dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA Extraction and Sequencing For each individual, total RNA was extracted after removing the shell and digestive tracts to avoid environmental contaminations using standard protocols as described in  Gayral et al. (2011) , and a nonnormalized cDNA library was prepared from 5 mg RNA. The libraries were sequenced on a Genome Analyzer II or Hiseq 2000 (Illumina, Inc. ) to produce 100-bp single-end fragments (Illumina reads). In addition, for one individual of G. truncatula, also a normalized randomprimed cDNA library was prepared and sequenced for half a run using a 454 Genome Sequencer FLX Titanium Instrument (Roche Diagnostics). Reads were trimmed of low-quality terminal portions using the SeqClean program (http://compbio. dfci. harvard. edu/tgi/, last accessed  May 26, 2015).
Transcriptome Assembly, Read Mapping, and Coding Sequence Prediction De novo transcriptome assembly based on the 454 (one individual in G. truncatula) and Illumina reads was performed following strategies B and D in  Cahais et al. (2012) , using a combination of the programs Abyss and Cap3. Reads were mapped to predicted cDNAs (contigs) using the BWA program, setting the mismatch penalty to 10. Contigs covered at 2. 5X (X = diploid sample size) or less across all individuals were discarded. Open-reading frames (ORFs) were predicted using the program transcripts_to_best_scoring_ORFs. pl, which is part of the Trinity package. Contigs carrying no ORF longer than 200 bp were discarded.
Genotype and SNP Calling At each position of each ORF and each individual, diploid genotypes were called according to the method described by  Tsagkogeorga et al. (2012, model M1)  and improved by  Gayral et al. (2013) , implemented in the "reads2snps" program. This method first estimates the sequencing error rate in the maximum-likelihood framework, calculates the posterior probability of each possible genotype, and retains genotypes supported with probability higher than a given threshold (0. 95 here)-otherwise missing data are called. We required a minimum coverage of 10X per position and per individual to call a genotype. Then, SNPs were filtered for possible hidden paralogs (duplicated genes) using a likelihood ratio test based on explicit modeling of paralogy ("paraclean" option of the reads2snps software;  Gayral et al.
Orthologous Genes Identification and Annotation Orthologous pairs of ORFs, hereafter called genes, from the focal and the outgroup species were identified using reciprocal best hits on BLASTn results, a hit being considered as valid when alignment length was above 130 bp, sequence similarity above 80%, and e value below e À50  (Gayral et al. 2013). Outgroup sequences were added to within-focal species alignments using a profile-alignment version of MACSE  (Ranwez et al. 2011) , a program dedicated to the alignment of coding sequences and the detection of frameshifts. Genes were only retained if no frameshift was identified by MACSE, and if the predicted ORF in the focal species was longer than 100 codons. To retrieve orthologous genes between the two focal species P. acuta and G. truncatula, reciprocal best BLAST hit procedure was repeated on the final set of retained genes for each species.
2007 ). SNPs sampled in less than 12 (six diploid individuals) or 8 (eight haploid individuals) sequences in P. acuta and G. truncatula, respectively, were disregarded as far as SFS was concerned. We used the unfolded spectra to fit the models with the diffusion approximation method implemented in the dadi software  (Gutenkunst et al. 2009 ). To take possible polarization errors into account, we added an error rate as an extra parameter that was jointly fitted in the model (see dadi's code in supplementary material S2, Supplementary Material online).
The statistic DoS = d N /(d N + d S ) À p N /(p N + p S ) overcomes these limitations  (Stoletzki and Eyre-Walker 2011). A positive DoS indicates evidence of adaptive evolution, DoS = 0 indicates neutral evolution, and negative values indicate evidence of purifying selection. An FDR  (Storey and Tibshirani 2003)  was applied to correct for multiple tests in both analyses ("qvalue" package under R environment). Given that selective pressures are expected to be relaxed on certain traits in selfing species, some specific genes should show stronger evidence of relaxed selection, particularly genes involved in reproduction (e. g. , mating, resource allocation or sexual conflicts; Gl emin and Galtier 2012, and references therein). Moreover, genes involved in stress response are more susceptible to be under positive selection  (Clark et al.
Genes not assigned to either the "reproduction or the "response" classes were assigned to the "other/unknown" class. We used a multilocus extension of the MK test implemented in the software MKtest v2. 0  (Welch 2006; Obbard et al. 2009). We allowed (the proportion of adaptive substitutions) and f (the proportion of nonsynonymous mutations that are neutral) to vary among classes, assuming the other parameters of the model to be the same for all classes.
This method takes demographic effects into account as in  Eyre-Walker et al. (2006). Because misinference of the ancestral state in the unfolded spectra can give spurious signature of selection or gBGC  (Hernandez et al. 2007) , we used an extension of this method that takes polarization error into account  (Gl emin et al. 2014). The method also allows estimating the mutation bias toward AT or unpreferred codons, noted here. As for the synonymous and nonsynonymous SFS, we resampled polymorphic sites according to a hypergeometric distribution to obtain a sample of 12 (P. acuta) and 8 (G. truncatula) sequences. ÁRSCU and frequency spectra were computed using custom PERL scripts kindly provided by Yves Clement. Selection on codon usage and gBGC can be confounded if preferred codons are most often GC-ending.
Acknowledgments This work was supported by the  French Centre National de la Recherche Scientifique , by the  Agence Nationale de la Recherche  ( ANR-11-BSV7-013-03 ), and by  European Research Council  advanced grant  232971  (PopPhyl). This publication is the contribution ISEM 2015-076 of the  Institut des Sciences de l'Evolution de Montpellier  ( UMR 5554 ). The authors thank  Benoît Nabholz  and  Yves Clement  for providing us scripts for data analyses,  Jean-Pierre Pointier  for sampling and suggestions on figure 1, and  Jonathan Romiguier  for BLASTP analysis. They acknowledge  D. Rondeleau ,  J. Dos Santos ,  Y. Caron ,  D. Bilton ,  R. Slapnik , and  M. Kalbe  for providing samples. They also thank the associate editor and three anonymous reviewers for their helpful comments on the manuscript.
Amplicon library preparation The 454 sequencing technique requires amplicon primers containing a directional GS FLX Titanium primer sequence (which includes a four base library "key" sequence) at the 5 0 portion of the oligonucleotide in addition to the gene-specific sequence at the 3 0 end. Multiplex Genotype Identifier (MID) sequences defined by Roche  [32]  (S2 Table ) were added between the primer A (or B) and gene-specific sequences to allow for automated software identification of samples after pooling and sequencing  [33]. For the 57 selected gene fragments (S3 Table ), 57 primer pairs were designed according to the Access Array System for 454 Sequencing Platform User Guide  [34]  and loaded on the Fluidigm Access Array. The PCR products generated on the 48. 48 Access Array IFC (Fluidigm 48.
Sequencing and sequence data analysis for SNP calling Raw reads obtained from 454 pyrosequencing were pre-processed by removing low quality reads and adapter/primer sequences using PRINSEQ  [35]. We considered as low quality reads the short reads (<150 Bases) with primers dimmers. The other reads, were automatically identified and sorted by MID and specific gene primers using SFF Tool commands of Newbler software  [36]. For each sequenced gene of each variety, 454 pyrosequencing reads were aligned independently using SeqMan NGen software version 7. 0  [37]  with the following assembling parameters: Match size: 12; minimum Match Percentage: 80; Minimum Sequence length: 150. Consensus genomic sequences were generated from alignments. Raw data are available in EMBL-EBI (European Nucleotide Archive), under the study accessions number: PRJEB8550.
KASPar genotyping SNP genotyping was performed using KASPar technology (KBioscience; http://www. kbioscience. co. uk/). The KASPar Genotyping System is a competitive, allele-specific dual Förster resonance energy transfer (FRET)-based assay for SNP genotyping. Primers were designed by LGC Genomics based on the SNP locus-flanking sequence (approximately 50 nucleotides either side of the SNP). Detailed information for all SNP markers can be found in S4 Table. Additional details about this genotyping method can be found in Cuppen  [38]. The fluorescence signals of PCR products were measured with Fluostar Omega (BMG) and genotype calling was made with KlusterCaller software (LGC Genomics).
Genetic analysis of the SNP data SNP numbers and locations were identified from sequence data using SniPlay online software  [39, 40]. Unbiased expected heterozygosity (He), observed heterozygosity (Ho), and F Stat parameters (F w  [41]  and F ST ) were calculated using Weir & Cockerham  [42]  estimator from the GENETIX v. 4. 03 software  [43]. The search for SNPs diagnostic for each taxon was based on G ST parameter  [44]  estimations for the concerned taxa considering two subpopulations: (1) the concerned taxon (Ti), and (2) a theoretical population of the three other basic taxa (T-i). Analysis was performed from the estimated allele frequency of each taxon considering the same population size for each taxon to estimate the frequency of the two subpopulations (Ti and T-i) and the whole population (Tot) frequency.
G ST estimations were computed using Excel software: G ST Taxoni ¼ He Tot À He Ti þHe TÀi 2 He Tot where He is the expected proportion of heterozygous loci per individual (He = 1 -∑ pi 2 , where pi is the frequency of a given allele in the considered population or subpopulation). Values of G ST range from 0 to 1. Low values indicate that little variation is proportioned among subpopulations, while high values denote that a large amount of variation is found among subpopulations. In this study, G ST Taxoni = 1 indicated that the taxon i was totally differentiated from the three other basic taxa and probably fixed for a mutant allele that most likely occurred in the taxon i after its separation. SNPs presenting a G ST value >0. 9 where considered as diagnostic SNP for the corresponding taxon.
For G ST values <0. 5, a SNP was considered as specific for a taxon when it was polymorphic within the taxon but fixed for the same allele in the other three taxa. The same criteria were used to select 32 additional diagnostic markers from SNP markers previously developed by our group  [8, 9]. Factorial Analyses (FA) from fragment sequences were performed using DARwin software  [45]. Principal Component Analysis (PCA) was performed using XLSTAT software from allelic frequencies. Genotypic genetic relationships were studied by Neighbour-joining analysis (NJA) based on the SNP data using DARwin software  [45]  with the simple matching dissimilarity index. This simple matching dissimilarity index was also used to infer the intra-and inter-taxa average differentiation. Population structure was inferred using the Structure program version 2.
SNP mining and genotype calling Fluidigm amplification followed by 454 sequencing produced 295,169 useful reads. The reads were classified according to their MID, and then Titanium sequences and MID sequences were removed using 454 software tools. All reads were attributed to one of the 1,482 (57 × 26) amplicons according to the fragment gene sequence. Forty-six gene fragment/variety amplicons did not have a corresponding read (3. 1%), and eight had insufficient read numbers for genotype calling. The average of reads per amplicon was 205. 55 (for 1,436 amplicons with reads). However, the distribution of the number of reads per amplicon was highly heterogeneous (Fig 1 ), and 414 amplicons (27. 94% of total gene fragments/varieties) had fewer than 50 reads. For C. micrantha (represented by a single accession), gene fragments with missing data were Sanger sequenced to complete the data set.
aurantium (three accessions), C. sinensis (three accessions), or C. paradisi, while four different genotypes were observed in C. aurantifolia (four accessions) and five in C. limon (six accessions). All secondary species displayed high heterozygosity values (Ho) compared with the four horticultural groups. Ho ranged from 0. 35 in C. paradisi to 0. 48 in C. limon. The mandarin hybrid, tangor, and tangelo groups displayed increasing average Ho values (0. 18, 0. 21, and 0. 25, respectively). However, substantial heterozygosity variations were observed between varieties within the tangor group (0. 13-0. 30), and the differences between the groups were thus not statistically significant. PCA was performed with the data from the 70 Citrus cultivars genotyped with the 105 KAS-Par SNPs. The population displayed a very strong structuration, with 85.
reticulata, C. maxima, C. medica, and C. micrantha). Among the four basic horticultural groups, no evidence of interspecific introgression was found for the analysed accessions of pummelos, citrons, and C. micrantha (Fig  8 ). Interspecific introgressions of C. maxima were observed in several mandarins, with higher contributions noted in Satsuma, 'King' and 'Carvalhal' mandarins. Lower C. maxima introgressions were found in 'Beauty', 'Nanfengmiju', 'Ladu', 'Se Hui Gan', 'Szibat' and 'San Hu Hong Chu'. Diagnostic marker analyses revealed a C. medica introgression in the 'Shekwasha' mandarin. No introgression was found for three mandarins ('Cleopatra', 'Sunki' and C. daoxianensis). Three  Probably some of the strong hypotheses of the Bayesian estimation performed by the Structure software  [46]  such as Hardy-Weinberg genotypic proportions and no linkage disequilibrium should not be encountered in the considered taxa pertaining to their reproductive mode (contribution of asexual reproduction), however we performed a Structure analysis (S1 File) to check if the estimated contributions based on the species diagnostic marker set should fit with the direct estimation of homozygosity/héterozygosity and PCA analysis.
The genome of strain PML1  (12)  presents genes related to plant growth promotion, including the genes for the production of indole-3acetic acid (accD). The genome presents one N-acylhomoserine lactone quorum-sensing system. Interestingly, the PML1(12) genome shows a large number of genes (690) for the metabolism of sugars and carbohydrates, including genes for the synthesis and transport of gluconate, an organic acid for which production has been previously linked with the ability of bacteria to weather minerals. The genome possesses 15 genes annotated as siderophore transporters, which indicates the ability of the strain to scavenge Fe-loaded siderophores synthesized by other bacteria. Nucleotide sequence accession number. This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession no.
ACKNOWLEDGMENTS This work was supported by grants from the  EC2CO program  of the CNRS to S. U. and P. O. and the  ANR JCJC Bactoweather  from the  French National Research Agency (ANR)  to S. U. The  UMR1136  and  UR1138  are supported by the  ANR  through the  Laboratory of Excellence Arbre  ( ANR-11-LABX-0002-01 ).
Like humans, de novo synthesis of lipids occurs mainly in the liver, whereas fat is deposited mainly at the visceral (abdominal) location. As stated above, only one study has reported the expression in vitro of a chicken FFAR to date, which was claimed to be FFAR1 in chicken hepatocytes  (Suh et al. 2008 ). Twenty-six chicken genes encoding paralogs of FFAR2 were accessible in old versions of Ensembl database (Ensembl release 70) but have been removed in the current version (Ensembl release 77). In this article, we experimentally confirmed the existence of FFAR2 paralogs, examined their patterns of expression in different tissues, and studied their evolution by gene conversion and positive selection. Comparisons were made with pigs, where FFAR2 has been previously detected in adipose tissue  (Hou and Sun 2008)  and intestine  (Colombo et al.
toulouse. inra. fr/, last accessed May 7, 2015). Distances and two-point LODs (logarithm of odds) were calculated through the Carthagene software  (De Givry et al. 2005). Maps were drawn with MapChart 2. 0  (Voorrips 2002). FFAR2 fragment from each positive hybrid was sequenced on an ABI 3730 sequencer (Applied Biosystem).
Genome DNA Resequencing Data Genome DNA resequencing was performed from 18 birds belonging to European broiler lines. The 18 DNA-Seq libraries were prepared using the TruSeq DNA Sample Preparation Kit (Illumina, San Diego, CA) according to the manufacturer's instructions. Briefly, paired-end libraries with a 250 bp insert size were generated. The libraries were quantified using QPCR Library Quantification Kit (Agilent), checked on a High Sensitivity DNA Chip (Agilent), and sequenced in pair end 2 Â 100 bp on a HiSeq 2000 (Illumina) using a TruSeq v3 Kit. Sequencing produced per animal an average 20. 4 Gb corresponding to a sequencing depth of 19. 7Â. For each animal, reads were aligned against the 26 gene sequences encoding paralogs of FFAR2 identified in 2011 by Ensembl, using BWA software  (Li and Durbin 2009).
RNA-Seq Data RNA-Seq was performed on liver and adipose tissue mRNA of 14-week-old prepubertal chicken (n = 8: four males and four females; two issued from fat lines and two from lean lines per sex) and pig (4. 5 months) (four males and four females) (SRA accession number SRP042257). RNA-Seq data from 4. 5-day chick embryos (n = 20) were obtained from a previous study (SRA accession number SRP033603; Fresard et al. 2014). The 32 mRNA-Seq libraries were prepared using the TruSeq RNA Sample Preparation Kit (Illumina) according to the manufacturer's instructions. Briefly, Poly-A RNA was purified from 4 mg of total RNA using oligo(dT) magnetic beads, fragmented into 150-400 bp pieces using divalent cations at 94 C for 8 min, and retrotranscribed to double-stranded cDNA using random primers. The resulting cDNA was purified using Agencourt AMPure XP beads (Beckman Coulter, Villepinte, France).
Libraries were then checked on an Agilent Technologies 2100 Bioanalyzer using the Agilent High Sensitivity DNA Kit and quantified by qPCR with the QPCR NGS Library Quantification kit (Agilent Technologies). After quantification, tagged cDNA libraries were pooled in equal ratios and a final qPCR check was performed postpooling. The pooled libraries were used for 2 Â 100 bp paired-end sequencing on one lane of the Illumina HiSeq2000 with a TruSeq SBS v3-HS Kit (Illumina). After sequencing, the samples were demultiplexed and the indexed adapter sequences were trimmed using the CASAVA v1. 8. 2 software (Illumina). Sequencing produced an average of 6. 67 Gb (i. e. , 33 million of pair end reads) per animal. Reads were then mapped to the chicken reference transcript database (Ensembl cDNA release 64) using BWA software (bwa-0.
Gene Conversion The 26 genes of the FFAR2 cluster used for the positive selection calculation were tested for intralocus gene conversion by using GENECONV version 1. 81  (Sawyer 1989) , which is a well-established method for detecting partial gene conversion  (Posada 2002). The analysis was performed on the codon-based alignment performed by the Muscle algorithm  (Edgar 2004 ) in the PhyleasProg pipeline  (Busset et al. 2011 ) and permitted to the program to look for pair of sequences which are sufficiently similar to be suggestive of nonreciprocal transfer of genetic information between the different sequences. Three P values GPR43 Genes in the Chicken Genome.
For the use of branch-site models, each branch of the phylogenetic tree was tested for positive selection. So we performed multiple test corrections by controlling for the false discovery rate using the R package QVALUE  (Storey and Tibshirani 2003). Results are considered significant with a threshold of q = 10% of false positives. Sites with posterior probabilities of Bayes Empirical Bayes analyses superior to 95% or 99% were considered as positively selected. No overlap was found between the two models because the branch-site model is detecting positive selection on a selected branch whereas the site model is detecting positive selection affecting the whole phylogenetic tree.
3D Structure Modeling A model of the 3D structure of FFAR2 has been constructed using Modeller (v9. 10)  (Martı ´-Renom et al. 2000) , the alignment shown in supplementary data S7, Supplementary Material online, deduced from a HHpred search  (So ¨ding et al. 2005 ) and, as template, the 3D structure of proteinase-activated receptor 1 (pdb 3vw7;  Zhang et al. 2012 ). The local reliability was evaluated using VERIFY3D (http://services. mbi. ucla. edu/Verify_3D/, last accessed May 6, 2015;  Bowie et al. 1991) , whereas the stereochemical quality was evaluated with PROCHECK  (Laskowski et al. 1993 ), 3D structures were manipulated using Chimera  (Pettersen et al. 2004) , and the sequence alignment was presented using ESPript  (Robert and Gouet 2014).
Validation by RNA Sequencing Data in Whole Embryos and Prepupertal Tissues The availability of RNA sequencing (RNA-Seq) data from whole body or specific tissues can be valuable to prove expression of particular genes. We produced on average 47 million reads per chicken whole embryos and 20 and 16 million reads for liver and adipose tissue, respectively, per prepupertal chickens. Reads were then mapped to the chicken reference transcript database (Ensembl cDNA release 64). Among the 26 Ensembl FFAR2 paralogs, 11 were observed with at least 10 reads in each tissue (supplementary data S2, Supplementary Material online). These results confirm existence of many expressed FFAR2 paralogs in chicken. It remains however to determine whether other paralogs could be detected when other tissues or other physiological conditions are considered.
Chromosomal Localization "Universal" primers were used to amplify a consensus fragment of FFAR2, giving rise to a unique band on agarose gels (supplementary data S8, Supplementary Material online). Sequencing of this band confirmed that we amplified several FFAR2 fragments. These fragments were mapped near the SEQ0067 marker (accession number AJ862640) and the ROS0264 marker (31. 7 cR, LOD = 10. 8, accession number XM_003643130; fig. 4 ), which is located inside the predicted LIG1 gene (ligase I, DNA, ATP-dependent). These two markers are located on linkage group E64 (ROS0264: chrLGE64:721094 + 721330, SEQ0067: chrLGE64:709088 + 709284, UCSC Galgal4). Two other markers in the vicinity of FFAR2 in the human genome were analyzed on the RH panel: MAG (myelinassociated glycoprotein) and USF2 (upstream transcription factor 2, c-fos interacting).
Both map to the same linkage group, not linked to E64 (fig. 4 ). This suggests that, contrary to the human genome, MAG and USF2 are not located near FFAR2 genes in the chicken genome, suggesting a genomic rearrangement. FIG. 3. -Amino acids that are critical for ligand binding are conserved within the FFAR gene family. Amino acid sequences of one of the chicken FFAR2 paralogs, and FFAR1, -2 and -3 human sequences were aligned. The well-conserved amino acids that are critical of ligand binding are indicated with colored stars. Sequence identities are reported white on a black background, whereas similarities are boxed. Secondary structures, as deduced from our modeling (supplementary data S7, Supplementary Material online, and fig. 8 ), are reported above the sequences.