dataesr/scientific-paragraphs-categorization huggingface dataset:
Finding Ciona orthologs of human disease genes Human disease gene data were obtained from: http://www. cbs. dtu. dk/suppl/dgf/disease_complexes/index. php (Lage et al. , 2008). A second set was obtained from the DisGeNET Database, GRIB/ IMIM/UPF Integrative Biomedical Informatics Group, Barcelona (http://www. disgenet. org/). We restricted analysis of this database to the manually curated set (http://www. disgenet. org/ds/Dis GeNET/results/curated_gene_disease_associations. tar. gz, retrieved April 2015) of 7108 human disease associated genes  (Pinero et al. , 2015). For 6012 genes, HGCN symbols (DisGeNET data) could be converted to ENSEMBL identifiers using data extracted from Biomart (ENSEMBL Release 79). Diseases associated with a complex (Data from Lage et al. (  2008 )) were transferred to the human genes belonging to that complex.
Validation of cliff algorithm and ORF predictions We validated the cliff algorithm by making an internal comparison between open reading frames defined by upstream stop codons and those without, and by analysis against NCBI RefSeq  (Pruitt et al. , 2014)  data using a set of 303 proteins with highly conserved N-termini across metazoa. In addition we validated the net output of the pipeline as a whole against the complete Ciona RefSeq data set. To assess the usefulness of the cliff algorithm in defining the 5′ ends of transcripts we compared the distribution of the peak enrichment of 5′ ends (N 100 /N 1000 ) with clusters size, comparing clusters with an in-frame stop codon upstream of the first ATG (which are therefore likely to be full-ORF) to those without a stop codon (which may be full-ORF or may be incomplete).
intestinalis. This nonredundant, well-annotated set of transcript sequences, with ongoing curation by NCBI staff, contains annotation of predicted coding sequence regions. Coding sequence coordinates are included in the GenBank data, and are additionally annotated where they are known to be truncated, at either the 5′ or 3′ end. We first compared 268 un-truncated RefSeq transcripts that had a conserved start position in C. elegans, D. melanogaster, zebrafish, chick, human and mouse and corresponded to Ciona clusters expressed at a high enough level to apply our Cliff algorithm (227 clusters, see Section 2 for details). Eleven of these clusters showed no cliff (false negative), though they included the 5′ end of the coding sequence. Two clusters had cliffs predicted at an appropriate position upstream of the start of translation, but our predictions for the ORF start disagreed with the RefSeq data.
This also suggests that instances of blocking of reverse transcriptase by secondary structure of the mRNA, which could have limited the pertinence of our method by creating internal cliffs within the coding part of the cDNA, are rare events. The data described here includes cliff scores down to the minimum value of 1; for the actual clone picking we took a more cautious approach, using a threshold of 10. We next tested how the ORFs defined in our clusters compared to all C. intestinalis RefSeq models. Using BLASTn we found alignments for 10,420 RefSeq transcripts against 10,035 EST cluster contig sequences, and, of these, 87% of the RefSeqs and 86% of the contigs agreed precisely on the predicted ORF. Where there were different predictions and we thought we could discriminate, we found 532 (5.
Gene representation and coverage of the full-ORF collection The utility of our full-ORF GATEWAY clone collection is determined to a large extent by its coverage of C. intestinalis protein coding genes. The previous sections indicated that full-ORF cluster sequences covered more than 10,000 RefSeq genes. As the ascidian community mostly makes use of the C. intestinalis Kyoto Hoya (KH) genome assembly and associated protein coding genes, refered to as KH gene models, released shortly after clone picking was completed  (Satou et al. , 2008a) , we analysed the coverage of this gene model set by full-ORF clones. The KH assembly and KH gene models made use of all available sequence data for this species, including the EST data generated for this project. The KH gene models are therefore not entirely independent of the clone sequences we were analysing, although our full-ORF predictions were not used in the gene modelling.
(2011)  and  Vidal et al. (2011) ). In a similar way, human genes associated with similar pathologies and disease status, possibly coexpressed in similar tissues, have been clustered into putative functional modules of disease complexes  (Lage et al. , 2008) , a strategy proven successful to discover novel links in human pathology (reviewed in Lage (  2014 )). Diseases, for which the whole complex is present in the Ciona genomes, and in our full-ORF collection, are thus promising for the development of an ascidian model. We thus characterized the repertoire of Ciona genes orthologous to human disease genes and their complexes as described by Lage and colleagues and in the independent human disease-associated gene database DisGeNET  (Pinero et al. , 2015)  To do this, we first established the scale of detectable orthology between these species.
Interestingly, in 63 out of the 351 nonredundant disease-associated complexes, all the genes in the complex have a Ciona ortholog. Our GATEWAY collection contains full-ORF clones for 1574 (85%) of the Lage disease-associated Ciona orthologs (Supplementary Table  1 ) and 2484 (79%) of the Dis-GeNET (Supplementary Table  2 ). 1745 human genes are in common (57% and 29%, respectively) between the two studies (labelled yes/Y versus no/N in Supplementary Table  2 ) of which 1170 (67%) have Ciona orthologs and 1011 (58%) full-ORF clones. In summary, our GATEWAY Ciona full-ORF clone collection covers human disease associated genes to 59% and 48% respectively, in the two datasets examined (with 33% and 17% that overlap). Analysis of embryonic expression patterns of orthologous gene pairs has shown that these are most similar between these species in the developing muscle tissues and nervous system  (Sobral et al.
We have however found in this study that assembly depth is very useful for assessing likely transcript 5′ ends (the cliff algorithm), as well as the relative abundance of different isoforms and the solidity of the contig assembly at key points. This problem has been recognised, and, for example, the Corset pipeline  (Davidson and Oshlack, 2014 ) maps short reads back to contigs after assembly to assess expression levels. As a consequence of such limitations the NCBI UniGene database  (Sayers et al. , 2012)  still exclusively defines representative gene transcripts on the basis of EST sequence clustering. Hybrid sequencing strategies, in which single-molecule sequencing of cDNA fragments of several kbs is corrected using short read RNA-seq are being developed to alleviate these issues  (Au et al.
Acknowledgements We thank  David Salgado  (then at IBDML, now at the  Medical School of Marseille ),  Jerome Belougne  and  Christophe Beclin  ( IMVT, Marseille, France ) for precious help in the setup phase of the project;  David Keys  and  Daniel Rokhsar , for supporting the project at  DOE Joint Genome Institute (DOE JGI) ;  Erika Lindquist  for sequencing and arraying of the large EST collection at  JGI ,  Genomic Technologies Department ,  Walnut Creek , and  Jane Grimwood  for rearraying the full-ORF collection at the  Stanford Human Genome Center ,  Stanford University, Palo Alto. Individual clones can be requested from the  Montpellier Genetic Collection  (http://www. igmm. cnrs. fr/spip. php?rubrique89&lang ¼ en), who can also be contacted for the batch distribution of the whole collection.
Funding This work was supported by the  U. S. Department of Energy Joint Genome Institute Office of Science  [Contract no. DE-AC02-05CH11231 ,  Community Sequencing Programme   05-SE-04 , to P. L]; the  United Kingdom Medical Research Council programme  [No. A252-5RG70  to M. G. ], the  French Centre National de la Recherche Scientifique  [to P. L. , U. R. ] and a grant from the  Agence Nationale pour la Recherche  ( TED ,  ANR-13-BSV2-0011-01 ).
The top pair production cross section is calculated at NNLO in QCD including resummation of next-to-next-to-leading logarithmic soft gluon terms with Top++2. 0  [34] [35] [36] [37] [38] [39]. The tchannel single-top events are generated by AcerMC  [40]  with CTEQ6L1 PDFs and Pythia 6  [41]  for hadronisation. The diboson events are produced with the Herwig generator and CTEQ6L1 PDFs. The diboson production cross sections are normalized to predictions at next-to-leading-order accuracy as calculated with  [42]. Generated events are processed with the ATLAS detector simulation program  [43]  based on the GEANT4 package  [44]. Effects from additional inelastic pp interactions (pile-up) occurring in the same bunch crossing are taken into account by overlaying minimum-bias events simulated by Pythia 8.
The combined normalization and shape uncertainties vary as a function of m ℓℓjj/J and range from 6% to 9% in the LR, 2% to 8% in the HR, and 11% to 47% in the MR. For all simulated samples, detector performance-related systematic uncertainties including the small-R jet energy scale and resolution, large-R jet energy, mass and momentum-balance scales and resolutions, the lepton reconstruction and identification efficiencies, and lepton momentum scales and resolutions are also considered. The large-R jet energy and mass scale uncertainties are evaluated by comparing the ratio of calorimeter-based to track-based measurements in dijet data and simulated events, and are validated using a data sample of high-p T W bosons produced in association with jets. A Kolmogorov-Smirnov (KS) test  [52]  is then performed between the nominal and systematically varied distributions for a given systematic uncertainty source to determine if it has a sizeable effect on the shape of background and signal estimations.
Conclusion In summary, a search for narrow, heavy resonances produced in pp collisions and decaying to diboson final states at the Large Hadron Collider has been performed. The data sample analysed, corresponding to an integrated luminosity of 20 fb -1 at √ s = 8 TeV, was recorded with the ATLAS detector. No significant excess over the Standard Model background expectation was found. Upper limits on the production cross section times branching ratio and mass exclusion limits are derived for W ′ bosons in the theoretical framework of an Extended Gauge Model and for gravitons in warped extra dimensions in the context of the bulk Randall-Sundrum model. The results represent a significant improvement over previously reported limits by ATLAS  [16]  due to increased pp collision energy and data set size as well as the development of new techniques to analyse heavily boosted decays of bosons.
acknowledge the support of ANPCyT, Argentina; Yer-PhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; EPLANET, ERC and NSRF, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, DFG, HGF, MPG and AvH Foundation, Germany; GSRT and NSRF, Greece; RGC, Hong Kong SAR, China; ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; BRF and RCN, Norway; MNiSW and NCN, Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZ Š, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Cantons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States of America.
Numerical implementation For the numerical implementation, the presented model was transformed into a dimensionless form by using the characteristic parameters of the system. The open source code DuMux was used as the basic framework  (Flemisch et al. 2011). DuMux is based on DUNE  (Bastian et al. 2008)  and was developed for the simulation of multicomponent and multiphase flows and transports in porous media. A 2D unstructured grid is imported in the format of ALUGrid  (Dedner et al. 2014). The spatial discretization is done by the Box-method (vertex-centered finite volume method; Helmig 1997). This method is a node-centered finite volume method based on the Galerkin's finite element method. Its advantage compared with finite-volumes and finite-elements is that unstructured grids can be used, while it is also mass conservative.
S1C ) and also an RNase A-treated NS sample for peak calling analyses (Supplemental Fig. S1B, C ), which is an appropriate background control used in these experiments. Supplemental Figure  S1C  shows the data reproducibility and the low background obtained with sheared genomic DNA and the RNase A-digested NS sample before incubation with λ-Exo. Supplemental Figure  S2  demonstrates that the presence of G4 in the sequence did not affect λ-Exo digestion in our conditions. We combined two complementary software tools to detect origins. SICER identifies wide genomic zones enriched in reads, whereas SWEMBL highlights the precise position of peaks  (Supplemental Fig. S1D; Methods). This joint analysis returned 65,019 high-confidence peaks that identified the major IS of DNA replication and 35,512 initiation zones (IZ) that encompassed one or several closely associated IS.
Initiation sites are characterized by specific oligonucleotide profiles Our previous analyses  (Cayrou et al. 2011 ) relied on a generic motif discovery tool (MEME)  (Bailey et al. 2015) , based on the detection of overrepresented motifs. We extended this analysis by characterizing the positional distribution profile of each oligonucleotide ("k-mer") around the IS. We used the RSAT position-analysis program  (van Helden et al. 2000)  to count the occurrences of all k-mers (from 1 to 8 b) in non-overlapping windows (50 bp) from -1 to +1 kb relative to the IS summits, according to the Watson and Crick strand. Observed occurrences were compared with the random expectation using a χ 2 test. Significant k-mers were clustered according to their position profiles (Fig. 5C ). kmer clusters were further assembled and used as seeds to extract motifs (sequence logos) (Fig.
Read density maps Read occurrence profiles were obtained by extracting 7 kb on each side of the summit of each peak (10-kb data provided in Supplemental Material) and counting the reads per 100-bp bins. Data processing was done in R (R Core Team 2014) by applying k-means clustering on the read occurrence profiles with various numbers of clusters. For visual purposes, the resulting clusters were sorted according to their average density profile to highlight the relationships between the central peak and its first closest neighbor. On the density map, pairs of neighbor peaks appear twice: once centered on the leftmost peak, and once on the rightmost peak. Consistently, our clustering procedure revealed symmetrical clusters. We labeled clusters as "L" or "R," respectively, to denote the left-side or right-side location of the secondary peak.
For further analysis, we regrouped the symmetrical pairs of clusters (e. g. , cluster 10L and 10R form cluster 10). We developed a custom R library (ocpR; available in Supplemental Materials and http://tagc. univ-mrs. fr/origins/) to automate data processing and test the impact of different parameters (k, summit flanking size) that allow data clustering, ordering, and visualization. Based on the visual inspection of the resulting heatmaps, we retained k = 19 clusters. Clustering results with other k values are in Supplemental Material. To assess the clusters' relevance, we performed two negative controls: (1) k-means clustering on randomized bins (random permutation of read count values inside each row independently); and (2) random selection of genome fragments using the RSAT program random-genome-fragments.
Overlap with genomic features and data from other sources Genomic annotations were downloaded from the UCSC Table Browser tool (Mouse genome version NCBI37/mm9 NCBI37). Overlapping of origins with the genomic annotations was done as in the Supplemental Material. Data on histone marks and transcription factors in mESCs were downloaded from the GEO database (http://www. ncbi. nlm. nih. gov/geo/). The accession numbers are detailed in Supplemental Table  S1.
Ab initio motif discovery The RSAT program position-analysis  (van Helden et al. 2000)  was used to detect oligonucleotides (k-mers) with positional biases around peak summits. The occurrence of all k-mers from 1 to 8 nt were counted in 50-bp-wide non-overlapping windows from -1 to +1 kb relative to origin summits. The difference between observed and expected distribution (window-specific Bernoulli model) was measured with a χ 2 test, and the top 100 ranking significant k-mers were clustered based on their positional profiles of occurrences. Clusters of co-occurring k-mers were used to define position-specific signals, which were used to orientate each peak for the polarity-oriented analysis of MNase occupancy.
An emerging concept in transcriptional regulation is that a class of truncated transcription factors (TFs), called microProteins (miPs), engages in protein-protein interactions with TF complexes and provides feedback controls. A handful of miP examples have been described in the literature but the extent of their prevalence is unclear. Here we present an algorithm that predicts miPs and their target TFs from a sequenced genome. The algorithm is called miP prediction program (miP3), which is implemented in Python. The software will help shed light on the prevalence, biological roles, and evolution of miPs. Moreover, miP3 can be used to predict other types of miP-like proteins that may have evolved from other functional classes such as kinases and receptors. The program is freely available and can be applied to any sequenced genome.
Materials and Methods 2. 1. miP3. miP3 is a command line program that predicts microProteins from a sequenced genome. It is implemented in Python. As input, it needs a FASTA file with all proteins in a given genome, a FASTA file with a class of proteins for which miPs are to be identified, for example, transcription factors, and a file with a list of unwanted domains, for example, DNA binding domains. To lower runtime, it makes use of the local BLAST+ tools  [9]. As input, miP3 takes FASTA-formatted TF sequences to query against a database of proteins from a genome using BLASTP and a list of DNA binding domain IDs from Interpro database  [10]. After the initial BLAST searches, a list of potential miPs is returned in a FASTA-formatted file. The putative miPs and their target TFs are subjected to InterproScan  [11]  to map protein domains.
Results and Discussion The miP3 algorithm detects putative miPs through sequence similarity with TFs and uses a number of filters to discard potential false positives. The algorithm is summarized in a diagram (Figure  1 ) and Pseudocode 1. Two types of BLAST searches are performed to identify putative miPs that share sequence similarity with TFs. First, a file containing TF sequences of an organism is used by miP3 as query in a BLASTP search against all proteins shorter than 550 amino acids in the genome with a default -value cut-off of 1e-7. The protein length filter has been set at 550 amino acids because all miPs characterized to date and the average size of proteinprotein interaction domains are smaller  [12]. To our knowledge, the Arabidopsis thaliana LITTLE SIPPER is the largest miP (541 aa) characterized to date  [2].
To leverage this information, miP3 removes putative miPs that are longer than their target TFs with a 10% size tolerance. The 10% size tolerance was set to allow the inclusion of miPs that carry longer linker regions while excluding proteins that are much larger than the TFs and less likely to be miPs. miP3 also removes putative miPs that are predicted to carry domains different from their target TFs through the InterproScan software  [11]. The performance of this version of the miP3 program was tested in Arabidopsis thaliana. To determine the best set of -value thresholds, we compared a number of combinations of -value thresholds for the three BLAST runs against a set of characterized miPs in A. thaliana (Supplemental Tables  1  and 2 ). We used the most stringent set of -value thresholds at the highest recall rate (59% recall) as default parameter values in the software.
The software set with the default parameters detected 10 of the 17 characterized miPs in A. thaliana (Supplemental Table  2 ). The false negatives fall into three categories (Supplemental Table  2 ): (1) one characterized miP does not have a protein sequence available in TAIR  [15] ; (2) one characterized miP was too divergent in sequence from the TFs; (3) 5 atypical bHLH miPs contain the HLH domain but are missing the basic residues, which is currently not detectable by domain mapping using InterproScan. It is difficult to assess precision without extensive experimental validation, which is beyond the scope of this paper. However, if we use physical interaction between a miP and its target TF as a criterion for being a true positive, we can assess precision based on the status of physical interactions of predicted miPs that have not yet been experimentally characterized.
Five putative miPs that were predicted by miP3 to target A. thaliana homeodomain transcription factors were previously tested for physical interaction with their target TFs in yeast two-hybrid assays (Supplemental Table  2  and [2] ). If we consider the three predicted miPs that failed to interact physically with the target TFs as false positives, the precision would be 40%. However, this is likely to be an underestimation because a number of characterized miPs have been shown to interact only with a partner of their target TFs and not with the target TFs themselves  [2]. Currently the miP3 program relies on protein domain mapping based on the domain profiles available in the Interpro database, which can miss some miPs. For example, if a protein has sufficient sequence similarity to resemble a DNA binding domain but has other characteristics that prevent it from binding to DNA (e.
Conclusions miP3 has been designed to detect miP/TF couples that share sequence similarity. All miPs characterized to date are homologous to their target TFs. Nevertheless, we cannot exclude the existence of miP/TF couples that might have diverged considerably in sequence or evolved through convergent evolution. The software will help shed light on the prevalence and evolution of a potentially universal miP function. Moreover, the design of the software allows the prediction of any group of proteins that have evolved from different types of proteins by domain loss. For example, to search for proteins that are similar to kinases but have lost the kinase domain in a genome, one simply needs to replace the TF sequences with kinase sequences and the DNA binding domain list with a kinase domain list.
set evalue all, evalue small, evalue reblast, DBD domains open transcription factors as input small proteins database, all proteins database, transcription factors database ← initialize BLAST reference databases putative miPs ← new list for transcription factor in transcription factors do TF domains ← InterproScan(transcription factor) subjects all ← BLAST transcription factor against all proteins database for subject in subjects all do if length(subject) ≤ 550 and length(subject) ≤ 1. 1 * length(transcription factor) and evalue of subject ≤ evalue all then subject domains ← InterproScan(subject) if subject domains not in DBD domains and length(subject domains) < length(TF domains) BLAST transcription factors against small protein database for subject in subjects small do.