dataesr/scientific-paragraphs-categorization huggingface dataset:
Here we report the main results of a pilot project (FR-AgENCODE  [19] ) launched at the beginning of the FAANG initiative. The broad aim was to generate standardized FAANG reference datasets from four livestock species (cattle, goat, chicken, and pig) through the adaptation and optimization of molecular assays and analysis pipelines. We first collected a panel of samples from more than 40 tissues from two males and two females of four species: Bos taurus (cattle, Holstein breed), Capra hircus (goat, Alpine breed), Gallus gallus (chicken, White Leghorn breed), and Sus scrofa (pig, Large White breed), generating a total of 4115 corresponding entries registered at the EMBL-EBI BioSamples database (see "Methods" section). For molecular characterization, three tissues were chosen to represent a "hub" organ (liver) and two broad immune cell populations (CD4+ and CD8+ T cells).
This allowed the acquisition of a partial representation of energy metabolism and immunity functions, as well as the optimization of the protocols for experimental assays for both tissue-dissociated and primary sorted cells. In addition to the transcriptome, we analyzed chromatin accessibility by the assay for transposase-accessible chromatin using sequencing (ATAC-seq,  [20] ), and we characterized the three-dimensional (3D) genome architecture by coupling proximity-based ligation with massively parallel sequencing (Hi-C,  [21] ) (Fig. 1 ). Using this combination of tissues/assays, we assessed the expression of a large set of coding and non-coding transcripts in the four species, evaluated their patterns of differential expression in light of chromatin accessibility at promoter regions, and characterized active and inactive topological domains of these genomes.
High-depth RNA-seq assays provide gene expression profiles in liver and immune cells from cattle, goat, chicken, and pig For each animal (two males, two females) of the four species, we used RNA-seq to profile the transcriptome of liver, CD4+ and CD8+ T cells (see "Methods" section, Fig. 1  and Additional file 1: Table  S1 ). We prepared stranded libraries from polyA+ selected RNAs longer than 200 bp, and we sequenced them on an Illumina HiSeq3000 (see "Methods" section). Between 250M (chicken) and 515M (goat) read pairs were obtained per tissue on average, of which 94% (chicken) to 98% (pig) mapped to their respective genome using STAR  [22, 23]  (see "Methods" section, Additional file 1: Figure  S1  and Tables  S2-S4 ). As an initial quality control step, we processed the mapped reads with RSEM  [24]  to estimate the expression levels of all genes and transcripts from the Ensembl reference annotation (hereafter called "reference" genes/transcripts/annotation) (Additional file 1: Fig.
1. Experimental design overview. For each species, samples from liver and T cells of two males and two females were processed by RNA-seq, ATAC-seq, and Hi-C assays. See Additional file 1: Table  S1  for a complete list of experiments performed and available datasets Table  S2  and Figure  S1 ; Additional file 2). As expected, a large majority of the reads (from 62% in cattle to 72% in goat) fell in annotated exons of the reference genes (Additional file 1: Figure  S2 ). In spite of the specialized scope of this initial study limited to liver and immune cells, a large share of all reference genes were detected (from 58% in chicken to 65% in goat), even considering only transcript isoforms with an expression level higher than 0. 1 TPM in at least two samples (see "Methods" section and Table  1 ).
Among those, 10 and 29 genes showed significant overexpression in CD4+ and CD8+ cells respectively (Additional file 1: Table  S6 ). We searched for the human orthologs of theses genes in the baseline expression dataset of human CD4+ and CD8+ αβ T cell subsets generated by the Blueprint Epigenome Project  [33, 34]  and considered their relative enrichment in each cell subset. With one exception (ACVRL1), all genes were found to be expressed in human CD4+ and/or CD8+ αβ T cells and 25 of them showed a relative enrichment in CD4+ (or CD8+) human cells consistent with our data across the four species. Out of these 25 genes, six and eight genes, respectively, could be associated with CD4+ and CD8+ T cell differentiation, activation, and function according to the literature (Additional file 1: Table  S6 ).
Analysis of new transcripts improves and extends gene structure annotation In order to test if our data could improve the reference gene annotation for each species, we used STAR and Cufflinks to identify all transcripts present in our samples and predict their exon-intron structures. We then quantified their expression in each sample using STAR and RSEM (see "Methods" section and Additional file 1: Figure  S1 ) and only retained the transcripts and corresponding genes expressed in at least two samples with TPM ≥0. 1. We identified between 58,000 and 85,000 transcripts depending on the species (Table  1 , Additional file 5), hereafter called "FR-AgENCODE transcripts". To characterize these FR-AgENCODE transcripts with respect to the reference transcripts, we grouped them into four positional classes (see "Methods" section): (1) known: a FR-AgENCODE transcript whose exon-intron structure is strictly identical to a reference transcript (i.
The color of each heatmap cell also reflects the similarity (Pearson correlation) between each sample pair (the lighter, the higher). Hierarchical clustering is performed using one minus the squared Pearson correlation as a distance and the complete linkage aggregation method were of the alternative class, therefore enriching the reference annotation with new splice variants of known genes (Additional file 1: Table  S7 ). The proportion of completely novel transcripts was relatively high for cattle, which is likely due to the incompleteness of the UMD3. 1 version of the Ensembl annotation used at the time of the study (Table  1 , Additional file 1: Figure  S2 , S9 and Table  S7 ). In order to identify interesting new transcripts involved in immunity and metabolism, we first selected the novel FR-AgENCODE coding transcripts that unambiguously project to a single human coding gene.
The structural features of these predicted lncRNA transcripts were consistent between the four species: lncRNAs are spliced but with fewer exons (1. 5 vs. 10) and higher median exon length (660 vs. 130 bp) compared to mRNAs (Additional file 1: Figure  S12 ). LncRNAs are also smaller than mRNAs (1800 vs. 3600 bp). Notably, the lower number of exons and consequent smaller size of lncRNAs compared to mRNAs could also be due to the weaker expression of lncRNAs, which makes their structure more difficult to identify  [39]. In addition to the coding/non-coding classification, FEELnc can also categorize lncRNAs as intergenic or intragenic based on their genomic positions with respect to a provided set of reference genes (usually protein coding), and considering their transcription orientation with respect to these reference genes.
These numbers were significantly higher than expected by chance as measured by shuffling peak positions (p value < 10 -3 , permutation tests). Moreover, this subset of overlapping peaks have significantly higher q-value signal scores than their non-overlapping counterparts (p value < 2. 2 × 10 -16 , Wilcoxon tests), which confirms the existence of a common signal between the datasets. To further characterize functional regulatory sites in our samples, we compared chromatin accessibility between liver and T cells. The ATAC-seq peaks of each species were quantified in each sample and resulting read counts were normalized using a loess correction (see "Methods" section). A differential analysis similar to the one used for RNA-seq genes was then performed on normalized counts (see "Methods" section).
It also confirmed, in line with several studies in model species  [4] [5] [6] 8]  the value of combining molecular assays on the same samples to simultaneously identify the transcriptomes and investigate underlying regulatory mechanisms. In the context of the global domesticated animal genome annotation effort, lessons learned from this pilot project confirm conclusions drawn by the FAANG community regarding the challenges to be addressed in the future  [13]. 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 meta-analyses of several datasets  [16].
Bioinformatics and data analysis All software used in this project along with the corresponding versions are listed in Additional file 1: Table  S3. The reference gene annotation was obtained from the Ensembl v90 release (pig: Sscrofa11. 1, chicken: GalGal5, cattle: UMD3. 1, goat: ARS1). Since Capra hircus was not part of the Ensembl release, we used the NCBI CHIR_ARS1 annotation (see Additional file 1: Table  S2 ).
RNA-seq 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 [22, 23] : 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 Additional file 1: Figure  S1 )  [64]. 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  [65, 66]  with the max-intron-length (100000) and overlap-radius (5) options, guided by the reference gene annotation (-GTF-guide option) (  [64] , Additional file 1: Figure  S1 ).
All cufflinks models were then merged into a single gene annotation using Cuffmerge 2. 2. 1  [65, 66]  with the -ref-gtf option. The transcript and gene expressions on both the reference and the newly generated gene annotation were quantified as TPM (transcripts per million) using RSEM 1. 3. 0 [24]  on the corresponding transcriptome alignment files (  [64] , Additional file 1: Figure  S1 ). The newly generated transcripts were then processed with FEELnc version 0. 1. 0 [36]  in order to classify them into "lncRNA", "mRNA" and "otherRNA" (Additional file 1: Figure  S1 , Tables  S10-11 , Figure  S9 ). 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  [67]  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 (Additional file 1: Figure  S3 ) and FR-AgENCODE genes with TPM 0. 1 in at least two samples (Additional file 1: Figure  S11 ).
RNA-seq normalization and differential analysis To perform the differential analysis of gene expression, we used the expected read counts provided by RSEM  [24]. RNA-seq library size normalization factors were calculated using the weighted Trimmed Mean of Mvalues (TMM) approach of  [69]  as implemented in the R/Bioconductor package edgeR  [29]. The same package was used to fit three different per-gene negative binomial (NB) generalized log-linear models  [70]. • 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-or under-expressed in liver versus T cells. This analysis was done separately for each species (Additional file 1: Figure  S5 -S7) and subsequently for genes identified for all species (Additional file 1: Fig. S8 ), using the three following ontologies: biological process (BP), molecular function (MF) and cell compartment (CC), and using the GOstat R/Bioconductor package  [72] ) for only those genes with a human ortholog.
FR-AgENCODE gene conservation between species In order to obtain gene orthology relationships, we projected FR-AgENCODE transcripts from the four livestock species to the human GRCh38 genome using the UCSC pslMap program (https://github. com/ ENCODE-DCC/kentUtils/tree/master/src/hg/utils/ pslMap, v302). More precisely, we used the UCSC hg38To[species. assembly]. over. chain. gz chain file for each species (created in-house for goat following UCSC instructions) and retained only the best hit for each transcript (according to the pslMap score). We further required each FR-AgENCODE gene to project to a single human gene that did not strandedly overlap any other projected FR-AgENCODE gene. Syntenic conservation of lncRNAs Briefly, a lncRNA was considered as "syntenically" conserved between two species if (1) the lncRNA was located between two orthologous protein-coding genes, (2) the lncRNA was the only one in each species between the two protein-coding genes, and (3) the relative gene order and orientation of the resulting triplet was identical between species.
In particular, trended biases are problematic as they can potentially inflate variance estimates or log fold-changes for some peaks. To address this issue, a fast loess approach  [78]  implemented in the normOffsets function of the R/Bioconductor package csaw  [79]  was used to correct differences in log-counts vs log-average counts observed between pairs of samples. As for RNA-seq, we used two different differential models: Model 1 for tissue pair comparisons, Model 2 for T cell versus liver comparisons (see corresponding "RNA-seq" section for more details).
ATAC-seq peak TFBS density In order to identify Transcription Factor Binding Sites (TFBS) genome-wide, we used the FIMO  [80]  software (Additional file 1: Table  S3 ) to look for genomic occurrences of the 519 TFs catalogued and defined in the Vertebrate 2016 JASPAR database  [81]. We then intersected these occurrences with the ATACseq peaks of each species and computed the TFBS density in differential vs non differential ATAC-seq peaks. Among the predicted TFBSs, those obtained from the CTCF motif were used to profile the resulting density with respect to Topological Associating Domains from Hi-C data (Fig. 7 , Additional file 1: Figure  S25 ).
Comparison between ATAC-seq peaks and ChIP-seq histone mark peaks Pig liver H3K4me3 and H3K27ac ChIP-seq peaks from the Villar et al. study  [42]  were downloaded from ArrayExpress (experiment number E-MTAB-2633). As these peaks were provided on the 10. 2 pig genome assembly, they were first lifted over to the 11. 1 pig genome assembly using the UCSC liftover program (https://genome. sph. umich. edu/wiki/LiftOver). About 86. 7% (9632 out of 11,114) of the H3K4me3 peaks and 91. 8% (31,161 out of 33,930) of the H3K27ac peaks could be lifted over to the 11. 1 genome assembly. The median peak size was 1944 bp for H3K4me3 and 2786 bp for H3K27ac, and the peak size distribution was very similar for the initial 10. 2 and the lifted over 11. 1 peaks. As for genome coverage, the H3K4me3 and H3K27ac peaks covered 0.
Chromatin accessibility conservation across species In order to investigate the conservation of chromatin accessibility across our 4 livestock species, we used the human GRCh38 genome as a reference. After indexing the softmasked GRCh38 genome (main chromosomes) using lastdb (last version 956, -uMAM4 and -cR11 options, http://last. cbrc. jp/), we used the lastal program followed by the last-split program (-m1 and -no-split options) (last version 956, http://last. cbrc. jp/) to project the 104,985 cattle, 74,805 goat, 119,894 chicken, and 149,333 pig ATACseq peaks onto the human genome. In doing so and consistent with the phylogenetic distance between our species and human, we were able to project 72. 6% (76,253) cattle, 73. 7% (55,113) goat, 12. 3% (14,792) chicken, and 80. 1% (119,680) pig peaks to the human genome.
Chicken ATAC-seq samples clustered completely separately from mammal ATAC-seq samples. T cell samples from cattle and goat were also closer to each other than to liver samples. To shuffle the 104,985 cattle, 74,805 goat, 119,894 chicken, and 149,333 pig ATAC-seq peaks, we used the bedtools shuffle program on their respective genomes and projected these shuffled peaks to the human genome as was done for the real peaks. We also compared the human hits to the combined set of 519,616 ENCODE human DNAse I peaks from two CD4+, two CD8+ and one "right lobe of liver" samples (experiment accessions ENCSR683QJJ, ENCSR167JFX, ENCSR020LUD, ENCSR316UDN, and ENCSR555QAY from the encode portal https://www. encodeproject. org/, by merging the peaks from the 5 samples into a single set of peaks using bedtools merge).
We found that 23. 1% (48,893 out of 212,021) of the human hits obtained from the real ATAC-seq peaks overlapped human DNAse I peaks, whereas only 8. 5% (21,159 out of 249,943) of the human hits obtained from shuffled ATAC-seq peaks overlapped human DNAse I peaks. This further supports the biological signal present in these data. Finally we used the phastcons measure of vertebrate sequence conservation obtained from the multiple alignment of 100 vertebrate species genomes including human (hg38. phastCons100way. bw bigwig file from the UCSC web site https://genome. ucsc. edu/). For each human hit, we computed its phastcons score using the bigWigAv-erageOverBed utility from UCSC (https://github. com/ ENCODE-DCC/kentUtils).
5. 3  [49]  at the 10 kb resolution using the matrix balancing normalization (arrowhead -r 10000 -k KR option), and with Armatus V2. 1  [55]  with default parameters and gamma=0. 5 (Additional file 1: Table  S3 ). Graphical visualization of the matrices was produced with the HiTC R/Bioconductor package v1. 18. 1  [48]  (Additional file 1: Table  S3 ). Export to JuiceBox  [84]  was done through Juicer Tools V1. 5. 3 (Additional file 1: 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. For these processes, each chromosome was considered separately.
Withinchromosome 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  [48]. 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 a homonegenous assignment across chromosomes to be obtained, without relying on the reference annotation. In line with what was originally observed in humans, where the first PC was the best criterion for separating A from B compartments (apart from a few exceptions like chromosome 14 for instance  [21] ), we also observed a good agreement between the plaid patterns of the normalized correlation matrices and the sign of the first PC (Additional file 1: Figure  S24 ).
In order to recover conserved segments, we chained the alignments using a python inhouse script (program available on demand, used with stranded mode, coverage=0. 4, score=3000, and length_cutoff=5000). Doing so, we managed to project 90% of the mammalian and 5% of the chicken TAD boundaries onto the human genome. Similar to what was done for ATAC-seq, for each projected TAD boundary, its best hit (according to the chaining score) was retained. The median length of those best hits represented 95% and 78% of the initial query size for mammals and chicken respectively. Merging these best hits on the human genome (using bedtools merge), we obtained 16,870 human regions with a median length of 44. 6 kb (similar to the initial TAD boundary size of 40 kb). Out of those, 16,468 were considered non ambiguous (i.
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 the following:  Y. Gallard ,  UE Le Pin  ( Gouffern en Auge ),  F. Bouvier  and  T. Fassier ,  UE Bourges (Osmoy) ,  S. Ferchaud ,  UE GenESI (Magneraud/Rouillé ),  Y. Baumard ,  UE PEAT (Nouzilly) ,  C. Berri  and  J. Gautron ,  UR BOA (Nouzilly) ,  G.
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 ,  A. Rau  and  E. Crisci  are supported by the  AgreenSkills+ fellowship program  with funding from the  EU 's  Seventh Framework Program  under grant agreement  FP7-609398. E. Crisci  was also supported by the  Animal Health and Welfare ERA-Net  (anihwa) -project  KILLeuPRRSV. 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.
Availability of data and materials Experimental protocols for tissue sampling and molecular assays are publicly available at  [18]. Sample records are available at the BioSamples database  [85]  under submission codes GSB-99 and GSB-721  [86]. Biological material from the 16 animals (tissue samples and aliquots) are stored at the INRA CRB-Anim BioBanking facility and are available on request. Raw sequences and metadata are available at the EMBL-EBI's European Nucleotide Archive ENA and at the FAANG Data Coordination Center using accessions PRJEB27455 (RNA-seq), PRJEB27111 (ATAC-seq) and PRJEB27364 (Hi-C)  [18]. Additional data and results (including annotation files, list of differentially expressed genes with normalized expression values, annotated ATAC-seq peaks with raw read counts, differentially accessible peaks with normalized read counts, Hi-C matrices, TADs and A/B compartments) are available at  [19].
Mapping CNN To SW26010 Considering the memory access characteristics of the SW26010 processor, the efficiency of accessing the main memory from the CPE using global read/store (gld/gst) is extremely low. The calculated data should be prefetched into the CPE's local memory (LDM) by means of DMA transfer when designing the algorithm. At the same time, considering the limited size of the LDM, it is necessary to keep the data reuse as much as possible when designing the algorithm. By analyzing the convolution algorithm, we can find that there are two forms of data reuse: one is the Output-Stationary, that is, output matrix is always kept in the LDM. When the matrix multiplication and addition operation associated with output matrix is completed, output matrix will be written back to the main memory.