dataesr/scientific-paragraphs-categorization huggingface dataset:
Compositional analysis 18 The seeds and forage of the soybean materials harvested from the field trials in North America in 2011 (Table  5 ) were analysed for 101 constituents (eight in forage and 93 in seeds), including the key constituents recommended for soybean by OECD (2001). Eighteen seed constituents 20 with 50% or more sample values below the lower limit of quantification were excluded from the comparative analysis, which therefore included 83 endpoints (eight in forage and 75 in seeds, of which 17 fatty acids). The GMO Panel has already assessed data on the composition of soybean 305423 and soybean 40-3-2 (EFSA GMO Panel, 2010b, 2012a, 2013). The composition of soybean 40-3-2 was found equivalent to that of its conventional counterparts (A5403 and Dekabig) and commercial soybean varieties (EFSA GMO Panel, 2010b, 2012a).
Chickens were observed three times daily for clinical signs; deaths were recorded and necropsy performed on all birds found dead. Body weight and feed intake were measured every 7 days. At the end four birds per pen were taken for carcass evaluation (yield, dressing percentage, weight of thighs, breast, wings, legs, abdominal fat, kidney and whole liver). Performance and carcass trait tolerance intervals were constructed using data from the three groups of animals fed diets containing the non-GM commercial varieties. Differences between the control and 305423 9 40-3-2 groups (treated or not treated with the intended herbicides) were evaluated to determine if observed values were contained within the respective interval. The individual bird (not the pen) was considered as the experimental unit.
Also, survival of soybean plants outside cultivation areas is limited mainly by a combination of low competitiveness, the absence of a dormancy phase and susceptibility to plant pathogens and cold climatic conditions. The applicant presented agronomic and phenotypic data on soybean 305423 9 40-3-2 gathered from field trials conducted in soybean growing areas in North America (Section 3. 4. 1). The data showed differences with the non-GM comparator and the three additional comparators (the negative segregant and the two parental lines, soybean 305423 and soybean 40-3-2) for several endpoints and equivalence with the non-GM reference varieties for all the endpoints. Due to the low survival capacity of soybean, the observed differences are unlikely to change the fitness (e. g. survival, fecundity, competitiveness) or invasiveness characteristics of soybean 305423 9 40-3-2 plants.
InnoStamp 40 ® for Multiplexed Printing of Biomolecules Using MacroStamp TM InnoStamp 40 ® (Figure  1 ) is a fully automated microcontact printer (Innopsys, Carbonne, France), which assembles different modules corresponding to every step of the microcontact printing process. It utilizes magnetic fields generated by a set of magnets (NdFeB magnets, 25 mm × 25 mm × 12 mm) to manipulate magnetized PDMS stamps, allowing a homogeneous printing of oligonucleotides. The entire microcontact printing cycle consists of:.
Data Acquisition and Statistical Analysis All slides were analyzed using InnoScan 710 (Innopsys, Carbonne, France), which simultaneously scans slides at two wavelengths (PMT 532 nm and 635 nm: 100%). Images were captured using Mapix software (Innopsys, Carbonne, France). Circle grids were aligned on each spot for each image. The fluorescence intensity of each spot was automatically calculated as the median of fluorescence signal minus the background signal. Fluorescence intensity of 100 spots deposited by microarrayer or printed by InnoStamp 40 ® were analyzed for each condition. When probes were printed, the fluorescence signals of the 25 best-defined circular patterns on 4 independent millimetric pillars, located at different positions on the microarray, were collected. For each condition the mean of fluorescence intensities was calculated.
Acknowledgments: This work was supported by the  National Research Agency (Agence Nationale pour la Recherche) ,  "BIOTECHNOLOGIES" program  ( ANR 2010 BIOT 004 06 : Project  INNODIAG  to  Jean Marie François  and by the  "pôle de compétitivité" Cancer-Bio-Santé from Midi Pyrénées, France. Julie Fredonnet  was financially supported through the  ANR (Agence nationale de la Recherche)  grant and  Julie Foncy  was recipient of a  Cifre fellowship  from  ANRT with Dendris SAS. We also thank our colleagues at the  GeT-Biochip Platform Toulouse Genopole, LAAS-CNRS  and  Dendris Company  for fruitful discussion and support during this work.
Introduction The amino acid sequence of a protein reflects the selective constraints underlying its fitness and, more generally, the evolutionary history that led to its formation  [1]. A central problem is to decode this information from the sequence, and thus understand both the "architecture" of natural proteins, and the process by which they evolve. With the dramatic expansion of the sequence databases, a powerful strategy is to carry out statistical analyses of the evolutionary record of a protein family  [2] [3] [4] [5] [6]. With the assumption that the principal constraints underlying folding, function, and other aspects of fitness are conserved during evolution, the idea is to start with an ensemble of homologous sequences, make a multiple sequence alignment, and compute a matrix of correlations between sequence positions-the expected statistical signature of couplings between amino acids.
Results The SCA begins with an alignment (M sequences by L positions) representing a sampling of homologous sequences expected to share common selective pressures (Fig  1  and S1 Text, section A). Standard sequence database searching algorithms (BLAST, PSI-BLAST, etc. )  [29]  together with automated alignment tools (e. g. PROMALS  [30] ), or available databases of multiple sequence alignments such as PFAM  [31]  seem to provide suitable sequence alignments. Since SCA concentrates on conserved features of protein sequences (see below), it is relatively robust to variations in alignment quality, but will depend on the depth and diversity of sampling of homologous sequences. While an alignment of a protein family is in principle sufficient for an analysis of coevolution, taxonomic and functional annotations and atomic structures are valuable for interpretation.
In this work, we use a PFAM-based alignment of the G protein family (PFAM, PF00071, version 27. 0), and an alignment of S1A serine proteases modified from Halabi et al. [10]. After the pre-processing steps with default values for thresholds, we obtain a final G protein sequence alignment of 4978 sequences by 158 positions (3364 effective sequences) and a final S1A alignment of 1344 sequences by 205 positions (928 effective sequences). In what follows, we will assume that sequence weights are applied and for simplicity we will simply denote M 0 by M. No calculations below explicitly depend on its value; we shall only assume that M 0 is large enough to give good estimates of amino acid frequencies (M 0 > 100). An interesting point is that for nearly all alignments, the number of "variables" (L × 20 possible amino acids) is typically on the order of or greater than the number of "samples" (M).
5 × M 0 ). Structure of evolutionary conservation: For a large and diverse alignment (M 0 > 100, minimally), the evolutionary conservation of each amino acid a at position i taken independently of other positions is measured by the statistical quantity D a i , the Kullback-Leibler relative entropy of f a i given q a , the background distribution of amino acids: D a i ¼ f a i ln f a i q a þ ð1 À f a i Þ ln 1 À f a i 1 À q a : ð1Þ q is computed over the non-redundant database of protein sequences. If gaps are considered, and q 0 represents the fraction of gaps in the alignment, a background frequency for gaps can be taken as q 0 , and then q a ¼ ð1 À q 0 Þq a for the 20 amino acids. Also, D i ¼ P 20 a¼0 f a i lnðf a i = q a Þ defines the overall conservation of position i taking all amino acids into account.
Discussion A fundamental goal in biology is to understand the architectural principles of proteins-the pattern of constraints on and between amino acids that underlies folding, biochemical activities, and adaptation. An emerging approach is to leverage the growing databases of protein sequences to statistically infer these constraints from large and diverse ensembles of homologous sequences. This strategy has two defining features that distinguish it from the more traditional direct physical study of specific model proteins. First, by averaging over the space of homologs, the statistical approach emphasizes the general constraints shared by many related proteins over those that are idiosyncratic to particular proteins. Second, by quantitatively examining the structure of correlations, the statistical approach provides models for the global pattern of cooperativity between amino acids.
Materials and Methods Multiple sequence alignments were obtained from previous work  [10]  or from the PFAM database (release 27. 0, accession codes PF00071 (G proteins), PF00186 (DHFR), and PF13354 (class A β-lactamases)), and were subject to pre-processing with default parameter values as described in Box 1. Reference sequences/structures selected for each family were rat trypsin (PDB 3TGI), human Ras (PDBs 5P21 and 4Q21), E. coli DHFR (PDB 1RX2), and E. coli TEM-1 β-lactamase (PDB 1FQG), and with sub-sampling to the number of effective sequences, yielded the following final alignment statistics: S1A serine proteases (928 effective sequences by 205 positions), G proteins (3366 effective sequences by 158 positions), DHFR (1157 effective sequences by 151 positions), β-lactamase (497 effective sequences by 200 positions).
All calculations were carried out using a new python implementation of the statistical coupling analysis (pySCA v6. 2), following the algorithms described in Box 1 and in the main text. Step-by-step tutorials for executing the analysis for the four protein families are provided in the S3 Text and accompany the toolbox distribution. The pySCA toolbox is available for download through GitHub (https://github. com/reynoldsk/pySCA), and with online instructions at http:// reynoldsk. github. io/pySCA.
This work was supported by  Agence Nationale de la Recherche  ( ANR-10-PDOC-004-01 , O. R. ), the  Gordon and Betty Moore Foundation (KAR) , the  National Institutes of Health  ( RO1EY018720-05 , R. R. ), the  Robert A. Welch Foundation  ( I-1366 , R. R. ), and the  Green Center for Systems Biology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We assessed the feasibility of both SNP datasets, UNEAK and transcriptome mapping, to investigate the diversity of 91 samples of wild pearl millet sampled across its distribution area. Both approaches produced several tens of thousands of single nucleotide variants, but differed in the way the variants were identified, leading to differences in the frequency spectrum associated with marked differences in the assessment of diversity. Difference in the frequency spectrum significantly biased a large set of diversity analyses as well as detection of selection approaches. However, whatever the approach, we found very similar inference of genetic structure, with three major genetic groups from West, Central, and East Africa. For non-model crops, using transcriptome data as a reference is thus a particularly promising way to obtain a more thorough analysis of datasets generated using genome reduction techniques.
These steps depend on several parameters, such as minimum coverage, for a read to be considered as a TAG, or the number of mismatches between two TAGs to be considered as alternative copies of one locus or different loci. The TASSEL "no reference genome" pipeline is implemented in the UNEAK (Universal Network Enabled Analysis Kit) module  (Lu et al. , 2013). SNPs are identified by drawing simple networks of reciprocal TAGs that only differ by 1 bp mismatch. Significant effects of pipeline parameters on SNPs identified and population genetics inferences have been highlighted for Stacks  (Catchen et al. , 2013; Mastretta-Yanes et al. , 2014; Rodríguez-Ezpeleta et al. , 2016). To our knowledge, the effects of the UNEAK calling approach on population genetics have not yet been investigated. An alternative strategy would be to map genomic reads from RRL approaches directly on a transcriptome.
UNEAK Pipeline Raw sequences were processed with a modification of the TASSEL-GBS pipeline  (Glaubitz et al. , 2014) : the UNEAK pipeline  (Lu et al. , 2013). With the UNEAK pipeline, the alignment of TAGs to a reference genome is replaced by the creation of a pair of TAGs and network filtering to enable SNP discovery  (Lu et al. , 2013). Briefly, good reads were defined as reads carrying a perfect barcode match with no Ns in the 64 bp following the barcode. Reads were subsequently trimmed to 64 bp (excluding barcodes). Unique 64-bp sequence TAGs that were present five or more times across all samples were retained and used to identify "TAG pairs, " with a default error tolerance rate (ETR) of 0. 03, as described in  Lu et al. (2013). Reciprocal "TAG pairs" with only 1 bp mismatch were considered as putative SNPs.
4. 7  (DePristo et al. , 2011)  to handle indels. SNPs and genotypes were called using UnifiedGenotyper. A total of 236,897 SNPs were then filtered for no more than three mismatches per 10 bp window, a HARD_TO_VALIDATE mapping quality (MQ) filter was applied [MQ0 ≥ 4 && ((MQ0/(1. 0 * DP)) > 0. 1], and filtering was performed for QUAL (Quality) and QD (Quality by Depth) parameters which derived from Illumina quality scores (QUAL ≤ 60; QD ≤ 6. 87 quantile 5%). The 121,279 remaining SNPs were then filtered for DP using the median value, and the percentage of missing data per SNP (≤10%). It is important to note that the additional quality filters cannot be applied in the UNEAK pipeline since Illumina quality scores are not used and not kept through the pipeline. All command lines are available in Supplementary Data File S1, and datsetes are availbale at https://sites.
Overlap between the Two SNP Datasets We aligned the Hapmap file of TAG sequences on the transcriptome using BWA version 0. 7. 5  (Li and Durbin, 2009)  with -n 3, allowing for a maximum number of three alignments to output. We only report TAGs that had a unique hit. In order to identify SNPs shared by the two datasets, we identified TAGs among the 21,913 final UNEAK SNPs that aligned to the transcriptome and extract the SNP position. We then compared the position and the alleles to identify homolog SNPs in the TM dataset.
Diversity Statistics and Population Genetics Structure We performed most analyses in the R environment (R Core Team, 2015 3 ). We performed a principal component analysis (PCA) using SMARTPCA  (Patterson et al. , 2006; Price et al. , 2006)  as implemented in the R package SNPRelate  (Zheng et al. , 2012). We used the R package Adegenet  (Jombart and Ahmed, 2011)  to estimate heterozygosity values, and the R package Pegas  (Paradis, 2010)  for F-statistics. We used the sNMF software to identify population structure  (Frichot et al. , 2014). This software gives similar results to those obtained with STRUCTURE  (Pritchard et al. , 2000)  but it is much faster and can handle a very large number of SNPs. Finally, the folded site frequency spectrum (SFS) was calculated and used to estimate w , π and Tajima's D  (Tajima, 1989).
Mapping and SNP Discovery Both pipelines produced a similarly high number of SNPs. With the UNEAK pipeline, we were able to identify 262,928 biallelic SNPs. After filtering for depth (DP ≤ 51, 50. 5% filtered) and missing data (NA ≥ 0. 1, 41. 2% filtered), we obtained 21,913 good quality SNPs. With the TM approach, a total of 16,399,078 cleaned reads with a mean size of 92 bp mapped on 36 918 contigs. The mean coverage was 41. 33 ± 44. 2 and the mean MQ was 24. 5. We identified 238,897 biallelic SNPs with a median depth of 90×, after filtering we obtained a total of 22,262 good quality SNPs. Specific filters (SNP clustering, mapping and 3 http://www. R-project. org quality filters) from TM pipeline removed nearly 50% of SNPs, while subsequent filters for depth (DP ≤ 90) SNPs and missing data (NA ≥ 0.
1) removed 25 and 13. 5%, respectively (Figure  2 ). The final sets of SNPs revealed that the quality of the two approaches was equivalent. The UNEAK final set of 21,913 SNPs had a mean DP per site and per sample of 7. 24 ± 3. 63 sd and an average missing rate per sample of 0. 04 ± 0. 04 sd. The TM final set of 22,262 SNPs had a mean DP per site and per sample of 8. 68 ± 12 sd and an average missing rate per sample of 0. 03 ± 0. 03 sd. Within the TM final set, 56% of SNPs were found within a distance of 64 bp. The missing rates per sample between the UNEAK and TM dataset were highly correlated (r = 0. 95). However, we had an average of 70% inflate number of missing data with UNEAK, since the average missing rates UNEAK:TM ratio was 1. 7 ± 1. 8 sd. In addition, we tested direct mapping of the 262,928 UNEAK TAG 64 bp on the transcriptome.
A total of 21,410 TAG loci (8%) mapped on 13,177 transcriptome contigs (26%). The mapping was relatively good since 94% of the mapped TAGs had a unique hit, among which 96% had a perfect 64 bp match. The mean MQ of these unique hits was 34 ± 9 sd. Among the 21,943 good quality TAGs, we found 3,146 TAGS (14%) that had good alignment on 2,382 (5%) contigs. Among those, we retrieved 822 SNPs common to the two datasets. Nearly all UNEAK SNPs had a MAF > 0. 05 (Supplementary Figure  S1  and Table  S1 ). The correlation coefficient between allele frequencies estimated by both pipelines for shared SNPs was very strong (r = 0. 98).
Genetic Structure and Genetic Diversity The two datasets showed very similar inference of genetic structure. We identified K = 3 grouping populations geographically in a Western, Center, and Eastern clusters with both datasets (Figure  3 ). Correlations between admixture values from both approaches within each cluster were high with r > 0. 99. The results of a PCA were similar (Figure  3 ). Both datasets showed the same three geographic clusters and the correlation between PCA coordinates was very high (r > 0. 99). Comparing UNEAK and TM PCA, only one sample (sample 5726B1) was in a different position in the two plots. This individual had 17 times more missing data with the UNEAK dataset than with the TM dataset despite missing rates <0. 05%. This very high ratio of missing data between datasets might explain its outlier status.
More generally, the regression of PCA coordinates between the two pipelines showed that most individuals qualified as slight outliers had three times more missing data in the UNEAK pipeline than in the TM pipeline. However, overall, we observed very good individual quality and a very strong congruent inference of population structure irrespective which pipeline was used. In contrast, genetic diversity assessment was affected differently depending on the pipeline. Heterozygosity values were almost two times higher with the UNEAK dataset than with the TM dataset (Table  1 ). For F-statistics, F IS was slightly but significantly higher with the TM dataset and F ST was significantly (two times) lower. When we compared observed SFS and expected SFS for a population at equilibrium, the UNEAK dataset clearly did not retrieve the expected amount of low frequency SNPs (Figure  4 ).
DISCUSSION In this study, we compared two bioinformatics pipelines and their impact on population genetics statistics. Investigating genomic diversity is still challenging for non-model organisms with large genomes. RRL sequencing approaches, such as RNA-seq and GBS approaches, have been proposed to reduce genome complexity. NGS data obtained can be handled by different pipelines including Stacks and TASSEL. Here, we preferentially used the TASSEL pipeline because is the most commonly used pipeline for crops studies. We therefore first used the UNEAK approach implemented in TASSEL and proposed and tested an alternative strategy in which NGS genomic reads were directly mapped on the pearl millet transcriptome. This strategy was guided by the observation that species transcriptomes are becoming progressively more accessible thanks to transcriptional studies and that it would be advantageous to use it  (Russell et al.
GBS Reads Biased Toward Coding Regions The quality of our two final datasets is as good as the datasets used in other population genetic studies with final coverage ranging from 5 to 10 and missing values rates below 0. 3. Many RRL datasets may have low coverage in studies whose design aims for more individuals or loci to increase the accuracy of population genetic parameters (Alex  Buerkle and Gompert, 2013). Surprisingly, we found that non-negligible numbers of UNEAK TAGs mapped to the transcriptome. Combosch and Vollmer (2015)  found that about 15% of RAD loci mapped to 10% of transcriptome contigs. Our results are similar with 8% of TAG loci that mapped to 26% of transcriptome contigs. We originally expected a very low mapping rate since we were only mapping to the expressed genome. One possible explanation is the choice of the restriction enzyme used.
Effect of Pipelines on SNPs Identified Pipeline specifics influence the number of SNPs discovered and their distribution properties. There are major differences in how SNPs are called between pipelines, because pipelines deal somewhat differently with sequencing errors, base quality values, SNP calling and genotype calling methods and in our case, TAG catalog construction vs. transcriptome mapping. We now review some of the differences between the two approaches and how such differences could impact our results. Among all the parameters that can affect SNPs discovery, coverage is one of the most important. For instance, error rates are expected to increase with low coverage (<20×;  Andrews and Luikart, 2014). To limit the impact of coverage in both our pipelines, we filtered SNPs with a depth above the median value for each dataset (51× for UNEAK, 90×for TM).
Both final datasets had similar coverage and similar missing rates. Thus, it that sense, it would have little effect on number of SNPs discovered and population genetics estimates between datasets. Another possible bias comes from repetitive regions in the genome, such as paralogs, and is not always easy to identify with NGS data. Different filters can be used to reduce the effect of unidentified paralogs. Paralogous regions are expected to align to multiple locations in the genome  (Hohenlohe et al. , 2012)  and SNPs within paralogs genes are expected to show more than two alleles  (Freedman et al. , 2014). We only considered biallelic loci in the two datasets, since in RRL approaches, the problem of paralogs can be effectively addressed by ploidy-based filtering  (Ilut et al. , 2014). With the TM approach, we were able to apply an additional filter on MQ to reduce paralogous regions.
, 2013; Davey et al. , 2013; Gautier et al. , 2013). By construction, in RRL based on restriction enzymes, reads will start at the same mapping position which is the RE site, therefore applying PCR duplicates filter will not be possible unless a paired-end sequencing approach and random sheering is used  (Davey et al. , 2013)  but recently a new protocol has been proposed by introducing "adaptor tags" allowing PCR duplicate discrimination  (Tin et al. , 2015). In conclusion, for both approaches we used, filtering for PCR duplicates was not possible and we therefore expected both UNEAK and TM datasets to underestimate heterozygosities. This is congruent with the strong correlation observed between estimated frequencies by both approaches for the shared SNPs. The amount of SNPs allowed within a genomic windows is important since regions with too many SNPs are not reliable and may (i) contain many sequencing errors, (ii) be associated with paralogs.