dataesr/scientific-paragraphs-categorization huggingface dataset:
ChIP DNA QC was performed using BioAnalyzer and using standard protocols sequenced using SOLiD and IonProton platforms, keeping the minimum read size >35 bases (mean read lengths 46-52 bases). ChIP were performed in 5 replicates, pooled and sequenced in triplicates. Raw sequence data was mapped to human genome (hg19) using LifeScope 2. 5 software (Life Technologies) following manufacturer's best practice guidelines and maximum stringency for unique mapping. Only uniquely mapped reads were retained in the resulting BAM file. Peak calling was done using MACS software at default settings for statistical significance (p value 1E-05) using UPPMAX at Uppsala University Next Generation Sequencing Analysis and Data Storage facility. The peaks thus obtained were analyzed using various bioinformatics tools as necessary.
For discriminative motif searches, where one set of sequences was used as a negative data set, a background Markov Model file was generated for the negative file. The distribution of reads in peaks was calculated in bins of 200 bps (¡500 to ¡300 till C300 to C500) and the results were plotted with center of bins as locations from Alu start site. Intersections of different genomic coordinates for the purpose was done using BEDTools. 56 For signal mapping, footprints of the CGGBP1 binding around the peaks were created using the SICTIN software and custom R-scripts in 150 bp segments ranging from ¡1kb to C1kb from Alu start and K-means clustering was performed to find out the major patterns of read pileup in and around peaks using a previously reported method. 43 he ChIP seq data is uploaded at NCBI GEO (ID: GSE53571, http://www.
Acknowledgments We acknowledge the freely available software tools MEME Suit, PRALINE, ELM, DAVID and GALAXY bioinformatics tool cluster. Uppsala Genome Center  is acknowledged for ChIP sequencing. The computations were performed on resources provided by  SNIC  through  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX)  under project "b2013008. "BEA facility at  Karolinska Institute  is acknowledged for Affymetrix Microarray expression analysis.
0 arrays (Affymetrix, Sta. Clara, CA, USA) according to manufacturer's protocol. Affymetrix GeneChip Operating Software version 1. 4 (GCOS) was used to manage GeneChip array data and to automate the control of GeneChip fluidics stations (FS450) and scanner (GeneChip® Scanner 3000). Data from this experiment have been deposited in the National Center for Biotechnology Information (NCBI) and are available in the GEO DataSets site under accession number GSE57065.
Reverse transcription and quantitative PCR Total RNA was reverse transcribed into cDNA using the SuperScript III reverse transcription-polymerase chain reaction (PCR) system (Invitrogen Life Technologies, Carlsbad, CA, USA) according to the manufacturer's instructions. mRNA expression was quantified using quantitative real-time polymerase chain reaction. Briefly, PCR reactions were performed using a LightCycler instrument with the Fast-Start DNA Master SYBR Green I real-time PCR kit according to the manufacturer's instructions (Roche, Basel, Switzerland). Thermocycling was performed in a final volume of 20 μL containing 3 mM of MgCl 2 and 0. 5 μM each of the required primers. PCR was performed with an initial denaturation step of 10 min at 95°C followed by 40 cycles of a touchdown PCR protocol (10 s at 95°C, 10 s annealing at 68°C to 58°C, and 16 s extension at 72°C).
mRNA expression of genes was investigated using specific cDNA standards. The cDNA standard was prepared from purified PCR amplicons obtained for each candidate genes: TBX21 (FP: 5′-TGTGACCCAGATGATTGTGCT-3′, RP: 5′-AG CTGAGTAATCTCGGCATTC-3′), GATA3 (FP: 5′-AAGCGAAGGCTGTCTGCAG C-3′, RP: 5′-GGGTCTGTTAATATTGTGAAGC-3′), CX3CR1 (FP: 5′-AGTCTGA GCAGGACAGGGTG-3′, RP: 5′-GTCCCAAAGACCACGATGTCC-3′), HLA-DRA (FP: 5′-GCCTCTTCTCAAGCACTGGGA-3′, RP: 5′-CCACCAGACCCACAGTC AGG-3′), IRAK-3 (FP: 5′-CTCGGAATTTCTCTGCCAAG-3′, RP: 5′-GTGGGAGG ATCTTCAGCAAA-3′), and for the housekeeping gene peptidylpropyl isomerase B encoding for cyclophilin B; PPIB (FP: 5′-GGAGATGGCACAGGAGGAAAGA-3′, RP: 5′-GGGAGCCGTTGGTGTCTTTG-3′). The second derivative maximum method was used with the LightCycler software to automatically determine the crossing point for individual samples.
Gene lists were created using cutoff of FDR <0. 05 and twofold change. Hierarchical clustering was performed using the gene expression module from Partek. Euclidian distance method after normalization by shift mean columns to mean of zero and scale to standard deviation of 1 was used. Gene ontology, functional enrichment, and canonical pathways analyses were performed using Ingenuity Pathway Analysis (IPA)  [12, 21]  (www. ingenuity. com). Fisher's exact test was used to calculate the p value for determining the probability that each function or pathway assigned to the dataset was due to chance alone. The Human Genome U133 Plus 2. 0 array was used as the reference.
05. The 'p value' column represents the range of significance for the specific functions included in the high level categories described. The p value is a measure of the likelihood that the association between a set of genes in the dataset and a related function is due to random association. p values <0. 05 indicate a statistically significant, non-random association. The p value is calculated by the Fisher's exact test. The 'percentage in tGDE' column represents the percentage of genes found in a function compared to the total number of gene differentially expressed (n = 8,885). The 'percentage in function' column represents the percentage of genes found in a function compared to the total number of genes described by IPA to be associated with the function. CD247), MHC genes (CD74, HLA-DRA, and HLA-DRB1), or transcription factors (GATA3, NFATC2, NFATC3, STAT4, TBX21, and TP53).
Data for the comparison of septic shock patients versus healthy volunteers are represented at the onset of shock (H0) and 24 and 48 h after (H24, H48). (B) Highlight on the 52 genes that have expression direction consistent with decreases in activation of mononuclear leukocytes process. functions remained quite stable in the SAPSII-high patients group (from 13 at H0 to 10 at H48) and the number of inhibited functions increased over time (50 at H0 to 70 at H48). The same functions were enriched in both groups reflecting that the overall changes (direction of the responses) in gene expression at H0 between the two severity groups was very similar (Figure  5C ). For example, cellular and metabolic functions (e. g. , cellular development or hematological systems) were found significantly enriched and mostly inhibited in both groups.
First, our healthy volunteers' cohort was not age/sex matched with patients (5 men and 20 women in healthy volunteers group). In our opinion, this is not a major issue as important results were mostly obtained from comparison between patients regarding kinetics approach. Second, as the SAPSII-high group tends to be older, the delayed and impaired immune response might have been related to age rather than severity. An additional analysis using ANOVA indicated that the modulation of gene expression was not related to age of patients (data not shown). Third, a reduced number of patients was enrolled and despite a high level of severity (median SAPSII = 45; median SOFA = 10), a low mortality rate was observed. We were therefore not able to study gene expression according to prognosis. Fourth, gene expression analyses were performed in whole blood samples that do not reveal the entire genomic dysfunctions leading to organ failure.
The AAV8RSM was also tested for sterility, endotoxin, and mycoplasma. Altogether, this international effort led to the generation of more than 4040 vials of an rAAV8RSM that is stored at ATCC (No. VR-1816) and is available to the community. Finally, to cross-validate the use of the AAVRSMs, we conducted a side-by-side comparison of rAAV2RSM versus rAAV8RSM in terms of vector genome titration and capsid content using non-serotype-dependent techniques. The data generated by four laboratories within the AAV8RSM Working Group were in agreement with the mean values reported by the community for the rAAV2RSM  (Lock et al. , 2010)  and the rAAV8RSM (data from this article).
Upon receipt, the RSM was evaluated by the 16 testing laboratories according to the posted protocols, and the data were recorded on the assay spread sheets provided, which contained the necessary calculations for titer determination. The raw data that emerged from the testing laboratories for the three quantitative titer assays (Table  3 ) were statistically analyzed to determine true mean titer values and 95% confidence intervals. The distribution was first visualized as histograms (Supplementary Fig. S3 ), and it was noted that the data did not appear to be normally distributed. Since valid estimation of the mean and confidence interval relies on an underlying normal distribution, it was clear that some form of transformation was warranted. Common statistical transformation methods employed are square root, natural log, and log base 10.
An assumption we made when calculating the mean values and confidence intervals is that each test result is independent of another; however, this assumption does not take into account that all institutions submitted duplicate (and in some cases triplicate) test results for the assays. To assess the degree of correlation between samples, Pearson coefficients were determined for the first two duplicate samples per institution, and the coefficients obtained were 0. 71 for vector particle titer, 0. 96 for vector genome titer, and 0. 97 for infectious units titer. These results indicated that there was a significant correlation within institutions and that the assumption that each result is independent was violated. To account for this correlation, the transformed data were modeled, using a linear random effect modeling approach  (Littell et al.
, 2001; Weiss et al. , 2009). In each case, the rAAV8RSM was analyzed alongside an internal laboratory-standard AAV vector. The consensus data from the 14 testing laboratories that carried out the purity test estimated that the rAAV8RSM was greater than 99% pure and confirmed that VP1, VP2, and VP3 co-migrated with the AAV capsid proteins of the internal vector standards (Fig. 2  and data not shown).
Comparison of rAAV2RSM versus rAAV8RSM Having an increasing repertoire of AAV serotypes entering the clinical arena is encouraging for the field of AAVmediated gene transfer. However, the characterization of the AAV preparations and the comparison of data between studies will become more complex without the appropriate use of common standards. As an example, here we have compared the vector genome titers and the capsid content of rAAV2RSM and rAAV8RSM using non-serotype-dependent techniques. This comparative study was performed in four institutions independently within the AAV8 RSM Working Group. The vector genome titer was measured by qPCR according to the SOPs written for the rAAV8RSM and using the primers and probe targeting the SV40 polyA. Of note, these experiments were performed before the collection of the data from the 16 laboratories and the statistical analysis shown in Table  4.
Quantification of total capsid content is critical for clinical trials, since immune responses are clearly dependent on the vector capsid  (Manno et al. , 2006; Nathwani et al. , 2011). The availability of ELISA methods and RSMs will help the field to better characterize vector preparations used in clinical trials. Even if an ELISA is not available for a specific serotype or chimeric AAV vector, it is possible to use other methods to measure total capsids, such as electronic microscopy (see Materials and Methods section: rAAVSRSM characterization assays)  (Grimm et al. , 1999) , optical density  (Sommer et al. , 2003) , or SDS-PAGE gel staining (Fig. 3 ). rAAV2RSM and rAAV8RSM could be used to evaluate total capsid load for those alternative AAV serotypes. A stability monitoring program that tracks physical and infectious titers over time is ongoing for the AAV2RSM generated in 2006, and data from several years have been collected (data not shown).
Acknowledgments This work was supported by the  Clinigene network of excellence (European Commission   FP6 ,  Clinigene ,  LSHB-CT-2006-018933 ). We thank  Odile Cohen-Haguenauer  for conceiving, writing, and submitting the  Clinigene European Network of Excellence. We also thank the support of several partners of the Clinigene AAV platform to the  AAV8RSM  project:  Robin Ali ,  Muriel Audit ,  George Dickson ,  Anne Douar ,  Amit Nathwani ,  Amos Panet ,  Thierry Vandendriessche , and  Bas Blits. We are also indebted to  Liz Kerrigan  at  ATCC  for coordinating the distribution of materials to testing laboratories, and  Keith Carson  (ISBiotech) and  Denise Gavin  ( FDA ) for useful discussions and guidance. We acknowledge the generosity of members of the  AAV RSM Working Group  who participated in the production and characterization of the rAAV8RSM.
0. The reaction was stopped by dilution with 5 ml of ice-cold water. The reaction mixtures were filtered immediately through GF/C membranes (GE Healthcare) and the filters were washed with 10 ml of ice-cold water and transferred to scintillation fluid (Opti-Phase HiSafe II; Pharmacia LKB). Radioactivity was measured in a liquid scintillation spectrophotometer (Tri-Carb 2200 CA; Packard Instrument Co. ) equipped with a d. p. m. correction facility. For nonspecific adsorption of [ 14 C] lactic acid was added at time zero after the cold water. All experiments were repeated at least three times, and the data reported represents average values. Data obtained is represented as the mean 6 SD of triplicate measurements.
Microarray analysis Detailed protocols are described at http://www. transcriptome. ens. fr/sgdb/protocols/. The S. cerevisiae microarrays used are fully described in Array express (www. ebi. ac. uk/microarray-as/ aer/entry; accession number A-MEXP-337). The microarray experiments were conducted as previously described  [60]. Raw data were normalized using global lowess followed by print-tip median methods, with background removal, as implemented in Goulphar  [61]. Experiments were carried out 2 times, with dye swapping. The microarray data are available in Table  S2  and fully available at the GEO database (accession number: GSE60983). The statistical significance of the expression variations measured was addressed by using the TMEV version of SAM with a FDR of 5%, a S0 calculated by the Tusher method and using the exact number of permutation  [62] [63] [64].
Identification of NRPS/PKS pathways Genomic analysis identified 452 biosynthetic gene clusters including 190 NRPS, 162 PKS, and 100 hybrid gene clusters encoding both NRPS and PKS enzymes from 89 cyanobacteria out of the 126 genomes of the Cyano-GEBA dataset covering the diversity of the phylum  [1]  (Table  1 ). The PKS contain at least one ketosynthase domain and belong to type I modular systems, subdivided into cis-and transacyl transferase, and type I iterative PKSs, as well as type III PKSs  [21]. Furthermore, various mixed polyketide pathways were found such as type I/ type III PKSs. The NRPS contain either a typical NRPS with, minimally, adjacent condensation and adenylation domains or a NRPS-like lacking of condensation domain  [22, 23]. Hybrids contain PKS linked to NRPS modules, which results in the production of polyketide-peptide hybrid metabolites.
Thus, the initial 452 gene clusters grouped into 286 CFs. As for several examples of NRPS/PKS clusters, these CFs are potentially involved in biosynthesis pathways. However, some pathways may not be functional while others produce notorious compounds or even known compounds for which the biosynthesis has not yet been investigated. A rank-abundance curve describing the distribution of the CF among our dataset reveals that most of the CFs sharing highly similar gene content in synteny are spread only into 2 to 3 genomes, and one up to 25 strains (Additional file 2: Figure  S3 ). The long tail consisting of orphan CFs unique to each genome evidenced a high diversity of the NRPS/PKS gene clusters in Cyanobacteria and a largest one to discover with function of further sequencing effort. The 225 orphan CFs are distinct from the shared CFs by their size as they are limited to their single version of NRPS/PKS genes without any tailoring enzyme.
The other half of the gene clusters were orphans, each forming a putative family by its own. subsequent losses. On the other hand, all ten CFs of Crocosphaera watsonni WH0003, the six CFs of Coleofasciculus chthonoplastes PCC 7420 or the only CF of Acaryochloris marina MBIC11017 located on one of its plasmids were present only in those genomes of the analysed dataset. Therefore, we performed a Hierarchical Clustering analysis of the 286 pathways (Additional file 3), which indicated a species clustering not coherent with the species phylogeny (Figure  1 ), and thus confirms that the NRPS/PKS gene clusters widely spread in this dataset are likely not vertically inherited as a whole in the phylum. BLAST analyses of the proteins of the 452 clusters against the non-redundant database of NCBI indicated that the closest homologs are always found within the Cyanobacteria.
Evolution of the pathways The impact of HGT on the evolution of these pathways at the phylum level is difficult to estimate due to the complexity of our dataset. Dinucleotide signature analysis of the clusters distinguished 132 clusters with atypical genomic signature (δ*-differences ≥55  [30] , indicated in the lowest pattern of Figure  4 , Additional file 1: Table  S1 ). Among them, 10 are particularly biased (δ*-differences ≥90  [30] ), suggesting an external acquisition from a distantly related organism, notably CF-3 in Synechococcus sp. WH8016, CF-8 in Fischerella sp. PCC 9339, CF-10 in Cylindrospermopsis raciborskii CS-505, CF-20 in WH5701 and 6 orphan clusters in diverse (See figure on previous page. ) Figure  1  Distribution of shared and orphan NRPS/PKS gene clusters detected in Cyanobacteria.
While the phylogeny of C domains found in our dataset (Additional file 2: Figure  S4 ) showed clustering into previously described C-domain subtypes, the phylogenetic analysis of KS domains revealed the supported clustering of all KS involved in PUFA and enediyne biosynthesis (Additional file 5; see below). A close examination of the clusters composing our CFs highlights different evolutionary scenarios. The clusters of CF-9 (anatoxin-a), CF-20 or CF-32 showed a conservation of gene order and content (Figure  2 , Additional file 2: Figure  S5 ). On the contrary in CF-5, CF-26, and CF-39 (Figure  5 , Additional file 2: Figure  S2 , S5), the clusters went through more complex evolution schemes involving gene duplication, indels and/or inversions as well as domain deletion/substitution. The KS and C domains phylogenies of CF-39 showed strong synteny conservation in filamentous cyanobacteria (Oscillatoria spp.
Further roles for secondary metabolites in Cyanobacteria Most secondary metabolite pathways are not linked to the cyanobacterial lifestyle in their environments e. g. marine vs fresh water, but some obviously benefit to the harbouring organisms to cope with their habitats. In our dataset, two examples covering several cyanobacterial genomes illustrate such benefice. The first example concerns the PKS PUFA gathered in CF-1 which are involved in the production of heterocyst glycolipids as the last maturation step of the heterocyst rendering it impermeable to oxygen produced by adjacent cells, and thus, able to fix atmospheric nitrogen  [31, 32]. The evolutionary data of the PKS PUFA within Cyanobacteria clearly supported their vertical inheritance, as the KS PUFA were forming a supported and large monophyletic clade with the enedyine KS in the KS phylogenetic tree (Additional file 5).
Discussion Cyanobacteria are a prolific source of natural products, many of which have complex chemical structures  [4, 6, 40]. The large-scale genomic analysis at the phylum-level presented here shows an impressive genetic diversity underlying known and cryptic cyanobacterial metabolism. Indeed, the 286 distinct NRPS/PKS gene cluster families found in this dataset will increase as other already known pathways described in previous works on Cyanobacteria are not represented in the present study. Often highlighted through human health hazard point of view, the clusters encoding major toxins, protease inhibitors and other known bioactive compounds are not predominant in these pathways. Most of the CFs seems to be spread in various groups throughout the phylum, but closely related cyanobacteria shared also similar CF patterns  [41, 42].
In our dataset, only three PKS pathways were more largely disseminated within a given group of cyanobacteria (CF-20 in picocyanobacteria of clade d, CF-8 in unicellular, baeocystous unicellular and one filamentous of the clade g, and CF-1 in heterocystous of clade h) coherent with a vertical inheritance and some subsequent losses, and presumably giving them a certain benefit. However, the diversification and the large distribution of the NRPS/PKS gene clusters are inconsistent with global vertical inheritance followed by repeated losses in the current lineages as observed for some of the toxins  [15]. Different mechanisms impacted the evolution of these gene clusters in Cyanobacteria shown by the CF-1 and CF-8 largely distributed in a given clade but also spread discretely in other clades.
The ecological function of secondary metabolites is unclear despite continual expression of some clusters, which appear more involved in the physiology of the producing organism  [46]. In this dataset also, we evidenced NRPS/ PKS gene clusters that might have alternative and adaptive functions notably in heterocystous cyanobacteria to boost their primary metabolism with specific PKS PUFA pathways dedicated to heterocyst glycolipids. Indeed, as the CF-1 and CF-3 clusters are present in Gloeobacter violaceaus PCC 7421 at the root of the cyanobacterial phylum, and the KS of these two CFs emerged the same way than in other bacteria, it is likely that these clusters predate the Cyanobacteria. CF-1 was subsequently kept in the heterocystous clade for the heterocyst maturation and the advantage of nitrogen fixation without inhibition of oxygen resulting from photosynthesis.
Dataset and strains The 126 cyanobacterial genomes of the CyanoGEBA dataset  [1]  were retrieved from public databases (Additional file 1: Table  S4 ). All genomes were reannotated using the MicroScope platform  [56]  with the exception of the genomes of Nostoc azollae 0708, Crocosphaera watsonii WH0003 and Arthrospira platensis Paraca, which were too fragmented. The PCC strains used in this study are available at Pasteur Culture collection of Cyanobacteria (http://cyanobacteria. web. pasteur. fr).
Detection of genes coding for natural products Natural product biosynthesis gene clusters were identified using a combination of the genome mining softwares met2db  [57] , antiSMASH  [58] , and NaPDoS  [59]. Adenylation domain substrate specificity predictions for NRPS enzymes were made using NRPSpreditor2  [60]. Annotations were refined manually using CD-search  [61] , BLASTP  [62]  and InterProScan  [63]  to identify conserved domains. We estimated the number of gene clusters for each genome using the three methods. The NRPS modules encoded in typical modular NRPS gene clusters contained at least adjacent condensation (C) and adenylation (A) domains, and the NRPS-like clusters which lack the C domain were encompassed with the NRPS clusters as they were proved to actively produce secondary metabolite without a proper C domain  [22].