dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, the mosaic nature of a global annotation effort that gathers contributions from various partners worldwide emphasizes the challenge of translating recent advances from the field of data science into efficient methods for the integrative analysis of 'omics data and the importance of future metanalysis of several datasets. Altogether, these annotation results will be useful for future studies aiming to determine which subsets of putative regulatory elements are conserved, or diverge, across animal genomes representing different phylogenetic taxa. This will be beneficial for devising efficient annotation strategies for the genomes of emerging domesticated species.
RNA-seq pipeline Prior to any processing, all RNA-seq reads were trimmed using cutadapt version 1. 8. 3. Reads were then mapped twice using STAR v2. 5. 1. b [48, 49] : first on the genome indexed with the reference gene annotation to quantify expression of reference transcripts, and secondly on the same genome indexed with the newly generated gene annotation (FR-AgENCODE transcripts) (see below and Figure  S1 )  [50]. The STAR --quantMode TranscriptomeSAM option was used in both cases in order to additionally generate a transcriptome alignment (bam) file. After read mapping and CIGAR-based softclip removal, each sample alignment file (bam file) was processed with Cufflinks 2. 2. 1  [51, 52]  with the max-intron-length (100000) and overlap-radius (5) options, guided by the reference gene annotation (--GTF-guide option) (  [50] , Figure  S1 ).
All cufflinks models were then merged into a single gene annotation using Cuffmerge 2. 2. 1  [51, 52]  with the --ref-gtf option. The transcript and gene expressions on both reference and newly generated gene annotation were quantified as TPM (transcripts per million) using RSEM 1. 3. 0 [53]  on the corresponding transcriptome alignment files (  [50] , Figure  S1 ). The newly generated transcripts were then processed with FEELnc version 0. 1. 0 [34]  in order to classify them into "lncRNA", "mRNA" and "otherRNA" (Figure  S1 , Tables  S9-10 , Figure  S10 ). The newly generated transcripts with a TPM value of at least 0. 1 in at least 2 samples were called FR-AgENCODE transcripts and kept as part of the new annotation. The 0. 1 threshold was chosen knowing that the expression values of polyadenylated transcripts usually go from 0.
PCA based on gene expression Principal Component Analysis (PCA) was performed using the mixOmics R package  [55]  on the RNA-seq sample quantifications of each species. This was done using the expression (TPM) of two different sets of genes: reference genes with TPM 0. 1 in at least two samples (Figure  S3 ) and FR-AgENCODE genes with TPM 0. 1 in at least two samples (Figure  S15 ).
RNA-seq normalization and differential analysis To perform the differential analysis of gene expression, raw read counts were first measured by counting the number of RNA-seq reads overlapping the exonic regions of each gene in each library using bedtools  [57]. RNA-seq library size normalization factors were calculated using the weighted Trimmed Mean of M-values (TMM) approach of  [58]  as implemented in the R/Bioconductor package edgeR  [18]. The same package was used to fit three different per-gene negative binomial (NB) generalized log-linear models  [59]. • In Model 1, the expression of each gene was explained by a tissue effect; because all three tissues (liver, CD4, CD8) were collected from each animal, an animal effect was also included to account for these repeated measures: log µ gi s i = β g,tissue(i) + γ g,animal(i) , where µ gi represents the mean expression of gene g in sample i, s i the TMM normalization factor for sample i, tissue(i) ∈ {liver, CD4, CD8} and animal(i) ∈ {1, 2, 3, 4} the tissue and animal corresponding to sample i, and β g,tissue(i) and γ g,animal(i) the fixed tissue and animal effects, respectively, of gene g in sample i.
GO analysis of differentially expressed genes For each species, GO term enrichment analysis was performed on the genes found to be over-expressed in liver versus T cells, and reciprocally. This analysis was done separately for each species (Figures  S4-S6 ) and then on genes present in all species (Figure  S7 ), using the three following ontologies: biological process (BP), molecular function (MF) and cell compartment (CC), and using the GOstat R/Bioconductor package  [61] ). This analysis tests the differential genes against all the genes, and here we always restricted analyses to genes with a human ortholog.
FR-AgENCODE transcript coding classification The FR-AgENCODE transcript models were also classified according to their coding potential. For this, the FEELnc program (release v0. 1. 0) was used to discriminate long non-coding RNAs from protein-coding RNAs. FEELnc includes three consecutive modules: FEELnc filter , FEELnc codpot and FEELnc classifier. The first module, FEELnc filter , filters out non-lncRNA transcripts from the assembled models, such as transcripts smaller than 200 nucleotides or with exons strandedly overlapping exons from the reference annotation. This module was used with default parameters except -b transcript_biotype=protein_coding, pseudogene to remove novel transcripts overlapping protein-coding and pseudogene exons from the reference. The FEELnc codpot module then calculates a coding potential score (CPS) for the remaining transcripts based on several predictors (such as multi k-mer frequencies and ORF coverage) incorporated into a random forest algorithm  [62].
In order to increase the robustness of the final set of novel lncRNAs and mRNAs, the options --mode=shuffle and --spethres=0. 98,0. 98 were set. Finally, the FEELnc classifier classifies the resulting lncRNAs according to their positions and transcriptional orientations with respect to the closest annotated reference transcripts (sense or antisense, genic or intergenic) in a 1Mb window (--maxwindow=1000000). It is worth noting that between 83 and 2,718 lncRNA transcripts were not classified because of their localization on the numerous unassembled contigs in livestock species with no annotated genes.
Contrary to RNA-seq counts, ATAC-seq counts exhibited trended biases visible in log ratio-mean average (MA) plots between pairwise samples after normalization using the TMM approach, suggesting that an alternative normalization strategy was needed. In particular, trended biases are problematic as they can potentially inflate variance estimates or log foldchanges for some peaks. To address this issue, a fast loess approach  [66]  implemented in the normOffsets function of the R/Bioconductor package csaw  [67]  was used to correct differences in log-counts vs log-average counts observed between pairs of samples. As for for RNA-seq, we used three different differential models: Model 1 for tissue pair comparisons, Model 2 for T cell versus liver comparisons and Model 3 for tissue pair and sex comparisons taking the sex effect into account (see corresponding RNAseq section for more details).
In any of the pairwise tissue comparisons or any of the four species, 26% to 56% of ATAC-seq peaks identified as differentially accessible (DA) in Model 1 or 3 were commonly identified as DA by both Models 1 and 3. Although sex-specific effects appeared to be stronger in the ATAC-seq data than in the RNA-seq data, for simplicity and consistency, we have focused our discussions on results from Models 1 and 2 in the manuscript, unless specifically discussing sex effects. ATAC-seq peak TFBS density. In order to identify Transcription Factor Binding Sites (TFBS) genome-wide, we used the FIMO  [68]  software (Table  S3 ) to look for genomic occurrences of the 519 TFs catalogued and defined in the Vertebrate 2016 JASPAR database  [69]. We then intersected these occurrences with the ATAC-seq peaks of each species and computed the TFBS density in differential vs non differential ATAC-seq peaks (Figure  S18 ).
Hi-C data analysis pipeline Our Hi-C analysis pipeline includes HiC-Pro v2. 9. 0  [70]  (Table  S3 ) for the read cleaning, trimming, mapping (this part is internally delegated to Bowtie 2 v2. 3. 3. 1), matrix construction, and matrix balancing ICE normalization  [71]. TAD finding was done using Armatus v2. 1  [45]  (Table  S3 ). Graphical visualization of the matrices was produced with the HiTC R/Bioconductor package v1. 18. 1  [44]  (Table  S3 ). Export to JuiceBox  [72]  was done through the Juicer Tools v0. 7. 5 (Table  S3 ). These tools were called through a pipeline implemented in Python. Because of the high number of unassembled scaffolds (e. g. for goat) and/or micro-chromosomes (e. g. for chicken) in our reference genomes, only the longest 25 chromosomes were considered for TAD and A/B compartment calling.
Within-chromosome Pearson correlation matrices were then computed for all pairs of bins based on their distancecorrected counts and a PCA was performed on this matrix. The overall process was performed similarly to the method implemented in the R/Bioconductor package HiTC  [44]. Boundaries between A and B compartments were identified according to the sign of the first PC (eigenvector). Since PCAs had to be performed on each chromosome separately, the average counts on the diagonal of the normalized matrix were used to identify which PC sign (+/-) should be assigned to A and B compartments for each chromosome. This allowed to automatically obtain an homogeneous assignment across chromosomes. In line with what was originally observed in human, where the first PC was the best criterion for separating A from B compartments (apart from few exceptions like the chromosome 14 for instance  [15] ), we also observed a good agreement between the plaid patterns of the normalized correlation matrices and the sign of the first PC (Figure  S23 ).
Figure4A novel lncRNA conserved across multiple species. Phylogenetic representation based on the NCBI taxonomy of the 22 annotated species from fishes to mammals using iTOL [73]  (A) Three gene syntenic region centered on CREMos lncRNA with NCBI IDs for lncRNAs already annotated in reference databases and distance between entities in nucleotides ; The cases where CREM and CREMos genes are overlapping are indicated by the "0*" distance. (B) Expression of the 3 genes in cattle, goat, chicken and pig: CREMos is generally less expressed in liver than in CD cells (in cattle, chicken and pig) whereas CREM is generally more expressed in liver than in CD cells (in cattle, chicken and goat).
Acknowledgements We would like to thank the  FAANG community  for the general support and in particular  A. Archibald  ( Roslin Institute, UK ),  L. Clarke ,  P. Harrison  ( EMBL-EBI, UK ) and  M. Groenen  ( WUR, Netherlands ) for their collaboration in the organization of the project, and  B. Rosen  ( ARS, USDA ) for providing information about the goat sexual chromosomes. We wish to thank all field operators at  INRA  experimental animal facilities, units and platforms in France for the access and the assistance in animal handling and sampling, including:  Yves Gallard ,  UE Le Pin  ( Gouffern en Auge ),  Frédéric Bouvier  and  Thierry Fassier ,  UE Bourges (Osmoy) ,  Stéphane Ferchaud ,  UE GenESI (Magneraud/Rouillé) ,  Yannick Baumard ,  UE PEAT (Nouzilly) ,  Cécile Berri  and  Joel Gautron ,  UR BOA (Nouzilly) ,  Gilles Gomot ,  Jean-Philippe.
Funding This study has been supported by the  INRA "SelGen metaprogramme" , grant " FR-AgENCODE: A French pilot project to enrich the annotation of livestock genomes " ( 2015-2017 ). S. Djebali  and  A. Rau  are supported by the  Agreenskills fellowship program  with funding from the  EU 's  Seventh Framework Program  under grant agreement  FP7-609398. Additional financial support for tissue biobanking was provided by the  CRB-Anim infrastructure  project,  ANR-11-INBS-0003 , funded by the  French National Research Agency  in the context of the " Investing for the Future" program.
g. , References  [23] [24] [25] ). These types of eye tracking devices have several advantages in comparison with the traditional ones, as they have a low-cost and a typically a small size, which facilitates their transport and the use of multiple trackers at the same time  [26]. An excellent presentation of the current panorama in low-cost eye tracking services (methods, datasets, software etc. ) is provided in a recent review article by  Ferhat and Vilariño (2016)   [27]. Among the existing commercial devices, Eye Tribe (The Eye Tribe © , The Eye Tribe, Copenhagen, Denmark) has already been used in several research studies and applications (e. g. , References  [28] [29] [30] [31] ). Based on the results of a recent study, presented by  [26] , the accuracy and the precision of this device may be considered comparable with these of well-established trackers.
Dalmaijer (2014)   [32]  was the first to question whether this new low-cost eye tracker could be used for research purposes. He compared the accuracy and precision of the tracker from The Eye Tribe (60 Hz sampling rate) with that of the high-quality Eye-Link100 (from SR Research, 1000 Hz sampling rate). He concluded that the device was suitable for analyses related to fixations, points of regard and pupillometry, but that the low sampling rate hinders it use for high-accurate saccade metrics. The paper also introduces some open-source toolboxes and plugins (e. g. , Pytribe, PyGaze plugin for OpenSesame, and Eye Tribe Toolbox for Matlab) to communicate with the Eye Tribe Tracker. This study was further extended by  Ooms et al. (2015)   [26] , who measured the accuracy and precision of the Eye Tribe Tracker at 30 Hz and 60 Hz, comparing the recordings registered through different tools (JAVA using the API and OGAMA) and applying different fixation detection tools (OGAMA and EyeMMV).
The obtained results were compared with those registered with a high-qualitative eye tracker from SMI (RED250) which recorded eye movements at 60 Hz and 120 Hz. These authors also concluded that, when set-up correctly and used in a correct context, the Eye Tribe tracker can be a valuable alternative for the well-established commercial eye trackers. Popelka et al. (2016)   [33]  also compared the results of the Eye Tribe tracker with those registered with an SMI RED250, but they performed a concurrent registration with both eye trackers. The data recorded from the Eye Tribe tracker was registered using HypOgama, which is a combination of OGAMA (to record the eye tracking data itself) and Hypothesis (to register additional quantitative data). They also registered minimal deviations between both systems.
When focusing on the studies which actually used the eye tracker, most of them did not mention the sampling rate at which the recordings were registered. Nevertheless, the experiments had to be set-up, the data registered and finally analyzed using some kind of software. Most of them do not mention specific software or indicate they used the accompanying SDK (e. g. , PeyeTribe-a python interface)  [34]. Other authors managed to combine the Eye Tribe tracker with other equipment such as EEG or Emotive EPOC  [29, 35].
Research with Webcams or DIY Eye Trackers Besides the rise of the commercially available low-cost eye trackers, such as the Eye Tribe Tracker, other related trends could be noticed in scientific research: Do-it-yourself (DIY) or home-made eye trackers. These DIY eye trackers are typically constructed using a webcam and an IR light source (e. g. , Reference  [23] ). These eye trackers are also often accompanied by custom made open-source software tools, such as open Eyes  [36] , ExpertEyes  [25]  and ITU Gaze-Tracker  [37]. The reported costs for these DIY eye trackers varies from 17 to 70 euros and the reported accuracies from "less than 0. 4 • "  [25]  to 1. 42 •  [23]. Recently, webcams (stand-alone or integrated in a laptop, without additional IR-lighting) are also used to track a users' eyes (e.
Apparatus All data were registered with an Eye Tribe Tracker (see Table  1  for specifications). For the AP, the eye movements were recorded at 30 Hz and 60 Hz. Furthermore, the device was connected with three types of monitors on which different settings for the resolution were selected. An overview of the specifications of the three monitors can be found in Figure  3. The default resolution of the monitors is underlined, and the colors indicate which resolutions were available on multiple monitors. The data of the RPs was only recorded at 60 Hz, using M2 with its default resolution. software takes a multi-layered detection approach, similar to what is described by Gómez-Poveda and Gaudioso (2016)  [21] , starting with the distinction of the shape of the head. Therefore, a curved surface is required, which was approximated by the egg-shape.
Apparatus All data were registered with an Eye Tribe Tracker (see Table  1  for specifications). For the AP, the eye movements were recorded at 30 Hz and 60 Hz. Furthermore, the device was connected with three types of monitors on which different settings for the resolution were selected. An overview of the specifications of the three monitors can be found in Figure  3. The default resolution of the monitors is underlined, and the colors indicate which resolutions were available on multiple monitors. The data of the RPs was only recorded at 60 Hz, using M2 with its default resolution. Next, five real participants were invited to take part in the test. These were all employees of the Department of Geography at Ghent University who were asked to execute a simple task on the screen (see Section 3.
Apparatus All data were registered with an Eye Tribe Tracker (see Table  1  for specifications). For the AP, the eye movements were recorded at 30 Hz and 60 Hz. Furthermore, the device was connected with three types of monitors on which different settings for the resolution were selected. An overview of the specifications of the three monitors can be found in Figure  3. The default resolution of the monitors is underlined, and the colors indicate which resolutions were available on multiple monitors. The data of the RPs was only recorded at 60 Hz, using M2 with its default resolution.
Procedure All tests were conducted at the Eye Tracking Laboratory of the Department of Geography at Ghent University. The eye movement data was recorded using PyTribe, a Python wrapper for the Eye Tribe tracker-based on PyGaze7-which can be downloaded from GitHub  [32, 71]. The tests with the AP did not require any calibration as the AP's gaze is fixed on one point. In total 50 trials of five minutes each were recorded with the AP (25 different "monitor x resolution"-combinations and two sampling rates), which were saved in a text file. This file contains a timestamp (in ms), state (how many eyes were detected), the x and y locations of the gaze (left and right eye separately and combined, including the raw and average values) and information on the pupil (size and position). For each of the 50 trials, the deviation (Euclidean distance between target and gaze position) was calculated expressed in pixels and in visual angle, considering a theoretical viewing distance of 50 cm, 55 cm, 60 cm, and 65 cm for the left eye, right eye, and the combined value.
The tests with the RPs were preceded by a calibration procedure-using the Eye Tribe UI-based on five calibration points. Similarly, as with the AP, all eye movements were recorded using the PyTribe code, in which the stimuli (including their timings) could be implemented. The data is processed using different manual and automatic procedures in order to optimize its quality. The Eye Tribe Tracker provides two sets of positions-The raw positions (raw) and average (avg) positions. The latter are already initially processed by the Eye Tribe server while recording-smoothed positions. First these two data sets are manually filtered based on two criteria:.
2. The recordings indicated by a state "8" are removed. This corresponds to the situation where the eye tracker was unable to determine the position of the right and left eye. Next, the data (avg and raw, both unfiltered and filtered) is further processed by applying a fixation detection which is capable of removing noise. The identification of fixation events is based on the implementation of the dispersion-based algorithm of EyeMMV toolbox  [52]. This toolbox was selected because  (1)  it is open-source, and (  2 ) it has the capability to filter out noise when detecting samples within the raw data. This algorithm implements both spatial and temporal criteria: The spatial dispersion threshold is implemented in two steps, while the temporal one corresponds to the minimum value of fixation duration.
This way it can be discovered if there is a systematic deviation between the recorded values and the real values, which allows comparing precision versus accuracy. Furthermore, the raw versus avg data sets (both unfiltered) are considered. The graphs clearly indicate that a smaller deviation is registered when comparing to the real target position (related to precision) instead of the mean position values (related accuracy). Furthermore, the deviations are larger in the X than in Y. Figure  9  shows the deviations (expressed in visual angle, at a viewing distance of 60 cm) for each of the target points when processing the original gaze data (13 targets) in X (left) and Y (right). The values are, on the one hand, compared to the real X and Y positions of the targets and, on the other hand, to the mean value of the recorded X and Y positions (M(avgX) or M(avgY)).
This way it can be discovered if there is a systematic deviation between the recorded values and the real values, which allows comparing precision versus accuracy. Furthermore, the raw versus avg data sets (both unfiltered) are considered. The graphs clearly indicate that a smaller deviation is registered when comparing to the real target position (related to precision) instead of the mean position values (related accuracy). Furthermore, the deviations are larger in the X than in Y. fixated and a fixed distance around each target point. This approach is similar to the results obtained when the 13 points were displayed separately. Figure  9  shows the deviations (expressed in visual angle, at a viewing distance of 60 cm) for each of the target points when processing the original gaze data (13 targets) in X (left) and Y (right).
The values are, on the one hand, compared to the real X and Y positions of the targets and, on the other hand, to the mean value of the recorded X and Y positions (M(avgX) or M(avgY)). This way it can be discovered if there is a systematic deviation between the recorded values and the real values, which allows comparing precision versus accuracy. Furthermore, the raw versus avg data sets (both unfiltered) are considered. The graphs clearly indicate that a smaller deviation is registered when comparing to the real target position (related to precision) instead of the mean position values (related accuracy). Furthermore, the deviations are larger in the X than in Y. Figure  10  focusses on the deviations for each of the target position, compared to their real position. For the 13 targets, a large difference between the filtered and unfiltered data is apparent, which is not the case for the data when there were five targets.
This is also confirmed by the Univariate ANOVA test presented in Table  4 , which also shows the interaction effect between filtering and the type of target. Although the overall values for the five-target stimuli are much lower, no significant difference is found. The difference between the avg and raw values is also not found to be significant. Figure  10  focusses on the deviations for each of the target position, compared to their real position. For the 13 targets, a large difference between the filtered and unfiltered data is apparent, which is not the case for the data when there were five targets. This is also confirmed by the Univariate ANOVA test presented in Table  4 , which also shows the interaction effect between filtering and the type of target. Although the overall values for the five-target stimuli are much lower, no significant difference is found.