dataesr/scientific-paragraphs-categorization huggingface dataset:
All protein sequences were collected from the National Center for Biotechnology Information (NCBI) and the US Department of Energy Joint Genome Institute (JGI). Reference proteins are listed in Supplementary Material Table  S1  and others were collected with protein ID used as sequence codes in the phylogenetic trees. Each data set was aligned by ClustalW enclosed in the free software Molecular Evolutionary Genetics Analysis version 5 (MEGA5,  Tamura et al. 2011). Neighbor-joining trees derived by applying the Jones-Taylor-Thornton (JTT) distance matrix calculation method and bootstrap values were calculated with 1000 replications. All the steps were performed with MEGA5. Vector cloning and biolistic transformation: Homologs of target proteins (Sec4 and SynA) were selected by BLAST protein searches in NCBI and JGI websites and phylogenetic analyses confirmed their putative functions (Figs  5  and 6 ).
Acknowledgements The authors wish to thank  Laure Wingertsmann  for her technical support. Funding is acknowledged from the  ERC  " Diatomite " and  ANR DiaDomOil  projects. We additionally thank the  French Government "Investissements d'Avenir" programmes MEMO LIFE  ( ANR-10-LABX-54 ) and  PSL* Research University  ( ANR-11-IDEX-0001-02 ).
Acknowledgements Financial support of the ' Fonds voor Wetenschappelijk Onderzoek FWO ' (Grants  G0607. 09,G. 0459. 10 ,  G0990. 11,G. 0197. 11, G. 0259. 12, G. 0962. 13 ,  G0B55. 14 ), the  K. U. Leuven Research Fund  ( GOA 2011/03 ,  CREA2009 ,  OT/12/059 ), the  Flemish government  (long term structural funding-Methusalem funding  CASAS METH/08/04 ), the  Hercules foundation  ( HER/08/21 ), the  Belgian Federal Science Policy Office  ( IAP-VI/27 ), the  EU   FP7 programme  ( ITN-SUPERIOR PITN-GA-2009-238177 ), the  European Research Council  (starting grant PLASMHACAT # 280064  to H. U. starting grant LIGHT # 307523  to M. B. J. R. and advanced grant FLUOROCODE # 291593  to J. H. ) and the  Japan Science and Technology Agency PRESTO  programme to H. U. are gratefully acknowledged. G. L.
Retrograde Flow Analysis Time-lapse images of freely moving or colliding hemocytes containing actin labeled with either Lifeact-GFP or Moesin-cherry were acquired at 5 s/frame (when imaging actin alone) or at 10 s/frame (when imaging actin with other fluorescent probes). For collision analysis, cells were chosen such that the cells collided once over the duration of the time course. Cells were manually segmented prior to analysis. To quantify retrograde flow rates in lamellae, the cell body was manually segmented and data points within this region discarded. Pseudo-speckle analysis was performed as described previously  (Betz et al. , 2009). See Extended Experimental Procedures for a more detailed description of retrograde flow analysis.
ACKNOWLEDGMENTS We would like to thank  M. Peifer  and  D. Kiehart  for fly lines. We appreciate comments on the manuscript from  B. Baum ,  G. Charras ,  N. Davis ,  M. Dionne ,  P. Martin ,  E. Paluch , and  W. Razzell. We also would like to thank  N. Tapon , and  P. Jordan  and  D. Alibhai  from  LRI Light Microscopy  for help with laser ablations. B. M. S. was supported by a  Biotechnology and Biological Sciences Research Council (BBSRC)  project grant  BB/F020635/1. T. B. was supported by  French Agence Nationale de la Recherche (ANR)  grant  ANR-11-JSV5-0002. J. R. D. was funded by the  Medical Research Council (MRC). A. L. was funded by the  BBSRC. We also thank the  CMCBI at KCL  for their support.
To gain molecular-level information about the dendrimers in the biological conditions, all-atom MD simulations of the 13 dendrimers in solution were carried out at 37 °C in presence of explicit water molecules and NaCl (150 mM). Each molecular system was equilibrated during 200 ns of MD simulation (Supplementary Fig. 1 ). Different data were extracted from the equilibrated phase MD trajectories. Figure  6a  reports the equilibrated size data (that is, the radius of gyration, R g ) for the    dendrimers in solution. Comparison of the R g data with the number of terminal groups indicates that neither the size (generation) of the dendrimers (we used generations 1 and 2), nor the number of terminal functions are exclusively important criteria for the biological activity of each molecule. For instance, the activity of dendrimer 4-G 1 (8 terminal functions) is marked þ þ , as that of dendrimers 2-G 1 , 3-G 1 and 5b-G 1 (12 terminal functions); nevertheless, dendrimers 6b-G 2 , 7b-G 2 and 8a-G 2 also have 8 terminal functions, but no activation properties towards monocytes.
Acknowledgements This work was financially supported by  Rhodia  (grant to S. F. and A. O. ),  CNRS  ( Centre National de la Recherche Scientifique ),  INSERM  ( Institut National de la Santeet de la Recherche Medicale ),  UPS (UniversitePaul Sabatier)  and  FRM (Fondation pour la Recherche Medicale , grant  DCM20111223039 ).
Recurrent somatic heterozygous gain-of-function mutations of EZH2 have been identified in DLBCL, most notably affecting tyrosine 641 (Y641), inducing increased H3K27me3  [6, 7]. More recently, multiple studies have shown cell lines with EZH2 mutations to be dependent on the higher catalytic activity of mutant EZH2 Y641 for proliferation, leading to the development of novel EZH2 inhibitors for therapeutic use, capable of reversing malignant phenotype  [8] [9] [10] [11]. Two EZH2 inhibitors are currently being tested in phase 1 and 2 clinical trials both in patients with and without EZH2 Y641 mutations (NCT01897571 and NCT02082977), but no study has specifically examined which patients would be most susceptible to benefit from this treatment and how to screen for them. Patients with EZH2 gain-of-function mutations have been pinpointed as ideal EZH2 inhibitor recipients  [8] [9] [10] [11] ; nevertheless, in today's targeted therapy era, it seems essential to establish a method of detecting optimal candidates for EZH2 inhibitor treatment.
PGM data analysis Torrent Suite™ version 4. 0 (Life Technologies) software was used to perform primary analysis, including signal processing, base calling, sequence alignment to the reference genome (hg19) and generation of Binary Alignment/Map (BAM) files. BAM files were used by Torrent Suite™'s Variant Caller to detect point mutations as well as short insertions and deletions using the PGM Somatic Low Stringency profile. VCF files generated by Variant Caller were annotated by ANNOVAR  [14]. Samples were considered of sufficient quality when more than 90% of targeted bases were read at least 20 times with sequencing and mapping precisions of at least Q20. Only frameshift deletions and insertions, nonframeshift deletions and substitutions, splicing, nonsynonymous, stopgain or stoploss Single Nucleotide Variations (SNVs) were kept.
Variants present in dbSNP (version 138) and absent in COSMIC (version 64) were discarded, as were variants with a predictive SIFT score > 0. 05  [15]. A normal probability plot defined thresholds separating true positives (confirmed by Sanger sequencing, TVC score ≥ 22) from true negatives (discredited by Sanger sequencing, TVC score < 9. 5) and highlighted a gray zone (9. 5 < TVC score < 22) in which variants must be confirmed by Sanger sequencing or pyrosequencing. Further verification by Sanger sequencing was performed using a BigDye ® Terminator v3. 1 Cycle Sequencing Kit (Life Technologies) and an ABI PRISM 3130 analyzer (Life Technologies). Primer sequences are provided in Supplementary Table  3. Further verification by pyrosequencing was performed using the PyroMark PCR kit (Qiagen, France) with internal and sequencing primers designed using PyroMark software (Qiagen).
NGS mutational profiles allow more thorough understanding of IHC methylation profiles In order to better understand the unexpected IHC methylation profiles observed for certain patients of our cohort, we performed an NGS analysis of their mutational profiles using our Lymphopanel set of genes based on literature data obtained from whole exome sequencing  [22]. To this end, we sequenced all Y641 EZH2 mutant patients, as well as all WT EZH2 patients with mutant-like IHC methylation profiles. We also included 12 additional patients to extend NGS analysis to a total of 15 Y641 EZH2 mutant patients (13 GCB, 1 ABC, 1 other) and 17 WT EZH2 patients (13 ABC, 2 GCB, 2 other). NGS results were sorted by quality scores and Sanger or pyrosequencing when possible, as described in the methods section (detailed in Supplementary Table  4 ).
c). Therefore, this strategy is better suited for sex chromosomes that have either a low or intermediate level of divergence. However, it will still provide X copies of sex-linked genes in old systems, as long as appropriate SNPs are present in the dataset (Figure  1. c). Crosses can be obtained in any organism that can be grown in controlled conditions and are a common resource since they are needed for building genetic maps. Hundreds of sex-linked genes were identified this way in species with unavailable fully sequenced genomes such as Silene latifolia  (Bergero and Charlesworth, 2011; Chibalina and Filatov, 2011)  or in species without any genomic resource such as Rumex hastatulus  (Hough et al. , 2014)  and Rumex acetosa  (Michalovova et al. , 2015). Although this RNA-seq cross-based strategy is very promising for studying sex chromosomes in nonmodel organisms, the existing approaches have a number of limitations due to the fact that inference of sex-linkage was done with empirical filters and without a statistical framework.
Once RNA-seq reads have been mapped to a reference transcriptome, individuals need to be genotyped in order to study allele segregation in the cross (Figure  1 ). Genotyping was either done by filtering the number of reads at each locus with fixed thresholds  (Bergero and Charlesworth, 2011; Chibalina and Filatov, 2011)  or by using genotypers designed for DNA-seq data  (Hough et al. , 2014; Michalovova et al. , 2015). This is problematic in the case of sex-linked genes where the Y allele is frequently less expressed than the X (reviewed in  Bachtrog, 2013)  and can be confounded with a sequencing error in RNA-seq data. Read number thresholds determined empirically for a given dataset can be sub-optimal for another dataset with different sequencing depth. In many cases, progeny individuals were pooled separately for each sex for sequencing in order to lower costs  (Bergero and Charlesworth, 2011; Chibalina and Filatov, 2011; Michalovova et al.
, 2015). However, this makes it impossible to differentiate between all individuals of the pool or only a few being heterozygous, a criteria that is crucial to disentangle sex-linked genes from autosomal ones (Figure  1 ). Finally, sex-linked genes were filtered for having more than a given number of sex-linked SNPs  (Chibalina and Filatov, 2011; Hough et al. , 2014) , and for not having any autosomal SNPs  (Bergero and Charlesworth, 2011; Michalovova et al. , 2015). These arbitrary filters clearly limit the application of this strategy to any dataset and probably prevent the detection of many true sex-linked genes. Also, a method allowing the study of UV systems is currently lacking. Here, we propose a probabilistic method called SEX-DETector that solves the caveats of previous RNA-seq cross-based approaches and works on any sex chromosome type (XY, ZW, UV).
Inferences An Expectation Maximisation (EM) algorithm is used to estimate the parameter values of the model θ = (π, α, β, ε, p). Detailed equations of the EM algorithm can be found in Supplementary text S1. Once parameters have been estimated, the posterior probabilities of the hidden data are computed given the observed data: S ktj = P S ktj |G is the posterior probability of segregation type j at position t in contig k, given the observed data G. Using Bayes' rule we get: S ktj = P S ktj P G S ktj ) j P S ktj P G S ktj ) = π j m,n α m β jn ir h,d hd (φ hd,r jmn ) G ir kt j π j m,n α m β j n ir h,d hd (φ hd,r j mn ) G ir kt , with h ∈ ( ε, 1 -ε), d ∈ ( p jr , 1 -p jr ) and: Then, the segregation type of each position in the dataset can be inferred using the posterior segregation type S ktj.
The model described above is adapted to X/Y and Z/W systems. Another version of the SEX-DETector model was written for U/V systems and can be found in Supplementary Text S2. Bayesian Information Criterion (BIC) test for the presence of sex chromosomes The Maximum Likelihood framework of the method allows the use of a model selection strategy to assess the presence of sex-linked genes in the dataset. A model M with the three possible segregation types can be compared to a model with only autosomal segregation type using the BIC, defined such that BIC(M) = -2 log L(θ M ) + θ M log n Where BIC(M) is the BIC value of model M, L(θ M ) is the likelihood of the model, θ M is the number of free parameters of the model and n is the sample size. The model with the lower BIC value is chosen. It is also possible to test for a X/Y versus a Z/W system by comparing both BIC values.
Mapping, genotyping and segregation inference Illumina reads from the 10 individuals of the cross were mapped onto the assembly using BWA (version 0. 6. 2, bwa aln -n 5 and bwa sampe,  Li and Durbin, 2009). The libraries were then merged using SAMTOOLS (Version 0. 1. 18, Li, Handsaker, et al. , 2009). The obtained alignments were locally realigned using IndelRealigner  (GATK, DePristo et al. , 2011; McKenna et al. , 2010)  and were analysed using reads2snp (Version 3. 0, -fis 0 -model M2 -output genotype best -multi alleles acc -min coverage 3 -par false,  Tsagkogeorga et al. , 2012)  in order to genotype individuals at each loci while allowing for biases in allele expression and not cleaning for paralogous SNPs as X/Y SNPs tend to be filtered out by paraclean (the program that removes paralogous positions,  Gayral et al.
Simulations Simulated genotypes were used in order to test the effect of various parameters on the sensitivity and specificity of SEX-DETector. Sequences were first simulated for two parents (or a single parent in the case of a UV system) using the program ms to generate a coalescent tree  (Hudson, 2002 , see Supplementary -n 1 0. 5 -ej UV divergence 2 1 -eN UV divergence 1). Then, allele segregation was randomly carried on for a given number of progeny of each sex, using the segregation pattern determined when generating sequences with ms and seq-gen (see Supplementary Table  S1  for segregation tables). θ = 4N e µ was set to 0. 0275 as estimated in S. latifolia by  (Qiu et al. , 2010). µ was set to 10 -7 , which implies that 4N e was equal to ∼70,000. Contig lengths were randomly assigned from the observed distribution of contig lengths of the S.
latifolia assembly presented previously. Equilibrium frequencies used for seq-gen were retrieved from SEX-DETector inferences on the observed S. latifolia data. The transition to transversion ratio was set to 2 as inferred by PAML  (Yang, 2007)  on S. latifolia data  (Kafer et al. , 2013). The rate of genotyping error (ε) was set to 0. 01 and the rate of Y genotyping error (p 2het ) was set to 0. 13 as inferred by SEX-DETector on the observed S. latifolia data. Five types of datasets were simulated, with ten repetitions for each set of parameters and 10,000 contigs simulated for each dataset: • Effect of X-Y divergence: Five different X-Y divergence times in units of 4N e generations were tested, either S. latifolia X-Y divergence time (4. 5My) or 10 times or 100 times older or younger. The proportion of X-hemizygous contigs among sex-linked contigs was set accordingly to X-Y divergence time: 0.
5 My, the X-Y divergence time in unit of 4N e generations varied accordingly to the value of theta. The proportion of sex-linked contigs was set to 10%. • Effect of the number of individuals in Z/W and X/Y systems: Nine different numbers of offspring individuals of each sex were tested for the X/Y system: 2, 3, 4, 5, 6, 7, 8, 12 or 16 individuals of each sex. Sex chromosome size was set to 10% and X-Y/Z-W divergence to 4. 5 My. • Effect of the number of individuals in U/V systems: Eight different numbers of offspring individuals of each sex were tested for the U/V system: 1, 2, 3, 4, 5, 6, 7 or 8 individuals of each sex. Sex chromosome size was set to 10% and U-V divergence to 4. 5 My. For each simulated dataset, segregation types were inferred using SEX-DETector and were compared to the true segregation types in order to compute sensitivity and specificity values.
The probability for a genotyping error to occur ε accounts for possible differences between observed and true genotypes (due to sequencing, mapping or genotyping errors). A specific Y genotyping error parameter p fits the high genotyping error rate for Y alleles due to degeneration and lower expression. Testing our pipeline's performance using a Silene latifolia dataset The SEX-DETector pipeline (Figure  2 ) was run on a cross dataset sequenced by RNA-seq in the plant Silene latifolia. This dioecious species was interesting for benchmarking our method/pipeline as its sex chromosomes are well known: they are relatively recent (∼5 MY old,  Rautenberg et al. , 2010)  but old enought to be clearly heteromorphic (the X is 400 Mb and the Y is 550 Mb,  Matsunaga et al. , 1994). X-Y synonymous divergence ranges from 5 to 25%  (Bergero, Forrest, et al.
, 2007) , S. latifolia thus represents a system of intermediate age. Also, a tester set of 209 genes for which segregation type has been established is available in this species (Supplementary Table  S2 ). The dataset that was used here consists of a cross (two parents and four offspring of each sex). RNA-seq data were obtained for each of these individuals tagged separately and the reads were assembled using Trinity and then Cap3, the final assembly included 46,178 ORFs (Table  1 ). RNA-seq reads were mapped onto this assembly (see Supplementary Table  S3  for library sizes and mapping statistics) and genotyping was done for each individual using reads2snp. SEX-DETector was run on the genotyping data to infer autosomal and sex-linked genes (Table  1 ). Figure  3  shows examples from the tester set.
For some genes, all SNPs show clearly the same correct segregation type (Figure  3. A-C), whereas in some genes mixed segregation patterns were inferred, which we attribute to co-assembly of recent paralogs or other assembly/mapping problems (see Figure  3. D). These mixed cases can be filtered by the user, although they can happen in true sex-linked genes as is the case in Figure  3. D. We used our tester set to measure the performance of our pipeline, i. e. estimate its sensitivity (the capacity to detect true sex-linked genes) and specificity (the capacity not to assign autosomal genes as sex-linked, see Materials and Methodss). 83% of the known sex-linked genes expressed in the RNA-seq data used here (i. e. flower bud) were detected, indicating a high sensitivity. We obtained a specificity of 99% for this dataset as one gene, OxRZn, was supposedly wrongly assigned as a sex-linked gene by SEX-DETector.
However, this gene was earlier assessed as autosomal on the basis of the absence of male specific alleles  (Marais et al. , 2011)  and SEX-DETector assigned it to a sex-linked category because of two clear X-hemizygous SNPs, without genotyping error. It is therefore likely that OxRZn is in fact a true positive and more research on that gene is required. Comparing our pipeline to others using a S. latifolia dataset We compared the performance of our pipeline to those used in previous work on inferring sex-linkage with RNA-seq data in S. latifolia  (Bergero and Charlesworth, 2011; Chibalina and Filatov, 2011; Muyle, Zemp, et al. , 2012). Those pipelines differ in many ways, and the data themselves can be different. In previous work, offspring individuals of the same sex were sometimes pooled before sequencing  (Bergero and Charlesworth, 2011; Chibalina and Filatov, 2011).
We used again the tester set of 209 S. latifolia genes with known segregation types, which we blasted onto each dataset to find the corresponding contigs and their inferred segregation type (for details see Supplementary Table  S2 ). Because the different pipelines require different types of data (pooled progeny versus individually-tagged offspring) and with different read coverages, we computed sensitivity on all known genes (expressed or not). Our pipeline outperformed other pipelines in terms of sensitivity, while specificity was comparable (Table  2 ), which indicates that SEX-DETector can uncover more sex-linked contigs, without increasing the rate of false positives. As further analysis showed, this was due to overly conservative filtering in previous work. To exclude false positives, genes with at least 5 sex-linked SNPs were retained in previous studies.
Progeny genotypes were obtained by random segregation of alleles from the parents and a genotyping error layer was added (see Materials and Methodss). 10,000 contigs were simulated for each dataset. In order to know how many offspring of each sex should be sequenced to achieve the best sensitivity and specificity trade-off using SEX-DETector, we varied the number of progeny individuals in the simulations. For an X/Y or Z/W system, optimal results were obtained when sequencing five progeny individuals of each sex (Figure  5. A); sequencing more progeny individuals did not improve the results further. This suggests that sequencing 12 individuals (two parents and five progeny individuals of each sex) may be sufficient to achieve optimal performances with SEX-DETector on an X/Y or Z/W system. For a U/V system, two progeny individuals of each sex seems sufficient to obtain optimal SEX-DETector performance (Figure  5.
SEX-DETector identifies unknown sex chromosomes using model selection It is common that the sex determination system is unknown in species with separated sexes, i. e. it is unknown whether they have sex chromosomes and if they do, what the system is (Z/W or X/Y). The likelihood-based framework of SEX-DETector allows us to test for these assumptions by comparing the model fit to the data using the Bayesian Information Criterion (BIC, see Materials and Methodss). In species for which sex determination is unknown, it is possible to compare models with and without sex chromosomes, and, if sex chromosomes are detected, it is possible to compare models with X/Y or Z/W system. This model selection strategy was tested on empirical and simulated data. In the S. latifolia dataset, the best model inferred by SEX-DETector was a model with sex chromosomes as expected, with 1357 sex-linked contigs (which represents 9% of the contigs with a posterior probability higher than 0.
8). In the Silene vulgaris dataset (a species without sex chromosomes), no sexlinked contigs were inferred, the best model fit to the data was thus a model without sex chromosomes as expected (see Materials and Methodss). In order to know from which proportion of sex-linked genes sex chromosomes can be detected, we compared models on simulated data with varying numbers of sex-linked contigs out of 10,000 simulated contigs (Table  3  and Supplementary Table  S4 ). When no sex-linked contigs were simulated, as expected the best model was the one without sex chromosomes. This was also the case when a single sex-linked contig was simulated. In this case, SEX-DETector could not detect it due to lack of information in the dataset. When ten or more sex-linked contigs were simulated, the best model was the one with sex chromosomes as expected.