dataesr/scientific-paragraphs-categorization huggingface dataset:
Most of the databases show a steady rate of increase over time, while some, such as ProDom and PANTHER, fluctuated in size. The percentage of entries integrated into InterPro for each member database is shown in Table  2. As can be seen, the level of integrated entries is in excess of 90% for all databases, except CATH-Gene3D, Superfamily, PAN-THER and ProDom. The former two both define domains based on known 3D structures and tend to represent the broadest entries in InterPro. However, as they are based on two different structural classification resources (CATH  (22)  and SCOP  (23) , respectively), there can be differences in scope and domain boundaries, which can make integration problematic. However, the two databases have been working towards greater alignment through the Genome3D project  (24) , which should help address this issue.
The current release of PANTHER, meanwhile, provides almost 60 000 signatures, far in excess of any other member database, resulting in a considerable backlog of entries to work through. In addition, two PANTHER releases involved significant    changes (see Figure  3 ): in 2010 a new phylogenetic tree reconstruction algorithm and reference sequence set were introduced  (25) , and in 2013 subfamily boundaries within families were refined to increase consistency. Any entries removed from PANTHER during the rebuilding process are also deleted from InterPro, so that the integration of PANTHER entries into InterPro is not an entirely cumulative process. Finally, ProDom is an automatically generated clustering of sequence space, so the regions provided by ProDom do not always reflect protein family domain boundaries; they also lack annotation.
Targeted curation of GO terms for bacterial metabolism Microme (http://www. microme. eu) is a resource for bacterial metabolism that aims to support the large-scale inference of biochemical pathways directly from genome sequence. To this end, Microme has developed a Genome-Reaction Matrix (GRM) containing inferred reactions from thousands of genomes that can be used to build draft metabolic networks and models. Several sources are used for the reaction sets, including InterPro annotations and GO terms, and curated associations of GO functions to reactions in the RhEA database  (26). In order to improve the accuracy and coverage of the inference methodology used in the GRM, a Microme curator spent 10 weeks working with the InterPro team, manually reviewing almost 4000 In-terPro entries relating to transporters and proteins involved in metabolism, by extracting evidence from published experimental data.
Approximately 10% of the InterPro entries examined were then associated with new or improved GO terms. These new annotations were used to increase the coverage of metabolic and transport reactions in Microme. On a data-set of 5423 genome sequences present in Microme release 3. 0, the number of gene-reaction associations increased by 9% (to a total of 4 730 692), the number of genome-reaction associations by 9% (to a total of 2 162 546), and the average number of reactions per genome also by 9% (to a total of 399). The improved annotations were also propagated to UniProtKB, through the InterPro2GO pipeline, resulting in improved annotations for over 2. 5 million sequences, as of release 2014 07. As an additional beneficial outcome, 35 new GO terms were created as part of this work, 74 new reactions were curated into RhEA (with the curation of additional chemical entities in the ChEBI database  (27)  where necessary to support this) and 113 new GO-RhEA mappings were created.
New website features Sequence search. In response to user feedback, the sequence search facility on the InterPro website has been refined to include an 'Advanced option', allowing users to select which member database and/or additional sequence feature prediction algorithm (i. e. Coils, Phobius  (28) , TMHMM  (29) , SignalP (30)) to run. This facility is available under the 'Search'--'By sequence' tabs at the top of the InterPro homepage.
Domain architecture search interface. A new domain architecture search tool has been developed and made available via the website (http://www. ebi. ac. uk/interpro/search/ domain-organisation); this allows the InterPro database to be searched with a particular domain, or set of domains, returning all of the domain architectures and associated proteins that match the query. This makes it easy to rapidly identify all of the different domain combinations, where one type of domain co-occurs with another, or where a particular domain is followed by another domain (e. g. an SH3 domain is found C-terminal to a protein kinase domain, or vice versa), and to list all of the proteins in UniProtKB with a matching domain architecture. The tool is specifically designed to work with InterPro entries of type domain, which represent distinct functional or structural units that may be found in different proteins with a range of overall functions.
It is these entries that typically recombine to provide functional diversity and are the subject of combinatorial searches. InterPro entries of type family (representing groups of evolutionarily related proteins that share common functions) are not catered for, since such entries tend to be near full length and typically do not undergo recombination. The tool makes use of a specialized graph-theory-based algorithm that rapidly searches through all domain matches within InterPro and returns proteins that match the domains in the order specified in the query. As InterPro integrates data from a number of different member databases whose domain boundary predictions do not always agree, InterPro domains may overlap. This is in contrast to the 'beads on a string' representation that is sometimes used to display domain architectures.
Users can launch the tool using an interactive panel on the left-hand side of the page. Clicking on this launches a pop-up window with a searchable list of all of the domains in the database (see Figure  4 ). Once an appropriate domain has been identified from the list, it can be added to or removed from the query using plus and minus buttons next to its name. The same domain can be added to a query multiple times (e. g. to identify all of the proteins in UniProtKB predicted to have two or more pleckstrin homology domains). Once the required domains have been selected, pressing the 'Apply' button performs the appropriate query. The different domain architectures matching the query, along with the number of proteins matching each domain architecture, are displayed graphically (as shown in Figure  5 ).
AVAILABILITY The InterPro database and related software are freely available for download and distribution, provided the appropriate Copyright notice is supplied (as described in the accompanying Release Notes). Data can be downloaded in a flat-file format (XML) and via the Web interfaces described in the text. The InterProScan software is available: (i) as a browser-based tool for analysing single protein sequences (http://www. ebi. ac. uk/interpro/search/sequencesearch/); (ii) programmatically via Web services that allow up to 25 sequences to be analysed per request (SOAPbased service documented at http://www. ebi. ac. uk/Tools/ webservices/services/pfa/iprscan5 soap and REST-based service at http://www. ebi. ac. uk/Tools/webservices/services/ pfa/iprscan5 rest) and (iii) as a downloadable package for local installation (https://code.
DISCUSSION The rate of growth of sequence data has increased massively in recent years, following the take-up of Next Generation Sequencing (NGS) methodologies. As a consequence, the rate of deposition of nucleotide (and thus protein) sequence data has greatly increased: at the beginning of this century, UniProtKB contained ∼470 000 entries; the current total is ∼80 million, ∼40 million of which were added in the last year alone. A key challenge facing bioinformatics is the accurate and consistent annotation of these sequences. The scale of this task is illustrated in Figure  6. This graph shows the growth of the manually annotated Swiss-Prot section of UniProtKB versus the exponential increase in UniProtKB/TrEMBL, which has no manual annotation associated with it. Over the last 10 years, UniProtKB's team of dedicated expert Swiss-Prot curators have manually annotated ∼400 000 protein sequences.
However, this figure is approximately the number of sequences now entering UniPro-tKB per week, and the rate of sequence deposition is growing. Clearly, even the most extensive manual efforts cannot meet the challenge of annotating these data. Automated annotation transfer is also beyond the scope of all-against-all pairwise-alignment methods, such as BLAST, due to the sheer scale of the task. Given this, InterPro with its scalable, signature-based approach is more important than ever in providing annotation. The resource has not only managed to keep track with the massive growth of UniProtKB in recent years, but has actually increased its coverage (from 79. 6% of proteins in the database in 2011 to 83. 5% in 2014). This is owing to a number of factors: the on-going development of new signatures by its partner databases (see Figure  3 ); the continued integration efforts of InterPro's curators (with only a small percentage of signatures from the majority of member databases awaiting integration--see Table  2 ); and extensive refactoring by InterPro's developers to ensure that production pipelines continue to scale with the burgeoning data volumes.
While InterPro provides a scalable option in terms of the compression of query terms into signatures, it is important for signature-based protein family databases to constantly review the algorithms and approaches used. The acceleration in search speed using HMMER3 compared with HM-MER2 offers a 1000-fold improvement  (31) , such that a single signature takes, on average, only 12 CPU minutes (using a 2. 5 GHz Intel Xeon) to be calculated against 80 million protein sequences. The databases that have adopted HMMER3 do not currently present scalability issues. Other databases, such as HAMAP, hope to achieve similar improvements soon by using the new pfsearchv3 algorithm for their searches, which are currently significantly slower than HMMER3-based methods. Whilst it is advantageous to have diversity between methods, it is important that performance speed is also considered.
Based on the projected growth of UniProtKB, it will not be many years before 20 million sequences will be routinely added to each release of the sequence database. The considerable power of InterPro in propagating annotation is illustrated in the targeted GO curation work. Here, focused curation of a small subset of InterPro entries (∼15% of the database) by an expert curator generated a cumulative 'snowball' effect, improving annotation for over 2 million of UniProtKB sequences, with positive knock-on effects on a host of other resources (Microme, RhEA, GO, Reactome and ChEBI). We are very interested in exploring similar collaborations in future, as this approach potentially allows us to rapidly (and with minimal overhead) improve annotation of proteins that may not be adequately covered in UniProtKB at present.
Measurements of IL-1b and IL-8. Concentrations of rabbit IL-1b and IL-8 in the BALF and in the crude homogenate of each pulmonary lobe were assessed by ELISA (Euromedex). The release of human IL-1b and IL-8 was quantified by ELISA using DuoSet ELISA kits (R&D systems, Lille, France). Statistical analysis. In vitro and in vivo data were analyzed by an unpaired t-test and Mann-Whitney analysis, respectively, using Prism software (GraphPad, San Diego, USA). For in vitro data, means and standard deviations are shown. Two tailed p values are shown: n. s. : not significant, * p,0. 05; ** p,0. 01; *** p,0. 0001.
Acknowledgments We thank  Frank deLeo  and  Binh Diep  for providing strains,  Y. Jamilloux  and  F. Ader  for stimulating discussions. This work was performed within the framework of the  LABEX ECOFECT  ( ANR-11-LABX-0042 ) of  Universitede Lyon , within the  program ''Investissements d'Avenir''  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). We thank  Philip Bastable  for helpful comments on the manuscript.
Proliferation Assay ARIAIII Cell sorter was used to transfer 1 000 B16-F10 viable cells (i. e. propidium iodide-negative cells), dissociated from oneweek-old tumorspheres or from adherent monolayers, into each well of a normal adherence 96-well plate containing DMEM supplemented with 10% FBS and 1% PS. The plate was placed into an IncuCyte TM FLR imaging system (Essen Biosciences, Welwyn Garden City, UK) within a regular cell culture incubator (37uC, 95% humidity, 5% CO 2 ). Four different areas per well were monitored (106 magnification, phase contrast) with the IncuCyte TM every 4 hours during one week. Proliferation was measured with the IncuCyte TM software and the doubling time was determined using a linear regression model.
More strikingly, the tumorigenic potential of the B16-F10 tumorspheres-forming cells was lower than that of the B16-F10 adherent cells, confirming all the in vitro data we obtained. Very recently, Collura and co-workers published similar findings  [34]. However, since tumorspheres were also found to be more efficient in initiating tumors than adherent monolayers in the case of several other cancer cell lines  [35] [36] [37] [38] , this suggests that the effect  of tumorspheres culture on tumorigenicity strongly depends on the studied cell line. An increasing number of authors report that CSC present post-EMT cells characteristics  [8, [10] [11] [12]. The expression of three EMT-related genes (coding for E-cadherin, Snai1 and vimentin) was evaluated in B16-F10 tumorspheres using RT-qPCR analysis.
BAC and fosmid DNA was labeled by nick translation using the CGH Nick Translation Kit and Spectrum Green-dUTP and Spectrum Red-dUTP (Vysis, Downers Grove, IL, USA), respectively, according to the protocol provided. Chromosomes were counterstained using 4′,6-diamidino-2-phenylindole (DAPI) (Serva Feinbiochemica, Heidelberg, Germany) and mounted in antifading solution (Vector Labs, Burlingame, CA, USA). Slides were analysed with a Leica Axioscope fluorescence microscope. Images were merged using a cooled CCD camera (Pieper, Schwerte, Germany) and CytoVision software (Applied Imaging, San Jose, CA, USA).
Sequencing of HCCS DNA from whole blood, buccal swabs or lymphoblastoid cells was isolated by standard procedures. The coding region of the HCCS gene (exons 2-7; GenBank accession no. NM_005333. 4) including flanking intronic sequences was amplified from genomic DNA. Primer sequences and PCR conditions are available on request. Amplicons were directly sequenced using the ABI BigDye Terminator Sequencing Kit (Applied Biosystems, Darmstadt, Germany) and an automated capillary sequencer (ABI 3500; Applied Biosystems). Sequence electropherograms were analysed using Sequence Pilot software (JSI Medical Systems, Kippenheim, Germany).
Cloning of a mutation bearing PCR product to analyse for mosaicism We cloned the amplicon of HCCS exon 6 in the pCR2. 1 TOPO TA Cloning® Vector (Invitrogen, Karlsruhe, Germany). We picked bacterial clones and amplified them using standard protocols to identify those that contained a copy of the amplicon. We sequenced 138 PCR products to permit identification of those clones to contain mutation or wild-type bearing amplicons using the ABI BigDye Terminator Sequencing Kit (Applied Biosystems) and the automated capillary sequencer ABI 3500 (Applied Biosystems). Sequences were assembled and compared using the software SeqMan (DNASTAR, Madison, WI, USA).
PCR conditions are available on request. For PGK1: PCR products were digested with BstXI at 55°C for 3 h and the enzyme was inactivated at 65°C for 20 min. For MAOA: PCR amplicons were precipitated with ethanol and 3 M NaAc (pH 5. 8), and the precipitate was dissolved in H 2 O. After submitting the fraction to digestion with SacI at 37°C for 1 h, the enzyme was inactivated at 65°C for 20 min. The resulting amplification products (AR locus) or digested fractions (PGK1 and MAOA loci) were run on an ABI 3500 automated sequencer and the peak areas were calculated by GeneMapper Software v4. 1 (Applied Biosystems). To account for preferential allele amplification, values for the digested DNA were normalized with those for the undigested DNA of each proband. The XCI pattern (expressed arbitrarily as a ratio of the smaller: larger allele) was calculated by applying the previously reported formula  [22] : skewing = (d 1 /u 1 )/(d 1 /u 1 ) + (d 2 /u 2 ), where d 1 and d 2 represent the two raw peak area values of the digested sample, and u 1 and u 2 represent the raw peak area values of the undigested sample.
Chromosomal analysis and FISH Routine cytogenetic analysis was performed in patients 1 and 3-6 diagnosed with MLS syndrome and revealed an apparently normal female karyotype in patients 1, 3, and 6. Patients 4 and 5 showed a structurally abnormal karyotype with 46,X,del(X)(p22) (Table  1 ). We confirmed the terminal Xp deletion by FISH using BAC RP11-163I1 encompassing the HCCS gene in patients 4 and 5 and a subtelomeric Xp probe together with a probe covering the STS gene (Xp22. 32) in patient 4 (data not shown). The estimated minimum size of the two deletions was ≥11. 2 Mb. FISH with the STS and X centromere probes on metaphase spreads of the parents of patient 4 revealed the expected number of signals (data not shown). High resolution molecular karyotyping using Agilent Human Genome CGH 44 K oligonucleotide arrays (Agilent, Santa Clara, CA, USA) with the ISCA design (www.
Sequence analysis of HCCS We Sanger-sequenced the coding exons 2-7 of HCCS in patients 2 and 3. In patient 2, we identified the heterozygous de novo nonsense mutation c. 589C > T (p. R197*) in exon 6 (Figure  3A ) (paternity confirmed; data not shown). In leukocyte-derived DNA of patient 3, we detected the 2bp deletion c. 524_525delAG in exon 6 that results in a frameshift and introduction of a premature termination codon (p. E175Vfs*30) (Figure  3B ). The sequence profile showed slightly lower signals for the mutant variant superimposed on the wild-type sequence suggesting that the mutation was present in the mosaic state (Figure  3B ). To confirm somatic mosaicism of the 2-bp deletion in HCCS in patient 3, we amplified exon 6 from leukocyte-derived DNA, cloned the amplicon and subjected a total of 138 individual E.
coli colonies to PCR followed by sequencing. We identified 102 wild-type alleles (73. 9%) and 36 alleles with the mutation c. 524_525delAG (26. 1%) demonstrating somatic mosaicism in patient 3 (data not shown). Thus, patient 3 had a mixed population of in vivo lymphocytes: ~52% of lymphocytes carry the heterozygous c. 524_525delAG mutation and ~48% carry two wild-type HCCS alleles. We next investigated DNA from two other cell types of patient 3 and sequenced HCCS exon 6 in DNA isolated from buccal cells and a lymphoblastoid cell line (LCL). As shown in Figure  3B , the peak height of the two sequence profiles representing the two different HCCS alleles in patient 3 was similar in buccal cell-derived DNA. In contrast, the sequencing pattern of the mutant HCCS allele is not any more visible in DNA isolated from LCLs (Figure  3B ).
Acknowledgements We are grateful to the patients and their families who contributed to this study. We thank  Inka Jantke  and  Dennis Zorndt  for skilful technical assistance,  Angela Fritsch ,  Birte Lübker  and  Fanny Kortüm  for help with FISH experiments, and  Cornelia Kraus  for XCI analysis at the FMR1 locus. This work was supported by a grant from the  Deutsche Forschungsgemeinschaft  ( KU 1240/6-1  to KK).
Microscopy and Flow Cytometry Nuclei were visualized by DAPI (49-69-diamino-2-phenylindole, SIGMA) staining of cells fixed in 70% ethanol. Pictures were taken using a Leica Microsystems DMRD Microscope with a 100x oil immersion objective and a Princeton CoolSnap fx cooled CCD camera setting the binning at 1. For observation of living cells expressing Rad52YFP or Sty1GFP protein the exposure time was of 5 s and binning was set at 2. Image capture software was MetaView (Universal Imaging) whereas image processing software was ImageJ. DNA content was analyzed by staining fixed cells with sytox green (Invitrogen) followed by flow cytometry analysis with FACSCalibur flow cytometer (Becton Dickinson). Data were plotted using CellQuest software.
Fluctuation Test and HR Rate Estimation Fluctuation test was done as follow: 9 Ade2 His+ colonies were independently inoculated in 10 ml of YE-rich medium and incubated at 30uC with agitation till saturation. Cells were plated on YE-rich medium to estimate viability. About 1. 8610 6 viable cells for each culture were plated on EMM medium lacking adenine to estimate the frequency of Ade+ recombinants. Plates were replicated on EMM lacking adenine and histidine to estimate frequency of Ade+ His+ recombinants. Frequencies were analyzed by MSS-MLE (Ma-Sandri-Sarkar Maximum Likelihood Estimator) method with the program FALCOR (Fluctuation AnaLysis CalculatOR) to estimate the rate of recombination  [34]. For each strain at least three independent experiments of nine cultures each were performed.
Mutation at S365 of Rad52 Prevents Rad52 Phosphorylation in rad51 and mus81 Null Cells Through in silico analysis (KinasePhos, NetPhos and GPS softwares), we identified several possible phosphorylation sites in fission yeast Rad52 protein. We concentrated on two possible phosphorylatable sites by MAPK located at the C-terminal of the protein: Threonine 333 (T333) and Serine 365 (S365). By site directed mutagenesis, we mutated T333 to A and S365 to G and the mutated sequences were integrated at the genomic locus as described in material and methods. We were unable by different strategies to obtain stable haploid or diploid strains expressing Rad52 mutated at T333, suggesting that this mutation is highly deleterious for cell survival and might act as a dominant negative. A haploid strain expressing at the endogenous locus the mutated Rad52-S365G protein tagged with YFP (rad52-S365G-YFP) was obtained.
Wild type and mutant cells were synchronized in early S phase by HU and then released into cell cycle. Cell cycle progression was followed by flow cytometry analysis and by monitoring cells that passed mitosis (2 nuclei). Both strains progressed through cell cycle with the same kinetics and none of these strains showed the presence of abnormal mitosis, indicating that checkpoint function is retained (Figure  3C ). In addition, we have previously shown that Rad52 is not phosphorylated when cells are exposed to 12 mM HU where the replication checkpoint is fully activated or when cells are released from the HU block  [30]. In conclusion, rad52-S365G-YFP sensitivity to HU is not linked to checkpoint failure or to lack of HU induced phosphorylation. As for rad51+ mus81+ wild-type background (Figure  3A ), the rad52-S365G-YFP allele didn't change the cell morphology or Rad52 foci formation and frequency characteristic of rad51-d or mus81-d cells (data not shown)  [32, 36].
After incubation, they were rinsed once in 10 mM PBS and incubated with 300 nM DAPI, then rinsed 3 times in 10 mM PBS, once in distilled water, and coverslipped with gel mount. The finished slides were then photographed using either an Olympus FV500 laser scanning confocal microscope or a Zeiss Axioplan fluorescence microscope. Confocal digital images were cropped and adjusted using Adobe Photoshop for color balance and intensity. All immunofluorescence experiments included slides (WT and +/Lc) with no 1antibody incubation as a control for nonspecific immunolabeling. The specificity of both the mouse monoclonal (ABR Affinity BioReagents) and the rabbit polyclonal (Upstate/Millipore) anti-3 antibodies was also tested with Western blots using homogenized cerebellar tissue from WT and +/Lc mutant mice (data not shown).