dataesr/scientific-paragraphs-categorization huggingface dataset:
1, and pairwise comparisons between effects (stages, strains, and stage*strain) were carried out with the Tukey-Kramer Honestly Significant Difference (HSD) test  (Kramer, 1956). The second regression analysis was performed to estimate effects for each strain (presumed to be constant across all time points), effects for each stage (presumed to be constant across all strains), and effects for each strain at each stage, allowing for the identification of stage-specific differences among strains for each PCA component. Genes from the filtered dataset (Data S2) were binned based upon their patterns of expression as a function of stage and/or strain to identify strain-dependent differential expression. Standard linear pairwise correlation analysis (Pearson correlation) was also used to quantify relationships between strains from each PCA score (Table  1 ).
Differential gene expression analysis: Significance of Microarrays (SAM) Differential gene expression between stages of development was determined using Significance Analysis of Microarrays  (Tusher, Tibshirani & Chu, 2001)  with a median FDR = 0 using the TM4 package  (Saeed et al. , 2003). Binning of individual time points was based on the developmental stages defined by the regression analysis described above. † Significant effect after multiple testing correction (FDR < 0. 1). The dataset used as input for the PCA and regression analyses is provided in Data S2.
Three genes with low variation in expression by time point or strain (Actb, Rpl10, Rpl13a) were used to normalize expression values of the target genes. A geometric mean of the control genes was determined for each biological triplicate to generate a normalization value. Following normalization, relative expression values were calculated for each target gene (Fggy, Saa3, Wnt11, Wif1) within all biological groups. The genes for validation were selected because of their relevance to fibrosis-related biological processes that are known to differ among inbred strains of mice  (Walkin et al. , 2013). The significance of differences between strains in relative expression arbitrary units (A. U. ) was assessed by Tukey Multiple testing corrected ANOVA using Prism v7. 0a (GraphPad Software). Primer sequences used for qPCR were as follows: Actb, 5 -GTGACGTTGACATCCGTAAAGA-3 , 5 -GCCGGACTCATCGTACTCC-3 ; Rpl10, 5 -CGTGGTGTCCCTGATGCTAAG-3 , 5 -GTTGGCACAAATACGGGCAG-3 ; Rpl13a, 5 -AGCCTACCAGAAAGTTTGCTTAC-3 , 5 -GCTTCTTCTTCCGATAGTGCATC-3 ; Fggy, 5 -CGGCTCTAGTGGACCAGAGA-3 , 5 -ATGTGCATCAATCCCTTGAACA-3 ; Saa3, 5 -AGAGAGGCTGTTCAGAAGTTCA-3 , 5 -AGCAGGTCGGAAGTGGTTG-3 ; Wnt11, 5 -ATGCGTCTACACAACAGTGAAG-3 , 5 -GTAGCGGGTCTTGAGGTCAG-3 ; Wif1, 5 -CCTGCCGAAATGGAGGTAAA-3 , 5 -GCTGGCTCCATACCTCTTATTG-3.
Annotation term enrichment analysis Annotation enrichment analysis was performed to guide the interpretation of gene sets associated with specific developmental stages and for those contributing to individual principal components (PCs). Probe set identifiers from the Affymetrix arrays were first mapped to mouse gene identifiers using Mouse Genome Informatics (MGI) database Batch Query tool  (Bult et al. , 2010). Sets of mouse gene symbols or gene accession identifiers were submitted for Gene  Ontology (2015)  and Mammalian Phenotype Ontology  (Smith & Eppig, 2012)  term enrichment analysis using VisuaL Annotation Display (VLAD) v1. 5. 1  (Richardson & Bult, 2015). Mouse genes that were converted to human homologs were also analyzed using the Reactome Pathway Browser v52  (Croft et al. , 2014)  to identify enrichment of homologous pathway annotations.
Mouse and human gene homology Gene homology between mouse and human was used to compare transcriptional profile results from this study to those from a previous study of human lung development  (Kho et al. , 2010). A gene homology resource containing 10,266 genes was generated manually using homology assertions from Panther  (Mi, Muruganujan & Thomas, 2013)  and Homologene  (Sayers et al. , 2012). If one gene mapped to multiple homologs, each homolog was represented in the final set. For example, the mouse alcohol dehydrogenase 1 (Adh1) gene is homologous to ADH1A, ADH1B, and ADH1C in human; the carboxylesterase (CES) gene in human is homologous to Ces1d, Ces1e, and Ces1g in mouse. Many-to-one homolog relationships were identified in 18 instances. Overlap between lists of homologous genes was assessed with the GeneVenn tool  (Pirooznia, Nagarajan & Deng, 2007)  using the NCBI GeneID for the human gene as the primary gene accession identifier.
Strain-independent principal components 1-3 define a murine developing lung characteristic subtranscriptome (mDLCS) The first PC (55. 1% of the sample variation) was significantly correlated (P < 0. 0001) with developmental time point, capturing the patterns of gene expression across the entire developmental timeline. Over 50% of the genes in our filtered dataset (Data S2) had relatively high (positive) or low (negative) loading values on PC1. GO term enrichment analysis of genes contributing to the prenatal signal (PC1 pos ) revealed enrichment of genes associated with nucleic acid metabolic process (GO:0090304) and RNA processing (GO:0006396). Genes previously associated with lung cell differentiation were among for strain-independent principal components 1-3 along developmental time points and stages (x-axis).
We downloaded these annotations from the MGI database (v6. 01; 12/22/15) based on MGI's unified gene catalog (genome assembly GRCm38)  (Zhu et al. , 2015). The resulting list included 209 genes annotated to the GO term of lung development (GO:0030324) and its child terms and 1,076 genes annotated to the MP term ''respiratory system phenotype'' (MP:0005388) and its child terms; 134 genes shared both ''lung development'' and ''respiratory system phenotype'' annotations. The gene list from previous transcriptomic studies in mouse and the gene list created from GO and MP terms overlapped by 32 genes. To ensure an accurate comparison, 9 and 27 genes that were not assayed by the Affymetrix Mouse 1. 0ST array were excluded from the GO and MP gene sets, respectively. Genes with relevant GO and MP annotations that were not included in the mDLCS because they failed the variance threshold were also removed from the comparison (83 and 496 genes, respectively).
3%) were differentially expressed between all three strains. Strain-dependent gene expression was validated by qPCR (Fig. S11 ) in four target genes  (Fggy, Saa3, Wnt11, Wif1). The qPCR results confirmed the trends observed in the array data despite the variation observed among biological replicates (Table  S2 ). All strain effects for Saa3 and Wif1 were confirmed by qPCR; that is, Saa3 expression is greatest in C3H and indistinguishable between AJ and B6 whereas Wif1 expression is greatest in AJ and indistinguishable between B6 and C3H. The qPCR results for Fggy were consistent with the trend of AJ < B6 < C3H; however, the expression differences were not statistically significant between AJ and B6. Similarly Wnt11 expression was greatest in B6 but that B6 was not statistically significant different from the AJ strain.
Comparative analysis of the mouse and human embryonic developing lung characteristic subtranscriptomes To investigate the genomic elements of lung development that are conserved between human and mouse we performed PCA on a reduced set of murine samples (E12. 5-E16. 5) that corresponded to the time points assayed in a previously published human Developing Lung Characteristic Subtranscriptome dataset containing 3,209 genes  (Kho et al. , 2010). We generated a prenatal mDLCS using the union of rank-ordered genes from PC1-3 that resulted in a set of 3,077 genes. Most of the genes (2,302) in the prenatal mDLCS overlapped the mDLCS that included all 26 developmental time points. Homologous human genes could not be identified for 464 of the prenatal mDLCS genes and homologous mouse genes could not be identified for 201 of the hDLCS genes, leaving 2,634 genes in the prenatal mDLCS for comparison to the 2997 hDLCS gene set.
The genes from the prenatal mDLCS genes for which no human homolog could be found included predicted genes, microRNAs, long non-coding RNAs (lncRNAs), and species-specific immune-related proteins (e. g. , major histocompatibility complexes). There were 771 genes shared in common (RF = 2. 3, P < 2. 4 × 10 -134 ) between the two datasets following the conversion of the mouse genes to human homologs; 2,226 genes were unique to the hDLCS and 1,861 genes were unique to the prenatal mDLCS (Fig. 5 ). There are two primary reasons for the lack of overlap of specific genes between the mouse and human subtranscriptomes. First is the difference in the genes represented on the two gene expression Affymetrix array platforms (Mouse 1. 0ST and the Human 133 Plus 2. 0). Mouse homologs for 68 genes included in the hDLCS were not assayed by the Mouse 1.
The 1,861 genes represented only in the mDLCS were enriched for high-level biological processes (cell cycle, DNA replication) as well as ECM organization, specifically non-integrin membrane-ECM interactions (LAMA3/4, LAMB1, COL4A1/4A2, COL2A1), ECM proteoglycans, degradation of the ECM, and integrin cell Table  3  Summary of Reactome pathway enrichment results for the 771 genes represented in both mouse and human embryonic developing lung characteristic subtranscriptomes. Entities refer to proteins, molecules, sequences, and other physical complexes associated with a given pathway in Reactome database; entities found are those associated with input gene sets and total entities refers to all entities in a given pathway. FDR represents multiple testing corrected p-values for enrichment. The previous human lung transcriptome study by  Kho et al.
The PSG sub stage defined in humans lasts approximately five weeks whereas the comparable developmental time in mice occurs over several hours. To definitively address the presence of a novel pseudoglandular stage in mouse would require sampling lung development at a much finer temporal scale (hourly) than was performed for the current study (one sample per day). An alternative explanation to the lack of a PSG substage in our murine dataset may lie in the fact that only male mice were used in this study; recent work in humans has revealed gender-based differences in transcriptional modulation surrounding this substage  (Kho et al. , 2015).
DISCUSSION AND CONCLUSION In this report we present the results of the most comprehensive characterization of gene expression during normal murine lung development to date. The dataset consists of gene expression measured at 26 time points from E9. 5-P56 in three common inbred strains of mice. Using a combination of Principal Component and least squares regression analysis we identified strain independent and strain dependent patterns of genome wide transcription during pre-and post-natal development. These analyses provide significant impact for both basic and translational research into mammalian lung development through the generation of a high-resolution molecular framework of murine lung development, comparative genomic analyses of human and mouse lung development, and the identification of putative pathways associated with respiratory pathology.
0 ST gene arrays where probesets are largely targeted to protein coding regions. In sharp contrast, the human lung expression dataset used U133 Plus 2. 0 arrays, which has probesets targeted primarily to 3 untranslated regions (UTRs). Prior studies have frequently identified developmental and cell-specific transcript isoform variation specifically in the 3 -UTR. The two array designs will have different sensitivity to such variations, potentially resulting in very different measurements, even with common transcript expression profiles, and subsequently changing the identification of genes with enough variation for inclusion into downstream analysis. In contrast to the modest overlap of individual genes, the enriched annotations associated with the genes in the mDLCS and hDLCS revealed many shared functional properties.
lungmap. net/). The normal mouse lung gene expression time course data available from LungMAP includes only two embryonic and two post-natal time points for a single strain (C57BL/6). Our data support a much finer-grained assessment of the temporal dynamics of gene expression across lung development and the ability to identify possible strain-specific differences in expression. In contrast to our study, the LungMAP resource includes some data for cell-specific expression (from sorted endothelial cell, myeloid cell, and type II pneumocytes) at different development time points. These cell-specific expression data can be integrated with our data to develop hypotheses about which cell types may be contributing to the tissue level gene expression patterns observed in our data. Although a full treatment of combining the data set reported here with LungMAP data is beyond the scope of this report, the expression data for the angiogenin (Ang ) gene serves as an example of the complementary nature of these data resources.
The general pattern of increased expression of Ang over development is observed in both data resources (see www. lungmap. net and lungdevelopment. jax. org). However our data reveal that the relative level of expression of Ang is much higher in C57BL/6J than in A/J or C3H/HeJ. This strain-specific difference is not possible to detect in the existing LungMAP data. The cell-specific expression data from LungMAP suggests that type II pneumocytes are contributing most to the increased expression of angiogenin between postnatal day 7 and day 28. This testable hypothesis would not be possible to formulate using our data set alone. Strain-specific differences in gene expression patterns were identified that serve as the basis for future investigations into the genetic basis for well-known differences in adult lung physiology and respiratory disease susceptibility among inbred mouse strains.
Comparison of developmental gene expression data between mouse and human highlighted conserved biological processes as well as organismal differences that may be important for using the mouse as a model for human respiratory biology and disease. The raw data files and processed data for this study can be accessed at NCBI's Gene Expression Omnibus (GEO) data archive (GSE74243). Expression patterns across stages (and time points) of lung development for all genes measured by the Mouse Affymetrix Gene 1. 0ST array can be queried, visualized, and downloaded at http://lungdevelopment. jax. org/.
ACKNOWLEDGEMENTS The authors are indebted to  Dr. Marge Strobel  and  Ms. Rita Thibodeaux  for their guidance and assistance with establishing the mouse colonies used for this study. Ms. Karen Hammond  and  Ms. Elsie Cough  served as the  Animal Care Technicians  for the study. The gene expression assays were performed  Ms. Sonya Kamdar  and  Ms. Sandy Daigle  of  The Jackson Laboratory Genome Technologies Scientific Services core. Dr. Nazira Bektassova  assisted with the use of the deSNPer software. Mr. Keith Sheppard  ( The Jackson Laboratory Computational Sciences core ) developed the database interface underlying the public gene expression visualization website. Mr. Jesse Hammer  ( The Jackson Laboratory MultiMedia group ) assisted with the preparation of figures for this manuscript. This project was completed by KJB in partial fulfillment of his graduate dissertation research in the  Graduate School of Biomedical Sciences and Engineering (GSBSE)  at the  University of Maine, Orono.
For the fourth method, we maximized the mean generalized coefficient of determination (CD method)  [33, 34]. The CD method provides a measurement of the expected reliability of predictions based on the pedigree. Briefly, a specified number (eight in our case) of individuals with the highest CD values are removed one-by-one, with the individuals causing the largest decrease in mean CD being retained. This process is repeated until the desired number of individuals remain. We evaluated these four methods by simulating 100 replicates corresponding to 10 different datasets (simulated genotypes and phenotypes), each with 10 different samplings of the G2 generation. Status number (N S ,  [35] ) was estimated as N S ¼ 1 2 F , where F is the mean inbreeding value calculated from the realized kinship matrix; see the methods below.
Genotyping and linkage disequilibrium analysis The DNA extraction method and the Illumina Infinium array used to genotype the reference population have been described elsewhere  [38]. SNP clustering was performed with GenomeStudio (Genotyping module V1. 9, Illumina, San Diego, USA), with the manual checking of each SNP. One G2 individual, with a call rate below 0. 98 and a 10 % GenCall score below 0. 24, was removed. We analyzed 8411 SNP loci: genotyping failed for 2429 (low fluorescence intensity, GenTrain score below 0. 35), 1539 were monomorphic and 4443 were polymorphic (52. 8 %). The pattern of SNP inheritance was checked with MERLIN  [39]. SNPs presenting an aberrant inheritance pattern or for which more than 2 % of values were missing were removed from subsequent analyses. For the remaining 4436 polymorphic SNPs, the mean GenTrain score was 77.
Genetic relationship matrices Kinship coefficients between individuals of the threegeneration pedigree were estimated from pedigree and genomic data. Two expected additive genetic relationship matrices (matrix A) based on pedigree were derived. The first, A P , used only data for the maternal parents and corresponds to polymix breeding, in which only the maternal parents are known. For the second (A F ), the full pedigree was used. G0 plants were considered to be unrelated and no population structure was identified  [20]. In parallel to pedigree-based matrices, a realized genomic relationship matrix (matrix G) was also calculated, as described by Van Raden  [47]. G ¼ M-P ð Þ M-P ð Þ 0 2 X p i 1-p i ð Þ ð1Þ where M and P are two matrices of dimension n (number of individuals) × p (number of markers).
The importance of effective population size for prediction accuracy was highlighted by Resende, MDV et al. [26] , in a study using empirical datasets for Eucalyptus with contrasting effective population sizes: N e = 11 and N e = 51. The level of relatedness between the calibration and validation sets also affected GEBV estimates. We found a 0. 17 difference in prediction accuracy between low (S1) and high (S2) levels of relatedness. Our findings are consistent with previous results for white spruce  [24]  and mice  [53]. Similar results have been reported for eucalyptus, in which the GS model was applied to populations other than that used to build the prediction model  [26]. In cases of a higher marker density, yielding a more stable linkage phase between markers and QTLs across populations, population-specific models have also been described  [54].
Acknowledgments The authors would like to thank the experimental unit of  INRA Pierroton  ( UE 0570 ) and the  GIS "Groupe Pin Maritime du Futur " for planting and the field trials and making the necessary measurements. This work was supported by the  European Union Seventh Framework Programme  (Procogen, no. 289841 ) and the  INRA SelGen metaprogram. JB was awarded a postdoctoral fellowship from the  "Conseil Général des Landes ".
Standardized sample preparation  (Gunata et al. , 1985; Voirin et al. , 1992)  and analytic procedures are routinely performed for evaluation of grape and wine volatiles and their precursors. Grape juices or wines are extracted to a non-ionic solid phase, free volatiles are eluted by a non-polar solvent (pentane or a mixture of pentane and dichloromethane), and glycosylated volatiles are eluted by a more polar solvent (ethyl acetate or methanol). The free fraction can then be directly analyzed by gas chromatography. The bound fraction is enzymatically hydrolyzed before the analysis. Gas chromatography allows for simultaneous analysis of hundreds of volatiles with good resolution of structurally similar molecules. Furthermore, coupling to a mass spectrometer enables reliable identification of compounds by searching mass spectra databases even when analytical standards are not available.
Many research laboratories used this method to investigate how aroma is influenced by grape variety, grape ripening, environment and different winegrowing and winemaking techniques. While these studies have without doubt addressed some of these important questions, a comprehensive picture of wine aroma integrating these valuable data has not been painted yet. We collected, curated and analyzed these data to answer the following questions. What are the components of grape and wine aroma? At which concentrations are they present? To what extent are they glycosylated? We compared these parameters between grapes and wines. We then compared these data to the existing knowledge on the origin and evolution of aroma components with a particular focus on their biosynthesis. This meta-analysis adds to our understanding of wine aroma composition and development.
Data Collection We collected published datasets on free and glycosylated grape and wine volatiles. We used search term "grape aroma profiling free glycosylated" on Google Scholar and reviewed citations of included publications. We reviewed 104 publications that reported profiling of grapes or wines. Foutry-five of them were excluded because they only contained information on free volatiles, and 8 because they only contained information on glycosylated volatiles. To ensure the compounds were annotated correctly, only publications that contained information of Kovats retention index were included. Further 31 publications were therefore excluded, which resulted in 19 selected publications (Table  1 ). Publications or samples that described Vitis species other than Vitis vinifera subsp. vinifera were excluded from all quantitative analysis, but used for validation of retention indices.
Data Curation Molecule names and their Kovats retention indices were aggregated in one file and manually checked for duplicates (synonyms), which resulted in 385 unique molecules. They were assigned to one of the 16 classes of volatiles (aliphatic alcohol, ketone, aldehyde, ester, or acid, monoterpene, norisoprenoid, sulfur-containing, phenol, aromatic alcohol, ketone, aldehyde, ester, or acid, nitrogen-containing or other). Two hundred and forty-four unique molecules appeared in less than three publications and were excluded from further analysis because of insufficient information. Kovats retention indices of the remaining 141 compounds were compared and in the cases where standard deviation of the retention indices exceeded 50, outliers were excluded (annotation of a compound was considered incorrect in this particular publication and was excluded from the dataset).
, 2006b). Retention indices of (Z) and (E)-8-hydroxylinalool were first reported in  (Winterhalter et al. , 1986)  and the author claimed the NMR spectra were in agreement with previously published data. However, in a subsequent publication  (Chassagne et al. , 1999) , which is also cited in the widely used database of volatiles Pherobase 1  , the retention indices of the two compounds are inverted even if the author received standards from the author of  (Winterhalter et al. , 1986). Recent publication  (Hofer et al. , 2014)  confirms the correct elution order is (Z)-, followed by (E)-8-hydroxylinalool, as reported in the original publication  (Winterhalter et al. , 1986).
Origin and Evolution of Wine Aroma During the winemaking process the aroma undergoes major changes, in particular deglycosylation of aroma compounds synthesized in grapes and biosynthesis of new compounds. To analyze these changes quantitatively we selected 43 compounds with sufficient number of available data points (>30) across the 82 volatile profiling datasets. For each compound we computed the total concentration (the sum of free and bound concentration) and percentage that is glycosylated (bound/total concentration), and tested for differences between these values in grapes and wines (Figure  2 ). These differences in concentration and degree of glycosylation are largely characteristic of each class of molecules.