dataesr/scientific-paragraphs-categorization huggingface dataset:
Then the spectra from the 12 different colonies were imported into the MALDI BioTyper software (version 2. 0, Bruker) and analysed by standard pattern matching (with default parameter settings) against the main spectra of 6252 bacteria. Scores ranging from 1. 32 to 1. 56 were obtained for strain FF8 T , suggesting that it was not a member of any known species. The reference mass spectrum from strain FF8 T was incremented in our database (Fig. 4 ). The gel view highlighted spectrum differences with other Flaviobacteriaceae species (Fig. 5 ). a Evidence codes are as follows: IDA, inferred from direct assay; TAS, traceable author statement (i. e. , direct report exists in the literature); NAS, nontraceable author statement (i. e. , not directly observed for the living, isolated sample, but based on a generally accepted property for the species or on anecdotal evidence).
Genome Sequencing Information Genome project history The organism was selected for sequencing on the basis of its phylogenetic position, 16S rRNA similarity and phenotypic differences with other members of the family Flavobacteriaceae. It was the second genome within Weeksella genus and the first genome of W. massiliensis sp. nov. The GenBank accession number is CCMH00000000 and consists of 54 contigs. Table  4  shows the project information and its association with minimum information about a genome sequence (MIGS, version 2. 0) compliance  [19] ; associated MIGS records are also summarized in Supplemental Table  S1.
massilliensis strain FF8 T , HG931340 (GA: CCMH00000000); Weeksella virosa strain DSM 16922 T , NR074495 (GA: CP002455); and Empedobacter falsenii strain NF 993 T , AM084341. Sequences were aligned using MUSCLE  [42] , and phylogenetic tree inferred by maximum likelihood method with Kimura 2-parameter model from MEGA 6 software  [43]. Numbers at nodes are percentages of bootstrap values obtained by repeating analysis 1000 times to generate majority consensus tree. E. falsenii was used as outgroup. Scale bar = rate of substitution per site of 0. 1.
Genome annotation Open reading frames (ORFs) were predicted using Prodigal  [20]  with default parameters, but the predicted ORFs were excluded if they were spanning a sequencing gap region. The predicted bacterial protein sequences were searched against the GenBank database [21] and the Clusters of Orthologous Groups (COGs) database using BLASTP. The tRNAScanSE tool  [22]  was used to find tRNA genes, whereas ribosomal RNAs were found by using RNAmmer  [23]  and BLASTn against the GenBank database. Lipoprotein signal peptides and the number of transmembrane helices were predicted using SignalP  [24]  and TMHMM  [25] , respectively. ORFans were identified if their BLASTP E value was lower than 1e-03 for alignment length greater than 80 aa. If alignment lengths were smaller than 80 aa, we used an E value of 1e-05.
Such parameter thresholds have already been used in previous works to define ORFans. Artemis  [26]  was used for data management, and DNA Plotter  [27]  was  for visualization of genomic features. The Mauve alignment tool (version 2. 3. 1) was used for multiple genomic sequence alignment  [28]. To estimate the mean level of nucleotide sequence similarity at the genome level, we used calculated the average genomic identity of orthologous gene sequences (AGIOS) parameter using an in-lab pipeline named Marseille Average Genomic Identity (MAGi). Briefly, this software combines the Proteinortho software [29] for detecting orthologous proteins in pairwise comparisons of genomes, then retrieves the corresponding genes and determines the mean percentage of nucleotide sequence identity among orthologous ORFs using the Needleman-Wunsch global alignment algorithm.
The script was created to calculate the average genomic identity of orthologous gene sequences (AGIOS) between genomes by MAGi. The MAGi script created to calculate AGIOS values written in Perl and BioPerl modules. Genome-to-genome distance (GGDC) analysis was also performed using the GGDC Web server (http://ggdc. dsmz. de/) as previously reported  [30, 31].
Number of genes associated with general COGsTotal is based on total number of protein-coding genes in annotated genome. 96New Microbes and New Infections, Volume 8 Number C, November 2015 , amoxicillin/clavulanic acid, penicillin, imipenem, gentamicin doxycycline but resistant to nitrofurantoin, vancomycin, trimethoprim-sulfamethoxazole and metronidazole. The G + C content of the genome is 35. 9%. The 16S rRNA and genome sequences are deposited in GenBank under accession numbers HG931340 and CCMH00000000, respectively. The type strain FF8 T (= CSUR P860 = DSM 28259) was isolated from the urine of a 65-year-old man with acute cystitis at Hôpital Principal de Dakar, Senegal.
83 General function prediction only S 116 4. 85 Function unknown - 823 33. 64 Not in COGs COGs, Clusters of Orthologous Groups (COGs) database. a NMNI New Microbes and New Infections © 2015 The Authors. Published by Elsevier Ltd on behalf of European Society of Clinical Microbiology and Infectious Diseases, NMNI, 8, 89-98 This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4. 0/) ceftriaxone.
INTRODUCTION Germline mutations are being found to underlie an increasing number of primary immunodeficiency (PID) diseases. With current advances, and the improved quality and decreasing cost of high-throughput sequencing (HTS), it is now possible to detect the full set of gene variants in PID patients, through techniques such as whole-exome sequencing (WES) or wholegenome sequencing (WGS). The genome of each patient contains about 20,000 coding variants and hundreds of thousands of non-coding variants  (1) (2) (3). It is not straightforward to identify the PID-causing gene from the HTS data for a patient  (4, 5) , and the most widely used approach involves selecting known disease-causing genes as candidate genes for further investigation. Thus, in this approach, variants are identified as candidate PID-causing genes only if they concern genes already listed among those known to cause PIDs (229 such genes have been identified to date)  (6, 7).
GENERATION AND PLOTTING OF GENE NETWORKS For visual depiction of the distribution of the 229 currently known PID genes within the full human genome, we used the predicted HGC biological distance between all human genes, selecting only direct biological connections  (5, 13)  between genes, to lower the complexity of the network. We then used the NetworkX python package for complex network analyses and visualizations  (14) , applying the spring layout function, which estimates the localization of each gene in a two-dimensional space with the Fruchterman-Reingold force-directed algorithm  (15). The nodes of known PID genes were plotted three times larger than those of the other human genes.
COMPUTING RESOURCES AND PROGRAMING LANGUAGES This project was performed on a Mac Pro machine with 12 cores and 128 GB RAM. Biological distances between genes were calculated by the HGC and server. Data extraction, statistical simulations, and network analyses and visualizations were performed with the Python programing language. FGA phylogenetic analyses and visualizations were performed with the R programing language for statistical computing 3. The programs and online server used in this study (in particular for ranking candidate genes by biological distance from core genes and FGA trees generation) are freely available to non-commercial users with step-bystep instruction at http://lab. rockefeller. edu/casanova/HGC and http://hgc. rockefeller. edu, and the scripts for the minor technical procedures in this study are all available from the authors upon request.
We plan to use this list, together with the list of 229 known PID genes, in investigations of the HTS data for PID patients. A hit for one of these genes in this analysis would be associated with a higher likelihood of the gene being PID-causing. It is important to note that as in any high-throughput genomewide analysis, the choice of input data, algorithm, and even small fine-tuning is likely to strongly affect the outcome. However, we attempted here to provide a reliable prediction where false negatives (i. e. , true PID genes that are not in the final provided list of candidate genes) are minimized -an aim that is expected to be achieved by following a biologically plausible hypothesis of the tight intra-relatedness of PID genes, together with having a small number of false positives, achieved by estimating the biological relevance of the gene candidates.
The main limitation of the approach described in this study is that it is still expected to contain a large number of false positives: although the final list of 3,110 genes removes about 85% of irrelevant protein-coding genes, the function of a large proportion of human genes is poorly understood. Future advances in characterizing the functions of human genes and updates to genome-wide curated databases of gene ontologies and biological/genetic pathways should significantly improve the predictive power of our (and other in silico) described methodology. The procedures described here could be used to infer novel disease gene candidates for other disease groups. For example, as information about known cancer driver genes is available (39) 4 , 4 http://cancer. sanger. ac. uk/cancergenome/projects/cosmic/ an extended list of cancer gene candidates could potentially be identified by HGC-predicted biological distance analysis of known cancer genes, to generate a final list of predicted novel candidates on the basis of biological function relevance.
ACKNOWLEDGMENTS We thank  Laurent Abel ,  Capucine Picard ,  Bertrand Boisson ,  Shen-Ying Zhang ,  Stephanie Boisson-Dupuis ,  Anne Puel ,  Jacinta Bustamante ,  Emmanuelle Jouanguy ,  Xiao-Fei Kong ,  Janet Markle , and  Guillaume Vogt  for sharing known and novel PID gene data and advice. We thank  Yelena Nemirovskaya  and  Eric Anderson  for administrative support. YI was supported in part by grant #  UL1 TR000043  from the  National Center for Advancing Translational.
DamID analysis A peak-finding algorithm with false-discovery-rate (FDR) analysis was developed to identify significant binding sites (PERL script available on request). All peaks spanning eight or more consecutive probes (900 bp) over a twofold ratio change were assigned a FDR value. To assign a FDR value, the frequency of a range of small peak heights (0. 1-1. 25 log 2 increase) were calculated within a randomized data set (for each chromosome arm) using 20 iterations for each peak size. This was repeated for a range of peak widths  (6-15 consecutive probes). All these data were used to model the exponential decay of the FDR with respect to increasing peak height and peak width, therefore enabling extrapolation of FDR values for higher and broader peaks. This analysis was performed independently for each replicate data set.
Comparison with expression profiling data The data set from  Freeman et al. (2003)  was retrieved directly from the publication. For Egger's data set, the raw data were retrieved and analyzed as described in the paper  (Egger et al. 2002)  (intensities. 50 and fold change. 1. 5) with a more restrictive P-value (,0. 001). The data set from  Altenhein et al. (2006)  comprising the filtered and tested genes for the gcm gain of function (GOF) and gcm loss of function (LOF) was retrieved, and all genes giving nonspecific or negative in situ hybridizations (ISHs) were removed to make the Venn diagrams in Figure  2. The R package VennDiagram was used to draw the diagrams in Figure  2 , B and C  (Chen and Boutros 2011). The gene names for all data sets then were converted to FlyBase gene numbers (Fbgn) for comparison with the DamID genes using the FlyBase conversion tool  (dos Santos et al.
To confirm the data obtained with the reporter plasmids, we analyzed the effects of Gcm on the endogenous genes and measured the levels of their transcripts in S2 cells by qPCR. S2 cells were transfected with ppacGcm because Gcm is expressed at extremely low levels in those cells  (Cherbas et al. 2011). We were able to show significant induction of CycA and E(spl)m8 expression, but no induction was observed for CG30002 or ptc (Figure  3E ). Such a negative result might be due to the facts that S2 cells do not contain the appropriate cofactors, the genes are in a repressed chromatin state, and/or S2 cells have low transfection efficiency. Indeed, FACS analysis of S2 cells transfected with the Gal4 expression vector ppacGal4 and UAS-GFP vectors revealed that only 4. 92% of the cells express GFP.
Protein domain enrichment analysis To annotate the genes identified in the DamID screen, we first performed a protein domain enrichment analysis using DAVID bioinformatics resources  (Huang et al. 2009a,b). The analysis showed enrichment for genes coding for proteins containing basic Helix-Loop-Helix (bHLH) domains (17 genes) and Homeobox domains (26 genes), which are characteristic of transcription factors, but the most enriched family is the Ig domain-containing protein (DAVID protein domain enrichment: 7. 79-fold; P = 1. 3 3 10 214 ; FDR = 2. 0 3 10 211 ) (Figure  6A ). Most of the Ig genes are involved in or at least expressed during nervous system development (Figure  6B ) and code for guidance molecules [reviewed in  Patel and Van Vactor (2002) ]. This includes two fibroblast growth factor receptors (Htl and Btl), two netrin receptors (Unc-5 and Fra), six members of the Beaten path family (Beat), three members of the Down syndrome cell adhesion molecule family (Dscam), two fasciclins (Fas), three roundabout proteins (Robo), and several others, including seven members of the defective proboscis extension response family (Dpr) and two Dpr interactors  (Ozkan et al.
Acknowledgments We thank the  Developmental Studies Hybridoma Bank  and the  Bloomington Stock Center  for reagents and flies, as well as  J. Veenstra  ( INCIA UMR5287 CNRS, France ) for the gift of the anti-DH31 antibody and  B. Altenhein  ( University of Mainz, Germany ) for fly strains. We thank  K. Jamet  for initial bioinformatics analyses. We thank  C. Diebold ,  C. Delaporte , and the  Institut de Génétique et de Biologie Moléculaire et Cellulaire  for technical assistance. We also thank the members of our laboratory for valuable input and comments on the manuscript. This work was supported by grants from the  Institut National de la Santé et de la Recherche Médicale , the  Centre National de la Recherche Scientifique , the  University of Strasbourg, Hôpital de Strasbourg , The fondation  Association pour la Recherche sur le Cancer , the  Ligue National Contre le Cancer , the  French National Cancer Institute , and the  Agence Nationale de la Recherche (ANR).
A. P. and P. C. were funded by the  Fondation pour la Recherche Médicale  and by the  ANR , respectively. A. Popkova also benefited from a short development traveling fellowship to visit the  laboratory of A. Brand in Cambridge (UK). The  Institut de Génétique et de Biologie Moléculaire  et Cellulaire also was supported by a  French state fund  through the  ANR Laboratoire d'excellence. T. D. S. and A. H. B. were funded by  Wellcome Trust   Programme grants   068055  and  092545  to A. H. B. A. H. B. acknowledges core funding to the  Gurdon Institute  from the  Wellcome Trust  ( 092096 ) and the  Cancer Research UK  ( C6946/A14492 ).
1 ). Next, we determined how MITF suppression changes the global responsiveness of human MZ7 melanoma cells (MITF high group) to increasing amounts of TNF-a. We identified genes with at least twofold change by TNF-a in each of the experimental conditions and found that a reduction of MITF greatly sensitizes to low concentrations of TNF-a and increases the number of TNF-regulated genes (Fig. 2c ). Heatmap clustering showed that hyperresponsiveness to TNF-a was not restricted to genes that were either unchanged or moderately induced by MITF loss (cluster 1; Supplementary Data 4), but was also evident for genes (cluster 2; Supplementary Data 5) that were co-suppressed with MITF loss (Fig. 2d ). Thereby, TNF counteracted certain parts of the siMITFmediated transcriptional effects (Fig. 2e , left and right panel).
MITF low MITF high log2 expression 0 -5 5  we used the transcription factor target gene set collection (C3) of the BROAD Molecular Signature Database (MSigDB v4. 0) for GSEA, to identify transcription factor programmes linked to the MITF low cell state in panels of human melanoma cell lines (Fig. 3a ). This analysis revealed three predominant and significantly enriched transcription factors SRF, NF-kB and AP-1. The unbiased identification of NF-kB gene sets corroborated an antagonistic relationship between MITF and NF-kBdriven inflammatory pathways (Fig. 3b ). Thus, we asked whether MITF high and MITF low melanoma cell lines differ in nuclear levels of the key NF-kB subunit p65/RelA at baseline or in response to TNF-a. Contrary to our expectation, we found that nuclear translocation of p65/RelA was comparable and irrespective of the MITF status (Fig.
4a ). This result was independently validated by interrogating our two published RNA sequencing (RNA-seq) data sets (GSE61967) from 501mel melanoma cells that consistently showed upregulation of c-JUN on knockdown of MITF (log 2 fold changes: 1. 8/1. 4)  19, 35. We further asked whether MITF could be directly involved in the transcriptional repression of c-JUN. Using our recently described MITF chromatin immunoprecipitation sequencing (ChIP-seq) data set we investigated the MITF binding profile (triple haemagglutinin (HA)-tagged MITF (3HA-MITF)) in the genomic region of c-JUN (Fig. 4b )  35. We identified several significantly enriched MITF-binding sites also containing E-box motifs, a known binding motif of MITF  19, 35. Two binding sites (P2 and P3) coincided with an ENCODE-annotated enhancer region (H3K27ac-enriched).
We consequentially hypothesized that the antagonistic interrelationship between MITF and c-Jun represents an important determinant for the reciprocal cross-talk of melanoma and immune cells in the tumour microenvironment. To address this, we conducted a bioinformatic approach using RNA-seq gene expression data from The Cancer Genome Atlas (TCGA) melanoma cohort (Skin Cutaneous Melanoma, n ¼ 470) that was accessed through the R-based computing platform of the cBioportal project  [41] [42] [43]. Melanoma samples were sorted by increasing MITF levels and we plotted for each sample the respective expression of c-JUN, FOSL1, the TNF-a response signature MLANA and the dedifferentiation makers AXL, NGFR and EGFR (Fig. 6a-d  and Supplementary Fig. 7a-c )  6, 21, 24, 44. To better visualize interrelationships against the background of heterogeneous human tumour samples, we made use of the large sample size and plotted moving averages of the gene expression levels (Supplementary Software 1).
MZ7 cells were obtained from Thomas Wo ¨lfel (Mainz, Germany). Key driver mutations were confirmed by Sanger sequencing and BRAF mutations were functionally addressed by sensitivity to BRAF inhibitors. Gene expression profiles of untreated cultures were compared with a previous study (GSE4843) and showed consistent results. For siRNA tranfections, 300,000 cells were seeded per well in a 12-well plate and reverse transfected with 30 pmol siRNA (Dharmacon) using 3 ml RNAiMAX Lipofectamine (Life Technologies) according to the manufacturer's instructions. The medium was changed after 24 h. All recombinant human cytokines were from Peprotech and were used at the following concentrations: CCL2 100 ng ml À 1 , interferon-g 1,000 U ml À 1 , IL-1b 1,000 U ml À 1 , IL-6 1,000 U ml À 1 , IL-8 150 ng ml À 1 , TNF-a 1,000 U ml À 1 , if not indicated otherwise.
For the separation of nuclear and cytoplasmic fractions, the NE-PER kit (Thermo Scientific) was used according to the manufacturer's instructions, with one additional PBS wash of the nuclear pellet. The lysates were separated using 10% SDS-PAGE and transferred to a nitrocellulose membrane (GE Healthcare) by wet blotting for 80 min at 70 V (both systems by BioRad). After blocking for 1 h with 5% BSA (GE Healthcare) in TBS with 0. 5% Tween, membranes were probed with primary antibodies overnight at 4 °C in 2. 5% BSA in TBS with 0. 5% Tween. The proteins were detected using IRDye680LT and IRDye800CW secondary antibodies and the Odyssey Sa Imaging system (LI-COR Biosciences). The following antibodies were used: b-actin (clone C4, Santa Cruz sc-47778, 1:1,000), histone H1 (clone AE-4, Santa Cruz sc-8030, 1:250), Mitf (Atlas Antibodies, HPA003259, Sigma-Aldrich, 1:250), c-Jun (clone 60A8, #9165 Cell Signaling, 1:250), p65/RelA (D14E12, Cell Signaling #8242, 1:1,000), Fosl1/Fra1 (clone R-20, Santa Cruz sc-604, 1:250), b-tubulin (clone D2N5G, Cell Signaling #15115, 1:2,000), IkBa (clone L35A5, Cell Signaling #4814, 1:1,000), JNK (polyclonal, Santa Cruz sc-571, 1:250), phosphoJNK (polyclonal, R&D AF1205, 1:250), ERK (p42/44 mitogen-activated protein kinase, polyclonal rabbit, #9102, Cell signaling 1:1,000); ladder: Broad Range Marker (Santa Cruz sc-2361).
Heavily pigmented mouse melanomas were bleached before staining (20 min at 37 °C in 30% H 2 O 2 and 0. 5% KOH, 20 s in 1% acetic acid and 5 min in TRIS buffer). Stained sections were examined with a Leica DMLB immunofluorescence microscope. Images were acquired with a JVC digital camera KY-75FU and processed with Adobe Photoshop. Flow cytometry (FACS) of tumour tissues. Transplanted melanoma infiltrating immune cells were isolated as described previously and stained with fluorochromeconjugated monoclonal antibody specific for mouse CD45 (clone 30-F11, 1:200), CD11b (clone M1/70, 1:200), Gr1 (clone RB6-8C5, 1:200), CD8a (clone 53-6. 7, 1:200) and CD4 (clone GK1. 5, 1:200; all from BD Pharmingen) according to standard protocols. Data were acquired with a FACSCanto Flow Cytometer (BD Biosciences) and analysed with FlowJo software (TreeStar, V7.
6. 5 for Windows). Microarray analysis of gene expression. RNA from melanoma cell lines was isolated as described above, quantified fluorimetrically and assayed for integrity with the Agilent Bioanalyser (Agilent Technologies). One hundred nanograms of RNA was converted to biotinylated cRNA using one round of amplification with the Illumina Labelling Kit (Illumina) and one round of T7 polymerase amplification and hybridized to Illumina Human Beadchips HT12 v4. 0. After hybridization and staining, the arrays were scanned in an Illumina Bead Station and the images processed using Illumina Bead Studio software. Raw microarray data were extracted from the Illumina BeadStudio software and imported into the R statistical programming environment and the Bioconductor platform using the beadarray package.
Variance stabilization (vsn package) and normalization including log 2 transformation of gene expression values were performed, followed by quality assessment of the fit. Gene expression data have been deposited in the GEO database with the following accession numbers: GSE71798, GSE71881 and GSE71886. Central R-based computer code is provided as Supplementary Software. Gene expression analysis of melanoma cell line panels. Affymetrix raw CEL files from the BROAD melanoma cell line panel of 88 melanoma cell lines were downloaded from the BROAD melanoma portal (https://www. broadinstitute. org/ software/cprg/?q=node/46) and normalized by Robust Multichip Average using the affy package. The log 2 -transformed gene expression values were mean centred for heatmap visualization. Gene expression data of the GSE7127 data set (n ¼ 63 melanoma cell lines) were downloaded as Affymetrix raw CEL files and processed as described above.