dataesr/scientific-paragraphs-categorization huggingface dataset:
Each individual trace represents a snapshot of the locus conformation within a single cell (see reconstruction with two different orientations). c-Data quality assessment: (top) The N-matrix represents the number of times that each pair of DNA loci was detected in the dataset, and is indicative of their detection efficiency. (Bottom) The distribution of pairwise distances between DNA-FISH spots in the same chromatin trace is plotted to ensure that there is no major error in the analysis (detection threshold, etc. ). d-Traces computed by pyHiM were sorted based on RNA expression profiles in NC14 fly embryos and assigned to specific cell types (e. g. mesoderm vs. neuroectoderm). Specific long-range interactions and chromatin organization are observed for each cell type. e-Fast 2D analysis based only on the projected 3D data is used to optimize parameters and test data quality.
Proteomic quantification and analyses Proteome was quantified using the super-SILAC method and an LC-MS/ MS analysis. Proteomic data were initially explored using a principal component analysis (PCA), followed by clustering using ConsensusClusterPlus. Identified unsupervised protein groups (uPGs) were compared with the transcriptomic NMIBC  [15]  and MIBC subtypes  [16] ; characterized using a protein expression differential analysis, a gene set enrichment analysis (GSEA) pathway enrichment analysis, asso-.
Kolmogorov-smirnoff test Fig. 1  -Workflow for proteomic data acquisition and correlation between omic data. (A) Diagram of super-SILAC mass spectrometry workflow. Protein quantities were given as ratios between the light (tumor) and heavy (four cell lines) versions of peptides (see the Supplementary material). (B) Density plots of mRNA/protein Spearman correlations: overall density of mRNA/protein correlations (top), for selected pathways from the Reactome Pathway Database (center), and for transcriptomic independent components identified by Biton et al  [28]  (bottom). Kolmogorov-Smirnoff adjusted p values are as follows: *** p < 0. 001, ** p < 0. 01, and * p < 0. 05 (Supplementary Table  2 ). (C) The mRNA/CNA and protein/CNA correlations. Orange and purple points have significantly higher protein/CNA and mRNA/CNA correlations (adj.
47; Fig. 1B , upper panel) was within the range reported in previous studies (0. 39-0. 53)  [20] [21] [22] [23] [24]. The mRNA/protein correlations can differ significantly depending on the biological processes  [21, 23, 24]. This was also observed in our series considering Reactome Pathway Database gene sets (Fig. 1B , middle panel). We also investigated the biological processes that we previously identified in bladder cancer using an independent component (IC) analysis from transcriptomic data (Fig. 1B , lower panel)  [28] , and found high correlations for mRNA/protein most associated with ICs. Next, we examined the effects of copy number alterations (CNAs) on mRNA and protein levels (Fig. 1C  and 1D , Supplementary Table  3 , and Supplementary material). The mRNA/CNA correlations were significantly higher (KS test p <10 -15 ) than the protein/CNA correlations (Fig.
6B  and 6C ). In the data of Xu et al's  [26]  study, uPG-E corresponded to a subset of U-I and contained almost all FGFR3-mutated tumors (p = 0. 001), U-II tumors displayed high expression of uPG-B markers spanning both NMIBC and MIBC, and U-III corresponded to both uPG-A and uPG-D tumors. Thus, despite using distinct proteomic methods and source material, both datasets showed strong concordance with four out of the five uPGs (uPG-A, uPG-B, uPG-D, and uPG-E).
Data sharing: LC-MS raw data are available through PRIDE under accession number PXD022435. Copy number alteration data are available on GEO under accession number GSE198328. Processed data are freely available on GitHub (https://github. com/csgroen/cit_blca_proteomics/ tree/main/analysis/data). All relevant codes used for analyses and generation of figures are available on GitHub (https://github. com/csgroen/cit_ blca_proteomics). A docker image of the R analysis environment is also provided to ensure reproducibility.
Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short-and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation.
In general, the transcript level of every gene appears to be influenced by one or more eQTL. In humans, the genetic basis of gene expression variation was dissected via GWAS on a large dataset of 49 tissues obtained for up to 838 individuals  (Ferraro et al, 2020). This study clearly highlighted tissue-specific gene expression patterns and detected a large number of eQTL across tissues. The detected variants are mostly local eQTL (i. e. , located close to the genes they influence), while distant eQTL (i. e. , located far from the genes they influence) remains difficult to identify in such context due to the large genomes, limited sample size and low statistical power. As most of gene expression variation does not arise from local eQTL, part of the variance is therefore still unexplored. More recently, a population-scale transcriptomic analysis was performed on more than 1,000 Saccharomyces cerevisiae yeast isolates leading to a deeper view of the genetic control of gene expression  (Caudal et al, 2023).
05 in a dataset of 1,011 yeast genomes  (Peter et al, 2018). Based on this resource, two independent surveys have shown that these variants contribute disproportionately to growth variation in a large number of conditions observed across yeast natural isolates  (Bloom et al, 2019, 19; Fournier et al, 2019). Although there is now strong evidence for the impact of such variants on organismal traits, their effect on molecular traits, such as gene expression variation, has been poorly studied at the population level. Regarding the structural variants (SVs), they have long been recognized as an important source of genetic diversity and phenotypic variation in all organisms  (Weischenfeldt et al, 2013). A few studies have provided catalogs of SVs in large populations of various organisms, including human, tomato, and yeast  (Alonge et al, 2020; Li et al, 2023; O'Donnell et al, 2022; Liao et al, 2023) , however, these catalogs are still non-exhaustive.
In total, we analyzed 2,002,550 transcriptomic measurements, providing a detailed analysis of the hybrid transcriptomes. The expression abundance (as the mean of log2(tpm+1)) and dispersion (as the mean absolute deviation of log2(tpm+1)) of each gene in the diallel population correlates with those determined in a large population of 969 S. cerevisiae natural isolates (R=0. 79 and R=0. 72, respectively) (Figure  S1C-D )  (Caudal et al, 2023). In addition, gene expression levels are also correlated between the homozygous hybrids generated in the current dataset and the original parental isolates that were characterized in the previously generated population-level dataset  (Caudal et al, 2023)  (R=0. 73) (Figure  S1E ).
Impact of the low-frequency SNPs on gene expression variation Next, we sought to explore the contribution of rare and low-frequency genetic variants (i. e. , with a MAF<0. 01 and <0. 05, respectively) to the observed gene expression variation. In fact, genetic variants considered by GWAS must have a relatively high frequency in the population to be detectable, usually over 0. 05 for relatively small datasets  (Visscher et al, 2017). Rare and low-frequency variants are therefore excluded from association studies. However, our diallel panel provides a powerful strategy to assess the impact of low-frequency variants that are initially present in the population, i. e. in the 1,011 S. cerevisiae collection here  (Peter et al, 2018). In fact, each parental genotype is present several times, increasing the frequency of these variants in this new population and allowing their detection in GWAS.
This observation highlights that low-frequency variants strongly contribute to gene expression variation in a natural population. This is very important as much of the detected genetic polymorphisms in a population, such as the 1,011 yeast genomes dataset, are low-frequency variants with almost 92. 7% of the polymorphic sites associated with a MAF lower than 0. 05  (Peter et al, 2018). Nevertheless, it is also worth mentioning that alongside this enrichment, we also found that overall, the effect sizes of these variants are smaller than those of the common variants. Our diallel panel, however, showed some limitations in exploring the impact of SVs on gene expression variation. In fact, we could only detect a total of 19 SV-eQTL probably because very few SVs were taken into account compared to SNPs in the genotype matrices (763 SVs versus 31,818 SNPs).
For a given hybrid, if accessory genes that have orthologs in the reference genome were annotated as absent, we merged their reads counts to those of their reference genome counterparts. mRNA abundance was then normalized by calculating the transcripts per million value (tpm) of each gene. This gave us a list of tpm values for 6917 genes. We filtered out genes that have a zero tpm value in more than half of all samples, leading to a final dataset of 6186 genes  (Tsouris et al, 2023).
Genome assembly and structural variant detection Raw fastq files were processed using Porechop (v. 0. 2. 4; github. com/rrwick/Porechop) in order to remove adapters and barcodes. We further treated these reads using Filtlong (v. 0. 2. 1; github. com/rrwick/Filtlong) to remove reads shorter than 1000 nucleotides. For each strain, reads were then assembled using both Canu (v. 2. 2)  (Koren et al, 2017)  and SMARTdenovo  (Liu et al, 2021). Canu assemblies were generated using the options -nanopore-raw and genomeSize=12m while we used the default options for SMARTdenovo. We used the same fastq reads to polish both assemblies per strain using Medaka (v. 1. 7. 2; github. com/nanoporetech/medaka). We then set out to detect SVs using MUM&Co (v 3. 8) (O  'Donnell & Fischer, 2020)  separately on each genome assembly, using the option -g 12000000.
Unique identifiers were also added to each SV using a combination of bcftools  (Danecek et al, 2021)  and the R package vcfR (v1. 12)  (Knaus & Grünwald, 2017). We then used Jasmine  (Alonge et al, 2020; Kirsche et al, 2023) ( to collapse SV calls during two rounds. First, for each strain, SV calls from both Canu and SMARTdenovo assemblies were collapsed into 24 strains specific VCF files. During the second round of merging, the 24 VCF files and a 25 th containing direct SV calls from the publicly available Σ1278b genome  (Dowell et al, 2010)  were collapsed into one final VCF file for downstream analyses. Then, since the 26 th parent is a haploid version of the reference strain we assigned all the SVs as absent in that parent. Finally, SVs linked to transposable elements were detected using BLAST  (Johnson et al, 2008) and a database containing LTR and Ty sequences  (Bleykasten-Grosshans et al, 2021).
Acknowledgments This work was supported by a  National Institutes of Health (NIH)  grant  R01  ( GM147040-01 ), a  European Research Council (ERC)   Consolidator grant  ( 772505 ) to J. S and a  French National Research Agency (ANR)  young investigator grant ( ANR-22-CE12-0023-01 ) to J. H. It is also part of  Interdisciplinary Thematic Institutes (ITI)   Integrative Molecular and Cellular Biology (IMCBio) , as part of the  ITI 2021-to-2028 program  of the  University of Strasbourg ,  CNRS , and  Inserm , supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ). J. S. is a Fellow of the  University of Strasbourg Institute for Advanced Study (USIAS)  and a member of the  Institut Universitaire de France.
Data availability All Oxford Nanopore sequencing reads are available in the European Nucleotide Archive (ENA) under the accession number PRJEB64478. The 1002 Yeast Genome website -http://1002genomes. u-strasbg. fr/files/diallel_RNAseq provides access to: -Datafile 1: MAF of the SNPs across the diallel population and the population of 969 natural isolates.
Diffusion profiles comparison to unveil immune diseases similarities The scores resulting from a random walk using a given seed can be regarded as a diffusion profile and represent a network-based molecular signature. Diffusion profiles obtained starting from different seeds can then be compared to reveal signature proximities. Here, we propose to compute and compare the diffusion profiles obtained using 131 immune diseases (Supplementary Table  S13 ) as seed in MultiXrank applied to several multilayer networks. We created eight hematopoietic cell-specific multilayer networks, each composed of four different monoplex/multiplex networks (Figure  3 , Materials and Methods). The first two monoplex/multiplex networks incorporate gene and disease interactions sourced from public databases. These two layers are the sames across all the eight multilayer networks.
The remaining two layers encode Promoter Capture Hi-C (PCHi-C) fragment interactions and Topologically Associating Domain (TAD) interactions observed in various hematopoietic cell lines, extracted from a dataset generated in  [20]. It's important to note that the PCHI-C fragment layer and the TAD layer are unique to each hematopoietic cell line, and hence vary across the eight multilayer networks (Supplementary Section 1. C). The strength of this multilayer network constructions lies in its capacity to combine non cell-specific generic gene and disease interactions with data regarding genomic interactions unique to hematopoietic cell lineages. In addition, the genomic interaction layers allow us to consider data representing the 3D conformation of DNA and non-coding regions of the genomes.
This 3D conformation of DNA is a key to understanding, for instance, genomic structural variations that are key players in the study of diseases  [39]. We demonstrated that these genomic data maintain the signal of the hematopoietic cell type. Indeed, the PCHi-C fragments and TAD datasets capture the tree lineage of hematopoietic cells (Supplementary Section 4. A, Supplementary Figure  S6 ). We also demonstrate that this lineage signal is captured in the RWR scores obtained from applying MultiXrank to the eight multilayer networks (Supplementary Section 4. B, Supplementary Figures  S7  and S8 ). Here, we aim to apply MultiXrank on the eight hematopoietic multilayer networks using as seeds 131 different immune diseases to obtain the disease diffusion profiles. We consider the diffusion profiles as disease signatures.
Construction of the biological networks The different studies presented in this manuscript explore different biological networks summarised in Supplementary Table  S1. Detailed information on the biological networks used in each study are provided in Supplementary Section 1. In study 1, for node prioritisation in leukemia, we used a multilayer network composed of a gene and a drug multiplex networks, connected by bipartite genedrug interactions (Supplementary Section 1. A, Supplementary Table  S1 ). In the gene multiplex network, we encode 3 types of gene interactions: protein-protein interactions, molecular complexes and pathways gathered from public databases. In the drug multiplex network, we encode 4 types of drug interactions: adverse interactions, experimental drug combinations, computationally predicted drug interactions and pharmacological interactions.
The two multiplex networksare connected with gene-drug associations extracted from the repoDB database  [60]. In study 1, for node prioritisation in epilepsy, we used Hetionet  [18] , an network containing 11 types of nodes (Supplementary Section 1. B, Supplementary Table  S1 ). The first multiplex network encodes 3 types of gene interactions: coexpression, physical interaction and regulation. The second and third monoplex networks encode for disease similarities and drug similarities, respectively. The three monoplex/multiplex networks are connected with bipartite gene-drug interactions, gene-disease interactions and disease-drug interactions. The multilayer network also contains other node types (including pathways, biological process and pharmacologic classes) that are connected to the gene, drug and disease networks via bipartite interactions.
In study 2, Gene-Disease associations were predicted from MultiXrank output scores obtained by exploring a multilayer network composed of a gene multiplex network and a disease monoplex network, connected by gene-disease bipartite interactions (Supplementary Section 1. A, Supplementary Table  S1 ). The multiplex network encodes gene interactions, identical to the one used in node prioritisation for leukemia. The monoplex network encode disease interactions. The gene-disease bipartite associations were extracted from an outdated version of the DisGeNET database (v2. 0, 2014). Finally, in study 3, we obtained MultiXrank scores from the exploration of eight hematopoietic multilayer networks (Supplementary Section 1. C, Supplementary Table  S1 ). Those networks are composed of a disease monoplex network, a gene multiplex network and two genomic monoplex networks.
The gene multiplex network encode gene interactions, and is identical to the one used in node prioritisation for leukemia. The disease monoplex network encodes disease phenotypic proximities. The two remaining networks encode genomic information: PCHi-C fragment interactions and TAD interactions. Importantly, the gene and disease networks are identical in the eight hematopoietic networks, whereas the PCHi-C and TAD networks were computed for each hematopoietic cell type from a dataset obtained in  [20]. We describe in detail the processing pipeline applied to obtain the PCHi-C fragment and TAD layers in Supplementary Section 1. C.
Supervised classification We created the gene-disease associations dataset from an outdated version of Dis-GeNET (v2. 0, 2014). We obtained 1914 gene-disease associations. We generated a negative dataset by randomly picking 3828 pairs of gene and disease nodes that are not considered associated in DisGeNET v2. 0 (2014). For each positive and negative gene-disease association defined in the training dataset, we used both the gene and the disease nodes as seeds when running MultiXrank (parameters defined in Supplementary Table  S8 ). Then, we trained Random Forest and XGBoost binary classifiers (Supplementary Table  S9 ) based on the MultiXrank output scores for predicting gene-disease associations. The evaluation of the binary classifiers is done on an updated version of the gene-disease associations dataset; DisGeNET v7.
0 (2020). The test dataset contained 7218 novel positive gene-disease associations, and the negative dataset is randomly selected twice as many (i. e. 14 436) negative associations. We ran MultiXrank using as seeds the gene and disease nodes of each association of the evaluation dataset, using the same parameters used for obtaining MultiXrank scores for the training dataset (Supplementary Table  S8 ). Then, the MultiXrank output scores were used as input of the previously computed Random Forest and XGBoost models (trained on the data obtained with the gene-disease association of DisGeNET v2. 0 (2014)) to predict their label. Finally, we compared the predicted labels to the true labels. We report the results for each model in Supplementary Table  S10. The full procedure is detailed in Supplementary Section 3 and Supplementary Figure  S5.
Funding The project leading to this preprint has received funding from the " Investissements d'Avenir" French Government program  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ), from  Excellence Initiative of Aix-Marseille University  -A*MIDEX and from the  Inserm Cross-Cutting Project GOLD. A. Bap gratefully acknowledge support from the  Turing-Roche  strategic partnership.
Availability of data and materials The MultiXrank package is available on GitHub: https://github. com/anthbapt/MultiXrank. MultiXrank can be installed with standard pip installation command: https://pypi. org/project/MultiXrank. It is associated with complete documentation: https://multixrank-doc. readthedocs. io/en/latest. The code and data used in this study are available on Github: https://github. com/galadrielbriere/ApplicationsMultiXrank.
ACKNOWLEDGEMENTS We acknowledge research support from:  Kidney Research UK  project grant  JFS/RP/008/20160916  (SJK, ASW and IB);  Medical Research Council  Project grant  MR/T016809/1  (ASW and FML);  Engineering and Physical Sciences Research Council (EPSRC)/Medical Research Council (MRC) Centre  of  Doctoral Training  grant  EP/L014904/1  (KR);  European Union  ( SYBIL European Community's Seventh Framework Program ,  FP7/2007-2013 ,  602300  (SJK, SW),  Wellcome Leap Human Organs Physiology and Engineering (HOPE) Initiative  (ASW,  SJK  and  IB );  Kidneys for Life pump priming projects  2021 (ASW, SJK, IB and KMR). Support is also acknowledged from: the  Malaysian Ministry of Higher Education and International Islamic University Malaysia (FT)  and an  Erasmus Scholarship  (SH).
( ENCODE) Project chromatin immunoprecipitation-sequencing data. It includes 1,121 human regulatory tracks with ChIP-seq data for 247 sequence-specific TFs across 43 different cell types and conditions. We selected 20-kb upstream parameter for the options "Putative regulatory region," "Motif rankings database," and "Track rankings database" to identify the targets. Motif sequences of HNF1B were explored with in silico predicted and experimentally validated DNA-binding motifs collection databases (JASPAR2018_CORE_vertebrates_nonredundant, Jolma 2013, TRANSFAC, UniPROBE mouse, HOCOMOCO v11 and Swiss.
ACKNOWLEDGEMENTS This work was supported in part by  National Institutes of Health  grants  P50 AA024333  (J. D. , S. D. , D. S. A. , L. E. N. , J. M. B. ),  R01 DK120679  (J. M. B. ),  RF1 NS133812  (J. M. B. ),  P01 HL147823  (J. M. B. ),  U01 AA026938  (L. E. N. , J. M. B. ),  R01 DK130227  (J. M. B. ),  U01 AA026264  (L. E. N. ),  R01 HL128268  (J. D. S. ),  JDRF JDRF201309442  (J. S. ), the  Glenn and   AFAR Junior Faculty  Grant  A22068  (J. S. ),  R01 DK133479  (J. S. );  Hatch  Grant  WIS04000  (R. J. , J. S. ),  K01 DK128022  (R. N. H. ), and  UL1TR001998  (R. N. H. ). The authors would like to thank  Dr. Philip Hawkins  ( Babraham Institute ) for providing Mboat7 knockout first mice.
The photon signals were collected through a hardware correlator (ALV5000, ALV GmbH) connected to a time-correlated single photon counting (TCSPC) module (SPC830, Becker & Hickl) for FLIM measurements. To obtain appropriate photon counts, FLIM images were acquired between 90 and 240 s. The data were analyzed using the software SPCImage (v. 8. 5, Becker & Hickl) that fits the decay of each pixel with a binning of two to obtain >15,000 counts associated to each pixel to optimize the fit. The data were also analyzed with the FlimFIT software (v. 5. 1. 1, Imperial College London).