dataesr/scientific-paragraphs-categorization huggingface dataset:
Journals do not refer in any consistent way to animations, nor are animations linked to the EMDB and PDB entries on which they are based. A dedicated, sustainable public archive for animations based on experimental structures that could be referred to by journals would be very useful. To facilitate citations, animations could have accession codes and metadata linking them to the parent PDB and EMDB entries. It is clear that the field of cellular structural biology is poised for exciting developments in the coming years that will yield new insights on the 3D structure and dynamics of organelles, cells and samples as well as the cellular context of molecular complexes and machines. To fully realize the potential of the emerging 3D cellular imaging techniques, it is imperative to address the issues of data archiving (including deposition, curation, validation, analysis, integration and dissemination) as soon as possible while the fields are still relatively young and their communities small.
ACKNOWLEDGMENTS We thank  P. Haslam  for help with the manuscript and logistical organization of the workshop. The workshop was supported by grants from the  Wellcome Trust  to the PDBe (088944) and OME (095931). Work on the EMDB at the PDBe is supported by the  European Commission   Framework 7 Programme  ( 284209 ), the  US National Institutes of Health National Institute of General Medical Sciences  ( R01 GM079429 ) and the  UK Medical Research Council  ( MR/L007835 ), with further support to the PDBe by  EMBL-EBI  and the  Wellcome Trust  ( 088944 ). Work on the OME at the  University of Dundee  is supported by the  Wellcome Trust  ( 095931 ) and the  Biotechnology and Biological Sciences Research Council  ( BB/G022585 ).
Implementation Models were implemented in R664 version 3. 0. 2 [22]. TheR-Scripts are included as part of the material (Script S1, S2, S3, S4). Dynamic regression was performed with function glm (package {stats)  [22]  for Poisson regression and glm. nb (package {MASS})  [23]. The expected number of counts at time 6 were estimated with the predict function of the respective package. Alternative regression models were evaluated using the Akaike information criterion (AIC). In addition adjusted deviance (Deviance/df) was used as a measure of goodness of fit (GOF). The receiver operating characteristic (ROC) curve was generated in R by simulation. Counts for negative weeks were sampled from a Poisson distribution (function rpois in package {stats}) with lambda equal to the predicted value for each week in 2011 and 2012 (n = 53000).
Counts for positive weeks were generated by sampling values from the fitted outbreak distribution (function rnbinom in package {stats}) and adding to the baseline. SE and SP were calculated for values of Log10(V) between -1 and +3 in steps of 0. 01. The expected PPV for each value of V was calculated as above using the prior odds for outbreak from three scenarios. Threshold values for posterior odds (O post *) were estimated using the Solver function of Microsoft Excel 2007.
Sources of data As a proof of principle the value of evidence framework was applied to neurological and respiratory syndromes in French horses. The associated time series are named NeurSy and RespSy, respectively. These data are collected through the passive surveillance system ''RESPE'', the French network for the surveillance of equine diseases (http://www. respe. net/). This system collects the declarations from veterinary practitioners registered as sentinels who fill online a standardized questionnaire depending on the syndrome concerned. Along with their declaration, veterinarians send standardized samples for the laboratory diagnosis. Tests for equine influenza, equine herpes 1 and 4 and equine arteritis viruses are implemented in the case of a respiratory syndrome, West Nile and equine herpes 1 viruses in the case of a nervous syndrome.
In our study, we used these weekly time series. Data from 2006 to 2010 were used to train our models and define the background noise of each time series when no outbreak occurs. We only used the data on the number of cases with no positive laboratory test result in order to remove the outbreaks from our datasets and obtain these outbreak free baselines. Then, different regression models were tested. No real outbreak of West Nile disease and divergent strains of equine influenza (New-Influenza) occurred during this time. Instead fictive test data were used for demonstrating outbreak detection. The baselines in the test data were based on NeurSy and RespSy data from 2011 to 2012 where unexplained aberrations, not related to the diseases of interest, were filtered out and fictive outbreaks inserted based on historical data.
Case study 2-Respiratory syndromes and equine influenza (RespSy) The same approach was successfully applied to the RespSy dataset. However, in this case the analysis indicated a significant degree of overdispersion in the weekly counts. Using the same regression model (counts , sin(2p t) + cos(2pt) + log(histmean)) the NB model had lower AIC (1141 vs 1284) and GOF closer to one (1. 14 vs 2. 54) compared to the Poisson model. The theta parameter for the NB distribution was 1. 78, and resulting in a much wider confidence interval for the expected number of cases in a non-outbreak situation (Figure  S2 ) compared to the Poisson model (Figure  S3 ). When the NB and Poisson models are applied to the same test dataset (Figure  S4 , S5) the latter will report a value of evidence for the inserted peaks (D, E) that is several orders of magnitude higher than does the NB model.
The same conclusion holds for seasonal patterns. The overdispersion in the RespSy dataset is largely due to veterinarians sampling several horses in a stable upon suspicion. Thus, in this special case it might be possible to handle the overdispersion by pre-processing the data to remove redundant cases, provided that the same pre-processing is applied to new data on weekly basis. However, when the mechanism behind overdispersion in baseline counts is not so transparent that automatic filtering out redundant cases is possible the NB model will support a correct interpretation of the value of the peak in the count data. As indicated in Figure  4  the tradeoff between SE and SP differs between seasons. This is natural since in case the (prior) probability of an outbreak differs between seasons the average sensitivity SE avr and specificity SP avr will be given by: SE avr ~Pn i~1 (d i |SE i |P i ) P n i~1 (d i ) ðEq:16Þ SP avr ~Pn i~1 (d i |SP i |(1{P i )) P n i~1 (d i ) ðEq: 17Þ where: SE i = sensitivity in season i SP i = specificity in season i P i = (prior) probability of outbreak in season i d i = (relative) duration of season i Thus, by incorporating prior knowledge about the seasonality of the diseases of interest it is possible to achieve a high average sensitivity without sacrificing the PPV and SP.
Massively parallel (next generation) sequencing We used the Illumina HiSeq2000 platform, with single-end 50 cycle sequencing. Sequencing was performed by the McGill University and Génome Québec Innovation Centre in Montreal, Quebec. Data was downloaded onto our servers in FASTQ format. Illumina de-indexed data was first processed using FastQC tool v0. 10. 0 (http:// www. bioinformatics. babraham. ac. uk/projects/fastqc/), which provides a user-friendly overview of raw sequencing data. The programs fastx_clipper, fastx_trimmer and fastx_collapser [part of the FASTX-Toolkit Figure  5  Pre-sequencing quality control of bisulfite-converted DNA libraries. A) Cloning and subsequent EcoRI release of library inserts reveals a random size distribution as revealed on an agarose gel. The extra band in some lanes represents enzyme cut sites present in the inserts.
Alignment There are many excellent aligners for bisulfite sequencing data including Bismark  [13] , BSMAP  [14, 15] , and RMAP  [16] , BS-Seekeer2  [17]  and some of these have been recently assessed  [18]. We opted to use Bismark  [14]  for its flexibility and compatibility with downstream processing tools such as MethylKit  [19] , which we use routinely for post-alignment processing. All user-set features were set to default, except for: --best, --n 2, anddirectional. For data visualization, we wrote a custom Perl script to convert Bismark output alignment files into the SAM (Sequence Alignment/Map) format for the visualization of the alignment reads into the Integrated Genomic Viewer  [20]. This script is available for download at: www. mcgill. ca/psychiatricgenetics/tools-0. Further description of bioinformatic details can be found in the results section.
Using imprinted regions and cloning to finalize alignment and library quality assessment There are many known imprinting clusters in the human genome, though the tissue specificity and extent of methylation at each site are not precisely known  [27, 28]. To provide reference maps for expected methylation levels in human brain tissue and to demonstrate how these regions can be used as positive controls for a BisSeq experiment, we began by selecting known imprinted CpG islands previously documented in sperm  [29]. Next, we assessed the same CpG islands in our sample using reads across all lanes and combining the data for each subject, though we included two CpG islands from genes that should not be imprinted, in other words, two regions where we did not expect to observe any methylation. Table  2  shows the results from this experiment and we note the relatively narrow ranges across subjects for each locus.
4 M amino acids, larger than that of any other investigated animal except the zebrafish, which is known to retain excess protein duplicates from a recent teleost-specific genome duplication (Fig. 5a  and Supplementary Tables  16  and 17 ). We detected 4,471 ancient domain types (that is, nonvertebrate-specific domains) in the lancelet, which is a higher number than in any examined vertebrate (Fig. 5a ; Supplementary Tables  16  and 17 ). Lancelets also preserve 144-193 (depending on criteria) ancient domains that were not found in several investigated vertebrates (Supplementary Tables 18-20; Supplementary Note 11). Because the Pfam database is biased towards vertebrates, we expect that there may be many undiscovered domain types present in lancelets and other invertebrates that are absent in vertebrates.
Using a de novo method, we identified 941 candidate novel domains that are conserved in the two lancelets but absent in vertebrates; the 375 most confident candidates were distributed in 1,884 proteins (Supplementary Figs  30  and 31 ; Supplementary Note 11). We functionally verified one of the candidates, the ApeC domain (deposited in the Pfam database under accession PF16977), as a novel pattern recognition domain for bacterial peptidoglycan  31. We also used a BLAST-clustering method to directly measure the sequence diversity of all protein domains (vertebrate-specific domains included) in humans, mice, zebrafish, tunicates and lancelets (Supplementary Note 11). Our results suggest that lancelets have the highest domain sequence diversity (Fig. 5b ). These findings suggest that lancelets have higher protein diversity than many (if not all) vertebrates, which is particularly striking considering the lancelet's compact genome size.
RNA-seq. Transcriptomes from multiple Chinese lancelets representing different developmental stages, tissues were sequenced to a total of B120 Â using both the Illumina GAIIx platform and the 454 platform. The de novo transcript assemblies were created using Newbler and Trinity  55. All reads were mapped to the reference genome using GMAP/GSNAP  50  to accommodate high polymorphism. Genomebased transcript assemblies were created from mapped reads using Cufflinks  56. Gene prediction and functional annotation. Protein gene models were obtained by integrating the results of de novo gene prediction, homology-based and transcriptome-based prediction. Multiple prediction sets, including cDNA alignments by PASA  57  , protein alignments by GeneWise 58 , RNA-seq alignments by Cufflinks  56  , ab initio data sets from Augustus  59  and GlimmerHMM 60 and RNAseq-based predictions by Augustus  59  , were combined into a non-redundant gene set using EVidenceModeler  57.
The initial combined prediction set was fed to Augustus  59  for a new round of evidence-based prediction for alternatively spliced isoforms. Proteins were annotated by sesearching against the InterPro database 61 , the Pfam domain database  62  , the gene ontology database  63  and the KEGG database  64. Polymorphism and population structure. SNPs, indels and translocations were called based on the refined whole-genome alignments between haploid assemblies and individual assemblies using customed Perl scripts. Synonymous versus nonsynonymous polymorphism rates, nature selection and population structure were analysed using PAML  65  and MEGA  66. Amplified mitochordial sequence fragments from lancelet populations were analysed using MEGA  66. Divergence and phylogenetic analysis. Sequence divergence analysis was based on gene orthologues.
Gblocks  68  was used to remove the less-conserved sites. Bayesian and maximum-likelihood analyses were used using Phylobayes  69  and PhyML  70  , respectively. Molecular dating was carried out using both Phylobayes and PhyML (Phytime). Proteome diversity and domain combinations. Protein sets from up to 25 species (aforementioned) were analysed by sesearching against the Pfam database 62 (both Pfam-A and Pfam-B). All protein isoforms of a gene were used for analysis. Different cutoff criteria (E-value and alignment coverage) were used for comparison. The Pfam database is biased towards the vertebrates (particularly mammals); hence, we separated the vertebrate-specific domain types from those ancient protein domain types that are present in non-lancelet invertebrates. We also performed a direct comparison of domain diversity between human, mouse, zebrafish, ascidians and lancelet using a Blastclust-based method.
Conserved non-coding elements. CNE were identified between Chinese and Florida lancelets, human versus mouse, human versus opossum, the worms C. elegans versus C. briggsae and the insects D. melanogaster versus D. mojavensis. A reciprocal-best whole-genome alignment method (that is, the aforementioned LASTZ-chainNet method  47  ) was used to identify CNEs between two genome sequences. Only cis-regulatory elements, microRNAs and long non-coding RNAs were retained in the CNE data sets, whereas all the other entities such as coding regions, pseudogenes, TEs and other RNA genes were filtered. Software and data. Genome data of the Chinese lancelet, including reference and alternative assemblies, annotations, proteins, transcripts and reconstructed TE sequences, are accessible on our website: http://mosas.
Acknowledgements This work was supported by project  31171193  (NNSF), 2008AA092600 (863), 2013CB835300 (973), 2011CB946101 (973) and 91231206 (NNSF) and by projects from the  Guangdong Province Key Laboratory of Computational Science and the Guangdong Province Computational Science Innovative Research Team. We thank  Dr Daniel Chourrout ,  Dr Linda Holland ,  Dr Chris Gunter  and  Dr Hector Escriva  for their comments and help in manuscript preparation. We thank  Roger Winer ,  Lei Du  and the 454  Life Sciences Corporation  for their help on pyrosequencing.
Brassica gene identification Sequence searches by similarity to A. thaliana AtGLN1 coding sequences were performed in the GenBank and Genoplante databases using the BLAST algorithm  (Altschul and Lipman, 1990 ) and the A. thaliana AtGLN1 coding sequences AtGLN1. 1 (NM_123119, At5g37600), AtGLN1. 2 (NM_105291, At1g66200), AtGLN1. 3 (NM_112663, At3g17820), AtGLN1. 4 (NM_121663, At5g16570), and AtGLN1. 5 (NM_103743, At1g48470). The BlastN option was used to recover B. napus, B. rapa, and B. oleracea ESTs and complete mRNAs as well as genomic sequences of B. napus and B. rapa  (Cheng et al. , 2011;  http://brassicadb. org/brad/). Contigs of ESTs were built using the CAP3 algorithm  (Huang and Madan, 1999)  and validated through multiple sequence alignments with ESTs and AtGLN1 coding sequences, allowing a manual proofreading.
Sequence analysis A global alignment of coding sequences from mRNA, inferred coding sequences, or newly created contigs from ESTs was generated with ClustalW  (Thompson et al. , 1994). Distance matrixes were computed using the Dnadist algorithm with a Kimura 2 nucleotide substitution model, and bootstrap analysis was performed with 1000 iterations. A consensus unrooted tree was then generated using the Neighbor-Joining method. All algorithms are contained in the Phylip 3. 67 package available at the MOBYLE platform (http://mobyle. pasteur. fr). The NCBI Conserved Domain Database  (Marchler-Bauer et al. , 2011)  was searched with translated B. napus and A. thaliana GLN1-coding sequences. A multiple protein sequence alignment was generated with the ClustalW algorithm.
Arabidopsis thaliana genes were selected from among the list established by  Czechowski et al. (2005). Their respective coding sequences were used to retrieve highly similar B. napus ESTs from GenBank using the BlastN algorithm. One B. napus EST per reference gene was selected to design qPCR primers (Supplementary Table  S4 ). For each run, single product amplification was confirmed by melt curve analysis. PCR products from each primer pair and genotype were sequenced in a preliminary analysis. The amplification efficiency was assessed for each genotype, with each primer pair using a dilution curve method over six orders of magnitude, on a pool of cDNAs from different tissues and modalities. Selected primer pairs have efficiencies >1. 8. The results reported were obtained from four biological replicates and two reverse transcriptions as technical replicates.
Identification of GLN1 coding sequences in EST databases of Brassica napus and its progenitors Brassica oleracea and Brassica rapa A total of 588 B. napus ESTs, 126 B. rapa ESTs, and 36 B. oleracea ESTs highly similar to one or several of the five AtGLN1. 1-AtGLN1. 5 mRNA-coding sequences from A. thaliana were isolated from public and private Genoplante databases (Supplementary Data File S1 at JXB online). Sequence assembly and alignments with AtGLN1 mRNA sequences revealed different groups of transcripts that allowed ESTs to be grouped and 16 individual contigs that might correspond to different BnaGLN1 genes of B. napus to be extracted (Supplementary Data File S2). Eight contigs for B. rapa and seven contigs for B. oleracea were also isolated (Supplementary Data Files S1, S2). The BnaGLN1, BraGLN1, and BolGLN1 contigs from B.
1. Cytosolic glutamine synthetase (GS1) phylogenetic tree. DNA coding sequences (CDS) were aligned using Clustal. The distance matrix was computed using Dnadist with a Kimura 2 nucleotide substitution model (bootstrap analysis, 1000 iterations). A consensus unrooted tree was then generated using the Neighbor-Joining method from the Phylip 3. 67 package. Black and grey dots indicate bootstrap values >90% and 50%, respectively. All programs are available at www. mobyle. pasteur. fr. a Incomplete CDS sequence when compared with the A. thaliana reference sequence. Except for the four BnaGLN1. 1_C1, BnaGLN1. 1_C2, BnaGLN1. 2_C1, and BnaGLN1. 2_C2 contigs, no other BnaGLN1 sequences have ever been described previously in the literature or reported in databases as glutamine synthetase gene products.
According to the BRAD annotation, this inserted region has been described as an long terminal repeat (LTR) transposon of 3746 bp on the minus strand. The identified The sequencing programme performed at URGV allowed identification of BnaGLN1 sequences in the B. napus Darmorbzh genome (SEQ-POLYNAP, ANR-09-GENM-021). The BraGLN1 protein sequences were deduced from the identified BraGLN1 genes using the BRAD tool, and used to search the database of protein sequences built from B. napus genomic sequence analysis (unpublished data). From the BnaGLN1 protein sequences identified, genomic sequences were recovered (Supplementary Data File S4 at JXB online). Interestingly, 16 putative BnaGLN1 genes and two putative BnaGSL genes (coding for the GS2 isoform) were found. The 16 BnaGLN1 genomic sequences (Supplementary Data File S4) were used to analyse similarities with the B.
napus contigs and to create a phylogenetic tree (Supplementary Fig. S4 ). Interestingly, each genomic sequence was closely associated with one contig sequence, suggesting that all the genes with the 16 BnaGLN1 contigs had been found. The deduced mRNA sequences (Supplementary Data File S5), obtained using FGENESH software available on the SoftBerry website (http://linux1. softberry. com/berry. phtml?topic=fgenesh&group =programs&subgroup=gfind), showed very a high similarity with the contig sequences (Table  4 ) and allowed the gene structures to be deduced (Fig. 2 ). Except for BnaGLN1. 3_C5 and BnaGLN1. 3_C6, similarities between mRNA and associated contigs were near 100% (Table  4 ). Knowing that contig sequences and mRNA sequences are obtained from different B. napus genotypes (Supplementary Data File S1), this indicates that there is almost no polymorphism between the different BnaGLN1 coding sequences regarding the various genotypes of B.
Expression of BnaGLN1 genes is modified depending on the nitrogen regime and leaf senescence A first analysis of EST distribution between libraries and BnaGLN1 contigs led to the conclusion that BnaGLN1 genes are probably differentially expressed according to tissue and developmental stage (Supplementary Data File S1 at JXB online). The BnaGLN1 gene expression was monitored at the vegetative stage measuring transcript levels by quantitative realtime RT-PCR in samples of taproot, crown, limbs, and veins of plants grown under low or high nitrate conditions. Plants grown under low or high nitrate conditions grew 13 and 17 leaves, respectively. F v /F m and SPAD measurements on all the leaf ranks (numbered from the bottom leaf to the top leaf) were done to estimate the relative leaf senescence status of each leaf.
METhODS Methods and any associated references are available in the online version of the paper. Accession codes. The tongue sole whole-genome shotgun project has been deposited in DDBJ/EMBL/GenBank under the project accession PRJNA73987 (with the associated accession code AGRG00000000). RNA-Seq and miRNA sequencing reads have been deposited in the NCBI Sequence Read Archive (SRA047922 and SRA122228), and BAC sequences have been deposited in the GSS division of GenBank (JQ003878-JQ003881). Note: Any Supplementary Information and Source Data files are available in the online version of the paper. and air dried. The dmrt1 BAC DNA was extracted and labeled by nick translation using a Nick Translation System (DIG-Nick Translation Mix, Roche). The BAC FISH probe contained 1 µg of labeled dmrt1 BAC DNA, 50 µg of sonicated salmon sperm DNA and 10 µg of Cot-1 DNA.
After hybridization in a moist chamber at 37 °C for 24 h, chromosome slides were subjected to a series of washing steps (2× SSC (TaKaRa) for 5 min, 50% formamide for 5 min and 1× SSC for 5 min). Signal detection and amplification were performed using sheep anti-digoxigenin and FITC donkey anti-sheep (Dig-Nick Translation Mix, Roche, 11745816910). FISH staining was performed with propidium iodide. Image capture was carried out with a NIS-element fluorescence microscope (Nikon) and then analyzed by the LUCIA system and Adobe photoshop software (Supplementary Note). RT-PCR and bisulfite PCR (BS-PCR) analysis. Total RNA was isolated and purified from all the samples using a traditional phenol method  78  , and the RNA concentration was measured using Nanodrop technology. Primers for quantitative RT-PCR (qRT-PCR) analysis were designed using the Primer Premier 5 program.