dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Cryptococcus transcription factor mutant collection. We first selected putative TFs using the published DBD TF prediction database (http://www. transcriptionfactor. org/)  6. The C. neoformans H99 strain, a serotype A platform strain, contains 188 TFs (148 predicted from Pfam and 96 from SUPERFAMILY). Because these TFs were predicted based on the first version of the annotated H99 genome database, we updated this database with reference to the most recent version (ver. 7) of the annotated H99 genome database  4  , which resulted in a final prediction of 178 TFs (Supplementary Data 1). Orthologue mapping based on the BLAST e-value matrix demonstrated that C. neoformans contains several evolutionarily distinct groups of TFs (Supplementary Data 3). The Cryptococcus DBD TFs were classified based on their DBDs (Fig.
1a ). Nearly 44% of these TFs (78) contain a fungal Zn2-Cys6 DBD, and among these, 40 also harbour a fungal-specific TF domain. Several TFs contain more than two TF domains (Supplementary Data 1). To analyse the functions of the TFs, we deleted 155 putative TF genes out of 178 using homologous recombination. To perform a large-scale virulence test, dominant nourseothricin-resistance markers (NATs) containing a series of signature tags of distinct oligonucleotide sequences were employed (Supplementary Data 2). The genotypes of all TF mutants were confirmed by performing Southern blot analysis to verify both the gene deletion and the absence of any ectopic integration of each genedisruption cassette. To accurately validate the phenotype and exclude unlinked mutational effects, we generated more than two independent TF mutants for all 155 TFs, including 4 TFs (HXL1, ATF1, MBS1 and SKN7) that we previously reported  [7] [8] [9]  , and thus obtained a total of 322 strains.
(Supplementary Data 1). Phenotypic profiling of the Cryptococcus TF mutant library. For the 322 TF mutants constructed, we performed a series of phenotypic analyses for the following phenotypic classes: growth, differentiation and morphology, stress responses, antifungal drug resistance, virulence-factor production and in vivo virulence (Fig. 1b ). This overall phenome data set generated for the TF mutant collection is illustrated together with a colour scale in the phenome heat map in Fig. 2  and Supplementary Data 4. Data for transcript levels of each TF measured by RNA sequencing analyses under six distinct growth conditions were obtained from a recent H99 genome analysis report  4  and also demonstrated as a heat map (Fig. 2 ; Supplementary Data 4). The phenotypic analysis revealed that about 93% of the TF mutants (145/155) exhibited at least one discernable phenotype, suggesting a high functional coverage of this TF mutant collection.
Almost 85% of the TFs (132/155) have not been functionally characterized before in C. neoformans. All of these phenome data are publicly available in the Cryptococcus neoformans TF database (http://tf. cryptococcus. org). TFs governing growth and differentiation of C. neoformans. C. neoformans undergoes both saprobic and pathogenic life cycles in natural and animal host environments. Therefore, it must be capable of growing at temperatures ranging from ambient (25 °C)    Galleria virulence heat map scales ARTICLE to high (37-39 °C). Deletion of some TFs (BZP2, CUF1, LIV4, GAT5, FZC6 and NRG1) resulted in temperature-independent growth defects (Supplementary Fig. 1 ). The growth defect of the cuf1D mutant was due to its inability to uptake copper, because external addition of CuSO 4 restored its wild-type (WT) growth (Supplementary Fig.
Similarly, the STM scores of the zap104D and liv1D mutants were also decreased ( À 5. 528 and À 3. 875, respectively)  10. Furthermore, the hxl1D, nrg1D and bwc2D mutants also showed highly reduced STM scores. The 11/40 TFs identified by the STM analysis (GAT201, PDR802, HXL1, BWC2, NRG1, FZC1, HOB1, USV101, ZFC2, SRE1 and FZC31) were also discovered using the insect model. The virulence assay data from the insect model were statistically significantly correlated with the STM-based infectivity data from the murine model based on the Pearson correlation coefficient (PCC) analysis (Fig. 7 ). Among the novel virulence-related TFs that were screened using only the STM-based murine model, the fzc31D and ddt1D mutants exhibited highly reduced STM scores ( À 4. 328 and À 4. 832, respectively). Our phenome database revealed that the fzc31D mutant exhibited increased susceptibility to osmotic, oxidative and cell membrane stresses, which might collectively affect virulence.
neoformans genome, and as such the data presented here provide comprehensive insights into transcriptional networks in diverse basidiomycetous fungi and human fungal pathogens. Our Cryptococcus TF mutant library and its phenome database have an exceptionally high functional coverage (93%) compared with those performed in other pathogenic ascomycetous fungal species. Homann et al. 11  constructed 166 TF mutants in C. albicans and tested their phenotypic traits under 50 different growth conditions and found that over the 50% of them exhibit at shown in the graph were calculated using the Log-rank test to measure statistical differences between the WT strain (H99) and each TF mutant strain. The insect infected with a second independent mutant for each TF exhibited similar survival patterns (Supplementary Fig.
5 Â 10 7 cells per ml) was loaded into a haematocrit capillary tube, which was subsequently placed vertically to allow cells to be packed by gravity for 10 days. The packed cell volume ratio was measured by calculating the ratio of the length of the packed cell volume phase to the length of the total volume phase (cells þ medium). The relative packed cell volume of each mutant was measured by calculating the ratio of the mutant packed cell volume ratio to the WT packed cell volume ratio. Triplicate technical experiments with two or more independent strains were performed. Statistical difference in relative packed cell volume was determined by one-way analysis of variance with Bonferroni's multiple-comparison test using Prism 6 (Graphpad software). For monitoring melanin production, each strain was cultured at 30 °C in liquid YPD medium for 16 h, spotted on Niger seed agar medium containing 0.
The output TF genomic DNA library was obtained as follows. Seven-week-old female A/Jcr mice (Jackson Laboratory) anaesthetized with intraperitoneal injection of Avertin (2,2,2-tribromoethanol) were infected through intranasal inhalation of 5 Â 10 5 cells (in 50 ml) of the pooled TF mutants and sacrificed with an overdose of Avertin at 15 days post infection. For each set of assays, we used three mice. Two independent mutants for each TF were tested in a separate STM set assay. Lungs were dissected and homogenized in 4 ml of sterile PBS. Each lung-tissue lysate was spread on YPD media containing 100 mg ml À 1 of chloramphenicol, incubated at 30 °C for 3 days and collected by scraping to isolate output genomic DNA. Both input and output genomic DNAs were extracted using the CTAB method  55. Quantitative PCR analysis was performed with the various tag-specific primers listed in Supplementary Data 2 using a MyiQ2 Real-Time PCR detection system (Bio-Rad).
We used the 2 À DDCt method to determine the STM score  10, 56. To determine the input of genomic DNA with a specific tag, a DCtin (DCttag-input) was calculated by comparing the CtACT1 with average of Cttag from genomic DNA of pooled TF strains (Cttag À CtACT1). To determine the output genomic DNA with a specific tag, a DCtout (DCttag-output) was calculated by comparing the CtACT1 with average of Cttag from genomic DNA from each lung of the sacrificed mouse (Cttag À CtACT1). The STM score of each TF mutant was determined as the Log 2 2 À DDCt (DCtout À DCtin). Cryptococcus transcription factor database. The genome and transcriptome data collected for 178 TFs were processed using the protocol of the standardized genome data warehouse in Comparative Fungal Genomics Platform (CFGP 2. 0; http://cfgp.
snu. ac. kr/)  57. The TF family name for each gene was described based on the prediction of Fungal Transcription Factor Database (http://ftfd. snu. ac. kr/)  58. For detailed information of the predicted genes, pre-computed results of eight bioinformatics programs were provided (InterPro scan, Signalp 3. 0, PSortII, TargetP, ChloroP, SecretomeP, predictsNLS and TMHMM2)  [59] [60] [61] [62] [63] [64] [65] [66]. To browse genomics contexts together with key biological features, Seoul National University Genome Browser (SNUGB; http://genomebrowser. snu. ac. kr/)  67  was incorporated for use with the Cryptococcus TF Database (http://tf. cryptococcus. org). In the pages of Browse Scaffolds, Browse Gene Models and 3 gene-family browsers, direct links to the SNUGB module were provided. MySQL 5.
0. 81 (source code distribution) and PHP 5. 2. 6 were used for administrating the database and developing web interfaces, respectively. Web pages were provided through Apache 2. 2. 9 web server. Construction of Pearson's correlation networks. We calculated PCC scores by using Prism 5. 0 (GraphPad Software Inc. ) based on the results of phenotypic tests (strongly resistant phenotype: 3; moderately resistant phenotype: 2; weakly resistant phenotype: 1; WT-like phenotype: 0; weakly sensitive phenotype: À 1; moderately sensitive phenotype: À 2; and strongly sensitive phenotype: À 3). Networks were visualized using Cytoscape software 3. 2. 0 based on the PCC scores. Statistical analyses. Statistical analyses were performed using Prism 6. 0 (GraphPad Software Inc. ). Statistical difference was determined using Bonferroni's multiple-comparison test.
Acknowledgements We thank  Hanna Na ,  Moonyoung Park ,  Yongjae Song ,  Yooyeon Kim ,  Kyeongseon Ryu ,  Changho Lee ,  Ja-Kyung Yoon ,  Seung-Yeol Lee ,  Heejung Ahn ,  Jihwan Lee ,  Hyunsoo Kim  and  Sunghyun Kim  for their technical assistance in constructing the TF mutant library. This work was supported by  National Research Foundation of Korea  grants (nos 2008 -0061963  and  2010-0029117 ) from  MEST  (to Y. -S. B. ). This work was also supported in part by  NIH/NIAID   R01-AI050438-10  to J. H. and  ANR  ( 2010-BLAN-1620-01  program YeastIntrons) to G. J.
Whole-genome microarray analysis Three independent transfections were performed with 50 nM siRNA TMPRSS2-ERG III, siRNA TMPRSS2-ERG IV and siRNA Control on VCaP cells. Total RNAs of untreated and transfected cells were extracted using RNeasy mini-kit (Qiagen, Courtaboeuf, France). Protocol is detailed in Array Express (Accession Number: E-MTAB-2838) and previously described by Gilbert-Sirieix  [22]. The mRNA was then labelled using fluorescent low-input linear amplification kit (Agilent Technologies, Massy, France). Briefly, reverse transcription was performed using M-MLV reverse transcriptase. Then, cyanine 3-labelled cDNAs were generated using T7 RNA polymerase. Hybridizations were carried out for 17h at 60°C on Agilent human whole genome oligo microarray 8x60k, either with 1 μg of untreated cells or with siRNA TMPRSS2-ERG III, siRNA TMPRSS2-ERG IV or siRNA Control transfected cells.
Slides were scanned using Agilent 2505 C DNA microarray scanner and microarray images were analysed with Feature extraction software version 10. 7. 3. 1 (Agilent Technologies). Raw data files were normalized with Limma procedure. We defined up-or down-regulations as ratios greater than two-fold between VCaP cells transfected with siRNA (TMPRSS2-ERG III, TMPRSS2-ERG IV and Control sequences) and untreated VCaP cells, acquired with a fold discovery rate 0. 05 and a minimum intensity ! 100. In order to interpret the biological meaning of the genomic data, we used the ingenuity software (www. ingenuity. com). Then, the authenticity of microarray data was validated by RT-qPCR, of few of the TOP 10 up-or down-regulated genes by siRNA TMPRSS2-ERG III and IV. Gene-specific primers were designed using the Oligo Explorer 1.
ebi. ac. uk/arrayexpress/), under the accession number: E-MTAB-2838. Then we compared the expression profile of cells treated with siRNAs TMPRSS2-ERG III and IV to siRNA Control and detected: i) 21 of 44000 probe sets down-regulated when VCaP cells were transfected with the siRNA TMPRSS2-ERG III, ii) 172 probe sets (up or down) with the siRNA TMPRSS2-ERG IV, iii) 88 probe sets with the siRNA Control (Fig  3a ). Contrary to cells treated with the siRNA TMPRSS2-ERG III (100% down-regulated), the repartition of up-or down-regulated genes is almost equally distributed when cells are transfected with siRNA TMPRSS2-ERG IV (48% up vs 52% down) or siRNA Control (47% up vs 53% down) (see Fig  3a). We therefore crossed the data obtained for the three siRNAs (see Venn diagram, Fig  3b ) and found that only three genes are co-repressed by both siRNAs against the TMPRSS2-ERG junction oncogene (ERG, ADRAA2 and SPOCK2).
Concerning the treatment by the siRNA TMPRSS2-ERG IV, a larger number of genes was found to be regulated with a good balance between up-and down- regulations (Fig 3a  and 3b ). The first TOP-10 gene (GREM1) found to be down-regulated in microarray analysis was detected to be up-regulated by RT-qPCR, whereas the down-regulation of the second TOP-10 gene (PAPSS2) was confirmed by RT-qPCR (Table  3 , IV). Then, we analysed by IPA software (Ingenuity Pathway Analysis) the associated network functions for genes specifically regulated by siRNA TMPRSS2-ERG III or IV. Concerning siRNA TMPRSS2-ERG III, only one associated network involved in "cellular movement, development, growth and proliferation" was found to be relevant and accounts for six down-regulated genes (S4 Table ). The siRNA TMPRSS2-ERG IV seems to be implicated in the regulation of several networks involved in cellular movement, morphology and proliferation (S4 Table ).
Acknowledgments The microarray analysis was performed in the " Genomics and Bioinformatics" platform of the Institute Gustave Roussy. The authors want to thank  Justine GUEGAN ,  Cedric OREAR  and  Doctors Philippe DESSEN  and  Guillaume MEURICE  for performing and analysing microarray experiments. The animal studies were performed within the animal facility of the  Gustave Roussy Institute  and authors gratefully acknowledge the excellent technical support of  Olivia BAWA  ( Histology Platform ) for the preparation and immunohistochemical analysis of histological slides. We also want to thanks  Dr Karine Debbasch  ( University Paris Descartes, Maison des Langues ) for the English editing of the manuscript.
Since the establishment step is fundamental to the trypanosome life cycle within its vector, our investigation has once again focused on the tsetse fly midgut, where the ingested trypanosome is established (or not). To investigate global infection dynamics at key early time points, samples were collected at 3, 10, and 20 days post-feeding on either trypanosome-infected or non-infected bloodmeals. The analyses were performed using an RNA-seq de novo assembly approach-"a revolutionary tool for transcriptomics"  (Wang et al. , 2009). Our report presents results of transcriptome read assembly. G. p. gambiensis and G. m. morsitans being two different species, functional annotation was performed with reference to a broad panel of insect data bases including G. m. morsitans. Here, we identified DEGs in susceptible and refractory tsetse.
De novo Assembly and Transcriptome Analysis To construct the G. p. gambiensis assembled whole transcriptome, all short reads obtained from infected, stimulated and non-infected, non-stimulated tsetse flies at 3, 10, and 20 days post-feeding were first assembled into contigs with no gap, using the de novo transcriptome assembly software programs Velvet and Oases  (Zerbino and Birney, 2008). Each read was then mapped back to the contigs using the Bwa short-read aligner  (Li and Durbin, 2009) , to generate the gross count per contig for each biological replicate representing the different conditions. The assembled contigs were annotated by BLASTX alignment (E < 0. 00001) to protein databases such as the NCBI NR (http://www. ncbi. nlm. nih. gov), Swiss-Prot (http:// www. expasy. ch/sprot), ensembl-pep, refseq-rna, refseq-protein, and FlyBase databases.
Contig sequences were deposited at the NCBI Transcriptome Shotgun Assembly (TSA) Database under BioProject PRJNA260242. Gene Ontology (GO) annotation assignment  (Ashburner et al. , 2000)  was used to perform functional gene annotation by mapping GO terms using the NCBI NR, GO (http://www. geneontology. org/), and UniProts (http://www. ebi. ac. uk/UniProt/) databases; E-value cutoff of 10 -5  (Conesa et al. , 2005).
Per Condition Assembly Read pairs were first cleaned from remaining sequencing adapter sequences using the trim_galore script (http:// www. bioinformatics. babraham. ac. uk/projects/trim_galore/; Smallwood et al. , 2014). Over-represented reads were then filtered out using the normalize_by_kmer_coverage. pl script from the Trinity software package  (Haas et al. , 2013). In the next step, invalid base calls were discarded by extracting the longest sub-sequence without Ns from each read. Specifically, if the length of the longest sub-sequence did not exceed half of the sequence length, the read, and its pair were removed. The final step was performed using an in-house script. Nine assemblies using nine different k-mers  (25, 31, 37, 43, 49, 55, 61, 65, and 69)  were performed on pre-processed input data.
Each assembly produces a transcripts. fa file and each raw transcripts. fa file is organized into loci. Rather than referring to genetic loci, each locus is actually a collection of similar sequences including (but not limited to) splice variations and partial assemblies of the longer transcripts in the locus. We chose to keep only the best transcript for each locus, using the script OasesV0. 2. 04OutputToCsvDataBase. py (http://code. google. com/p/oases-to-csv/; Schulz et al. , 2012). Subsequently, all files were merged and anti-sense chimeras (accidentally produced by the assembly step) were cut with a homemade script. Identical contigs produced by different k-mers were removed using the cd-hit-est program  (Li and Godzik, 2006). Because different k-mers sometimes construct different transcript parts, we used TGICL  (Pertea et al.
Meta-assembly All single condition contig fasta files were concatenated, and each contig was renamed by adding the condition name to the beginning of its name. The longest open reading frame (ORF) of each contig was then searched using the getorf program from EMBOSS  (Rice et al. , 2000). A cd-hit clustering was performed on ORFs with a sequence identity ≥0. 9, in order to extract from each cluster the contig with the longest ORF, or the longest contig (if the ORF sizes were identical). A clustering using cd-hit-est was then done on selected ORFs with a sequence identity ≥0. 95. Input reads from all conditions were then mapped back to the contigs using bwa  (Li and Durbin, 2009). Contigs with very low coverage (<1 mapped read per million) were filtered out.
Analysis of DEGs DEG's were identified using the DESeq software, version 1. 16. 0  (Anders and Huber, 2010). This method represents widely accepted and complementary analytical approaches for RNASeq data. The raw read counts were produced by realigning the read on the contigs. These counts were used as inputs for DESeq to calculate the normalized expression for each contig in the different samples (e. g. , trypanosome-stimulated and nonstimulated tsetse flies at 3 days and trypanosome-infected and non-infected tsetse flies at 10 and 20 days). Differential expression was then reported as fold change, with associated p-values. DESeq calculates p-values using a negative binomial distribution that accounts for technical as well as biological variability. The resulting raw p-values were corrected for multiple tests using the False Discovery Rate  (Benjamini and Hochberg, 1995).
Contig pairs whose read numbers displayed a greater than two-fold difference between the selected conditions (with p < 0. 05) were identified as DEGs. The DESeq approach is well suited for count data (i. e. , read counts), as is the case for RNA-Seq experiments, and the method estimates variance in a local fashion for varying signal strength  (Trapnell et al. , 2010). For functional analysis, all DEGs were mapped to terms in the GO database, which requires E-values adjusted for multiple testing to be <0. 001. The annotation of all significant genes was further supplemented by BLASTX, conserved domains, and literature searches. Using these combined approaches resulted in a functionally driven classification.
SNP Identification Many SNP panels have been build using an RNA-Seq assembly reference for species without reference genome  (Salem et al. , 2012; Swaminathan et al. , 2012 ; see also GATK Program which is the classical tool to evidence SNPs). In order to call putative variants (SNPs, insertions and deletions), the alignment files were cleaned from reads with low mapping quality and PCR duplicates using the samtools software, v 0. 1. 19-44428cd. The remaining reads were recalibrated and realigned with the GATK Program 2. 4-9-g532efad  (DePristo et al. , 2011) , which was also used for variant calling (Unifiedgenotyperalgorithm). Variants were filtered using a Phred quality score of 20. Variants were deposited at the NCBI single nucleotide polymorphism database (dbSNP) with the accession number SUB833398.
Transcriptome Sequencing by RNA-seq and de novo Assembly Twelve RNA-seq libraries were prepared from total RNA extracted from pooled midguts of non-stimulated and noninfected (refractory) tsetse flies [representing 3 (NS3), 10 (NI10), and 20 days (NI20) post-bloodmeal uptake] and from pooled midguts of stimulated or infected tsetse flies [representing 3 (S3), 10 (I10), and 20 days (I20) post-bloodmeal uptake]. A total of approximately 520 million raw reads (50 million paired-end reads, 2×100 bp) representing approximately 165 GB of sequence data were generated from 12 independent 200 bp insert libraries. Prior to de novo assembly, the quality of the reads was assessed using the base-calling quality scores  (Cock et al. , 2010 ) from Illumina's base-caller RTA software. Most reads displayed Phred-like quality scores at the Q20 level, indicating a sequencing error probability of 0.
01%. After trimming and cleaning, between 58,370,072 and 87,387,674 read pairs were kept, depending on the sample library. De novo assembly was then performed using the Velvet software. Oases is a de novo transcriptome assembler designed to produce extended contigs from short read sequencing technologies in the absence of any genomic reference. It clusters the contigs from a preliminary assembly by Velvet into small groups and uses a de Bruijn graph-based algorithm to construct transcript isoforms  (Schulz and Zerbino, 2010). The contigs, produced by Velvet, were post-processed using Oases. This yielded a total of 16,936 contigs ranging from 137 to 4836 bp (average length: 2302 bp), with an N50 at 3036 and a N90 at 1215 (Table  1 , Figure  1 ). The assembled transcriptome size is 38,986,687 bp.
Functional Annotation and Classification of Assembled Contigs A total of 12,806 contigs could be annotated (out of 16,936) from which 9698 were annotated with reference to the sequences recorded in the Refseq-proteins database. Subsequently, 1303 and 1805 contigs were annotated with reference to the sequences recorded in the Refseq-rna and Swiss-Prot databases, respectively. In the end, 24. 39% of the contigs could not be annotated. Nevertheless, these orphan sequences may be of great interest, as they could refer to putative G. p. gambiensis and T. b. gambiense specific biological functions (Figure  2 ), and therefore specific genes. Our annotated dataset including 16,936 contigs (Supplementary Table  S1 ) is most likely representative of the G. p. gambiensis gene catalog. In terms of the total number of hits, BLASTX hits and top hits were mostly identified with Ceratitis capitata (5443 hits), Drosophila melanogaster (1656 hits), Trypanosoma brucei (838 hits), Drosophila willistoni (626 hits), Drosophila virilis (608 hits), and Drosophila mojavensis (561 hits).