dataesr/scientific-paragraphs-categorization huggingface dataset:
2015) , some of which have been applied to Musa research (e. g. Risterucci et al. 2009 ). Yet SSR markers are still used as an effective tool in elucidating the genetic diversity in many plant species, including bananas  (Nicolai et al. 2013; Mbanjo et al. 2012; Gross-German and Viruel 2013; Liu et al. 2015; Kitavi et al. 2016). The main advantage of the SSR genotyping approach resides in its capability of systematically adding new information to existing data sets. The newly analyzed accessions can be added to the existing database of SSR profiles, while the optimized and undemanding methodology assures comparability of results gathered over different time points, at a reasonable cost. The maintenance of genebanks of vegetatively propagated plants is expensive and timedemanding. Therefore, it requires a rationalized approach dealing with properly characterized germplasm and ensuring the management of true-to-type germplasm.
Fragment analysis Purified PCR products were diluted 40-fold in Hi-Di formamide containing the internal size standard (GeneScan TM -500 LIZ size standard; Applied Biosystems, Foster City, CA, USA) and loaded onto a capillary electrophoresis DNA analyzer (ABI 3730xl, Applied Biosystems) after 5 min denaturation (95 °C). Electrophoretic separation and signal detection were carried out with default module settings. In order to reduce the cost and increase throughput of the genotyping platform, samples were multiplexed for electrophoretic separation. Up to fourfold multiplexing was applied by combining four PCR products, labelled with four different fluorescent dyes (6-FAM, VIC, NED and PET) into a single sample for loading. The resulting data were analyzed and called for alleles using Fig. 1  Estimation of ploidy level in Musa accessions.
Genetic diversity analysis using distance-based methods The extent of genetic diversity among all samples was evaluated using the Neis genetic distance coefficient calculation  (Nei 1973 ) and subsequent cluster analysis was done using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA;  Michener and Sokal 1957). To enable joint analysis of all ploidy levels (29, 39 and 49) the genotypic data was converted into binary (coded by 1/0 = presence/absence) and analyzed as a dominant marker's record  (Weising et al. 2005). Dendrograms were constructed based on the results of UPGMA analysis and visualized in FigTree v1. 4. 0 (http://tree. bio. ed. ac. uk/software/ figtree/). The first dendrogram that comprised all analyzed samples (not shown) was used to identify problematic accessions whose position in the dendrogram did not agree with their current classification and were therefore not used to build the ''core subset''.
The core dataset comprised reliable accessions only, for which the clustering pattern agreed with the classification based on morphological descriptors. The genotyping and evaluating pipeline is shown in Fig. 2. The dissimilarity index threshold of 0. 25 was used to assess the grouping of the accessions on the dendrogram. Bootstrap support for individual branches was calculated on 1000 replicates and values above 35% (0. 35) were used to confirm the fundamental subclustering pattern. To evaluate the informative power of individual SSR loci, several characteristics were calculated, such as Polymorphism information content (PIC;  Botstein et al. 1980) , allele number, observed heterozygosity (H o , in the diploid dataset) and major allele frequency using Powermarker v3. 25  (Liu and Muse 2005).
Ploidy level estimation Flow cytometry was used to estimate the ploidy in all 495 accessions that were received as fresh leaf tissues. The remaining accessions were received either as lyophilized leaf tissues (151) or DNA samples from the Reference DNA collection (49; http://www. musanet. org/ Musagenotyping centre/genomicDNA). Ploidy of these later accessions was retrieved from the Musa germplasm information system database (MGIS; http://www. crop-diversity. org/ mgis/). For a majority of the samples, the expected ploidy was confirmed (Online Resource 1). However, some accessions e. g. Gebi (ITC0877) and Mwitu Pemba (ITC1545) reported to be triploid AAB, were found to be diploid. Moreover, accessions Tongkat Langit Papua (ITC1716) and M. borneensis (ITC1531) showed ambiguous results with DNA content between diploid and triploid, or between triploid and tetraploid for accession Sar (ITC0898) and SUP 1 (AAB, Indonesian Triangle mission 2).
Distance-based genetic diversity analysis The informative power of the 19 SSR loci used in the genetic diversity study (expressed as the Polymorphism Information Content; PIC) ranged between 0. 561 and 0. 933 with an average value of 0. 789 (Table  1 ). The highest observed heterozygosity within the diploid dataset was observed for the marker mMaCIR01 (H o = 0. 623), whereas the locus mMa-CIR307 had the lowest H o value (H o = 0. 250). The UPGMA analysis of the dataset comprising all ploidy levels together grouped the accessions into clusters, which in general conformed to the morphological traits-based classification into subgroups (in 84% of accessions) as reported by germplasm donors to the collection (Musa Germplasm Information System (MGIS), Bioversity International. Accessed on February 15, 2016 at http://www.
crop-diversity. org/banana/). However, in 16% of cases, the classification reported did not agree with the clustering observed in the dendrogram. These accessions are listed in Online Resource 2 and the discrepancy between the current classification and the results obtained here are possibly caused by mislabeling or handling error, or due to misclassification before introduction into the genebank. This set of 71 ''to be verified'' accessions was excluded from the dataset that was used for the final dendrogram construction. Furthermore, 24 accessions for which classification was incomplete or not provided, and eight synthetic hybrid accessions were removed from the dataset, in order to increase the resolution of the ''core subset'' tree. The remaining 591 accessions, representing only entries with coherent classification and genotyping data, were used to construct a final SSR dendrogram.
2012). Another wild species, M. itinerans, ubiquitous from NE India in the west to Taiwan in the east  (Ha ¨kkinen et al. 2008 ) is morphologically a very distinct species with characteristic long rhizomes, which was, however, not reflected by the cluster analysis. Indeed, the three representatives of this species were scattered over the dendrogram instead of being grouped in one block (black boxes on Fig. 3 ), which could be attributed to underrepresentation of the sample in the present dataset or potential mislabeling. On the other hand, it may also indicate the limitations of the 19 SSR marker set used. The selected 19 SSR markers proved to work very well for verifying classification of diploid and triploid cultivars subgroups (as described further below), and for some M. acuminata subspecies (see above).
However, there seem to be limitations when it comes to certain species/subspecies, such as M. itinerans, M. acuminata ssp. errans and others (see Table  2 ), which are rather underrepresented in the dataset. As compared to the result of the first study where the platform was applied  (Christelovaet al. 2011) , the resolution has greatly improved by enlarging the dataset, which is clearly demonstrated by the AA cv. clusters, or triploid subgroups diversification (see further below). Only future results comprising the whole diversity of the germplasm available for genotyping would give clear answer and show the borders for the application of the platform.
Conclusions Recently, plant genetic diversity has been frequently assessed with high-throughput nextgeneration sequencing methods, such as Genotyping by Sequencing (GBS) or SNP-based DNA chips (e. g. DArT or DArTSeq). Although such methods are powerful and offer low costs per data-point, they may not be suitable for long-term projects in which entries need to be analyzed in batches, or even one by one over extended periods of time. The comparability of results gathered at different time points cannot be assured unless the whole set of accessions is analyzed again, which may not be economical. In contrast, SSR genotyping allows processing even single samples over time. Moreover, with each new accession, new alleles may be added into the dataset thereby increasing the resolution of the analysis and improving the classification.
Acknowledgements We thank  Marie Seifertova  ´for her excellent technical assistance and  Ludvı ´k Urda  and  Alz  ˇbe ˇta Ne ˇmec ˇkovafor assisting with the flow cytometry measurements. This work was supported by the  Ministry of Education, Youth and Sports of the Czech Republic  ( Grant award   LO1204  from the  National Program of Sustainability I and awards INGO II  No. LG12021  and  INGO II  No. LG15017 ). The authors would furthermore like to thank all donors who supported this work through their contributions to the  CGIAR Fund  (http://www. cgiar. org/who-we-are/cgiar-fund/fund-donors-2/),and in particular to the  CGIAR Research Program for Managing and Sustaining Crop Collections (Genebanks-CRP). We also thank  Vincent Johnson  for his kind review and editing before submission. We dedicate this article to the late  Dr.
01). In order to validate these transcription factorpromoter interactions in planta, we performed two types of assays. Transcription factors were overexpressed using a b-estradiolinducible system  (Coego et al. , 2014)  and expression of the respective target gene was measured 24 h after induction (Supplemental Data Set 3). In addition, myc-tagged transcription factors were assessed for their ability to drive expression of the GUS reporter gene fused to the target promoter in Nicotiana benthamiana leaves (Supplemental Data Set 3). Altogether, 71 of the 101 transcription factors in the network were tested in these in planta assays and a total of 63 interactions were successfully validated in planta (Figure  5 ; Supplemental Data Set 3 and Supplemental Figure  8 ). We hypothesize that these transcription factors represent an important upstream regulatory component of PRC2 gene expression.
Cloning Strategies All oligonucleotides used in this study are described in Supplemental Data Set 4. All PCR-amplified fragments were completely sequenced after subcloning, and only the clones without PCR-induced errors were used for subsequent cloning steps. For promoter amplification, Col-0 genomic DNA was used as template. For coding region amplification, Col-0 cDNA was used as template, except for the CLF coding region, which was amplified from genomic DNA and thus contains introns. For the generation of the transcriptional GUS fusions, each respective PCR product was introduced into pENTR D-TOPO (Invitrogen) and subsequently recombined into the pGWB4 and pGWB5 destination vectors  (Nakagawa et al. , 2007)  with the exception of the CLF promoter, which was assembled to Venus-N7 (rapidly folding YFP variant) by Hot Fusion reaction  (Fu et al.
, 2005)  by a Multisite Gateway reaction (Invitrogen). The design of the amiRNA for MSI1 was performed following WMD3 software  (Ossowski et al. , 2008)  and cloned into pENTR D-TOPO. Afterwards, a Multisite Gateway reaction was performed in combination with the promoter of WOL (kindly provided by Anthony Bishopp, University of Nottingham) and pK7m24GW  (Karimi et al. , 2005). The resulting plasmids were introduced into Agrobacterium strain GV3101 carrying the pSoup plasmid  (Hellens et al. , 2000) , and Col-0 wild type in addition to MSI1 pro : MSI1:GFP were transformed using floral dip  (Clough and Bent, 1998). Transformation into the MSI1 pro :MSI1:GFP background served as a control to ensure precise tissue-specific silencing of MSI1 with the designed amiRNA.
Quantification of transcription factor and PRC2 gene expression was performed by RT-qPCR. We calculated the mean from three independent experiments (biological replicates) and from the average of three technical replicates per biological replicate. Each biological replicate captures expression from ;200 roots of each respective genotype. In each case, the DDCt was calculated relative to a Ubiquitin10 control (At4g05320). In all cases, significance was tested using a t test (*P < 0. 05, **P < 0. 01, and ***P < 0. 001). We used Cytoscape software  (Shannon et al. , 2003)  for data visualization and GO analysis of the network.
ChIP-Seq Data Analysis Reads were filtered by length and quality and aligned to the Arabidopsis (TAIR10) genome using Bowtie  (Langmead et al. , 2009)  and the parameters "-v2 -m1-best-strata -S". SCICER software was used to determine the differentially methylated islands using a 200-bp window size, 200-bp gap size, and a false discovery rate of 0. 005. The genomic regions containing the histone modification were determined using windowbed software  (Quinlan and Hall, 2010)  and 21000 bp upstream and downstream of the gene body for H3K27me3 and 200 bp upstream and 200 bp downstream for H3K4me3. Genes that overlap in at least three of the four biological replicates were considered as high confidence genes for the downstream analyses. Root cell-type-specific expression of the H3K27me3 and H3K4me3 affected genes was obtained from  Brady et al.
ACKNOWLEDGMENTS M. d. L. was supported by  EMBO  and  HFSP  postdoctoral fellowships. S. M. B. and F. R. were supported by the  France-Berkeley Fund. S. M. B. was supported by a  Katherine Esau Junior Faculty Fellowship  and a Hellman Fellowship. Work by A. K. M. and F. R. with the technical assistance of  Erwann Caillieux  was supported by the  European Union Seventh Framework Programme Network of Excellence EpiGeneSys  (to V. Colot) and the  CNRS. A. K. M. is the recipient of a grant from the  French Ministère de la Recherche et de l'Enseignement Supérieur. Work in the  Sugimoto lab  was supported by the  Grant-in-Aid for Scientific Research on Priority Areas  to K. S. ( 26291064). This project was supported by the  University of California Davis Flow Cytometry Shared Resource Laboratory  with funding from the  NCI   P30 CA0933730 , by  NIH   NCRR C06-RR12088 ,  S10 RR12964 , and  S10 RR 026825  grants, and with technical assistance from  Bridget McLaughlin  and  Jonathan Van Dyke.
Electrophysiology Brain slices were prepared 24 hr after the last behavioral test for each animal. Signals were amplified and recorded with Multiclamp 700B, controlled with pClamp 10. 3 software via a Digidata 1440A interface (Molecular Devices). STDP protocol consists of pairing pre-and postsynaptic stimulations 100 times at 1 Hz, with a delay of $15 ms and the neuron held at À70 mV in the current clamp configuration. Medium spiny neurons were recorded for 10 min of stable baseline (0. 1 Hz) and for at least 35 min after the STDP protocol. Chemicals used were AM251 (4 mM), SR141716A (1 mM), and JZL184 (1 mM). In Vivo Pharmacological Experiments For JZL184 i. p. injections, mice were given i. p. injections of vehicle (22. 5:100 HBC:H 2 O) or JZL184 (16 mg/kg) 6 hr before the elevated plus maze test.
ACKNOWLEDGMENTS This research was financially supported by  INRA ,  Agence Nationale de la Recherche  ( ANR-MoodFood ),  FRM , and the  Region Aquitaine. T. L. was the recipient of a postdoctoral fellowship from the  SocieteFrancaise de Nutrition  and the  Labex BRAIN. C. B. -B. was funded by  ANR-MoodFood  and the  European Agreenskills program  ( COFOUND FP7-267196 ). The authors thank  Claudine Tridon ,  Cindy Testa , and  Mathieu Cadet  of the animal facility. We thank  Pierre Trifilieff ,  Agnes Nadjar , and  Corinne Joffre  for helpful and fruitful discussions and  Dr. Henry Martin  for proofreading the manuscript.
Background: The program RepeatMasker and the database Repbase-ISB are part of the most widely used strategy for annotating repeats in animal genomes. They have been used to show that avian genomes have a lower repeat content (8-12 %) than the sequenced genomes of many vertebrate species (30-55 %). However, the efficiency of such a library-based strategies is dependent on the quality and completeness of the sequences in the database that is used. An alternative to these library based methods are methods that identify repeats de novo. These alternative methods have existed for a least a decade and may be more powerful than the library based methods. We have used an annotation strategy involving several complementary de novo tools to determine the repeat content of the model genome galGal4 (1.
RepeatMasker (RM) is the most widely used library-based method in genome sequencing projects  [16, 17]  and is typically used in association with Repbase, a repeat library that is freely available to academics  [18]. The TEs of numerous genomes have been annotated with RM and a private, inaccessible, library at the Institute for System Biology (ISB)  [19]. The main limitation of such library-based approaches is that the annotations depend very heavily on the quality of the reference database, including completeness and accuracy of the consensus sequences. By contrast, signature based methods focus on traits that are unique to certain TEs or repeats. For example, the program LTR Finder detects specific DNA organization patterns as well as a chain of signatures (motifs) specific to retroviruses to detect LTRretrotransposons  [20].
Tandem repeat finder (TRF), another signature method tool, is dedicated to detecting all types of uncomplicated tandem repeats such as simple repeats, microsatellites, minisatellites and satellite DNAs  [21]. Finally, DNA de novo consensus methods combine a range of detection tools. The REPET package is a pipeline that uses both de novo and signature-based methods  [22] [23] [24]  and may be used to include a library-based step  [25]. de novo consensus methods such as REPET have been limited until now by their need for powerful resources for calculation and storage which has restricted their application to small eukaryotic genomes (~10 Mbp to 500 Mbp). However, advances with computing clusters and a recent REPET update have opened the way for the use of this software package with larger genomes such as those of vertebrates.
Detection and annotation of repeats in galGal4 Strategy for detecting and annotating repeats in galGal4 Our approach for accurately estimating the repeat content of the galGal4 model was based on published data and analysis of individual repeat types (such as those described above, as well as others methods that are detailed below). The resulting strategy (Fig. 2 ) was organized into five steps. First used the program Red to estimate the total number of repeats  [15]. Second, TRF was used to analyse SSRs  [21]. Third was the TE annotation, which demanded the most investment of resources. We used the software package REPET  [22] [23] [24]  because it has been extensively tested and had been shown to be more efficient than the RepeatScout  [64]  and RepeatModeler  [65]  packages. We were aware that REPET annotations do not always recover 100 % of annotations calculated by the other two packages  [13] , but decided that these were ultimately only small differences.
Furthermore, we found that even these small differences were minimized by our use of TRF prior to REPET, which we found to be more efficient at locating SSRs than either the REPET. We performed the REPET analysis in three successive detection steps (Fig. 2 ) in order to dig deeper for fragmented repeats than RM. Our fourth step in the annotation strategy was to annotate the dark matter (DM) as proposed by Maumus et al (2014)  [25] , using a library containing all repeated copies longer than 500 bp detected in step 3 and the TEannot program  [66]  rather than RepeatMasker (RM)  [67]. For our final step we used the available annotation of CNVs in galGal4  [11, 68].
Profiles of SSRs in galGal4 (STEP2) The proportion of SSRs in the galGal4 model has been estimated, using RM, to be 1. 73 %  [61]. We reinvestigated this number by examining the diversity and number of microsatellites using the FASTA program of the GCG computer package  [8]  and sputnik  [9]  while those of satellite DNAs were investigated using a variety of molecular approaches (for a review see  [44] ). Using TRF, which can detect SSRs with repeated units from 1 bp to 2 kbp, we found that the assembled genome contained 3. 73 % SSRs and the unassembled genome contained 12. 74 % SSRs, for a total coverage of 4. 08 % in the gal-Gal4 model. These proportions are at least twice as large as those found with RM (2. 36-folds with rates varying from 1. 11 to 9. 13-folds, depending on the chromosome; Table  2  and Fig.
Repeats with sequences that are well conserved can be annotated using default values in the REPET pipeline. We used a library containing all repeated copies of the REPET annotation and the TEannot program to access the DM, the older and/or fragmented TE segments (Fig. 2 , STEP 4). Our aim was to use a population of genomic copies as a probe to detect more divergent repeats (see Methods). Computational constraints obliged us to select only TE copies >500 bp (33,757 copies). The 33,757 copies used at this step each originated from one of 222 consensuses calculated by REPET. Annotation of the DM increased the TE coverage in the galGal4 model to 4. 7 % (Fig. 4b ).
7 % of galGal4 (Fig. 4c , Additional file 5). This proportion of TE coverage may be compared to the 9. 74 % coverage in the ISB TE annotation  [61]. Almost all (99. 7 %) DM annotations (4. 41 % of coverage in galGal4) were new and only 0. 3 % of them extended existing REPET annotation (0. 035 % coverage in galGal4). The sum of SSR and [TE + DM] coverages suggests that there are at least 19. 78 % repeats in galGal4. But this estimate was corrected by intersecting SSRs and [TE + DM] annotations using bedtools software (Fig. 5a ). Because [TE + DM] includes 1 % coverage by SSRs, the amount of annotated repeats was 18. 78 %, which was 1. 64 times more dense than the ISB annotation  [61]. Intersections were also calculated with the CNV annotation  [68] , as were those obtained with Red and P-clouds.
Although Dfam improves significantly the sensitivity and takes better account of TE sequence variations, it is still of limited use for detecting the diversity of rearrangements of TEs such as the non-LTR retrotransposons and, to a lesser extent, some LTR-retrotransposons and DNA transposons. We have borrowed the concept of the TE model developed by the creators of the program RepeatExplorer  [80, 81]  to describe a "TE species". This concept is also included in the philosophy of REPET  [22, 24]. It assumes that a TE model is composed of a main consensus sequence (the most complete version of the TE) plus all the consensuses detected as variants. Using this concept, our final 3f library contains 499 consensuses distributed among 34 TE models (TEs or repeat "species" listed in Table  4 , correspondences between Repbase and ISB consensuses and the 34 TE models are shown in Additional file 7).
The final clustering steps were performed manually using information from sequence databases because BLASTclust in TEdenovo does not calculate models that are consistent with the galGal4 sequences. Our 34 TE models were in striking contrast to the ISB annotation  [61]  that describe 317 different TE consensuses (TEs or repeated "species", from which 65 consensus sequences corresponding to repeated genes encoding structural RNA -tRNA, U RNA, 5S RNA, rRNA, etc -must be removed). The many Repbase and ISB consensuses corresponding to non-gene repeats (252) was partly due to fragmentation of a significant number of repeats intp several consensus sequences associated with a single TE species. Thus, 21 of our TE models were split into 81 different Repbase and ISB consensuses (Additional file 7). Furthermore, there were 171 Repbase and ISB TE consensuses involved in the ISB annotation  [61]  that were found in 81,805 annotations covering 2 % of the genome without.