dataesr/scientific-paragraphs-categorization huggingface dataset:
Whole-genome sequencing and sequence alignment to the reference The whole genome of 274 animals corresponding to both French dairy and beef breeds (Table  1 ) were used for 2 × 100 bp paired-end sequencing on an Illumina HiSeq 2000 with a TruSeq SBS v3-HS Kit (Illumina). Sequence alignments were carried out using the Burrows-Wheeler Alignment tool (BWA-v0. 6. 1-r104) [6]  with the aln option with default parameters for mapping reads to the UMD3. 1 bovine reference genome  [7]. Potential PCR duplicates, which can adversely affect the variant calls, were removed using the MarkDuplicates tools from the Picard package version 1. 4. 0  [8]. Only properly paired reads with a mapping quality of at least 30 (-q = 30) were retained. The resulting BAM files were then used for all subsequent analyses.
Variant annotation All variants were annotated with the Ensembl variant effect predictor (VEP) pipeline v81  [10]  based on the Ensembl version 81 transcript set and using dbSNP build 143. The effect of the amino acid changes was predicted using SIFT  [11, 12] , a sequence homology-based tool that can determine whether an amino acid substitution in a protein is deleterious or tolerant.
Functional characterization of protein-coding genes with LoF variants A set of 8337 gene products was used for gene ontology (GO) enrichment and functional analyses, using the GO  [13]  and the KEGG (Kyoto Encyclopedia of Genes and Genomes)  [14]  database resources. The Cytoscape  [15]  ClueGO plugin  [16]  was used to identify the biological functions to which genes contribute. The enrichment of biological terms and groups were set as follows. First, we used the enrichment tests based on the hyper-geometric distribution. Second, we set the statistical significance to 0. 05 (p ≤ 0. 05), and we used the Benjamini-Hochberg adjustment to correct the p value for the terms and the groups created by ClueGO. Third, we used fusion criteria to reduce the redundancy of related terms that have similar associated genes.
Whole-genome sequencing and read mapping Two hundred and seventy four animals corresponding to both French dairy and beef breeds were selected for whole-genome sequencing (Table  1 ), of which 62 whole-genome sequences were already published  [1] ; the Illumina short reads are available at the European Nucleotide Archive (ENA) with study accession number PRJEB9343 (http://www. ebi. ac. uk/ena/data/view/ PRJEB9343). Overall, 103 billion raw paired-end reads 100-bases long were generated, which resulted in over A total of 146,944 genome variants were identified as bi-allelic in our dataset but contained more than two alleles in the Ensembl variation 83 database. Of these 146,944 genome variants, only 95 positions that displayed a single variant type in our dataset overlapped with multiple variant types in the Ensembl variation 83 database.
For the remaining 146,849 positions, a single variant type was observed in both databases, of which 129,356 (88. 1%) SNPs and 17,493 (11. 9%) InDels were identified in our dataset. Among the 129,356 discrepant SNPs, 99. 3% (128,407) were reported to be tri-allelic SNPs and only 0. 7% (949) corresponded to InDels in the Ensembl variation 83 database. Of the 17,493 discrepant InDels, 67. 3%  (11, 770)  corresponded to tri-allelic SNPs and 32. 7% (5723) were also InDels but with multiple alleles in the Ensembl variation 83 database. In addition, we identified 88,289 positions that displayed one type of variant (i. e. SNP or InDel but not both) in our dataset but which overlapped with multiple variant types in the Ensembl variation 83 database. We also identified 517,417 variants for which the alleles differed between our dataset and the Ensembl 83 variation database.
Evaluation of sequencing genotypes To evaluate the quality of our sequencing data-derived genotypes, we performed three different analyses. First, we used the ratio of transitions over transversions (Ts/ Tv) as a diagnostic measure to assess the quality of our sequencing data. The average Ts/Tv ratio observed in our whole-genome sequencing data was 2. 12 and ranged from 2. 05 on BTA6 to 2. 35 on BTA25 (Fig. 1 ). This average rate is within the same range as those observed in other species. For example, in human whole-genome sequence data, the genome-wide Ts/Tv ratio ranged from 2. 0 to 2. 2  [19, 20]. In mouse and pig, similar ratios were reported i. e. about 2. 0  [21]  and 2. 04 ± 0. 28  [22] , respectively. DePristo et al. [19]  indicated that the Ts/Tv ratio should be around 2. 1 for whole-genome sequencing and that lower ratios may indicate that the sequencing data includes false positives caused by random sequencing errors.
Therefore, the Ts/Tv ratio estimated in our study is indicative of good sequencing data quality. Second, we measured the call rate by estimating the percentage of samples presenting a known genotype for each variant. On average, 95% of the variants were called in more than 90% of the samples with 13% (3,655,506) of the variants being genotyped in all 274 samples (Fig. 2 ). Third, we compared our sequencing data-derived genotypes to SNP array-derived genotypes using the Illumina High-Density (HD) Bovine SNP BeadChip ® which includes 777,962 SNPs  [23]. Overall, both genotyping data sources were available for 152 samples. The average genotype concordance rate was around 99. 1% and ranged from 91. 7 to 99. 8% (see Additional file 3). We also observed a dependency of chip genotype concordance on sequencing depth (see Additional file 3; Fig.
3 ). Lower accuracy rates were found for samples with a low depth of coverage (less than 10×). For 21 samples, the concordance rate was less than 98% but their depth of coverage was higher than 11×. Of these 21 samples, 20 had a concordance rate between 95 and 97% and were considered as acceptable. The observed lower concordance rates could be partly due to lower sequence data quality compared to the rest of our sample set. The low missing rate and high concordance rate observed in our study can be explained by the good average genome-wide sequence coverage of the mapped reads in our data. Indeed, more than 86% of our samples were sequenced at least at an average 10-fold coverage. Another reason is the efficiency of our variation calling strategy.
Functional annotation of small genome variants Functional annotation of the identified small genome variants was carried out using the Ensembl VEP annotation software  [10]. Overall, 66% of the annotated variants were located in intergenic regions and almost 30% were identified within gene intronic sequences (Table  3 ). The remaining 4% were located within gene-coding, upstream and downstream regions. Of these, 85,038 variants were located within the 5′ or 3′ untranslated regions (UTR), 171 were located within genes coding for micro RNAs (miRNAs), 96,711 missense mutations were identified within gene coding regions, 358 InDels were predicted to cause inframe insertions and 814 InDels were predicted to cause inframe deletions. Overall, we identified 2120 variants that affected splice sites.
Integrated Genome Viewer (IGV) visualization and inspections of BAM files for animals carrying these mutations revealed that four of the seven frameshift mutations were spurious variant calls (results not shown). The three remaining frameshift variants could be visualized and confirmed by IGV and were therefore considered as true variants. First, a five nucleotide insertion (-/CACGT) at position 66,552,044 on BTA1 was identified in two Blonde d' Aquitaine animals. This frameshift mutation was absent in both the Ensembl database and in the most recent 1000 bull genomes project dataset which contains small genomic variations for 1577 animals corresponding to 48 different breeds (Daetwyler HD, personal communication). This mutation affects the results in the addition of ten new amino acids followed by a new downstream termination site.
The two other frameshift mutations were identified in the Charolaise breed. The first one is a GACC insertion at position 149,472 on BTA19 and is located within an olfactory receptor gene coding sequence (ENS-BTAG00000045560). This variant was identified in three samples in our dataset and is also present in the Ensembl database. It leads to a frameshift mutation within the  7tm_1 (PF000001) pfam domain at amino acids 81 and 82 and creates 39 new amino acids followed by a termination site, thus producing a truncated protein, which contains only 26% (82 of 311 amino acids) of the wild type protein. The second frameshift mutation is a four nucleotide (-/AGTT) insertion identified at position 21,913,213 on BTA18. It was identified in two samples in our panel but it is absent in the Ensembl database.
It is located within the retinoblastoma-like 2 (RBL2) coding gene region and leads to a frameshift mutation within the RB_B box (PF01857 pfam domain) at amino acids 890-891, thus introducing 26 new amino acids before creating a premature termination site. Thus, a truncated protein representing only 78% (890/1140 amino acids) of the wild type protein is produced. RBL2, also called pRb2/p130, is a member of the retinoblastoma family of tumor suppressors  [37]  and its expression was reported to be altered in several cancer types  [38] [39] [40]. RBL2 interacts with the E2F4 and E2F5 transcription factors and results in negative regulation of the cell cycle  [41]. It is also involved in many other cellular processes, such as regulation of apoptosis and differentiation  [37]  and control of the length of telomeres  [42].
Conclusions In this study, we performed a pan-genome assessment of small genome variations in cattle using whole-genome sequence data. Analysis of WGS data of 274 animals from both dairy and beef cattle breeds allowed the identification of over 28 millions small variations, among which we identified more than 25 million SNPs and around 3 million small insertions and deletions. To assess the quality of both our sequencing data and calling approach, we analyzed the transition to transversion ratio and the call rate, and we also compared the sequence-derived genotypes with array-derived ones. Results from all these analyses confirmed the efficiency of our sequencing data as well as the good quality of our variant calling procedure. Annotation of these variants revealed several deleterious missense and loss-of-function variants, among which we identified several mutations that were previously reported to be associated with either dairy or beef traits.
Acknowledgements This work was funded by  INRA  ( GenSSeq project from Selgen metaprogram ), the  Agence Nationale de la Recherche  and  Apis-Gene jointly  (Cartoseq  ANR-10-GENM-018 , Gembal  ANR-10-GENM-0014 ), the  Limousin Région (BovSeq) ,  Apis-Gene (ReproSeq, Akelos), Valogene (Valoseq)  and the  INRA Animal Genetics division  ( BOVATAX AAPGA 2012 ).
Identification of SNPs and small InDels A search for small genome variations with the GATK- UnifiedGenotyper software resulted in the identification of 28,164,518 variants, of which 25,210,883 were SNPs, 1769,413 small deletions and 1184,222 small insertions. Almost 87% of the deletions and 93% of the insertions identified in our study were 1 to 3 bp long (see Additional file 2). The largest deletions and insertions identified were respectively 58 and 29 bp long (see Additional file 2). Overall, 73% of the identified variants (20,647,361) were known in the Ensembl variation 83 database (build 143). The remaining 27% were considered as novel variants and should contribute to better highlight the genetic variabil- ity in cattle.
Videomicroscopy, confocal microscopy, and image acquisition Parasites were collected within a few hours following spontaneous egress from the HFF monolayers and washed in Hanks' Balanced Salt Solution (HBSS) supplemented with 1 % FCS (HBSS-FCS). Time-lapse video microscopy was conducted in Chamlide chambers (LCI Corp. , Seoul, Korea) installed on an Eclipse Ti inverted confocal microscope (Nikon France Instruments, Champigny sur Marne, France) with a temperature-and CO 2 -controlled stage and chamber (LCI Corp. , Seoul, Korea), equipped with a CoolSNAP HQ2 camera (Photometrics, Roper Scientific, Lisses, France) and a CSU X1 Yokogawa spinning disk (Roper Scientific, Lisses, France), three lasers (with excitation wavelength λ 491, 561, and 642 nm), and three dichroic mirrors. The microscope was piloted using Metamorph software (Universal Imaging Corporation, Roper Scientific, Lisses, France), and images of parasitecell interaction were acquired with settings including 1 or 2 frames/s for up to 40 min, and 1 frame/30 s for up to 6 h when assessing the viability of internalized parasites.
Image analysis Image stacks for every event of interest were prepared and annotated with time, bar scale, and arrows with Metamorph software from the raw image data file. Next ImageJ software (Rasband, W. S. , ImageJ, US National Institutes of Health, Bethesda, MD, USA, http://imagej. nih. gov/ij/, 1997-2014) was used to add some additional labels on the time lapses, movies, and maximal projections from confocal z stacks. In some video sequences, the "Manual tracking" plug-in was used to track in time the spatial xy positions of the parasite's constriction site  [17].
Scanning electron microscopy ARPE-19 cells were grown at 80 % confluency for 24 h on poly-L-lysine-coated glass coverslips and incubated with either RH-MyoA, LoxMyoA, or ΔMyoA tachyzoites for 4-5 min (MyoA + ) and 15-20 min (ΔMyoA). After cell fixation in 2. 5 % glutaraldehyde in 0. 1 M cacodylate buffer (pH 7. 2) (1 h, 4 °C), samples were washed in 0. 1 M cacodylate buffer (pH 7. 2) (12 h, 4 °C), ethanol dehydrated, and critical point dried in CO 2 atmosphere with an Emitech K850 apparatus (Quorum Technologies, Laughton, UK). Coverslips containing the infected monolayers were attached to SEM aluminum holders and were gold coated using a JEOL SEM instrument, JPC-1200 (JEOL, Freising, Germany) and analyzed with the Scanning Electron Microscope SU3500 (Hitachi, Tokyo, Japan). Digital images were recorded, and photocompositions were realized with ImageJ and Photoshop software.
ΔMyoA tachyzoites were settled on top of the cells by gentle centrifugation (2 min, 250 g) and incubated at 37 °C and 5 % CO 2 for 15 min before PFA fixation. Following sequential labeling of extra-and intracellular tachyzoites with anti-TgP30 and anti-TgGRA1 antibodies and differential fluorophore-coupled secondary antibodies, respectively, prior and post TX-100 permeabilization, Hoechst 33242 was added to label all nuclei (mammalian cells and parasites), and the samples were automatically scanned at a magnification of × 20 under an Olympus Scan^R automated inverted microscope (3 wells per condition, 16 fields of acquisition per well). Image processing with the Cell^R software successively included signal-to-noise ratio optimization to allow cell nuclei segmentation, channel-associated image detection, and image subtraction (extracellular zoites subtracted from extra-plus intracellular tachyzoites), and intracellular tachyzoite segmentation using an edge detection algorithm.
The whole assay including imaging procedure was applied to samples of untreated pre-starved ARPE-19 and HFF cells that were incubated in complete medium supplemented with 20 % FCS but no phenol red with ΔMyoA tachyzoites to which Alexa 594 dextran (500 ug/mL) was added for 10 min after the parasite centrifugation step. Samples were fixed in PFA, permeabilized, and stained with anti-T. gondii ROP2 antibodies to observe macropinosomes and nascent PVs. Control of the efficiency of macropinocytosis inhibitors was performed under the conditions used for invasion assays and assessed by quantifying the number of macropinosomes positive for fluorescent Alexa 594 dextran (500 μg/ mL, 10 kDa) as described  [56]. Statistics were performed with GraphPad Prism software. To check whether JLY treatment was cytotoxic for the U2OS cells, we assessed viability by pre-loading them with cell-permeant calcein-AM (0.
Acknowledgements We are grateful to  Pr Pascale Gueirard  ( Faculté Medecine et Pharmacie, Clermont-Ferrand, France ) and  Geraldine Toutirais  ( PTME, Museum National d'Histoire Naturelle, Paris ) from the electron microscopy and microanalysis facilities for their assistance with SEM. We thank  G. Milon  ( Institut Pasteur, Paris ),  V. Delorme-Walker  ( Scripps Institute, La Jolla, CA ),  D. Ferguson  ( University of Oxford, Oxford, UK ),  G. Charras  ( UCL, London ), and  A. Chitnis  for careful reading and editing of the manuscript. Funding IT's laboratory has been funded by the  Fondation pour la Recherche Médicale  ( FRM-DEQ20100318279 ).
Quantification of c-fos positive (c-fos ? ) nuclei Quantification was performed by identifying spot positions. c-fos ? nuclei were counted with ICY software (http://icy. bioimageanalysis. org/) after acquiring images using a digital camera (Nikon DXM 1200) of an Olympus BX600 microscope coupled to software (Mercator Pro; Explora Nova, La Rochelle, France). The constant use of a 10 9 Plan Apo objective allowed us to have good resolution for c-fos immunochemistry. The focus was set on the upper face of each section before digitization. Each region of interest (ROI) was delimited on the screen for each picture based on the mouse atlas  (Paxinos and Franklin 2001). ICY software directly counts the number of cells in the ROI. The density of cell per square micrometer was calculated after and normalized in relation to the control.
HPLC dosage Amount of dopamine (DA), serotonin (5-HT) and noradrenaline (NA) was quantified using high performance liquid chromatography (HPLC) techniques. Prior to analysis, brain tissues were crushed in 350 lL of 0. 2 M perchloric acid and centrifuged at 22,000g for 20 min at 4 °C. The supernatants were collected and filtered through a 10 kDa membrane (Nanosep, Pall) by centrifugation at 7000g. Then, a 20 lL aliquot of each sample was analyzed for 5-HT by fluorometric detection  (Kema). The amounts of catecholamines (dopamine and noradrenaline) were measured by electrochemical detection on a serial array of coulometric flow-through graphite electrodes (CoulArray, ESA)  (Gamache). Analysis, data reduction, and peak identification were fully automated. Results were expressed as fentomoles/milligram of fresh tissues  (Gamache et al.
Sub-group formation To distribute animals among groups regarding their performances we calculated the mean of 30 last trials (i. e. when performances was stable) and used a k-mean clustering separation with Statistica Ò software (version 12)  (Timmerman et al. 2013) , so that animal belonged to a set that had the closest mean to its own performance value. Three groups were defined: animals which chose mostly advantageous options at the end of the experiment, thereafter called ''safe'' group, animals which explored the different options at the end of the experiment, thereafter called ''risky'', and animals which exhibited an intermediate behavior and distributed their choices between sporadic risky choices and high proportion of advantageous choices, thereafter called ''average''.
Acknowledgments This research was funded by the  Direction Generale de l'Armement (DGA , Grants  # PDH-1-SMO-2-505 ), the  CNRS  and  Paris Sud University. We thank  A. Faure  for helpful comments, A. Cressant,  N. Samson ,  B. Poly ,  M. Lefe  `vre,  S. Antoine  for technical or experimental assistance. We particularly thank  F. Canini  for his attentive reading of the manuscript.
Both genomic coordinate data (e. g. , ChIP-seq peak coordinates) and quantitative data (e. g. , ChIP-seq signal intensity log ChIP Input ) were generated. From the simulated data, multiple logistic regression model parameters were then estimated by maximum likelihood. We first simulated 100 genomic coordinate and 100 quantitative datasets that comprised 6 proteins and learned models without considering any interaction terms. In Fig  2a , we plotted true against estimated parameter values. We reported a very good accuracy for parameter estimation for both genomic coordinate and quantitative data with R 2 = 99. 5% (p < 1 × 10 -20 ) and R 2 > 99. 9% (p < 1 × 10 -20 ) between true and estimated parameter values, respectively. Because some proteins might be rare over the genome and only involved in some 3D domain borders, we studied parameter accuracy for simulated proteins with varied ChIPseq peak numbers.
Parameter estimation was highly accurate even for proteins with a low number of peaks over the genome (R 2 = 97. 4% for 50 peaks; S1 Fig). In addition, we sought to assess how parameter estimation is affected by 3D domain border inaccuracy of few kilobases. We observed that with a border inaccuracy equal or lower than 2 kb, parameter estimation was still accurate (R 2 > 70. 9%, S2 Fig). We then simulated 100 genomic coordinate and 100 quantitative datasets that comprised the same 6 proteins and learned models with all two-way (e. g. X 1 X 2 ) interaction terms. In Fig  2b , we plotted true against estimated parameter values corresponding to interaction terms only. Parameter estimation accuracy was still high for both genomic coordinate data (R 2 = 94. 6%, p < 1 × 10 -20 ) and quantitative data (R 2 = 99.
MLR outperforms enrichment test and random forests to identify drivers of TAD borders We then sought to assess how multiple logistic regression (MLR) efficiently identifies genomic features that influence TAD borders, comparing with other approaches commonly used to assess the link between TAD borders and genomic features. We compared our model with enrichment test (ET)  [4]  and non-parametric model  [23]. For the non-parametric model, we used random forests (RF) which are very similar to the model used in  [23] , but for which a scalable implementation allowed high resolution analysis (https://github. com/aloysius-lim/bigrf). For this purpose, we first simulated 100 datasets comprising 11 genomic features {X 1 , X 2 ,. , X 11 } that were similar to real ChIP-seq data (see Subsection Materials and Methods, Data simulation, second paragraph).
We then compared empirical type I error rate for a significance threshold α = 10 -5 between enrichment test and multiple logistic regression for which p-values on beta coefficients were available (Fig  3c ). Even for a high odds ratio of 8,  Comparison of MLR beta parameters with ET beta parameters and RF variable importances obtained from 100 simulated datasets including 11 genomic features. Among the genomic features, variables X 1 and X 10 were chosen to be causal. For a method, a blue check mark denotes a causal or non-causal variable that was correctly identified as causal (resp. non-causal). A black x mark denotes a causal or non-causal variable that was incorrectly identified as non-causal (resp. causal). b) Percents of causal variables ranked first by ET, MLR and RF computed from 100 simulated datasets and varying odds ratios.
Here the causal variables and their number were randomly drawn at each simulation. c) Type I error rates for MLR and ET computed from 100 simulated datasets. RF were not included because no p-values were available. The significance threshold α was set to 10 -5. Simulated data were the same as in b). d) Comparison of MLR with ET and RF to detect known or suspected architectural proteins in human using GM12878 cell ChIP-seq data. Receiver operating characteristic (ROC) curves were computed from Wald's statistics for ET, from beta parameters for MLR, and from variable importances for random forests. Computations were carried out at 1 kb resolution. MLR had a low error rate of 16%. Conversely enrichment test showed a high error rate of 75% even for an odds ratio of 2. We also compared MLR with ET and RF using real data in human.