dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein localization assay in human cell lines Human skin melanoma SK-MEL-2 CLTA-TagRFP-T gene-edited cells (SK-MEL-2 CLTA EN-1 )  (Doyon et al, 2011)  were maintained under 5% CO 2 at 371C in DMEM/F-12 (Invitrogen) supplemented with 10% FBS (fetal bovine serum, HyClone). SK-MEL-2 CLTA EN-1 cells were transiently transfected with plasmids encoding GFP-tagged candidate clones (all N-terminally tagged except for SH3D19, which was C-terminally tagged) using Lipofectamine 2000 according to manufacturer's protocol. Total internal reflection fluorescence (TIRF) microscopy images were captured using MetaMorph software on an Olympus IX-81 microscope using a Â 60 NA 1. 49 objective and an ORCA-R2 camera (Hamamatsu). The system was maintained at 371C using a WeatherStation chamber and temperature controller (PrecisionControl).
At 16-24 h before imaging, cells were seeded onto uncoated glass coverslips in growth medium. During imaging, cells were maintained in DMEM without phenol red that was supplemented with 5% FBS, 10 mM HEPES and 1 Â GlutaMAX. A 488-nm solid-state laser (Melles Griot) and a 561-nm diode-pumped solid-state laser (Melles Griot) were used to excite GFP and RFP fluorophores, respectively. Simultaneous two-color TIRF images were obtained using a DV2 image splitter (MAG Biosystems) to separate GFP and RFP signals. Image sequences were captured at a 2-s per frame interval. Image and kymograph analyses were performed using ImageJ software.
Peptides were identified by mass spectrometry as described  (Rush et al, 2005; Guo et al, 2008). In total, 1139 human proteins were identified with this approach. We compared this data set with the predictions by orthology of human endocytosis proteins based on our worm SH3 interactome. The 55 predicted novel worm endocytosis proteins with human orthologs can be mapped to 143 human proteins. Twenty eight of them overlap with the phosphoproteomics predictions (P ¼ 7. 9 Â10 À 9 , assuming a universe of 20 000 proteins, see Supplementary Table  15 ). All proteins that overlap with predicted human endocytosis list were in the endosome fractions except SFPQ and LASP1 (26 out of 28), which were only found in the lysosome fraction.
The score for any two GO terms is calculated based on their information content and ontology graph position. As recommended in previous work  (Guo et al, 2006) , given a PPI involving two proteins, with A annotated by M terms and B annotated by N terms, we computed GO term similarity between all possible M Â N term pairs and used the highest score as the SIM score between proteins A and B. Protein pairs involving a protein with no GO annotation do not receive an SIM score. We then compared the distribution of the similarity scores between our positive (Y2H) and random PPI sets. We created 100 random PPI networks by selecting 100 random worm genes from the entire genome as interactors for each of our 69 bait genes. SIM scores were calculated for all resulting random PPIs and these were compared with our Y2H data using a number of metrics: (i) the percentage of PPIs with a SIM score, (ii) the sum of all SIM scores, (iii) the average SIM score, which is the sum of SIM scores divided by the number of PPIs with an SIM score, and (iv) the overall average SIM score, which is the sum of SIM scores divided by the number of all PPIs (Supplementary Table  5 ).
GO gene function term enrichment and construction of the enrichment map We used the DAVID v6. 7 service to compute GO term enrichment for our yeast and worm interactomes (http://david. abcc. ncifcrf. gov/; Dennis et al, 2003; Huang da et al, 2009). The worm SH3 interactome contains 520 unique proteins, mapped to WormBase (WB) gene identifiers using WormMart  (Schwarz et al, 2006). DAVID recognized 518 of these, which were used in subsequent DAVID functional analyses. The yeast SH3 interactome was previously published  (Tonikian et al, 2009). DAVID recognized 370 yeast proteins which were used in DAVID functional analyses. The enrichment map in Figure  4  was built using the Enrichment Map Cytoscape Plugin  (Merico et al, 2010). Functional modules were manually grouped and labeled using illustration software.
Protein interaction conservation To probe the degree of SH3-mediated protein interaction conservation from worm to yeast and human, proteins from the worm SH3 domain interactome, restricted to interactions with SH3 domains as baits, were mapped to their yeast and human orthologs. Protein interactions in yeast were retrieved from the BioGRID database version 3. 1. 78 (Breitkreutz et al, 2008)  and a previously published SH3 interactome  (Tonikian et al, 2009). Protein interactions in human were retrieved from BioGRID version 3. 1. 78. When comparing worm with yeast, 98 proteins from the worm network had orthologs in yeast (Supplementary Table  19 , only proteins involved in interactions in either worm or yeast are shown in Figure  5 ). To compute statistical significance of this overlap, the total number of possible interactions was estimated as the number of conserved baits (10) times the number of conserved preys (90).
Negative example proteins (105 in total) used to compute ROC curves were randomly selected from the Worm proteome, excluding ones with known endocytosis function in any organism. ROC curves were computed for each cross-validation run by varying k and used to compute an average AUC of 0. 69 (Po10 À 4 ). The k-core with k ¼ 3 was used to make endocytosis predictions as it corresponded to a good ROC score, resulting in 86 novel proteins predicted to be involved in endocytosis (Supplementary Table  13 ). study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author contributions: XX, DG, and GDB wrote the manuscript. XX and GDB led and coordinated the project. CB initiated the project. CB and GDB supervised the project. XX, DG, and GDB analyzed the data.
DG performed the bioinformatics analysis. XX cloned yeast two-hybrid (Y2H) baits. XX and LL performed Y2H screens. J-FR helped with ORFeome Y2H screening. RT cloned phage display baits. AP and AL performed phage display assays. RT and SSS supervised the phage display assays. JC, ATC, and DB performed co-localization assays. DGD supervised co-localization assays. LS performed TBC-2 assay. AA obtained binding data for TBC-2 and AMPH-1. BDG designed and supervised the TBC-2 assay. AG and MLG performed the mass spectrometry assay. MLG analyzed the mass spectrometry data. MV and DEH provided human ORF clones, worm ORFeome Y2H library and supervised ORFeome Y2H screening. YZ, BC, SS, and TH performed sequencing of Y2H and phage display hits. XY, YAS, and KS-A helped with GFP cloning. JL helped with GO analysis.
Acknowledgements We thank  Q Li ,  H Huang ,  J Gu ,  R Isserlin ,  H Fares ,  A Audhya ,  A Shi ,  S Pant , and  D Carranza  for experimental and computational support. This work was supported by the  Canadian Institutes of Health Research  (Grants  MOP-84324  to GDB and  MOP-93725  to SSS) and the  US National Institutes of Health  (Grants  R01 GM067237  to BDG,  R01 GM65462  to DGD and  NHGRI R01 HG001715  awarded to MV and DEH). DG acknowledges the financial support of  EMBO  ( EMBO-ALTF 241-2010 ) and  SNSF  ( PBELA33-120936 ). The funders had no role in.
Data availability Phage display data and other data from this paper are available for download from http://www. baderlab. org/Data/SH3Worm. Phosphoproteomics data are available from the PhosphoSitePlus database for the following cell line samples under the following accession numbers: LAN-6 End1, End2, and Lys, which can be accessed using the base URL: http://www. phosphosite. org/curatedInfoAction. do? record=9391186 (CS6151), 9391899 (CS6152), 9391606 (CS6153, Akt substrate); and 9390610 (CS6121), 9391322 (CS6122), 9391006 (CS6123, pY); SMS-KCN End1, End2, Lys: 4148078 (CS5180), 4148082 (CS5181), 4148086 (CS5182, pY); 4318900 (CS5322),.
Alternatively, defective interfering particles could have served as a template  32, 33  , as the DNA forms had breakpoints and rearrangements similar to those identified in RNA1 defective interfering particles 11. Of note, we also identified DNA forms derived from FHV RNA2 that were similar in sequence to RNA2 defective interfering particles 32 (data not shown). We then infected S2n cells with FHV at a multiplicity of infection (MOI) of 0. 5 and monitored the appearance of FHV DNA over time by PCR. The DNA form was detectable as early as 12 h after infection (Fig. 2b ). Because reverse transcriptases encoded by retrotransposons and endogenous retroviruses are widespread in insect genomes  34, 35  , we determined if we could detect reversetranscriptase activity in S2 cells. We detected robust Mn 2+ -dependent reverse-transcriptase activity in extracts of S2n cells (Fig.
Methods and any associated references are available in the online version of the paper. Accession codes. NCBI Small Read Archive: sequences, SRA045427. After viral infection, viral genomes (viral RNA) or dsRNA intermediates (viral dsRNA) are propagated (red). Those viral forms are reverse-transcribed by cellular reverse-transcriptase activity into DNA forms (green) that may integrate into the host genome or be processed into extrachromosomal circular DNA. The sequences of viral origin, now in a DNA form, will produce transcripts (black) that form dsRNA, recognized by Dicer 2 (Dcr2) and further processed by a small RNA-related pathway. When a viral small RNA from those transcripts reaches the RNAinduced silencing complex (RISC), the ongoing infection is contained and the acute infection is controlled.
Each unique sequence was given a unique identifier followed by the number of sequences that it represented in the library. First, unique sequences were filtered with Blastall software (National Center for Biotechnology Information) with the parameters '-W 9 -F F -e 1-g F', first against the Drosophila melanogaster reference genome (ftp://ftp. flybase. net/releases/FB2010_03/dmel_r5. 26) and then against FHV RNA1 and RNA2 (NC_004146. 1 and NC_004144. 1, respectively) and their defective interfering particles (GU393238. 1 for RNA1, and GU393239. 1, GU393240. 1 and GU393241. 1 for RNA2). At each filter, all the unique sequences with a high-scoring segment pair above 18 bases with 0 and 1 mismatches were eliminated. This first step eliminates all nonchimeric small RNAs with a good match in either the host genome or the virus genome.
Second, the Blastall software was used with the parameters '-e 100 -W 9 -F F -q -100 -g F' for selection, among the remaining unique sequences, those with 9-12 bases with the most similarity to FHV RNA1 or RNA2 and their defective interfering particles without gaps or mismatches. Unique sequences without similarity or with similarity of more than 12 bases were not selected. A second blast analysis was done on the selected unique sequences with the parameters '-e 10000 -W 9 -F F -q -100 -g F' for selection of the unique sequences with 9-13 bases of similarity with the Drosophila reference genome. Unique sequences without similarity or with similarity of more than 13 bases were not selected. Then, an 'in-house' script was used for comparison of the two Blastall series (versus FHV and versus Drosophila), with selection of only the unique sequence with at least 1 'hit' against FHV and 1 'hit' against Drosophila and whose positions were considered nonoverlapping.
Results that allowed 0, 1 and 2 overlapping bases between the two hits were examined for all the unique sequences. Only unique sequences representing at least five reads on the original small-RNA library were retained. This second step was specifically designed to 'fish out' chimeric transcripts with high confidence. Of note, to detect chimeric small RNAs that unambiguously map partly to Drosophila and partly to FHV, we applied very stringent mapping and filtering parameters. By doing this, we may have lost many chimeric reads that were unable to pass the filters, and thus the final numbers are low (Fig. 4d  and Supplementary Fig. 6 ). For comparison of small-RNA libraries with or without AZT treatment, the following procedures were used. For miRNA analysis, the mirBase database was used as reference.
Mapping was done with Bowtie software, with applying a seed of 21 nts with a maximum of 2 mismatches. Each miRNA was quantified in the presence or absence of AZT with SAMtools. FHV small RNAs were mapped with Bowtie software for the alignment of short DNA sequences, and a maximum of two mismatches was allowed. The mapping of siRNAs was annotated for each position of FHV RNA1. Silencing assay. S2 cells (~1 × 10 6 ) were transfected with dsRNA with Effectene (QIAGEN). The dsRNA was generated by in vitro transcription from T7 promoter-flanked PCR products, with. 2 g dsRNA used per condition in six-well plates with a 2 ml final volume of Schneider's medium. After 3 d of dsRNA treatment, cells stained with propidium iodide, followed by analysis with a FACSCalibur and CellQuest Software. RNA blot analysis.
ACKNOWLEDGMENTS We thank members of the  Saleh lab , the  Antoniewski lab  and the  Vignuzzi lab  for discussions and technical support;  M. Vignuzzi ,  J. Chandler  and  R. van Rij  for critical reading of the manuscript;  A. Schneemann  ( The Scripps Research Institute, La Jolla, CA ) for antibody to FHV;  P. Vargas  for assistance with flow cytometry; and  A. Pelisson  for advice on retrotransposition. Supported by the  French Agence Nationale de la Recherche  ( ANR-09-JCJC-0045-01  to M. -C. S. ), the  European Research Council  ( FP7/2007-2013 ERC 242703  to M. -C. S. ),  Institut National de la Santé et de la Recherche Médicale  (for work in the laboratory of G. C. ) the  European Research Council  ( ERC 243312 , Retrogenomics; for work in the laboratory of G. C. ), the  French Ministry of Research  (C.
Assay Miniaturization and Optimization HuH-7 cells were seeded in 384-well m clear-plate black (Greiner Bio-one, Germany) at 5610 3 cells per well containing 0. 5% DMSO vehicle, and treated with 50 mM CPZ or infected with CHIKV-118-GFP at a multiplicity of infection (M. O. I. ) of 0. 5 for 24, 48, 72 and 96 hrs at 37uC, 5% CO 2 under humidified conditions. The wells were fixed with 4% PFA containing 5 mg/ mL 49,6-diamidino-2-phenylindole (DAPI, Sigma-Aldrich, USA) for 30 min at room temperature, then washed twice with 80 mL DPBS and finally filled with 50 mL DPBS. Confocal fluorescence images of DAPI-stained nuclei from 5 different fields of the wells were acquired using the Operetta High Content Imaging System (Perkin Elmer, USA) with 206objective lens, and analyzed using a customized plug-in on the Image Mining platform software developed in-house.
Data Normalization, Compound Activity, and Assay Quality Control The measured RFU values obtained from the resazurin reduction assay was normalized as percent viability using the formula: % viability~RFU sample {mRFU CC100 À Á RFU MOCK {RFU CC100 ð Þ Â Ã |100% where m represents the mean value, and the RFU CC100 , RFU sample , and RFU MOCK are the measured RFU of the CPZ 50 mM -treated HuH-7, CHIKV-infected HuH-7 treated with test compound or 0. 5% DMSO vehicle, and MOCK-infected HuH-7, respectively. The percent inhibition, which reflects the inhibitory effects of the compounds against CHIKV infection in HuH-7 cells, was calculated using the formula: % inhibition~RFU sample {mRFU DMSO À Á RFU MOCK {RFU DMSO ð Þ Â Ã |100% where m represents the mean value, and the RFU sample , RFU DMSO and RFU MOCK are the measured RFU of the CHIKV-infected HuH-7 treated with test compounds, 0.
Counter-screening against Dengue Virus Serotype 2 (DENV2) The inhibitory property of the CHIKV primary screening hits against DENV2 infection was investigated by retrieving the results from the a high-content, high-throughput screening of the BioFocus kinase inhibitor library against DENV2 infection in Huh-7. 5 cells (a derivative of the HuH-7) that was reported previously  [34]. In that screening campaign, Huh-7. 5 (PTA-8561, U. S. Patent Number 7455969) was inoculated with DENV2 (BR DEN2 01-01, GenBank JX073928) (M. O. I. 0. 5) in the presence of 10 mM compounds and incubated under humidified conditions at 37uC, 5% CO 2 for 72 hrs. Percent infection and cell number was determined using an image-based immunofluorescence detection of dengue E protein in infected cells by flavivirus group-specific aE monoclonal antibody 4G2 and nuclei staining with DAPI, and analyzed using another customized plug-in of the Image Mining platform.
Image-Based High-Content Assay of CHIKV Infectivity CHIKV-118-GFP infection of HuH-7 in the presence of the hit compounds for 24 hours was analyzed by image-based highcontent assay. HuH-7 cells were seeded in 96-well m clear-plate black (Greiner Bio-one, Germany) at 5610 4 cells per well and kept under humidified conditions at 37uC, 5% CO 2 for 24 hrs. CHIKV-118-GFP was inoculated at a M. O. I. of 0. 5 in the presence of 50 mM MPA, 50 mM CQ, 20 mM hit compounds or 0. 5% DMSO vehicle and incubated under humidified conditions for 24 hrs at 37uC, 5% CO 2. Cells were fixed and stained with 4% PFA containing DAPI (5 mg/mL) for 30 min at room temperature, then washed thrice with DPBS. Confocal fluorescence images from 10 different areas of the well were acquired using Operetta (206 objective lens) and analyzed by the customized plug-in software in the Image Mining platform.
Chemical Structures of Anti-CHIKV Compounds The chemical structures of the 6 confirmed hit compounds are shown in Figure  4. Based on the structural analysis using the molecule-clustering module from Pipeline Pilot, 4 of the compounds -CND0335, CND0364, CND0366, CND0415 share a benzofuran scaffold, with a common substitution of 4-methoxy-2-methylphenyl at the 3-or 4-positions in the benzofuran core plus amide linker with hydrophobic groups at the 7-position. Two other compounds, CND0545 and CND3514, are singletons having a pyrrolopyridine or a thiazol-4-carboxamide base scaffold, respectively.
In MS research, 118 studies that have reported QoL as an outcome were performed with MS patients in the Clinical Trials registry (ClinicalTrials. gov, December 31, 2012). Despite the acknowledged need to consider QoL issues, QoL assessment remains under-utilized in MS clinical practice  [8]. QoL assessment may be considered to be an "unfulfilled promise"  [9] [10] [11]. Therefore, these issues should be explored and understood to promote both the use and usefulness of measuring QoL in MS clinical practice. Here, we explore the difficulties for clinicians to choose and determine the most appropriate QoL measure, to be convinced by the clinical utility of the QoL assessment implementation in clinical practice and to interpret QoL scores.
The Ac H-NOX proteins lack a critical tyrosine residue in the non-polar distal heme pocket, making it likely that they are for nitric oxide (NO) rather than oxygen signaling  [34]. Both Dd and Ac are responsive to light, although the photoreceptor that mediates phototaxis in Dictyostelium has yet to be identified  [35]. We identified two rhodopsins both with Figure  1  Measures of the composition of the Ac genome based on sequence similarity. For each protein, the best BLASTP hit to the nonredundant database, that is, the match with the lowest e-value, was recovered and the classification of the corresponding organism was extracted according to NCBI taxonomy. The central bar represents the full complement of annotated Ac genes exhibiting a best BLASTP hit respectively against the four kingdoms -Eukaryota (blue), Bacteria (red), Archaea (green) and viruses (purple) -with orphan genes depicted in yellow.
Sequencing and assembly Genome assembly was carried out using a two-step process. Firstly, the Illumina reads were assembled using the Velvet  [96]  short read assembler to generate a series of contigs. These assembled contigs were used to generate a set of pseudo-reads 400 bp in length. These pseudo reads were then assembled in conjunction with the 454 FLX and Sanger sequences using version 2. 3 of the GS De Novo Assembler using default parameters (Table  S1. 1. 1 in Additional file 1). The assembly contained 45. 1 Mb of scaffold sequence, of which 3. 4 Mb (7. 5%) represents gaps and 75% of the genome is contained in less than 100 scaffolds. For assembly statistics see Table  S1. 2. 1 in Additional file 1. In order to determine the coverage of the transcriptome, we aligned our genome assembly to a publicly available EST dataset from GenBank (using the entrez query acanthamoeba EST) AND 'Acanthamoeba castellanii' [porgn:txid5755]).
Gene structure prediction Gene finding was carried out on the largest 384 scaffolds of the Ac assembly using an iterative approach by firstly generating gene models directly from RNA. seq to train a gene-finding algorithm using a genome annotation pipeline followed by manual curation. Firstly, predicted transcripts were generated using RNA. seq data from a variety of conditions (Table  S1. 4. 1 in Additional file 1) in conjunction with the G. Mo. R-Se algorithm (Gene Modelling using RNA. seq), an approach aimed at building gene models directly from RNA. seq data  [97]  running with default parameters. This algorithm generated 20,681 predicted transcripts. We then used these predicted transcripts to train the genefinder SNAP  [98]  using the MAKER genome annotation pipeline  [99, 100]. MAKER is used for the annotation of prokaryotic and eukaryotic genome projects.
It identifies repeats, aligns ESTs (in this case the transcripts generated by the G. Mo. R-Se algorithm) and proteins from (nr) to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations. The 17,013 gene predictions generated by MAKER were then manually annotated using the Apollo genome annotation curation tool  [101, 102]. Apollo allows the deletion of gene models, the creation of gene models from annotations and the editing of gene starts, stops, and 3' and 5' splice sites. Models were manually annotated examining a variety of evidence, including expressed sequence data and matches to protein databases (Section 1 of Additional file 1). Out of a total of 113,574 exons, 32,836 are exactly covered and 64,724 are partially covered by transcripts and 7,193 genes have at least 50% of their entire lengths covered by transcript data.
Functional annotation assignments Functional annotation assignments were carried out using a combination of automated annotation as described previously  [103]  followed by manual annotation. Briefly, gene level searches were performed against protein, domain and profile databases, including JCVI in-house non-redundant protein databases, Uniref  [104] , Pfam  [105] , TIGRfam HMMs  [106] , Prosite  [107] , and InterPro  [108]. After the working gene set had been assigned an informative name and a function, each name was manually curated and changed where it was felt a more accurate name could be applied. Predicted genes were classified using Gene Ontology (GO)  [109]. GO assignments were attributed automatically, based on other assignments from closely related organisms using Pfam2GO, a tool that allows automatic mapping of Pfam hits to GO assignments.
Acknowledgements This work was funded by the  Irish Science Foundation (SFI)  grants  05/RP1/ B908  and  05/RP1/908/EC07  awarded to BJL. The authors thank the  Broad Institute  and the investigators of 'the  Origins of Multicellularity Sequencing Project, Broad Institute of Harvard and MIT ' [110] for making data publicly available and the  Liverpool Centre for Genomic Research  for provision of 454 sequencing data. IL and MH were funded through grants from the  Austrian Research Fund  ( Y277-B03 ), the  European Research Council  ( Starting Grant   EVOCHLAMY 281633 ), and the  University of Vienna  ( Graduate School 'Symbiotic Interactions '). BT was supported by a grant from the  Natural Sciences and Engineering Research Council of Canada  (grant # 184053 ). TRB is funded by the  Centre for Biosciences.