dataesr/scientific-paragraphs-categorization huggingface dataset:
AmoebaDB ID and Genbank ID refers to the accession number of the gene in AmoebaDB (http://amoebadb. org/amoeba/) and NCBI GenBank (http://www. ncbi. nlm. nih. gov/genbank/), respectively; iron deficiency refers to the fold changes of expression level in the iron deficiency condition as compared with the normal condition; Iron def + Hb refers to the fold-change of the expression level in iron deficiency following by hemoglobin supplementation as compared with the normal condition; low iron refers to the fold-change of expression level in low iron as compared with the normal condition; Please see methods for definition of iron deficiency, low iron, and normal iron condition. NM: non-modulated. Gene description AmoebaDB ID Genebank ID Iron deficiency Iron def + Hb Low iron Genes that are preferentially upregulated in iron deficiency Acyl-CoA synthetase EHI_153060 XM_651318 5.
Acknowledgments We thank  Profs. J. A. Hoffmann ,  J. Imler  and  D. Gubb  for critical reading of the manuscript. We are indebted to  Profs. T. Ip  and  S. Hirose  for the kind gift of The EMBO Journal Vol 33 | No 20 | 2014 ª 2014 The Authors The  EMBO Journal Akirin specifies NF-jB selectivity François Bonnay et al.
RNA-seq analyses The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus http://www. ncbi. nlm. nih. gov/geo/  [40]  and are accessible through GEO Series accession number GSE54173. Table  1  contains the read statistics for the raw reads coming off the sequencer. Detailed statistics on the quality of the reads were calculated with FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). The 50 base pairs raw sequences were aligned on the C. elegans ce10/W220 genome with TopHat  [41]  using the Ensembl annotations provided with the Illumina iGenomes. The htseq-count software (http://www-huber. embl. de/users/anders/HTSeq) was used to count the number of reads aligned to each gene. These counts were then normalized relative to the sequencing depth with DESeq  [42].
Acknowledgments We thank  Dr. Odile Neyret-Djossou  from the sequencing platform and  Alexis Blanchet-Cohen  from the bioinformatics platform at the  Institut de Recherche Clinique de Montréal (Québec, Canada ) for their valuable help. We thank  E. R. Paquet  from the  Centre de Recherche sur le Cancer de l'Université Laval  ( Quebec City ) for the transcription factor analyses. Nematode strains were provided by the  Caenorhabditis Genetics Center , which is funded by the  National Institute of Health National Center for Research Resources. This work was supported by the  Canadian Institutes of Health Research  to M. L. and  M. J. S. M. J. S. is a  Senior scholar  from the  Fonds de la Recherche du Québec-Santé. Received: 7 April 2014 Accepted: 15 October 2014 Published: 27 October 2014.
Data were deposited in Gene Expression Omnibus (Series GSE60764). For the second experiment, 50 ng total RNA was amplified and labeled with Cy3 using Low input Quick Amp labeling Kit (Agilent technologies, Santa Clara, California) according to manufacturer's instruction. Labeled cRNA was then fragmented and hybridized over 1 Sureprint G3 Mm9 8 × 60K array (Agilent technologies), allowing the study of 8 samples in parallel and interrogating over 40K annotated transcripts of Mm9 assembly. Hybridization was performed according to manufacturer's instructions. Raw fluorescence data were extracted using Genespring software v12 (Agilent technologies) and normalized using the 75th centile method in each array. To allow for an easy comparison between the two experiments, we then took for each gene the average expression value of all transcripts.
We removed from further analysis the genes for which median fluorescence was under 100 (NimbleGen data) in the first experiment, under 10 in the second experiment (Agilent data). Values were then log2 normalized and subtracted of the median value for each replicate. Data were deposited in Gene Expression Omnibus (Series GSE60770). To uncover FOXL2 targets in the first experiment, we conducted a paired Significance Analysis for Microarrays using the Multiexperiment Viewer program v4. 9 (http://www. tm4. org/mev. html). Each array treated with a control siRNA pool was paired with the corresponding array treated with a FOXL2 targeting siRNA pool, giving 12 independent pairs of arrays. Delta value was adjusted to the minimum value allowing a median FDR = 0. The differentially expressed genes were then sorted according to the average absolute difference between the control siRNA/control siRNA and control siRNA/FOXL2 siRNA conditions, and all genes above a threshold of 0.
The reads in fastq files were imported on Galaxy server (https://usegalaxy. org/). Reads were mapped on mm9 Canonical Female genome using Map Bowtie for Illumina tool with commonly used settings. Unmapped reads were then removed using the Filter SAM tool. Peak calling was performed using MACS tool using the following settings: Tag size: 50; Band width: 160 (first experiment) or 174 (second experiment); Mfold: 20; new peak detection method: yes; and other settings unchanged. Input DNA analyzed in parallel was used as a control for peak detection. Peaks with an FDR <0. 05 were kept for further analysis. De novo motif discovery was conducted using the MEME-chip pipeline on MEME server (http://meme. nbcr. net/meme/). Data were deposited in Gene Expression Omnibus (Series GSE60858).
Acknowledgements This work was supported by  Centre National de la Recherche Scientifique (CNRS) ,  La Ligue Nationale contre le Cancer  and l' Université Paris Diderot-Paris7. We thank  Claude Thermes ,  Delphine Naquin ,  Erwin van Dijck ,  Maud Silvain , and  Yan Jaszczyszyn  from the  CNRS ' IMAGIF platform for their most kindful help. Animal studies were conducted according to the guidelines of the  Animal Experimentation Ethical Committee Buffon  ( CEEA-40 ), project  CEB-25-2012. All high-throughput data are available at the Gene Expression Omnibus database and can be accessed through the SuperSeries  GSE60859.
Cloning of fut8 cDNA from Sf9 cells A partial cDNA sequence was obtained using a reverse transcription -polymerase chain reaction (RT-PCR) approach and degenerate primers (Table  S1 ). This 860-bp (base pairs) sequence was compared to all non-redundant coding sequences (cds) in the GenBank database using the BlastX program  [39]  and was identified as belonging to a fut8 gene, with a high identity score (51% aa identity with human FUT8). This sequence was then used to design new exact-match primers for 39-and 59-RACE. The full-length cDNA sequence of Sf9 fut8 gene is available under the accession number KC538901. Sequence analysis of this full-length cDNA sequence showed the presence of three in-frame ATG codons as potential initiation codons (Figure  1 ). Inspection of the sequences immediately flanking the initiation codon of genes expressed in Sf9 cells (n = 168)  [40]  allowed us to propose a consensus sequence (Table  1 ) for initiation of translation (most frequent for gene expression with high or low efficiency) that was in agreement with the start translation sites reported by Cavener and Ray  [41]  for invertebrates.
As intron position analysis relies on consistent aa alignments, FUT8 sequences were aligned using Clustal Omega. The high aa identity observed in FUT8 catalytic domain (from exon 4 {E4l} to exon 11 {E11l}) allowed us to compare the intron-exon structure in this region. In contrast, the N-terminal region showed a very low level of aa conservation (Figure  S1 ). As previously reported  [44] , when the insertion site was conserved, the intron phase was conserved as well. Several intron insertion sites were very well conserved (Table  4  and Table 6 ), for instance i3l, i5l, i7l and i9l in chordata, hemichordata (S. kowalevskii) and echinodermata (Strongylocentrotus purpuratus). Recombinant FLAG-tagged Sf9 FUT8 was produced using the baculovirus-Sf9 cell system and purified using anti-FLAG M2 affinity gel.
Analysis of the structure and evolution of the fut8 genes in arthropods showed that the high evolutionary rates found for instance in the coleoptera and diptera branches (see branch length differences. Molecular phylogenetic analysis using the Maximum Likelihood method. Evolutionary analyses were conducted in MEGA5  [67]  and the evolutionary history was inferred using the Maximum Likelihood method based on the Whelan and Goldman model  [72]. This analysis involved 92 FUT8 aa sequences and the final dataset contained 336 positions (42% of 787). The bootstrap consensus tree inferred from 1050 replicates is taken to represent the evolutionary history of the analyzed taxa. The percentage of trees (only those. 75%) in which the associated taxa clustered together is shown next to the branches  [73].
After incubation at 30uC for 4 hours, the reaction was stopped with 150 ml of water and samples were precipitated with 1 ml of 5% PTA and processed for scintillation counting. The transfer of [ 14 C]-fucose is expressed in cpm. In silico a1,6-FucT sequences retrieval and phylogenetic analysis Only eukaryotic core a1,6-FucT sequences were considered for this study. Homologous fut8 sequences were searched by querying all genomic and expressed sequence tag (EST) divisions of the National Center for Biotechnology Information (NCBI database, Washington, DC, USA), as described previously for sialyltransferases  [22, 64]  (Table  S2 ). DNA and aa sequences were analyzed using DNA Strider  [65]. PSORT II (http://expasy. org) was used for the prediction of protein localization sites in cells. Homology searches were performed using the BLAST program  [39].
of Allergy and Infectious Diseases to investigate host-pathogen interactions at a systems level. This program generated 47 transcriptomic and proteomic datasets from 30 studies that investigate in vivo and in vitro host responses to viral infections. Human pathogens in the Orthomyxoviridae and Coronaviridae families, especially pandemic H1N1 and avian H5N1 influenza A viruses and severe acute respiratory syndrome coronavirus (SARS-CoV), were investigated. Study validation was demonstrated via experimental quality control measures and meta-analysis of independent experiments performed under similar conditions. Primary assay results are archived at the GEO and PeptideAtlas public repositories, while processed statistical results together with standardized metadata are publically available at the Influenza Research Database (www.
Lipidomics analysis of mice infected with various influenza A virus strains of varying pathogenicity have also been reported  10. Similarly, the disease severity of SARS-CoV correlated with the dysregulation of the urokinase pathway and an increase in fibrinolysin pathway activity  7. An integrated network interrogation approach using both transcriptomic and proteomic data sets predicted a subset of key regulator molecules responding to SARS-CoV and influenza infections that could be used as host targets for therapeutic intervention  11, 12. Genetic modification of non-structural protein 16 in SARS-CoV showed enhanced susceptibility to type I and III interferon responses, making it a good vaccine strain candidate  13. To support the broad dissemination of research datasets from these SysBio centers and other supported programs, DMID established the Bioinformatics Resource Centers for Infectious Diseases (BRC) program to provide public database and analysis resources for the infectious disease research community.
A data dissemination working group, consisting of representatives from NIAID, the SysBio centers, and the BRCs was established to develop a data management and release approach that would maximize the reuse potential of the generated data. The working group leveraged existing public archiving strategies to store the primary assay results and devised metadata standards to represent the experiment descriptions and interpreted results in a consistent and standardized way. The final derived host response data and associated structured standardized metadata were made publically available at the BRC appropriate for the system under study. As the virus-centric BRCs, the Influenza Research Database (IRD, www. fludb. org)  14 and the Virus Pathogen Database and Analysis Resource (ViPR, www. viprbrc.
org)  15  host data produced by the Systems Influenza and Systems Virology centers. Here we describe 47 transcriptomic and proteomic datasets from 30 studies generated by these SysBio centers and made available through public resources. These studies characterized the host response to infection by members of the Orthomyxoviridae and Coronaviridae virus families, including pandemic influenza A H1N1 virus, highly pathogenic H5N1 avian influenza (HPAI) virus, severe acute respiratory syndrome coronavirus (SARS-CoV), and Middle East respiratory syndrome coronavirus (MERS-CoV); all of which are capable of causing severe respiratory infections and pose significant threats to humans on a global scale  4, 16. Host responses were evaluated in either in vivo (mouse) or in vitro (human cell line) model systems using three major experimental designs: longitudinal time-course, dose response, and genetic modification (involving both genetically manipulated viruses and hosts) comparisons.
The excess liquid was blotted onto a Kimwipe and the dry slides placed into a light tight slide box to be scanned at once. Dry slides were scanned on an Agilent DNA microarray scanner (model G2505B) using the XDR setting. Raw images were analyzed using the Agilent Feature Extraction software (version 9. 5. 3. 1) and the GE1-v5_95_Feb07 extraction protocol. (CA04M001, ICL004, ICL006, ICL010, ICL011, ICL012, IM001, IM002, IM004, IM005, IM006A, IM006B, IM007, IM009, IM010, ECL001, SCL005, SCL006, SHAE002, SHAE003, SHAE004, SM001, SM003, SM004, SM007, SM009, SM012, SM014, SM015, SM019, SM020).
Agilent microarray processing and expression analysis Normalization and QA/QC: Raw data consisted of the output from the Agilent Feature Extraction (AFE) software. Data was read into the R/Bioconductor Framework and visualization of raw intensity level was performed using boxplots, histograms, and correlation plots. All intensities were background corrected using the norm-exp method and quantile normalized using Agi4x44PreProcess and RMA Bioconductor packages. Distribution of control probes was examined and then those probes were removed. Descriptive statistics were reported for non-control replicate probes and the intensity distributions were examined via histograms. All non-control replicate probes were then summarized by taking the mean intensity measurement. To assess the quality of the reading for each probe, five Agilent QC flags were considered.
5| and an adjusted false discovery rate (FDR) P value of o0. 05 for a given time point. Software Utilized: Custom R Bioconductor workflow that includes the Agi4x44 Pre-Process and RMA Bioconductor packages  36  plus in house scripts for QA/QC visualization and differential expression analysis. (CA04M001, ICL004, ICL006, ICL010, ICL011, ICL012, IM001, IM002, IM004, IM005, IM006A, IM006B, IM007, IM009, IM010, ECL001, SCL005, SCL006, SHAE002, SHAE003, SHAE004, SM001, SM003, SM004, SM007, SM009, SM012, SM014, SM015, SM019, SM020). Positive-strand cDNA synthesis, fragmentation, and single-stranded cDNA for affymetrix microarray analysis cDNA was synthesized from isolated RNA (250 ng from each sample) using the Ambion WT kit (Life Technologies, Grand Island, NY, USA -http://tools. lifetechnologies.
Affymetrix microarray processing and expression analysis Probe intensities were measured using the Affymetrix GeneChip Scanner 3000 and processed into CEL files using the Affymetrix GeneChip Operating Software. Microarrays were normalized at the gene level using the BrainArray custom CDF (Entrez Gene, Version 14) for probeset definitions and RMA as implemented in the justRMA function of the Bioconductor package affy for background adjustment, quantile normalization, and summarization. Linearized data was imported into Genedata Analyst software (Genedata AG, Basel, Switzerland). ANOVA was performed on all samples (100 balanced permutations) and genes with a permutation Q-value of 0. 01 or less and fold change of at least 2 (between any two groups) were exported. (SBRI_AA_E1).
Reversed-phase capillary LC-MS-MS and LC-MS analyses Capillary LC-MS/MS analysis was used to generate separate accurate mass and time (AMT) tag databases for virus-infected cells and lung homogenates (see below). For this, dried peptide fractions from pooled samples were reconstituted in 30 μL of 25 mM ammonium bicarbonate, pH 7. 8, and analyzed using a 4-column custom-built capillary LC system coupled online to a linear ion trap mass spectrometer (LTQ; Thermo Scientific, San Jose, CA, USA) by way of an in-house manufactured electrospray ionization interface. Electrospray emitters were custom made using 150 μm outer diameter (o. d. ) x 20 μm inner diameter (i. d. ) chemically etched fused silica. Reversed-phase capillary columns were prepared by slurry packing 3-μm Jupiter C18 bonded particles (Phenomenex, Torrence, CA, USA) into a 75 μm x 65 cm fused silica capillary (Polymicro Technologies, Phoenix, AZ, USA) using 0.
A dynamic exclusion window of 1 min was used to discriminate against previously analyzed ions. The temperature of the heated capillary and the electrospray ionization (ESI) voltage were 200°C and 2. 2 kV, respectively. Following AMT tag database generation, capillary LC-MS analyses were performed on all virusinfected and mock-infected samples to generate quantitative data using the AMT tag approach. For this, individual dried peptide samples (see above) were reconstituted in 30 μL of 25 mM ammonium bicarbonate, pH 7. 8, and analyzed in either duplicate or triplicate and in random order using identical chromatographic and electrospray conditions as for capillary LC-MS/MS analyses. The LC system was interfaced to an Exactive Orbitrap mass spectrometer (Thermo Scientific), and the temperature of the heated capillary and the ESI voltage were 250°C and 2.
Development of the AMT tag database for virus-infected model systems A novel AMT database was generated for peptides within the specific model system, using mock-infected and virus-infected samples. To generate the AMT tag database, aliquots of the virus infected or mock-infected samples were combined to make the appropriate sample pools. Each pool was subjected to strong cation exchange fractionation as described above, and each fraction was analyzed by capillary LC-MS/MS. The SEQUEST analysis software was used to match the MS/MS fragmentation spectra with sequences from the appropriate UniProt/Swiss-Prot protein database. When searching, SEQUEST used a dynamic mass modification on methionine residues corresponding to oxidation (15. 9949 Da) and a static mass modification on cysteinyl residues to account for alkylation by iodoacetamide (57.
0215 Da). Peptides passing the following filter criteria were stored as AMT tags in a Microsoft SQL Server database: 1) SEQUEST DelCn2 value ≥ 0. 10 (normalized Xcorr difference between the top scoring peptide and the second highest scoring peptide in each MS/MS spectrum) and 2) for the mouse lung tissue database, a SEQUEST correlation score (Xcorr) ≥ 1. 6, 2. 4, and 3. 2 for fully tryptic peptides with 1+, 2+, and 3+ charge states, respectively, and Xcorr ≥ 4. 3, and 4. 7 for partially tryptic or non-tryptic protein terminal peptides with 2+, and 3+ charge states, respectively. While for the human cell line database, a SEQUEST correlation score (Xcorr) ≥ 2, 2. 6, and 3. 5 for fully tryptic peptides with 1+, 2+, and 3+ charge states, respectively, and Xcorr ≥ 2. 5, 3. 6, and 4. 1 for partially tryptic or non-tryptic protein terminal peptides with 1+, 2+, and 3+ charge states, respectively.
These filter criteria resulted in estimated peptide false discovery rates of o2% based on target-decoy database searches for both lung and cell samples. Nontryptic peptides were excluded, and a minimum peptide length of 6 amino acid residues was required. The elution times for these peptides were normalized to a range of 0 to 1 using a predictive peptide LC normalized elution time (NET) model and linear regression. A NET average and standard deviation are assigned to each identified peptide if the same peptide is observed in multiple analyses. Both calculated monoisotopic masses and observed NETs of identified peptides were included in the AMT tag database. From the primary data records corresponding to the data used to generate the AMT tag database (PASS00416 and PASS00417), the user can download the raw data, the assembled database, and parameter files necessary to recreate the AMT-tag approach.
Processing of quantitative LC-MS datasets Quantitative LC-MS datasets were processed using the PRISM Data Analysis system  37  , which is a series of software tools developed in-house (e. g. Decon2LS  38  and VIPER  39  ; freely available at http://ncrr. pnl. gov/ software/). Individual steps in this data processing approach are reviewed here  40. The peptide identities for detected features in each dataset (i. e. a single LC-MS analysis) were determined for features matched to AMT tags with high confidence based upon the accurate measured monoisotopic masses and NETs for each of the peptides in the filtered AMT tag databases within initial search tolerances of ± 6 ppm and ± 0. 025 NET for monoisotopic mass and elution time, respectively. The peptides identified from this matching process were retained as matrices for subsequent data analysis.
Proteomic data statistical processing The integrated LC-MS peak intensity values (i. e. abundances) for the final peptide identifications were processed in a series of steps using MatLab R2010b that included quality control, normalization, protein quantification, and comparative statistical analyses. Quality control processing was performed to identify and remove peptides with an insufficient amount of data across the set of samples  41  , and LC-MS runs that showed significant deviation from the standard behavior of all LC-MS analyses  42  , using a significance level of 0. 0001 (ref. 43). The peptide abundance values were normalized across the technical replicates using a rank invariant subset of peptides (p-value threshold of 0. 1) followed by median absolute deviation centering of the data.
Data Records Study designs for all experiments, including relevant repository identifiers, are summarized in Table  1  (Available online only), with individual RNA experiment samples detailed in Table  2  (Available online only) and individual protein experiment samples detailed in Table  3  (Available online only). Sample tracking from animal subjects to experiment samples is given in Table  4  (Available online only). Primary assay results (txt and mzXML files) are archived in public repositories as listed below (Data Record 1 and 2). Derived data resulting from processing and hypothesis testing accompanied by structured metadata describing both the experimental and data analysis methods are available at the Influenza Research Database and the Viral Pathogen Resource (Data Record 3).
Data Record 1 Primary data for Agilent and Affymetrix microarray experiments are available at the NCBI Gene Expression Omnibus (GEO, http://www. ncbi. nlm. nih. gov/geo/) under the accession numbers GSE37569 (Data Citation 1)  8  , GSE45042 (Data Citation 2)  5  , GSE28166 (Data Citation 3) 4,45 , GSE37571 (Data Citation 4), GSE43203 (Data Citation 5), GSE43204 (Data Citation 6), GSE33263 (Data Citation 7)  8, 45  , GSE36328 (Data Citation 8)  6  , GSE37572 (Data Citation 9) 8 , GSE43301 (Data Citation 10)  8  , GSE43302 (Data Citation 11)  8  , GSE44441 (Data Citation 12), GSE44445 (Data Citation 13)  8  , GSE40792 (Data Citation 14), GSE37245 (Data Citation 15) 9 , GSE33267 (Data Citation 16)  5, 12  , GSE37827 (Data Citation 17), GSE47960 (Data Citation 18)  11  , GSE47961 (Data Citation 19)  11  , GSE47962 (Data Citation 20)  11.