dataesr/scientific-paragraphs-categorization huggingface dataset:
1-B ). Interestingly, similar observations were made in Drosophila where Y-linked TE expression is stronger in individuals with an additional Y chromosome (i. e. XYY) compared to XY individuals, regardless of their age  [14]. The present results suggest that a similar disruption of TE regulation is present in young individuals with a 47,XYY karyotype in humans. Furthermore, in Drosophila, Y-linked TE expression is stronger in aged flies compared to young flies for XY and XYY individuals but not for XXY individuals  [14] , which is in line with the trend we found in humans (Fig. S16 ). The analyses carried out herein support both predictions of the toxic Y chromosome hypothesis, particularly when using the dataset that we have generated. We show an over-expression of TE related to the number of Y-chromosomes in the karyotype, and suggest that a toxic Y effect may contribute to the sex gap in longevity in humans, as shown in Drosophila ((14, 15) but see  [16] ).
GTEx dataset The Genotype-Tissue Expression (GTEx) project regroups samples from a multitude of post-mortem organ donors and from a multitude of tissues. Samples were realized in PAXgene tissue fixation containers. After RNA extraction, libraries were prepared using an automated variant of the Illumina TruSeq RNA sample preparation protocol (Illumina Inc, California, USA) which was a non-stranded protocol using polyA selection. Then, libraries were sequenced in paired-end mode 76x2 on a HiSeq 2000 or 2500 ® (Illumina Inc, California, USA) (see https://gtexportal. org/home/methods and  [27] ). To constitute the GTEx dataset, we extracted raw expression data (FASTQ containing Illumina reads) from the human expression atlas database GTEx via the dbGaP portal (dbGaP accession number phs000424.
v8. p2) after authorization. We focused on expression data obtained from blood, a tissue with one of the largest numbers of samples in the GTEx database. Thus, we considered only data of RNA-seq ("Assay. Type" variable) for which a file with sra extension was available ("DATASTORE. filetype" variable) and generated from RNA extracted from whole blood ("body_site" variable). To reduce data heterogeneity and to get closer to the individuals we sampled in parallel to constitute our dataset (see below), we focused on: data collected in non-latino nor hispanic white subjects ("RACE" and "ETHNCTY" variables) and excluded data from subjects with persistent infection by the human immunodeficiency virus (HIV), the hepatitis C virus (HCV) or the hepatitis B virus (HBV) ("LBHCV1NT","LBHBHCVAB", "LBHIV1NT", "LBHIVAB", "LBHIVO", and "LBHBSAG" variables), or with dementia ("MHALZDMT" and "MHALZHMR" variables).
The median number of paired-reads (after trimming, see below) was about 44. 6 million (Min: 27. 0 -Max: 141. 1 million) for the 80 FASTQ of the no disease group and was about 44. 3 million (Min: 27. 0 -Max: 141. 1 million) for the 102 FASTQ in the group including cancer and cardiovascular diseases. Dataset generated in this study: biological sample collection for 46,XX; 46,XY; 47,XXY; 47,XYY individuals Twenty-five subjects were included: 13 had a sex chromosome aneuploidy (nine patients with a 47,XXY karyotype and four with a 47,XYY karyotype) and 12 had normal chromosomal formula (six subjects with 46,XX karyotype and six with 46,XY karyotype). Median age was 30. 0 years old (interquartile interval: 25. 0 -35. 0). Pairwise Wilcoxon test showed a significant difference in the age distribution only between 46,XX and 46,XXY (P = 0.
Bioinformatic processing Studying the TE expression is challenging due to their repeated nature. We used a methodology adapted to repeated sequences to map and estimate the expression level of TE. From this point, FASTQ files from GTEx dataset or our dataset were processed in the same way, except that kallisto, TEcount and TEspeX tools were used in non-stranded mode for GTEx dataset and in stranded mode for our dataset when necessary. See pipelines in Fig. S3  and S17. Input files FASTQ files were used after verification and improvement of quality with the FastQC ® and Trimmomatic ® softwares  [28]. After trimming, read quality was good. The whole human reference transcriptome (Human Release 32 GRCh38/hg38, gencode v32) was downloaded from the University of California Santa Cruz (UCSC) table browser (https://genome.
The human reference sequence file for repeats (including TE copy sequences) was downloaded on the U C S C t a b l e b r o w s e r ( https://genome. ucsc. edu/cgibin/hgTables?hgsid=791366369_Zf0cNT7ykVM0ZQ0zErZRArSgvMEO&clade=mammal&org=Human&db= hg38&hgta_group=allTracks&hgta_track=rmsk&hgta_table=0&hgta_regionType=genome&position=chr1 %3A11%2C102%2C837-11%2C267%2C747&hgta_outputType=primaryTable&hgta_outFileName=) with output format = "sequence" (fasta file) and RepeatMasker track from the "Dec. 2013 GRCh38/hg38" assembly of the human reference genome. This file contained sequences of 4886205 repeat copies, after excluding transfer RNAs, ribosomal RNAs, small RNAs, repeats U1 to U13, and satellites (microsatellites, GSATX, HSAT5). From this file, we built the "rosetta" file (Data S1) which provided a correspondence between the 4886205 repeat copy sequence names and the repeat family names (1270 repeat families).
Alignment and read counting Using the transcript sequences from the whole human reference transcriptome, gene expression was quantified with Kallisto software (v0. 46. 1)  [30]. The --rf-stranded option was added to deal with reverse stranded RNAseq data in our dataset only. Alignment to the reference transcriptome resulted in high mapping efficiency (Table  S7 ). Alignment and read counting on repeat families from the human repeat reference sequence file were realized with TEcount (v1. 0. 0) software from TEtools (https://github. com/l-modolo/TEtools). This tool uses Bowtie2  [31]  as the aligner and we added the --nofw option to deal with reverse stranded RNAseq data in our dataset only. TEcount uses also the "rosetta" file to sum the read counts per repeat copy into read counts per repeat family  [32].
Data preparation Differential expression (DE) analysis was performed separately for genes and for TEs. Two files were prepared for each purpose. As a same gene can have several transcripts, the tximport R package (v1. 14. 2, Bioconductor) was used to sum the read counts per gene transcripts into read counts per gene using the GTF file mentioned above. Then, a file (txi file) containing the gene identifier (Ensembl nomenclature), the average gene length, and the estimated counts for all genes per sample was obtained. For DE analysis on TE, we applied on the repeat family counts a normalization factor calculated only on the gene counts to avoid masking relevant biological information. Indeed, if all the repeat families in a sample were overexpressed, this relevant biological information would be erased by the normalization factor if this one was only calculated on repeat family counts  [32].
To estimate the normalization factor, the gene identifiers and counts per gene were extracted from the txi file and merged with the TEcount file containing the repeat family names and counts per repeat family per sample. The length could not be included in the merged table since this variable was not available from the TEcount table (each repeat family is a collection of copies displaying a diversity of lengths, it is thus difficult to estimate the length of a TE family). This merged table was then imported in the DESeq2 tool (command DESeqDataSetFromMatrix). From this step, we removed counts on the following 24 snRNA, snpRNA, scRNA, or remaining satellites and rRNA repeat families: 7SLRNA, 7SK, LSAU, D20S16, REP522, SATR1, SATR2, ACRO1, ALR/Alpha, BSR/Beta, CER, 6kbHsap, TAR1, SST1, MSR1, SAR, GA-rich, G-rich, A-rich, HY1, HY3, HY4, HY5,5S.
This allowed us to only consider the 1246 families corresponding to TE families out of the 1270 repeat families contained in the rosetta file. For the DE analysis on genes, as the length was available for genes, a more reliable procedure was performed. The raw txi file was directly imported in DESeq2 (command DESeqDataSetFromTximport) and the average length of each gene could be used as a correction term (offset) in the DESeq2 statistical model described below to consider the length bias  [33, 34].
Differential expression analysis The R package DESeq2 R (v1. 26. 0, Bioconductor)  [33, 34]  was used to normalize the read counts to remove any depth sequencing bias or bias related to sequence length (when possible), and to fit a negative binomial model on the log2 of the normalized counts (dependent variable) according to two independent variables: sex (factor with 2 levels: female, male) and age group (factor with 5 levels:  [20, 30] , ]30,40], ]40,50], ]50,60], ]60,70]) for the GTEx dataset, or karyotype (factor with 4 levels: XX, XY, XXY, XYY) and batch (factor with 2 levels: first batch, second batch) for our dataset. Indeed, we found a significant effect of the sequencing batch in our dataset that had to be taken into account in the statistical model (Fig. S12  and S13 ). The statistical model provided variation of expression of a gene or a TE family between two conditions expressed as log2 fold-change (log2FC) (i.
e. log base 2 of the ratio of predicted normalized counts between the two conditions). Log2FC were compared to 0 with a Wald test. A likelihood-ratio test (LR test) was also performed to assess whether adding an independent variable in the model provided relevant information compared to the null model, i. e. to test whether gene or TE family expression was globally impacted by an independent variable having more than two classes (age group or karyotype variables). A 0. 05 False-Discovery Rate (FDR) threshold value was used for significance and p-values were adjusted for multipletesting using the Benjamini & Hochberg procedure  [35]. The thresholds for the outlier and the low mean normalized read count filters were set as default. Graphics were generated after the data transformations proposed by the DESeq2 package authors: variance stabilizing transformation for PCA, adaptive shrinkage for MA plots, log2 transformation of the normalized counts for heatmaps  [33, 36].
The command removeBatchEffect (for unbalanced batches) from the limma R package (v3. 46. 0) was used before PCA representations, heatmap representations and before building boxplots of normalized counts according to the karyotype to deal with batch effect in our dataset. When necessary, gene name, gene description, and gene localization were determined using the GTF file and the command gconvert from the R package gprofiler2 (v0. 1. 9)  [37]  from the Ensembl gene (ENSG) identifier.
Further analysis GO term enrichment analysis: A gene ontology (GO) analysis was performed on significantly differentially expressed genes (DE genes) in the six pairwise karyotype comparisons (47,XYY vs 46,XX, 47,XXY, 46,XX, 46,XY vs 46,XX, 47,XYY vs 46,XY, 47,XXY vs 46,XY, 47,XYY vs 47,XXY) after adjustment on the batch effect. The R package gprofiler2 (v0. 1. 9) was used  [37]  with the g:SCS algorithm for multiple testing correction. The significance threshold was set at 0. 05.
RNA To test the robustness of our results, we re-analyzed our dataset with another procedure which also permitted to avoid the spurious or non-autonomous expression of TE from exonic sequences and long non-coding RNA sequences. To do that, we used a tool named TEspeX (v2. 0. 1)  [39] , with "--strand reverse" option to deal with reverse stranded RNAseq data. We provided four input files to TEspeX. The first was a file containing the consensus TE sequences (1179 sequences) generated from the Dfam database using the instructions here https://github. com/fansalon/TEconsensus, keeping only "LINE" "SINE" "DNA" "LTR" "RC" "Other" "Retroposon" repeat classes (except for 7SLRNA). The second and third files containing cDNA and long non-coding RNA sequences (fasta files), respectively, were downloaded from https://www.
gencodegenes. org/human/ (content = "Protein-coding transcript sequences" and "Long non-coding RNA transcript sequences", regions = "CHR", Release 43 GRCh38/hg38), as suggested by the creators of the tool. As we worked with total RNAseq data, we took advantage of the "--mask" TEspeX option to also remove spurious TE expression from introns of genes carried by the X or Y chromosome. We did not remove expression from intronic insertions of autosomal genes to meet our computer resources. For this purpose, we downloaded the intron sequences of genes carried by the X or Y chromosome on the UCSC table browser https://genome. ucsc. edu/cgibin/hgTables?hgsid=814613547_qUmIEGan4K2f39cHvb9AgrKdRVdK&clade=mammal&org=Human&db=h g38&hgta_group=genes&hgta_track=knownGene&hgta_table=0&hgta_regionType=genome&position=c hr1%3A11%2C102%2C837-11%2C267%2C747&hgta_outputType=primaryTable&hgta_outFileName= with output format = "sequence" (fasta file), track "GENCODE V43" from the "Dec.
Acknowledgements We thank  Yasmine Zerdoumi  and  Maxime Vallée  for their help in the data generation and data analysis, and  Véréna Landel  ( DRS, Hospices Civils de Lyon ) for reading the manuscript. The  Genotype-Tissue Expression (GTEx)  Project was supported by the  Common Fund of the Office of the Director of the National Institutes of Health , and by  NCI ,  NHGRI ,  NHLBI ,  NIDA ,  NIMH , and  NINDS. The GTEx data used for the analyses described in this manuscript were obtained from: dbGaP at http://www. ncbi. nlm. nih. gov/gap through dbGaP accession number phs000424. v8. p2. Data, scripts, code, and supplementary information availability Fastq files of our dataset were submitted to European Genome-phenome Archive (EGA ID of the study:  EGAS00001007462 ). Scripts and code were described in detail in the Methods section but can be supplied upon request.
Genome sequencing, assembly and annotation. The full genome sequences of strains G5813/1+2, G1650, G1705, 8756-13 and 14/OD_0492 were obtained using PacBio sequencing technology, and deposited in GenBank under the accession numbers LR738858. 1, LR739234. 1, LR739233. 1, LR739235. 1, and LR739237. 1, respectively. Whole-genome sequencing of M. feriruminatoris strains F11561, L13461, L14815, L14822, L15181, L15220, L15407 and L15568 was performed using a combination of Oxford Nanopore (ONT) and Illumina (paired-end 250 bp library) technologies (Table  S1 ). The ONT reads were base-called using Guppy and demultiplexed using qcat (v. 1. 0. 3, available at https://github. com/nanoporetech/qcat). The Illumina reads were trimmed using Trimmomatic (v. 0. 36, available at https://github. com/usadellab/Trimmomatic) (Bolger et al.
, 2014) , and the Illumina adapters, i. e. the first 5 bp and both ends, were removed using a 5-bp sliding window with a phred score of under 20. Quality of the Illumina reads was assessed before and after trimming using fastqc (v. 0. 11. 5, available at https://www. bioinformatics. babraham. ac. uk/projects/fastqc). The ONT reads were filtered using filtlong (v. 0. 2. 0, available at https://github. com/rrwick/Filtlong), and reads with a length <250 bp or sharing <87% identity with the trimmed Illumina reads were excluded. The long reads were assembled using canu (v1. 8, available at https://github. com/marbl/canu) (Koren et al. , 2017)  with an expected genome size of 1 Mbp. The initial assembly was polished by iterative alignment of the trimmed Illumina reads using bwamem (v. 0. 7. 15, available at https://github.
com/lh3/bwa) (Li and Durbin, 2009)  followed by correction using pilon (v. 1. 22, available at https://github. com/broadinstitute/pilon) (Walker et al. , 2014). The final manual polishing was done by iteratively performing a variant calling pipeline and correcting the variant positions in the assemblies between each iteration. The variant calling pipeline used bwa-mem to map the Illumina reads. GATK (v. 3. 7, available at https://github. com/broadinstitute/gatk/) IndelRealigner  (McKenna et al. , 2010)  was then used to perform local realignment around indels, read mate coordinates were added using the SAMtools (v. 1. 5, available at https://github. com/samtools/samtools) (Li et al. , 2009)  fixmate command, duplicate reads were marked with the Picard toolkit (v. 2. 18. 9, available at https://broadinstitute.
github. io/picard), and finally the variant positions were called using GATK HaplotypeCaller with ploidy set to 1. The corrected genomes were then manually circularized and annotated using prokka (v. 1. 12, available at https://github. com/tseemann/prokka) (Seemann, 2014). The completed genomes were submitted to GenBank (Table  1  In order to compare core and pan-genomes of M. feriruminatoris and related species, all the proteins of a combined set including the M. mycoides cluster members, M. yeatsii, M. putrefaciens, Mesoplasma (Me) florum and M. feriruminatoris were clustered based on sequence similarity. Three sets of genomes were compared, each with their own core and pan-genome reconstructed based on these clusters. One set was formed with the 14 M. feriruminatoris strains, the second set was formed with the 4 M.
yeatsii and M. putrefaciens strains, and the third set was formed with the 16 M. mycoides-cluster strains available in databases. Me. florum was excluded from the sets, but its proteins were used as an outgroup during the clustering process. Synteny of the M. feriruminatoris genome was analyzed by whole-genome alignment using Mauve (v. 20150226, available at https://darlinglab. org/mauve/mauve. html) (Darling et al. , 2004 (Darling et al. , , 2010)). Synteny of the single-copy core genes in all the Mycoplasma genomes was visualized using detection was performed using ISfinder (available at https://isfinder. biotoul. fr/) (Siguier et al. , 2006).
The evolution model for tree construction was determined using ProtTest (v. 3. 4. 2, available at https://github. com/ddarriba/prottest3) (Darriba et al. , 2011) , and in both cases the CpREV model  (Adachi et al. , 2000)  was identified as the best model. The trees were then created with RaxML (v. 8. 2. 12, available at https://github. com/stamatak/standard-RAxML) (Stamatakis, 2014)  using the GAMMA model of rate of heterogeneity, and 450 and 150 bootstrap replicates were made for the M. feriruminatoris tree and the M. mycoides tree, respectively, using the autoFC bootstopping criterion to determine the number of replicates.
The pL15220 plasmid was longer than most of the plasmids described so far within the M. mycoides cluster but close to the size of pMyBK1 plasmid from M. yeatsii (3,432 bp)  (Breton et al. , 2012) , and had a G+C content of 26. 5%. The strongest pairwise nucleotide identity with mycoplasma plasmids was obtained with pMmc-95010 (1,850 pb and 48. 6% of identity)  (Thiaucourt et al. , 2011) , despite a noticeable difference in size of circa 1,400 bp. The pL15220 plasmid encodes two CDSs, including a hypothetical protein and a predicted CopGfamily transcriptional regulator sharing 82% nucleotide identity with its homolog in pMmc-95010 and a second CDS (649 aa) with no homolog in databases (Figure  S2 ). Noticeably, three regions of 94 to 293 nt shared 76%-100% nucleotide identity with three different regions of the L14822specific MICE, a feature already described in work comparing the plasmid and MICE of the Mmc 95010 strain  (Thiaucourt et al.
, 2011)  and that suggests genetic exchanges among these MGEs. The pL15220 plasmid does not encode a Rep2 protein with a pfam01719 domain shared by most replicases  (Breton et al. , 2012). This finding is consistent with the absence of a double-strand origin (dso) where the Rep proteins normally cleave (at a conserved site TACTAC(C)G/A) the positive DNA strand to start the rolling-circle replication  (Moscoso et al. , 1995). In contrast, an sso block (lagging-strand initiation site) similar to pMmc-95010 was identified next to the HP encoding gene. Its mosaic structure together with the presence of a CDS encoding a hypothetical protein with no homologs in databases suggests that the pL15220 plasmid may belong to a new plasmid family whose emergence was marked by recombination events with other mycoplasma plasmids and MICEs.
yeatsii GM274B (RefSeq accession no. NZ_CP007520). Finally, the genome of Mesoplasma florum L1 (RefSeq accession no. NC_006055) was also used as an outgroup for construction of the phylogenetic tree. get_homologues (v. 05032019, available at https://github. com/eead-csic- compbio/get_homologues) (Contreras-Moreira and Vinuesa, 2013) with the COGtriangle (Kristensen et al. , 2010) clustering algorithm. Multiple sets of genomes were analyzed. One set included the M. feriruminatoris genomes only and was used to identify the core genome and pan- genome of this species. A second set included the M. feriruminatoris genomes as well as the Mmc GM12 genome (RefSeq accession no. NZ_CP001668) and was used to build the M. feriruminatoris phylogenetic tree. A third set included the M. feriruminatoris genomes, multiple complete.
The necessity for an initial adaptation period of the animals was confirmed. Also, the importance of conducting an effective data cleaning procedure of the raw database automatically collected by the WoW was corroborated. Adaptation of naive ewe lambs enabled the required voluntary passages across the weighing platform and a high volume of individual and daily data after 2-3 weeks. Close monitoring of individual growth was then possible after performing sound data cleaning. A good agreement was demonstrated between WoW LW and a reference LW value (measured with a standard static scale). At the individual level, even with the lowest number of LW values collected with WoW, it was possible to monitor variations in LW at daily intervals. The establishment of an early warning system to help farmer decisionmaking could therefore be possible.
Subject Livestock Farming Systems Type of data The datasets are provided i. e. the raw database downloaded from the WoW indicator and subsequent datasets after the raw dataset was filtered from outliers, using different cleaning procedures. In each case, the same information is contained including the individual animal identification (ID), the experimental group, the week, the date, the exact time of passage through the WoW, the individual WoW_LW measurement, the LW_estimated, the SD_min and SD_max. In the case of the static scale (GS) database, the same information is contained including the individual ID, the date and the static LW value for each individual at that time (i. e. one LW reference value per animal per week)   González-García, E. , Alhamada, M. , Pradel, J. , Douls, S. , Parisot, S.
Phase II: Fully automated data collection The second phase lasted 10 weeks (Fig. 2 ) and aimed to evaluate (i) the possibility of using the WoW system for growing animals from this Mérinos d'Arles breed under grazing condition and (ii) the feasibility of automatically measuring their LW with accuracy, and the resultant calculations of individual growth rates (Average Daily Gains -ADGs, g/d). The progression in the number of passages and biologically plausible LW records variables were also recorded. The GS measurements continued to be performed every Tuesday at 0900 am and the data transmission was performed almost every day at the same hour (0900 h) when no lamb was on the platform. During this second phase, a total of 25 172 WoW records were collected.
Database development and outlier detection All statistical analyses were performed using R software (R Core Team, 2021). Daily downloaded raw WoW records were registered in CSV file format on a laptop, and data from the GS measurements were added in an individual sheet in the Specification Table. Using the GS, individual lamb ADG between each of the two weekly measurements was calculated. Due to this calculated ADG, each daily LW was estimated for each ewe lamb. Before further analyses, different data filtering procedures were carried out with R using the dplyr package to manipulate data. Firstly, records were removed if they did not capture the RFID identity of the individual, or if the registered LW was equal to zero. Then, a three-step data filtering approach was followed to detect and remove lamb misbehaviours and outliers.