dataesr/scientific-paragraphs-categorization huggingface dataset:
Human transcriptional regulatory expansion This fourth release of ReMap comes with a significant update of the Human regulatory catalog by adding a large number of new datasets, new transcriptional regulators and biotypes. We curated, processed and analysed 4121 ChIPseq datasets targeted against TRs collected from GEO and ENCODE databases since the last release. Since 2015 we provide consistency and comparability between datasets by processing raw data through our standardized ReMap pipeline, which includes read filtering, read mapping, peak calling, and quality controls (See 'Materials and methods'). In contrast to other databases, the manual curation and annotation of studies we process are at the foundation of the ReMap project. It involves analysing the warehouse study design descriptions and reading submitted materials and methods from GEO or in the manuscripts to curate heterogeneous experimental information.
Furthermore, to correct the diverse quality of DNA binding experiments the pipeline contains quality controls and filtering steps (See 'Materials and methods'). After applying our quality filters, we retained 2828 datasets (65%) from the 4121 new deposited ChIP-seq datasets (Supplementary Figures  S1, S5-S6 ). As a result, the updated Human regulatory atlas contains 182 416 820 peaks, derived from 8,103 datasets (Figure  1A ), which includes 1210 TRs (Figure  1B ). More precisely 181 426 344 peaks spread over 1002 TRs come from ChIP-seq studies, while 990 476 peaks spread over 208 TRs come from ChIP-exo studies. A large ChIP-exo study was processed (GSE151287), but the resulting peaks were inconsistent with published regions, and not added in this re-lease. When compared to ReMap 2020, the large data gain is dispersed over practically all TRs (Figure  1C , light blue bars).
We observe that the most studied transcription factors (e. g. ESR1, AR, FOXA1, TP53), transcriptional repressors (e. g. CTCF), and CRFs (e. g. BRD4) display, as expected, more datasets than other DNA-binding proteins. Nonetheless, all of the top 15 TRs show additional datasets integrated in ReMap 2022 (Figure  1C , light blue bars). The top 10 biotypes present in the human catalog correspond to the most common cell lines used in genomics (e. g. MCF-7, K-562; Figure  1D ). Our uniform data processing contributes to an updated ReMap 2022 human regulatory atlas of 182M binding regions revealing an unprecedented view of the regulatory landscape and complexity. To illustrate this complexity with dense co-localizations of peaks creating tight clusters (CRMs), we have been tracking the Human ELAC1 promoter since 2015.
This genomic region (chr18:50 967 094-50 970 983) highlights the 2022 catalog expansion compared with 2020 (Figure  2A ), and across all four catalogs with 229, 1037, 2554 and 3546 binding regions respectively (Supplementary Figure  S8 ). Three clusters of peaks can be observed, one large at the promoter embedding a second cluster after the transcription start site (TSS) at about +500 bp and +2 kb from the Gencode TSS  (31). Interestingly these clusters concords with locations of candidate Cis-Regulatory Elements (cCREs) derived from ENCODE data  (8). The third cluster located further up the first intron has been described in depth in precedent ReMap papers  (10, 15, 16)  to illustrate how combining data from various sources enhances genome annotations. Indeed, this third cluster contains 179 FOXA1 peaks in the 2022 catalog (93 in 2020, 60 in 2018, 15 in 2015) including one peak from ENCODE (7) (Supplementary Figure  S9 ).
The ReMap database provides three majors atlases, the main catalog containing all binding regions, a non-redundant set and a CRMs atlas. Indeed, to show a discrete repertoire of binding regions in the genome, the redundant binding regions are merged for identical TRs resulting in a multi-cell, multi-tissue regulatory map of 68M nonredundant binding regions (see 'Materials and Methods' for details). The CRM atlas (3. 4M regions) has been improved with a new methodology that relies on detecting peak density along the genome, reinforcing the identification of regulatory hotspots by the integration of ChIP-seq from various cells, antibodies, and laboratories. Overall, this 2022 update of the human catalog expands the genome regulatory space revealing complex regulatory architectures strength-ening the identification of DNA bound regions across thousands of experimental evidences.
Arabidopsis thaliana regulatory update The A. thaliana 2022 release focuses on updating the regulatory catalog as illustrated in Figure  1E -H and in Figure  2. In this update, we curated, processed and analysed 223 new ChIP-seq datasets against TRs submitted in GEO since our previous release (Figure  1E ). These datasets were processed uniformly using the ReMap pipeline. After applying quality controls and filters, 185 datasets (79%) were retained then integrated with the current catalogue leading to a total of 694 datasets (Figure  1E , Supplementary Figure  S2 ). The 2022 Arabidopsis regulatory catalog contains 4. 8M binding regions for 423 TRs in 23 biotypes and 14 ecotypes (Figure  1F-H ). This update shows a growth in both the number of peaks and the number of TRs. In fact, the number of peaks is almost 2-fold superior to the 2020 Arabidopsis regulatory catalog (Figure  1F ).
Mouse transcriptional regulatory catalog A new regulatory catalog for M. musculus is included in this fourth release of ReMap, as a result of a one-of large-scale integration of public murine DNA-binding assays. While few Mouse regulatory atlases from consortiums  (8, (32) (33) (34)  are available for viewing in genome browsers, none offer significant details and depth of transcription factors occupancy map. To create this unique Mouse regulatory atlas we have collected, curated, uniformly processed, and analysed 7317 ChIP-seq datasets against TRs, precisely 7050 from GEO and 167 from Mouse ENCODE (Figure  1I ). After applying quality controls and filters 5503 datasets (76%) were retained (Supplementary Figure  S3 ). Our analyses lead to a final Mouse regulatory atlas of 123 207 170 binding re-gions for 648 TRs in 373 different murine biotypes (Figure  1J-L ).
The top three most represented TRs are the transcriptional repressor Ctcf, the polycomb protein Suz12 and the double-strand-break repair protein Rad21 homolog Rad21 (also a member of the cohesin complex) (Figure  1K ). The most commonly represented biotypes (cells or tissues) are the mouse Embryonic Stem Cells (mESC), Mouse Embryonic Fibroblast (MEF) and Bone Marrow Derived Macrophage (BMDM) (Figure  1L ). A recent study  (35)  has experimentally quantitatively identified over 60% of Mouse TFs (n = 941) out of the approximated 1500 TFs encoded in the mammalian genome  (6). The complex regulatory architecture of the Mouse ReMap catalog is illustrated in the vicinity of the Rho GTPase-activating protein 26 (Arhgap26) gene (Figure  3A ), combined with the Mouse ENCODE cCREs annotation  (8). We observe a good correlation between cCREs regions and ReMap peak clusters (99.
6% overlap), with some clusters (e. g. first cluster) yet undescribed by ENCODE. While our catalog contains 648 transcriptional regulators (TRs), which may represent ∼44% of the current TF census  (6) , it provides a unique collection of manually curated and uniformly processed ChIPseq datasets from heterogeneous sources. When comparing with the Human atlas, 412 transcriptional regulators are found in common with the Mouse catalogs (Figure  1M ), allowing exploration of evolutionary conservation of cisregulatory modules  (36, 37). We present a unique collection of regulatory regions in Mouse as a result of a large-scale integrative analysis of ChIP-seq experiments for hundreds of transcriptional regulators.
Drosophila transcriptional regulatory catalog Finally, this new ReMap release allows the Fruit Fly community to browse and study the regulatory landscape of the D. melanogaster genome as we present a regulatory catalog of Drosophila ChIP-seq studies in diverse experimental conditions and various tissues and cells. A total of 1308 datasets were processed with 790 datasets from GEO and 525 from ENCODE (461 modERN, 69 modENCODE)  (38, 39). These datasets were manually curated and annotated using the Flybase database  (26)  for the official gene name convention. After applying our quality controls and filters 1,205 datasets (92%) were retained (692 from GEO and 514 from ENCODE, Supplementary Figure  S4 ). Our analyses lead to a final Drosophila regulatory atlas of 16 634 486 binding regions for 550 TRs in 17 different fly biotypes (Figure  1N-O ).
Less is more In this release we focused on improving the content of the ReMap catalogs by extending our filtering procedures. With updates adding thousands of published datasets, the ReMap catalogs have expanded dramatically for Human, but also for the new Mouse catalog. With a range of 100-200M peaks, a small fraction of spurious peaks or datasets may influence or bias the characterisation of the regulatory landscape. Indeed, ReMap has reached a point where the identification of regulatory occupancy regions by adding large quantities of datasets may have been achieved. High quality redundant ChIP-seq evidence, illustrated by the FOXA1 peaks in ELAC1 gene (Supplementary Figure  S9 ), allows to improve the identification of ReMap occupancy regions. To remove spurious peaks, two sets of controls were added in our quality control steps, named QC2 in Figure  1.
These filters were applied to new catalogs, and retroactively for Human and Arabidopsis. We discarded datasets with less than 100 peaks or with more than two times the number of annotated genes, according to the Ensembl gene annotation statistics (Supplementary Figure  S5 ). A few datasets passing our initial quality controls would generate an unusual amount of peaks, potentially affecting the occupancy repertoire. The second filter removes peaks whose length are outside set cutoffs. Ranges were defined as a minimum of 50bp and a maximum upper cutoff for which we have 99% of catalogue, either 1. 5kb or 2kb (See Material and Method, Supplementary Figure  S6 ). This discards large peaks spanning multiple regulatory regions, those peaks not being informative for the transcription factor repertoire identification, or definition of CRMs.
In Human, these filters (QC2) remove 97M peaks as they are applied retroactively to the entire catalog (279M without QC2), 71M peaks removed in Mouse, 1. 1M in Drosophila and 0. 1M in Arabidopsis. However, following the open science principles, the peaks filtered out are available in the download section as 'filtered out' peaks. We believe a conservative approach will benefit the ReMap catalogs by removing uninformative peaks and datasets, leading to clearer regulatory catalogs.
Interactive tour and fast search The ReMap 2022 release comes with an improved user experience. On the ReMap homepage, as well as on the header of the site, we provide an interactive tour walking users through the main features of the website. The tour is activated by clicking on the 'Tour' button right in the middle of the homepage or header bar. The tour dynamically shows the different types of catalogs available on the website (all peaks, non redundant and CRMs), how to browse the ReMap catalogs, download and search the database. This newly introduced interactive tour is a useful feature to better understand the ReMap database, the data content and its functionalities. The ReMap database can be browsed by using the navigation links on the left sidebar, or searched for individual TRs, specific biotypes (cell lines or tissues) using the simple or advanced search box.
ReMap users can now filter which peaks are displayed by selecting specific biotypes and/or by specific TRs (e. g. FOXA1 in MCF-7). This new feature enables better flexibility especially as the ReMap database increases in size. Additionally, the peak names displayed on the browser can be adapted for a better visualisation. To illustrate and explain these browsing options we added multiple videos. These will guide users to make the best of the ReMap catalogs. Furthermore, to represent the depth of regulatory regions, we added a density track on top of the ReMap catalog track. By adding these browsing features, our objective is to make it easier for researchers to explore the variety and the complexity of ReMap catalogs when combined with other biological tracks.
CONCLUSION AND FUTURE DIRECTIONS This fourth release of the ReMap database pursues its commitment to provide manually curated datasets and highquality regulatory catalogs for the research community. For this 2022 update, we processed 13 000 ChIP-seq datasets starting from raw data. With four species, this version of ReMap continues the long-term goal of maintaining accessible, browseable, high-quality regulatory catalogs. The ReMap 2022 database comes with many updates, (i) a substantially expanded human regulatory catalog followed by (ii) an update of the Arabidopsis thaliana regulatory catalog; (iii) a new regulatory atlas for Mus musculus with more than 7,000 datasets curated and processed; (iv) the first Drosophila melanogaster regulatory atlas for the Fly scientific community; (v) an improved CRM identification method, (vi) a conservative filtering approach to improve our catalogs; (vii) an improved user experience with an interactive tour and (viii) updated genomic track hubs in different assemblies for better visualization in genome browser.
Finally, since 2018 each ReMap update is incorporated into the JASPAR pipeline to infer new and updated TF binding profiles  (17, 18). Overall, the ReMap database reaches 327M binding regions across four regulatory atlases. We anticipate that upcoming functional studies will provide additional experimental regulatory evidence giving rise to even larger catalogs. While manual curation and annotation are the foundation of ReMap, we intend to evolve to more conservative approaches in future releases, such as directing our efforts towards redundant peaks to build robust regulatory regions. Our long term goal of providing qualitative high quality catalogs will focus on redundant occupancy evidence for new releases, rather than releasing quantitative catalogs consisting in incremental inventories.
(J) Size of the Mus musculus regulatory catalog, before and after QC2 (yellow). (K, L) Number of datasets for the top 15 TRs and top 6 biotypes. (M) Transcriptional regulators shared between the Human and Mouse regulatory catalogs. (N) Analysed ChIP-seq datasets for the ReMap 2022 Drosophila melanogaster catalog, removed datasets from QC2 in yellow. (O) Size of the Drosophila melanogaster regulatory catalog, before and after QC2 (yellow). (P,Q) Number of datasets for the top 15 TRs and top 6 biotypes.
ACKNOWLEDGEMENTS We would like to thank  Nathalie Arquier  and  Laurent Perrin  for their expertises and scientific discussions regarding the curation and annotation of Drosophila melanogaster ChIP-seq data. We would like to thank the  JASPAR Team  led by  Anthony Mathelier  from  NCMM Norway  for constant scientific feedback on the ReMap catalogues. Finally, we would like to thank  Maximilian Haeussler ,  Gerardo Perez  and the  UCSC Genome informatics groups  for their help with public track hubs and their latest hub development, also the  Ensembl and Ensembl Plant group  for their help with the Human, Mouse, Fly and Arabidopsis track hubs.
FUNDING PhD Fellowship to F. H. from the  Provence-Alpes-C ôte d'Azur Regional Council (Région SUD) ;  Institut National de la Santé et de la Recherche Médicale  ( IN-SERM ); PhD Fellowship to P. D. L. from the  French Ministry of Higher Education and Research (MESR) ;  HPC resources of Aix-Marseille Université  financed by the project  Equip@Meso  [ ANR-10-EQPX-29-01 ] of the  program 'Investissements d'Avenir ' supervised by the  Agence Nationale de la Recherche. Funding for Open Access charge: INSERM ,  MarMaRa , this project has received funding from the  Excellence Initiative of Aix-Marseille University  -A* Midex a French "Investissements d'Avenir programme"-Institute MarMaRa   AMX-19-IET-007.
FTO knockdown in SW620 cells phenocopied the CRC1 cells (Fig. S1c-h ). To connect FTO levels with in vivo tumor initiation potential we inoculated immunodeficient mice (athymic nude) with increasing numbers of sh-FTO or sh-CTL cells (Fig. 1f ). Seven weeks later, tumor xenografts (diameter > 100 mm 3 ) were counted and harvested. Remarkably, as few as hundred sh-FTO cells were capable of initiating tumor formation in 4/5 mice vs. 1/5 for sh-CTL cells (Fig. 1g ). Extreme limiting dilution software analysis (ELDA) shows that the frequency of tumor-initiating cells is seven-fold higher in sh-FTO cells, with a highly significant p-value (p = 0. 00035) (Fig. 1g ). Based on these findings, we conclude that diminished FTO expression promotes the CSC phenotype. FTO silencing confers resistance to chemotherapy in colorectal cancer cell lines.
This observation is also consistent with previous studies showing cancer-specific effects of m 6 A regulators. METLL3 activity promotes tumorigenesis in AML by enhancing BCL2 and PTEN translation  50  , whereas in glioblastoma stem cells, it suppresses growth and self-renewal by reducing expression of ADAM19  45. Interestingly, Kaplan-Meier survival analysis from cancer database shows that overall survival of colorectal cancer is higher with lower FTO. This is contradictory with our data that connects reduced FTO expression with enhanced chemoresistance and tumor initiation, as emphasized by our results from cell lines derived from CTCs, the source of lethal metastases. At least two parameters can account this inconsistency. First, we show that FTO expression in colon cancer cells is finely regulated at the post-transcriptional level, though the precise underlying mechanism remains to be determined.
Methods Cell lines. Patient-derived colon cancer cell lines (CRC1, CPP-14/25/43/6/19/30/36) were derived from CRC surgeries provided by CHU-Carémeau (Nîmes, France, ClinicalTrial. gov Identifier#NCT01577511) within an approved protocol by the french Ethics Committee: CPP (Comité de Protection des Personnes) Sud Méditerrannée III. We have complied with all relevant ethical regulations for work with human participants, and informed written consent was obtained for all the patients. CRC1, CPP-14/25/43 cell lines were derived from primary tumors and CPP-6/19/ 30/36 from metastatic tumors. CTC44 and CTC45 are circulating tumor cell lines derived from blood of metastatic chemotherapy-naïve stage IV CRC patients  31. HCT-116 (ATCC® CCL-247™) and SW620 (ATCC® CCL-227™) are commercially available colon cancer cell lines derived, respectively from primary and metastatic human tumors.
After two washes with distilled water, coverslips were mounted on slides with Fluoromount-G (Invitrogen). Fluorescent pictures were acquired at room temperature on an AxioImager Z1 microscope (Carl Zeiss, Inc. ) equipped with a camera (AxioCam MRm; Carl Zeiss, Inc. ) and Plan Apochromat (63×, NA 1. 4) objective, the Apotome Slider system equipped with an H1 transmission grid (Carl Zeiss, Inc. ), and Zen 2 imaging software (Carl Zeiss, Inc. ). mRNA purification. mRNA was purified from total RNA with two rounds of GeneElute mRNA purification kit (Sigma). rRNA was removed using Ribominus kit (Invitrogen) according to the manufacturer's instructions. Nucleoside mass-spectrometry analysis. This part was performed as previously described  39. Briefly, 400 ng of RNA was digested by FTO detection by immunohistochemistry.
Acknowledgements This work was generously supported by  Occitanie Region/FEDER  ( PPRi ,  SMART  project),  Ligue contre le Cancer, SIRIC Montpellier Cancer (INCa-DGOS-Inserm   6045 ),  Cancéropole GSO and Labex NumeV (GEM   flagship  project,  ANR 2011-LABX-076 ). We thank  Plateforme de Proteomique Clinique  (PPC, https://ppc-montpellier. com),  Montpellier Genomix  (http://www. mgx. cnrs. fr)sequencing facility as well as iExplore animal facility, in particular  Denis Greuet  and  Steeve Thirard. We thank the ATGC bioinformatic platform, whose servers hosted our bioinformatic analyses; ATGC is a member of both the " France Génomique" network  [ ANR-10-INBS-0009 ] and the  Institut Français de Bioinformatique  [ ANR-11-INBS-0013 ].
Code availability Data analysis was performed using free software detailed in the material and methods (i. e. FastQC v0. 11. 5, SortMeRNA v2. 1b, Kallisto v0. 45. 0 and R v3. 5. 1). Statistics and graphics were performed using R packages (DESeq2, ggplot2, biomaRt) and an online program gProfileR. All the scripts used are hosted on a private gitlab repository and could be available from the corresponding author on reasonable request (contact: rivals@lirmm. fr).
Tissue microarray (TMA). TMA was constructed with FFPE tumor samples collected in the frame of the Clinical and Biological Database BCBCOLON (Institut du Cancer de Montpellier-Val d'Aurelle, France, Clinical trial Identifier #NCT03976960). Adenomas, primary adenocarcinomas, and metastatic lesions were sampled as two cores of 1 mm diameter. All samples were chemonaive. Tumor samples were collected following French laws under the supervision of an investigator and declared to the French Ministry of Higher Education and Research (declaration number DC-2008-695). Study protocol has been approved by the french Ethics Committee: CPP (Comité de Protection des Personnes) Sud Médi- terrannée III (Ref#2014. 02. 04) and by the local translational research committee (ICM-CORT-2018-28). We have complied with all relevant ethical regulations for work with human participants, and informed written consent was obtained for all patients.
As single-cell transcriptomics becomes a mainstream technology, the natural next step is to integrate the accumulating data in order to achieve a common ontology of cell types and states. However, owing to various nuisance factors of variation, it is not straightforward how to compare gene expression levels across data sets and how to automatically assign cell type labels in a new data set based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of cohorts of single-cell RNA-seq data sets, while accounting for uncertainty caused by biological and measurement noise. We also introduce single-cell ANnotation using Variational Inference (scANVI), a semi-supervised variant of scVI designed to leverage any available cell state annotations -for instance when only one data set in a cohort is annotated, or when only a few cells in a single data set can be labeled using marker genes.
We demonstrate that scVI and scANVI compare favorably to the existing methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings such as a hierarchical structure of cell state labels. We further show that different from existing methods, scVI and scANVI represent the integrated datasets with a single generative model that can be directly used for any probabilistic decision making task, using differential expression as our case study. scVI and scANVI are available as open source software and can be readily used to facilitate cell state annotation and help ensure consistency and reproducibility across studies.