dataesr/scientific-paragraphs-categorization huggingface dataset:
e. probes, transcripts, genes or, perhaps, corresponding proteins. Their structure is therefore incompatible with the ability of RNA-sequencing to lead to new discoveries (e. g. when new transcript isoforms are assembled and/or new loci identified) and not adapted to ChIP-or Methyl-seq analyses of specific chromatin regions, the boundaries of which cannot be strictly defined. The so-called genome browsers, a new type of database, have emerged to meet these requirements  (10). UCSC's famous website  (11)  is a pioneer in this regard. The implementation of new modules  (12, 13)  makes it possible to create even more flexible and intuitive browsers. These allow the hosting, visualization, customization, retrieval and analysis of various types of genomics data in a single environment, thus enabling researchers to extract and share data easily and construct new hypotheses from them.
W110 Nucleic Acids Research, 2015, Vol. 43, Web Server issue Here we present the ReproGenomics Viewer (RGV), a cross-species genomic toolbox for the reproductive community. The system is based on the implementation of a 'JBrowse genome browser'  (20)  and a 'Galaxy bioinformatics workflow environment'  (21) (22) (23). It was developed to provide a one-stop genomic working environment and aims to assist scientists in the analysis and the mining of a wide range of high-throughput repro-genomics data, including sequencing data. RGV allows hosting, visualization and direct comparison of users' data to published genomics studies as well as to relevant genetic variations linked to reproduction. One way it does this is by enabling various genomic file format conversions. These genomic coordinates can be converted not only between genome releases of a given species but also and more importantly between different species.
All these experiments took place in a wide spectrum of model organisms, including Homo sapiens  (25, 30, 36) , Gorilla gorilla  (36) , Macaca mulatta  (36) , Mus musculus  (24) (25) (26) (27) (28) (29) (31) (32) 35) , Rattus norvegicus  (33, 34) , Monodelphis domestica  (36) , Ornithorhynchus anatinus  (36) , Gallus gallus  (29, 36)  and Saccharomyces cerevisiae (as sporulation in yeast is the developmental process analogous to spermatogenesis in higher eukaryotes  (38) (39) (40) (41) ). Taken together, these published data sets currently represent 342 samples, 168 of vertebrates. In a critical step, we also gathered allele and genotype frequency data and significant genetic association findings from such public databases as GWAS and ClinVar  (42, 43). The control vocabulary provided by both projects enabled us to split genetic association studies into two categories: reproductive and non-reproductive symptoms.
THE RGV BACKBONE: DATA PROCESSING AND OR-GANIZATION The backbone of the RGV is the series of tools for processing and organizing data within the system (Figure  1A ). Four types of information were manually extracted and curated for each study, including: the scientific name of each species and the genome release with which the experiments were performed and analyzed; the associated scientific publication; each biology topic investigated in the study; and the high-throughput technologies performed. Then each sample of a given data set underwent a series of automatic conversions to make it fully compatible with the RGV system (Figure  1B ). Briefly, for a given sample X analyzed under a genome release r-1 of Species Y, five processing steps were sequentially performed: (i) each of the various input data formats (bedGraph/BED, WIG, bigWig) was converted into a simple tab-delimited text file (BED); (ii) as some differences can occur even in the same genome release of a given species, the resulting BED data file might have needed to be modified to standardize, for example, the chromosome names that might differ between the Ensembl, UCSC and NCBI databases; (iii) the standardized BED file was then converted into an indexed binary format (bigWig or bw) to enable fast remote access to the data; the pairwise alignments between genome assemblies and between species provided by UCSC made it possible to convert genome coordinates in the resulting bigWig file from a genome release r-1 of the species Y into (iv) the current assembly r of the same species Y and then (v) the current assembly r of another species Z.
The RGV working environment To host genomics tools essential for data comparisons between genome releases and above all between species, we implemented a 'Galaxy bioinformatics workflow environment'. Briefly, Galaxy is an open web-based platform for genomic research that provides users with an easy-to-use web interface to create complex biological workflows by tools that simply need to be dragged and dropped. It is worth mentioning that the 'RGV Galaxy session' is available without creating an account. Users are, however, strongly invited to create an account to have access to their history, saved analyses, data sets and workflows. By default, this environment contains a myriad of tools designed mainly to assist   (33). The organization of the data is based on the information manually extracted from the publication (species name, genome release, biological topic and technology).
(B) The 'RGV data processing' workflow used to convert data file formats, standardize data files and then to convert genome coordinates between assemblies (r -1 → r) and between species (species Y → Z). (C) Screenshot of the JBrowse 'Available tracks' menu illustrating the 'in-house' organization of the published data sets embedded in the RGV system in several categories, such as 'Biological topics', 'Technologies', 'Publications' and 'Species'. The 'RGV data processing' workflow described in the RGV backbone section (Figure  1B ) was conveniently implemented as a Galaxy module. This pipeline is based on the implementation of three tool suites: UCSC tools (44), bedtools  (45)  and CrossMap  (46). The former is used for all data file format conversions in either bedGraph or bigWig formats. The second is employed for the data standardization step.
To corroborate RGV's usefulness, we decided to test its ability to identify novel human loci dynamically expressed during male gamete development and conserved across species. We first integrated three RNA-sequencing studies in the 'JBrowse session': a tissue profiling project including samples from human testis and three other tissues (ovary, brain and placenta) published by Necsulea et al. (36) ; then we added two high-resolution expression profiles of male germ cells, one in rats  (33)  and the other in mice  (32). Next, we analyzed the human testis sample provided by Necsulea et al. and assembled the transcripts with the cufflinks tool suite  (50). We then sought to identify novel intergenic and multi-exonic loci that are expressed in human testes and have a meiotic and/or postmeiotic expression pattern in rodents (data not shown).
Third, we plan to develop community tools that will greatly facilitate collaborative work and stimulate the emergence of novel forms of collaboration in our research field. Finally, we will be enhancing the features and functionalities of the 'RGV-Galaxy working environment'. In particular, we intend to embed the 'JBrowse genome browser' directly into the Galaxy environment. Users will thus be able to entirely customize, and eventually share, their own personal genome browser session with their ultra-highthroughput data sets. This integration of JBrowse within the RGV-Galaxy working environment will also facilitate communications and data export between the two sessions. Another crucial point involves the direct implementation of several workflows for analysis of NGS data (e. g. RNAseq, ChIP-seq) within the Galaxy environment.
CONCLUSIONS We report the development of the RGV, a webserver-based toolbox for reproductive scientists. The system combines specific solutions for ultra-high-throughput data management, curation and organization, with data conversion across releases and species (CrossMap), genome browsing (JBrowse session) and a bioinformatics workflow environment to deploy analysis pipelines (Galaxy session). RGV currently embeds 15 published data sets related to germ cell development from nine eukaryotic species. We intend to complete RGV's repertoire with other related biological processes, other model organisms and other technologies of interest related to reproductive biology in the near future. This may help scientists and clinicians who work on reproduction to compare their own data sets to relevant    (36)  as well as in rodent meiotic and post-meiotic germ cells  (32, 33).
The amount of transcript determined in each tissue/cell and in each study is displayed as color-coded red heat maps. Red histogram bars represent the sequence conservation score distributions between 100 species as provided by the UCSC genome browser (phastCons scores, y-axis ranges from 0 to 1). TCONS 00962903 detection at the RNA level was further confirmed by RT-PCR in four rat (B) and mouse (C) tissue samples, including total testis (TT), brain (BR), kidney (KI), liver (LI) and lung (LU). RT-PCR analysis was also performed in four human tissue samples (D), including total testis (TT), epididymis (EP), seminal vesicle (SV) and prostate (PR), as well as five isolated testicular cell populations (E) including Leydig cells (LC), peritubular myoid cells (PC), Sertoli cells (SC), spermatocytes (Spc), round spermatids (rSpt) and total testis (TT) as positive control.
ACKNOWLEDGEMENTS We thank  Céline Le Béguec ,  Yoanne Ando Randriamanantena ,  Laetitia Guillot ,  Francois Moreews ,  Raphaël Charles ,  Aurélie Lardenois  and  Michael Primig  for stimulating discussions and/or beta-testing RGV. We acknowledge the  GenOuest bioinformatics facility  for hosting the software. We also thank  Dominique Mahe Poiron ,  Nathalie Dejucq-Rainsford  and  Nathalie Rioux-Leclerq  for providing the human samples.
Neuroimaging pipelines are known to generate different results depending on the computing platform where they are compiled and executed. We quantify these differences for brain tissue classification, fMRI analysis, and cortical thickness (CT) extraction, using three of the main neuroimaging packages (FSL, Freesurfer and CIVET) and different versions of GNU/Linux. We also identify some causes of these differences using library and system call interception. We find that these packages use mathematical functions based on single-precision floating-point arithmetic whose implementations in operating systems continue to evolve. While these differences have little or no impact on simple analysis pipelines such as brain extraction and cortical tissue classification, their accumulation creates important differences in longer pipelines such as subcortical tissue classification, fMRI analysis, and cortical thickness extraction.
Introduction Neuroimaging pipelines are known to generate different results depending on the computing platform where they are compiled and executed  (Krefting et al. , 2011; Gronenschild et al. , 2012). Such reproducibility issues, also known as computing noise, arise from variations in hardware architectures and software versions. The state-of-the-art solution to deal with these issues is to restrict studies to a single computing platform (hardware and software), which has several drawbacks: (i) results may not be reproducible over time, when the computing platform used to produce them becomes obsolete; (ii) the use of High-Performance Computing (HPC) is limited to homogeneous sets of platforms, while available platforms are increasingly versatile; (iii) in some cases, homogenizing computing platforms is not even feasible, for instance when shared databases are processed in different institutions.
On GNU/Linux, a dominant OS in neuroimaging  (Hanke and Halchenko, 2011)  and in HPC 1  , applications rely on the GNU C library, glibc 2  , which includes a mathematical library, libmath. New versions of glibc are released regularly, and subsequently adopted by distributions of the GNU/Linux OS, sometimes several years later. We focus on the differences generated by different library versions, which we call inter-OS differences for dynamically-linked applications, and inter-build differences for statically-linked applications. Inter-run differences, that is, differences between runs of the same application on the same platform may also occur, for instance when applications use pseudo-random numbers (this particular case can be addressed by forcing the seed number used to initialize the pseudo-random number generator).
This paper reports on our experiments with three of the main neuroimaging tools: the FMRIB Software Library (FSL,  Jenkinson et al. , 2012) , Freesurfer  (Fischl, 2012), and CIVET (Ad-Dabbagh et al. , 2006). We quantify the reproducibility of tissue classification (cortical and subcortical), resting-state fMRI analysis, and cortical thickness extraction, using different builds of the tools, deployed on different versions of GNU/Linux. We also identify some causes of these differences, using library-call and systemcall interception. The paper closes with a discussion suggesting directions to address the identified reproducibility issues.
Operating Systems and Applications Table  1  summarizes the platforms used in our experiments. We used two HPC clusters with Red-Hat-like Linux distributions: (A) CentOS release 5. 10, running glibc 2. 5 released in 2006, and (B) Fedora release 20, running glibc 2. 18 released in 2013. We installed Freesurfer 5. 3. 0 and FSL 5. 0. 6 on these clusters using the 64-bit binaries released on their respective websites 3,4. We used the Freesurfer CentOS 4 (1) and CentOS 6 (2) builds 5  , and the FSL CentOS 5 (1) and CentOS 6 (2) builds 6. We compiled and installed CIVET version 1. 1. 12-UCSF on cluster A, and used the same build on cluster B. Freesurfer releases mainly consist of statically-linked executables and tcsh scripts. Dynamically-linked executables and Perl scripts are also present, in the mni directory where the minc tools are installed.
The main differences between the Cen-tOS 4 and CentOS 6 builds are the version of the gcc compiler potentially used to compile them (gcc 3. x on CentOS 4 vs. gcc 4. y on CentOS 6), and the glibc versions embedded in the executables (glibc 2. 3 on CentOS 4 vs. 2. 12 on CentOS 6). FSL and CIVET consist of dynamically linked executables which depend on libmath and other libraries. FSL also contains Tcl (provided with the FSL release), bash and Python scripts, while CIVET has Perl and bash scripts. All data movements and task executions on the clusters were performed with the CBRAIN platform for High-Performance Computing  (Sherif et al. , 2014).
FSL: Resting-state fMRI We used 37 resting-state fMRI (RSfMRI) data arbitrarily selected from an ADNI-GO 7 dataset (site 130). All fMRI volumes were collected on a 3T Achieva Philips Medical Systems scanner with a gradient echo EPI (TR/TE = 3000/30 ms; Flip Angle = 80. 0 • ; 64. 0 × 64. 0 inplane isotropic resolution of 3. 3125 mm and slice thickness of 3. 313 mm). Each RSfMRI dataset contained 140 volumes. Structural images were obtained using a manufacturer T1W MPRAGE sequence. RSfMRI analysis was carried out using Probabilistic Independent Component Analysis (ICA,  Beckmann and Smith, 2004)  as implemented in MELODIC (Multivariate Exploratory Linear Decomposition into Independent Components) Version 3. 14. We executed MELODIC with FSL build 1, with the default parameters and different initializations of the random seed: (a) fixed, and (b) variable (time-based), which is the default.
Freesurfer and CIVET: Surface Segmentation and Cortical Thickness Extraction Cortical thickness maps were generated with Freesurfer and CIVET from the same ICBM dataset used in Section 2. 2. In our Freesurfer analysis, we performed all stages of cortical reconstruction using the recon-all pipeline, with qcache option enabled. In our CIVET analysis, we used the default options with the following additional specifications: an N3 spline distance of 200 mm, 12 • of freedom for the linear registration, and the tlink metric with a smoothing kernel size of 20 mm FWHM (full-width at half maximum) for the cortical thickness. Cortical thickness maps were computed in each subject's native space. For Freesurfer, these thickness maps were then resampled to Freesurfer's default fsaverage surface template as a common space, while cortical thickness maps for CIVET were resampled to CIVET 1.
1. 12's default MNI152 surface template. Resampled thickness files from both Freesurfer and CIVET were imported to the SurfStat MATLAB toolbox  (Worsley et al. , 2009)  for statistical analyses. To directly compare the effect of build and OS on cortical thickness, a difference score between processing conditions (cluster A-B or build 1-2) was calculated with SurfStat for the cortical thickness of every subject at every vertex, and a Generalized Linear Model (GLM) was computed consisting simply of the formula Y = 1.
5 million calls: they are type-3 differences in function expf() which manipulates singleprecision floating-point representations. The result produced by expf(), stored in variable b, is encoded as 24 58 95 40 in hexadecimal (little-endian convention). On the other hand, with glibc 2. 18, the program prints: expf(1. 540518522262573242187500000000) =4. 6670098304748535156250 The result produced by expf(), stored in variable b, is encoded as 25 58 95 40 in hexadecimal (little-endian convention): 1 bit is flipped compared to the result obtained with glibc 2. 5. These numerical differences, which originate in changing implementation of expf() between glibc 2. 5 and 2. 18, are a cause of the inter-OS differences in FSL FAST.
The trace shows no inter-OS difference until 43 million calls, where type-3 differences start to appear in function cosf(), soon followed by type-1 differences in ceilf() and floorf(), and type-2 differences in cosf(), sinf(), ceilf(), floorf(), and logf(). Mismatches appear at 43. 9 million calls, indicating that inter-OS differences have an impact on the control flow of the program. An inspection of the source code shows that the bounds of a few loops are determined from floorf() and ceilf() 10  , which is a plausible explanation for these mismatches. Type-3 differences come exclusively from function cosf() The result produced by cosf(), stored in variable b, is encoded as d8 b3 5d 3f in hexadecimal (littleendian convention). With glibc 2. 18, this program prints: which cosf(0. 523598790168762207031250000000) =0.
The result produced by sinf(), stored in variable b, is encoded as 9a 0c 2d 3d in hexadecimal (little-endian convention). With glibc 2. 18, the program prints: sinf(0. 042260922491550445556640625000) =0. 042248342186212539672851562500 The result produced by sinf(), stored in variable b, is encoded as 99 0c 2d 3d in hexadecimal (little-endian convention): again, 1 bit is flipped compared to the result obtained with glibc 2. 5. These numerical differences, which originate in changing implementation of sinf() between glibc 2. 5 and 2. 18, are a cause of the inter-OS differences in mcflirt.
Statically building programs improves reproducibility across OSes, but small differences may still remain when dynamic libraries are loaded by static executables, as observed with Freesurfer. When static builds are not an option, software heterogeneity might be addressed using virtual machines (VMs) as tested in CBRAIN  (Glatard et al. , 2014) , or lighter container environments such as Docker 11. Specific Linux distributions such as Neurodebian  (Halchenko and Hanke, 2012 ) could be used with these environments to guarantee a wide reproducibility within the community. However, such solutions are only workarounds: differences may still arise between static executables built on different OSes (as seen in our Freesurfer study), or between dynamic executables executed in different VMs. Although it would not improve numerical stability, a more rigorous way to address reproducibility issues would be to use higher-precision representations of floating-point numbers, and to avoid using functions operating on single-precision numbers (e.
Limitations Our results cover some of the main neuroimaging analysis tools (Freesurfer, FSL and CIVET), executed on RedHat-like Linux operating systems which are widely used in neurosciences. To cover a large spectrum of OSes, we used the oldest still-supported version of CentOS and the latest version of Fedora which anticipates on the coming CentOS versions. This encompasses 7 years of glibc development, from version 2. 5 in 2006 to 2. 18 in 2013, and a much longer range of Linux distributions. For instance, our study gives an idea of reproducibility issues that will arise when upgrading platforms to the recently-released CentOS 7 distribution, which is based on glibc 2. 17. The range of operating systems tested in this study remains, of course, limited. We expect that comparing intermediate glibc versions would only reduce the magnitude of the reported effects.
Other Linux distributions, for instance Debian and Ubuntu, are very likely to suffer the same reproducibility issues as long as they are based on glibc. Similar issues are also very likely to occur on non-Linux operating systems, see for instance differences observed between Mac OS 10. 5 and 10. 6 by  Gronenschild et al. (2012). Our study is limited to compiled application programs. Applications written with interpreted languages such as MATLAB and Python would most likely behave differently. Compilation options were also not considered in this study and are likely to impact the reproducibility of results. For instance, the gcc C compiler has several options that speed-up floating-point operations at the cost of numerical correctness. Using such options to compile programs that are sensitive to small numerical differences is very likely to compromise inter-OS reproducibility, too.
Some of the differences observed between Freesurfer builds are likely to originate from the use of different versions of gcc to compile these builds. Gronenschild et al. (2012)  report the effects of Freesurfer version, workstation type, and OS version on anatomical volume and cortical thickness measurements. Their study was conducted with different versions of Freesurfer (4. 3. 1, 4. 5. 0, and 5. 0. 0). We deliberately chose not to compare different versions of the tested pipelines. Instead, we focused on differences that originate in the system libraries. The Freesurfer versions used by  Gronenschild et al. (2012)  were dynamically linked (version 5. 0. 0 was linked statically on Linux, but dynamically on Mac), while the current one (5. 3) is statically linked. Thus, the difference reported by  Gronenschild et al.
Krefting et al. (2011)  studied the reproducibility of Freesurfer 5. 0. 0 on Mac OS 10. 6, CentOS 4, and SUSE Linux 10. 1. They report that the CentOS 5 and CentOS 4 Freesurfer builds gave identical results, but that results obtained with the same build were different across operating systems. This seems in contradiction with our results (we found that different Freesurfer builds give different results). A possible explanation for these differences is that the authors used a dynamically-linked version of Freesurfer 5. 0. 0, as suggested when they report that different implementations of dynamically linked libraries may explain their findings.
Acknowledgments We thank  Fabrice Bellet  for his rigorous administration of the cluster at Creatis used in this work (cluster B). We also thank  Compute Canada  and  Calcul 13 Québec 14  for providing the infrastructure (cluster A) to perform the experiments presented in this paper. This work is in the scope of the  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the program " Investissements dAvenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). This paper also acknowledges the support of the  National Science Foundation  under grant # ACI-1148515. Data collection and sharing for the fMRI part of this study was funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ) and  DOD ADNI (Department of Defense  award number  W81XWH-12-2-0012 ).