dataesr/scientific-paragraphs-categorization huggingface dataset:
The fact that the cell lines in the GTEx data set were created in very different waysone transformed and the other a primary cell linesuggests that the global alterations we observe in both types of cell lines in terms of transcriptional patterns may be associated with growing in culture, the lack of tissue context, and decreased cellular heterogeneity. Some of the changes may also be associated with the transformation process as we observed smaller changes in expression and regulation of cell cycle genes in fibroblasts compared to LCLs. Our regulatory network analysis captured differences in the cell line regulatory processes that could not have been captured using more standard approaches such as differential expression analysis. For example, we identified TFs, such as SP1 and SP3, that were not differentially expressed between cell lines and their tissues of origin, but targeted different genes.
GTEx data The GTEx version 6. 0 RNA-Seq data set (phs000424. v6. p1, 2015-10-05 released) was downloaded from dbGaP (approved protocol #9112). Using YARN R package (version 1. 0. 0) we performed quality control, gene filtering, and normalization preprocessing  [58]. We grouped related body regions using gene expression similarity. For example, skin samples from the lower leg (sun exposed) and from the suprapubic region (sun unexposed) were grouped as "skin. " We filtered and normalized the data in a tissue-aware manner using smooth quantile normalization [github. com/stephaniehicks/ qsmooth]  [59]. The final data set contains 549 research subjects (188 females and 361 males) comprising 38 tissues (which included two cell lines), 30,333 genes, and 9435 samples. We filtered sex-chromosome and mitochondrial genes (retaining 29,242 genes).
We reduced the data set to only cell line and tissuespecific paired samples, which comprised 127 subjects with whole blood and LCL samples, and 244 subjects with skin and primary fibroblast cell line samples; 89 subjects have data across all four groups. For the skin samples, an equivalent number of samples were obtained from the lower leg (n = 123), and from the suprapubic region (n = 121). We kept only the 27,175 genes with at least one TF binding motif in its promoter region (see section: Gene regulatory networks), so that we could use the same set of genes for differential expression and gene regulatory network analysis. GTEx version 6. 0 RNA-Seq was annotated using the GENCODE release 19 (GRCh37. p13). Thus, we defined the different types of genes (protein coding, antisense, pseudogene, lincRNA, and other) according to the same genome annotation downloaded from http://www.
Differential expression analysis Differential expression analysis was performed using voom available in the limma Bioconductor R package (version 3. 26. 9) [24]. We performed four analyses using only paired samples between the groups of comparison: 1) LCL (n = 127) and Blood (n = 127), 2) Fibroblast (n = 244) and Skin (n = 244), 3) LCL (n = 89) and Fibroblast (n = 89), 4) Blood (n = 89) and Skin (n = 89). Multiple testing correction was performed using Benjamini-Hochberg. Genes with adjusted p-values less than an alpha of 0. 05 and an absolute log 2 fold change greater than 2 were considered differentially expressed.
Pathway enrichment analysis We performed GSEA to determine the biological functions related to the differential expression between cell lines and tissues  [25]. All genes were ranked by the moderated t-statistic produced by voom differential expression analysis. We used pre-ranked GSEA program (Java command line version 2-2. 0. 13) to calculate a running-sum statistic. We used the gene sets obtained from the KEGG pathway database that was downloaded from the Molecular Signatures Database (MSigDB) (http://www. broadinstitute. org/gsea/msigdb/collections. jsp) ("c2. cp. kegg. v5. 0. symbols. gmt"). We performed 1000 gene set permutations to assess the statistical significance, and considered gene sets with FDR < 0. 05 significant. We only considered gene sets of size greater than 15 and less than 500 genes after filtering out those genes not in the expression data set, or 176 gene sets in total.
The illustrations of the subnetworks were done using Cytoscape default yFiles Organic layout (version 3. 4. 0) [62]  where each edge connects a TF to a target gene, and the edge weight is represented by the color shade. To assess the robustness of the regulatory network models, we used bootstrap sub-sampling of the RNA-Seq datasets from the 89 paired samples across all four groups. We did multiple random selections of 40 paired samples, and generated 100 networks for each group: LCL, blood, fibroblast, and skin. We then calculated the average and standard deviation of the edge weights across the bootstrapped networks and compared to the network obtained from all the samples. We also compared degree centrality measures (more specifically, the weighted out-degree and in-degree differences between the corresponding cell line and tissue networks) estimated from the bootstrapped networks and the network obtained using all the samples.
ENCODE Chip-Seq data Chip-Seq on GM12878 (type of LCL) targeting the TFs SMAD5, IKZF1, USF1, and USF2 were downloaded from the ENCODE Project (https://www. encodeproject. org, accessed 2016-06-03). We used the narrow peak data processed by ENCODE from 2 biological replicates (accession: ENCFF553HHF, ENCFF001VEJ, ENCFF002CIB, ENCFF001VFQ). Then, to identify genes bound by each of these TFs we used bedtools (v2. 17) to annotate peaks that fall within the promoter region of a gene (same promoter regions used for the network reconstruction, ranging from -750 bp to +250 bp of the TSS).
Funding This work was supported by grants from the  US National Institutes of Health , including grants from the  National Heart, Lung, and Blood Institute  ( 5P01HL105339 ,  5R01HL111759 ,  5P01HL114501 ,  K25HL133599 ), the  National Cancer Institute  ( 5P50CA127003 ,  1R35CA197449 ,  1U01CA190234 ,  5P30CA006516 ), and the  National Institute of Allergy and Infectious Disease  ( 5R01AI099204 ). Additional funding was provided through a grant from the  NVIDIA foundation. CML was supported by  Sao Paulo Research Foundation (FAPESP)  grant  2014/19062-9. The funding agencies had no role in the study design; collection, analysis and interpretation of the data; in writing the article; and in the decision to publish.
This situation can thus lead to difficult legal situations. Simultaneously, various institutions and the general public now demand more information about the use of genetically modified organisms (GMOs). In response, researchers are seeking new database solutions to address the triple challenge of research competition, legal constraints, and institutional/public demands. To help plant biology laboratories organize, describe, store, trace, and distribute their seeds, we have developed the new program SeedUSoon, with simplicity in mind. This software contains data management functions that allow the separate tracking of distinct mutations, even in successive crossings or mutagenesis. SeedUSoon reflects the biotechnological diversity of mutations and transgenes contained in any specific line, and the history of their inheritance.
, 2008; Nieuwland et al. , 2012). Although they include such functions as plant, seed and plasmid management modules along with genotyping indications, these programs are not capable of independently tracking individual mutations through successive crossings and seed amplifications. In fact, they can only follow the general relationship between seed batches, tracking all seed batches derived from an individual plant. Despite the presence of these programs, we could not identify any software designed to specifically track the inheritance of mutations or transgenes within the complex history of seed collections, which would also be capable of reflecting the ever-growing diversity of biotechnological applications for plant mutagenesis and transgenesis. We therefore decided to create a new seed and plant database solution that utilizes strong genetic concepts, including mutation inheritance and independent genotyping of each mutation.
At the same time, we wanted to provide a simple and intuitive interface that would respect the habits of individual laboratories and their members. To answer this need, we have developed the "SeedUSoon" software. Its intuitive and flexible user interface permits the tracking of plant lines along with plants and seed batches, and it includes a graphical representation of the genetic link between related plant lines arising from crosses or secondary mutagenesis. Mutations inherited from parental lines can easily be identified using our software, and transgenic (GMO)/non-transgenic (non-GMO) types of mutations can be color-coded for fast visual identification. The program can be easily customized to the needs of each laboratory through an administrative module, for use with different plant models or mutagenesis techniques, for instance.
Implementation We developed the SeedUSoon user interface (version 1. 1. 0) using the platform-independent Java programming language 1. This choice allows the software to operate on any system running Java 1. 8 or higher. It has been tested with the Windows XP, Windows 7, Windows 8, and Mac OS X (up to Mavericks) operating systems. The SeedUSoon software needs to connect to a database (client/server architecture) that can be present on the same computer, or preferably on a server for multiple user access. The computer hosting the database must run MySQL (version 5. 5. 35 or higher). Extended computer knowledge is only necessary for the database installation.
Software and Start-Up Database Availability SeedUSoon is distributed under a proprietary license, and is free exclusively for academic purposes (i. e. , non-profit institutions). Non-academics interested in the program cannot access the software and must contact us directly. Academics can sign the proprietary license agreement through the project website 2. Once completed, this provides access to the download page for the SeedUSoon software, a start-up database, and the installation procedures. An example of exported data, a template form to load customized laboratory information, demonstration movies, FAQs and access to updated versions of the software will be posted on the project website. Specific questions can be directed to the project leaders by using the dedicated email address SeedUSoon@cea.
Easy Customization of SeedUSoon: Adaptation to the Laboratory Context Personalized "lab" guidelines SeedUSoon contains a customizable document that will provide laboratory members with specific guidelines and rules decided within their own laboratory. An "Our lab" icon is always visible on the SeedUSoon main page and provides access to this document (Figure  1C-d ). This document specifies how to name lines and successive generations, and describes which file formats are acceptable for upload into the database. In addition, it provides details on the organization of the laboratory's common seed stock, how to store seeds, and protocols for seed selection, transformation, etc. A window asking the user to upload the manual will appear following the first activation of the "Our lab" icon. After this initial upload, the document will automatically open whenever any user clicks on "Our lab.
Customization of the user module Parameters and methods susceptible to change between laboratories are presented in scroll-down menus when in the user mode. These menu options are customizable, but can only be edited by the database administrator in the administrative mode (see the corresponding section for details). This allows the software options to closely match the habits and protocols of each laboratory, while preserving a certain consistency. Through these scroll-down menus, the user will have access to a specific selection of laboratory member's names, plant species, ecotypes, strains, plant resistances, and mutation methods in use in the laboratory. New entries or modifications to the scroll-down menu options can be made at any time during the database lifetime, and corresponding plant lines will be updated accordingly.
Searching for Available Plant Lines or Seed Batches A search engine is located at the bottom of the user interface (Figures  1C-g  and 2 ), which can provide access to all "Plant lines" present in the database (by clicking on "Show all lines"), or only a subset of lines when "Search lines by criteria" is selected (Figure  2 ). The first field ("Gene, Line, Genetic feature") can be used to search a keyword throughout all plant line names, gene names and genetic features recorded in the database. Alternatively, users can select more restrictive query criteria by completing the fields specifically associated with the 4 individual sub-parts of a plant line datasheet. These fields can be among the general properties of the line, genetic feature properties, and plant or seed batch properties (including seed batch name or ID, person involved, etc.
New record in the database The user can create a "Plant line" record de novo, by entering any available information in the empty fields of the new database entry (Figure  3 ). Most fields are optional and can be completed later (mandatory and facultative fields are listed in Supplementary Figure  1 ) to avoid any wrong assumptions when recording data (arising from erroneous "guess work"). Data can easily be saved, completed or modified at any time by clicking on the lock buttons (Figures  1C-m,n  and 3 ). When starting the "new" plant line wizard, the user will only be required to complete three mandatory fields: the line name, the person associated with this datasheet, and the plant species. No other additional information is needed in the case of a WT plant. If the plant line contains one or several mutations (or transgenesis), the user can click on "Add a transgenesis" or "Add an endogenous gene mutagenesis.
Crossing two previously recorded plant lines SeedUSoon is capable of predicting the genetic configuration of plants resulting from the crossing of two plant lines that are already present in the database. The software will import all important properties from the parental lines and create a new plant line that combines this data (mix of ecotypes, set of combined genetic features, etc. ). Only intra-species crosses are permitted by the software. After starting the "Cross" wizard (Figure  4 ), the user will only need to specify which parents were used as male/female; optionally, the seed batches used for the crossing can be included. This will generate a new datasheet, to which the user can allocate corresponding seeds or plants (the user can also specify whether the mutations are homozygous or heterozygous).
Secondary mutagenesis of a recorded plant line The "MUTAG" wizard can be used if a new mutagenesis has been applied to a plant line previously recorded in the database (Figure  5 ). As with the "Cross" wizard, "MUTAG" will import all the genetic properties from the mother line into the new line (species, ecotype, and genetic features). The user will only need to complete the fields corresponding to the new "Genetic feature" selected for the secondary mutagenesis (Figure  5 ). Similarly to the "Cross" wizard, the data inherited from the parental line cannot be edited in the datasheet of the resulting line.
Importing/exporting a line It is possible to export a database entry from a "Plant line" (with or without the corresponding seed batches) into a single file that can easily be sent to collaborators (Figure  6 ). This file is generated using an exchange format (. json). Although this format can only be partially read in a text processor such as WordPad, it permits a very complete data exchange between two SeedUSoon databases, including all attached files (plasmid sequence, phenotyping results, etc. ). The export can be performed either from the Research result table (Figure  6A ) or directly from the opened plant line datasheets (Figure  6B ). The simultaneous export of several lines is only possible through the Research result table, however, no seed batches can be exported along with the line information in this case.
When exporting directly from the plant line datasheet, it is possible to assemble the information from one or several seed batches (with the exception of sensitive/personal information; see Supplementary Figure  1 ). Any plant (from the plant table) that is linked to a seed batch (identified as its mother plant) will be exported as well. Reciprocally, a plant line can be created by importing data from other databases. To import data, the user must click on the "Import arrow" at the upper left corner (Figures  1C-k  and 6C ), specify a name associated with this new database entry, and select the. json file. If the import contains options that are not available in the database scroll-down menus, a popup window will warn the user that the administrator must create the corresponding choices via the administration mode.
One or several "Genetic features" can be included during the creation process. The resulting "tree" shows that this line does not depend on any parental lines present in the database (no parental lines indicated in the tree). The icons at the bottom provide access to the number of descendants from the plant line, and to an export function of the graphical representation. FIGURE 4 | Simulated crossing of two lines using the "cross" wizard. A new plant line can be created using the simulated crossing between two parental lines present in the database. This line will inherit all genetic features from the parents. It is possible to add an extra genetic feature during the crossing process for the specific case of pollen mutagenesis, where crossings are combined with additional mutagenesis. The resulting "genealogical tree" reflects the relationship between the parental and resulting plant lines.
A plant line protected by an Material Transfer Agreement (MTA) is indicated in red. The icons at the bottom provide access to the number of descendants from the plant line, and to an export function of the graphical representation. FIGURE 5 | Creation of a line using the "MUTAG" wizard in order to apply a secondary mutagenesis to an existing plant line. A new plant line can be created by secondary mutagenesis (addition of a genetic feature) to a parental line already present in the database. The new line will inherit all genetic features from this parental line, and combine it to the new genetic feature. The resulting "genealogical tree" reflects the relationship between the parental and resulting plant lines. The icons at the bottom provide access to the number of descendants from the plant line, and to an export function of the graphical representation.
Administrative Mode The "Admin" icon (Figure  1C-f ) provides access to the administrative mode (only for users with administrative rights) to be able to customize the user interface, create SeedUSoon user accounts, and specify their rights. All customization choices recorded from the administrative mode (on a single computer) will be effective for all computers that connect to the same database. The administrative mode is a very simple interface organized in eight tabs, each giving access to a table with editable content (Figure  8A ). A wizard for generating new entries can be activated by clicking on "New" at the bottom of each table. Existing entries can be separately edited or deleted using the buttons at the right of each table.
Scroll-Down Menu Customization All tabs (aside from the one used to define "SeedUSoon users") are dedicated to the customization of the user's module (Figure  8B ). This allows the administrator to specify the options available in the scroll-down menus presented to the users. The software comes with a set of pre-recorded options for each tab, which can easily be edited by the database manager. For each tab entry, the software will automatically verify and count lines, features or experiments containing the corresponding scrolldown menu option. This will help the manager visualize the relevance of certain fields, in order to only delete unused menu options.
Persons The "Persons" tab (Figure  8B ) corresponds to current or past laboratory members who contributed to the generation or the analysis of any plant line recorded in the database. This category should not be confused with the previously mentioned "SeedUSoon users" category. If a "Person" leaves the laboratory and no new entries will be generated under this name, it is possible to deactivate (hide) the name from the scroll-down menus when generating new plant line entries. This limits the length of scroll-down menus in laboratories with a high turnover of members. To do this, the administrator must uncheck the box "Shows up in the "Persons" Menu (User mode)" in the central column of the "Persons" table (Figure  8B ). Previous entries containing a reference to this person will still display the name.
CONCLUSION AND FUTURE DEVELOPMENTS SeedUSoon is a new plant line database software, built upon a strong genetic foundation. The software's ability to track the history of mutations or transgene inheritance, in addition to the possibility to record related seed batches, provides the user with a more clear and organized view of the genomic context of their biological material. SeedUSoon contains novel functions related to MTA tracking and easily distinguishes between GMO and non-GMO plant lines, to facilitate administrative and legal compliance. Exporting data between databases is also greatly simplified by the import/export functions. Our intention when we started to design SeedUSoon was to improve the management of our own laboratory plant lines and seed collections. Nevertheless, from the start, the software was also meant to be able to adjust to the context and habits of any other plant laboratories conducting basic research.
We achieved this goal by developing a customizable user's module, and by integrating choices for field entries that are respectful of individual user habits. To help managers or PIs standardize entries in their own database or seed collection, a customizable "Laboratory guidelines" document is easily accessible from the software. Several additional functions were requested during the development of SeedUSoon. The current version of the software was designed in order to implement most of these requests in the future. For instance, the possibility to connect to different SeedUSoon databases using a simple Login/Logout could be advantageous to access distinct databases dedicated to specific projects. We also took into account future functions that can print labels (with customizable content, including unique ID numbers and plant line names), or export data in a diversity of formats (to generate files necessary for GMO certification, for instance).
In collaboration with our Intellectual Property department, we considered the possibility of generating MTAs prefilled with plant line information, which would only require the addition of the recipient identification and the approval signatures. This feature would greatly facilitate and stimulate this procedure, since the signing of MTAs when sending seeds to other research groups is hard to implement in many laboratories. User feedback (through the project website and the dedicated email address) will be important in helping us decide on the strategy for future SeedUSoon developments. Similarly, the design of the current version was improved by the feedback from users of previous versions of SeedUSoon. In the current configuration, this software has already helped laboratories organize hundreds of plant lines, from their generation to the organization of seed collections.