dataesr/scientific-paragraphs-categorization huggingface dataset:
innatedb. com; Additional file 7: Table  S7 ). For the global GO annotation, only antiapoptosis was found significantly enriched (annotated for the genes BIRC3, FAS, MCL1, NFKBIA, TNF, PIM2). Genes in this smaller cluster of 81 members include several negative regulators of innate/inflammatory pathways (ATF3, IL1RN, NFKBIA, NFKBIZ, SOCS1, SOCS3) (Additional file 6: Table  S6 ). The pattern of expression of these genes is similar to that of cluster 4, with cluster 17 genes clearly activated in LPS-stimulated macrophages and in lymph nodes or blood from pigs infected with Salmonella (Additional file 8: Figure  S1 ). Interestingly, in contrast to cluster 4, several datasets did not show activation of genes represented by the probesets in cluster 17 genes on average, including lymph nodes infected with PCV2 or PRRSV.
A third cluster contains 48 probesets (cluster 26), containing probesets representing several T-cell related genes (CD2, CD3D, CD3E, CD8A, CD8B, LCK) as well as  other immunity genes such as IL7R, GZMB and STAT4. This cluster appears to document gene expression specific to T cells and neutrophils, as expression is detected in lymph node and whole blood datasets, and absent in macrophages (Additional file 9: Figure  S2 ). The global annotation identified regulation of immune response as well as several T cell receptor terms as enriched in this cluster (Additional file 6: Table  S6 ). The pattern of expression is not clearly correlated with response to immune stimulation, which is consistent with the observation that these genes are not enriched for J-Y IR or InnateDB genes, but are enriched for genes with GO:0002376 annotation (Additional file 7: Table  S7 ).
Especially for cluster 4, which is significantly enriched for probesets annotated as human immune response genes, these data provide foundational information that can be used for human-pig comparisons at several levels. For example, global comparisons of promoter sequence elements between pig and other species can be performed using draft genome assembly information, as shown recently for an analysis of the CYP27B1 gene promoter  [36]. Previously, substitution of human orthologous promoters was useful in prediction of sets of NFκB target genes in the pig  [28]. Given the recent expansion of transcriptomic datasets on immune response, especially for those analyzing response to infection of specific tissues or cell types, the pig genome will now be invaluable in bioinformatic approaches to recognize known and novel regulatory motifs in immune response genes.
Conclusions Our computationally-facilitated, manual annotation of immune response genes provided expert-level curation of 1,369 gene models and 3,472 transcripts, of which 1,172 annotated using sequence available only from other organisms. This extensive annotation provided evidence for gene expansions in several immune response families, and identified artiodactyl-specific expansions in the cathelicidin and type 1 Interferon families. We found gene duplications for 18 genes, including 13 immune response genes and five non-immune response genes discovered in the annotation process. We used a functional approach to discover and accurately annotate porcine immune response genes. Using co-expression analysis of transcriptional profiling data from studies on blood, macrophages, as well as lymph nodes, we identified a large cluster (n = 619 probesets) that exhibited a correlated positive response upon infection across a number of pathogens or due to different immune stimuli.
Among 4,245 initially proposed immune genes to annotate, only 2,990 were found on the Ensembl predicted gene list. The remaining genes were mapped using cDNA sequences to the pig assembly. With several updates from various sources, the final list contained 4,347 immune genes. A GBrowse portal displayed the alignments of the genes and BAC clones. With a coordinatebased query mechanism, an interactive web portal (http://www. animalgenome. org/cgi-bin/host/ssc/gene2bacs) was set up on a MySQL database-enabled system for curators to locate the clones of interest. The portal also provided ortholog genes and Refseq match information to assist annotation.
Annotation methods using new Otterlace software The Otterlace/Zmap  [45, 46]  manual annotation software was used by authorized external users via the Wellcome Trust Sanger Institute (WTSI) SingleSignOn system using their Institute email addresses. The manual annotation approach used was that of the "Gatekeeper"  [45]  where the external annotation is subjected to integrated QC within the software followed by extensive QC from professional annotation staff in the human and vertebrate analysis and annotation (HAVANA) team. When a clone of interest was identified, it was linked to the assembly sequence chooser in Otterlace. This region of the chromosome was opened by the annotator and large-scale data analysis from our annotation pipeline  [45] , such as searching mRNA, EST and Protein sequence libraries, was performed on WTSI systems.
Gene family selection and description of ortholog checks Across four mammalian genomes (human, mouse, bovine and porcine), we identified a number of expansions in gene families important in the immune system. We targeted several gene families for a detailed analysis of expansions across species; families were chosen from a preliminary analysis done in humans, mice and pigs  [49]. Artiodactyl-associated families were included based upon expansions noted in the bovine genome  [9]. Unambiguous 1:1 orthologs for each species were initially determined from the corresponding human or mouse gene in Ensembl. For each gene, additional family members were determined by including genes that were listed as ambiguous orthologs (1:many) or by a separate Ensembl within species search for paralogs. Each Ensembl predicted gene transcript was aligned against the NCBI reference sequence database using BLAST  [73]  to determine the corresponding NCBI loci and reference sequence or other family members that may have been missed due to areas of the genome that were not sequenced.
Results are summarized in Table  2  and detailed in Additional file 1: Table  S1. These data were used to extract a gene list of 597 unambiguous orthologs across the four species that were used for Venn analysis using Venny (http://bioinfogp. cnb. csic. es/ tools/venny/index. html) (Figure  1 ). Defensins or interferons were not included in this analysis because 1:1 orthologies could not be assigned for the great majority of genes, due to the extremely high similarity of these gene family members within and across species. Genes for which there is evidence for transcription but that are missing from genome build 10. 2 are summarized in Additional file 2: Table  S2.
Co-expression analysis of immune response gene expression profiling datasets Gene expression profiling datasets for the Affymetrix porcine genome array (platform GPL3533) were collected from GEO (http://www. ncbi. nlm. nih. gov/geo/) and ArrayExpress (http://www. ebi. ac. uk/arrayexpress/) databases. Datasets were filtered based on the following criteria: 1) availability of raw data (. cel files) and 2) a pathogen/pathogen component treatment challenge was part of the experimental design. All raw data (. cel files) were downloaded and the probe set expression levels were estimated using the robust multi-array average (RMA) method  [74]. The quality of the raw data from each dataset was analyzed using the arrayQualityMetrics package in Bioconductor (http://www. bioconductor. org/) and scored on the basis of 5 metrics, namely maplot, spatial, boxplot, heatmap and rle.
Any array failing on more than one metric was removed from the dataset. The collected data included four unpublished datasets from the pig research community (one from CKT and three from DAH/TAA; three datasets are now published: E-MTAB-505  [75] ; GSE30956  [36] ; and GSE27000  [34] ). In total, 188 chips across 8 experiments fit this criterion, and were used as input for the cluster analysis (Additional file 5: Table  S5 ). To find genes that responded similarly to an infection or stimulus across all groups, we used Robinson's within-group correlation (Equation 1,  [76] ) to calculate pair-wise correlations for each probeset. These correlations emphasized similar responses to the known stimulus present in each dataset. A tgf matrix was created using a minimum correlation of 0. 52, which empirically maximized cluster size while retaining functional annotation enrichment.
In this context, nodes represented individual probesets (genes/transcripts) and the edges represented Pearson correlation coefficients between them. The network was clustered into groups of genes sharing similar profiles using the MCL algorithm with an empirical MCL inflation value (1. 8) and a global graph for each network was created showing the MCL clusters. Several functions within BioLayout Express 3D were then used to explore and characterize the clusters created. In addition, cluster gene lists were exported and annotated using R scripts and GO-SLIM tools (at www. ebi. ac. uk) to find over-representation of GO terms and other functional annotations. Significance of enrichment of annotations was calculated using a modified Fisher's exact test  [77]. False discovery rate for this enrichment was also calculated and presented  [78].
ebi. ac. uk/goldman-srv/prank/) and MUSCLE  [80] , respectively. Positive selection results were computed with site models (Model 1a vs. 2a, Model 7 vs. 8 and Model 8a vs. 8) and branch-site models of PAML  [81]. Branch-site models were designed to detect signals of local episodic positive selection in order to determine whether different species underwent selective pressure. Bayes Empirical Bayes (BEB) method implemented in PAML was used to estimate posterior probabilities of selection on each codon, and probabilities > 0. 95 were considered significant. After the Prank alignment, multiple sequence alignments were refined by GBLOCKS  [82] , improved by a home-made Perl program. All positive selection results found specific to pig were checked manually. In the context of multiple testing, we calculated q value measures as an extension of the false discovery rate, using the q value package of R.
The q value attached to each individual branch described the expected proportion of false positives among all branches equal to or more extreme than the observed one. Therefore, the thresholding of the estimated q values at alpha level =5% produced a list of significant branches so that the expected proportion of false positives was alpha. For the modeling of the 3D structure, a BLAST  [83]  was performed to find an approaching structure in PDB database  [84]  in order to use it as a template to calculate a model with MODELLER  [85]. If a PDB sequence matched correctly with submitted protein, evolutionary results were directly visualized onto its modeled structure. The amino acids found under positive selection were mapped on potentially functional domains for the proteins CASP8, GBP1, HEXB and PPP2R5C.
Acknowledgements We thank  Christelle Hennequet-Antier  ( INRA ) for her advice and help for the q value computations and  David Enard  ( Stanford University ) for his advice and information on positive selection of whole genomes. We thank  Danielle Goodband  for her excellent technical support, and the COBRE core facilities in the  Department of Anatomy and Physiology at Kansas State University  (funded by  NIH   P20-RR017686 ) for technical and equipment support. We thank  Yasaira Rodriguez Torres  for help with Otterlace annotations. We acknowledge funding from the following sources: •  USDA-NRSP8 Bioinformatics Coordination (JMR, ZH, EF, CKT, JKL, RPYC, MB) • USDA-NRSP8 Pig Genome Coordination  funds (CKT, JKL, RPYC, MB) •  USDA-NRI-2009-35205-05192  and  US-UK Fulbright Commission  ( CKT) • INRA, Animal Genetics Department funds (CRG) • USDA/ARS  Project Plan #  1235-51000-055-00D  ( HDD) • MAFF grant  ( Integrated  research project for plant, insect and animal using genome technology, No.
8, supplemented with protease inhibitors (Complete, Roche Applied Science)). Membranes (MbLϩ and MbLϪ) were recovered by ultracentrifugation (100,000 ϫ g, 4°C, 1 h), extensively washed in buffer A, and resuspended in 200 l of buffer A before storage at Ϫ80°C. Recovery of lysosomes in the fractions resulting from differential centrifugation and from the Nycodenz gradient was followed by ␤-galactosidase activity measurement  (38). These data along with the protein amounts recovered in each fraction allowed calculation of purification factors as compared with the initial homogenate. Protein concentration was evaluated using a Micro BCA TM protein assay kit (Thermo Scientific). Chloroform/Methanol Extraction-Chloroform/methanol (CM) fractionation of proteins was performed according to Salvi et al.
Peptides were extracted from gel pieces in three 15-min sequential extraction steps in 30 l of 50% acetonitrile, 30 l of 5% formic acid, and 30 l of 100% acetonitrile. The pooled supernatants were finally dried under vacuum. NanoLC-MS/MS Analysis-The dried extracted peptides were resuspended in 30 l in 4% acetonitrile, 0. 5% trifluoroacetic acid and analyzed by on-line nanoLC-MS/MS (Ultimate 3000 and LTQ-Orbitrap, Thermo Fisher Scientific). The nanoLC method consisted of a 40-min gradient ranging from 5 to 55% acetonitrile in 0. 1% formic acid at a flow rate of 300 nl/min. Peptides were sampled on a 300-m ϫ 5-mm PepMap C18 precolumn and separated on a 75-m ϫ 150-mm C18 column (Gemini C18, Phenomenex). MS and MS/MS data were acquired using Xcalibur (Thermo Fischer Scientific) and processed automatically using Mascot Daemon software (version 2.
1, Matrix Science). Database Searching and Criteria for Protein Identification-Consecutive searches against the IPI_rat_decoy_database (based on the IPI-Rat version 3. 48 database; 80,082 entries including the reverse ones) were performed for each sample using Mascot 2. 1 (Matrix Science, London, UK). ESI-TRAP was chosen as the instrument and trypsin as the enzyme, and two missed cleavages were allowed. Precursor and fragment mass error tolerance were set respectively at 15 ppm and 1 Da. Peptide variable modifications allowed during the search were: acetyl (N-terminal), oxidation (M), dioxidation (M), and trioxidation (C). Proteins identified with a minimum of one unique peptide and with a score higher than the query threshold (for a p value of peptide Ͻ0. 01) were automatically validated using IRMa  (42).
The filtered results were downloaded into an MS identification database, in which the peptide false discovery rate (FDR) was of 2. 38%. (FDR ϭ 2 ϫ reverse/(reverse ϩ forward)). A homemade tool 2  was used for the compilation, grouping of proteins identified by a same set or subset of peptides (according to the principle of parsimony) and final comparison. Peptides shorter than hexamers were rejected at the grouping step. A last filtering step retained only protein groups identified by at least two unique peptides. All keratin isoforms and trypsin were deleted from the results. All MS data are available on the Pride database site  (43)  as Pride project 22847. Protein Annotation-Gene names were retrieved from the IPI-Rat or Uniprot databases. Uncharacterized proteins (IPI sequence set) underwent a Blastp process against the mammalian Uniprot database section (released February, 2011).
Top protein hits with at least 10e-05 e-value and a query coverage greater than 50% were kept and manually checked for relevance. The query coverage represents the percent of the query length that is included in the aligned segments and is calculated over all segments. When several protein groups corresponded to the same gene name, they were all kept. The TMHMM version 2. 0 server (Center for Biological sequence analysis, Lyngby, DK) was used for predictions of membrane-spanning regions (i. e. transmembrane domains). Protein functional annotation and subcellular localization information, either experimental or predicted, were collected from the IPI, Uniprot, or QuickGO sites and from the bibliography. Spectral Counting and Semi-quantitative Analysis-For each identified protein p, the spectral count values (sc p,s ϭ number of spectra assigned per protein in a given sample s) were determined with the homemade hEIDI software (supplemental Tables  S1  and S2 ).
Inserts were amplified by PCR using the Phusion polymerase (New England Biolabs) and the commercial plasmids as template and cloned for heterologous expression of GFP or YFP fusion proteins. Original plasmids, DNA accession numbers, primers, and expression vectors are given in supplemental Table  S3. Cell Culture and Fluorescence Studies-HeLa cells were from the American Type Culture Collection (ATCC) and were grown in DMEM/ GlutaMAXI supplemented with 10% FBS. Media and serum were from PAA Laboratories and Invitrogen, respectively. Cells were transiently transfected using electroporation or lipofection with Lipofectamine 2000 and processed for epifluorescence 2 days after transfection. Cells were fixed at room temperature in 4% paraformaldehyde. Antibodies were used at the following dilutions: mouse monoclonal anti-human LAMP1, 1:2,000 (H4A3, Developmental Studies Hy-bridoma Bank); Cy3-conjugated donkey anti-mouse, 1:1,000 (The Jackson Laboratory).
All resulting samples were subsequently separated by SDS-PAGE prior to LC-MS/MS analysis of in-gel digested samples. FTCD (Golgi) were depleted from L and enriched in PS to different extents. Although they all display some enrichment in MbLϩ, TGN38 is the only one for which this enrichment is comparable with that of LAMP2. Both the mitochondrial ATP synthase OSCP subunit and the endoplasmic reticulum BiP were depleted in MbLϩ. Protein Identification-From the three biological replicates, we generated 959 MS analyses. After first pass filters, this resulted in 1,398,920 spectra, 368,147 of which could be assigned to 4,097 nonredundant rat gene products from the IPI-Rat database. According to the principle of parsimony, protein isoforms that could not be segregated by the identified peptides were counted as one unique gene product.
The 4,097 gene products corresponded to 24,316 nonredundant peptide sequences. All corresponding protein and peptide information is available in the Pride database under project number 22847 and in supplemental Table  S2. Further filtering excluding trypsin and keratins as contaminants and retaining proteins identified by at least two unique peptides led to a list of 2,385 nonredundant gene products, hereafter named the MbL2385 list. In this list, 528 proteins were present in the MbLϩ fraction only, 157 in the MbLϪ fraction only, and 1,700 in both fractions (supplemental Tables  S4a  and S5a ). Thus, most of the proteins were common to both MbLϩ and MbLϪ fractions, in agreement with the limited resolution power of subcellular fractionation and the high sensitivity of mass spectrometers. To evaluate the content in IMPs identified in our samples, transmembrane domains were predicted by use of the TMHMM 2.
3  with supplemental Fig. S1 ). Abundance information was extracted from spectral count data (supplemental Table  S1 ), according to the spectral counting semi-quantitative approach  (44, 46). The relative abundance for any given protein was derived from the Nsc calculated as indicated under "Experimental Procedures" using merged data issued from all MbLϩ or MbLϪ samples (supplemental Table  S1 ). MbLϩ and MbLϪ Fractions Display Different Organellar Profiles-We then analyzed the known or predicted subcellular localization of proteins from the MbL2385 list by manually collecting this information in protein databases (IPI, Uni-protKB, and QuickGO) and bibliography. For IPI entries without any attributed gene name, homologs were previously searched in a mammalian subset of the Uniprot database using Blastp.
Extraction from protein databases or bibliography, and analysis of known or predicted functional annotation showed that all defined functional processes were represented in the Lys-734 list, although only two "polypeptide transport" annotated proteins remained (Fig. 5B ). Transporters, channels, and pumps of ions and small molecules represented the most abundant functional class, despite its third position by protein number. Metabolism-associated proteins were the most numerous but ranked second in abundance (supplemental Table  S5e ). As for the 94 proteins without subcellular annotation, one-third had no functional annotation either; more than onequarter were various metabolic enzymes; and the remaining were distributed between the "miscellaneous," "transporters, channels, and ion pumps," and "receptors and signaling" classes with rather similar abundances (Fig.
Acknowledgments-We thank  L. Aubry  and  G. Klein  for their stimulating support and  L. Aubry  for critical reading of the manuscript. We thank  A. Kraut  for technical help;  C. Adam ,  D. Barthe , and  V. Dupierris  for the homemade software support, and  Y. Vandenbrouck  for Blast searches. We also thank  M. Court  and  C. Masselon  for writing the SpI analysis script. Author contributions: J. G. , B. G. , M. J. , and A. J. conceived the project. J. T. and A. C. prepared samples for proteomics analysis. S. K. J. performed the mass spectrometry analysis. A. J. analyzed proteomic data. M. M. and A. J. performed statistical analysis. A. J. , C. S. , C. I. , and Q. V. performed molecular biology experiments and subcellular localization analyses. C. B. supervised software developments for protein identification and parsimony analysis.
In this study, the emission ratio and emission factor of C 2 H 6 are calculated from ground-based FTS measurements and compared to two recent studies (within the BORTAS project) of the emission ratio and emission factor from airborne and satellite observations  (Lewis et al. , 2013; Tereszchuk et al. , 2013). Furthermore, this paper compares results from the chemical transport model GEOS-Chem to the ground-based FTS observations. This paper is organized as follows. Subsequent to this introduction, the second section introduces the different instruments and measurement sites used in the campaign, data analysis and the retrieval methods. In the third section, the measurement time series are presented, and the retrievals are compared with the other instruments. The fourth section discusses the results of this study.
Methodology and datasets For the analysis of the smoke plume observed during the BORTAS-B campaign in July 2011, we use measurements taken in Halifax and Toronto. This section focuses on the instruments, retrieval processes and the methods used for the instrument comparisons, as well as on the chemical transport model GEOS-Chem, to which the observations are compared. The observations were taken at DGS using two FTSs (the Portable Atmospheric Research Interferometric Spectrometer for the Infrared (PARIS-IR) and the Dalhousie Atmospheric Observatory DA8; herein the DAO-DA8) and a CIMEL sun photometer. Additionally, observations from a second DA8 (TAO-DA8) and a second CIMEL sun photometer were used, both of which are located in Toronto. However, these two instruments are not co-located. The TAO-DA8 is operated at the University of Toronto, while the sun photometer is located at Environment Canada (43.