dataesr/scientific-paragraphs-categorization huggingface dataset:
Submission to the MetaboLights repository for metabolomics data currently places the burden of reporting instrument and acquisition parameters in ISA-Tab format on users, who have to do it manually, a process that is time consuming and prone to user input error. Since the large majority of these parameters are embedded in instrument raw data files, an opportunity exists to capture this metadata more accurately. Here we report a set of Python packages that can automatically generate ISA-Tab metadata file stubs from raw XML metabolomics data files. The parsing packages are separated into mzML2ISA (encompassing mzML and imzML formats) and nmrML2ISA (nmrML format only). Overall, the use of mzML2ISA & nmrML2ISA reduces the time needed to capture metadata substantially (capturing 90% of metadata on assay and sample levels), is much less prone to user input errors, improves compliance with minimum information reporting guidelines and facilitates more finely grained data exploration and querying of datasets.
Introduction MetaboLights, a database of experimental and derived metabolomics data  (Kale et al. , 2016) , currently requires studies to be submitted in the ISA-Tab file format  (Sansone et al. , 2008) , a hierarchical structure consisting of three components (Investigation, Study and Assay). ISA-Tab allows experimentalists to describe and record metadata in a simple format that aids reproducibility and shareability of experimental data and results. In addition to being a requirement for MetaboLights, the format is used for data centric journals (GigaScience, Scientific Data) and can facilitate data analysis  (Gonz alez-Beltr an et al. , 2014; Lekschas and Gehlenborg, 2016). Data acquisition within a metabolomics experiment involves a wide range of instrument and data pre-processing parameters.
Monitoring and recording those parameters is essential to maximize the reproducibility of experimental data. Currently MetaboLights utilizes the ISA software suite  (Rocca-Serra et al. , 2010) , particularly ISAcreator, to capture this information in ISA-Tab syntactic elements. Manual entry of >40 potential parameters and associated ontology references for MetaboLights submission can be a laborious, time-consuming and error-prone process. However, the majority of this instrument metadata exists already within the instrument vendor data files and corresponding open source data formats. The use of freely available open source data formats for both mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy is advantageous for software development as it allows a single route to access instrument data and allows metadata to be standardized between vendors.
The open source MS file format mzML  (Martens et al. , 2011)  was created in 2008 as part of the Human Proteome Organization (HUPO) Proteomics Standards Initiative working group for mass spectrometry (PSI-MS). The mzML format amalgamated many of the benefits of the older MS formats (mzData  (Orchard et al. , 2004) ) and mzXML  (Pedrioli et al. , 2004)  while adding new features. Importantly, the mzML format has a MS controlled vocabulary and is easily extendable  (Martens et al. , 2011). The imzML format is a relatively recent extension designed specifically for imaging MS  (Schramm et al. , 2012). The nmrML file format is a newly developed file format for NMR data, while not an extension of mzML, it is built around the same principles (http://nmrml. org/). Through the use of python packages, we automated both the extraction of the instrument metadata and the subsequent population of an ISA-Tab structure.
ISA-Tab The ISA-Tab structure consists of three file types. The Investigation file describes the overall objectives of a project as well as defining factors, protocols and parameters. The Study files describe the subjects studied and their characteristics and sampling methods. For every Study file there are one or more associated Assay files describing a measurement (e. g. metabolic profiling) and the technique (e. g. NMR). The instrument metadata extracted from either mzML, imzML or nmrML files are predominately used to automatically populate fields within the Assay file(s).
Implementation and availability The mzML2ISA & nmrML2ISA software can be used as an application programming interface (API), via the command line interface (CLI) or via a graphical user interface (GUI). To allow integration into Galaxy, a popular workflow platform for metabolomics and other 'omic data analysis  (Afgan et al. , 2016) , the python packages have been wrapped into a Galaxy compatible tool format. Additionally, mzML2ISA & nmrML2ISA are available as tools within the PhenoMeNal Galaxy public instance and as Docker Containers as detailed in the supplementary materials. See Supplementary Table  S1  for full list of nmrML2ISA & mzML2ISA software and Supplementary section S2 for further implementation details. Instruction on how to use the tools and examples for common use cases can be found in the documentation http://2isa.
Workflow Due to the nature of the analytical technologies and structural differences between MS and NMR file formats, the software described here is divided into mzML2ISA (encompassing mzML and imzML formats) and nmrML2ISA (nmrML format only). However, the same workflow applies for both technologies (see Fig. 1 ). From a user supplied collection of XML based data files and some experimental information (minimally a study identifier), the metadata are extracted from the XML data files. The Python Pronto package (https://pypi. python. org/pypi/pronto) is used to extract parameters referring to their accession number within either the HUPO PSI-MS ontology  (Mayer et al. , 2013) , imagingMS ontology  (Schramm et al. , 2012)  or nmrCV (http://nmrml. org/). Some instrument metadata is not available within the XML file format (e.
g. chromatography parameters); such meta data can be added by the user manually. Additionally, study design or wet lab experimental metadata (e. g. sample preparation details) can also be added by the user manually. The GUIs developed provide the easiest route for users who wish to provide additional metadata at this stage. The metadata is then converted into the ISA-Tab file format where a large number of fields are automatically populated. Depending on the level of additional metadata already provided by the user, the ISA-Tab files may require review and expansion to meet annotation requirements  (Fiehn et al. , 2007; Sansone et al. , 2007). These remaining fields can be manually added through the ISAcreator tool. The metadata generated from the XML file parsing is stored as a Python dictionary that can be rendered in JSON (JavaScript Object Notation), making it accessible to other software tools and analysis independent of ISA-Tab generation.
The remaining fields are then populated manually using the standard ISAcreator software. 7) The completed ISA-Tab structure can then be submitted to MetaboLights. 8) Additionally, the parsing components of mzML2ISA & nmrML2ISA can be used as standalone Python packages to extract metadata as either a python dictionary or JSON for integration in other analysis pipelines See Supplementary section S2 for full details of all MetaboLights studies used for assessing software in this paper.
Extracted experimental metadata Up to 23, 42 and 46 instrument metadata terms for mzML, imzML and nmrML respectively are automatically extracted and parsed into the assay component of the ISA-Tab structure. Extracted terms for the XML files include generic instrument descriptors (e. g. the instrument name, manufacturer and software), data transformation descriptors (e. g. file conversion details) and platform specific descriptors (e. g. mass analyzer, detector and m/z range for MS derived XML files). Where possible, extracted terms from each file format should be found within a relevant controlled vocabulary. Full details of the extracted terms can be found at http://2isa. readthedocs. io/en/latest/ (see the extracted terms sections).
MetaboLights case studies The mzML2ISA & nmrML2ISA packages have parsed and created ISA-Tab structures for all MetaboLights studies that have associated mzML, imzML, nmrML files. The resulting ISA-Tab structures have then been successfully validated using the ISAvalidator (https:// github. com/ISA-tools/ISAvalidator-ISAconverter-BIImanager) tool. See Supplementary Table  S2  for details of the 21 studies tested. Using sample sets of 50 XML files (either mzML, imzML or nmrML) derived from MetaboLights, XML to ISA-Tab conversion is completed less than 45 seconds. The exact time will vary depending on file type and size. See Supplementary Table  S3  for details.
Discussion The mzML, imzML and nmrML data file formats used in metabolomics provide a parameter rich, technical layer of metadata that we have exploited here to both improve the reliability of MetaboLights submissions and increase the ease and speed of submission. Additionally, the generated ISA-Tab structures can be used with subsequent downstream analysis with software such as Risa (Gonz alez-Beltr an et al. , 2014) and the Refinery Platform  (Lekschas and Gehlenborg, 2016). However, to reach core information for metabolomics reporting compliance (CIMR) (BioSharing: Bsg-s000175), derived from the Metabolomics Standards Initiative  (Fiehn et al. , 2007; Sansone et al. , 2007) , a range of distinct descriptors still have to be reported into the ISA-Tab structure. Typically, these are experimental design key descriptors (predictor variables, replicate information, study group information), subject to sample relationships, as well as biological sample characteristics, and sample separation procedures if relevant (e.
g chromatography). The development of the ISA-API (https://github. com/ISA-tools/isa-api) will further facilitate the automation of these missing descriptors, especially regarding experimental design, and aid in reducing the barrier and time required to submit metabolomics data to repositories. Indeed, instrument vendor raw files may harbour many more parameters that are often absent in the open source equivalent files. Metadata extraction and integration into an open source file format can however be an involved process to implement for each instrument vendor API. Additionally, licensing issues can limit full exploitation of the vendor raw files. Further work that brings together the metabolomics and proteomics community, along with the instrument vendors, could help identify and integrate useful but missing parameters into the appropriate open source format.
Regulation by miRNAs represents a powerful mechanism to accurately fine-tune gene expression of several a priori distinct biological processes. The growing interest for miRNAs has led to the creation of a specialized database, miRBase  [9] , which current release (version 21) gathers information on 35 828 mature miRNA products across 223 species. This database has been instrumental to disseminate useful information about miRNAs. miRNA genomic clusters are often cotranscribed as related miRNAs  [10, 11]  which share the same seed region and/or belong to functionally related miRNA families  [12, 13]. A typical example is given by the miR-34/449 superfamily which exhibits extensive genomic redundancy by encompassing six homologous miRNAs (miR-34a, miR-34b/c, and miR-449a/b/c) located on three distinct genomic loci  [14] [15] [16] [17] [18] [19].
miRNA sequences may undergo several modifications such as uridylation, adenylation, RNA editing, or methylation that affect their properties and functions  [21]. Recent advances in high throughput sequencing technologies have also refined the definition of miRNA variants, named 5 0 -or 3 0 -isomiRs, which differ from the canonical miRNAs by one or more bases at the 3 0and/or 5 0 -end. Seldom, the sequence reported in miR-Base accounts for a very small fraction of a given miRNA  [22] [23] [24] [25] [26] [27] [28] [29] [30]. Although isomiRs have been reported in many datasets, their biological relevance and contribution to the functional repertoire of a specific miRNA gene remain controversial  [29, 31, 32, 33, 34]. Noisy measurements can hardly account for the high frequency of some isomiRs which are not randomly distributed  [35] , suggesting these isomiRs have a regulated production and a biological relevance.
RNA and small RNA libraries were generated using the NEB next small library prep set for SOLiD (New England Biolabs, Ipswich, MA, USA) and sequenced on the Applied Biosystems SOLiD 5500 wildfire system following the manufacturer's instructions. All data generated from RNA sequencing were stored on microarray and next generation sequencing information system Mediante  [36]  and submitted to GEO repository under super series GSE77252 gathering GSE90764 series for small RNA sequencing of undifferentiated and fully differentiated HAECs (n = 3 donors), GSE77249 series for small RNA sequencing of synthetic microRNA transfection, and GSE77251 for transcriptome analysis in HAECs overexpressing canonical miR-34abc/449abc, 5 0 -iso-miR-34b, or 5 0 -isomiR-449c.
Data analysis SOLiD data were analyzed with lifescope v2. 5. 1, using the small RNA pipeline for miRNA libraries and whole transcriptome pipeline for RNA-seq libraries with default parameters. Annotation files used for production of raw count tables correspond to miRBase v18 for small RNAs and Refseq Gene model v20130707. The abundance of isomiRs was quantified using custom java program. For each miRNA from miRBase v18, we extracted the number of reads starting at the exact miR-Base position versus the number of reads starting at position À2, À1,+1, and +2 from the miRBase start position, then we computed for each miRNA the percentage of reads mapping to each isomiR. Data generated from RNA sequencing were analyzed with Bioconductor (http://www. bioconductor. org). Differential expression analyses were performed using the EDGER package V 3.
14. 0 (Bioconductor). Briefly, dispersion was estimated using the parameters method = 'deviance' and robust = TRUE, and statistical significance was assessed using exact tests. P-values were adjusted for multiple testing using the Benjamini and Hochberg method which controls the false discovery rate (FDR)  [37]. Thousand one hundred and ninety-six genes down-regulated in at least one condition compared to control were selected based on an average expression level log 2 CPM (counts per million) > 3 and a log 2 FC (fold change) ≤ À0. 9 in at least one condition. Log 2 CPM data were centered and scaled per gene. An unsupervised hierarchical clustering was performed with the R package pheatmap using Pearson's correlation distance and complete linkage. The relative enrichment of sequences in the 3 0 -UTR in gene lists from GSE77251 and ranked by increasing fold change was identified using SYLAMER  [38]  version 08-123; online available at www.
genomique. info/sylamer. 'MicroCible' is a bioinformatics tool developed in-house to predict miRNA targets (http://www. genomique. info: 8080/merge/index?action=MICIB,  [39] ) by scanning transcripts sequences for the presence of 'miRNA seed' complementary sequence. This search can be performed for different 'seed' match type, a minimal free energy binding cutoff (deltaG≤ À20 was used for Fig. 2B, C ), and the location of the potential targeting site (i. e. 3 0 UTR or entire transcript).
FUCCI-A549 cells were transfected 24 h after seeding with either miR-Neg, canonical miR-34/449, 5 0 -isomiR-34b, or 5 0 -iso-miR-449c at 10 nM using Lipofectamine RNAi Max Reagent (Invitrogen) in OPTIMEM (Invitrogen) according to the manufacturer's instructions. Cellular fluorescence was then assessed 72 h after transfection by microscopy (LSM 780, Zeiss). Using IMAGEJ software, fluorescent cells were counted positive for one color (corresponding to cell cycle phases) if the fluorescence reached a defined threshold.
Mature miRNAs and isomiRs target distinct transcripts Next, we explored the inhibitory effects of canonical miR-34/449 and their isomiR variants on several miR-34/449 targets, including ARHGAP1, ARHGDIB, R-Ras, and Notch1, which we previously described to be involved in MCC differentiation  [15, 19, 47]. After examining their expression level in several RNA sequencing datasets (Fig  2 ), these targets were validated using quantitative RT-PCR, western blot, and luciferase reporter assay in primary HAEC cultures, A549 cells, and/or HEK293 cells (Figs  3 4 ). Figure  3  shows that canonical miR-34b and miR-449c induced similar effects on these miR-34/449 targets compared to other canonical miR-34/449 members. On the opposite, 5 0 -isomiR-34b and 5 0 -isomiR-449c, i. e. the sequences referenced in miRBase, exhibited much weaker effects on targets related to small GTPase pathway (ARHGAP1, ARHGDIB, and R-Ras)  Pearson's correlation distance was used both for genes and samples, and pairwise average-linkage was used as clustering method.
(B) Predicted target genes either for canonical miR-34b, miR-449c, 5 0 -isomiR-34b, or 5 0 -isomiR-449c were selected (see Materials and Methods). Venn diagrams show the number of predicted gene targets for each canonical miRNA or isomiR that were down-regulated (log 2 FC≤ À0. 9 and log 2 CPM>3) following 48-h overexpression of canonical miR-34b (blue circle), miR-449c (magenta circle) versus 5 0 -isomiR-34b (red circle) or 5 0 -isomiR-449c (red circle), as indicated upon each circle. The intersection of two circles indicates the number of predicted targets commonly down-regulated in both conditions as indicated. (C) Individual miRNA mimics for miR-34b, miR-449c, 5 0 -isomiR-34b, or 5 0 -isomiR-449c were transfected (10 nM, 72 h) in proliferating HAECs to examine target recognition induced by either canonical miRs or isomiR variants using RNA sequencing data (GSE77251) analyzed with Sylamer  [38].
Acknowledgements This work was supported by  CNRS ,  Inserm , the  French Government (Agence Nationale de Recherche, ANR)  through the ' Investments for the Future '  LABEX SIGNALIFE  ( ANR-11-LABX-0028-01 ) and  FRANCE GENOMIQUE  ( ANR-10-INBS-09-03  and  ANR-10-INBS-09-02 ),  Canceropôle PACA , and by grants from  ANR  ( Asthmatreat ,  ANR-12-BSV1-0023-02 ),  Vaincre la Mucoviscidose ,  FRM  ( DEQ20130 326464 ), and Ligue contre le cancer. Data from this work are the object of a CNRS patent  PCT/FR2010/ 000539. Sequencing was performed by  UCA GenomiX Platform of the University Côte d'Azur.
Data analysis was performed using the ΔΔCt method  [10]  with normalization of the raw data to housekeeping genes. NormFinder  [11]  and geNorm  [12]  algorithms were used to determine the optimal normalization genes. A set of 4 invariant genes was used for each specific PCR array: BIRC2, CASP8, FADD and TP53BP2 for the Apoptosis array; MTA1, MTA2, PNN and RAF1 for the Cancer pathway array; LDLRAP1, OSBPL5, PMVK and SNX17 for the Lipoprotein signaling and cholesterol metabolism array; ADH5, ALDH, CES2 and SMARCAL1 for the Drug metabolism array and BTRC, CTBP1, EP300 and PPP2CA for the Wnt pathway array. Homogeneity of gene expression in the group of normal tissues was validated through splitting randomly the samples into two groups. Gene expression showed no significant difference in fold-changes between the two groups.
Data integration into biological networks To obtain biological information about this set of deregulated genes in CRC as compared to NT, we searched for direct (physical) and indirect (functionally associated with no direct interaction) associations between 111 genes identified by transcriptome analysis in the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database (version 10. 5)  [13]. Briefly, interactions in STRING are derived from multiple sources (experimental/biochemical experiments, curated databases, genomic context prediction, co-expression and automated text mining). Confidence level of edge, i. e. interaction between 2 nodes or protein/gene, is computed in a combined score (ranging from 0 to 1). Interactions and transcriptome data were imported and used for network generation in the Cytoscape environment (version 3.
[19] , but decreased in the study of Yu et al. [20]  and showed no change in TCGA study  [17]. GPX1 mRNA was increased in our study and in that of Yagublu et al. [21]  and a small increase in TCGA study  [17] , but decreased in that of Nalkiran et al. [22]. To confront our data to other available data sets, we used these same genes to test for the correct separation of NT and CRC using the colorectal carcinoma cohort (COADREAD) from The Cancer Genome Atlas (TCGA) a b Fig. 1  (See legend on next page. ) dataset (gdac. broadinstitute. org). We performed a Principal Component Analysis (PCA) using expression data for 47 genes selected in our study (fold-change >1. 7, retrieved in more than 75% of CRC samples). Similarly to our hierarchical clustering (Fig. 1b ), the PCA clearly distinguished two groups: NT and CRC, thereby validating the expression data from our sample (Fig.
2 ). Furthermore, based on expression of the same 47 genes, colon and rectum carcinoma were undistinguishable on PCA plots. A similar observation was made from PCA performed with other datasets (sets of deregulated genes related to functional Qiagen classification and all of the 111 deregulated genes identified with our samples) (Additional file 4). Finally, the colorectal tumors could not be separated according to the grading stage or the APC and KRAS mutational status of (Fig. 2 , Additional file 4), indicating that the deregulated gene expression of genes identified with our samples was independent of tumor grades and of APC and KRAS mutations. A high proportion of the deregulated genes found here have not been reported in CRC, although the very large majority had been described in other cancers, mostly solid cancers, at the mRNA and/or protein levels (Table  2 ).
In silico protein pathways analyses In view of the overall good correlation between mRNA and protein expression changes, we went on to build up presumptive protein-protein interaction maps. We used the STRING database and the Cytoscape environment to integrate interactions data into biological networks (See figure on previous page. ) Fig. 1  Colorectal carcinoma classification based on gene expression profiling. a Heat map analysis of genes deregulated in colorectal carcinoma (CRC) as compared to normal tissue (NT). Representation of the log 2 -transformed fold-change values from 111 cellular genes identified as significantly deregulated in colorectal carcinoma (CRC), compared to normal tissue (NT), with an absolute fold-change >1. 7 and a q-value <0. 05 (Student-t-test adjusted by FDR) using Heat Map analysis (median centered data performed in Cluster and TreeView)  [63].
The association of Lovastatin and Camptothecin essentially did not modify the response to Camptothecin, except for the NME1 gene that showed decreased expression in response to the combination of treatments. Finally, we searched for potential associations between the four drugs and deregulated gene products in CRC by interrogating the STITCH database  [29]. Interestingly, many links were identified with genes having a high degree or clustering coefficient, predicting a high impact on the network in the case of combination of Lovastatin and anticancer drugs (Additional file 10).
In view of the overall good correlation between mRNA and protein expression changes, we went on to build up putative protein-protein interaction maps, using the STRING interaction database. Looking at the individual PCR-based pathways, it appeared that almost all cancer proteins were densely connected. The MYC and the IL8 proteins were the most up-regulated proteins and the IGF1 and FGFR2 proteins the most down-regulated, in at least 75% of the CRC samples. These observations agreed with previous reports  [30] [31] [32] [33] [34] [35] [36]  and further emphasized the major roles played by these proteins in the control of CRC development. The SNCG protein was not interacting with any of the other proteins from the pathway, yet its down-regulation occurred in more than 75% of the CRC samples.