dataesr/scientific-paragraphs-categorization huggingface dataset:
As a result, unanticipated solutions emerged. For example, a proposition was to relay all the data points, captured by a motion suits sensors, to the audience, rather than combining this information into a single representation. Participants therefore explored different representations and modalities such as numbers, colors, sounds, curves, etc. As a result, the emotion could be interpreted by the spectator himself. Another original solution suggested using the dancer's movements could be used to sculpt a virtual 3D object. The resulting 3D object would be returned to the audience through different projectors or augmented reality glasses. In the following paragraphs, we present the data we collected from the protocol analysis. Even if the differences between these two case studies make them not rigorously comparable, this double analysis aims at showing the general trends we observed in the use of the MACS method with the ASUR model.
Application to data in the literature Method 1 was applied to retrieve B using published data. There have been few simultaneous measurements of extinction and reflectance of snow reported in the literature. Earlier studies dealt with broadband extinction  (O'Neill and Gray, 1972; Fukami et al. , 1985)  so that general features were retrieved (such as the increase of the broadband e-folding depth with depth) but no quantitative information on grain shape can be inferred. Studies with spectrally-resolved measurements include the works of  Grenfell and Maykut (1977)  in Arctic,  Beaglehole et al. (1998)  in Antarctica,  Perovich (2007)  on mid-latitude seasonal snow,  France et al. (2011a)  in Antarctica,  France et al. (2011b)  in Svalbard and  France et al. (2012)  in Alaska. Spectral diffuse reflectance and AFEC were measured or deduced from measurements.
However, this is counterbalanced by the higher probability of absorption between scattering events in snow with large B, because the path in the grains is longer. In short, the increase of photons paths within the grains for larger B is counterbalanced by the shortening of total path lengths in the snowpack due to lower g G. This explains the limited sensitivity of albedo to the shape of the grains. The low sensitivity was noticed experimentally by  Domine et al. (2006)  who studied snow samples with various crystal shapes and confirmed later by  Gallet et al. (2009). They demonstrate indeed that the reflectance of snow samples of known SSA (Domin é et al. , 2001) is well reproduced using DISORT-Mie code  (Wiscombe, 1980; Stamnes et al. , 1988) , i. e. for a medium of independent spheres. In this paper, we exploit the sensitivity of light extinction to estimate the grain shape parameter B using measurements of reflectance, density and profiles of intensity within the snowpack, and TARTES model.
For example, the non conservative K232A substitution in the acylCoA:diacylglycerol acyltransferase (DGAT1) gene has a major effect on milk yield and composition  [5]. Several of these genetic markers are now available and used in industry marker-assisted selection programmes  [11, 12]. Because of its economical importance Bos taurus was one of the first mammals to have its genome sequenced. In August 2006, the sequence of the cattle genome was released by the Human Genome Sequencing Center at Baylor College of Medicine  [13]. During the sequencing more than 2. 2 million putative single nucleotide polymorphisms (SNPs) were identified and deposited in public databases  [14]. The Bovine Genome Sequencing Consortium has since discovered approximately 62,000 extra highquality SNPs  [15]. These SNPs have been used to develop a whole-genome cattle SNP genotyping microarray  [16].
A transcriptomic-based sequencing approach offers a cheaper alternative to identify a large number of polymorphisms and possibly to discover QTNs. In the present study, we performed a gene-based SNP discovery analysis in bovine Longissimus thoraci, using a whole-transcriptome sequencing approach. To our knowledge, this represents the first study done in bovine muscle. For this purpose, muscle samples from three different Limousin bulls were analysed. We have identified more than 34,000 putative SNPs, including more than 60% novel polymorphisms. To evaluate the accuracy of the SNPs detected, 48 putative SNPs were genotyped. One-hundred percent concordance was observed when a stringent SNP quality criterion was chosen. The RNA-Seq data and the collection of newly discovered coding SNPs improve the genomic resources available for cattle, especially for beef breeds.
RNA sequencing To obtain a global view of the bovine Longissimus thoracis transcriptome at single-nucleotide resolution, poly(A)-enriched mRNA from three Limousin bull calves were retrotranscribed and subjected to highthroughput sequencing. The three RNA-Seq libraries were barcode-tagged and sequenced on one lane of an Illumina HiSeq2000 sequencer. Sequencing of cDNA libraries generated a total of 125,781,357 raw paired-end reads with a length of 100 bases, resulting in a total of 25 gigabases. The reads were de-multiplexed to assign reads to each sequenced sample according to its barcode index. Approximately 36 to 46 million paired-end reads were obtained for each library. Reads from each sample were then mapped back to the bovine reference transcriptome. We used the set of Bos taurus Ensembl transcripts v61 RefSeq genes as the reference transcriptome.
This set contains transcripts for 22,915 known or novel genes but also pseudogenes. Based on mappings done using the Burrows-Wheeler Aligner (BWA) programme, 63% to 67% of the mapped reads were aligned properly paired (Table  1 ). Transcriptome contamination was negligible (0. 19%-0. 24%). A total of 19,752 transcripts (16,287 genes) were identified, with at least one paired-end read in all samples analysed. Similar RNA-Seq read mapping rate and the number of genes identified were obtained in other RNA-Seq bovine studies  [33] [34] [35] [36] [37] [38]. For example,  Wickramasinghe et al. (2012)  found that ~65% of the RNA-Seq reads they generated while sequencing the milk transcriptome mapped uniquely onto the bovine genome. They also found that ~17,000-19,000 genes were expressed in milk  [35].
Baldwin and collaborators found, this time, by sequencing the rumen epithelium that ~71% of the reads mapped onto ~17,000 different genes  [36]. Gene expression was normalised as paired-end reads mapped per million total uniquely mapped paired-end reads (FPKM). Amongst these transcripts, 14,298 (72%) were identified with more than 1 read per million in at least one library. Some transcripts were represented by many reads. Moreover, 50% of the reads mapped to only 77 transcript sequences and 90% mapped to 2,878 transcripts. The top twenty of these transcripts are shown in Table  2. Amongst these transcripts, several are associated with energy metabolism (cytochrome c oxidase subunit I, II and III, cytochrome b, ATP synthase subunit alpha, NADH dehydrogenase subunit I and NADHubiquinone oxidoreductase chain 3) or locomotion (alpha skeletal muscle actin, troponin T, myosin regulatory light chain 2, tropomyosin beta chain, myoglobin, myotilin, myosin 1 and myosin 7).
SNP discovery and annotation For SNP calling, BWA was used to map the pairedreads from each sample to the bovine reference genome sequence. The SAM tools package was used for SNP discovery using stringent parameters (e. g. minimum coverage of 8 reads and mapping quality of 20). SAMtools can identify single base substitutions as well as small insertions and deletions; however, only SNPs were considered in the current analysis. In total 34,376 different SNP positions were detected with the RNA-Seq reads. Amongst these SNPs, 8,974 (26%) were homozygous in all three sequenced samples, corresponding presumably to differences between Limousin and the Hereford bovine whole-genome reference sequence  [13]. A comparable number of SNPs were discovered by Canovas et al. (2010) using a similar total number of RNA-Seq reads (~118 millions reads).
They identified ~100,000 SNPs located in genes expressed in milk samples from Holstein cows. However, only 33,045 SNPs (32%) were polymorphic within their seven Holstein cows  [33]. In our study, we found that there were 30,998 biallelic SNPs mapping to coding regions, 38. 6% of which were previously found and recorded in dbSNP. This high percentage of novel SNPs, even though there are currently more than 9 millions SNPs in the public SNP database dbSNP (version 133), suggests that a large fraction of the genetic variability present in Limousin cattle still remains to be discovered. The proportion of transition substitutions were A/G, 36%, and C/T, 37%, compared to transversions A/C, 7%, G/T, 7%, A/T, 4% and C/G, 9%. This corresponds to a transition:transversion ratio of 2. 65:1. The observed transition:transversion ratio is closed to the expected ratio (2:1) if all substitutions were equally likely.
The broader gene coverage when sequencing DNA versus RNA might contribute to the discrepancy in the rate of nscSNPs found between whole-genome and transcriptome-based studies. The deleterious effect of non-synonymous SNPs were analysed using the SIFT and PolyPhen algorithms. In order to use these programmes, sequences flanking the bovine nscSNPs were mapped onto the human genome and custom scripts were used to extract the human position orthologous to each bovine SNP position. We selected only bovines nscSNPs for which the two bases before and the two bases after the SNP exactly matched the human sequence. The human chromosomal position and the bovine alleles were combined to produce "pseudo human" variant positions and then used to query SIFT and PolyPhen. Using this conservative approach, we could retrieve the human "orthologous" position for 206 different bovine nscSNPs.
Fifty damaging nscSNPs were found by both SIFT and PolyPhen-2 algorithms, including 5 high-confidence nscSNPs for which all three Limousin animals are homozygous (Additional file 2: Table  S2 ). Gene Ontology analysis was performed with all genes containing nscSNPs. Out of the 2,438 genes, 1,092 (45%) were assigned to one or more GO annotations. In total 3,589, 2,892 and 8,172 GO terms were obtained for biological processes, cellular components and molecular functions, respectively. GO term analysis showed a significant enrichment of specific GO terms when comparing the annotations of SNP-containing genes against all unique transcripts from the bovine reference transcriptome. A summary of the classification of these genes into major biological process, cell component and molecular function categories is presented in Additional file 3: Table  S3.
Genes encoding proteins from the cytoskeleton and the extracellular matrix, or involved in cell cycle and cellular response are significantly over-represented. This finding might be explained by the high level of expression of these genes, that likely translates into greater sequence coverage and ultimately in a larger proportion of SNPs being identified in specific functional groups of genes. No significant enrichment in KEGG terms/pathways was found. The positions of the 34,376 different SNPs predicted with the RNA-Seq reads were compared to the position on the UMD3. 1 bovine genome assembly of know quantitative trait loci (QTLs) deposited in the public database AnimalQTLdb  [39]. 32,631 SNPs were located in 3,855 different QTL regions (Additional file 4: Table  S4 ). For example, 2,116 different SNPs are found in 16 QTL regions for meat tenderness score; whereas 14,560 SNPs are within 121 QTL regions for marbling score.
The 48 selected SNPs were genotyped on the three original Limousin bull calves used for the RNA-Seq work, using llumina's GoldenGate BeadXpress system. From the 48 SNPs that were genotyped, 11 SNP assays failed to work (23%), equivalent to a conversion rate of ~77%. We had 100% call rate for all remaining 37 SNPs with these three DNA samples (Table  4 ). A similarly low assay conversion rate was obtained in a recent SNP genotyping project using Illumina's GoldenGate BeadXpress system and was due to failure in the synthesis of some of the oligonucleotides (unpublished data). A comparison between genotypes obtained by direct genotyping and predicted from the RNA-Seq data show 23 discrepancies (20%) (Table  4 ). A quick survey shows that discordant genotyping calls occur when genotypes have been predicted from the RNA-Seq data with a low probability (score below 20).
Population genetics screens To test the usefulness of the identified SNPs, the 48 selected nscSNPs were assessed by genotyping a total of 90 bovine samples (including the three Limousin samples used for the RNA-Seq work) representing the 9 major breeds used in France, an African taurine breed (Watusi), and two other Bovinae species (European bison and Greater Koudou). As reported above, 8 SNP assays failed to work in all samples. SNP call rate ranged from 55% (rs42555633) to 100%, whereas the call rate for bovine DNA samples ranged from 93% to 98%. The majority (95%) of the selected SNPs with working assays, generated data with the European bison and the Greater Koudou samples (35/37 and 27/37 SNPs, respectively) (Table  5 ). This could be expected since the markers were developed from (conserved) intra-genic regions.
Functional candidate mutations The discovered coding SNPs, especially the 8,407 highconfidence SNPs may have a direct functional effet and some of them may be involved in the genetic variability of meat quality traits. Among the high-confidence non synonymous coding SNPs, we have identified a single polymorphism resulting in a premature stop codon. SNP rs135279925 (ENSBTAT00000007104:c. 1093C>T) is located within the 10 th and last exon of CD46, a membrane cofactor protein. This variant leads to a three amino acid shortened protein. None of the sampled animals were homozygous for this mutation. The corresponding bovine gene (ENSBTAG00000005397) has three known different transcripts encoding 343, 361 and 367 amino acid long proteins. The nscSNP modifies the longest bovine protein version; however, as the last three amino acids are not conserved within the bovine proteins or between species, the polymorphism is unlikely to have a functional impact.
For example, we found a high-confidence non synonymous coding SNP (rs109813896: BTA1 g. 134130474G>C) in the gene encoding the mitochondrial propionyl-coA carbolylase beta subunit (PCCB), which is involved in the catabolism of propanoate, an important intermediate in the metabolism of several amino acids. Yang and collaborators  [63]  have shown that a polymorphism in PCCB is associated with fat weight, in pig. Interestingly, the bovine PCCB gene lies within a QTL region for fat thickness at the 12 th rib  [64]. PCCB could therefore be a good candidate gene for this trait. We also found seven high-confidence nscSNPs (including previously discovered SNPs: rs136458240, rs211315064 and rs209586352) in the gene encoding the heparin sulfate proteoglycan 2 (HSPG2, ENSBTAG00000017122). This gene encodes a large proteoglycan that is a component of the extracellular matrix.
Mapping reads to reference transcriptome and gene expression counts The Bos taurus reference transcriptome was downloaded from Ensembl (version 63, Bos_taurus. Btau_4. 0. 63. cdna. all. fa). To align the reads back to the assembled reference transcriptome the BWA programme (version 0. 5. 9-r16) was used  [68]. Reads were mapped for each sample separately to the assembled transcriptome. The BWA default values were used for mapping. Properly paired reads with a mapping quality of at least 30 (-q = 30) were extracted from the resulting BAM file using SAMtools  [69]  for further analyses. Properly paired is defined as both left and right reads mapped in opposite directions on the same transcript at a distance compatible with the expected mean size of the fragments (<500-bp). Custom scripts were developed to identify paired-reads mapping to single locations and with the expected distance.
Read pairs mapping to separate chromosomes were discarded for the present study. Transcriptome contamination was assessed by mapping with BWA reads on a sequence library, containing E. coli, phiX and yeast genome sequences. The number of pairedreads uniquely aligning to transcribed regions of each transcript was calculated for all genes in the annotated transcriptome. The transcript paired-read count was calculated as the number of unique paired-reads that aligned within the exons of each transcript, based on the coordinates of mapped reads. The expression level of each gene was calculated in FPKM (fragments per kilobase per million sequenced reads) using a custom script based on Tapnel et al. (2010)  [70].
Polymorphism identification BWA was also used to map reads onto the bovine genome reference sequence (version UMD3. 1,  [71]. Only reliable properly paired BWA mapped reads were considered for Single Nucleotide Polymorphism (SNP) calling. Indels were not considered because alternative splicing impedes reliable indel discovery. SNPs were called using the SAMtools software package. Genotype likelihoods were computed using the SAMtools utilities and variable positions in the aligned reads compared to the reference were called with the BCFtools utilities  [72]. SNPs were called only for positions with a minimal mapping quality (-Q) of 30, a minimum coverage (-d) of 4 and a maximum read depth (-D) of 10,000,000.
Functional annotation of detected SNPs The functional effect of the newly discovered SNPs on known transcripts were analysed using Ensembl's Variant Effect Predictor v2. 5, following local installation  [73]. The deleterious effect of non-synonymous SNPs were analysed using the SIFT (Sorting Intolerant From Tolerant; http://sift. bii. a-star. edu. sg; [74]  and PolyPhen-2 (Polymorphism Phenotyping 2; http://genetics. bwh. harvard. edu/pph2/; [75]  programmes. In order to use these two programmes, sequences flanking the bovine nscSNPs were mapped onto the human genome (version GRCh37/ hg19) using MegaBLAST  [76]  and custom scripts were used to extract the human position orthologous to each bovine SNP position. The human chromosomal position and the bovine alleles were then used to query SIFT and PolyPhen.
Default settings were used for both programmes. We refered to damaging SNPs, SNPs that were identified as damaging and not tolerated, using PolyPhen-2 and SIFT, respectively. In order to evaluate whether SNP-containing genes were significantly enriched for specific gene ontology (GO) terms and KEGG pathways compared to all annotated bovine genes, gene enrichment analyses were conducted using the FATIGO tool of the online software suite Babelomics (http://babelomics. bioinfo. cipf. es; [77]. Genes were assigned their Ensembl identities as input for Babelomics. Only one copy of each gene was used. Default parameter settings were used for the analysis. Statistical assessment of annotation differences between the two sets of sequences (SNP-containing genes versus all the other bovine genes) was carried out for each FATIGO analysis, using the Fisher Exact Test with correction for multiple testing.
Population genetics analyses Genetic diversity parameters within each population were calculated using the GENETIX 4. 05. 2 software package  [78]. Tests for deviation from Hardy-Weinberg equilibrium were performed by the GENEPOP 3. 4 software  [79] , using the exact test of  Guo and Thompson (1992)   [80]. Genetic differentiation among and within the populations was estimated based on F-statistics (F ST ) according to Weir and  Cockerham (1984)   [81] ) using the GENEPOP and GENETIX software packages. Test for population differentiation was performed as implemented in GENEPOP. The Reynolds genetic distance (D R ) was calculated for each pair of populations based on allele frequencies  [82]  using the GENETIX software. Principal component analysis (PCA) was performed using the GENETIX programme from allele doses for each individual.
Acknowledgements The authors would like to thank  Hubert Levéziel  for his help, the different cattle breeding societies that provided semen and blood samples for the animals analysed in this study and  Yves Amigues  and colleagues at  LABOGENA  for their help in DNA preparation. The RNA-Seq work was funded by the  INRA Animal Genetics Department  ( BovRNA-Seq  project). The sampling of the Limousin Longissimus thoraci biopsies was part of the  Qualvigène  project, funded by  Agence Nationale de la Recherche  (contracts  ANR-05-GANI-005  and  ANR-05-GANI-017-01 ) and  APIS GENE  (contract  01-2005-QualviGenA-02 ). The authors wish to thank the anonymous reviewers for their valuable comments and suggestions, which were helpful in improving our manuscript.
09 G/C C/C C/C 6 rs42722878 8 101,639,394 ENSBTAG00000020243 T/C 0. 18 0. 04 0. 36 0. 04 0. 59 0. 25 0. 41 0. 17 0. 04 T/C C/C C/C 7 rs42722887 8 101,642,585 ENSBTAG00000020244 G/A 0. 27 0. 09 0. 36 0. 04 0. 59 0. 25 0. 41 0. 17 0. 04 G/A G/G G/G 8 rs42722900 8 101,645,192 ENSBTAG00000020245 C/T 0. 04 0. 04 0. 14 0 0. 41 0. 25 0. 27 0. 08 0. 04 C/C C/C C/C 9 rs42722901 8 101,645,255 ENSBTAG00000020246 C/T 0. 24 0. 09 0. 35 0. 04 0. 59 0. 25 0. 41 0. 17 0. 04 C/T C/C C/C 10 rs42306198 8 111,749,876 ENSBTAT00000008586 G/A 0 0. 04 0. 09 0. 04 0. 18 0 0 0. 08 0. 04 G/G G/G 11 rs17870317 9 34,687,597 ENSBTAT00000038044 T/G 0. 33 0. 32 0. 45 0. 45 0. 41 0. 50 0. 32 0. 67 0. 32 T/T T/T T/T 12 rs17870361 9 61,258,934 ENSBTAT00000015037 C/T 0. 24 0. 14 0. 04 0. 73 0. 14 0. 17 0. 32 0 0. 04 C/C C/C 13 rs43626955 10 51,842,959 ENSBTAT00000007206 A/C 0.
82 1 0. 86 0. 58 0. 68 C/C C/C 28 rs42015934 22 51,561,550 ENSBTAT00000007217 C/T 0. 23 0. 04 0. 14 0. 04 0. 04 0. 17 0. 18 0. 33 0. 09 C/C C/C C/C 29 rs42451508 25 21,535,844 ENSBTAT00000008398 G/A 0. 14 0. 09 0. 27 0. 04 0. 31 0. 83 0. 41 0. 33 0. 27 G/G G/G G/G 30 rs42174698 29 26,367,840 ENSBTAG00000001660 T/C 0. 36 0. 50 0. 91 0. 54 0 0. 50 0. 04 0 0. 41 C/C C/C 31 rs17871172 29 26,368,230 ENSBTAG00000001661 C/T 0 0. 04 0 0 0. 04 0 0 0. 08 0 C/T C/C C/C 32 rs42188070 29 45,033,799 ENSBTAT00000023514 C/T 0. 14 0. 09 0. 54 0. 14 0. 18 0. 25 0. 18 0. 25 0. 27 C/C C/C C/C 33 rs29024659 X 81,605,181 ENSBTAG00000002585 C/T C/C C/C.
RNA-seq experiments are now routinely used for the large scale sequencing of transcripts. In bacteria or archaea, such deep sequencing experiments typically produce 10-50 million fragments that cover most of the genome, including intergenic regions. In this context, the precise delineation of the non-coding elements is challenging. Non-coding elements include untranslated regions (UTRs) of mRNAs, independent small RNA genes (sRNAs) and transcripts produced from the antisense strand of genes (asRNA). Here we present a computational pipeline (DETR'PROK: detection of ncRNAs in prokaryotes) based on the Galaxy framework that takes as input a mapping of deep sequencing reads and performs successive steps of clustering, comparison with existing annotation and identification of transcribed non-coding fragments classified into putative 5 0 UTRs, sRNAs and asRNAs.
Moreover, even in model genomes, RNA-seq experiments will need to be carried out in multiple different conditions in order to enable the discovery of RNAs expressed under specific growth conditions. As an illustration of how ncRNA annotation lags behind DNA sequencing, consider that most current publicly available genome annotations have no ncRNA annotated other than tRNAs and rRNAs. For instance, while the Escherichia coli K12 MG1655 strain NCBI annotation file has a solid set of 63 annotated ncRNAs, this is an exception, as even closely related E. coli strains have little or no ncRNAs annotated. Species such as Salmonella enterica, Bacillus subtilis or Staphylococcus aureus, in which hundreds of ncRNA have been published and stored in public databases such as RFAM  [6] , have their annotation files devoid of any ncRNA gene (except for tRNAs and rRNAs).