dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We want to thank  Camille Grasso  and  Kate Watkins  for insightful comments at various stages of the present research, and  Suong Nguyen  for her help with setting up the stimulation protocol and the analysis scripts. This work, carried out within the  Institute of Convergence ILCB  ( ANR-16-CONV-0002 ), has benefited from support from the  French government (France  2030), managed by the  French National Agency for Research (ANR)  and the  Excellence Initiative of Aix-Marseille University  (A*MIDEX). The funders have/had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Metagenomic DNA was extracted with a custom extraction protocol involving mechanical and enzymatic lysis  (Stearns et al. , 2015). The LucKi Birth Cohort Study was approved by the Medical Ethical Committee of Maastricht University Medical Centre (MEC 09-4-058). LucKi is designed according to the privacy rules that are stipulated in the Dutch "Code of Conduct for Health Research. " Microbiome profiling was performed by next-generation sequencing of the 16S rRNA V3-V4 hypervariable gene region. Thereafter, a DADA2-based pipeline was used to identify amplicon sequence variants (ASVs;  Stearns et al. , 2015). Lastly, a centered log ratio (CLR) transformation of data using the ALDEx2 R package was performed to account for the compositional nature of microbiome data, whenever appropriate  (Gloor et al.
Data pre-processing and exploratory analysis Selecting informative individuals and taxa and filtering out random noise was achieved with the following prevalence filter: only ASVs with a prevalence exceeding 15% survived the filtering. Prevalence indicates the percentage of samples in which a microbe was detected. The average sequencing depth was 57,392 read counts, while the range was  [11, 123, 105, 921] , with an interquartile range of 25,346. Pre-processing was done on the merged data of 155 newborns across months 6 and 9. No infants were removed. Out of 1,144 taxa, only 95 (8%) remained after data preprocessing. These 95 taxa were considered for subsequent analyses. In addition, we defined two new classes of microbes: appearing or disappearing microbes-taxa that meet the 15% threshold at month 9 (appearing), or at month 6 (disappearing), but were filtered out by joint-time point pre-processing.
Global microbial network construction For the remainder of this manuscript, we refer to a global network as a network computed over a set of independent individuals. For instance, such a set can refer to all infants at month 6 after birth. We computed a global network for each time point, by selecting microbial taxa as nodes and calculating association strength via microbial association graphical model analysis (MAGMA;  Cougoul et al. , 2019)  on all newborns at each time point (81 at time point 6 m and 74 at 9 m). MAGMA uses copula Gaussian graphical models for which marginals are modeled with zero-inflated negative binomial generalized linear models, and sparseness is induced via a graphical Lasso strategy. From a practical point of view, we used the rmagma R package to derive MAGMA-networks (https://gitlab.
Unique use of individual-specific network methodology The ISNs were constructed using  Kuijjer et al. (2019b). It builds on a global reference network from which individuals are iteratively extracted and perturbation effects are used to derive an ISN with individual-specific edges. Motivation for this particular way of ISN construction included that it is easy to implement via the LIONESS software  (Kuijjer et al. , 2019a)  and has a straightforward interpretation: On average, and for an asymptotically large number of individuals, Kuijjer's ISNs average to the global reference network. Moreover, unlike for samplespecific networks, approaches such as those described in  Liu et al. (2016b) , edges in Kuijjer's ISNs go beyond differential cooccurrences: for each individual a tailored "co-occurrence" network is reconstructed.
Since ISNs are computed outside the MNDA framework, our pipeline can be combined with any type of ISN, as long as the edges are individual-specific. Compared to the work done by Mac Aogáin et al. (  2021 ), our work has some important differences. First, the authors construct microbial co-occurrence networks on CLR-transformed abundances and Pearson correlation. Pearson correlation is the base measure of association implemented in the LIONESS software, which was initially showcased on gene expression data. Friedman and Alm (2012)  showed that even though correlations on the CLR transformation are more accurate than Pearson correlation, they are not as accurate as the SparCC algorithm. Second, the authors continued their analysis with edges as units of analysis. Our work exploits individual-specific networks to define individual-specific ASV neighborhoods (hence sets of edges).
Limitations and future work We did not apply our approach to shotgun metagenomics (WMGS), which may be seen as a limitation. In favour of applying MNDA to 16S rRNA gene data, we first observe that we used an Amplicon Sequence Variant approach on our data, rather than the Operational Taxonomic Unit-based clustering approaches that have been the default approach until a few years ago. As ASVs are exact sequences, this implies that comparisons can be made to a reference database at a much higher resolution allowing for more precise identification. Depending on the length of the 16S rRNA gene region being sequenced, identification down to the species level can even be achieved for many, but not all, ASVs. Indeed, a recent comparative analysis of 16S rRNA gene and metagenomic sequencing of the same pediatric faecal samples as in our study showed that the microbial diversity was similar between WMGS and 16S rRNA gene data and even slightly lower when using WMGS.
Funding This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  H2020 Marie Skłodowska-Curie  grant agreement No:  860895  (TranSYS) [BY, FM, BS, and KV]. The  LucKi Gut study  was funded by a grant from The  Netherlands Organization for Health Research and Development (ZonMw)  through the  European Union Joint Programming Initiative- A Healthy Diet for a Healthy Life  (received by JP and MM; project:  529051010 ). NB was supported by a  Kootstra Talent Fellowship  from the  Faculty of Health, Medicine and Life Sciences of Maastricht University.
2C ). To explore the biological pathways associated with the chromatin profiles that could distinguish Luminal from Basal bladder cancers, we ranked genes based on the MCA outputs for Dim 2 and performed Gene Set Enrichment Analysis (GSEA  [34, 35] , Fig. 2D ). As expected, for the basal cluster, we found that active chromatin (E2) was strongly enriched at genes involved in decreased Luminal differentiation, while repressive chromatin (E4) was strongly depleted for these genes (Fig. 2D ). Interestingly, genes involved in increased tumour aggressiveness, stemness, extracellular matrix, epithelialmesenchymal transition and invasion were also enriched in active chromatin and depleted for repressive chromatin in the basal cluster (Fig. 2D ). Taking an alternative approach, we derived Basal and Luminal gene signatures from an independent publicly available scRNA-seq dataset (GSM4307111  [36] ) and compared these genes with the chromatin states associated with the basal and non-basal clusters identified in Fig.
For example, a large SE mapped close to KLF5, whose expression was correlated with SE activity mostly in Luminal samples, as previously reported  [43]  (Fig. 3B ). In contrast, KLF7 was regulated by a SE mostly active in Basal samples (Fig. 3B ). We then performed pairwise differential analyses between subtypes (Figs. 3C, S3E ). The comparison between Basal and Luminal samples identified 379 subgroup-specific SEs (Fig. 3C , Table  S3 ). By comparing the differential SEs to RNA-seq differential expression analysis, we confirmed that luminal-gained SEs showed significantly higher activation levels in Luminal samples relative to Basal samples and vice-versa for basal-gained SEs (Fig. 3D ). We then validated the subgroup-specific SE-associated genes identified in our samples in a larger dataset, leveraging the gene expression profiles of the TCGA-BLCA MIBC cohort across molecular subtypes (n = 406)  [40, 44].
Having established the SE landscape in bladder cancer, we next sought to determine which master regulators control the subtype-specific transcriptional programs. To this end, we overlaid the genomic coordinates of subgroup-specific peaks inside SEs with publicly available ChIP-seq datasets  [48, 49]. Our analysis revealed that Luminal-specific SEs were significantly enriched in several TF binding sites (Fig. 4A , Table  S5 ), including known regulators of Luminal subtypes FOXA1, GATA3, and ESR1  [3, 14, 40]. Basalspecific SEs were enriched in binding sites of a different set of regulators, including components of the AP-1 complex (FOSL1, FOSL2, JUND, JUNB), as well as SMAD2/3, NFkB, and STAT3. Further DNA motif enrichment analysis comparing Basal differential peaks inside subgroup-specific SEs over Luminal ones, again identified AP-1 as a potential regulator of Basal enhancers, as well as FOXA1, FOXA1:AR, GATA, and GRHL1/2/3 for Luminal enhancers (Fig.
4B , Table  S5 , Homer  [50] ). However, motif binding and ChIP-seq data are not available for all known TFs. To overcome this issue, we designed a method to identify subgroup-associated TFs and their co-regulated networks based on our differential SEs and the large transcriptomic cohort from the TCGA  [40]  (Fig. 4C ). We selected TFs that were regulated by differential SEs (Basal vs Luminal), according to annotations from Lambert et al. [51] , and that were differentially expressed in TCGA Ba/Sq vs Luminal subgroups. To identify and evaluate the regulons (group of genes regulated in response to one transcriptional regulator) of the 75 resulting TFs, we inferred the protein activity of these selected TFs using the VIPER algorithm  [52]. VIPER infers the protein activity score for each TF based on the expression of a set of genes/targets that are most directly regulated by the given TF, which was obtained from the gene regulatory network inferred by the ARACNe-AP algorithm on the TCGA-BLCA expression dataset  [53].
In fact, we found that TFs associated with the Luminal network showed strong expression correlation in our dataset, TCGA-BLCA and in CCLE bladder cell cohorts  [56]  (Fig. S5A-C ) while expression correlations of Stroma-Rich or Basal-associated TFs (e. g. , ZEB1, SPI1) were lower for urothelial cell lines in the CCLE  [57]  compared to those found in primary tumours (Fig. S5A-C ). This indicates that expression of those TFs could be dependent on growing conditions and/or interactions with the tumour microenvironment. To validate our Luminal-and Basal-specific TF networks, we analyzed public single-cell RNA-seq data of a tumour presenting both a Luminal and a Basal cell population (GSM4307111  [36] , Fig. S5C ). The Luminal-associated TFs FOXA1, GATA3 and PPARG were mostly expressed in the Luminal cell cluster, whereas ZBED2, HMGA2, and KLF7, newly identified as part of the Basal TF network, were mostly expressed in the Basal cell clusters (Fig.
7C ). As little is known about the ZBED2 TF, we used ARACNE/VIPER algorithms to identify the ZBED2 regulon based on TCGA-BLCA expression data. Interestingly, FOXA1 was predicted as a ZBED2 target, with the most negative weight, whereas two genes associated with Basalspecific SEs (IL7R and CAV1) were in the top 10 positive ZBED2 regulon weights (Fig. S8B ). Using ZBED2 ChIP-seq data from pancreatic cancer cell lines  [55]  (the only ZBED2 ChIP-seq reported so far), we found a high confidence ZBED2 peak in the FOXA1 promoter (Fig. 7D , left). Analysis of the RNA-seq data from the same study revealed that ZBED2 overexpression triggered downregulation of FOXA1 (Fig. S8C , p = 0. 004). In our data, we found that the ZBED2 SE was highly enriched in FOXA1 binding in SD48 luminal cells, whereas FOXA1 binding was significantly decreased in 5637 Ba/Sq cells, and negatively correlated with ZBED2 expression (Fig.
7D , right). Overall, these findings suggest that FOXA1 and ZBED2 could negatively regulate each other to promote or maintain Luminal or Basal identity, respectively. On the other hand, ZBED2 has been shown to inhibit STAT2 and dampen inflammation by direct competition with IRF1 for Interferon Responsive Element binding in the pancreas  [55]. We therefore sought to determine if ZBED2 is involved in the downregulation of interferon signaling in bladder cancer, potentially through interfering with FOXA1-activated pathways. Intriguingly, ZBED2 expression in the TCGA-BLCA cohort positively correlated with interferon gamma associated gene expression (Fig. S8D ), which could be an indication that ZBED2 increases in response to inflammation at the cell population level, or vice versa. We then examined the correlation between ZBED2 expression and different cellular pathways at the single-cell level using publicly available scRNA-seq data  [36].
In the previous publications (refs. [27, 28] ), TF binding motif analysis in enhancer regions was applied to identify subtypespecific transcriptional regulators. Here, we further characterized subgroup-associated master regulators and co-regulated networks for both Luminal and Ba/Sq subgroups using two complementary approaches, in order to overcome issues linked to low expression, unknown binding motifs or multi-partner complexes. First, we identified TFs with enriched binding sites or DNA motifs in subgroup-specific enhancers. Second, we combined SE activity in our cohort with regulon analysis of TCGA data to identify master regulator networks for Luminal and Basal subgroups. The first approach, based on public ChIP-seq data, validated the role of TFs involved in urothelial differentiation in Luminal SE activity, namely FOXA1 and GATA3, but also revealed that the AP-1 complex   regulates Basal SEs.
We applied the approach for molecular classification of BCa cell lines. We tested the robustness of the method using transcriptome data of 124 samples of 47 BCa cell lines profiled by two different transcriptome profiling method (mRNA-seq and Affymetrix HuST1. 0 exon array) from four different sources, described as follows: 1. Cancer Cell Line Encyclopedia (CCLE) dataset, 36 cell lines with one sample for each, RNA-seq, downloaded from the DepMap Data Portal release19Q4, as transcripts per million normalized (TPM) counts; 2. MD Anderson (MDA), 30 cell lines with one sample for each, RNA-seq, downloaded from Gene Expression Omnibus with an accession GSE97768, as raw counts and then relative logexpression normalized (RLE); 3. In-house data of our lab team oncologie moleculaire (OM), 53 samples of 33 cell lines including xenografts (xeno), Affymetrix HuST1.
0 exon array, robust multichip average (RMA) normalized; 4. In-house data of our lab team oncologie moleculaire (OM), 5 samples of 5 cell lines, RNA-seq, RLE normalized. The reference tumours for RNA-seq cell line samples were TCGA-BLCA RNA-seq tumour transcriptomes (n = 430) and normalized in accordance with the cell line normalization, namely TPM for CCLE samples and RLE for MDA samples. The reference tumours for microarray samples were CIT MIBC tumours profiled with Affymetrix HuST1. 0 exon array (n = 160), which were normalized with RMA. Our previous MIBC consensus molecular classification framework was applied to the reference tumour transcriptomes, and the NE-like and Stroma-rich subtypes were aggregated as 'double-negative'. The analyses were performed separately for each dataset. Briefly, the normalized cell line and reference tumour transcriptomes were first merged by taking the intersection of the transcriptome features.
Then, the top 2000 genes with largest between-class variances among the reference tumours calculated by F-test were used as selected subtype-relevant features for subsequent analysis. One thousand 2-dimension tSNE embedding spaces were then generated using 1000 random-sequences. For each sample of the cell lines, KNN-based classification (k = 6) was performed in each tSNE embedding, and the predicted molecular class with the highest frequency was considered the molecular class for the cell line sample. The predicted molecular subtypes were basically consistent across different profiling method and datasets and were coherent with prior knowledge. For example, the FGFR3-altered cell lines RT4, UMUC14, and SW780 were consistently classified as LumP subtype and the squamous BCa cell line SCaBER was consistently classified as Ba/Sq subtype, independent of dataset and profiling techniques.
In brief, genes corresponding to each SE were annotated using ROSE (ROSE_geneMapper. py, identifying overlapping and proximal genes), but also with GREAT using "Basal plus extension parameters", as the candidate proximal genes regulated by the SE  [41, 72]. The spearman correlation coefficients between SE activity (H3K27ac read counts, log2RPKM normalized) and the expression of the candidate genes (RLE normalized) were calculated in the tumours. The gene whose expression showing the highest correlation with the activity of the corresponding SE was determined as the gene most likely regulated by the SE. The SE-gene relationships within the top 1% were also given, not limited to the proximal genes. The number of germline single nucleotide polymorphisms (SNPs) within a given SE as well as their association with bladder cancer (median -log10 p-value) was provided based on the UK biobank GWAS summary statistics (Neale lab Round 2, ukb-d-C67, extracted from the MRC IEU OpenGWAS database)  [73].
The germline SNPs falling within the SEs and with published GWAS-level association with BCa or with -log10P. value > 5 in GWAS summary statistics (PhenoScanner v2 database) were provided as GWAS SNPs within the SEs  [74]. For the genes most likely regulated by a given SE, we provided their median CERES dependency score of all and urothelial cancer cells from the Cancer Dependency Map database  [56] , as well as the p-value for difference between the two. We checked if any bias compared to the background in mutation type (missense, non-sense, synonymous, etc. ) for the protein coding genes by Chi-square test. We checked if they were within the list of established cancer genes, including the COSMIC Cancer Gene Census and Network of Cancer Genes 6  [75, 76]. Chromatin state analysis and correlation with expression.
Dimension reduction and functional ontology analysis. We performed dimension reduction and visualization taking directly the categorical format of the above described selected features using multiple correspondence analysis (MCA). To explore the biological significance of the regions that contributed to the dimension that distinguishes the non-basal and basal clusters, the chromosome segments' loading estimates to the Dim 2 were extracted from the MCA outputs and regions with a p-value < 0. 05 for the loading estimate were included (n = 12,198). Genes mapped to Dim 2 contributing regions were pre-ranked by loading estimates for gene-set enrichment analysis (GSEA) by which we identified multiple biological gene sets / ontologies associated with Dim 2. The gene sets collections were retrieved from the Broad Institute Molecular Signature Database, spanning the H (hall mark gene sets), C2 (curated gene sets, e.
DATA AVAILABILITY The datasets supporting the conclusions of this article are available in the GEO repository under accession numbers: GSE193889 for Tumours ChIP-seq GSE193886 for Normal and Cancer cell culture ChIP-seq GSE195768 for Tumours RNAseq GSE195608 for Normal and Cancer cell culture RNA-seq GSE196595 for functional assays 3'RNA-seq This paper analyzes existing, publicly available data: GSE141606 (ZBED2 ChIP-seq), GSE141605 (ZBED2 Overexpression in PDA cell lines), GSM4307111 (scRNAseq Tumor public data), CCLE (https://depmap. org/portal/download/) and TCGA (TCGA data portal).
FUNDING The work was supported by grants from Ligue Nationale Contre le Cancer: (HN-K, JF, CG, X-YM, LC, FD, CK, YA, FR, IB-P) as an associated team (Equipe labellisée), the "Carte d'Identité des Tumeurs" program initiated, developed and funded by Ligue Nationale Contre le Cancer, and a post-doctoral fellowship supporting HN-K. JF was supported by the Fondation ARC pour la recherche sur le cancer, LC by FRM (Fondation Recherche Médicale) and X-YM was supported by a fellowship from ITMO Cancer AVIESAN, within the framework of Cancer Plan. The work was also supported by a "PL-Bio" project funded by INCa (2016-146), the French Ministry of Education and Research, the CNRS, and the Institut Curie. ChIP-sequencing was performed by the GenomEast platform, a member of the 'France Genomique' consortium (ANR-10-INSB-0009).
enable data management and analysis activities that enhance our understanding of life on Earth and facilitate biodiversity preservation and restoration. This white paper, prepared by the ELIXIR Biodiversity Community, summarises the current status and responses, and presents a set of plans, both technical and community-oriented, that should both enhance how ELIXIR Services are applied in the biodiversity field and how ELIXIR builds connections across the many other infrastructures active in this area. We discuss the areas of highest priority, how they can be implemented in cooperation with the ELIXIR Platforms, and their connections to existing ELIXIR Communities and international consortia. The article provides a preliminary blueprint for a Biodiversity Community in ELIXIR and is an appeal to identify and involve new stakeholders.
, 2021; Lewin et al. , 2022). Connecting such molecular sequence data with biodiversity research infrastructures (see Extended Data  (Waterhouse, 2023) ) and resources is a critical step towards facilitating exchange of knowledge, sharing, and interoperability of large and complex datasets  (Waterhouse et al. , 2022). As a European life sciences infrastructure, ELIXIR strives to coordinate bioinformatics resources from across Europe to enable researchers to access and analyse life science data, to improve the value and impact of life science research on public health, the environment, and the economy. The need for informatics solutions to address key societal challenges inspires many scientists from across the ELIXIR Nodes to increasingly engage in different aspects of biodiversity research.
Four authors were added as they contributed substantially to the manuscript revisions, especially the newly added parts about the Community goals and Implementation Study. Any further responses from the reviewers can be found at the end of the article the increased risk of new human diseases  (Frumkin & Haines, 2019) , the collapse of ecosystem services, the degradation of natural resources, and the increased possibility of a global food crisis  (FAO, 2019). At the same time, scientists and naturalists do not even know what is being lost, as around 80% of biodiversity at the species and population levels remains undescribed and/or underrepresented in inventories and databases  (Mora et al. , 2011; Costello et al. , 2013; Moura & Jetz, 2021; Bispo et al. , 2022; Boekhout et al. , 2022; Chimeno et al.
With ELIXIR's strengths in molecular sciences, a "hub and spokes" model would help augment opportunities to connect molecular-focused bioinformatics tools, protocols, and resources with the many other biodiversity-related infrastructure and stakeholder organisations. Building on these strengths in data science and a connected network across Europe, ELIXIR can contribute to coordinated efforts designed to support and grow the many existing initiatives in the domains of biomonitoring, ecosystem health, and biodiversity research.