dataesr/scientific-paragraphs-categorization huggingface dataset:
This combined analysis confirmed that the largest phylogenetic cluster of non-sg1 clinical isolates comprised mainly of Sg6 ST187 and Sg6 ST68. In contrast to several other molecular types such Sg6 ST407 which had only been isolated over a period of 5 years, Sg6 ST187 and Sg6 ST68 strains seemed clonally stable, since isolates with identical sequence types have been obtained for 25 to 30 year periods of our repository (initiated in 1980). For the purposes of further analysis we selected strain Thunder Bay, Sudbury and Mississauga isolates as the representative ST187, ST68 and ST407, respectively. The high-prevalence of Sg6 among non-Sg1s was also confirmed through an inspection of the European Working Group for Legionella Infections (EWGLI) database (http://www. ewgli. org/) and has been reported independently elsewhere  [41].
The most Prevalent Molecular Types of Sg6 Strains Share a High Degree of Genomic Similarity To identify the factors that may be contributing to the population structure of L. pneumophila in clinical settings, we performed whole genome sequencing of Sg6 str. Thunder Bay. The L. pneumophila Sg6 str. Thunder Bay (accession number: CP003730) genome consists of a 3455167-bp chromosome with an average GC content of 38. 24% (Table  S1 ). A total of 3052 proteinencoding genes are predicted, 2299 (75. 3%) of which have been assigned a putative function. A BLAST map analysis of Thunder Bay against five reference Sg1 strains showed several conserved regions (Figure  4A ). Furthermore, high genomic strand asymmetry identified through the GC skew suggested no abnormalities in the assembly of the genome  [57]  (Figure  4A ).
To determine plasticity of the Sg6 genomes, we selected two additional strains from our clinical repository, Sg6 str. Sudbury (Accession number PRJNA170020) and Sg6 str. Mississauga (Accession number PRJNA170014), and sequenced their genome using Illumina technology (paired-end). The genomes were de novo assembled into 29 and 37 contigs, respectively. By including these strains in our analysis, we report genomic data on most sequence types that were identified as Sg6 in our database. The genome size of Sg6 str. Sudbury (3351893 bp) and Sg6 str. Mississauga (3384301 bp) were comparable to that of the Sg6 str. Thunder Bay. Reference assembly of raw Illumina reads to the finished Sg6 str. Thunder Bay genome also indicated that Sg6 str. Sudbury was more closely related to Sg6 str. Thunder Bay (,4800 SNPs) than Sg6 str.
This is also in agreement with a previous study revealing that Sg1 LPS gene cluster spaning lpp0827 (galE) to lpp0843 of L. pneumophila Sg1 str. Paris is highly divergent from those of Sg6, Sg10, Sg12, Sg13 and Sg14  [59]. Some other predicted proteins that were outside of this cluster included MreD (lpg0813) and LvrB (lpg1258). The role of LvrB has been characterized in several bacterial pathogens and has been shown to be essential in the formation of the Type IVA (lvh) secretion system  [60, 61, 62, 63]. MreD has also been described in several bacteria, and has been shown to be involved in controlling cell wall biosynthesis and bacterial morphology  [64, 65]. In our study, no morphological difference was observed between the Sg1 and Sg6 using differential interference contrast microscopy (data not shown).
In a reverse comparison, 581 Sg6 str. Thunder Bay proteins were defined as divergent compared to the Sg1 core. Amongst these only 41 were considered divergent to all L. pneumophila in the Genbank database (Table  1 , Figure  4C ). Twenty three of these Sg6-specific proteins were identified from the O-antigen biosynthetic cluster, and included proteins like Wzm (lp6_749), Wzt (lp6_750), GalE (lp6_758) and WecA (lp6_759), whose role in Oantigen biosynthesis is well described  [66, 67, 68, 69]. Notably a previous study failed to identify the presence of many of the genes coding for O -antigen biosynthesis proteins by DNA microarray in all non-Sg1 tested  [42]. Our results suggest that this may have been due to low sequence homology of these genes with their Sg1 orthologs. Interestingly, the Thunder Bay O-antigen cluster was fully conserved in the Sg6 str.
However, no effect was observed for Sg1. Sg6 and Sg1 viability was not affected by decomplemented (heat inactivated) NHS, consistent with a Sg6-specific killing by heat-sensitive complement. These results suggest that Sg6 O-antigen is unable to provide any appreciable resistance to serum complement, whereas Sg1 O-antigen may allow the bacteria to escape complement mediated killing by naive human serum. We next measured the sensitivity of Sg1 str. Philadelphia and Sg6 str. Thunder Bay to the classical complement pathway using sera obtained from patients diagnosed with either Sg1 or Sg6 infections (Figure  7A ). Presence of either Sg1 or Sg6 specific antibodies was previously confirmed through IFA (data not shown). The Sg1 str. Philadelphia isolate showed a 72. 5% decrease in CFU after a 60 min incubation in sera containing Sg1 antibodies, an effect that was not observed in decomplemented patient sera.
The DNA was allowed to relax overnight and its quality was checked via a Q-Card. The DNA was next applied to a special cover slip using a wide-bore pipette, and as a result of capillary action the DNA was stretched in channels. The DNA was stained with a fluorescent dye and in the presence of an antifade solution, and was visualized through a laser microscope. The DNA concentration was adjusted to achieve 20-30 DNA fragments/ frame, with a minimum size of 150 kB. The diluted DNA was applied to the M-card, and was treated with NheI for 30 min. The digested pieces of DNA were visualized via the Optical mapping array and fragment length was assigned by the onboard software (MapManager). These fragments were aligned to generate a circularized map with a minimum coverage of 65 X. Optical maps for reference strains were constructed in silico by providing genome sequences from public databases.
DNA Extraction, Sequencing and Genome Assembly The genome of L. pneumophila str. Thunder Bay was sequenced using a combination of Sanger, Roche-454 and Illumina sequencing platforms. All general aspects of Roche-454 library construction and sequencing were conducted according to the manufacturer's directions. Preparation of Illumina libraries and sequencing were conducted as previously described  [126]. Sequencing using the Roche-454 platform generated individual reads at a minimum 196 coverage, while Illumina paired-end reads were obtained at 996 coverage. Initial genome assembly of 454 reads via the Newbler software package (Roche) generated 36 large contigs. The order of the contigs was determined via optical mapping and gaps between these contigs were closed via primer walking to generate a single molecule.
Genome Annotation and Comparative Genomics The single chromosome of L. pneumophila str. Thunder Bay was annotated using a modified version of the GenDB platform as previously described  [127, 128]. In short, ORF prediction was done in GenDB using REGANOR  [129] , which uses CRITICA  [130]  and Glimmer  [131]  in addition to RBSFinder  [132]  to make ribosomal binding site and gene predictions. Ribosomal predictions are done using tRNA-Scan-SE  [133]  and RNAmmer  [134]. All intergenic regions were elongated with a 25 base pair overlap on each end and run through a pipeline for identification of potential frameshifts and short genes overlooked by GenDB regional prediction pipeline. Regions found with significant BLASTX  [135]  hits to EMBL database  [136]  were identified, added as new CDS regions in GenDB.
All coding regions were ran through the functional annotation pipeline by running a BLAST of each region against the following databases: PFAM  [137] , TIGRFAM  [138] , KEGG  [139] , NCBI's non-redundant protein database, TMHMM  [140] , and SignalP  [141]. Comparative proteomics was conducted by first comparing the L. pneumophila str. Thunder Bay translated genes against Sg1 core, which has previously been described  [58]. Predicted proteins of L. pneumophila str. Thunder Bay that exhibited greater than 65% sequence identity were defined as the conserved group. These were then compared against the entire public database of known L. pneumophila proteins, in order to establish a list of unique proteins of L. pneumophila str. Thunder Bay. Furthermore, a list of unique proteins of the Sg1 core was compiled, by including proteins which showed less than 65% homology to L.
In the experiments comparing serum resistance of several serogroups, a single incubation time of 60 min was selected. Similarly, experiments involving immune human serum (IHS) were carried out for 60 min. As controls, bacteria were also incubated with heat inactivated serum for the corresponding periods. Next bacteria were pelleted and washed 3 X with PBS, and CFU were determined by plating several serial dilutions on BCYE agar and incubated as before. These CFU values were compared to the inoculums and the data is reported as % survival.
PUBLISHED ASTHMA ASSOCIATIONS ARE ENRICHED WITH eQTLs We extracted published asthma associations from the GWAS catalog maintained by the National Human Genome Research Institute. As of January 15, 2013, 52 distinct reference SNPs in or near more than 40 genes have been reported to be associated with asthma (Table  A1 ). According to the eQTL database (described in Materials and Methods), 20 of these 52 SNPs (38. 5%) are eQTLs. Using the proxy SNP search tool SNAP  (Johnson et al. , 2008) , we then obtained an extended list of 506 SNPs that were either in the GWAS catalog or in strong linkage disequilibrium (LD) with the 52 SNPs (r 2 ≥ 0. 8). We called all these 506 SNPs the extended set of asthma-associated SNPs. We calculated an eQTL enrichment p-value  (Hosack et al. , 2003)  using the MAGICS data.
There are 300,821 SNPs that passed quality control in the MAGICS data. Among these SNPs, 29 SNPs are in the GWAS catalog, and 64 SNPs are among the 506 extended asthma-associated SNPs defined previously. To account for the LD between SNPs in the calculation of enrichment p-value, we conducted LD pruning with the r 2 threshold of 0. 8 on the 300,821 SNPs. This resulted in 251,826 SNPs and 38 of them are extended asthma-associated SNPs according to the GWAS catalog. According to the eQTL database, 22,922 SNPs (9. 1% of 251,826 SNPs) are eQTLs, and 13 asthma associated SNPs (34. 2% of 38 SNPs) are eQTLs. The corresponding enrichment p-value is 6. 78 × 10 -5 , suggesting the asthma associations are enriched with eQTLs in the MAGICS data. Note that other analyses considered all the SNPs rather than the pruned set of SNPs.
WEIGHTS USING eQTL INFORMATION We calculated two kinds of weights using eQTL information for the MAGICS data. All the 300,821 SNPs passing quality control were considered. First, we defined a SNP as an eQTL SNP if it was labeled as an eQTL in the eQTL database (see details in Materials and Methods). There are 31,781 cis eQTL SNPs (10. 6% of 300,821 SNPs) according to the definition, and for each of them, we retrieved an eQTL p-value p eQTL. Next, we considered two choices of weights, the general weight and the binary weight. The general weight is w g = -log 10 p eQTL for an eQTL SNP, and w g = 1 otherwise. The binary weight takes only two possible values, w b = 3. 70 for any eQTL SNP and w b = 0. 68 otherwise. The two values of the binary weight were chosen to maximize the minimum power while keeping at least 10.
Since only 10. 6% SNPs are eQTL SNPs according to the eQTL database, and hypotheses for eQTL SNPs are up-weighted, eQTLs generally have higher ranks after weighting, and non-eQTLs' ranks are lower but the magnitude of changes is small. This is true for both the general and binary weights. When restricting to the 29 asthma-associated SNPs reported in the GWAS catalog, we also observed similar behaviors, suggesting that weighting hypotheses may improve power using informative weights, and sacrifice a little power using uninformative weights  (Roeder and Wasserman, 2009). We then looked at the Q-Q plots of the original and weighted p-values. For p-values greater than 0. 0001, the Q-Q curves (Figure  3 ) are similar between the original and weighted p-values, regardless of the weights used. For those p-values less than 0.
REANALYSIS OF THE GABRIEL DATA As another application, we reanalyzed the GABRIEL data using the eQTL weighted approaches. Since only the p-values are necessary for the use of eQTL weighting, we took the p-values of the meta analysis of 37 studies that were calculated based on imputed data. In total, there are 2,473,850 SNPs and their p-values available in the GABRIEL study, which include 267,350 out of 268,204 eQTL SNPs in the eQTL database. The weights based on eQTL information were calculated in the similar way to the MAGICS data analysis. We applied Bonferroni and Holm's methods with an FWER level of 0. 05, as well as the BH procedure with an FDR level of 0. 05. An effective ratio of 0. 30  (Li et al. , 2012)  was used to calculate the effective number of SNPs (2,473,850 × 0. 30). After obtaining the lists of significant SNPs using different methods, we report any SNPs identified by eQTL weighting that are not in LD with any SNPs identified by unweighted methods using an r 2 threshold of 0.
In the reanalysis of the MAGICS data  (Moffatt et al. , 2007) , we applied recently developed statistical methods that can improve power by weighting hypothesis  (Genovese et al. , 2006; Roeder and Wasserman, 2009). Using eQTL information obtained from an independent dataset, we employed weighted procedures that up-weighted eQTL SNPs and down-weighted non-eQTL SNPs while controlling for the FWER or the FDR. It has been proved  (Genovese et al. , 2006)  that any set of nonnegative weights can guarantee substantial power gain given informative weights and little power loss for uninformative weights. The property implies that the weighted procedures are robust to informativeness of weights and to the uneven coverage of genes and expression targets on the genome. We took advantage of this robustness and applied the procedures to an asthma study.
We found additional SNPs that were significantly associated with  asthma according to the weighting hypothesis methods. Some of them were interesting after we accounted for LD and compared them to literature. Our analysis was the first application of this approach to asthma GWAS studies, and the results successfully illustrated the use of eQTL weighting in the context of asthma studies. As another application, we also reanalyzed the GABRIEL meta-analysis p-values and reported corresponding results. It is noted that the weighted procedures can utilize eQTL information from a reference database. Multiple choices of eQTL databases have already been made available (e. g. ,  Yang et al. , 2010; Liang et al. , 2013) , and future efforts may provide even better reference of eQTL information. For example, the eQTL information considered in our reanalysis was obtained through a single platform (Affymetrix HG-U133 Plus 2.
Although gene expression may have tissue-specific patterns, a substantial fraction of eQTLs may be shared across tissues  (Ding et al. , 2010). Hence eQTLs developed from tissues that are not directly relevant to the outcome of interest, such as those from publicly available eQTL databases based on LCL, can be used to improve power on GWAS. It is possible that using eQTL information from relevant tissues may result in even more power gain, if such information is available. Besides the particular weighting hypothesis method  (Genovese et al. , 2006 ) we adopted, Bayesian methods are potentially alternative strategies to incorporate eQTL information. The use of Bayes factors has been applied to genetic association studies  (Wellcome Trust Case Control Consortium, 2007; Stephens and Balding, 2009).
THE MAGICS ASTHMA GWAS SAMPLES AND DATA The MAGICS (Multicentre Asthma Genetics in Childhood Study) study data  (Moffatt et al. , 2007) , part of the GABRIEL consortium, were reanalyzed by incorporating eQTL information. Quality control procedures were conducted similarly to a published protocol  (Anderson et al. , 2010). Individuals with missing phenotypes, elevated missing rates (≥5%), or outlying heterozygosity rate were removed. Markers with an excessive missing rate (≥5%), low MAF (<5%), or failing in the HWE test (p-value < 10 -5 ) were all excluded as well. The remaining dataset contains 1296 individuals (647 affected and 649 unaffected) genotyped across 300,821 SNPs. To account for possible divergent ancestry and population stratification, principal component analysis (PCA) was conducted using EIGENSOFT 4.
EXPRESSION QUANTITATIVE TRAIT LOCI DATA An eQTL database (http://www. hsph. harvard. edu/liming-liang/ software/eqtl/) resulting from an independent dataset was used as prior information to be incorporated in the GWAS. The sample contains 405 siblings from a panel of families of British descent (MRC-A)  (Dixon et al. , 2007). Global gene expression in LCLs was measured using Affymetrix HG-U133 Plus 2. 0 chips. All siblings were genotyped using the Illumina Sentrix HumanHap300 BeadChip (ILMN300K) and/or the Illumina Sentrix Human-1 Genotyping BeadChip (ILMN100K). The SNP genotype data were further imputed using the MaCH program, and each SNP was tested for association with probes in the gene expression data. Restricting to cis eQTLs (1 Mb region) and controlling for the FDR of 1%, there are 515,947 tests with logarithm of odds (LOD) scores greater than 3.
172, corresponding to 268,204 unique SNPs. In case a SNP has multiple p-values reported for associations with different probes, the minimal p-value was used for that SNP. These 268,204 SNPs are considered as eQTLs, and the database contains information of their physical positions, LOD scores, p-values, and residing or nearby genes. Details of the database are described by  Liang et al. (2013).
, w g,m ) has w g,j = -log 10 p eQTL if the jth SNP is an eQTL SNP with the eQTL p-value p eQTL , and w g,j = 1 otherwise. The particular form was intuitively chosen prior to the reanalysis of the GWAS data in consideration of avoiding up-weighting top eQTL SNPs too much. Both w b and w g were then normalized such that the means equal to 1, i. e. , w B = 1 and w G = 1.
REPORTED ASSOCIATIONS IN THE GWAS CATALOG Asthma-associated SNPs and genes reported in publications were retrieved from the online catalog of published GWAS on January 15, 2013. The catalog limits the associations to those with p-values less than 1. 0 × 10 -5 and records only one SNP with a gene or region of high LD unless there was evidence of independent association. The reported associations were compared against the findings in the asthma GWAS data we reanalyzed.
SIZE SIMULATION USING THE ASTHMA GWAS DATA Besides analyzing the MAGICS asthma GWAS data, we also conducted size simulations by permuting the disease status in the data. Logistic regression was considered where the dependent variable was the disease status (affected or unaffected) and the independent variables included a single SNP effect, gender, and the first principal component. The regression was applied to all the ∼300,000 SNPs across the whole genome. Five thousand permutations were done by permuting the disease status among all the individuals, and then the model was refitted for each SNP. In the end of simulations, 5000 permutation p-values were obtained for each of the ∼300,000 SNPs.
ACKNOWLEDGMENTS Lin Li and Xihong Lin's research was supported by grants from National Cancer Institute (R37 CA076404 and P01 CA134294). Martin Farrall is supported by the British Heart Foundation Centre for Research Excellence in Oxford and the Wellcome Trust core award [090532/Z/09/Z]. We thank the members of the GABRIEL consortium groups for providing data and summary results (a full list of the GABRIEL groups can be found in the Supplement of  Moffatt et al. , 2010).
N. Frontiers in Genetics| Statistical Genetics and Methodology May 2013 | Volume 4 | Article 103 | 8 "Y" in the eQTL column means the corresponding SNP is an eQTL SNP according to the eQTL database described in Materials and Methods. "nearGene-5" is an NCBI dbSNP function code, meaning that SNP is 5 to and 2kb away from a gene. Frontiers in Genetics | Statistical Genetics and Methodology May 2013 | Volume 4 | Article 103 | 12.
Three-dimensional structure of HeV XD The three-dimensional crystal structure of HeV XD was determined from X-ray diffraction data. The protein crystallized in a P2 1 space group with two molecules within the asymmetric unit of the crystal. Crystallographic phases were determined by single wavelength anomalous diffraction (SAD) using synchrotron data collected from crystals of the selenomethionine-substituted protein, and the structure was refined at 1. 65 A ˚resolution (Table  S2  in Text S1). The electron density was well defined throughout the structure, except for a few side chains on the surface, and the model of chain B includes four additional residues encoded by the expression vector (LEHH). The structure is composed of three ahelices, forming an anti-parallel three-helix bundle (Figure  3 ).
Elution was carried out using a gradient of imidazole (10-500 mM) in buffer A. Eluates were analyzed by SDS-PAGE for the presence of N. The fractions of interest were pooled and then injected onto a HiPrep 26/10 desalting column (GE, Healthcare). Elution was carried out in NMR buffer (50 mM BisTris, pH 6, 500 mM NaCl). The sample was concentrated using Centricon Plus-20 (molecular cutoff of 10,000 Da) (Millipore) and protease inhibitor cocktail (1/40) was added. The sample was immediately subjected to NMR and electron microscopy analyses. The concentration of nucleocapsid samples was roughly estimated using the theoretical absorption coefficient at 280 nm as provided by the ProtParam program at the ExPASy server  [75].
Measurement of residual dipolar couplings in XD Four types of residual dipolar couplings ( 1 D HN-N , 1 D Ca-Ha , 1 D Ca-C9 , 2 D HN-C9 ) were measured in XD using BEST-type experiments at a 1 H frequency of 600 MHz and 298 K  [90]. The protein was aligned in a suspension of filamentous phages (ASLA Biotech) at 11. 5 mg/mL giving rise to a residual deuterium splitting of 7 Hz. All spectra were acquired with sweep widths of 7. 5 ( 1 H), 1. 4 ( 15 N), 1. 2 ( 13 C9) and 2. 9 ( 13 Ca) where the number of complex points in each dimension was 512 ( 1 H), 40 ( 15 N), 80 ( 13 C9) and 80 ( 13 Ca). Spectra were processed using NMRPipe  [77]  and analyzed using CcpNmr  [78]. The alignment tensor was determined by fitting the experimental RDCs to the crystal structure of XD using the program Module  [91].
Supporting Information Text S1 This file contains six additional figures showing the assigned HSQC spectra of N TAIL and XD (Figures S1, S2 and S3), comparison of the X domains of Sendai, Measles and Hendra viruses (Figures  S4  and S5 ) and a model of the Hendra virus N TAIL -XD complex from NMR chemical shift perturbations (Figure  S6 ). In addition, the file contains four additional tables with chemical shift values of N TAIL and XD (Tables  S1  and S3 ), data collection and refinement statistics of XD (Tables  S2 ) and residual dipolar couplings of XD (Table  S4 ). Crystallographic coordinates and structure factors of XD have been deposited in the Protein Data Bank with accession code 4HEO. (DOC).
This work was carried out with financial support from the  French Agence Nationale de la Recherche (ANR)  through the specific programs '' Physico-Chimie du Vivant ''  ANR-08-PCVI-0020-01 , '' Blanc ''  ANR-09-BLAN-0100  and '' ASTRID ''  ANR-11-ASTR-003-01  (to SL),  ANR JCJC  ProteinDisorder (to MRJ),  ANR MALZ TAUSTRUCT  (to MB), the  Human Frontier Science Program  (long-term fellowship to RS), the  Rhone-Alpes region (Ph. D. fellowship to FY) , the  CNRS  and the  Direction Generale de l'Armement (Ph. D. fellowship to DB) and the  Finovi foundation  (to MB and RWHR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.