dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome sequencing, assembly and annotation After filtering 4. 74% of the initial reads, raw sequence data represented a total of 7,831,929 paired-end reads (15,663,858 reads in total). Among the remaining reads, 34,224 were assembled into a 16,625 bp circular sequence, representing the complete mitochondrial genome with an average sequencing depth of 209. 6. A circular map of the mitogenome and the assembly coverage are presented in Fig. 2. The sequence was deposited in GenBank under the accession number KM044501. A total of 38 genes (13 PCGs, 23 tRNAs, two rRNAs) and one control region were identified. Twenty-four genes are encoded on the majority strand and the others mapped to the minority strand. Seven gene overlaps where observed, the longest being an 8-bp region between tRNA Cys and tRNA Trp (Table  2 ), which is a peculiar feature in Arthropoda  (Bernt et al.
Indeed, heteroplasmy is often associated with tandem repetition  (Zhang and Hewitt, 1997). The complete nuclear ribosomal gene cluster was recovered. A total of 21,514 reads were assembled into an 8287 bp sequence comprising of 18S rRNA (1893 bp), ITS1 (1141 bp), 5. 8S rRNA (155 bp), ITS2 (941 bp) and 28S rRNA (4077 bp). The sequence was deposited in GenBank under the accession number KM278219. The mitogenome of B. colossus shares the same architecture and orientation as the other six mitogenomes of assassin bugs, except for the presence of an additional tRNA Arg gene, as will be described below. This gene arrangement (without the additional tRNA Arg ) is also found in Drosophila melanogaster  (De Bruijn, 1983)  and was the first to be determined, differing by a single tRNA translocation, from that of the chelicerate Limulus polyphemus, which is considered ancestral for Arthropoda  (Boore et al.
However, the higher-level relationships of Reduviidea remain poorly resolved and the addition of mitogenomic data for more taxa will surely provide useful phylogenetic information in the future. The 20 superfamilies represented in our dataset are monophyletic except for Miroidea. At the infra-order level, Pentatomorpha is monophyletic with the following relationships between the five superfamilies: Aradoidea + (Pentatomoidea + (Lygaeoidea + (Pyrrhocoroidea + Coreoidea))). These relationships are strongly supported in our analyses and do not confirm the results of previous studies based on a subset of the present mitogenomic data that placed Coreoidea and Lygaeoidea as sister groups  (Hua et al. , 2008; Yang et al. , 2013). Based on the tree generated by Bayesian inference, Nepomorpha is monophyletic with the following relationships between the six superfamilies: (Pleoidea + Corixoidea) + ((Notonectoidea + Naucoroidea) + (Nepoidea + Ochteroidea)).
ML analysis differs in positioning Pleoidea as a sister group of the remaining Nepomorpha. These results are inconsistent with those of a recent study based on molecular and morphological data  (Hebsgaard et al. , 2004). Interestingly, they also contradict a previous analysis of a subset of the present mitogenomic dataset by confirming the monophyly of Nepomorpha including Pleoidea, for which an infraordinal status was proposed  (Hua et al. , 2009). Gerromorpha and Leptopodomorpha are also monophyletic, but only two species of each were included in the study. Enicocephalomoprha was only represented by Stenopirates sp. Our analysis supports the paraphyly of Cimicomorpha that consisted of four different clades: (Cimicoidea + Naboidea), Reduvioidea, Miridae and Tingidae. Tingidae is placed as a sister group to all remaining Heteroptera.
, 1993; Xie et al. , 2008). Mitogenomic data provide a new insight in this regard, but more taxa should be added to the current database, especially in the poorly represented infraorders Enicocephalomorpha, Gerromorpha, Leptopodomorpha and Dipsocoromorpha. Our results are incongruent with current phylogenetic hypothesis of Heteroptera (e. g. Schuh et al. , 2009). On the other hand, they are in accordance with previous studies based on a subset of the present mitogenomic data  (Li et al. , 2011; Yang et al. , 2013). Analyses performed on individual genes by  Tian et al. (2008)  and  Schuh et al. (2009)  indicate that the monophyly of Cimicomorpha is only supported by nuclear DNA. The incongruence of phylogenetic analyses among different genomic regions is a well-known issue that can have various biological causes such as incomplete lineage sorting or rate variation among partitions  (Som, 2014).
Lin and Danforth (2004)  studied the differences in the pattern of nucleotide substitution among nuclear and mitochondrial genes and concluded that insect phylogenetic studies should increasingly focus on nuclear data. This raises the limitations of phylogenetic inferences from complete mitochondrial genomes only. While reducing stochastic errors by providing large molecular datasets, this approach is susceptible to potential site-specific bias and would benefit from a conjoint analysis with other genes. One advantage of the genome skimming approach used in this paper is that it allows the recovery of nuclear genes of phylogenetic interest in addition to the full mitogenome sequence. However, 18S and 28S data available to date in GenBank are too scarce (14 species out of 55 represented in our mitochondrial dataset) to perform a combined analysis.
Currently, there is a lack of correspondence between publicly available mitogenomic and nuclear data. The genome skimming-approach could provide an interesting improvement in this regard. In a single experimentation, it allows to recover the full mitogenome sequence as well as nuclear genes that are classically used for phylogenetic inference. We argue that future studies reporting full organelles sequencing with genome skimming approaches should systematically report the assembly of the nuclear genes of phylogenetic interest. Supplementary data to this article can be found online at http://dx. doi. org/10. 1016/j. gene. 2014. 09. 033.
Acknowledgments This work was supported by  PO-FEDER   TIMGED N°30195 (Trypanosomes d'Intérêt Médical en Guyane française -Epidémiologie et Diagnostic ) and " Investissement d'Avenir " grants managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ;  TULIP ,  ANR-10-LABX-41 ,  ANR-11-IDEX-0002-02 ) as well as project  METABAR  ( ANR-11-BSV7-0020 ). We would like to thank  Christine Aznar ,  Denis Blanchet ,  Jean-Pierre Dujardin  and  Jean-Michel Bérenger  for the help with obtaining and identifying the specimen. We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees  for providing computing and storage resources.
Background: Using motif detection programs it is fairly straightforward to identify conserved cis-sequences in promoters of co-regulated genes. In contrast, the identification of the transcription factors (TFs) interacting with these cis-sequences is much more elaborate. To facilitate this, we explore the possibility of using several bioinformatic and experimental approaches for TF identification. This starts with the selection of co-regulated gene sets and leads first to the prediction and then to the experimental validation of TFs interacting with cis-sequences conserved in the promoters of these co-regulated genes. Results: Using the PathoPlant database, 32 up-regulated gene groups were identified with microarray data for drought-responsive gene expression from Arabidopsis thaliana. Application of the binding site estimation suite of tools (BEST) discovered 179 conserved sequence motifs within the corresponding promoters.
Background In recent years large numbers of novel cis-regulatory sequences have been described that are conserved in stress-response genes, but the role of these elements and their binding transcription factors (TFs) remains unknown  [1] [2] [3]. The identification of the binding TFs is therefore a major challenge for bioinformaticians and experimentalists. Using database-assisted analysis of cissequences it is possible to generate hypotheses on the nature of the binding TF  [4] , but the experimental validation of these predictions is often missing. The current work investigates how efficient a database-assisted approach leads to the prediction of the correct TF or TF-families that bind to conserved cis-sequences in coregulated genes. The identification of cis-regulatory sequences has been facilitated using bioinformatic and web-queryable resources  [4] [5] [6] [7] [8] [9].
One approach is the detection of cis-elements annotated in existing resources by performing a database-assisted analysis  [10] [11] [12]. Another approach is the de novo discovery of conserved sequence patterns in sets of co-regulated genes without knowing if these sequences have been associated with a function before. This involves detection of over-represented sequences in the upstream region of co-regulated genes by using pattern mining programs such as MEME, AlignACE, CONSEN-SUS, Co-Bind, BioProspector, MITRA, or integrative frameworks such as BEST  [13] [14] [15] [16] [17] [18] [19]. To discover conserved sequence motifs in promoters of up-regulated A. thaliana genes, BEST was applied in the work presented here. Co-regulated genes were identified using drought responsive microarray expression data annotated to the PathoPlant database  [20, 21].
The identified motifs were classified with STAMP  [22]  and compared to known cis-regulatory sequences annotated to the AthaMap, PLACE and AGRIS databases  [23] [24] [25] [26] [27] [28]. Furthermore, the newly developed footprintDB repository, built on top of 3D-footprint, was employed to predict interacting TFs  [29, 30]. To confirm bioinformatic predictions, 15 cis-sequences were used for the isolation of interacting TFs in a yeast one-hybrid screening. The specificity of each TF-DNA interaction was further validated in yeast. Furthermore, two TFs were used for the generation of synthetic factors that activate reporter gene expression under the control of synthetic promoters harbouring corresponding cis-sequences in a P. patens expression system.
Identification of seven motif families in drought-responsive A. thaliana genes The goal of this work was the identification of transcription factors binding to cis-regulatory sequences conserved in promoters of drought-responsive genes. Thus, the first part aimed to identify sequence motifs harbouring such cis-sequences. Figure  1  gives an overview of the workflow. In a first step, microarray experiments annotated to the PathoPlant database were employed to identify gene sets two-to tenfold up-regulated by drought in roots and shoots. A total of 32 queries were performed. Additional file 1 lists the parameters for these queries and also the number of induced genes obtained in each of the 32 queries. The 32 queries yielded 32 upregulated gene groups containing up to 34 co-regulated genes. In a second step, conserved sequence motifs within the upstream region of the 32 co-regulated gene groups were identified with the software package BEST  [19].
For motif detection, 1,000 bp upstream of all genes in a co-induced gene group were screened with this software. This analysis resulted in 179 sequence motifs, 22 from roots, 46 from shoots, and 111 from combinations of timepoints or tissues. In a third step, all sequences from the 179 motifs were subjected to an in silico expression analysis  [31]. With this tool, motifs specific for drought responsive genes were identified. A total of 49 motifs containing cis-sequences that pass this analysis remained, 5 from roots, 14 from shoots and 30 from combinations of timepoints or tissues. These sequences and the alignments generating the 49 sequence motifs are shown in Additional file 2. Single sequences that pass the in silico expression analysis criteria are shown in bold in this file. These 49 sequence motifs were further analysed using STAMP (Step 4, Figure  1 ).
STAMP determines the relationship between the motifs and also calculates similarities to known cis-regulatory sequences  [22]. To illustrate the similarities among the 49 sequence motifs, a tree was generated and illustrated by the program MEGA  [32] , which is shown in Figure  2. Based on the relationship between the motifs, 7 motif groups were designated. STAMP was also employed for the identification of motif similarities by comparing the 49 motifs with known cis-elements from plant databases AthaMap, AGRIS and PLACE  [23] [24] [25]. Additional file 3 summarises these results. Several motifs from group I, that harbours 29 motifs, show significant similarities (low Evalue) to a G-box motif and to abscisic acid-responsive elements (ABRE) known as bZIP binding sites. For example with the PLACE database eight motifs show similarities with the GBOXLERBCS sequence  [33]  and nine motifs show similarities to four different ABREs from PLACE (ABREATRD22, ABREAZMRAB28, ABRETAEM, ABREMOTIFIIIOSRAB16B).
A cutoff of 50% interface similarity was observed to be adequate, increasing specificity to 0. 921, as shown in Additional file 4  [PredictionSummary]. Tested cis-elements were classified as predictable if the footprintDB repository contained at least one annotated TF with a significantly similar motif (STAMP E-value < 1E-3), which was homologous to the experimental Y1H-inferred factors. Thirteen of the 15 cis-elements tested in the Y1H experiment were accordingly called 'predictable', but only 5 of them  (2, 5, 6, 7, 12)  yielded true positives among footprintDB results. The rest had significantly divergent DNA motifs annotated in the database, with E-values over the cutoff, and therefore could not be identified (see Figure  5 ). It is important to note that in all predictable cases the correct binding TFs were identified by homology to annotated plant regulatory proteins, including A.
Using the STAMP web server as well as footprintDB, these show significant similarities (low E-value) to G-box and ABRE elements. However, no consistency in TF predictions was obtained with these database-assisted analyses using the other motifs. This first set of results exposed the natural limitations of this approach, mainly associated with the data content of the underlying scanned databases. For example, predictions using the STAMP webserver depend on previously annotated data derived from databases such as TRANSFAC, JASPAR, PLACE, AGRIS, and AthaMap  [23, 28, [40] [41] [42]. The content of these repositories is primarily derived from functionally known regulatory sequences, which are expected to be only a tiny fraction of all cis-elements. Similarly, foot-printDB (http://floresta. eead.
csic. es/footprintdb) contains TFs and their experimentally-associated DNA motifs from these and other databases. A main difference to the  STAMP webserver is that footprintDB motifs are associated to their cognate binding TFs, and their DNA-binding interfaces are annotated using 3D-footprint structural data  [29]. However, STAMP as well as footprintDB detect and predict TF-DNA interactions based on known data. Therefore, TF-DNA interactions not readily predicted with STAMP and footprintDB may represent novel or to date unknown TF-DNA interactions. This was substantiated in the present study by using sequences enriched in promoters of drought-responsive genes, but not readily associated with interacting TFs for experimental analysis using Y1H-screenings. Towards these ends two different sequences from motif group I were assayed.
While these Y1H results on their own already provide valuable biological insight, they also served in this work as a reference dataset. Indeed they were also used to benchmark the predictive value of the footprintDB approach for recognizing putative binding TFs with different parameter settings. Two STAMP E-value cutoffs were tested. While a stringent 1E-5 threshold yielded the most specific predictions, it was at the cost of a significantly reduced sensitivity (52%). On the contrary, a relaxed 1E-3 cut-off increased the sensitivity (78%) by compromising specificity. However, this loss of specificity could be corrected by lowering the BLASTP E-value cutoff to 1E-10. In addition, we observed a further gain in specificity by applying an interface similarity filter for candidate TFs, requiring them to have at least 50% similar interface residues to be considered.
Conclusions The work presented demonstrates the successful integration of several bioinformatic resources to predict and validate TFs interacting with conserved sequence motifs in co-regulated genes. Predictions were confirmed by using a yeast-one-hybrid approach to identify interacting TFs belonging to the predicted TF families. TF-DNA interactions were further experimentally validated in yeast and with a Physcomitrella patens transient expression system, leading to the discovery of several novel TF-DNA interactions. Our work establishes a novel approach to identify TFs interacting with conserved cis-sequences. This approach may facilitate the experimental identification of TFs because a candidate TF-family can be predicted bioinformatically using the footprintDB database. Although the limitation of this approach is the content of the databases used, footprintDB is particularly valuable because it contains data for TF-DNA interactions from many different species.
Methods Bioinformatic analysis to identify conserved cis-sequences in co-regulated genes In silico identification and bioinformatic analysis of sequence motifs overrepresented in drought stress-induced A. thaliana genes was based on microarray expression data from the AtGenExpress global stress expression data set  [61]. To identify genes up-regulated by drought, Affymetrix ATH1 microarray raw expression data were downloaded from NASCArrays (NASCARRAYS-141)  [62] , normalised using MAS5 algorithm  [63] , and scaled to a TGT of 100. An untreated control data set (NAS-CARRAYS-137) was also downloaded and identically processed. Array elements were assigned to genes according to an assignment table based on TAIR release 8 annotations  [64]. Expression data were imported into the PathoPlant database  [20].
The annotation procedure of cDNA microarray data and Affymetrix ATH1 data has been described earlier  [3, 21]. All data as well as links to the microarray source of the expression set can be found on the PathoPlant site at http://www. pathoplant. de/ documentation. php. Expression data was used to identify genes up-regulated upon drought stress. Genes showing at least a 2-fold induction compared to untreated control were defined as up-regulated. Using an SQL server query tool, 32 drought stress combinations at different time points and in different tissues were queried. PathoPlant's 'Microarray expression' online tool displays a similar functionality to the query tool described above and can be used to determine sets of genes co-regulated upon drought. For promoter analyses of drought-induced gene sets, sequences 1,000 nt upstream of the transcription start site, if known as for the majority of genes, otherwise 1,000 nt upstream of the ATG start codon were extracted using TAIR release 8 sequence annotation and converted into FASTA format.
To identify overrepresented motif sequences within these promoters, the BEST software package  [19] , locally installed on a Linux SuSE9. 2 system was used  [3]. The package combines 4 different motif-finding programs (MEME, AlignACE, CONSENSUS, BioProspector) and an optimization step. BEST was run with default parameters and predefined motif lengths of 5 to 10 nucleotides. The application of these parameters had previously shown to yield optimal results with promoter sequences from A. thaliana  [3]. Overrepresented sequence motifs identified by BEST were further used if detected by at least 2 out of the 4 motif finding programs. Such a BEST analysis with 33 gene sets yields a high number of enriched sequence motifs. In order to prevalidate these bioinformatically, a so-called in silico expression analysis was performed with all motif sequences (http://www.
pathoplant. de/expression_analysis. php). This analysis is based on PathoPlant's microarray expression database and correlates motif sequence occurrences with stress-specific gene expression data resulting in an evaluation of the sequence to identify the stress this sequence is most likely to be responsive for  [31]. Strict correlation criteria were applied by only considering motif sequences that displayed at least two stresses associated with drought among the first 3 highest ranked stresses. Identified sequence motifs in which at least one sequence was found to be enriched in promoters of drought stress responsive genes were submitted to the STAMP web server applying the recommended default parameters  [22, 40]. STAMP classified all motifs based on matrix alignment to a similarity tree given in Newick tree format that was displayed using MEGA  [32].
Groups containing similar motifs were generally defined by clustering single motifs on branches with lengths <0. 05. In one exceptional case a motif (shoots3hr_1) was grouped into motif group II based on its conserved core sequence GCC that is characteristic for AP2/EREBP binding proteins. This core sequence is also conserved in the other two motifs of motif group II. STAMP was also employed for the identification of motif similarities by comparison with known cis-elements from plant databases AthaMap, AGRIS and PLACE  [23] [24] [25].
The main difference to the STAMP webserver is that footprintDB motifs are associated to their cognate binding TFs, and their DNA-binding interfaces are annotated using 3D-footprint structural data  [29]. Interface residues are defined for being located within 4. 5 Angstroms with respect to at least one DNA nitrogen base in homologous structural complexes deposited at the Protein Data Bank. By taking advantage of these data, footprintDB is able to predict A. thaliana homologous TFs that are candidates to bind the input cis-sequences, calculating interface similarity with a custom scoring matrix motifs  [30]. The search parameters were: 1) STAMP E-value: 1E-3, 2) BLASTP E-value: 1E-10, 3) Arabidopsis protein source: TAIR9, containing 33,410 protein sequences  [64] , 4) Interface similarity cut-off: 50%.
Acknowledgements Part of the results have been achieved within the framework of the  Transnational (Germany, France, Spain) Cooperation STREG  within the  PLANT-KBBE Initiative , with funding from  Ministerio de Ciencia e Innovación ,  Agence Nationale de la Recherche (ANR)  and the  German Federal Ministry for Education (BMBF). The work of Z. K. , G. H. , A. S. , F. S. , C. B. , and L. B. was supported by the  STREG  project. W. X. 's work was supported by the  China Scholarship Council (CSC). The authors would like to acknowledge the support of  Jasmin Huebner  and  Nina Schmidt  in the generation and analysis of transgenic A. thaliana. We also acknowledge  F. Turck  ( MPI, Köln ) and  V. Colot  ( ENS, Paris ) for providing the REGIA library.
, 2010; Vazquez et al. , 2011). In this regard, a USUV specific real-time reverse transcription (RT) PCR assay for the detection of USUV in human blood and cerebrospinal fluid was developed recently based on sequences from Austria and Hungary  (Cavrini et al. , 2011). As the genetic variability of African USUV strains had not been taken into account  (Nikolay et al. , 2013) , the universal application of this assay developed previously might be constrained. Therefore, to ensure detection of a maximum of genetically diverse USUV strains from distinct geographic regions, this article presents a quantitative real-time RT-PCR assay based on USUV sequences from Europe and Africa. USUV sequences from South Africa, Senegal, Central African Republic, Austria, Hungary, and Italy (GenBank: KC754954, KC754955, KC754956, KC754957, AY453412, AY453411, EF206350, JF296698) were aligned to a USUV subtype strain (GenBank: KC754958) and closely related flaviviruses circulating in Africa as West Nile (GenBank: AY603654, DQ318019, DQ318020) but also Murray Valley Encephalitis (GenBank: NC000943, AF161266), and Saint Louis Encephalitis virus (GenBank: NC 007580).