dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis The GWAS was performed using Qxpak 5. 0 software  [72]. The additive effect of a SNP on each trait was estimate by mixed model  [73, 74]  following the model: y ij ¼ Xβ þ Zu þ s j;k þ e ij ; where: y ij represents the vector of observations from the i th pig at the j th trait ; X is the incidence matrix relating fixed effects in ß with observation in y ij ; Z is the incidence matrix relating random additive polygenic effects in u with observation in y ij ; s j,k represents the additive association of the k th SNP on the j th trait and e ij is the vector of random residual effects. Fixed effects included in ß were, sex (two levels), batch (five levels) and carcass weight as covariate. Polygenic effects were treated as random and distributed as N(0, Aσ u ) where A is a numerator of kinship matrix.
05 or 0. 01 or 0. 001, we employed a knowledge-based approach to identify the P-value threshold at which the information content, in terms of fatty acid regulation, is maximized. To this effect, we mined the literature and relevant databases to compile a list of 340 TF of which 34 were known to be related to FA metabolism. The distribution of these 34 TF relative to the entire set of 340 was explored at various P-value thresholds. The P-value threshold that maximized the number of FA-related TF was used as the optimal Pvalue to apply when developing the Association Weight Matrix. Quite importantly, others in the past have employed a knowledge-based approach to identify the critical P-value threshold. More recently, and in the context of GWAS, Yang et al. [75]  used an approach similar to ours to find the P-value that maximise the correlation between the proportion of significant SNPs and the heritability across 47 traits.
In detail, the process for choosing the threshold was as follows: Step1: A total of 340 TF were located within 2. 5 Kb of a SNP and therefore included in the initial dataset. For all these TF included in our dataset, those that are well known key regulators of the lipid metabolism were initially selected. Step2: For each gene, those involved in the lipid metabolism and also reported in the census of human TF by Vaqueriza et al. [76]  were included. Step3: The Human Protein Reference Database (HPRD) and the Biomolecular Object Network Databank (BIND) were mined. Then other TF that have been reported to interact with some of the TF retained in the two previous steps were selected. After these first 3 steps, a total of 34 TF were retained (Additional file 12: Table  S9 ). Step4: Subsequently we compare the distribution of the 34 TF at different P-values from P = 1 to P = 10 -4 versus the distribution of the total number of TF included in the AWM (340).
As a result, we have chosen P < 0. 035 as the threshold. This specific P-value maximizes the difference between both groups of TF (Figure  5 ), imposing an informed bias towards lipid metabolism to the network. After defining the threshold of P < 0. 035, the selection of SNPs for building AWM continued. Those SNPs that were either associated (P < 0. 035) with palmitoleic acid or with any ≥ 3 traits, and were located either ≤ 2,500 bp to or ≥ 850 kb from the nearest annotated gene (Sscrofa10. 2 assembly), were selected to build the AWM matrix. Per-mutMatrix software  [77]  was employed to visualize hierarchical clustering of traits (AWM columns) and genes (AWM rows) using Euclidean distance and the Average linkage method. To identify and report genegene or gene-SNP interactions we used PCIT algorithm  [14].
Cytoscape software  [78]  was used to visualize the gene network and also to perform overrepresented GO terms analysis, using BiNGO plugin  [79]. Node centrality values and network topological parameters were calculated using CentiScaPe plugin  [80]. Pathway enrichment analysis were performed using FATIGO tool form BABELOMICS  [81, 82]. Further, pathways analyses of the 730 predicted target genes (co-associated with the key TF) were performed using ClueGO, Cytoscape plugin  [83]. Pathway information was retrieved from the KEGG (http://www. genome. jp/kegg/) and BioCarta (http://www. biocarta. com/) databases. In all cases, the cut-off for considering a significance overrepresentation was established by Benjamini & Hochberg multiple testing correction of the P-value (FDR < 0. 05)  [84].
Expression and co-expression analysis In order to provide supporting evidence for the in-silico AWM-network predictions we obtained and explored gene expression data by reverse transcription quantitative Real-Time PCR (RT-qPCR). The expression pattern of the 3 Key TF (NCOA2, EP300, FHL2) in LD muscle, liver and adipose tissues was tested in two phenotypically divergent breeds for fat deposition traits (Iberian and Landrace which are also the founders of our studied population, five animals per breed). Finally, liver and adipose co-expression analyses of 55 (43 from the present study, and twelve: ACSM5, APOA2, ARNT, CYP7A1, FABP5, FADS3, HNF4a, LIPC, MTTP, PPARA, PPARD and ELOVL6 genes from Ballester et al. , 2013 submitted) and 40 genes, respectively, were performed using the PCIT algorithm  [14]  in 55 backcross animals.
A dissociation curve was drawn for each primer pair to assess the specificity of the amplification. Three reference genes (ACTB, HPRT1, TBP) frequently used in RT-qPCR experiments were tested as endogenous controls. Using the GeNorm software  [89] , the ACTB and TBP genes were selected as the best endogenous controls for all tissues. After ensuring the possibility to use the 2 -ΔΔCT method  [90] , data was analyzed using the RQ manager v1. 2. 1 and the DataAssist™v3. 0 softwares (Applied Biosystems). The 2 -ΔCT values were used to compare our data. The 48. 48 microfluidic dynamic array IFC chip (Fluidigm) was used to analyze the expression of 48 genes (44 target genes and 4 reference genes) in liver and adipose tissue of 55 backcross animals belonging to the same population in which the GWAS was performed.
5 μl of Assay Loading Reagent (Fluidigm), 2. 25 μl of DNA Suspension Buffer (Teknova) and 0. 25 μl of 100 μM primer pairs (500 nM in the final reaction) were mixed inside the chip using the IFC controller MX (Fluidigm). The thermal cycle was 60s at 95°C followed by 30 cycles of 5 s at 96°C and 20s at 60°C. A dissociation curve was also drawn for each primer pair. Data was collected using the Fluidigm Real-Time PCR analysis software 3. 0. 2 (Fluidigm) and analyzed using the DAG expression software 1. 0. 4. 11  [91]  applying the relative standard curve method (see Applied Biosystems user bulletin #2). Standard curves with a four-fold dilutions series (1/4, 1/16, 1/64, 1/256, 1/1024) of a pool of 10 cDNA samples were constructed for each gene to extrapolate the quantity values of the studied samples.
The PCR efficiencies were almost 100% in both tissues for all the assays (Additional file 13: Table  S10 ) with low coefficients of inter-assay variation of threshold cycle (<2. 4% in liver and <3. 5% in adipose tissue). Of the four endogenous genes tested (ACTB, B2M, HPRT1, TBP), ACTB and TBP were the genes with the most stable expression  [89]  in both tissues. The normalized quantity values of each sample and assay were used to compare our data. All the primers used in this study were designed using PrimerExpress 2. 0 software (Applied Biosystems) and are shown in Additional file 13: Table  S10. Prior to perform the Fluidigm Real-Time PCR, all the assays were tested for PCR specificity in an ABI PRISM 7900 Sequence Detection System (Applied Biosystems) using two-fold dilutions (1/20, 1/200) of a pool of ten cDNA samples and a minus RT control to check the presence of DNA contamination.
Acknowledgments This work was funded by  MICINN  project  AGL2011-29821-C02  ( Ministerio de Economía y Competitividad ), and by the  Innovation Consolider-Ingenio 2010 Program  ( CSD2007-00036 ,  Centre for Research in Agrigenomics). We thank  Dr. Nick Hudson  for insightful suggestions and manuscript proofread. Y. Ramayo-Caldas  was funded by a  FPU  PhD grant from the  Spanish Ministerio de Educación  ( AP2008-01450 ).
Regarding the propeller (as a mechanical system), the breakthrough represented by the introduction of the podded propulsion (years 1990s for first systems in the market) was also supported by several research projects. Among these projects the EUfunded STREAMLINE project, partly funded by the 7th Framework Programme of the European Commission (FP7), is the first project for which the effect of waves on cavitation and ventilation was explored in both experimental and numerical ways for a ship with pods. A major objective of the STREAMLINE project is to inves- tigate methods to fully optimise current state-of-the-art (SoA) systems including conventional screw propeller systems and pods. The key here is exploitation of advanced Computational Fluid Dynamics (CFD) methods to pursue improvements without dramatic vessel configuration changes.
COV2HTML is an interactive web interface, which is addressed to biologists, and allows performing both coverage visualization and analysis of NGS alignments performed on prokaryotic organisms (bacteria and phages). It combines two processes: a tool that converts the huge NGS mapping or coverage files into light specific coverage files containing information on genetic elements; and a visualization interface allowing a realtime analysis of data with optional integration of statistical results. To demonstrate the scope of COV2HTML, the program was tested with data from two published studies. The first data were from RNA-seq analysis of Campylobacter jejuni, based on comparison of two conditions with two replicates. We were able to recover 26 out of 27 genes highlighted in the publication using COV2HTML.
The second data comprised of stranded TSS and RNA-seq data sets on the Archaea Sulfolobus solfataricus. COV2HTML was able to highlight most of the TSSs from the article and allows biologists to visualize both TSS and RNA-seq on the same screen. The strength of the COV2HTML interface is making possible NGS data analysis without software installation, login, or a long training period. A web version is accessible at https://mmonot. eu/COV2HTML/. This website is free and open to users without any login requirement.
As more and more NGS data sets are made available, their analysis becomes more diverse and complex and, as a consequence, needs experienced and skillful experts. On the other hand, the main bottleneck for biologists to perform their own global and specific analysis is the use of dedicated tools without the help of a bioinformatics expert. Ideally, biologists should have a 'plug and play' software for such studies. Thus we developed COV2HTML, an accessible visualization and analysis tool for biologists, which consists of two distinct and highly dependent programs. The first one, MAP2COV, (i) calculates the genome coverage from the heavy mapping file, reaching several Gb, and (ii) extracts gene, rRNA, tRNA, and ncRNA information from annotation files to create light result files of a few Mb.
Implementation COV2HTML is a web interface designed for biologists that allows coverage visualization of NGS data before being studied. In order to ease both data loading and processing, COV2HTML uses its own coverage format instead of directly handling huge alignment map or coverage file. Thus, after having been mapped against the reference genome, the NGS data are converted by the tool MAP2COV provided with the visualization interface. MAP2COV is written in Python 2. 6 (http://www. python. org/) and is available both in command line and in ''one click'' graphical versions (require Python 2. 6 or 2. 7 and Tcl/Tk installed). COV2HTML is a web interface implemented in PHP 5. 0 (http://www. php. net/) to generate dynamic HTML content. The web version (https://mmonot. eu/COV2HTML/) is currently running on a PC server with Ubuntu Linux using an Apache HTTP server to support web services.
MAP2COV: Conversion of NGS data MAP2COV is useable on three operating systems (Linux, Mac OS X, and Windows) in command line or through a graphical interface written in Tcl/Tk (http://www. tcl. tk/ software/tcltk/license. html). For the two latter platforms, we used PyInstaller (http://www. pyinstaller. org/) to generate ''one click'' binary executables. MAP2COV works with three mandatory inputs (Fig. 1  annotated reference sequence previously used for the mapping assembly in GenBank  (Benson et al. , 2012)  or EMBL  (Kulikova et al. , 2007)  format, or two files, one containing the reference sequence in Fasta format, and the second one containing annotations in GFF3 format  (Stein, 2010)  or features nucleotide sequence in Fasta format; (ii) an alignment map file in SAM'/'BAM  (Li et al. , 2009) , ELAND (http:// ccg.
MAP2COV extracts first the interesting features in extenso CDS, rRNA, tRNA, ncRNA, and reference sequence length contained in complete GenBank, EMBL files or Fasta, GFF3 files. Feature information (ID, position, strand, type, and description) is recovered directly from these files. For the gene nucleotide sequences in Fasta format, an alignment step is performed to find out their position in the genomic sequence. The 'find' function of python is used to get back the position of the first exact match of each CDS in the genomic sequence plus and minus strand. In order to prevent that exactly duplicated genes correspond to only a single position on the reference genome, once a match has been found, one part of the corresponding sequence is masked by Ns in the reference genome before subsequent searches.
Only part of the match is replaced to avoid losing the information necessary for overlapping genes. As this step could not be 100% accurate, users are advised to use EMBL, GenBank, or GFF3 file whenever it is possible. Finally, intergenic region (IGR) elements are created and begin one base after the stop codon of the previous gene, and end one base before the start of the next gene, except in case of overlapping genes where IGR are ignored. IGR are numbered from the origin and their description contains the ID of the surrounding genes. The position and direction of the read matches present in the alignment file are used to calculate the coverage of the reference genome except for the WIG format, which contains only the genomic coverage information. In the case of alignments performed against more than one reference genome and stored in the same SAM/BAM file (e.
COV2HTML interface: Integration, visualization, and analysis of NGS data The compressed coverage files from MAP2COV have to be directly visualized by submitting them into the COV2HTML interface. COV2HTML uses simple HTML code that assumes the low-technology style of the interface, allowing larger browser compatibility (Supplementary Table  S1 ; supplementary material is available online at www. lie bertpub. com/omi) and better swiftness, with a high realtime reactivity. The interface is divided into 2 PHP web pages, «connexion. php» for data integration and «visualization. php» for data visualization. Data integration. In the «connexion. php» web page, analyses are entered into database or selected from down-drop lists. Each analysis is defined by the title and two conditions can be compared with a maximum of four replicates per condition (Fig.
2a ). Otherwise, COV2HTML can be used to visualize up to eight experiments of diverse type performed on the same genome. The title should be chosen to describe analysis carefully as there is no link between coverage files and experiments. A label of up to four characters could be attached to each condition and numbered per replicate, for example, 'WT1, WT2' (mandatory if statistical results are provided). Users could also integrate statistical results obtained from any source using a tabulated text file with the following columns: gene identifier (ID), each condition numbered per replicate (e. g. , WT1, WT2, rpoN1, rpoN2), each condition normalized value (e. g. , WT, rpoN), fold change value (FC), p value (pvalue), and a significativity column with 0 or 1 value (significant). Analysis integration into database takes approximately 30 seconds for each file, depending of the Internet connection (see below performance test).
, 2009). Then we used MAP2COV with the C. jejuni genome sequence [GenBank:AL111168] and the mapped file to test COV2HTML for each condition's replicate. A statistical result file was created by using information from Table  S3  available in the supplementary results of the publication. The second study contains stranded TSS and RNA-seq data sets on the Ar-chaea Sulfolobus solfataricus recovered from multiple conditions  (Wurtzel et al. , 2010). The authors provided raw data files for TSS and WIG-like coverage files for RNA-seq [GEO:GSE18630]. For the TSS approach, Bowtie  (Langmead et al. , 2009)  was used to create stranded condition mapping files ( + and -) which gather three pooled conditions. Then MAP2COV was launched to convert them into coverage files along with the S. solfataricus genome sequence [GenBank:NC_002754] which will be used to validate COV2HTML.
COV2HTML interface: A web and a local version The web interface https://mmonot. eu/COV2HTML/ runs on a PC server (Core I3 at 3,4 Ghz with 16 Gb of RAM and a 2-Tb HDD drive), with Ubuntu Linux using Apache HTTP server to support web services. Data generated by COV2HTML are stored in a MySQL database split into 10 sub-bases. Each sub-base is limited to 100 stored analyses (1000 analyses in total). We set a maximum genome size which can be uploaded at 10 megabases. This website is free and open to any users without login requirement. A local version of the COV2HTML interface can also be set up by downloading the PHP and SQL source files from http://cov2html. sourceforge. net/, together with a package that contains at least the Apache web server, MySQL, PHP, and phpMyAdmin. We suggest using the XAMPP Apache distribution (http://www.
COV2HTML web interface: Performance test We tested performances of the COV2HTML interface both for analysis uploading and visualization by using the Apache JMeter desktop application (http://jmeter. apache. org/). We performed several test plans simulating a succession of steps executed by JMeter during the data handling, integrating multiple criteria: simultaneous users, database load (0, 250, 500, 750, 1000 analyses) and use of secure Internet protocol or not (HTTPS, HTTP). The first test plan built in JMeter simulated 1, 3, 5, or 10 users simultaneously uploading data from the Campylobacter jejuni RNA-seq analysis (Case study 1) through the ''connexion. php'' web page, and then accessing to the ''visualization. php'' web page. The second test plan simulated 10, 50, 100, 150, 160, 180, 200, or 250 users simultaneously refreshing the ''visualization.
php'' web page every 5 to 10 seconds. These two test plans were launched 10 and 100 times, respectively, to collect mean response time and standard deviation (Supplementary Table  S2  and Supplementary Fig. S1 ). Both test plans showed that the mean insertion time depends on the number of simultaneous users and varies from 30 to 90 seconds according to the data tested. The maximum of simultaneous uploadings into the database is reached with 10 users; at this point some users could receive an error message. There is no difference in insertion or visualization time relative to the database load. Since the internet protocol (HTTP and HTTPS) does not influence the swiftness of the connection with our tests, we set the secure protocol as default. To limit database congestion, user sessions are deleted after 6 months if no analysis insertion or visualization has been done, and the 15 oldest analyses are removed when one sub-base reaches 100 analyses.
The aim of this work was to develop a program that makes NGS data analysis simpler and autonomous for biologists. COV2HTML can be used either directly by NGS platforms providing their own specific coverage files, or by biologists with coverage files converted by MAP2COV. For this last option, the coverage file for each experiment is created with MAP2COV by providing the reference genome information, the alignment file as well as the data type and optional parameters (Fig. 1 ). The analysis can be then performed in the COV2HTML connexion. php web page (Fig. 2 ), by filling the title box and by uploading the right coverage files. For each condition a short label can be attached and it is possible to compare two conditions with a maximum of four replicates per condition (Fig. 2a ). The statistics results could be also integrated by using a specific formatted file.
COV2HTML allows biologists to combine and visualize their raw data and data from publications prior to access sophisticated analysis tools. The security of the COV2HTML website https://mmonot. eu/COV2HTML/ is enhanced by default using a HyperText Transfer Protocol Secure (HTTPS). It provides authentication of the website by authoritatively signed certificates (Go Daddy Secure Certification Authority) and bidirectional encryption of communications between client and server. As the encryption could delay the website, we add the possibility to switch between HTTP and HTTPS protocols (lock image next to the analysis title in the ''connexion. php'' page). The security of the analysis is managed by a cookie coded inside the web browser with a combination of the date and a unique identifier from the database.
, 2011) , Generic Genome Browser (GBrowse)  (Stein et al. , 2002) , Javascript-based genome browser ( JBrowse)  (Skinner et al. , 2009) , and BamView  (Carver et al. , 2012). These interfaces are high-performance visualization tools for interactive exploration of large integrated genomic datasets. Although many features overlap with these interfaces (Table  4 ), COV2HTML offers facilities for the biologist users as follows. First, a complex installation procedure is mandatory for GBrowse or JBrowse (server) but IGV, BamView, and COV2HTML are given directly as a graphical interface. IGV and BamView are written in Java, making them portable between platforms in theory, whereas a web browser is sufficient for JBrowse (client) or the COV2HTML visualization interface. Second, the NGS results can be sent in many formats to biologists.
Finally, the analysis of genes or IGR coverage is not present in IGV, GBrowse, and JBrowse. BamView can normalize data with the read per kilobase per million of mapped reads (RPKM)  (Mortazavi et al. , 2008) , and write an output file that can be used for further statistical analysis. In COV2HTML, the interpretation of aligned reads is screened out by using filters on genes or In the publication, TSS considered by an accumulation of more than 10 reads. For COV2HTML, the filter used was «coverage > 10». As more and more NGS data become accessible online, scientists are attracted to exploit diverse experimental results. However this often requires bioinformatics skills, which may prevent them from undertaking such studies. Thus, COV2HTML has been designed as an easy and accessible interface to simplify analysis of NGS data recovered from laboratory experiments or from databases.
Acknowledgments We would like to thank  Corinne Levi-Meyrueis , Francoise  Norel-Bozouklian , and  Thomas Dubois , which have tested COV2HTML. We thank  Dr. Thierry Garnier  for suggestions and comments of the manuscript. This work was supported by  Institut Pasteur and  Grant  ERA-PTG/SAU/0002/2008  ( ERA-NET PathoGenoMics ) for  Bruno Dupuy  and  Marc Monot. Author contributions; MM and CB wrote the COV2HTML code. MO and MM wrote the MAP2COV code. EC tested the software. MM, EC and BD wrote the manuscript, all authors have contributed to it and have read and accepted the final version.
This work was funded by  Agence Nationale de la Recherche  Grants  ANR-12-BSV5-0014-02 ; the  European Commission Seventh Framework Programme  ( FP7/2007-2013 ) under grant agreements no. 241548  and  258068 ;  Labex Grenoble Alliance  for Integrated Structural Cell Biology; and CTBU Grant from  CEA  to  Gaëlle Letort. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.