dataesr/scientific-paragraphs-categorization huggingface dataset:
Yeast data were extracted (17 August2015) from YeastMine  48  , with the query 'Phenotype ¼ Haploinsufficient' or 'Phenotype ¼ Haploproficient'. Similar annotations for 169 murine genes were extracted (9/17/2015) from the Mouse Genome Informatics database or the MouseMine database  49. Human homologues for mouse and yeast genes were systematically determined using the 'Homology' tool of MouseMine and YeastMine. Of the 486 proteostasis genes studied, 284 were annotated as gene-dose-sensitive. All gene annotations can be found in Supplementary Table  3. Each edge connecting two gene nodes was scored for negative (loss or deletion) or positive (gain or amplification) copy-number change as follows. Given an edge between gene1 (G1) and gene2 (G2), edge scores were calculated as: For either (G1,G2) GISTICo0 (at least one gene is deleted): EdgeLossScore G1; G2 ð Þ¼MinimumðGISTIC1ÂGDS1; GISTIC2ÂGDS2Þ ð1Þ For both (G1,G2) GISTICZ0 (neither gene is deleted): EdgeGainScore G1; G2 ð Þ¼MaximumðGISTIC1ÂGDS1; GISTIC2ÂGDS2Þ ð2Þ Wherein GISTIC scores represent a range of ( À 2, À 1, 0, 1, 2) from À 2 as a double deletion, À 1 as a monoallelic deletion, 0 as no somatic change, 1 as a monoallelic gain and 2 as a gain of two or more alleles, and gene dose sensitivity (GDS) indicates the gene-dose sensitivity information (1 for no information, 2 for yeast information and 3 for mouse information).
Each cancer type has a unique distribution of chromosome losses and gains. Since a highly copy-number variable cancer may have a higher chance of a random loss or gain of a pathway than a relatively SCNA stable cancer, we compared the distribution of observed HAPTRIG module scores to that of the distribution of HAPTRIG module scores resulting from globally shuffled gene copy-number data from the same cancer cohort (Supplementary Software 1). Edge scores were then recreated using the shuffled gene data. Two distributions for each cancer type were thus created using identical calculations: an observed HAPTRIG module score distribution corresponding to observed tumour data, and a statistical comparison HAPTRIG module score distribution corresponding to randomized data To increase the confidence in the output P value, our automated HAPTRIG code creates 1,000 control network scores for each tumour and output P values are generated from the average log 10 (P value) resulting from these 1,000 control network comparisons.
A summary of the highest and lowest scoring five genes for each KEGG molecular pathway is provided in Supplementary Data 1. For quality control, a table of the top 10 ranked genes (as in the gene-impact prioritization) for each of the 187 KEGG pathways was generated and compared with the appropriate COSMIC tumour suppressor/oncogene gene set or STOP/GO gene set. Efficiency was calculated as the per cent of possible hits that were found to be present in the quality control table. Code availability. Complete HAPTRIG code is available as Supplementary Software 1. Demo data for input are provided as a convenience as Supplementary Data 5. Gene set enrichment analysis. TCGA OV data were used as the expression data set, with tumour copy number compared with normal tissue control copy number. Gene sets were the same as HAPTRIG.
Acknowledgements This work was supported by  NCI  grants  CA107263  (D. G. S. ), CA102310 (D. D. S. ). CA177519 (O. H. ) and by funding from the  Nine Girls Ask Foundation  (D. G. S. , D. D. S. ). The authors thank  Dr. Beth Levine  ( University of Texas Southwestern ) and  Dr. Albert La Spada  ( University of California San Diego ) for providing reagents.
benthamiana leaf samples (more than 515 M reads, Fig. 2a , Additional file 1: Table  S1 ). Following standard adapter trimming and read quality control, we applied a two-step filtering procedure (Fig. 2a ) to separate pathogen reads from plant host reads. First we mapped the pooled read dataset to the N. benthamiana reference genome and collected unmapped read pairs. Recovered reads were subsequently mapped to the N. benthamiana transcriptome  [53]. Reads not mapped to either host plant genome or transcriptome were used to run assemblies. Short reads (<60 nt) were filtered out to produce transcripts of better quality and coherence. Final de novo Trinity assemblies were run from 190 M pre-processed, properly paired and cleaned reads (Additional file 1: Table  S1 ). This yielded 57,579 'Trinity genes' corresponding to 100,303 transcripts with an average backwards alignment rate of 76%, indicative of an overall acceptable representation of reads and therefore reasonably good assembly quality  [54].
05) corresponding to 80,258 transcripts (Table  1 ). We further selected 13,997 Trinity genes (corresponding to 27,106 transcripts) having the best expression support (Additional file 2: Dataset 1). We assessed completeness of P. palmivora assembly by benchmarking nearly universal single-copy orthologues (BUSCO)  [55]  (Table  1 ) and compared them to the BUSCO content of P. infestans, P. sojae and P. parasitica transcriptomes. We identified 326 BUSCO genes (76% of eukaryotic BUSCO genes) in our P. palmivora assembly, 348 (81%) in P. infestans, 343 (80%) in P. sojae and 360 (84%) in P. parasitica (Table  1 , Additional file 3: Figure  S1 ). We also surveyed 14 publicly available Phytophthora genomes, yielding 20 additional BUSCO genes absent from all transcriptomes. Interestingly, the remaining 35 BUSCO genes were consistently missing from all analysed Phytophthora genomes and transcriptomes (Additional file 4: Table  S2 ).
Clustering of plant and pathogen samples reflects different temporal dynamics during infection To explore the temporal expression dynamics of plant and pathogen genes, we separately mapped initial reads back to the reference N. benthamiana transcriptome (https:// solgenomics. net/) as well as to our P. palmivora transcriptome assembly (Additional files 5 and 6: Dataset 2 and Dataset 3). Principal component analysis (PCA) of plant samples revealed a major difference between infected and uninfected samples (91% of variance; Fig. 3a ). Plant transcript profiles from infected samples could be further assigned into three groups: 6 hai, 18-24-30 hai and 48-72 hai (4% of variance; Fig. 3a ). Conversely, PCA analysis of P. palmivora transcript profiles identified two groups corresponding to early infection (6 to 24 hai) and late infection with MZ (48 and 72 hai), while 30 hai was kept apart (66% of variance; Fig.
In contrast to the pathogen, the plant transcriptome did not undergo sharp transitions over time and was instead characterised by steady up-or downregulation (Fig. 3e, c ). Therefore, we utilised repeated upregulation of a gene in at least two time points as a selective criterion to alleviate the absence of replicates resulting in 2078 up-and 2054 downregulated genes. From these we validated 5 out of 6 genes with low or no expression under control conditions and high expression levels during infection using qRT-PCR  (Additional file 3: Figure  S11 ). Gene ontology (GO) term analysis revealed that upregulated genes are enriched in biological processes related to hormone metabolism, abiotic stress (including oxidative stress, response to heat and wounding), defense, biosynthesis, transport, regulation of transcription and protein modification by phosphorylation and ubiquitination (Additional file 7: Dataset 4).
P. palmivora secretome prediction and annotation identify a set of effector candidate genes Pathogen-secreted effectors and hydrolytic enzymes are hallmarks of Phytophthora infection  [56]. Therefore, we probed our P. palmivora transcriptome for transcripts encoding secreted proteins. A TransDecoder-based search for candidate open reading frames (ORFs)  [57]  identified 123,528 ORFs from predicted Trinity genes (isoforms included). We then analysed the predicted ORFs using an automated pipeline for secretome prediction (Fig. 2b ) building on existing tools  [58] [59] [60]. The pipeline was designed to predict signal peptides and cellular localisation with thresholds specific for oomycete sequences  [61, 62]  and to exclude proteins with internal transmembrane domains and/or an endoplasmic reticulum (ER) retention signal.
We identified 4163 ORFs encoding putative secreted proteins. Partial translated ORFs which were not predicted as secreted were subjected to an additional analysis (Mslicer) (Fig. 2b ) and resubmitted to the secretome prediction pipeline. This improved procedure allowed us to rescue 611 additional ORFs encoding putative secreted proteins. In total, we identified 4774 ORFs encoding putative secreted P. palmivora proteins. We further selected a single representative secreted ORF for genes with sufficient expression support (transcripts per million (TPM) ≥1 in three or more samples). This yielded 2028 P. palmivora genes encoding putative secreted proteins (Additional file 8: Dataset 5). To maximise functional annotation of the P. palmivora secretome, we used an integrative approach (Fig. 2c ) tailored to the use of known short motifs characteristic of oomycete secreted proteins.
Amongst predicted cytoplasmic effectors, the most prominent category encompasses 140 RXLR effectors. Of these 123 have a conserved RXLR motif followed by a dEER motif. WY domains were found in 30 RXLR-EER effectors and 3 RXLR effectors. Some RXLR effectors are unusually long (>400 aa; the average length of RXLR effectors is 204 aa), suggesting that multiple effector domains are linked together. For instance, the effector domain of PLTG_07082 consisted of 8 internal repeats of a WY domain. It remains to be tested whether multiple WY domains within one effector fulfil different and independent roles. Pfam searches revealed one full-length RXLR effector protein (PLTG_09049) carrying a C-terminal NUDIX domain. Pfam predictions assigned to partial genes identified two putative effectors, one bearing a NUDIX domain PF00293 (PLTG_05400) and the other a MYB/ SANT domain PF00249 (PLTG_06121).
Notably, the putative CRN effector PLTG_06681 carries a C-terminal serine/threonine kinase domain (PF00069) and shows low sequence similarity (34%) to P. infestans effector CRN8  [65]. The P. palmivora secretome also contained a substantial number of apoplastic effectors (Table  2 ). We identified 28 genes encoding extracellular protease inhibitors, including extracellular serine protease inhibitors (EPIs) with up to five recognisable Kazal domains, several cystatins and cysteine protease inhibitors (EPICs) (Additional file 8: Dataset 5). PLTG_05646 encodes a cathepsin protease inhibitor domain followed by a cysteine protease and an ML domain (PF02221, MD-2-related lipid recognition domain). We also identified 28 proteins with small cysteine-rich (SCR) signatures, 18 of them being encoded in full-length ORFs, but only 6 in which the mature peptide is shorter than 100 aa.
Longer SCRs can harbour tandem arrangements (PLTG_08623). In one case an SCR is linked to an N-terminal PAN/ APPLE domain, which is common for carbohydratebinding proteins  [66]. Additionally the P. palmivora secretome contains 90 proteins carrying potential MAMPs, including necrosisinducing proteins (NLPs), elicitins and lectins. Out of 24 NLPs, 4 (PLTG_05347, PLTG_07271, PLTG_13864, PLTG_01764) carry a pattern of 20 amino acid residues which is similar to the immunogenic nlp20 motif (AiMYySwyFPKDSPVTGLGHR, less conserved amino acids in lower case)  [67]. Transcripts encoding elicitins and elicitors in the P. palmivora secretome belong to the group of highest expressed ones during infection (Additional file 8: Dataset 5). We identified six transglutaminases, five of them (PLTG_04342, PLTG_02581, PLTG_10032, PLTG_10034 and PLTG_10033) carrying a conserved Pep-13 motif  [28].
Despite the annotation efforts, 62% of the genes of the predicted secretome remained hypothetical and may contain novel classes of secreted proteins contributing to P. palmivora virulence. Inspection of the second largest tribe after RXLR effectors revealed 42 genes, all encoding a conserved N-terminal domain with similarity to the PTHR13842 family combined with variable Cterminal domains. Motif enrichment analysis identified an invariant motif HxGPCExxxDD, encoded in the Nterminal domain of all full-length transcripts (Additional file 3: Figure  S3a  and Additional file 8: Dataset 5). Homology modelling revealed structural similarity to lytic polysaccharide monooxygenases (Additional file 3: Figure  S3b )  [68]. However, a function for these proteins remains to be assigned. Given that their expression peaks during biotrophy (6-24 hai) and their conservation in 13 oomycete species (including representatives from genera Phytophthora, Plasmopara, Saprolegnia, Aphanomyces, Achlya, Thraustotheca and Albugo), they may represent a major class of apoplastic oomycete proteins.
Conserved RXLR effectors amongst P. palmivora isolates confer enhanced plant susceptibility to root infection We next focussed on the characterisation of four RXLR effectors upregulated during infection (Additional file 3: Figure  S5 ) and named them REX1 (PLTG_01927 [Gen-Bank:KX130348]), REX2 (PLTG_00715 [GenBank: KX130350]), REX3 (PLTG_00687 [GenBank:KX130351]) and REX4 (PLTG_13723 [GenBank:KX130352]). REX1-4 sequences from P. palmivora isolates with diverse geographic and host species origin (Additional file 9: Table  S4 ) were obtained by PCR and amplicon sequencing. Primers specific for REX1-4 generated amplicons from at least 13 of the 18 isolates (REX1: 15, REX2: 15, REX3: 16, REX4: 13, Additional file 3: Figure  S6 ) encoding proteins with high levels of amino acid sequence conservation.
Discussion We utilised a dual transcriptomics approach coupled to a semi-automatic secretome annotation pipeline to study the interaction between P. palmivora and N. benthamiana roots. While the pathogen transcriptome undergoes remarkable shifts in expression patterns throughout the infection, we see a steady response of the plant transcriptome with no detectable major shifts in sets of differentially expressed genes. We used our dataset to identify P. palmivora and N. benthamiana genes implicated in the interaction, uncovered a new class of HxGPCE effector candidates and characterised two conserved biotrophic P. palmivora effector proteins which confer enhanced infection susceptibility when expressed in planta. We show that one of them, REX3, suppresses plant secretion processes. Surveying the set of early transcriptionally activated plant genes resulted in the identification of an N.
Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analyses Total RNA was extracted from frozen, axenically grown mycelium with sporangia (sample MZ) and infected roots harvested at 3, 6, 18, 24, 30, 48 and 72 h after inoculation (hai) using the RNeasy Plant Mini Kit (Qiagen, Germantown, MD, USA). One microgram was reverse transcribed to generate first-strand complementary DNA (cDNA), using the Roche Transcriptor First Strand cDNA Synthesis Kit according to the manufacturer's instructions (Roche, Basel, Switzerland). Quality was assessed by electrophoresis on agarose gel. qRT-PCR experiments were performed with 2. 5 μl of a 1:20 dilution of first-strand cDNA and LightCycler 480 SYBR Green I Master mix, according to the manufacturer's instructions (Roche). Gene-specific oligonucleotides were designed with BatchPrimer3 software  [107]  (Additional file 11: Table  S3 ), and their specificity was validated by analysing dissociation curves after each run.
Plasmid construction The vector pTrafficLights was derived from pK7WGF2 (Plant System Biology, Gent University, Belgium). A cassette containing the signal peptide sequence of Nicotiana tabacum pathogenesis-related protein 1 (PR-1 [GenBank:X06930. 1]) fused in frame with the green fluorescent protein (GFP) was obtained by PCR using primers SP-F/SP-R (Additional file 11: Table  S3 ) and ligated into pK7WGF2 using SpeI and EcoRI restriction enzymes. The AtUBQ10pro::DsRed cassette was amplified from pK7WGIGW2(II)-RedRoot (Wageningen University, Netherlands) using primers RedRoot-F/Red-Root-R (Additional file 11: Table  S3 ) and ligated into pK7WGF2 using XbaI and BamHI restriction enzymes. The TIPTOP promoter (1230 bp, ending 46 bp before start codon) was PCR-amplified from N. benthamiana genomic DNA using primers TIPTOP-F2/TIPTOP-R2 (Additional file 11: Table  S3 ) and cloned into a pENTR/D-Topo vector (Life Technologies Inc.
Confocal microscopy Confocal laser scanning microscopy images were obtained with a Leica SP8 laser-scanning confocal microscope equipped with a 63× 1. 2 numerical aperture (NA) objective (Leica, Wetzlar, Germany). A white-light laser was used for excitation at 488 nm for GFP visualisation, at 514 nm for yellow fluorescent protein (YFP) visualisation and at 543 nm for the visualisation of tdTomato. Pictures were analysed with ImageJ software (http://imagej. nih. gov/ij/) and plugin Bio-Formats. Library preparation and sequencing N. benthamiana and P. palmivora messenger RNAs (mRNAs) were purified using Poly(A) selection from the total RNA sample and then fragmented. cDNA library preparation was performed with the TruSeq® RNA Sample Preparation Kit (Illumina, San Diego, CA, USA) according to the manufacturer's protocol.
De novo transcriptome assembly In order to capture the full complexity of the P. palmivora transcriptome, we pooled all the samples potentially containing reads from P. palmivora (Fig. 2 ): eight mixed (plant-pathogen, combining leaf and root infections), one exclusively mycelium and one mixed myceliumzoospores sample. Initial read quality assessment was done with FastQC (Babraham Bioinformatics, Cambridge, UK). Adapters were removed using Cutadapt  [111]. To exclude plant reads from the library, raw paired reads were first aligned to the N. benthamiana reference genome (v1. 01) using TopHat2  [112]. Unmapped reads (with both mates unmapped) were collected with SAMtools (samtools view -b -f 12 -F 256), converted to fastq with bedtools and processed further. To estimate the level of residual contamination by plant and potentially bacterial reads, the resulting set of reads was subjected to FastQ Screen against the UniVec database, all bacterial and archaeal sequences obtained from the RefSeq database, all viral sequences obtained from the RefSeq database, the N.
De novo assembly statistics and integrity assessment General statistics of the assembly were determined using the 'TrinityStats. pl' script provided with the Trinity release and independently using Transrate (http://hibberdlab. com/ transrate/) and Detonate (http://deweylab. biostat. wisc. edu/ detonate/) tools. Assembly completeness was estimated using the eukaryotic set of BUSCO profiles (v1)  [55]. BUSCO analysis was performed for the full transcriptome assembly and for the reduced assembly, obtained after retaining only the longest isoform per Trinity gene. BUSCO genes missing from the assembly were annotated with InterProsScan based on the amino acid sequences emitted from the corresponding hmm profile ('hmmemit' function from hmmer package, http://hmmer. org/). Overall expression support per assembled transcript was performed after transcript abundance estimation.
Protein prediction and annotation ORFs were predicted using TransDecoder software  [57]. At the first step, ORFs longer than 100 aa were extracted. The top 500 longest ORFs were used for training a Markov model for coding sequences; candidate coding regions were identified based on log-likelihood score. Additionally all the ORFs having homology to protein domains from the Pfam database and/or P. sojae, P. parasitica, P. infestans and P. ramorum protein sequences downloaded from the Uniprot database (accession numbers: UP000005238, UP000006643, UP000002640, UP000018817) were also retained (blastp parameters: max_target_seqs 1 -evalue 1e-5).
Secretome prediction For the automatic secretome prediction a custom script was written, employing steps taken for P. infestans secretome identification  [16]. Predicted proteins were subsequently submitted to SignalP 2. 0 (Prediction = 'Signal peptide'), SignalP 3. 0 (Prediction = 'Signal peptide' , Y max score ≥ 0. 5, D score ≥ 0. 5, S probability ≥ 0. 9), TargetP (Location = 'Secreted')  [113]  and TMHMM (ORFs with transmembrane domains after predicted signal peptide cleavage site were removed)  [114]. Finally, all proteins with terminal 'KDEL' or 'HDEL' motifs were also removed, as these motifs are known to be ER-retention signals  [115]. Exact duplicated sequences and substrings of longer ORFs were removed to construct a non-redundant set of putative secreted proteins. Taking into account possible fragmentation of de novo assembled transcripts, a custom Python script (M-slicer) was developed to rescue partial proteins with mispredicted coding sequence (CDS) coordinates.
Secretome annotation To annotate putative secreted proteins a complex approach was used, combining several lines of evidence: (1) blastp search against the GenBank non-redundant (NR) database with e-value ≤10 -6 ; (2) InterProScan (v5. 16) search against databases of functional domains (PANTHER, Pfam, Coils, Gene3D, SUPERFAMILY, SMART, PIRSF, PRINTS) with default parameters  [116] ; (3) RXLR and EER motif prediction using regular expressions; (4) WY motif prediction based on WY-fold HMM by hmmsearch function from the HMM3 package (http://hmmer. org/); (5) LXLFLAK and HVLVVVP motif predictions based on HMM model build on sequences of known CRN effectors; (6) NLS motif prediction by NLStradamus  [117]  (v1. 8, posterior threshold = 0. 6) and PredictNLS  [118]  with default parameters. The Tri-beMCL algorithm was used to cluster predicted putative secreted proteins with signal peptide and after signal peptide cleavage (mature proteins).
The tribing results were used as a soft guidance for functional annotation (proteins belonging to the same tribe are likely to have the same function). All obtained data were aggregated in the Additional file 8: Dataset 5: Annotated P. palmivora secretome. Functional categories were assigned based on manual curation of the resulting table. A 'hypothetical' category was assigned to proteins either having similarity to only hypothetical proteins or when the top 20 hits of blastp output did not show consistency in terms of distinct functional categories. Proteins having significant sequence similarity to ribosomal, transmembrane proteins or proteins with known intracellular localisation (e. g. heat shock proteins) and/or having respective domains identified by InterProScan were marked as false predictions.
Acknowledgements We acknowledge the experimental and annotation help from  Abhishek Chatterjee  and  Schornack lab. We would like to thank  Mike Coffey  and  Joe Win  for providing the pathogen isolates and list of oomycete RXLRs. We are indebted to  Diane Saunders ,  Liliana Cano ,  Jodie Pike  and  Sophien Kamoun  for generating leaf transcriptome sequences. We would like to thank  Ruth Le Fevre  and  Stuart Fawke  for proofreading the manuscript.
However, this is not generally true since the particle relaxation time can vary inside the domain based on the local flow-field. For instance in  [16] , a security factor is introduced between the simulation and particle time step in order to correctly catch the dynamic and thermal evolution of the liquid phase. To overcome such limitations and to extend the ELSA capabilities in dealing with evaporation in the dense spray region with a more physical characteristic time scale, a novel implicit evaporation modelling strategy has been developed in the framework of the open source code OpenFOAM. Considering that the attention here is mainly focused on the dense zone, only the Eulerian-Eulerian solver derived from ELSA has been considered for the present study. The next section is therefore devoted to the description of such E-E solver and to the characterization of the modelling strategy employed to include evaporation.