dataesr/scientific-paragraphs-categorization huggingface dataset:
However, with the explosion of sequence analysis needs, it became necessary to provide access to bioinformatics tools to a larger number of biologists. Many efforts were done to develop web interface to pilot bioinformatics tools and to browse databases. Then, the first tools combining web interfaces and the ability to automate treatments emerged, such as (i) The Biology Workbench  [35]  and (ii) PISE  [36]. However, due to technological limitations, web interface interaction with the user was limited. In parallel to the web interface development trends, standalone workflow management software was being developed to design and execute bioinformatics workflow. One of the first of these software solutions was Pipeline pilot  [37]  developed in 1999 by SciTegics (now part of Biovia products) for chemoinformatics and bioinformatics.
Quick Overview of Some Scientific Workflow Management Software for Big Data Analysis. Several workflow management types of software are available to design scientific workflows (see Table  1 ). We present a summary of some that have been used to analyse NGS data. [40] [41] [42]  is an open tool dedicated to perform complex analyses in a web-oriented collaborative environment and dedicated to users with no programming skills. The Galaxy framework encapsulates high-end computational tools and provides intuitive user interface while hiding the details of computation and storage management  [40]. The three objectives of Galaxy are to facilitate the accessibility and reproducibility of analysis and to assist the sharing of developed workflows  [41, 42].
Galaxy. Galaxy Accessibility. Galaxy users do not need to know the implementation details of the various tools. The principle is that, from a web page, users can connect to data and perform advanced genomic analysis, using tools built into the interface. To incorporate a new tool, the user has to write a configuration file that describes how to launch the tool and specifications for inputs and outputs. The approach used in Galaxy is less flexible than a programming language but it makes computation accessible for biomedical researchers averse to command lines. Reproducibility. For each analysis, a number of metadata types are generated, through which it will be possible to replicate the analysis. These include input and output datasets, tools used, and parameter values. Such types of metadata are sufficient to reproduce the analysis, but, in addition, Galaxy offers the opportunity for the user to enter annotations that should make it easier to understand the purpose of the analysis.
The analysis steps are assembled in a history, in which it is possible to copy or create versions. Sharing. Galaxy has repositories where users share objects: datasets, histories, and workflows but also has Pages, elaborated web documents that enable users to communicate in detail, via texts and graphics, about their analyses. Galaxy is implemented primarily in the Python programming language and is distributed as both a public web service and a downloadable package that can be deployed in individual laboratories. An interesting example of Galaxy is BioBlend, a unified application programming interface (API) coded in Python language that wraps the functionality of Galaxy and Cloud-Man APIs  [43]. BioBlend makes it possible to automate large data analysis within Galaxy using the Cloud environment of CloudMan  [44].
Kepler. We describe Kepler, despite the fact that this workflow management software has, in fact, rarely been used, up to now, in bioinformatics and NGS analysis. However, the capacity of Kepler to deal easily with distributed intensive computation infrastructures may lead to innovative development in Big Data analysis such as NGS data. Kepler is a powerful desktop workflow management software developed primarily for ecology and environmental studies. Kepler includes a graphical user interface (GUI) for creating workflows. Kepler allows scientists to run workflows within the GUI and independently with command lines. In addition, Kepler has powerful distributed computing options to distribute workflow execution on cluster, grid, or cloud. Recently, Kepler has been used for bioinformatics purposes.
For intensive computing and NGS analysis, the bioKepler project  [45]  has been initiated. The bioKepler module is an extension for Kepler that integrates bioinformatics components in order to execute bioinformatics tools (e. g. , BLAST  [46]  and HMMER  [47] ) and to facilitate the development of workflows for execution in distributed environments (grid platforms or cloud). In addition, Kepler also offers the possibility to integrate the R statistics suite  [48]  through the RExpression component. This gives access to all R packages dedicated to NGS analysis.
KNIME. Konstanz information miner (KNIME ) is an open source workflow management software for data mining, reporting, and integration  [49]. KNIME integrates various components and is used in numerous domains (e. g. , Business Intelligence, customer relationship management, and marketing), including life sciences. KNIME was developed to offer a highly scalable platform. KNIME allows the easy integration of data sources (databases and files), analysis, and visualization of data. Using an intuitive graphical interface, KNIME allows users to create data analysis workflows by "drag and drop. " To enhance collaboration between research teams, KNIME allows the creation and sharing of plugins to extend its capacities. Initially used for chemoinformatics  [50, 51] , numerous plugins for life sciences are now available.
Concerning NGS and Big Data, three extensions are proposed. (i) KNIME extensions for next-generation sequencing  [52]  is a set of free components and workflows dedicated to NGS analysis. (ii) Knime4Bio  [53]  is a set of components for the filtering and manipulation of NGS VCF format files. (iii) KNIME Big Data extension  [54]  is dedicated to high performance access and queries on Big Data. This is a commercial extension that offers a set of components for accessing Apache Hadoop Mapreduce framework (distributed storage and distributed processing on clusters) and Apache Hive (data warehouse software for querying and managing large datasets residing in distributed storage). In addition KNIME integrates the R statistics suite that gives access to all R packages dedicated to NGS analysis. A recent application of KNIME and R statistics was built for proteomics experiments, focusing on the screening for targets of a miRNA involved in neuroblastoma.
Pipeline Pilot. Pipeline Pilot (Accelrys, USA) was initially developed in 1999 by SciTegics (San Diego, USA) a company that became a subsidiary of Accelrys in 2004. Professional-oriented software Pipeline Pilot is a productivity platform dealing with Big Data and one of the most commonly used workflow management types of software in pharmaceutical companies. Pipeline Pilot has visualization possibilities through the reporting component collection  [56]  that allow the automation of reports and the publication of web applications. In addition, Pipeline Pilot has components for integration with the visualization suite Tibco Spotfire  [57] , which provides powerful visualization tools and efficient interactive dashboard conception. Pipeline Pilot has its own simple scripting language for "nonprogrammers.
Finally, as the R statistics package is widely used by scientists, Accelrys proposes an R statistics component collection  [62]  to easily integrate R in the designed workflow. [39]  is workflow management software created by the myGrid project. Taverna was one of the first open source workflow management types of software developed for creating scientific workflows. Taverna allows integration of SOAP and REST web services giving access to bioinformatics resources, databases, and tools  [63]  from numerous academic research institutes. As Taverna is widely used in academic research, a large number of components are available for scientists in various domains such as bioinformatics, chemoinformatics, medicine, astronomy, and social sciences. To enhance collaboration and workflow sharing, the myGrid consortium delivered the BioCatalogue  [64] , a centralized catalogue of life science web services, and myExperiment  [65] , a social web site for scientists, where scientists can share Taverna workflows.
Taverna. The Taverna Workbench One important point in Taverna is the possibility to not need to execute workflow within the Taverna Workbench. Indeed, the workflows can be run by (i) executing a command line, (ii) a remote execution server, to allow workflow running on distant machine, and (iii) through the online workflow designer and enactor OnlineHPC  [38]  giving access to high performance computer resources for intensive computation or Big Data analysis. A recent example of the use of Taverna was done by the School of Computer Science at the University of Manchester. They designed and implemented a Taverna-based data refinement workflow which integrates taxonomic data retrieval, data cleaning, and data selection to define a semiautomated workflow  [66].
4. 3. Limitations. These workflow tools facilitate data analysis by noncomputer users, allowing them to control bioinformatics tools and easily analysis pipelines. They nevertheless have some limitations. Indeed, the functional components encapsulate code written by someone who has a good knowledge of one or more programming languages (e. g. , Java for KNIME, specific script for Pipeline Pilot). The different bricks are not necessarily written in the same language, and the workflow may ultimately be constrained by a brick, written in a language that is less efficient compared to other bricks. This is not a problem for ad hoc analyses, but as soon as one user works in a production environment, with analyses that are performed routinely, it becomes a problem. Furthermore, if a brick is needed that does not yet exist (or a new data analysis method is needed) or if it does not match exactly the needs of the non-IT Biologist, he will not be able to intervene and he will have to seek help from developers.
The power of expression of these workflow tools is more limited than that of programming languages since they do not allow immediate transcription of an idea in the environment. Researcher's exploration capacity is thus limited by the existence or not of functional bricks in the environment. The user will then be obliged to make guided data analyses and will not be free to have an entirely exploratory approach. These environments eliminate the need to write command lines that seem obscure for noncomputer users, and they allow scientists to perform treatments, encapsulated in the different bricks, for which they will know the inputs and outputs, but ultimately users have no visibility on intermediate steps performed by the encapsulated scripts. Furthermore, these tools do not solve one of the main issues related to NGS, namely, the volume of data that generate problems of processing.
Trends in IT Technologies for Big Data in Management and Analysis of NGS Data Indeed, the use of a relational database may be inappropriate under the following conditions, especially with constraints of Big Data management and sharing. (i) The database cannot adapt to large traffic at an acceptable cost. (ii) The number of tables required to maintain the relational model rises too quickly for the corresponding amount of stored data. (iii) The relational model no longer meets the performance criteria because the model is no longer adapted to how the system has evolved. (iv) The database is subjected to a large number of temporary tables that store intermediate results. All these limitations were noticed by major actors of the web like Amazon Inc. and Google Inc. in the 2000s. Facing the growing amount of data from the web they have undertaken to develop new classes of databases.
NoSQL Database. The term NoSQL ("Not Only SQL") database is a class of distributed database management systems, designed mostly for the purpose of dealing with large datasets within an acceptable timeframe for the user. This designation emerged in the second half of the 2000s with the increasing number of distributed nonrelational databases  [67]. The NoSQL systems were developed in order to maintain short response time to queries, despite a very high query throughput. However, their architecture does not offer the same guarantees as relational databases (including constraints to conserve properties that guarantee that database transactions are processed reliably, namely, ACID for atomicity, consistency, isolation, and durability). In particular, the absence of a structural model for storing heterogeneous data within the same database and the necessity of rapid treatment results in the omission of some structural controls (e.
g. , table integrity). Often, the transition from relational database system to a NoSQL system is motivated by several reasons such as (i) a very large volume of data to store, (ii) frequent and massive write entries that have to be fast and reliable, (iii) data readings that have to be fast and consistent, (iv) a data model that can evolve and change on the fly (e. g. , adding new concepts and data), (v) ease of administration (backups and restoration), (vi) capability to have parallel data processing. Existing NoSQL solutions can be grouped into four main categories. (i) Key/value. This category can be viewed as a distributed hashmap. The data are simply represented by a key/value pair. The value can be a simple string, a serialized object. The interaction with the database is simple and limited to "PUT, " "GET, " and "DELETE" operations, which infers that more effort has to be put in the construction of the web application to manage complex querying.
Both "column-oriented" and "oriented document" categories are based on key/value systems; however the nature and structure of the value are different. Despite their increasing deployment in several domains, the use of NoSQL databases in bioinformatics is an emerging field. NoSQL databases have caught a lot of attention in bioinformatics because of their advantages in scalability, but authors question the timeliness of their use for data organization in biology. For example, the use of graph databases was discussed by Have and Jensen  [73]  who concluded that "Graph queries, formulated in terms of paths, can be concise and intuitive compared to equivalent SQL queries complicated by joins. " However, the authors pointed to the limitation of actual graph query languages to enjoy the full benefits of using a graph database.
Recent use of NoSQL technologies for NGS data has been reported. Taylor  [74]  discussed the application of Apache Hadoop Mapreduce and the HBase NoSQL system in bioinformatics for NGS data analysis. He showed that the scalability of Big Data oriented system Hadoop and HBase is, as expected, very high. In addition, he noticed that this architecture allowed the easy integration and analysis of large and heterogeneous data sources with a small number of HBase tables. This is an important result, since, in NGS analysis, the ability to cross data with other databases is a key for discovery. Another example is the SeqWare Query engine developed by O'Connor et al. [75]. The authors use the HBase NoSQL database and deploy their application on a cloud to offer high performance for access and queries on NGS human genome data.
The HBase database exhibits high performance for querying the data and, according to the authors, "enables a faster and more open exploration of results. " However, some authors tried to enhance NoSQL systems, as did Wang et al. , who designed a key/value data model to support fast query over large amounts of data with HBase  [76]. These authors obtained a threefold decrease of query time using NoSQL system compared to classical relational database system like Oracle. An interesting example of a successful use of a NoSQL database is the ncRNA-DB database developed by Bonnici et al. [77]. The authors created a database to integrate ncRNA interactions data from 7 of the online data sources. The data integrated include interactions with RNA, DNA, protein, and relationship to diseases. NoSQL databases are suitable for the integration of nonstructured data and can be easily implemented for Big Data management.
The authors used OrientDB, a distributed graph NoSQL database (http://www. orientechnologies. com/). Using graph NoSQL database allowed the authors to easily integrate several data sources as biological entities (genes, ncRNAs, RNAs, and diseases) and their relationships (physical interactions, functional relationships, and so on) can be modeled as a graph. The resulting graph is composed of nodes (biological entities) and edges (relations between biological entities). The ncRNA-DB offers a command line interface. The authors also developed a web interface for easily querying the database and exporting data in a text format. In addition, a Cytoscape  [78]  dedicated interface called ncINetView was developed to visualize the interaction graph. In this example we see that NoSQL databases offer a powerful alternative to SQL databases for Big Data integration.
We further note that the representation in graph form is particularly suitable for biological data representation in a database. Despite its scalability and performance, NoSQL is rarely used for NGS projects. The few existing examples include METEOR of INRA, used for the analysis of the impact of dietary intervention on gut microbial gene diversity  [79]. However, their performance must drive scientists to pay attention to developments of NoSQL systems and to find suitable applications to overcome the limitations of relational database systems for NGS data management.
A Quick Overview of Business Intelligence Solutions. The Big Data revolution first affected major web companies (Amazon, Facebook, Google, and so forth) and large companies involved in banking and insurance, with the increasing need to extract key information from datasets in order to help decision-making processes. In IT, it is the domain of Business Intelligence that addresses the issues of decisionmaking. Thus, major BI solution companies offer innovative solutions to collect, to manage, and to analyse heterogeneous, large datasets. BI seeks to extend collected data and also restructure, aggregate, and reformat data in order to present them in a form that helps decision-making of the end-user. Different stakeholders are present on the market and have each developed their decision-making tools for Big Data.
We can cite Cognos Business Intelligence (IBM), Reportive (CEGEDIM)  [80] , Oracle Business Intelligence Enterprise Edition (Oracle Corporation), and QlikView (QlikTech). All these solutions offer efficient platforms to manage and analyse Big Data. Despite their advantages, BI platforms have rarely been used in medical research and a fortiori on massive data sets from NGS. However, innovative BI solutions are used by some teams involved in NGS analysis. These tools perform well and are highly beneficial to laboratories with NGS Big Dataespecially those with high quality and industrialization requirements of their processes. As an example of BI solution benefiting Life scientists, we focus on the successful use of the Amadea software (Isoft, France) for NGS data analysis. This is one of the first tools from Business Intelligence which has been applied in Life Science  [81].
Thus, in silico work becomes highly similar to laboratory work ("wet lab") where the results of an experiment raise new hypotheses that can be tested easily with a new analysis workflow. The data morphing engine is developed in C++. The user interface allows the graphic elaboration of analysis workflows, by the "dragging-and-dropping" of needed components. Native components, directly linked to the engine, perform the basic operations. The combination of native components allows the creation of more sophisticated and dedicated components. The development of a new component does not require programming language knowledge. In addition, as the new component is a combination of elementary components, it takes full advantage of the data morphing engine performances. Thus, the scientist using Amadea has the same power of expression as if he was using a programming language; he has the ability to implement   1 ) output from the data source "chip array data, " (2) the output from "Entrez Gene" database, and (3) results from "Get Gene Info" component.
a missing brick in his analysis flow, without having to rely on someone else to develop it for him. This is a major difference with other existing workflow tools. This technology offers enormous flexibility for analysis. It allows one to circumvent the question of technological choices for loading and processing of the data. Indeed, in the same environment, it is possible to import, view, and compare information from relational or NoSQL database, XML, TXT, CSV, EMBL, GFF, SAM, and so forth. Furthermore, data are considered as "data heaps" which are displayed in tables made of rows and columns (spreadsheets). The user can use the data as he constructs his analysis workflow, without a predefined semantic. For example, in a workflow involving the Refseq databank  [82] , the user can select and use information about genes and, further down in the analysis process, decide to use information about proteins (see Figure  3 ).
Annotation Transfer on Yeast Genomes Using Amadea. Whereas assembly is usually included in sequencing services by sequencing companies, the structural annotation of genes, that is, the definition of gene structures including UTRs or alternatively spliced isoforms, is rarely an option and therefore remains a challenging task for many researchers. Therefore, many genomes are deposited in GenBank or EMBL/ENA without annotation or with a minimalist and often wrong annotation, which considerably limits their usefulness to researchers. Very early in the genomics era, gene predictors were developed, providing a fast and easy means to identify genes in assembled DNA sequences from bacterial genomes, such as the GeneMark programs that have been used since 1995  [33, 71, 83]. This type of ab initio gene predictors uses mathematical models to identify genes.
Today, the typical size of a FastQ file for a yeast genome (a 10-12 Mb-long genome with a coverage of 70x) is around 2 Go. Many laboratories, such as the INRA Micalis laboratory where one of us works, are sequencing large numbers of yeast species, leading to many genomes to annotate. Timeconsuming ab initio annotation methods are not suitable and annotation transfer has become our preferred methodology. The following existing automatic annotation transfer tools were tested and deemed inefficient: (i) GATU, dedicated to viral genomes  [88] , and (ii) RATT, based on synteny conservation, which only applies to closely related genomes  [89]. Therefore, the INRA Micalis team chose to use the Amadea platform for developing their own automatic annotation transfer software, which had to be able to deal with significant differences between genomes.
The Visualization Challenge. NGS is a powerful discovery tool for scientists seeking to glean new insights and concept from their data. However, the large amount of data leads to difficulties in their analysis and visualization. As visualization, in particular, plays a key role for discovery of new patterns and trends in large datasets, the lack of dedicated visualization tools is a major limitation to the interpretation of the data. Indeed, without Big Data-dedicated interactive visualization systems, much of the knowledge from NGS may go unrecognized. Thus, data visualization is fundamental to NGS interpretation and several groups involved in NGS data analysis have developed specific tools for this (see Table  2 ). We will not describe these tools in detail, but the majority are limited to a specific type of visualization (e.