencing metagenomic data.
General comments The authors have developed MetaGenSense, a web-based application for analysing metagenomics data. MetaGenSense also acts as a laboratory information management system, managing the metagenomics data and the results of their analysis by Galaxy workflows. The work done by the authors sounds promising since I am a fan of Galaxy and of the Django framework. However, I believe the paper can be much improved by providing additional details in a number of places. For example, it would be interesting to learn more about the two prototyped workflows for analysing metagenomics data which are alluded to in the "Pre-designed Galaxy workflow" section on page 5. The "Case study -use example" section could also be improved by providing screenshots of the MetaGenSense GUI which are relevant to each or some of the steps.
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 In the final paragraph, it is stated that MetaGenSense can be easily deployed but looking at the source code in https://github. com/F1000Research/MetaGenSense, there appears to be a lack of documentation 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.
We improved the documentation on our github repository (https://github. com/pgppasteur-fr/MetaGenSense) with elements facilitating deployment. We completed a README file containing well-informed installation and configuration instructions. We also wrote a UserGuide working with readTheDocs documentation tool ( http://metagensense. readthedocs. io). Don't hesitate to tell us if parts of the documentation are still a bit blurry. We will adapt the manuscript with all your minor corrections and suggestions. Thank you very much for your review. Competing Interests: No competing interests were disclosed.
the final paragraph, it is stated that MetaGenSense can be easily deployed but looking at the source code in https://github. com/F1000Research/MetaGenSense,there appears to be a lack of documentation 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. The "Case study -use example" section could also be improved by providing screenshots of the MetaGenSense GUI which are relevant to each or some of the steps.
the screenshots, we added a picture of the framework at each step of its ○ use. It is also available at the url: https://github. com/pgp-pasteur- fr/MetaGenSense/blob/master/doc/images/metagensense_complete. jpeg Moreover, as the screenshots are small, they are also available on our github repository in the doc directory. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Competing Interests: No competing interests were disclosed. Reviewer Report 19 June 2015 https://doi. org/10. 5256/f1000research. 6578. r8901 © 2015 Li P.
have read this submission and 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. MetaGenSense directly on a web browser. Instructions for download and use are available in the GitHub README file. However, as instructions for installation of the framework are now available, any developer can download and link MetaGenSense to his Galaxy. the final paragraph, it is stated that MetaGenSense can be easily deployed but looking at the source code in https://github. com/F1000Research/MetaGenSense,there appears to be a lack of documentation 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.
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.
Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models.
As the number and size of models increase, the availability of informative annotations becomes more important. Annotation techniques can then be applied to rule-based models that can represent in a compact way the complexity inherent in biological systems  (Blinov et al. , 2008; Danos and Laneve, 2004). Rule-based languages, such as Kappa  (Danos and Laneve, 2004; Danos et al. , 2007)  and BioNetGen  (Faeder et al. , 2009) , have emerged as helpful tools for modelling biological systems  (Ko ¨hler et al. , 2014). Rule-based modelling is widely used to concisely represent the combinatorial explosion of the state space inherent in modelling biological systems. Rule-based models comprise agents representing biological molecules and rules representing biological interactions between agents. These rules are sufficient to allow models to be simulated, but the biological meanings of the model entities are not directly accessible.
, 2012). These guides include graphs, depicting interactions of agents through rules, which are enriched with further biological information. Creating a model guide is a manual process and may not be time-efficient for large models. Recently, Klement and co-workers demonstrated embedding more structured comments into rule-based models  (Klement et al. , 2014). Data are added in the form of property/value pairs using a specific syntax; however, this study also focuses on presenting data for humans. Machine readable annotations have been applied to rule-based models using PySB, a programming framework for writing rules using Python  (Lopez et al. , 2013). A model object in PySB includes lists of agents and rules and also a list for machine-readable annotations. However, this approach is insufficiently general.
Therefore, patterns should also be annotatable. The issue of mapping annotations may also occur for rules that contain subrules (for example, as part of Kappa hybrid rules, additional rules can be defined). A subrule does not correspond to a single entity so it is difficult to unambiguously refer to in order to link biological information. Therefore, we extend the syntax of rule-based models to incorporate annotations. Existing metadata resources include machine readable controlled vocabularies and ontologies, Web services providing standard access to external identifiers and guidelines for the use of these resources. For example, the Minimum Information Requested in the Annotation of Models (MIRIAM) standard  (Le Novere et al. , 2005)  was proposed in order to standardize the minimal information required for the annotation of models.
, 2005)  to annotate genomic features and unify the semantics of sequence annotation; the BioPAX ontology  (Demir et al. , 2010)  to specify types of biological molecules and the Chemical Entities of Biological Interest (ChEBI)  (Degtyarenko et al. , 2008)  terms to classify chemicals. URIs of entries from biological databases, such as UniProt  (Magrane and UniProt Consortium, 2011)  for proteins and KEGG  (Kanehisa et al. , 2008)  for reactions, can also be used to uniquely identify modelling entities.