dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2004). The MSTM code is therefore appropriate for computing the radiative properties of aggregates. The MSTM code has found wider applications in the research field because of better accuracy and comparatively lower computational cost for fractal like particle compared to 335 other methods like the Discrete Dipole Approximation DDA  (Liu et al. , 2017). The MSTM manual notes a limitation that the nested spheres in the particle should not intersect each other. However, in the case of "aggregate and sphere" representation (Fig. 1f ) the monomers of the aggregate intersected with the sphere at few points. The application of the MSTM code over particles with few intersecting spheres were tested by comparing them to the results of the Geometric Optics Surface-wave (GOS) approach used in the 340 study by  He et al.
(2015). The results for the absorption cross section from both the methods were in good agreement with each other, summarized in the supplementary information of this manuscript. Therefore, the MSTM code was used for the case of "aggregate and sphere" representation where few intersecting spheres were present. The MSTM code and the Lorenz-Mie theory were used to calculate the extinction efficiency 𝑄 =>? , absorption 345 efficiency 𝑄 @AB , scattering efficiency 𝑄 B4@ , and the asymmetry parameter 𝑔. The asymmetry parameter 𝑔 is defined as the intensity-weighted average of the cosine of the scattering angle. The single scattering albedo (SSA) was further derived from the ratio of the scattering efficiency (𝑄 B4@ ) to the extinction efficiency (𝑄 =>? ) as: SSA = s $%& s ()*. (5).
The guideline of our computational study is that functional mechanisms underlying the association of risk loci with a disease may, in some cases, be mediated by a change occurring in TAD borders when embedded SNPs are in their at-risk-form. Accordingly, the increased disease risk due to the presence of non-coding SNPs in their variant form could presumably be better understood by investigating their location with respect to various features of the 3D genome organization, in particular its partitioning into TADs  [23]. We studied quantitatively and systematically, for each disease, the location of disease-associated SNPs with respect to TAD borders (Fig. 1 ). Different cell types and data obtained in different laboratories have been considered (overall 15 datasets). Based on a preliminary analysis  [29]  and on their different etiology, we analyzed separately 71 cancers and 378 non-cancer diseases and compared the distribution of their associated SNPs with respect to TAD borders.
For a fraction of diseases, the associated SNPs are preferentially located in TAD borders We used 15 high-resolution published Hi-C datasets, obtained in two different laboratories, for different human cell types and using different restriction enzymes. We first determined the 3D genome organization in topological domains (TADs) using TopDom algorithm. Then, for each of the 449 diseases considered, the potential over-representation of its associated SNPs in TAD borders (overall covering between about 300 to 500 Mb) has been assessed using a hypergeometric test (see Methods). Results are shown in Fig. 2A  in the form of a p-value histogram. The p-value for a given disease measures the statistical significance of the preferential location of its associated SNPs in TAD borders, or equivalently, the statistical significance of TAD-border enrichment in its associated SNPs.
Different enrichments are observed in embryonic stem cells (hESC) We consistently observe the above-described results for most cell types, as shown in Fig. 3B-G  and Additional file 1: Fig. S7. The relative dominance of cancers among diseases displaying TAD-border enrichment in their associated intergenic SNPs largely reaches statistical significance, with a few notable exceptions: umbilical vein cells (HUVEC, Fig. 3G ), embryonic stem cells (hESC, Additional file 1: Fig. S7A, E ), H1-derived cells (H1_ME, H1_NP, H1_TB, Additional file 1: Fig. S7F, G, H ). We further assess the different enrichment results between embryonic stem cells and differentiated cell types in a systematic comparison of hESC and IMR90 cell lines, for which several datasets from different studies are available, Fig. 4.
Consistent results are obtained for the two datasets obtained in hESC (Fig. 4 , blue panels), and the three datasets for IMR90 (Fig. 4 , pink panels), respectively. These results can be extracted more clearly by an aggregation over datasets for the same cell type (Fig. 4 , top panels). The comparison emphasizes the peculiarity of hESC, with no relative predominance of cancers among diseases displaying TAD-border enrichment in intergenic daSNPs. In H1 hESC and derived cells at early developmental stage (H1_ME, H1_NP, H1_TB), 3D genome partitioning in TADs is not significantly related to the location of cancer risk loci. Consistently, H1_MS cell line differs from the other H1-derived cell lines both in our analysis and in the original study  [38] , which has evidenced a genome-wide evolution of TAD structure in this series of H1-derived cell lines following their progressive differentiation.
Only diseases displaying enrichment for a majority of values of the TopDom window-size parameter k are included (see Methods). A Aggregation over five cell types (six datasets) using data at 10 kb-resolution from  [30]. B-G Detailed comparison for each of the six datasets, where the cell type is indicated above each panel, together with the restriction enzyme (DpnII or MboI) used in the Hi-C experiment. Stars indicate when the difference between cancers and non-cancer diseases is statistically significant (Fisher exact test, *p ≤ 0. 05, **p ≤ 0. 01, ****p ≤ 0. 0001). Analyses for data from  [19, [36] [37] [38]  are presented in Additional file 1: Fig. S7  analog of the diseasome networks introduced in  [41] , in which a link is drawn between two diseases when they share a related gene. We then compared the networks obtained when a link is drawn only when the diseases share a border SNP, i.
g. , in  [28] ). Such disruptions lead to aberrant gene expression within the affected TADs. However, very little is known about how genetic variations associated with cancer susceptibility are Fig. 5  Clustering of cancers and non-cancer diseases sharing daSNPs. In this network representation, red (resp. violet) nodes correspond to cancers (resp. non-cancer diseases) displaying TAD-border enrichment in daSNPs, orange (resp. light blue) nodes to other cancers (resp. other non-cancer diseases). An edge is drawn between two diseases when they share an associated SNP A located in a TAD border for a majority of values of the window parameter k (border SNPs, see Methods) or B belonging to the complementary set (non-border SNPs). C, D Same as (A, B) when considering only intergenic SNPs. The four networks have been visualized using NetworkX Python package.
Disease-associated SNPs (daSNPs) We used the version v1. 0. 2-associations_e94_r2018-09-30 of the GWAS catalog: www. ebi. ac. uk/ gwas/  [51]. We extracted all SNP entries associated with a disease EFO term (Experimental Factor Ontology), overall 449 EFO terms, distinguishing 71 cancers and 378 non-cancer diseases. Specifically, diseases were found by selecting all traits which fall into the disease subtree (EFO_0000408) from the EFO ontology, and subsequently, cancers are separated from non-cancer diseases by using the cancer subtree (EFO_0000311). A SNP can be associated with a disease multiple times in the GWAS catalog when it was found in distinct studies; we ignored this multiplicity by dropping duplicates of its identifier snpId. We classified the resulting 21,183 daSNPs into intergenic (40%), intronic (55%) or exonic (5%) type according to its parent category in The Sequence Ontology database, (http:// www.
Hi-C data We used the Cooler Hi-C database  [53]  at ftp:// cooler. csail. mit. edu/ coole rs, which provides published Hi-C data files in the. cool format, at 10 kb-resolution (bin size). Throughout our study, genomic coordinates refer to the hg19 genome version adopted in this database. We present in the main text results obtained with highresolution data from E. Lieberman-Aiden's laboratory  [30]  for the five native and non-cancerous cell lines available, namely GM12878 (human lymphoblastoid cell line, data obtained with MboI or DpnII restriction enzyme), IMR90 (fetal lung fibroblasts of Caucasian origin), HMEC (human mammary epithelial cells), NHEK (normal human epidermal keratinocytes) and HUVEC (human umbilical vein endothelial cells). We considered only normal cell types (and not cancer cell types) as we are interested in the genetic risk present at birth in all cells and wanted to exclude possible confounding features appearing during cancer development itself.
We also investigated datasets from B. Ren's laboratory  [19, [36] [37] [38] , obtained in pioneering experiments using a lower sequencing depth and an enzyme HindIII producing larger restriction fragments (see Additional file 1: Fig. S8 ), for several cell types: GM12878 in  [37] , IMR90 in  [19, 36]  embryonic stem cells (H1 hESC) in  [19, 38] , and cell lines derived from H1 hESC in  [38] , namely mesendoderm (H1_ME), neural progenitors (H1_NP) trophoblast-like cells (H1_TB) and mesenchymal cells (H1_MS). Overall, 15 datasets were examined in our study.
TAD determination We determined TAD coordinates using the TopDom algorithm  [31] , applied after a transformation of. cool files into count matrices using https:// github. com/ open2c/ cooler. Its principle is to count the number of contacts in a window sliding along the genome and to locate TAD ends at the minima of this count (see Fig. 1 , blue diamonds). Genomic regions having established only very few contacts in the experiment, labeled 'gap' by TopDom, were filtered out. The choice of using this algorithm is supported by comparative studies  [33] [34] [35]. Moreover, TopDom is based on quantifying the topological insulation between adjacent TADs, which is the feature that matters for gene (mis)regulation. In particular, recently evidenced long-range associations between TADs and their higher-order organization  [54]  will not be considered here.
TAD borders We defined TAD borders as regions of 20 kb located inside the TAD at the limits of this TAD. Their size has been chosen smaller than the median size (23 kb) of a human gene  [55]. This definition agrees with a topological characterization of TAD borders as regions across which the contact frequency displays a marked decrease  [56]  and is consistent with the use of TopDom for calling TADs. It differs from the notion of TAD boundary, considered, e. g. , in  [19, 24]  which is the-not always existing-linker region between two successive TADs along the genome (not belonging to any TAD), whereas a TAD will always have two borders. TAD borders cover from 8% up to 14% of the genome when TopDom parameter k varies, the smallest fraction being observed for k = 20 (see Additional file 1: File S2, Additional file 1: Table  S3  and, for the variation according to the Hi-C dataset, Additional file 1: Table  S4 ).
Enrichment histograms Histograms of corrected p-values (Fig. 2 ) are plotted and normalized separately for cancers and non-cancer diseases. The counts have been first aggregated over the six considered datasets from  [30]  and the values of the window parameter k of the TAD caller. In order to get a better display of the core features of the plots, the range of p-values has been truncated at log 10 (1/p) = 4. A few EFOs, with corrected p-values smaller than 0. 0001, are thus lying outside the displayed plot. It would be possible to choose a larger bin size or even to draw a smooth histogram, but this would rather dilute the information.
Comparison of TAD-border enrichment for cancers and non-cancer diseases Due to the small number of cancers (71) compared to non-cancer diseases (378), we compared the fraction of cancers and the fraction of non-cancer diseases displaying a significant TAD-border enrichment, considering either all their associated SNPs or only a sub-category (exonic, intronic and intergenic). Only diseases displaying a significant enrichment for a majority (more than 50%) of values of the window parameter k have been counted. The significance of a difference between the disease fractions has been assessed using Fisher's exact test. The comparison has been done for each of the six considered datasets (for 5 cell types) from  [30] , and also after aggregating the disease counts over these datasets.
Workflow The different steps described above have been gathered in an easy-to-execute pipeline, using Snakemake (https:// snake make. github. io,  [59] ) unifying the analysis of different datasets. Its rule graph is presented on Additional file 1: Fig. S1. Its code, written in Python, is freely available at https:// github. com/ kpj/ Genet icRis kAndT ADs. An order of magnitude of the typical numbers of diseases and SNPs of different categories involved in our analysis is provided in Additional file 1: File S2, Additional file 1: Table  S3 , Additional file 1: Table  S4.
Noncancer disease and cancer nodes (and among them, those displaying TAD-border enrichment) were underlined with different colors. Note that, each cancer or each noncancer disease is associated with an ensemble of border SNPs, of non-border SNPs, of intergenic border SNPs and of intergenic non-border SNPs, and could be present in more than one of the four networks. In Fig. 5 , the four networks have been visualized using NetworkX Python package. For each network, only diseases (nodes) having an associated SNP of the prescribed type are drawn. For a quantitative comparison of the four networks regarding their clustering and assortativity properties, we computed an indicator for any subset of nodes, e. g. , a group of nodes with the same color. This indicator, called network coherence, is defined as the z-score of the number of edges within the subset of nodes, compared to a thousand randomly drawn groups of nodes of the same size  [42, 43].
1 Mbp), for a total genome size of 797. 25 Mb. Automated structural annotation of the genome yielded 73,541 protein coding genes and 5,134 ncRNAs. Finally, the high completeness of the annotated genome was evidenced by a 97% (2255 genes) Busco recovery score on eudicots_odb10 (C:97. 0% [S:10. 2%,D:86. 8%], F:1. 1%,M:1. 9%,n:2326). The expression of 51,214 Mimosa genes was detected in our complete transcriptomic dataset, 43% of which were differentially expressed (8303 genes up/15423 genes down) during the symbiotic interaction with C. taiwanensis in at least one time point compared to non-inoculated roots (Table  S3  and S4 ). In addition, we generated the transcriptome of the Papilionoideae Lupinus albus, from the Genisteae tribe, inoculated with Bradyrhizobium sp. 1AE200 strain (Ledermann and Couzigou, unpublished) which forms lupinoid nodules  19  and whose genome has been recently sequenced  20.
In brief, we identified 3794/4781 (up/down) deregulated genes in response to Bradyrhizobium sp. 1AE200 compared to non-inoculated roots (Table  S1 , S3 and S4). These differentially expressed genes represent around 33% of the 26,204 Lupinus expressed genes. To obtain comparable datasets, raw RNAseq reads obtained in the presence or absence of their respective bacterial symbionts from 7 other nodulating species (Table  S1 ) were remapped on their respective genomes and differentially expressed genes (DEGs) were computed following the same approach as for M. pudica and L. albus. Due to sampling and sequencing depth heterogeneity among species, we used different fold-change thresholds to obtain comparable number of differentially expressed genes (See Material and Methods). For each species, we also concatenated all differentially expressed genes at any time point to estimate the whole symbiotic response for up and down regulated genes.
The ancestral transcriptomic signature of symbiont perception, nodule organogenesis and N 2 -fixation Next, we compared the plant responses to bacterial strains able/unable to reach different symbiotic traits (Figure  2A , Table  S9 ) to identify M. pudica genes whose expression is associated with these different traits (response to NF, nodule organogenesis, intracellular release of bacteria (hereafter called "Release"), intracellular persistence (hereafter called "Persist") and nitrogen fixation) (Figure  3A  and Table  S4 ). Transcriptomic responses to direct NF treatments were also available for two other Fabales, Medicago truncatula and Lotus japonicus  44, 45. Another dataset is available for Medicago truncatula obtained from lasercapture microdissections of the different nodule cell layers and corresponding to symbiosome release (FIId) and intracellular maintenance (FIIp), plant and bacteroid differentiation (FIIp, IZ) and nitrogen fixation (ZIII) (Table  S3  and S4)  46.
Despite this diversification, our analysis revealed an over-representation of DEOGs associated with N 2fixation and linked with the ancestral RNS gene set for M. pudica (Figure  3A ) and less species specific DEOGs identified in both M. pudica and the nodule ZIII of M. truncatula (Figure  3  and Table  S9 ). Most of these genes encode enzymes that have not been characterized yet (Table  S4 ). Although the mixed step of intracellular accommodation including the release of symbiosomes and their persistence in the Medicago and Mimosa dataset displays a peculiar evolutionary pattern (see below and Figure  3 ), this symbiotic stage also involved genes that are part of the ancestral transcriptomic response. Suppressors of plant defense in nodules, SYMCRK  55  and RSD  56  , as well as the transcription regulator EFD required for both plant and bacteroid differentiation in Medicago  57, 58  participate to this ancestral response.
Genome assembly The genome was assembled in three steps. In Step 1, Canu version 1. 8  71  was used to trim, correct and assemble the 5 815 198 subreads for a total size of 85 Gb, i. e. an estimated coverage of 94x. Program parameters were corOutCoverage=40, minReadLength=1000 and an input genome size estimate of 900Mb. In Step 2, the raw data, from PacBio Sequel bam files were aligned on the draft assembly. For this step we used the wrapper of minimap2  72  , pbmm2 include in the SMRT Analysis Software version 7. 0. 0 (https://www. pacb. com/products-and-services/analytical-software/smrtanalysis/). Program parameters were « pbmm2 align --preset 'SUBREAD' -c 70 -l 500 ». In the final step (Step 3), we used this mapping result in order to polish the draft assembly and generate a high-quality final assembly.
For this step we used the variantCaller command, include in the SMRT Analysis Software version 7. 0. 0, with the arrow algorithm. Program parameters were --algorithm arrow --minConfidence 40 --minCoverage 70 --coverage 100 --minReadScore 0. 65. CC-BY-ND 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted September 2, 2022. ; https://doi. org/10. 1101/2022. 09. 02. 505815 doi: bioRxiv preprint The final polished assembly produced have a total of 1343 contigs with a total length of 842 189 795 pb, a largest contig of 24 170 175 pb and a N50 contigs of 13 257 064 pb.
Data collection, optical map construction and genome scaffolding Labelled DNA was loaded on the Saphyr G1 chip according to Saphyr System User Guide (30247 -Bionano Genomics). Data processing was performed using the Bionano Genomics Access software https://bionanogenomics. com/support-page/bionano-access-software/). 480 Gb of molecules larger than 150 Kb with a N50 of 199 kbp were produced and represented 533X. It corresponded to 533x coverage of the 900 Mb estimated size of M. pudica genome. These molecules were assembled using RefAligner with default parameters. It produced 110 genome maps with a N50 of 16. 1 Mbp for a total genome map length of 833 Mbp. Finally, a hybrid scaffolding was performed between the polished PacBio assembly and the optical genome maps with hybridScaffold pipeline with default parameters.
Mimosa pudica genome structural annotation The Mimosa pudica gene models were predicted by the eukaryotic genome annotation pipeline egn-ep (http://eugene. toulouse. inra. fr/Downloads/egnep-Linux-x86_64. Four protein databases were used to detect translated regions: i) the proteome of Medicago truncatula A17 version 5 annotation release 1. 6 https://medicago. toulouse. inra. fr/MtrunA17r5. 0-ANR/), ii) the proteome of the previous Mimosa pudica Illumina genome  14  , iii) Swiss-Prot -October 2016 and iv) the proteome of Arabidopsis thaliana TAIR10 version. Proteins similar to REPBASE were removed from the three datasets (to avoid the integration of TE related proteins in the training steps). Chained alignments spanning less than 50% of the length of the database protein were removed. The proteome of M.
truncatula release 1. 6 was used as a training proteome by EuGene. Three input transcripts for EuGene were used. One transcriptome based on mapping predicted from the 136 RNAseq samples generated in this study (Table  S1 ). To obtain these transcripts, the raw fastq paired-end reads were cleaned by removing the adapters and the low-quality sequences using cutadapt  74  (v2. 1) and TrimGalore (v0. 6. 5, https://github. com/FelixKrueger/TrimGalore) with -q 30 --length 20 options. The cleaned reads were mapped against the Mimosa pudica genome assembly using HISAT2  75  (v2. 1. 0) with -score-min L,-0. 6,-0. 6 --max-intronlen 10000 --dta --rna-strandness RF options. Duplicated reads were removed using SAMtools  76, 77  (v1. 9) markdup command. Transcripts were predicted using Stringtie 78 (v2.
1. 4) with --fr -f 0. 8 on each sample. All 80 gtf sample files were merged together using stringtie --merge with standard options. Transcripts fasta file was generated using gffread  78,79 (v0. 11. 6)  with -w option. We also de novo predicted two transcriptomes from two batches of 10 samples of our same RNAseq data using DRAP pipeline 80 (v1. 92, http://www. sigenae. org/drap). runDrap was used on the 20 samples applying the Oases RNAseq assembly software  81. runMeta was used to merge assemblies without redundancy based on predicted transcripts with fpkm 1. These transcriptomes were employed as a training transcriptome by EuGene. Finally, 73,541 proteincoding genes, 1107 tRNAs, 114 rRNAs and 3913 ncRNAs were annotated. Genome assembly, annotation file and gene models are publicly available through MyGenomeBrowser  82  https://bbricpipelines.
Differential gene expression analysis All RNAseq libraries were mapped against their representative genome (Table  S1 ) using nextflow  83  (v20. 11. 0-edge) run nf-core/rnaseq 84 (v3. 0, 10. 5281/zenodo. 1400710) using -profile debug,genotoul --skip_qc --aligner star_salmon options. The workflow used bedtools  85  (v2. 29. 2), bioconductor-summarizedexperiment (v1. 20. 0), bioconductor-tximeta (v1. 8. 0), gffread  79  (v0. 12. 1), picard (v2. 23. 9), salmon  86  (v1. 4. 0), samtools  76  (v1. 10), star  87  (v2. 6. 1d), stringtie  78  (v2. 1. 4), Trimgalore (v0. 6. 6) and ucsc (v377). Differencially expressed genes (DEGs) for the different species and experiments were estimated using 'edgeR' 88 in R  89 (v4. 1. 2). Template script to estimate and identify DEGs is store on the following dedicated GitHub repository https://github.