dataesr/scientific-paragraphs-categorization huggingface dataset:
The existence of transcripts corresponding to contig12 and to the hypothetical proteins was first confirmed by RT-PCR on the same RNA used for the RNA-seq library construction (data not shown). Expression of the selected FD genes was examined in three FD-infected grapevine samples: sample 120, B68 and B75. To estimate the expression level of the selected FD transcripts, the mRNA absolute quantity was compared to the phytoplasma titer measured in the corresponding sample. The primers list, the corresponding amplification conditions and efficiencies, melting peak temperatures and correlation coefficients of qRT-PCR reactions were reported in Additional file 6. For each gene, the average Expression Index (EI, N°of transcript in single FD cells) in the three grapevine samples was calculated (Table  4  and Additional file 5).
Discussion Application of deep sequencing technologies to specifically study the transcriptome of intracellular obligate endosymbiont bacteria has been attempted in very few Transcription levels are expressed as the mean of RPK (reads per kilobase of transcript) values obtained in the two expression libraries. The first column (locus ID) reported the IDs of the annotated FD92 genes, with the only exception of contig12, which was obtained in this study. A brief gene description and the mean of reads per kilobase of transcript (RPK) are also reported. TMD = transmembrane domain predicted by TMHMM; SP = signal peptide predicted by SignalP-HMM v. 3. cases, because the selection of prokaryotic transcripts in a multitude of eukaryotic RNAs makes the accurate representation of the bacterial transcriptome particularly challenging.
These differences could be exploited for studying the FD genetic diversity within different strains. Besides genes related to protein synthesis, the functional classification highlighted a high percentage of unclassified genes. This was probably due to a) the lack of a good functional annotation for some known phytoplasma proteins, such as SpoVG, Imp, VmpA, VmpB, etc. …, and b) the presence of many hypothetical proteins whose functions are still unknown and thus not yet included in databases. It is noteworthy that the abundance of hypothetical proteins (six in total) among the FD most expressed genes is in accordance to the results obtained in L. intracellularis  [28]  and Wolbachia  [29] , in which seven and four out of the 20 most expressed genes encoded hypothetical proteins, respectively.
In prokaryotes, the protein component, which shows endonuclease, reverse transcriptase and maturase domains, is encoded by the intron itself and is essential for both retrohoming and retrotransposition  [39]. In the FD92 genome two almost identical copies of this group II intron were identified so far. One of these was artificially truncated at the 3′ due to its position at the end of a genomic contig, so only the other copy was analyzed in more detail. The complete genomic copy of this group II intron was very similar (77% identity, E-value = 4E-39) to the Onion yellows phytoplasma intron named OYPI1 (GenBank:AP006628 Region: 388234-390749)  [40] , but, unlike OYPI1, it seemed to be an ORF-less intron, as neither a reverse transcriptase nor any other protein-coding gene was detected within the intron sequence.
As already hypothesized for Onion yellows phytoplasma ORF-less OYPI2 (GenBank: AP006628 Region: 544682-545416)  [41] , isolate FD92 could also harbor in the gaps of its unfinished genome at least one full-length group II intron that may act in trans on the other ORF-less group II intron(s). It has, in fact, been shown that group II introns that became fragmented by genome rearrangements in eukaryotic organelles have the capacity to ligate independently transcribed coding sequences, splice accurately in vivo and finally produce a functional mRNA  [42]. The presence of a tra5 remnant at the 3′ of contig12 suggested that the absence of the retrotranscriptase in this copy could indeed be associated with some genome rearrangements. Finally, we cannot exclude that the missing part of the other copy of group II intron could encode itself a functional reverse transcriptase.
Mobile elements, in general, are known to play key roles during the emergence of host-adaptive strategies in bacteria  [45]  and group II introns, in particular, have been demonstrated to be responsible of some recent genomic rearrangements in the bacterial endosymbiont Wolbachia  [46]. Fourteen additional genes, including highly expressed hypothetical proteins and genes with possible involvements in the host-bacterium interactions, were examined by qRT-PCR. The positive correlation on a linear regression model between qRT-PCR and RPK values and the high R-squared value demonstrated that RNA-Seq data properly estimated the expression levels of the selected genes.
Sequence analysis Settings used for each of the following computer programs were specified in Additional file 7. Before read mapping and assembly, poor quality data were filtered out by Trimmomatic v. 0. 30  [51]. The partial FD92 draft genome (cluster FD2)  [52]  used in this work is available upon request at https://iant. toulouse. inra. fr/F. doree. Bowtie v. 0. 12. 7  [53]  was used to map reads to the FD92 genome as well as to the assembled transcripts for the evaluation of gene expression levels. Tophat v. 1. 4. 1  [54]  was preferred to Bowtie for mapping reads to the 12X Vitis vinifera genome  [55] , because it can identify splice junctions between exons. Given the low amount of FD-mapped PE reads (thousands) compared to other projects dealing with transcriptomes of free-living bacteria (millions), we decided to retain, only for the transcriptome assembly, the single-end (SE) reads, i.
e. those PE reads in which only one read of the couple passed the quality check. SE reads were not considered for the quantitative analysis. The minimum assembled contig length to be reported by Trinity assembler (Trinityrnaseq_r20131110)  [56]  was set to 150 bp to avoid losing short protein-coding RNAs, considering that 49-51 amino acid-long proteins are quite common in phytoplasmas (e. g. GenBank:WP_011161061. 1, GenBank:NP_950971. 1, GenBank:AAO61980, GenBank: AAO61987). After each Trinity assembly, the assembled sequences were first analysed by blastn  [57]  against the FD92 genome. Assembled nucleotide sequences were considered as significantly similar to the FD92 genome if they showed at least 95% identity across the alignment with an E-value ≤ 1E-20. Those without a match were then analysed by blastn against the NCBI "nt database" to discard sequences belonging to plant plastidial/mitochondrial genomes or other bacterial genomes.
Finally, sequences with no significant similarities to the NCBI "nt database" were analysed by blastx  [57]  against the NCBI "nr database" to retain only those with significant similarities (E-value ≤ 0. 0001) to Mollicutes predicted proteins. This last step was used to find coding sequences that could potentially be localized in the gaps of the unfinished FD92 genome. Sequence datasets obtained with different assembly strategies were merged using cap3 (version:4/15/05)  [58]. After each cap3 assembly, sequences were again checked against the FD92 genome to avoid misassemblies. A dataset enriched in FD-mapping PE reads was submitted to Sequence Read Archive (SRA) (http://www. ebi. ac. uk/ena/data/view/PRJEB6982).
In silico phytoplasma gene expression The expression level of each transcript was calculated by an ad-hoc Bash wrapper script calling Bowtie v. 0. 12. 7 and expressed as number of reads per kilobase of transcript (RPK). Given the low amount of FD-mapped reads, we decided to use RPK instead of RPKM (number of reads per kilobase per million reads mapped). RPK values were calculated using only PE reads that mapped concordantly, unambiguously and on the correct strand. To compare the RPK values of the two libraries, the RPK values obtained from library 120E were multiplied by a factor of 1. 3, which took into account the different number of FD-mapped reads in the two libraries. The expression level of each gene that was predicted to be part of a polycistronic transcript was evaluated separately.
The Shapiro-Wilk test and the Bartlett test revealed that the EIs of each gene in different samples were normally distributed, but did not show equal variances. The Welch-one way ANOVA for normally distributed and heteroscedastic data, followed by the Waller-Duncan test, was then used for multiple comparisons. All statistical analysis were performed with the R Stats package v 3. 1. 1 (http://www. R-project. org). The same secY primers and amplification conditions used for calculating the FD titer were also used to estimate the relative abundance of phytoplasma mRNA in sample 120. In this case, the single-copy ubiquitin gene of grapevine (GenBank:FQ378362. 1) was used instead of the 18S rDNA gene. Ubiquitin primers by Gutha and colleagues  [66]  were used to determine the transcript levels of this gene.
Acknowledgments This work was part of the " Nebbiolo Genomics " project sponsored by  Fondazione Cassa di Risparmio di Cuneo, Italy. The FD92 genome draft was obtained with the support of  INRA  and the  Interprofessional Council for Bordeaux's Wine (CIVB). The authors also acknowledge the financial support of the  Cost Actions FA0807 ("Integrated Management of Phytoplasma Epidemics in Different Crop Systems ") and  FA0806  ("Plant virus control employing RNA-based vaccines: A novel non-transgenic strategy"). We thank  Marta Vallino  and  Piero Caciagli  ( IPSP-CNR, Torino, Italy ) for discussions;  Mario Altieri  ( University of Verona, Verona, Italy ) and  Stefano Ghignone  ( IPSP-CNR, Torino, Italy ) for technical assistance in bioinformatics analyses;  Rafael Montero  ( Universitat de les Illes Balears, Palma, Spain ) for providing the primers and the plasmid to quantify the grapevine ubiquitin transcription levels.
Gas chromatography-mass spectrometry (GC-MS); Liquid chromatography-mass spectrometry (LC-MS) and 1 H Nuclear Magnetic Resonance (NMR) were employed. Principal Component Analysis (PCA) was applied to the NMR data. The partial least squares-discriminant analysis (PLS-DA) was used to integrate LC-MS and NMR data sets. All statistics were performed with the SIMCA-P + 12. 0 software.
The vast numbers also pose a problem in the identification of known and novel compounds. Nuclear magnetic resonance (NMR) is a convenient method for confirming the presence of known biomolecules and for assigning the chemical structure of novel ones. It can measure compounds in crude extracts or in vivo in a non-destructive nature  [6]. There have been several NMR-based geographical characterizations of plant species, e. g. studies on Coffea arabica  [7]  and on Withania somnifera  [8]. The advantage of using NMR is that no prior knowledge of the identity or quantity of the individual chemicals within the metabolomic pool is necessary. Therefore, the comparison of NMR profiles to differentiate groups is fast, convenient and a reproducible tool especially since the development of databases and software packages that can handle large datasets  [9].
Acquisition parameters were as follows: pulse width of 2. 8 μs (flip angle ≈ 30°), relaxation delay of 4 s, 64 K data points, spectral width of 9500 Hz (19 ppm) and 128 scans. All FIDs were processed using the Bruker TopSpin 2. 1 software with one level of zero-filling and a line broadening of 0. 7 Hz. Baseline correction was performed on each spectrum and spectra were referenced to the signal of TSP at δ 0. 00 ppm.
Chemometric analysis of the data The 1D 1 H NMR spectra were transferred to the KnowItAll® software (Bio-Rad, USA). The bin area method was used to segment the spectra between 0 and 13. 1 ppm with the variable size intelligent bucketing tool included in the KnowItAll® package. Bucket sizes ranged from 0. 01 to 0. 30 ppm. The spectral regions containing the NMR signals of DMSO (δ 2. 47-2. 57 ppm) and its 13 C satellites (δ 2. 36-2. 40 ppm, and 2. 63-2. 67 ppm), methanol (δ 3. 17-3. 20 ppm), H 2 O (δ 3. 33-3. 45 ppm) and dichloromethane (δ 5. 75-5. 80 ppm) were removed. A manual filtering procedure was applied to the whole spectrum to exclude buckets that contained only noise. A total of 119 variables were considered for subsequent statistical analyses. For spectrum normalization, integrated regions were divided by the total area of the spectrum and multiplied by the mean value of the corresponding family previously calculated.
Data were preprocessed by mean-centering. The unit variance (UV) scaling method was applied prior to analysis. Both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were performed with the SIMCA-P + 12. 0 software (Umetrics, Umeå, Sweden) and for the t-tests, the R software (R Development Core Team, 2012) was used. The predictive ability of the PLS-DA models was assessed from the values of Q 2 cum (> 0. 5), R 2 Y cum (> 0. 7) and R 2 X cum parameters. The statistical significance of R 2 Y and Q 2 parameters was also estimated through the response permutation test where the Y matrix was 999 times randomly permuted when the X matrix was fixed  [26]. For determining the discriminating variables between classes, loading plots, coefficient plots, variable importance in the projection (VIP) from PLS-DA models, and p-values (<10 -3 ) of the t-tests on the variables arising from the coefficient plot and VIP were considered.
25 min) was used. Analysis was performed using a network GC system (6890 N) coupled to inert XL EI/CI Mass Selective Detector (MSD) 5975B (Agilent Technologies Inc. , Palo Alto, CA) equipped with a CTC Analytics PAL autosampler had. Separation was achieved with a capillary column (Restek RTX200; trifluoropropylmethyl (30 m in length; 250 μm diameter; 1 μm in thickness)). The temperature was increased from 76°C (1 ramp min -1 ) to 320°C (4 ramp min -1 ) The run time was 72 min and helium gas was used as a carrier at a flow rate of 53. 7 ml min -1. The instrument was set to the following conditions: pressure of 62. 6 kPa, purge flow of 50 ml min - 1 for 2 min, flow rate of 1 ml min -1 (37 cm sec -1 ) and a data rate of 20 Hz. The mass spectrometer was operated in electron ionization (EI) mode at ionization energy of 70 eV, scanning from 35 to 600 m/z in positive mode.
Caffeic acid, rosmarinic acid, myo-inositol, glucose, galactose and mannose were used as standards to aid with identification of constituents in the extracts. All standards were purchased from Sigma-Aldrich (Germany) except for the mannose which was provided by Merck (Germany). Data were analyzed using the MSD Chemstation software which was linked to the National Institute of Standards and Technology (NIST) mass spectral search program library ver. 2. 0 d (2005; standard reference data program of the National Institute of Standards and Technology, USA) for peak identification of metabolites. A library match of 80% and above for metabolites from the NIST library were regarded as likely hits. The relative abundance of metabolites was recorded using the total ion chromatogram peak integration (Additional file 2).
Acknowledgements Financial support was obtained from the  National Research Foundation  (Pretoria; South Africa) and the  Division of Research Development (Stellenbosch University ; SU). The NRF travel mobility grant was used to visit the  NMR Biomedical Laboratory at Toulouse III University. Mr M. Nkomo  was a recipient of a postgraduate  Department of Botany and Zoology bursary. Cape Nature  is thanked for the plant collection permit. Dr Petra Wester ,  Mr Lucky Mokoena  and  Mr Fletcher Hiten  of the central analytical facility (SU) are thanked for their assistance. Professor Véronique Gilard  ( Toulouse III ) assisted with analyzing the LC-MS data. This work was partly conducted at the  Medical Research Council (Tygerberg, Cape Town ) and the  PROMEC  unit is thanked for use of their facilities.
Each stimulus was presented only once. Eight to twelve caterpillars were tested for each stimulus. The recording electrode was connected to the probe (AI 401, Axon Instrument, USA) of an extracellular amplifier (CyberAmp 320, Axon Instrument, USA), amplified × 1000 and filtered (10-2800 Hz Bessel band-pass). Data were sampled at 10 kHz with a data acquisition card (16-bit A/D; DT9803, Data Translation, USA) under the control of a custom program Raw data were transformed into a PI index, computed as the ratio of the difference between the number of larvae on each side of the arena divided by the total number of larvae. A ratio of +1 indicates complete attraction, -1 indicates complete repellency. In the absence of odor, no preference for either side was observed and the PI remained close to zero throughout the experiment.
Statistical Methods and Sequence Analyses Most statistical analyses were performed using the statistical software SPSS version 19. 0. The Kruskal and Wallis test  [37]  was done to see whether the differences in stability and transposition between hybrid families were significant. The Mann-Whitney test was used to compare genomic instability induced by transposition between hybrids and parental species. For identification of AFLP marker sequences, a similarity was searched between the query sequences and TEs sequences of Genbank and Repbase  [38]  databases using the parameters defined by the CENSOR search tool (http://www. girinst. org/ censor). The sequence data have been deposited in GenBank under the accession nos: JX997188; JX997190; JX997191; JX997192; JX997193; JX997194; JX997195: JX997196; JX997197; JX997198; JX997199; JX997200; JX997201; JX997202; JX997203; JX997204; JX997205; JX997206; JX997207; JX997208; JX997209; JX997210; JX997211; JX997212; JX997213; JX997214; JX997216; JX997217.
Results AFLP band patterns of 40 primer combinations were analyzed in each backcross generation and 28 in segmental hybrids. AFLP instability markers were selected in 3 backcross generations of hybrids (BC1, BC2, BC3) and in segmental hybrids between D. buzzatii and D. koepferae. Instability markers homologous to TE sequences by analysis in the data bases, were considered transposition markers, resulting probably from new insertions of TEs in the hybrid genome.
Transposition Markers The number of transposition markers was 5, 5 and 13 in the BC1, BC2 and BC3 generations, respectively, and 10 in segmental hybrids (Figure  2 ). A total of 33 transposition markers were detected in the hybrid genomes whereas only one was found in the parental species (D. buzzatii). All except 3 transposition markers (CGGGG22, CACAT21 and TGCGG21) were longer that 200 bp (Table  2 ) and showed a wide range of similarity (0. 1 to 0. 94) with the TE sequences. In a few cases, transposition markers showed homology to internal or coding regions of one, or two TEs. This internal homology probably occurs because these TE regions are the most conserved in the database. The percentage of genomic instability due to transposition (number of transposition markers divided by the number of total markers in each backcross generation, multiplied by 100) was 16.
This work was supported by research grants  CGL2006-13423-C02-01/02  from the  Ministerio de Educacion y Ciencia (Spain) (www. educacion. gob. es),grants  CGL2009-12912-C03-01  and  CGL2010-15395  from the  Ministerio de Ciencia e Innovacion (Spain) (www. educacion. gob. es), and grants  2005SGR 00995  and  2009SGR 636  from  Generalitat de Catalunya  (www. gencat. cat) to the  Grup de Biologia Evolutiva. ANR-09-BLAN-0103  (www. agence-nationale-recherche. fr)Genemobile and  Institut Universitaire de France  and the  CNRS GDRE Comparative Genomics. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Taxon Sampling and Molecular Biology We included 117 of the 147 described species (≈80%) and all genera of Australasian Hydroporini (Online Appendix 1 available on Dryad http://dx. doi. org/ 10. 5061/dryad. c5g23, Nilsson 2001 Nilsson , 2006;; Watts et al. 2008; Watts and Humphrey 2006, 2009; Hendrich 2008; Hendrich and Fery 2008; Hendrich and Watts 2009; Hendrich et al. 2014). Outgroups were 12 species of Bidessini, Hyphydrini, Laccornini, and Vatellini (Appendix 1): the closest tribes to Australasian Hydroporini within Hydroporinae  (Ribera et al. 2008). We included several genera for each tribe when possible to improve both phylogenetic resolution and branch length estimation. Coptotomus was selected to root the tree since Coptotominae has been found in sister position to the subfamily Hydroporinae  (Ribera et al.
2008). Since the genus Paroster mainly comprises rare, sometimes monotypic and excessively difficult to sample hypogean species, almost all the sequences used in this study (CO1 and 16S) were recovered from GenBank. In addition to previously sequenced species and in order to improve the placement of the genus in the group, we sequenced mitochondrial and nuclear markers (see below) for four epigean species of Paroster diving beetles  (Leys and Watts 2008). Total genomic DNA was extracted from legs, thoracic and head tissues of specimens kept in 96% ethanol using the DNeasy kit (Qiagen, Hilden, Germany). Using standard PCR protocols (Appendix 2) we amplified and then sequenced the following genes: ribosomal 16S (16S, 769 base pairs [bp]), cytochrome oxidase subunit 1 (COI, 741 bp), cytochrome b (CytB, 390 bp), ribosomal 18S (18S, 607 bp), histone 3 (H3, 321 bp), histone 4 (H4, 203 bp), and arginine kinase (ARK, 636 bp).
All sequences of the genus Sternopriscus were recovered from a recent publication  (Hawlitschek et al. 2012. ). The DNA sequences were eye corrected under GENEIOUS R6 (Biomatters, http:// www. geneious. com/), aligned using MUSCLE  (Edgar 2004 ) and the reading frames checked under MESQUITE 2. 75 (http://mesquiteproject. org). The different datasets used to infer phylogenetic relationships were generated under MESQUITE. All sequences were deposited in GenBank (accession Nos. HG965576-HG965750).
P1 (NoPart) [1] Unpartitioned dataset P2 (ByType)  [2]  One partition for the coding genes and one partition for the noncoding genes P3 (ByGenome)  [2]  One partition for the mitochondrial genes and one partition for the nuclear genes P4 (ByThree)  [3]  One partition for the mitochondrial coding genes, one partition for the nuclear coding genes and one partition for the noncoding genes P5 (ByFour)  [4]  One partition per codon position for the coding genes and one partition for the noncoding genes P6 (BySeven)  [7]  One partition per codon position for the mitochondrial genes, one partition per codon position for the nuclear genes and one partition for the noncoding genes P7 (ByGene)  [8]  One partition for each gene P8 (ByEight)  [8]  One partition per codon position for the mitochondrial genes, one partition per codon position for the nuclear genes and one partition for each noncoding gene P9 (BySixteen)  [16]  One partition per codon of each coding gene and one partition for the noncoding genes P10 (ByMax)  [17]  One partition per codon of each coding gene and one partition for each noncoding gene Notes: The number of partition(s) is given in square brackets for each partitioning scheme.
VOL. 64 the best partitioning scheme selected in BI using RAXML  (Stamatakis 2006). We performed 1000 Bootstrap replicates (BS) to investigate the level of support at each node. A calculated PP >0. 95 or a BS >70 was considered to indicate strong support for a given clade  (Felsenstein 2004). Divergence Time Estimation In order to account for the difficulty of estimating divergence times with confidence, we chose to perform three independent sets of analyses using BEAST 1. 7. 4  (Drummond et al. 2012b ). Prior to this, we tested whether or not the fragments contained in the sequence matrix evolve in a clockwise fashion, using PAUP*  (Swofford 2003)  to calculate the likelihood with and without enforcing a strict molecular clock. A likelihood ratio test (LRT) was carried out in the same software in order to compare both results, and since the molecular clock hypothesis was not statistically supported (P <0.