dataesr/scientific-paragraphs-categorization huggingface dataset:
1d ,e and Supplementary Data 1). Interestingly, the gene ontology term analysis also suggested KAT2A/2B-dependent regulation of novel pathways, such as actin-mediated cell contraction, protein transport, phosphorylation and regulation of centrosome duplication (Fig. 1d ,e and Supplementary Data 1). Overall, (a) KAT2A/2B knockdown efficiency. Tetracycline (Tet) inducible stable HeLa cell lines were used in which shRNAs either not targeting any endogenous transcript (Tet-shCtrl) or targeting KAT2A (Tet-shKAT2A) were expressed under doxycycline (Dox) induction. In the Tet-shKAT2A cell line, a siRNA against KAT2B (K2B) was also transfected. The knockdown efficiency of KAT2A, KAT2B and the acetylation of histone H3K9 was tested by western blot analyses. (b) In all, 398 potential KAT2A/2B acetylated protein targets were identified as being present in control samples (3/5), but absent in all KAT2A/2B KD-depleted cell lysates.
Imaging was done on a Zeiss LSM710 confocal microscope. Optical sections were acquired every 0. 12 mm, and planes containing centrioles were projected together. Images were processed using ImageJ, preserving relative image intensities within a series. Levels of PLK4-pS305 at the centrosome were quantified as follows. Cells were fixed with methanol, stained for PLK4-pS305 and g-tubulin to localize centrosomes. The total PLK4-pS305 signal was determined within a circular region of 20 px diameter (B1 mm) centred on the g-tubulin focus. After subtraction of the background present outside cells, the total centrosomal intensity per cell was determined as the sum of all centrosomal intensities. Homology modelling. Crystallographic structures of PLK4 were taken from the Protein Data Bank (PDB accession codes: 3COK, 4JXF) for both the active and inactive forms of the protein  63.
Owing to a lack of electron density, no structure was resolved for residues in the activation loop, and so a structure was constructed for this segment using the Modeller programme  64. In the active form, segments 158-189 and 212-226 were modelled using the structures of PLK2 and PLK3 as starting templates (accession codes: 4i6h and 4b6l, respectively). In the inactive form, residues 165-191 and 211-216 were modelled using an Aurora Kinase structure (accession code 4J8M) as a starting template. This latter structure was chosen because of its similarity with the fold of the N-terminal part of the activation loop of PLK4 (in the inactive structure of PLK4, see PDB ID 4JXF). In the 4JXF structure the DFG is in an inactive conformation  65. For each structure (active and inactive), 1,000 homology models were generated using the automodel module of Modeller  64  and their quality was evaluated using the DOPE score  66.
Proteins were identified by database searching using SEQUEST  74  within Proteome Discoverer 1. 3. Tandem mass spectra were searched against a non-redundant protein sequence database for Homo sapiens containing 20,276 protein sequence entries (Uniprot release from 2010-09). Cysteine residues were considered to be fully carbamidomethylated ( þ 57 Da statically added), methionine to be oxidized ( þ 16 Da dynamically added) and lysine to be acetylated ( þ 42 Da dynamically added). Three missed cleavages were permitted. Peptide mass tolerance was set at 2. 5 and 0. 8 Da on the precursor and fragment ions, respectively. Tandem mass spectra were filtered with XCorr values equal to 1. 5 ( þ 1), 2. 5 ( þ 2), 3. 0 ( þ 3) and 3. 2 (4 þ 3). Only peptides with a minimum length of seven residues and maximum deltaCn values of 0.
1% formic acid (FA) in H 2 O; B, 80% acetonitrile (acetonitrile (ACN)) and 0. 08% FA in H 2 O) followed with 10 min at 99% B. The total duration was set to 150 min at a flow rate of 200 nl min À 1. The temperature was kept constant at 40 °C. Peptides were analysed by Top 10-CID-HCD (collision induced dissociation and high-energy collisional dissociation) data-dependent MS. Tandem mass spectra were searched using SEQUEST HT within Proteome Discoverer 1. 4, against a non-redundant protein sequence database for H. sapiens containing 27,858 protein sequence entries (Uniprot, release 2014-11). Cysteine residues were considered to be fully carbamidomethylated ( þ 57 Da statically added), methionine considered to be oxidized ( þ 16 Da dynamically added) and lysine considered to be acetylated ( þ 42 Da dynamically added), and two missed cleavages were permitted.
Acknowledgements We are grateful to  V. Simanis  and  M. Bornens  for antibodies, to  A. Holland  for expression vectors, to  K. Oegema  for centrinone and to  N. Rochel-Guiberteau  for the baculovirus expression vector for KAT2B/PCAF. We thank  G. Duval  for generating the PLK4ac rabbit sera,  M. Oulad-Abdelghani  for ELISA tests,  P. Eberling  for peptide synthesis,  I. Kolb-Cheynel  for baculovirus overexpression experiments,  D. Devys  for critically reading the manuscript and for helpful comments, and  R. Stote  for modelling assistance, the IGBMC proteomic platform for help. M. F. was supported by a  European Commission  ( EC) Marie Curie fellowship  ( PIIF-GA-2009-255266 , SAGATAC). M. O. was supported by a fellowship from the  FEBS. Computing time was provided by the  Meso-center for High Performance Computing at the University of Strasbourg  and supported by the project  EQUIP@MESO.
This work was supported by funds from  CNRS, INSERM, Strasbourg University , and the  EC Marie Curie-ITN  ( NR-NET ) and the  Agence Nationale de Recherche  ( ANR-11-BSV5-010-02   Chromact ;  ANR-13-BSV6-0001-02   COREAC ;  ANR-13-BSV8-0021-03  DiscoverIID). This study was also supported by the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the frame programme  Investissements d'Avenir   ANR-10-IDEX-0002-02. ' L. T. and P. G. are recipients of  European Research Council (ERC)  Advanced grants ( ERC-2013-340551 ,  Birtoaction , to L. T. , and  ERC-2013-340227 , CENFOR, to P. G. ).
Furthermore, work on modeling PE-contamination has so far been disregarded in stand-alone scaffolders and the effect that PE-contamination has on scaffolding quality has not been examined before. We have addressed PE-contamination in an update of our scaffolder BESST. We formulate the problem as an Integer Linear Program (ILP) and use characteristics of the problem, such as contig lengths and insert size, to efficiently solve the ILP using a linear amount (with respect to the number of contigs) of Linear Programs. Our results show significant improvement over both integrated and standalone scaffolders. The impact of modeling PE-contamination is quantified by comparison with the previous BESST model. We also show how other scaffolders are vulnerable to PE-contaminated libraries, resulting in increased number of misassemblies, more conservative scaffolding, and inflated assembly sizes.
When designing a scaffolder, one can take the stance that PE contamination is (1) an experimental issue which should be controlled in the wet-lab and (2) it can be treated as noise in the MP that will get filtered away in an scaffolding optimization procedure anyway. Regarding (1), we probably have to accept PE contamination as a largely unavoidable difficulty and, in that case, we argue that the PE should be explicitly modeled in MP datasets to reduce errors. Although a decent MP library will contain more true MP reads than PE, and hence overshadow PE contaminants in many cases, assumption (2) will not hold for fragmented assemblies as there are many short contigs close to each other making PE links dominate MP links. The ambition can also be set higher: perhaps we can start utilizing PE contamination as valuable short-range information rather than nuisance that needs to be filtered away? One may also ask how sensitive the current generation of stand-alone scaffolders are to MP libraries with a high amount of PE contamination? If scaffolders have been designed for near ideal datasets, what can one expect in a more difficult situation? PE-contamination has been discussed in work on integrated scaffolders in end-to-end assemblers such as ALLPATHS-LG  (Gnerre et al.
The heuristic method is very fast and finds the correct order of contigs in all cases given the model assumptions, as seen on our simulated data. With the new implementation, BESST-v2 completes scaffolding of an assembly for the 20 Gbp spruce genome with five libraries in 24 hours (mapping time excluded). The new method is benchmarked in a large evaluation over a range of different assembly instances. With the use of PE contamination reads, the new version of BESST places more contigs in fragmented assemblies due to the extra short range information. To account for PE contamination in the scaffolding step is especially important for larger genomes where assemblies tends to be fragmented. This is seen from the significant reduction of errors in BESST-v2 over BESST on the more fragmented assemblies in our evaluation data (Supplemental Table  S13 ).
Datasets We have included a simulated data set, sim, which we developed the model on. First, a set of 354 random contigs was generated, each contig of size 5000 bp (probability 0. 2) or 500 bp (probability 0. 8). This gave 71 larger contigs and 283 smaller contigs. The largest stretch of smaller contigs consisted of 20 consecutive small contigs. The reference genome was created by concatenating these contigs, hence being of size 496500bp. Based on the reference genome, a MP library with 50x coverage of 2x100bp reads was simulated. The MP library had insert-size distribution N(3000,300) and 30% of the reads were PE contamination with insert size chosen from N(400,40). The larger contigs can be seen as "border contigs" that breaks the scaffolds into separate regions of smaller contigs, because there are no read pairs that can stretch over a large contig.
Evaluation method Assembly evaluation is known to be difficult  (Earl et al. , 2011; Salzberg et al. , 2012; Hunt et al. , 2014) , as there are many metrics to consider. Ultimately, the end result of an assembly should be as long error-free sequences as possible. However, two assemblies with the same lengths on error free sequences can still differ if one of the assemblers/scaffolders contains false joins, making the assembly look more contiguous. Thus, errors is another important metric. We choose to look at the adjusted E-size (the expected length of error free sequence in the assembly,  Salzberg et al. , 2012)  which we denote by E , and the number of errors. Moreover, we need an informative way to present the quality increase/decrease of an assembly from contigs to scaffolds. Since the GAGE data sets contains assemblies of varying quality (with respect to contig errors and E ) of each organism, we present the quality improvement between contigs and scaffolds.
(3) Errors: a gap between contigs is inserted due to a false join, or the gap sequence significantly differs in length to the real gap length. This is seen as a misassembly that decreases the quality of the assembly. When we discuss assembly inflation we will refer to these three causes as (1), (  2 ) or (3). We also report the ratio of scaffolded assembly size compared to initial contig size, labeled inflation in the tables, and find that some scaffolders are prone to (2). Note on evaluation. Snakemake (Köster and Rahmann, 2012) was used to run our evaluation pipeline. QUAST  (Gurevich et al. , 2013)  was used for evaluating the scaffolders as it uses alignments to a reference to identify misassembly breakpoints. Support for computing E-size was added. QUAST classifies a Table  1 : Results on the sim dataset.
Note on testing. It is of general importance to know what data has been used in the development of the algorithm. By modifying the ILP, we can obtain something that works well for our data but does not generalize to other data sets. We used simulated data sets similar to the dataset "sim" as problem instances for BESST-v2 when developing the ILP. Our method is extremely accurate for all contamination levels and contig sizes that we have tested. Therefore, it is not strange that we excel on the data set "sim" and BESST's results on this data set should be seen as a test of correctness in the implementation and formulation of the ILP, given its assumptions. However, these datasets also illustrate how vulnerable other scaffolders can be to paired end contamination in such scenarios. The Assemblathon 1 dataset with Minia's contig assembly was the data set where we discovered that paired end contamination affected BESST's result, and we ran BESST before and after the implementation to verify the improvement on this data set that more closely mimics a biological data set.
Inflated assembly sizes In general, all scaffolders increase the assembly size to some extent but there is large variation among them. On the sim data set, see Table  1 , all scaffolders except BESST-v2 and SCARPA inflates the assembly size between 18-106. 6% even though all contigs has the potential to be linked (as done in BESST-v2). SCAFFMATCH inflates the assembly size the most (106. 6%) and manual inspection reveals that this is due to a mix of cause (  2 ) and (3). SCAFFMATCH uses an approach (maximum matching) that only permits one neighbour of every contig to be joined; this is a limitation on assemblies where the insert size is relatively large compared to contigs, making mate pairs link to several neighbors. SCAFFMATCH addresses this limitation by including a separate insertion-step that attempts to place remaining singleton contigs, but it does not perform well on this data set.
SSPACE also inflates the assembly size of the dataset sim to a large extent, but has only one error. The inflation is from (2), which is a result of SSPACE's heuristic; SSPACE extends scaffolds greedily by choosing the neighboring contig with the most links and may therefore miss many small intermediate contigs. OPERA, SOPRA and BESST shows vulnerability by making orientation mistakes (as in Figure  1 ) due to creating many joins where the link is interpreted as a MP instead of a PE. SCARPA has low inflation on this data set. On the assemblathon3k dataset, see Table  2 , BESST-v2 is able to increase the adjusted E-size with a factor of 22 while keeping the size of the assembly relatively constant. SCAFFMATCH, SSPACE, On the GAGE datasets (Tables  3 4 5 ) the inflation levels vary significantly among scaffolders.
Such extreme inflation is clearly not correct as it almost doubles the assembly size, especially when the contig assembly size is already larger than the true genome size. We also argue that the more subtle inflation on the higher quality data sets are artifacts as BESST-v2 in general has the fewest errors and highest increase in contiguity with only a small increase in assembly size. Inflation/deflation level vary among the integrated scaffolders. For example, SGA contig assemblies is generally significantly larger than the genome size and SGA's scaffolder removes a lot of sequence in the scaffolding step. In the hs14 assemblies, the integrated scaffolders in Velvet, Bambus2, MSR-CA and SOAPdenovo inflates the assembly size the most and also contains significantly more errors than the other scaffolders.
Errors and contiguity The sim and assemblathon3k datasets show how strongly PE contamination can affect scaffolding (Tables 1-2). There are extreme differences in inflation, errors, and E s among the scaffolders on these two datasets. The differences in result between BESST and BESST-v2, as well as the number of errors of, e. g. , OPERA, SOPRA and SCAFFMATCH, indicate that a large part of the links created in the scaffolding graph are from PE contamination. BESST-v2 corrects all errors on sim and the majority of errors on assemblathon3k, which indicates that it is an efficient method for finding the correct ordering. The GAGE datasets (Tables  3 4 5 ) further show that PE contamination affects scaffolding significantly, especially when assemblies are more fragmented, as is common for more complex genomes (see Table  5 ).
On rhodo (Table  4 ), however, there is a large increase in errors, where almost the entire increase is on the two fragmented assemblies ABySS and SGA. Due to the number of failed runs by SOPRA on hs14 (from the time constraint), no general conclusion about trend can be made with the averaged data presented here. However, as with the other scaffolders, SOPRA is the most affected by fragmented assemblies and greatly increases the number of errors on SGA and Velvet on hs14 when contamination is present (Table  S12 -S14). SCAFFMATCH shows fairly consistent number of misassemblies and E s across all datasets and with a relatively good E s. However, it always has significantly more misassemblies than other scaffolders. With the GAGE libraries, BESST-v2 has the second fewest to fewest misassemblies and second highest to highest E s for almost all runs.
Geometry and boundary conditions The purpose is to perform the simulation using 2D and 3D modeling with FLUENT software, and to present the different results obtained. The sheet is modeled as a rectangle of length L = 70 mm and b = 50 width and thickness t = 6mm. A ring of outer diameter D = 22mm is drawn on the upper surface of the sheet, which corresponds to the shoulder. The figure  6  shows the surfaces designation that will be used thereafter. The pin is modeled as a cylinder of diameter d = 6 mm and height h = 5. 7mm. In FLUENT 3D model, the origin is in the center of the sheet and at mid-thickness. Thus, knowing the rotation direction of the tool and its advancing speed, the AS-side and the RS-side can be differentiated by positive or negative Y values. The meshing of reference used in the calculations is shown in figure  7.
The transient model was used to simulate the movement of the tool. Heat flow, defined in equation (  4 ) was applied to the tool surfaces. For the upper and side surfaces of the plate, the thermal boundary condition was fixed by convection between the air and the plate, with a heat transfer coefficient of 30 W / m 2 °C. For the lower surface of the plate, the heat transfer coefficient by convection between the plate and the anvil plate was set at 150 W / m 2 °C. The value of the heat transfer coefficient is found to be similar to that reported in the literature for modeling FSW aluminum alloy 2017A. The plate was initially at room temperature of 27 ° C. In the simulation process using the FLUENT software, the approach is to consider the metal material as a fluid, which is under the effect of the rotation and advance of the tool.
Robust phylum-level phylogeny of the Firmicutes supports distinct origins of Halanaerobiales and Negativicutes We gathered homologues of 47 ribosomal proteins from a local database of 205 Firmicutes taxa and 13 bacteria belonging to eight major phyla as outgroup (Materials and methods). We did not include the Tenericutes in the analysis, because their reduced genomes and fast evolutionary rates are likely to cause artifacts in deep phylogenies, but it is known that they phylogenetically belong to the Bacilli  (Davis et al. , 2013). We assembled the 47 ribosomal proteins into a large concatenated dataset (5551 amino acid characters) and carried out Bayesian analysis with a sophisticated site-heterogeneous model of protein evolution (CAT) that allows each site to evolve under its own substitution matrix and is robust against tree reconstruction artifacts that frequently affect deep phylogenies  (Lartillot and Philippe, 2004).
The Bayesian tree was well resolved at most nodes (Posterior Probabilities (PP) > 0. 95, Figure  2 ). Despite the weak signal and the stochastic errors frequently associated to small proteins such as ribosomal ones, topology congruence tests on individual markers showed a largely congruent phylogenetic signal, especially at high rank taxonomy level, justifying their combined analysis (Figure  2 -figure supplement 1 and Materials and methods). Maximum Likelihood (ML) analysis of the same concatenated dataset and the site-homogeneous LG model  (Le and Gascuel, 2008)  gave a largely consistent topology although it was much less resolved, especially at deep nodes (Figure  2 -figure supplement 2). With respect to previous analyses, the relative placement of Negativicutes and Halanaerobiales in the Firmicutes phylogeny was robustly resolved (Figure  2 ).
Such clustering is unusual as in E. coli for example these genes are scattered in different regions of the genome. However, the genes coding for the first four steps of LPS synthesis (lpxABCD) display a conserved synteny in diderm Bacteria at very large evolutionary distances, suggesting that they have similar evolutionary histories  (Opiyo et al. , 2010). Accordingly, synteny is also conserved  in Halanaerobiales and Negativicutes (Figure  3  and Supplementary file 1). We therefore searched for these four core LPS genes (lpxABCD) in a local databank of 121 genomes representative of 30 major bacterial phyla (Materials and methods and Supplementary file 2). As compared to what could be previously inferred from available genomic data  (Sutcliffe, 2010; Opiyo et al. , 2010) , we show the presence of homologues of the four core LPS coding genes in 26 major bacterial phyla, eight of which evidenced for the first time: Thermodesulfobacteria, Fibrobacteres, Ignavibacteria, Nitrospina, Chrysiogenetes, Cloacimonetes, Atribacteria, and Armatimonadetes (Supplementary file 2).
This suggests that LPS-diderm cell envelopes might be even more widespread in Bacteria than currently thought, leaving only four major phyla that appear to lack the coding capacity for LPS: Thermotogae, Caldiserica, Chloroflexi/Thermomicrobia, and Actinobacteria (Supplementary file 2). We assembled the four core LPS protein homologues into a concatenated dataset (898 amino acid characters) also including Halanaerobiales and Negativicutes, and obtained a Bayesian tree with the CAT+GTR+G 4 evolutionary model (Figure  4 , Materials and methods). In agreement with their conserved synteny, congruence tests showed that these four core LPS genes have a consistent phylogenetic signal at large evolutionary distances, in particular concerning the monophyly of major bacterial phyla, justifying their combined analysis (Materials and methods and Figure  4 -figure supplement 1).
New OM-related proteins? Finally, three genes encoding proteins with unclear annotation display a conserved synteny within the large conserved genomic cluster (Figure  3  and Supplementary file 1) and are present in all Negativicutes and Halanaerobiales genomes (Supplementary file 1), while absent from monoderm Firmicutes. The first one belongs to COG0816, which is annotated as 'Predicted endonuclease involved in recombination (possible Holliday junction resolvase in Mycoplasmas and B. subtilis)'. The second one belongs to COG4372, which is annotated as 'Uncharacterized conserved protein, contains DUF3084 domain'. The third one belongs to COG750, which is annotated as "Predicted membrane-associated Zn-dependent proteases 1", and is predicted to be in the OM by the PSORT prediction software (Materials and methods).
Phylogenetic analyses We assembled a local databank of 205 complete genomes from a wide representative sampling of Firmicutes, including 38 Negativicutes and 7 Halanaerobiales genomes available at the beginning of this analysis (Supplementary file 1). Exhaustive HMM-based homology searches were carried out on this genome databank by using the HMMER package  (Johnson et al. , 2010)  and as queries the HMM profiles of the complete set of 54 bacterial ribosomal proteins from the Pfam 29. 0 database (http://pfam. xfam. org, Finn et al. , 2016). Additional searches with tblastn  (Altschul et al. , 1997)  were used to identify eventually misannotated homologues in some genomes. Because it is unclear which bacterial phylum is closest to the Firmicutes, we included as outgroup 13 taxa representatives of eight major bacterial phyla (2 Actinobacteria; 2 Cyanobacteria; 1 Deinococcus; 2 Proteobacteria; 1 Spirochaetes; 3 Flavobacteria/Bacteroidetes/Chlorobi; 2 Plactomyces/Chlamydia).