dataesr/scientific-paragraphs-categorization huggingface dataset:
g, disease resistance) in plants and animals, and the potential applications of this technology are remarkable  [12] [13] [14]. The Xanthomonas genus comprises 27 species that collectively cause major diseases on more than 400 plant hosts, including a wide variety of economically important crops, such as rice, citrus, banana, cassava, cabbage, tomato, pepper and beans  [15]. There are currently 104 Xanthomonas genome sequences available from 11 species (http://www. xanthomonas. org/) and this number is likely to increase dramatically in the near future. Xanthomonas strains harbor quantitatively and qualitatively different repertoires of TALEs, and the total number of sequenced TALEs available in databases reaches roughly 100 (http://www. xanthomonas. org/). Despite great advances in TALE-based biotechnology, the collective role of TALEs during infection has not been systematically investigated and only a few transcriptional targets have been validated in host plants  [4, 5, [16] [17] [18] [19] [20] [21].
Yet, the ability to predict binding sites for TALEs is key to a better understanding of the disease process and to develop genetic resistance strategies which are greatly needed considering the impact of Xanthomonas on agriculture worldwide  [22]. Currently, a single TALE target prediction algorithm based on the published TALE-DNA code  [5]  has been released and is available in the TALE-NT suite  [23]. Despite its ability to correctly identify previously unknown targets of some TALEs  [5] , it fails to detect the genuine targets of others (see Results section). This is particularly true for several TALE-target pairs that exhibit RVD-nucleotide association patterns deviating from expected matches based on the documented specificities  [5, 16]. The effects of such imperfect RVD-nucleotide pairings on TALE activity still remain elusive and therefore represent an obstacle for the accurate prediction of TALE Effector Binding Elements (EBEs).
In this work, we used the available literature on TALE-RVD specificities from experimentally validated TALE-DNA interactions to develop new programs for TALE binding sites prediction. Our software outperformed the TALE-target finder module of the TALE-NT suite in differentiating positive and negative TALE-DNA interactions. It was subsequently employed to predict rice microarray data-supported new binding sites for TALEs from various X. oryzae strains. Because we had access to TALE repertoires of both the xylem vessel-associated X. oryzae pv. oryzae (Xoo), the causative agent of the bacterial leaf blight, and the mesophyll-associated X. oryzae pv. oryzicola (Xoc), the causative agent of the bacterial leaf streak, we could also compare the plant candidate target sets of these pathovars with contrasting tissue specificity.
New Approaches for EBE Predictions that use a Modified RVD-nucleotide Association Matrix In this work, two programs named Talvez and Storyteller were designed to predict TAL effectors binding sites in plant genomes. Similar to the reference target finder program from the TALE-NT suite  [23] , hereon referred to as Talent, both programs use a RVD-nucleotide association matrix to convert a sequence of RVDs for a given TALE into a positional weight matrix (PWM). However, we first refined the previous RVD-nucleotide association model published in  [4, 5]  to account for more recent insight into RVD-nucleotide specificities  [7, 9]. In our updated matrix (Table  S1 ), identical counts were assigned to all RVDs sharing the same second hyper variable residue (e. g. HG, NG), since it has been shown that this residue only is responsible for nucleotide specificity  [7, 9].
In addition, considering that cytosine binding at position 0 is possible and functional, albeit with lower activity  [9, 18] , prediction of cytosine binding at this position was allowed but was assigned a lower value than thymine. For predictions, both Talvez and Storyteller first convert a sequence of RVDs into a PWM based on this updated RVD-nucleotide association matrix. Talvez then uses the PWM to scan and score all possible EBEs with a log-likelihood function developed originally to study plant transcription factors binding sites  [24]. In contrast, Storyteller uses the PWM to generate a set of possible EBEs and constructs a hidden Markov model that is then fed to the program HMMER  [25]  to scan DNA sequences. (see Materials and Methods, program descriptions).
We reasoned that it may be counterproductive to strongly penalize suboptimal RVD-nucleotide pairings in distal regions of the alignment as this may unnecessarily diminish the overall score of legitimate hits relative to background. We therefore added a ''position correction'' parameter to the Talvez program in order to buffer this potential effect and to ultimately improve the performances of our prediction model. The value of the position correction parameter corresponds to a RVD position after which a scaled down matrix for RVD-DNA specificities is used instead of the standard matrix. In our scaled down matrix, non-perfect matches are no longer penalized and are assigned a count value slightly lower than perfect matches (See the Material and methods section and Table  S3 ). The Position Correction Parameter Improves EBEs Prediction Next, we addressed the effect of the position correction feature on both false positive and false negative rates on our validation set (Table  S2 ) by varying the position correction parameter of Talvez between 7 and 26.
Moreover, 6 of these 11 targets were ranked first among the predicted target promoters (Figure  3B ). Notably, with our dataset, this position correction value appeared to be a good compromise in the sense that it was the only one that, with the exception of Xa27, ranked all positive control targets within the top 200 best predictions of their cognate TALE (Figure  3B ). In conclusion, these results suggested that optimal RVDnucleotide pairings in the C-terminal region of TALEs DNAbinding repeat domain may contribute to a lesser extent than the N-terminal region to productive TALE-DNA associations. This property was integrated into Talvez by not penalizing mismatches after a certain RVD. Benchmarking of the position correction value in our validation and genomic control sets revealed that values above position 15, and particularly position 19 provided an appreciable gain in prediction performances over no position correction; hence it was subsequently employed when searching for TALE targets in the rice genome with Talvez.
Nipponbare genome. To this end, we cross checked Talvez predictions with experimental evidence of candidate target gene induction during compatible interactions in publicly available microarray data as a good estimate of true direct up-regulation of a gene by a TALE in a rice susceptible background. Talvez was used to identify candidate target genes for all sequenced TALEs from Asian Xoo and Xoc strains with associated microarray data of rice gene responses to infection in databases (Xoo PXO86, Xoo PXO99 A , Xoo MAFF311018, Xoc BLS256, 69 TALEs were used and 20 microarray comparisons; see Table  S4  and Table  S5 ). To minimize the false positive rate, the set of potential TALE target genes was delimited as the top scoring 200 genes for each TALE. This set of tentative target genes was then contrasted with experimental expression data.
The missing Tal7a/8a-LOC_Os01g68740 pair  [5]  was predicted by Talvez but did not meet our criteria for differential expression. Hence, we concluded that examining the differentially expressed top 200 Talvez predictions with a position correction value of 19 produces an exhaustive and high quality perspective on susceptible rice candidate TALE targets. The Inferred TALEs-candidate Target Genes Regulatory Network is Potentially Enriched for Functional Hub Genes By comparing Talvez predictions and microarray data with the parameters defined above, a set of 73 rice genes was predicted to be induced by 47 TALEs (Table  S7  and Datasets S1). On the other hand, for the remaining 22 TALEs, no differentially expressed target gene was found. The differentially expressed genes and their corresponding TALEs were organized into a TALE-gene network of 91 interactions (Figure  4 ), 17 of which correspond to our controls or to interactions already predicted in the literature (Table  S7 ).
This network contains 17 instances of simple one-to-one interactions between a TALE and a putative target. The remaining interactions are organized into more complex modules corresponding to multiple TALEs from identical or distinct strains predicted to target the same genes or conversely, single TALEs predicted to target multiple genes (Figure  4 ). The annotated functions in this candidate TALE target gene set are very heterogeneous, and no enrichment of any GO term was found using Singular Enrichment Analysis (SEA) (see Figure  S2A )  [34]. However, a possible commonality of TALE-target genes is their role in functional networks. Of the 73 candidate targets, 43 genes (58%) were found to be highly connected (degree. 5) in a Rice network of predicted functional interactions  [35]  (Figure  S2B ).
To support this notion with more discriminatory expression data, we identified Talvez-predicted genes specifically induced by individual TALEs by comparing the expression profiles of plants inoculated with wild-type bacteria and mutants for PthXo1 (GSE36272, PXO99 A vs PXO99 A ME2) and both PthXo6 and AvrXa27 (GSE36272, PXO99 A vs PXO99 A ME1). For the PthXo1 experiments, from the top 100 most induced genes, only the known Os8N3 target had a predicted EBE (Table  S8 ), indicating that this TALE probably does not have additional targets in the rice genome. Therefore, the other predicted target for PthXo1 detected in the PXO99 A versus PXO99 A T3SS mutant microarray data comparison on Nipponbare (Table  S7 ) is likely a false positive. In contrast, for the PthXo6/AvrXa27 experiment, in addition to the known PthXo6 target OsTFX1 (LOC_Os09g29820), up to six genes were significantly induced in the wild type versus double mutant strain comparison on at least one rice variety and had a high-scoring Talvez-predicted EBE for PthXo6 (Table  S8 ).
As a result, 24 additional TALEs were predicted to bind to the promoter of 33 genes in the network (Figure  S4 , Table  S7  and Datasets S2). Strikingly, analogous to the situation observed for the Japanese strain MAFF311018, two TALEs from the Korean strain KACC10331 were also predicted to target Os11N3 and Os12N3 (Figure  6 ), indicating that internal genetic redundancy among TALEs targeting MtN3/SWEET family members may be a recurrent feature of TALE repertoires. Interestingly, LO-C_Os12g41110 (OsCML5) and LOC_Os05g31620 (OsCML15) genes which both encode calmodulin-like proteins may represent another case of convergence towards a gene family (Figure  4  and Table  S7 ). OsCML5 was predicted to be targeted by AvrXa7 and AvrXa10, and was induced in the IR24 cultivar upon infection by Xoo strain PXO86.
Performance of Prediction Programs and Features of TALE-EBE Specificity We initially tested the performance of three programs for EBE prediction: Talvez, the program that performed the best, is based on transforming TALEs RVD sequences into PWMs and then scoring all possible binding sites in a promoter region using a loglikelihood function. Storyteller, another program developed in this work, took advantage of a faster pattern-search algorithm based on Hidden Markov models. While both programs had similar rates of true and false positives in the validation set, when searching the complete set of rice promoters Storyteller ranked control targets lower than Talvez. We are currently trying to incorporate additional corrections that may improve Storyteller's predictions while keeping its main advantage: greater speed.
In this respect, TALEs are interesting type III effectors because they manipulate the host physiology by direct transcriptional up-regulation of susceptibility genes. Hence, the ability to accurately predict TALE-binding elements in rice promoters enables a straightforward comparative perspective on individual TALEs targets as well as on the organization of TALE repertoires: how they are functionally structured with respect to their virulence target genes and how these repertoires may evolve. The performance of Talvez allowed a systemic insight into the sets of candidate target genes of X. oryzae strains. Putative targets for a total of 69 TALE sequences from four strains were identified in the O. sativa japonica genome using a combination of software predictions and expression data analysis.
It is noteworthy however that heterologous TALEs targeting MtN3/ SWEET family members do confer an increased virulence to Xoc  [52]. Even if we were unable to demonstrate a specific functional enrichment in our rice candidate target set, it is interesting to note that beyond sugars, TALEs may have evolved to manipulate additional transmembrane transport mechanisms because several candidate targets have an annotation suggestive of a role in ions (sulfate and potassium) and purine derivatives transport. Apart from the MtN3/SWEET family, OsTFX1 (LOC_Os09g29820, the PthXo6 target) has also been reported to be induced by Xoo strains from diverse geographical origins  [19]. In the network, MAFF311018 is the only strain with a complete TALE repertoire that is not predicted to target OsTFX1. However, this gene ranks in the 100 most induced genes 24 hours post inoculation with this strain (data not shown), revealing a possible false negative prediction or pointing to an erroneous TALE DNA sequence in databases.
Negative interactions consisted of mutated boxes of otherwise positive interactions that showed no induction activity in the experiments. Note that the interactions involving AvrBs4 and Bs4C (blue background in Table  S2 ) were not included in the analysis reported in Figure  1 , Figure  2  and Figure  3A. A subset of this validation set comprising known natural targets for TALE in the rice and Arabidopsis genome was used for whole-genome screenings (Table  1 , Table  S2 ). RVD sequences for TALEs in the validation set and for all other known TALEs referenced at the Xanthomonas Resource Database (http://www. xanthomonas. org/, http://bioinfo-prod. mpl. ird. fr/ xantho/x. org/gui/) were manually extracted from the available protein sequences at NCBI. Sequences from pseudogenes or TALEs with less than two RVDs were not included.
Receiver Operating Characteristics (ROC) Analyses The outputs of the prediction softwares obtained after screening the validation set of experimentally tested positive and negative interactions were analyzed as follows: for each TALE and each program, a minimum classifying threshold was defined as the lowest score for a genuine positive EBE, all EBEs scored above or equal to that threshold were considered positive predictions and all EBEs scored below that threshold were considered negative predictions. In this way a true positive corresponded to a genuine positive EBE scored equal or above the minimum threshold (i. e. scored higher than all negative), and a false positive corresponded to a genuine negative EBE scored equal or above the minimum threshold (i. e. scored higher or equal than a known positive).
Microarray Analyses and TALE-candidate Target Network Construction Routine computational tasks were performed in R with facilities provided by packages from the Bioconductor project  [59]. Primary expression data used for gene differential expression analysis corresponds to the following Gene Expression Omnibus (GEO) datasets: ''Comparative transcriptional profiling of rice undergoing infection by X. oryzae pv. oryzae or by X. oryzae pv. oryzicola'' (GSE16793), ''Comparison of transcriptional responses of two susceptible rice cultivars to strains of Xanthomonas oryzae'' (GSE36272). The Affymetrix probe-level expression data in CEL files was processed separately for each dataset using the Bioconductor affy package  [60]  with default parameters of the rma function that computes the RMA (Robust Multichip Average) expression measure after background correction, normalization and probe summarization.
To identify differentially expressed genes at a given time point after inoculation on susceptible plant cultivars, pair-wise comparisons that tested (i) a wild-type strain versus mock treatment (virulent strain effect), (ii) a wild-type strain versus a T3SS-defective mutant strain (T3SS effect) or (iii) a wildtype strain versus a mutant strain in a TALE gene (specific TALE effect) (Table  S5 ) were performed using the Bioconductor limma package as described in  [61]. The probesets that displayed a value of the log 2 -transformed fold change (logFC) equal or above 1 and an associated adjusted p-value (Benjamini and Hochberg's adjustment method) equal or below 0. 1 in a treatment comparison were deemed as differentially expressed. Their expression data (logFC and logFC rank) and associated limma statistics in that comparison were recorded in a custom sqlite database.
This database also stored the Talvez predictions on the corresponding 500 bp promoters set and was queried to retrieve TALE targets satisfying specific criteria as described in the results section. The mapping of Affymetrix probe sets on MSU6 Gene Model IDs was obtained from the www. ricechip. org website. Random TALEs -rice genes networks were constructed by first generating six random TALEs for each natural X. oryzae TALE encoded in PXO99 A , MAFF311018, PXO86 and BLS256 strains by shuffling their RVD sequence. Next, 100 random networks were assembled iteratively by randomly selecting one of the six shuffled TALE derivative for each initial natural TALE and querying the database for its associated Talvez-predicted targets that also met the microarray expression criteria. The distributions of node properties such as Talvez rank and connectivity measures (TALE and rice gene degrees) were computed in R with facilities from the graph package of Bioconductor.
Additional Analyses Perfect matches (PMs) in the validation set of positive and negative interactions were identified using an in-house Perl script that identified PMs in each position based on the RVD-nucleotide specificities used by Talvez. Connectivity of TALE target or random gene sets in was assessed by querying the Ricenet  [35]  server online (http://www. functionalnet. org/ricenet/search. html) and retrieving the complete output network (query and all predictions). TALE-gene network and functional networks for TALE targets obtained from RiceNet were visualized using Cytoscape  [62]. Singular Enrichment Analyses (SEA) for GO terms were performed on the AgriGO platform  [34]  against the O. sativa MSU7 nonTE background and by testing with Fisher and hypergeometric methods at 0. 05 significance.
Predictor Programs Availability Packages containing the source code of the Talvez and Storyteller programs are available in Datasets S3 and Datasets S4, respectively. A web interface and the source code of the Talvez and Storyteller programs are also accessible on our server at http://bioinfo. mpl. ird. fr/cgi-bin/talvez/talvez. cgi, and http:// bioinfo-prod. mpl. ird. fr/xantho/tales, respectively. Finally, the source code of the Talvez and Storyteller is also available for download on Sourceforge at https://sourceforge. net/projects/ talvez/and https://sourceforge. net/projects/storytellr/ respectively. Table  S1  Updated RVD-nucleotide association matrix used by Storyteller and Talvez to produce PWMs. Darker gray shading indicates increasing binding preferences for each RVD. Higher counts were assigned to well-studied RVDs (eg.
HD, NG). (XLSX) Table  S2  Validation set of controls used to test EBE prediction programs. RVDs and promoter sequences were extracted from the indicated references. Interaction class indicate the experimental results,+(positive) = the TALE binds to the EBE or is able to induce the expression of a gene located downstream of the EBE, 2 (negative) = the TALE is unable to induce the expression of a gene located downstream of the EBE. Raw prediction scores from each program are reported. Each prediction is classified as a true positive (TP), true negative, (TN), false positive (FP) and false negative (FN). Xca = X. campestis pv. armoriaceae, Xcv = X. campestris pv. vesicatoria, Xg=X. gardnerii, Xoc = X. oryzae pv. oryzicola, Xoo = X. oryzae pv. oryzae. The subset of interactions that were used for genomic screenings and that correspond to genuine targets in the rice or Arabidopsis genome are highlighted in green and underlined.
(XLSX) Table S4 Supplementary information on TALEs from X. oryzae strains used in this work. (XLSX) Table  S5  Description of the microarray data comparisons performed to identify TALE-regulated genes. Each row corresponds to a pair-wise treatment comparison used in this work to calculate Log Fold changes and select up-regulated genes. Information about the host plant genotype, the query strain, the appropriate reference treatment and the sampling time is provided. Number of TALEs indicates the potential number of TALEs that are expected to be queried for potential rice gene induction activity in that comparison (i. e. difference in TALE numbers between the tested strain and the control treatment). Accession numbers correspond to NCBI GEO Datasets accession numbers. (XLSX) Table  S6  Assessment of various position correction values for predicting induced TALE targets.
(XLSX) Table  S7  TALE-candidate target gene network data. Talvez predictions obtained with a position correction value of 19 and cross-referenced with microarray data are reported. Accession number corresponds to the TALE protein sequence available in public databases. RVD sequences were manually extracted from available protein sequences. TALE alias corresponds to a known common name for a given TALE. TARGET gene ID refers to the locus ID of genes containing a Talvez-predicted EBE in their promoters. Prediction scores and rank correspond to the Talvez predictions for the TALE-EBE pair after searching the rice promoters set. Evidence indicates whether the TALE-gene association is supported by gene-induction data in microarrays or if it corresponds to a TALE from a strain without associated microarray data that has a predicted target that is likely induced by TALE from our reference strains (''Conserved target'').
(XLSX) Table S8 Predicted EBEs in the promoter of differentially expressed genes in microarray comparisons addressing TALEspecific effects. LogFC values correspond to the log2 fold-change from comparisons between plants inoculated with either the wildtype or a TALE-mutant bacteri. EBE distances refer to the translation start site. (XLSX) Datasets S1 Cytoscape Session (. cys) file containing the primary, expression data supported TALE-rice gene network that served for the construction of Figure 4A. (CYS) Datasets S2 Cytoscape Session (. cys) file containing the conserved TALE-rice gene network that served for the construction of Figure S4. (CYS) Datasets S3 Script files for Talvez. (TAR) Datasets S4 Script files for Storyteller. (TAR).
Our work attempts to rectify this. In Business Intelligence (BI) analysis, the most popular visualization tools are dashboards. Dashboards  [8]  are collections of visual components (such as charts or tables) on a single view  [12] , that permit analysts to explore their data and quickly view different aspects of complex datasets. Nevertheless, simple collections of visual representations cannot be interpreted by untrained audiences; to become meaningful they require interpretation and explanation, often presented in a story narrative. We attempt to answer the following research questions: What are the actual practices of BI experts in creating and communicating visual stories to their audiences, and do current BI visualization tools support well this story creation and storytelling process? How can we enhance BI visual analysis tools with narrative capabilities, and are these capabilities effective in communicating analysis stories to others? Our work makes the following contributions: (1) Interviews with expert BI analysts (story "creators" and "readers"), provide a better understanding of current practices in creating BI stories.