dataesr/scientific-paragraphs-categorization huggingface dataset:
Read alignment Sequence reads of individual BAC pools were aligned using MOSAIK 1. 1. 0021 [48]  to both sequence datasets. First, the sequence datasets and paired-end reads from individual BAC pools were converted to binary data format using MOSAIKBUILD. Then, paired-end reads of each BAC pool were mapped to sequence data set using MOSAIKALIGNER. "Hash size" (k-mer length for alignment) and "alignment candidate threshold" (minimal alignment length to map the read) parameters were preoptimized prior to the analysis with respect to alignment sensitivity and time needed for the analysis. To calibrate the parameters, we mapped one row pool (rP) against the GenomeZipper sequence dataset with hash size ranging from 10 to 30, and alignment candidate threshold ranging from 20 to 80. Final alignment was done with the following settings: hash size 20; alignment candidate threshold 40; number of mismatches 3 with the GenomeZipper dataset, and 5 with the Ae.
tauschii sequence dataset. Number of mismatches should be selected carefully. It strongly depends on divergence of genotypes (or even species) used to produce reads and reference sequence and also on quality of sequence reads and reference used for alignment. For our data, we optimized number of mismatches for GenomeZipper with row pool rP (see Additional file 8). To estimate number of mismatches for Ae. tauschii sequence data set we aligned sequences available for bread wheat D genome and Ae. tauschii using blastn (data not shown). Non-uniquely mapped reads were resolved and filtered using MOSAIKSORT. Finally, coverage statistics for each sequence in both datasets was calculated using MOSAIKCOVERAGE. Text files produced by MOSAIK COVERAGE were used in further analysis to anchor sequences to BAC clones.
BAC address deconvolution A home-made perl script (https://github. com/cvikova/ seq_anchoring) was developed for deconvolution of readmapping information to positive BAC clone(s). For each sequence in both datasets the length of sequence region covered by reads from a particular pool was parsed from MOSAIKCOVERAGE text files (we call this parameter here after covered region). The BAC pool was considered positive for the sequence if its covered region was at least 80% of the sequence length. BAC clone addresses were then calculated from positive pools in three steps. 1) For sequences with only one positive result in each dimension (plate, row and column: 1-1-1) BAC address could be immediately assigned (anchor_type_1). 2) For sequences with more than one positive result in at least one dimension, all candidate BAC addresses were calculated.
Acknowledgement We are grateful to  Marie Seifertová  and  Radomíra Tušková  for excellent technical assistance and  Jan Vrána  and  Marie Kubaláková  for chromosome arm sorting. We thank  Michael Abrouk  for careful manuscript reading and critical comments. This work was supported by the  Czech Science Foundation  (grant No. and  Ministry of Education, Youth and Sports  (grant No. LO1204  from the  National Program of Sustainability I)  and computing supported by the  National Grid Infrastructure MetaCentrum  (grant No. LM2010005  under the programme Projects of  Large Infrastructure for Research, Development, and Innovations ).
Acknowledgments We would like to express our thanks to: " Centre Régional de Mesures Physiques de l'Ouest", Université de Rennes 1, France  for NMR spectra ( Dr Sourisak Sinbandhit ) and  MS ,  Prof. C. Roux  and  Prof. L. Sparfel-Berlivet  for expert advice on lichen taxonomy and statistics,  R. Bennison  for technical assistance, the  Biology and Health Federative Research Structure of Rennes (Biosit)  for use of the biosafety level 3 containment laboratory core facility,  Dr F. Chisari  from  The Scripps Research Institute (TSRI ) for Huh-7. 5. 1 cells and  Dr C. M. Rice  for the  FL-J6/JFH-5/C19Rluc2AUbi  construct.
Materials and Methods WT-Drosha and TN-Drosha plasmid construction BAC R26A26 (Genbank accession number: AC007084) was digested with EcoRV and NdeI restriction enzymes, and the resulting 4,5kb fragment containing the drosha gene was cloned in the SmaI/NdeI restriction sites of the puc19 vector to obtain the pucWT-Drosha vector. A Flag-HA tag was introduced at the 3' end (see S1 Text). To impair Drosha slicer activity  [65] , a point mutation in each RNAseIII domain of Drosha was introduced by PCR to produce the pucTN-Drosha construct (see S1 Text). pucWT-Drosha and pucTN-Drosha were digested with NdeI and XbaI and cloned in the KpnI and XbaI sites of the pUASp vector. The resulting pUASp-WT-Drosha and pUASp-TN-Drosha plasmids were introduced in the w 1118 strain to get P-element-mediated transgenes (BestGene Inc services).
RNA extraction and quantitative RT-PCR Total RNA was isolated from ovaries with Trizol, following the manufacturer's instructions. RNA was DNase-treated (Turbo DNA-free AM1907, Ambion). mRNA quantification. First strand cDNA was obtained by reverse transcribing 500 ng of total RNA using random primers and SuperScript III (Invitrogen). Quantitative PCR was performed using the LightCycler 1 480 SYBR Green I Master system (Roche). Each experiment was performed with biological triplicates and technical duplicates. Relative RNA levels were calculated by using the 2 (-ΔΔCt) method  [66]  and normalized to the appropriate control levels. The RT-PCR primers are listed in S3 Table. Data were analysed with the LightCycler software (Roche). miRNA and piRNA quantification. cDNA synthesis was carried out according to  [43]  with the following modifications: 100 ng of total RNA was polyadenylated and reverse transcribed in the same reaction tube with E.
Bioinformatic analysis of the sequencing data from the small RNA libraries and RNA-IP samples Sequencing data were annotated according to the sequences available in several reference databases. rRNA, tRNA and snRNA sequences were retrieved from modEncode (http://www. modencode. org/) [69] , miRNA sequences were retrieved from miRBase (http://www. mirbase. org/)  [70]  and mRNA transcript sequences were retrieved from Flybase (http://flybase. org/). The analysis of small RNA libraries was performed as described in  [67]. Briefly, after subtracting the reads matching abundant cellular rRNAs, tRNAs and snRNAs, the remaining reads were considered as bona fide small regulatory RNAs reads (siRNAs, miRNAs and piRNAs). miRNAs were separated from the other bona fide reads based on their identity with miRBase.
Accession numbers Small RNA data from Yb heterozygous and mutant ovaries were previously published (Handler et al. Embo J. 30:3977) and are available via the NCBI Gene Expression Omnibus (accession no. GSM767598 and GSM767599 respectively). Sequencing data concerning the small RNA and RNA-IP libraries generated in this study are available via the NCBI Gene Expression Omnibus (GEO) (http://www. ncbi. nlm. nih. gov/geo/) under accession no. GSE60974 (see S4.
Acknowledgments We thank the VDRC and Bloomington stock collections for transgenic RNAi lines,  J. Brennecke  and  C. Vaury  for the gypsy-lacZ and ZAM-lacZ reporters, respectively,  J. Brennecke , and  M. Siomi  for kindly providing antibodies, the  Fasteris facility  for small RNA-seq and  RNA-IPseq ,  H. Seitz  and  J. Dufourt  for valuable comments on the paper.
The work was funded by  ANR Jeunes Chercheurs  ( ESSORJCJC-1604 01 ), the  CNRS ,  ARC  fellowship. CA was funded by the  Fondation pour la Recherche Médicale  (FRM). AA and VS were supported by an  ARC  fellowship. BL was supported by the  ANR  ( ESSORJCJC-1604 01 ). Work in the DVV laboratory was supported by  NIH/NINDS   1R01NS069695. The funders had no role in study.
Introduction During the last decade, a huge amount of new genomes have been sequenced, leading to an abundance of available DNA resources. Nevertheless, most of recent genome projects stay unfinished, in the sense that databases contain much more incompletely assembled genomes than whole stable reference genomes  [1]. One reason for this phenomenon is that, for most of the analyses performed on DNA, an incomplete assembly is sufficient. Sometimes it is even possible to perform them directly from the sequenced data. Another reason is that producing a complete genome, or an as-complete-as-possible-genome is a difficult task. Traditionally, producing a complete genome consists of three steps, each of them computationally or financially difficult: the assembly, the scaffolding, and the finishing.
Finally, they proved that the minimization version of SCAFFOLDING (seeking a minimum-weight cover of the scaffold graph) is unlikely to be approximable within any constant ratio, even if the scaffold graph is a completed bipartite graph  [15]. On the positive side, two polynomialtime factor-3-approximation algorithms for the maximization version have been designed. The first is an O(n 2 log n)-time greedy algorithm, the second is based on the computation of a maximum matching (O(n 3 )-time). The theoretical aspects of the scaffold graph have been completed by extensive experimental results  [17]  for the maximization version on simulated and real datasets. The main contribution of the present paper lays in both the algorithmic exploration of exact methods for the scaffolding problem with structural parameters, and the initiation of a discussion on the scaffold graph properties.
Data In our experiments, we worked with two datasets, all derived from real genomes (see Table  1 ). The first one is a set of eleven contig sets, produced from real genomes using the following process: first, the genomes where taken off the NCBI nucleotide database (http://www. ncbi. nlm. nih. gov). Then, for each of them, a set of simulated paired-end reads was generated with the tool wgsim (  [28] ), with default parameters and a 20X mean covering depth. Thereafter, assembly was performed with the tool minia (  [29] ) with a k-mer size k = 29. Reads were mapped on the contigs with bwa (  [30] ), with default parameters and using the sampe method. The second dataset is composed of five scaffold graphs, already presented in  [31]  and  [17]. Some of them have been produced by simulating reads, other come from real paired-end reads libraries (see Table  1 ).
Finally, using the scaftools previously developed  in  [15, 31] , we produced the scaffold graphs corresponding to these datasets. See  [31]  for a more detailed explanation of this pipeline. The aim of this process is first to produce a benchmark of test graphs that are more realistic than uniformly generated graphs to test our algorithms on, second to study the different parameters that may be interesting to consider for parameterized algorithms, and finally to help to design a more realistic and convenient scaffold graph generator allowing to generate graphs directly, avoiding the complicated pipeline described above (see Table  2  and Table  3 ). The second dataset was used to study the influence of a preprocessing operation on the scaffold graph, aiming at filtering low informative edges.
However, Table  2  shows that, for instances that we expect to be seen in practice, these measures can be assumed constant and, thus, it might be worth considering a combination involving these parameters. Table  3  shows the same statistics for the second set of graphs where edges of small weight (that is, low confidence) were discarded (for weight thresholds of 10, 6, 3 and 0, yielding the original graph with all edges present). We notice that even with a light filtering, the parameters of the scaffold graph are considerably lowered, confirming that raw data suffers from significant noise. Thus, filtering low quality information not only increases the quality of the scaffolding, but also may lead to a significant leap to tractability. Note that, at the time of writing this article, the Staphylococcus Aureus genome is no longer available in the NCBI Nucleotide database ("This RefSeq genome was suppressed because updated RefSeq validation criteria identified problems with the assembly or annotation.
Implementation and first results We implemented the dynamic programming algorithm presented in Section 11 in C++ using boost and the treewidth optimization library TOL  [38]. We ran it on a selection of the generated data sets (see  Section 11)  for which the greedy fill-in heuristic produced tree decompositions of width at most 45. We chose (s p , s c ) = (3, 1) for the ebola and monarch genomes and (s p , s c ) = (20, 3) for the more complicated inputs. The tests were run on an AMD Opteron(tm) Processor 6376 at 2300 MHz. Figure  7  shows running times and memory consumption needed to produce a solution as well as details regarding the used tree decompositions. Figure  8  shows optimal solutions for the two smallest data-sets: ebola and monarch. To validate the proposed scaffolding, we compared the output to the alignment of the contigs on the reference sequence using megablast (  [39] ).
We are currently transferring the preprocessing rules to the general problem variant. We are highly interested in further graph classes that are closer to real-world instances than trees and on which the problem might be polynomial-time solvable. From an algorithmic point of view, we remark that only few bags of the used tree decompositions are small (see Figure  7 ). Thus, we envision a hybrid strategy of branching on the vertices in the largest bags before running the dynamicprogramming algorithm and using this "distance x to treewidth-x" parameter to re-analyze the problem. We intend to perform more extensive tests on diverse datasets, in particular comparing the quality of this approach to existing ones using, for instance, the criteria presented in  [9]. This work demands reliable benchmarks, with up-to-date and well assembled genomes.
Acknowledgements Publication of this work is funded by the  Institut de Biologie Computationnelle. This article has been published as part of  BMC Bioinformatics Volume 16 Supplement 14 , 2015: Proceedings of the 13th Annual Research in  Computational Molecular Biology (RECOMB) Satellite Workshop on Comparative Genomics: Bioinformatics. The full contents of the supplement are available online at http://www. biomedcentral. com/bmcbioinformatics/supplements/ 16/S14.
Measurement of Energy Values, Growth Performance, and Body Composition Database 1 (DB 1 ) comes from an experiment conducted in 1996 for the determination and prediction of AMEn and NE values of broiler diets  (Carré et al. , 2013 (Carré et al. , , 2014)). Thirty diets were given to male broiler chickens (IJV 915, ISA) from 3 to 5 wk age. The whole study was divided into 3 successive trials, each testing 10 different diets. Diets differed between trials, except Diets 11. 1, 11. 2, and 11. 3 used in Trials 1 through 3, respectively. Diets were attributed to trials in such a way that similar mean diet compositions were obtained for each trial. AMEn and NE determinations were carried out in the same trials, but they were not measured in the same birds. AMEn and NE values were measured in 2 different groups of birds, the ME and NE groups being placed in 2 different rooms with the same environment conditions.
Statistical Analyses Statistical analyses were performed using the Super-Anova software  (1989 to 1991, version 1. 11, Abacus Concepts, Inc. , Berkeley, CA). For regressions with no intercept, R 2  values were calculated in terms of the residual standard deviation (RSD) of the regression equation and σ 2 y (variance of the dependent variable) as (1 -RSD 2 /σ 2 y ). Trial effects were added in the regression models. In some cases, the trial effect observed in a regression model was used to correct the dependent variable; then the regression calculation was performed again with the same scheme without including the trial effect. This approach was especially developed for regression calculations without intercept. The variance analyses testing the diet effect were conducted by adding the trial effect in models and by considering the diet effect as being nested in the trial effect.
RESULTS Effects of diets on BW gain, feed intake, feed efficiency (FE), and protein efficiency were highly significant (P = 0. 0001; Table  3 ). Strong diet effects were observed on energy deposition as protein and lipid (P = 0. 0001; Tables  3  and 4 ). From the data shown in Table  4 , it can be calculated that AME partitioning averaged 19. 1 and 25. 4% for energy retained as protein and lipid, respectively. In Table  5 , the coefficients assigned to EDL and EDP in the multiple regression calculating AMEI represented the inverse of partial AME efficiencies for energy deposition as lipid and protein. Accordingly, partial AME efficiencies for lipid and protein energy deposition were   (Carré et al. , 2013). 3 Calculated values from measured CP and calculated (amino acid/CP) ratio. 4 Calculated by multiplying the content of the limiting amino acid (lysine or sulfur amino acids) by the ratio of CP to lysine or sulfur amino acids (the limiting amino acid) observed with Diet 26.
ACKNOWLEDGMENTS This study was supported by a grant from  Fonds SYPRAM  [funding from companies suscribing to  Association pour le Maintien de l'Elevage en Bretagne (AMEB) ,  Syndicat National des Industriels de la Nutrition Animale (SNIA) , and  Fédération Nationale des Coopératives de Production et d'Alimentation Animales (SYNCOPAC) ], Broiler Net Energy Project, October  1996-October 1998. We would like to thank  Dr. Jean Simon  for participation in the manuscript editing.
The genomics of economically important marine bivalves is studied to provide better understanding of the molecular mechanisms underlying their different reproductive strategies. The recently available gonad transcriptome of the black-lip pearl oyster Pinctada margaritifera is a novel and powerful resource to study these mechanisms in marine mollusks displaying hermaphroditic features. In this study, RNAseq quantification data of the P. margaritifera gonad transcriptome were analyzed to identify candidate genes in histologicallycharacterized gonad samples to provide molecular signatures of the female and male sexual pathway in this pearl oyster. Based on the RNAseq data set, stringent expression analysis identified 1,937 contigs that were differentially expressed between the gonad histological categories.
Recently,  Teaniniuraitemoana et al. (2014)   [29]  performed a gonad transcriptome analysis of P. margaritifera, using Illumina-based RNAseq, unraveling some molecular mechanisms involved in sex determination, sex differentiation and gametogenesis. In this study, the authors revealed the importance of pmarg-dmrt, pmarg-fem1-like and pmarg-foxl2 as starting points for further functional studies on the sex-determining cascade of the pearl oyster. In the present study, using the previously published RNAseq dataset  [29] , we applied a more stringent method of statistical selection on the differential expression levels in order to draw up a short list of candidate genes of the male and the female sexual pathway. These candidates were next assayed by real-time PCR in different sexes and stages of pearl oyster gonad.
Real-time PCR Candidate marker genes selected from the analysis of RNAseq dataset were assayed by realtime PCR on the 44 gonad samples. After removing RNAlater by pipetting and absorption, total RNA of each individual was extracted using TRIzol Reagent (Invitrogen) and treated with DNAse I using a DNA-free Kit (Ambion) following manufacturer's instructions. RNA concentrations were measured on an ND-100 spectrophotometer (Nanodrop Technologies) at 260 nm, using the conversion factor 1 OD = 40 μg mL -1 RNA. For each sample, 0. 5 μg of total RNA were reverse-transcribed using Transcriptor First Strand cDNA Kit (Roche) and amplified by real-time PCR on a Stratagene MX3000P. The amplification reaction contained 12. 5 μL 2X SYBR green qPCR Master Mix (Stratagene), 10 μL cDNA template, and 2. 5 μL of primers (4 μM) in a final volume of 25 μL.
Statistical analysis All the statistical analyses were performed using R v2. 15. 2, an environment and a language for statistical computing  [33]. Differential expression (DE) analysis. RNAseq quantification data of P. margaritifera gonads were taken from the study of  Teaniniuraitemoana et al. (2014)   [29]  (Bioproject PRJNA229186-Genbank accession number SRP033217) who analyzed the gonad transcriptome based on the Illumina sequencing of 36 cDNA libraries, each corresponding to gonad samples of different reproductive stages and sexes. The differential expression level of contigs between different gonadic categories was tested using the DESeq package v1. 11. 3  [34]  as described in  Teaniniuraitemoana et al. (2014)   [29]. In the present study, the differential expression of contigs was considered as statistically significant when the value of FoldChange was > 10 or < 0.
Differential expression analysis reveals new insight into P. margaritifera reproduction To provide an overall view of the transcriptional changes in the gonad of P. margaritifera, a stringent differential expression analysis was performed between the ten different gonadic categories on the RNAseq dataset already available  [29]. In accordance with the value of FoldChange > 10 or < 0. 1 and padj < 0. 001, the DESeq method identified 1,937 contigs differentially expressed between gonadic categories. A hierarchical clustering method was applied on the RNAseq samples to cluster them according to the similarity of expression pattern among the 1,937 differentially expressed contigs (Fig. 1 ). Analysis of the dendrogram generated identified two clusters: one male, gathering the samples of male sex, and one female, gathering the samples of female sex.
Concerning FP candidate genes, results of relative expression showed a significantly higher expression in female gonads overall, especially for pmarg-c43476 (F: 0. 0099 ±0. 0077; and FR: 0. 0104 ±0. 0107) (Fig. 4A, 4B  and 4C ). In the same way, relative expression of F and MP candidate genes showed levels equivalent to their expression found in the RNAseq dataset. Indeed, pmarg-vit6 (0. 39 ±0. 53) and pmarg-zglp1 (0. 005 ±0. 006) were significantly more expressed in F gonads (Fig. 4F  and 4G ), and pmarg-dmrt and pmarg-fem1-like were significantly more expressed in M gonads (0. 005 ±0. 004 and 0. 04 ±0. 02 respectively) (Fig. 4H  and 4I ). There was no significant difference between gonadic categories in the relative expression of early female pathway candidate genes (eFP), pmarg-c19309 and pmarg-c54338 (Fig.
1. Thus, following this MRT model of the sexual pathway of P. margaritifera, we can associate oysters with the particular pathways by analyzing the expression ratios of these four genes (i. e. , pmarg-c43476/pmarg-fem1-like, pmarg-foxl2/pmarg-c54338 and pmarg-foxl2/pmarg-fem1-like). To test the predictive ability of this MRT model of the sexual pathway of P. margaritifera on other datasets, we predicted the sexual pathways on which were engaged the RNAseq samples. To do this, we used the RNAseq normalized expression (estimatesizefactor, DESeq) of the samples. The regression tree prediction matched with the previous hierarchical clustering (Fig. 1 ). Samples of the male cluster were predicted to be on the male pathway and samples in the female cluster were predicted to be on the female pathway as shown in Table  2.
Acknowledgments The authors are indebted to  J. Y. Daniel ,  V. Quillien ,  V. Vonau ,  J. Fievet ,  C. Cabrau ,  L. Quintric ,  F. Marquer  and all the staff of the  GeT-PlaGe  (http://www. genomique. genotoul. fr/)and Sigenae (http://www. sigenae. org)core facilities for their helpful assistance. We thank  H. McCombie  for English language editing.
This study was financially supported by  ANR  funding ( POLYPERL project , reference  ANR-11-AGRO-006 ) and by the  French Government  through the Contract Project BiodiPerl 2011-2013. VT is a Ph. D. student financially supported by  Ifremer  and holds a grant from la  Délégation à la Recherche de Polynésie française. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.