dataesr/scientific-paragraphs-categorization huggingface dataset:
Instant data reuse Using the GeneProf data analysis suite  (6) , data sets from the public repository can be instantly integrated into ongoing experiments. This may enrich new projects by adding additional data without significantly increasing the cost and time required. In our previous work  (27) , we have found it useful to combine the Tag-seq gene expression data generated as part of our investigations of the downstream targets of Nanog with publicly available ChIP-seq data sets to prioritize the identification of putative direct target genes of this transcription factor. To do so, we have imported the binding peak data sets from two studies in the GeneProf database into our analysis workflow, mapped these peaks to the genes and integrated both sources of data with the expression data. The workflow describing this analysis is available as part of experiment gpXP_000385 (http://www.
Integration into external applications Bioconductor  (28)  libraries offer an unparalleled breadth of utilities that are widely used by bioinformaticians. Using GeneProf web services, it is trivial to load, for example, gene expression data for a particular gene, cell type or treatment condition directly into an active R session. The RCurl package provides all the functionality required to establish a connection to these web services, which can export data directly in an R-compatible binary format. Following on from previous examples, we may wish to further investigate the relation between the transcription factors Nanog and Esrrb. Using GeneProf web services, we can load the fully annotated gene expression values from currently 380 mouse RNA-seq data sets (30 July 2013) into R within seconds, making it possible to calculate their global correlation (Pearson correlation 0.
84) or to produce a scatter plot in moments (Supplementary Figure  S5 , Supplementary Methods). GeneProf's web services open up a plethora of possibilities for integrating GeneProf data with external applications and their use is by no means restricted to R. The online documentation of the web service API (http:// www. geneprof. org/webapi. jsp) provides further workedthrough examples for using the web services from the Unix command line, from the UCSC  (23)  or IGV  (24)  genome browsers, from the Galaxy  (7)  and Taverna  (25)  workflow engines, in external web applications via HTML and JavaScript and from within programs written in various programming languages (Perl or Java).
DOCUMENTATION The GeneProf website contains an extensive online manual detailing all aspects of the analysis system and the associated databases and providing a range of tutorials for new and returning users (http://www. geneprof. org/help_and_tutorials. jsp, http://www. geneprof. org/web api. jsp and http://www. geneprof. org/screencasts. jsp; see Supplementary Table  S3  for further links). Additionally, we have integrated a simple ticketing system enabling registered users to raise questions and concerns. Users raising queries can generally expect a response within one working day.
CONCLUSION The GeneProf database hosts an ever-increasing wealth of functional genomics data of considerable interest to the biomedical research community. Importantly, GeneProf goes beyond merely archiving high-throughput data, aiming to also process these data into useful formats that help to create novel insight. We are making every effort to keep this database up-to-date and as comprehensive as possible and thanks to GeneProf's unique integration of a database component directly with a data analysis suite, the data in the system is immediately and transparently accessible and reusable by researchers around the globe. GeneProf provides straightforward and quick means to exploit large-scale genomics data sets for both experimental and computational biologists and we aim to further improve its utility by adding additional functionality and data in future releases.
V. CONCLUSION In this paper, we have analyzed, theoretically and experimentally using Matlab simulations, the solving system using classical Cholesky , MGS-QR and MGS-Cholesky. Both analyses are concordant. As expected through the theoretical analysis, the experimental analysis has confirmed that the MGS-QR is more robust than the classical Cholesky and MGS-Cholesky. However this robustness has a cost in terms of execution time. Classical Cholesky requires twice less execution time than MGS-QR and around 1. 5 less than MGS-Cholesky. With the introduction of the MGS-Cholesky, we give an alternative to MGS-QR if we search a good robustness of the system solving against additive round-off error in the final solution with a slight degradation in execution time performance.
Modeling of Mouse MR1 Mouse MR1 sequence (UniProt accession number Q8HWB0, http://www. uniprot. org/) was used to model mouse MR1 with the homology-based web server Phyre  [14]. The crystal structure of human MR1  [15]  was used as a template and mouse MR1 was modeled with 100% confidence. The molecular graphic representation was created with PyMol. Proteinase K Digestion and Lipid Extraction E. coli cultures were grown overnight in Luria broth, sonicated, and the ,10 kD fraction was separated using Amicon spin columns. Ovalbumin protein (Sigma) was spiked into the fractions containing proteinase K (80 mg/ml), 1 mM CaCl 2 , 50 mM Tris and 10 mM 2-ME. Fractions were incubated at 37uC for 24 hours. After 24 hours, the proteinase K activity was inhibited by adding PMSF (40 mM). a-Galactosylceramide (Funakoshi co, ltd, Japan) was spiked into overnight E.
Stimulation of these hybridomas using the LM1. 8 fibroblast overexpressing MR1 (Fig. 1, A  and B ) provided us with the pattern of reactivity of these mutants to the presumably selfantigen(s) expressed in fibroblast cells and presented by MR1 molecules or, alternatively, to ligand(s) provided by the culture media. Several interesting observations can be drawn from these results. First, the requirement for residues encoded within the invariant TCRa chain, especially the CDR1a loop, is much more pronounced than it is for residues within the TCRb chain. Residues within the CDR1a (T26a, G28a, F29a, N30a, G31a), CDR2a (Y48a, V50a, L51a) and CDR3a (D92a, S93a, Y95a, I98a) loops were all necessary for the recognition of the selfantigen-MR1 complex. Based on the mouse TRAV1 (used by MAIT cells), TRAV11 (used by iNKT cells) and TRAV19 (used by conventional T cells) sequences, we searched databases for orthologue genes in sixteen different species of placental mammals and performed a phylogenic analysis rooted to the TRAV1S1 sequence from Oncorhynchus mykiss (Rainbow Trout) as a reference.
Design of the model: In order to forecast and quantify carbon diffusion across the interface, we have developed a mesoscopic model, which has been implemented into the software MatCalc  [3]. It is based on Onsager's equation, which postulates a linear dependence between the flux J k of a specie k and the chemical potential gradients µ i of all MATEC Web of Conferences species present in the system. The following system of partial differential equations is solved on each node of the one-dimensional mesh using a finite difference method of discretization:            ∂C k ∂t = ∂ ∂z (-J k ) J k = - n-1 j=1 D n k j.
∂C j ∂z The diffusivity matrix D n k j contains thermodynamic and kinetics contributions, which are calculated at each node in space at each time step from appropriate databases. The calculation consists in two steps: diffusion is first solved inside the matrix phases (BCC and FCC) only, then thermodynamic equilibrium allows calculating the phase fractions of the different phases by minimization of the Gibbs free energy of the system. Three different carbides (Fe 3 C, M 7 C 3 and M 23 C 6 ) are taken into account. The matrix composition is changed after precipitation and a new step of diffusion is performed.
This is also fully consistent with the observations. The nature of carbides has been studied on carbon extraction replicas by Transmission Electron Microscopy assisted by the ACOM (Automated Crystal Orientation Mapping  [4] ) software. An intense precipitation of large M 7 C 3 carbides (100-200 nm) has been found in the martensitic layer, whereas small coherent M 23 C 6 precipitates (20 nm) are visible in the austenitic matrix. A 3D reconstruction of the carbides population in the martensitic layer has also been performed by serial cutting with a Focused Ion Beam (FIB).
Background: Despite the recent sequencing of seven ant genomes, no genomic data are available for the genus Formica, an important group for the study of eusocial traits. We sequenced the transcriptome of the ant Formica exsecta with the 454 FLX Titanium technology from a pooled sample of workers from 70 Finnish colonies. Results: About 1,000,000 reads were obtained from a normalised cDNA library. We compared the assemblers MIRA3. 0 and Newbler2. 6 and showed that the latter performed better on this dataset due to a new option which is dedicated to improve contig formation in low depth portions of the assemblies. The 29,579 contigs represent 27 Mb. 50% showed similarity with known proteins and 25% could be assigned a category of gene ontology. We found more than 13,000 high-quality single nucleotide polymorphisms.
The D9 desaturase gene family is an important multigene family involved in chemical communication in insects. We found six D9 desaturases in this Formica exsecta transcriptome dataset that were used to reconstruct a maximum-likelihood phylogeny of insect desaturases and to test for signatures of positive selection in this multigene family in ant lineages. We found differences with previous phylogenies of this gene family in ants, and found two clades potentially under positive selection.
g. [42] [43] [44] ). Recently, two studies have proposed a phylogeny of this gene family in insects  [3, 5]. Both studies included only one or two ant species in their dataset. Those studies identified five major clades, but some of them were not well supported. Combining desaturase sequences from multiple social and non-social insect species would improve the phylogenetic reconstruction of this gene family and allow a stronger test if desaturase genes are under positive selection  [43]  which might be an adaptation of sociality. The objective of the present study was to characterise the transcriptome of Formica exsecta and annotate desaturase genes in this ant species to obtain extensive genomic resources for Formica ants. These resources will provide the means to tackle the genetic basis of important phenotypes for social insects such as CHC production/recognition and polygynous mating systems as well as look for patterns of positive selection on desaturase genes in social insects.
To obtain a good-quality de novo assembly of our transcriptome, we chose to test two different assemblers with varying parameters and to compare the resulting assemblies. In the second part of this study we functionally annotated the transcriptome and analysed genetic variation in the sample. Finally, we manually annotated the D9 desaturase gene family in the Formica exsecta transcriptome dataset, reconstructed a phylogeny of insect D9 desaturases and searched for the signature of positive selection on those genes  [46]. Tvarminne zoological station in Hanko, Finland. The ants were collected on land belonging to the University of Helsinki field station that, via Prof. L. Sundstro ¨m, gave permission for the colonies to be sampled. The ant population at this location is not considered as an endangered or protected species.
Transcriptome Assembly Read cleaning. Reads were extracted from SFF files into FASTQ format using sffextract (http://bioinf. comav. upv. es/ sff_extract/). Primer and adaptor sequences were masked using custom Perl scripts. Low complexity reads were screened with Tandem-repeat finder  [47]  (http://tandem. bu. edu/trf/trf. unix. help. html). Quality trimming was performed using filterfastq on a Galaxy server  [48] : parts of the sequences with the lowest quality scores (,25), mostly at 39 ends, were removed as well as sequences shorter than 50 bp. Poly-A tails were screened with trimest from the EMBOSS package  [49]  (http://emboss. sourceforge. net/apps/ release/6. 1/emboss/apps/trimest. html) and masked in the fastq file with Biopython. Contaminant or unwanted sequences were identified by searching (blastn  [50] ) raw reads against (i) the Wolbachia pipientis genome (e-value cut-off of 10e-45,  [51] ) (ii) rRNA coding genes of the Formica genus (e-value cut-off of 10e-45  [52] , http://www.
arb-silva. de/). These reads were removed from the data set using a Biopython script. The resulting FASTQ file was used as input for de novo assemblies. De novo assembly. To optimise the quality of the de novo transcriptome assembly, we compared two different assembler programs: Newbler 2. 6 (Roche), recommended in Kumar and Blaxter  [45]  for transcriptome assembly, and MIRA 3. 0  [53]. We tested two main assembly parameters for each program: minimum percentage identities from 85 to 95%, and minimum overlap length of 20 bp and 40 bp for MIRA, and 40 and 60 bp for Newbler. The ''-cdna'' mode was used for Newbler. We also tested a new option of Newbler 2. 6, the -urt option, which is dedicated to improve contig formation in low depth portions of the assemblies. Assembly metrics (number of contigs, the total length of assembly, the average contig length, and the number of singletons -i.
e. the number of unassembled reads) were computed on Galaxy. Qualitative assessment of the different assemblies was performed by measuring the coverage of a set of 50 known transcripts  [32]  already annotated in the genus Formica or its closely related ant species Camponotus floridanus. For each gene, the total percentage of its length covered by the assembly (completeness) and the maximum percentage covered by a single sequence (contiguity) were computed  [32]. BLAST of assemblies against the test transcripts, qualitative assessment of coverage and measures of contiguity and completeness were carried out on a Galaxy server. The final de novo assembly was chosen based on basic assembly metrics and performance in terms of completeness and contiguity. Clustering. Newbler is sometimes too conservative in its method for assembling reads.
This can cause alleles to be separated into different contigs, even after Newbler has performed a clustering step and grouped presumed alleles in ''isotigs''  [37]. When several contigs blasting to the same test transcript shared a high percentage similarity, we hypothesised that they were redundant contigs for the same transcript. We evaluated redundancy qualitatively in the trackster viewer on a Galaxy server, and empirically determined a minimum percentage identity of 98% to cluster contigs. Contigs of Newbler assemblies were clustered with uclust  [54]  on Galaxy, with the minimum identity set to 0. 98 and otherwise default parameters. Mapping. We used the recently available genome of C. floridanus to run a mapping assembly where de novo contigs were mapped onto C. floridanus predicted transcripts with bwa  [55]  (http://bio-bwa.
Transcriptome Annotation Similarity annotation. Contigs and singletons of the bestquality assembly were searched against the non-redundant (nr) protein database of NCBI with an e-value cut-off of 10e-5 (blastx program). The latest versions of ant protein datasets were downloaded from the Hymenoptera database  [56]  (version 3. 8 for Acromymex echinatior, 1. 2 for Atta cephalotes, 3. 3 for C. floridanus, 3. 3 for Harpegnathos saltator, 1. 2 for Pogonomyrmex barbatus, 1. 2 for Linepithema humile and 2. 3 for Solenopsis invicta)  [3, [5] [6] [7] [8]. Sequences that did not have any match in the nr database were blasted against the combined ant datasets with an e-value cut-off of 10e-5. Gene Ontology (GO) annotation. Contigs and singletons of the best-quality assembly were annotated with gene ontology terms with Blast2GO  [57]  online or pipeline version (http://www.
humile  [3] , Nasonia vitripennis  [3] , Tribolium castaneum  [59] , and Drosophila melanogaster  [3]. We used Table  S6  of the genome analysis of L. humile  [3] , which compares the percentage of genes matching a set of GO terms. Prediction of Open Reading Frames (ORF). For sequences with a significant blastx hit (10e-8), ORFs were predicted with the prot4EST pipeline  [60]  version 2. 2, modified to accept xml BLAST input files. For the remaining sequences, ESTScan2  [61]  was used to detect ORFs based on the bias in codon usage. The species-specific matrix, used in ESTScan2 to represent codon usage in Formica exsecta, was built by Prot4EST3. 1b from the output of blastx searches. The sequences without any ESTScan prediction were passed to getorf (EMBOSS package  [49] , http://emboss. sourceforge.
Analysis of Polymorphism SNP calling and filtering. Reads used for building contigs in the assembly were re-mapped to the de novo assembly using bwa  [55]  (http://bio-bwa. sourceforge. net/). The alignments were processed with samtools  [62]  (http://samtools. sourceforge. net/) and SNPs were called using VarScan  [63] , a tool that is adapted to SNP calling from pooled samples. By default, VarScan calls SNPs at position with at least 8X coverage, 2 reads supporting the variant allele, a minimum frequency of 5% for the variant and a minimum base-calling quality of 15. To be even more conservative and further minimise putative sequencing errors, only SNPs with at least 3 reads supporting the variant allele were kept. Highly variable contigs. The degree of variability of a given transcript can indicate selection acting on the gene.
Desaturase Gene Family Annotation and Analysis Annotation. We used sequence similarity with other insect sequences to identify desaturases in the F. exsecta transcriptome data set using blastx. The D9 desaturases of L. humile had been manually annotated in  [3] , Drosophila melanogaster desaturases were retrieved from flybase  [64]  (CG9747, CG9743, desat1, desat2, desatF, CG15531 and GC8630) and we manually annotated desaturases from the closely related ant species C. floridanus using available CDS predictions  [56]  (http://www. antgenomes. org/). Annotations were manually curated and final annotations confirmed with reciprocal BLAST searches against protein databases. Desaturase sequences homologous to sub-families other than the D9 family were not considered in subsequent analyses. Gene phylogeny.
Phylogenetic analyses were conducted on most of the D9 desaturase genes found in F. exsecta as well as other representative insect D9 desaturases. In addition to L. humile, C. floridanus and D. melanogaster sequences already retrieved for the annotation step, desaturase sequences from five other ant species (S. invicta, A. echinatior, A. cephalotes, P. barbatus, H. saltator) were retrieved by searching with blastp the combined dataset of expressed and predicted ant proteins  [56]  using L. humile desaturases as queries. The sequence set was also completed with desaturases from three other hymenopteran species (Apis mellifera, Apis floridanus and Nasonia vitripennis) searched in the hymenopteragenome dataset, as well as other hymenopteran and nonhymenopteran insect D9 desaturases (Bombus terrestris, B.
impatiens, Megachile rotundata, Acyrthosiphon pisum, Tribolium castaneum, Bombyx mori, Anopheles gambiae) retrieved using D. melanogaster desaturases as queries for blastp searches against the NCBI non-redundant protein database. Pseudogenes were excluded from the analysis. Partially annotated genes were excluded only when they were very short compared to other sequences. Accession numbers for all insect desaturases used in this study are provided in Table  S1. The amino acid sequences of 197 homologous genes were aligned using MAFFT v6  [65]. Gblocks  [66]  with low stringency parameter settings, and Guidance  [67] , were used to screen positions that were poorly aligned. We compared the results of those two programs to manually curate the alignment in Jalview  [68]. This resulted in a final trimmed dataset comprising 282 amino acid positions.
LG+G was found to be the best evolutionary model for the dataset as determined by ProtTest  [69]. A nucleotide alignment guided by the amino-acid alignment was obtained with translatorX. The best evolutionary model (GTR+I+G) was inferred with jModelTest  [70]. According to the best models for nucleotide and amino acid alignments, maximum likelihood trees were reconstructed using RAxML v7. 0. 4  [71] , and nodal support values were obtained by a 500replicate rapid bootstrap analysis. Selection analysis. To investigate difference in selective pressures among D9 desaturase genes of social Hymenoptera, sequences from A. mellifera, A. florea, B. impatiens, B. terrestris, L. humile, P. barbatus, S. invicta, A. echinatior, A. cephalotes, C. floridanus, H. saltator and F. exsecta were re-aligned independently and a maximum likelihood tree was obtained with the same methodology as for the general insect tree.
Tests for variation in selective pressures and for positive selection were performed using the codeml program of the PAML package  [46]  which estimates, by a maximum likelihood method, v ratios of the normalised nonsynonymous substitution rate (dN) to the normalised synonymous substitution rate (dS). v. 1 is considered to be evidence of positive selection for amino acid replacements, whereas v,1 indicates purifying selection. The comparison between models was assessed using Likelihood-Ratio Tests (LRTs) for hierarchical models  [72]. Only sequences from species of Hymenoptera (cf. phylogenetic analyses) were chosen for selection analyses. We aligned all hymenopteran protein sequences using MAFFT v6 and the nucleotide sequence alignment was guided by the protein sequence alignment with translator  [73].
Data Release Raw data were released on the short read archive (SRA) of NCBI as two sff files under accession number SRR837406 and SRR848070. The assembled transcriptome, gene ontology annotation and SNP dataset are available on the antgenome database (http://www. antgenomes. org/). L is the log of the likelihood, k the transition on transversion ratio, v the ratio of non-synonymous to synonymous substitution rates; ***significant at the 0. 001% level. The M2 null model assumes the same v 1 for all foreground branches. doi:10. 1371/journal. pone. 0068200. t002.
Transcriptome Sequencing and Assembly Sequencing statistics and read cleaning. Sequencing of the transcriptome of F. exsecta produced 1,051,346 raw reads, with a mean length of 374. 7 bp, for a total length of 390 Mb and a GC content of 35. 4%. We removed 333 reads blasting to the Wolbachia pipientis genome  [51] , 101 rRNA reads and 13,992 chimeric, lowquality or short reads (,30 bp). After read cleaning, the dataset included 1,036,920 reads with a mean length of 315. 5 bp (total: 327 Mb). Comparison of Newbler2. 6 and MIRA 3. 0. With default parameters, MIRA produced more (32,929 versus 17,955) and shorter (809 versus 893 bp) contigs than Newbler, which is consistent with a recent comparative study showing that MIRA is more conservative in its way of assembling reads  [82]. Moreover, the total length of the alignment was 26.
6 Mb with MIRA versus 16 Mb with Newbler (Table  S2 ). When the Newbler option -urt is activated, Newbler builds contigs from slightly overlapping reads and extends contigs to low-coverage extremities. This option increased the total length of assembly from 16 Mb to 23. 2 Mb, closer to the total length of MIRA assemblies, increased the number of contigs to 32,576 (Table  S3 ) and decreased the number of singletons to 3,769. The average contig length of this assembly was reduced to 722 bp, due to the formation of many new short contigs. Hence, activation the -urt option greatly improved the quality of the transcriptome assembled and made Newbler outperform MIRA on our dataset. When evaluating the quality of MIRA and Newbler assemblies using 50 test genes, we found that 15 transcripts annotated in species other than F.