dataesr/scientific-paragraphs-categorization huggingface dataset:
I. Kim et al. 2016). Proximity-based covalent biotin labeling techniques improve upon classic antibody-based pulldown methods by relying on the strong biotin-streptavidin interaction, which allows for harsh washes and therefore tends to have low levels of nonspecific binding (P. Li et al. 2017)   (Roux et al. 2018). Biotin labeling techniques are well-suited for surveying the spatial proximity between proteins within a narrow diameter  (Gingras, Abe, and Raught 2019). To date, only a few large-scale datasets of dozens or hundreds of bait proteins have been generated  (Antonicka et al. 2020; Go et al. 2021; Youn et al. 2018). In this study, we extended these efforts by applying proximity-dependent biotinylation (BioID) analysis to 50 human RBPs with a well-controlled and rigorous design. We validated the expression of individual fusion proteins by Western blotting (Fig.
Perturb-seq 68 RBPs were chosen for Perturb-seq analysis based on the clustering analysis of ENCODE eCLIP dataset and DeepBind dataset  (Alipanahi et al. 2015). Perturb-seq experiment was performed as previously described  (Datlinger et al. 2017). Briefly, a library of 205 sgRNAs (5 non-targeting sgRNAs and 200 sgRNAs targeting 100 genes, 2 sgRNAs per gene) was ordered as a pooled oligonucleotide library from Twist Bioscience with the following design: [ATCTTGTGGAAAGGACGAAACACCG]-[Protospacer Sequence]-[GTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGC] The library was PCR-amplified using Q5 Hot Start High-Fidelity 2X Master Mix (NEB) with the primers with the following sequences: 5'-ATCTTGTGGAAAGGAC-3' and 5'-GCCTTATTTTAACTTGCTA-3'. To clone libraries into CROPseq-Guide-Puro vector (Addgene 86708), the starting vector was digested with BsmBI following the protocol outlined in  (Sanjana, Shalem, and Zhang 2014).
Reanalysis of enhanced CLIP ENCODE data To reliably identify RNA targets of RBPs in K562 cells, we started with the raw eCLIP FASTQ files of 'released' K562 experiments for 120 RBPs that were available in the ENCODE database. The analysis was performed as follows: (1) the reads were preprocessed in the same way as in  (Van Nostrand et al. 2016 ) including adapter trimming with cutadapt (v1. 18)  (Martin 2011), (2)  preprocessed reads were mapped to the hg38 genome assembly with GENCODE v31 comprehensive annotation using hisat2 (v. 2. 1. 0) (D. Kim et al. 2019 ), (3) the aligned reads were deduplicated using the barcodecollapsepe. py script (https://github. com/YeoLab/eclip/tree/master/bin) as in  (Van Nostrand et al. 2016 ), (4) properly paired and uniquely mapped second reads were extracted using samtools (v.
1. 9, with -f 131 -q 60 parameters) (H. Li et al. 2009 ), (  5 ) gene-level read counts were obtained with plastid (v. 0. 4. 8) by counting 5' ends of the reads (Dunn and Weissman 2016), (  6 ) analysis of specific enrichment against size-matched control experiments was performed with edgeR (v. 3. 18. 1) for each RBP separately, considering only genes passing 2 cpm in at least 2 of 3 samples  (Robinson, McCarthy, and Smyth 2010). Reliable RNA targets of each RBP were defined as those passing 5% FDR and log 2 (Fold Change) > 0. 5, see Supplementary Table  S8. eCLIP target scores (TSs) used in datasets integration were estimated as -log10(P)*sign(logFC) for every "RBP-gene" pair separately.
Dataset integration The functional similarity of RBPs was estimated by joint analysis of eCLIP, BioID, and Perturb-seq data (Figure  1 , Figure  S1 ). First, TS Z-scores were calculated for every gene across RBPs separately for each type of experimental data (eCLIP, BioID, or Perturb-seq) in the same way as preys of the BioID data, see above and Figure  S1 (1). Next, cosine distance was computed for all 7140 pairs of different RBPs followed by ranking and calculation of empirical p-values defined as a fraction of RBP pairs with the cosine distance less than the score of the tested pair, see Figure  S1 (2). The empirical p-values were aggregated with logitp function from the metap R package (v. 1. 4), see Figure  S1 (3) and S1(4), raw (non-aggregated) p-values were used for the RBP pairs assessed in a single type of experiment  (George and Mudholkar 1983).
Perturb-seq analysis Cell Ranger (version 3. 0. 1, 10X Genomics) with default parameters was used to align reads and generate digital expression matrices from single-cell sequencing data. To assign cell genotypes, a bwa reference (H. Li and Durbin 2009)  database was created containing all guide sequences present in the library using bwa index command. The barcode-enrichment libraries were mapped to this database to establish the guide identities; to detect the cell barcodes, the barcode correction scheme used in Cell Ranger was used (the mapping of uncorrected to corrected barcodes was extracted from Cell Ranger analysis run of the whole transcriptome libraries; this mapping was then applied to the reads of barcode-enrichment libraries). UMI correction was performed by merging the UMIs within the hamming distance of 1 from each other.
MS data analysis (BioID2 mass spectrometry data) Data were quantified and queried against a Uniprot human database (January 2013) using MaxQuant MaxLFQ command  (Cox et al. 2014). Data normalization was performed in Perseus  (Tyanova et al. 2016) (version 1. 6. 2. 1). For batch correction, Brent Pedersen's implementation  (Pedersen n. d. )  of ComBat function from sva package  (Leek et al. 2012 ) was used. The protein abundances in "experiment" (biotin +) and "control" (biotin -) samples were compared using t-test for each protein individually.
ATAC-seq analysis ENCODE ATAC-seq pipeline  (Lee et al. 2016)  with default parameters was used for sequencing data processing and analysis. The differentially accessible peaks were identified with the DESeq2 package  (Love, Huber, and Anders 2014)  and annotated with the ChIPseeker package  (Yu, Wang, and He 2015). To perform a comparison against published ChIP-Seq data, the processed ChIP-exo results were downloaded from GEO (GSE151287)  (Lai et al. 2021) The data consisted of bed files containing 33 and 181 QKI peaks (two replicates) and a bigWig file with ZNF800 ChIP-exo signal (no ChIP-exo peaks were reported for ZNF800). In total, 234564 and more than one replicate and were used in the following tests. For QKI, the bed files with ChIP-exo peaks were merged, transferred to the hg38 genome assembly with UCSC liftOver and the numbers of differentially accessible (or not differentially accessible) QKI-KD ATAC-seq peaks that intersect (or do not intersect) ChIP-exo peaks were calculated using bedtools intersect (v.
Proximity-labeled proteins were ranked by their z-scores in the ZNF800-BioID dataset, where a higher score implies enrichment relative to control. Experiments were performed in biological triplicates using unlabeled samples as controls (three case vs. three control design). Gene-set enrichment analyses were performed on the resulting ranked list across all RBPs. Each enrichment analysis resulted with p-value and NES score for a given pair of RBP and a pathway. (E) Venn diagram of TAF15 RNA regulons. Shown are the numbers of genes that exhibit significant changes in splicing (155 genes with Bayes factor >=10), translation (919 genes with FDR < 0. 05), or stability (2,068 genes with FDR < 0. 05) upon TAF15 knockdown, as captured by RNA-seq, Ribo-seq, and RNA-seq with α-amanitin, respectively.
Acknowledgments The authors would like to thank the  Bordeaux Imaging center  for the help in superresolution imaging. We also thank  Zeiss  for their help with SIM. We express our gratitude to  E. O'Shea ,  S. Jaspersen ,  J-P. Javerzat ,  Wei-Lih Lee  and  B. Goode  for sharing reagents. We would like to thank  J-P. Javerzat  for helpful and constructive discussions about our work. DL, AML, JD and IS were supported by a grant from the  ANR-21-CE13-0023-01 , a  Ligue Contre le Cancer Régionale   -Dordogne  grant # 193366  and the  CNRS. MG and EN were supported by a  National Science Foundation  grant number  MCB-1846262. AR was supported by the  Conseil Régional d'Aquitaine  (# 20111301010 ) and the  CNRS.
Processing of the reads, computation of contact matrices, and generation of contact maps Reads were aligned and the contact data processed using Hicstuff (https://github. com/koszullab/hicstuff). Briefly, pairs of reads were aligned iteratively and independently using Bowtie2 in its most sensitive mode against the S. cerevisiae W303 reference genome. Each uniquely mapped read was assigned to a restriction fragment. Quantification of pairwise contacts between restriction fragments was performed with default parameters: uncuts, loops and circularization events were filtered as previously described (Bastié et al, 2021). PCR duplicates (defined as paired reads mapping at exactly the same position) were discarded. Contact maps from independent replicates were generated with the "view" function of Hicstuff, merged and normalized using the merge and balance functions of Cooler.
Acknowledgements This research was supported by the  European Research Council  under the  Horizon 2020 Program  ( ERC  grant agreement  771813 ) to RK,  Agence Nationale pour la Recherche  ( ANR-22-CE12-0013-01 ) to FB and RK. FB also received support from the  Fondation ARC pour la Recherche sur le Cancer  ( ARCPJA2022060005240 ) and the  Comité de l'Occitanie de la Ligue Nationale contre le Cancer. NB was supported by the  Ministère de l'Enseignement Supérieur and la Ligue Nationale contre le Cancer and Le comité départementale de la Ligue contre le Cancer de la Moselle. CC was supported by  Pasteur-Roux -Cantarini fellowship. We thank all our colleagues from the  laboratories régulation spatiale des génomes and organization du  noyau for helpful comments on the manuscript,  K. Nasmyth 's laboratory and  B.
Data Availability Raw data will be made available following peer-reviewed publication. Table S2 recapitulates the number of sequencing reads exploited for each condition tested. Reference genomes S. cerevisiae W303: gift from (O'Donnell et al. , 2022); C. glabrata: http://www. candidagenome. org/download/sequence/C_glabrata_CBS138/current/ Strains of this study are available from the corresponding authors. Code Availability Programs involved in the study are listed below. They include custom made, open source programs from the Koszul lab: HiCstuff (www. github. com/koszullab/hicstuff) version 3. 0. 1 Chromosight (www. github. com/koszullab/chromosight) v1. 3. 1 36 As well as programs published by others: Bowtie2 (version 2. 3. 4. 1 available online at http://bowtie-bio. sourceforge. net/bowtie2/) Samtools (version 1.
Gene atlases for livestock are steadily improving thanks to new genome assemblies and new expression data improving the gene annotation. However, gene content varies across databases due to differences in RNA sequencing data and bioinformatics pipelines, especially for long non-coding RNAs (lncRNAs) which have higher tissue and developmental specificity and are harder to consistently identify compared to protein coding genes (PCGs). As done previously in 2020 for chicken assemblies galgal5 and GRCg6a, we provide a new gene atlas, lncRNA-enriched, for the latest GRCg7b chicken assembly, integrating "NCBI RefSeq", "EMBL-EBI Ensembl/GENCODE" reference annotations and other resources such as FAANG and NONCODE. As a result, the number of PCGs increases from 18,022 (RefSeq) and 17,007 (Ensembl) to 24,102, and that of lncRNAs from 5,789 (RefSeq) and 11,944 (Ensembl) to 44,428.
Genome annotation databases are mainly supplied by short-read RNA-seq generated massively by the scientific community. In this context, to improve genome annotation completeness, especially for lncRNAs, one strategy is to combine both most popular reference genome annotations -"RefSeq" and "Ensembl" -and other additional databases. In this context, we provided in 2020  13  a chicken atlas integrating gene models from "Ensembl", "RefSeq" and other databases. However, since 2020, the new GRCg7b chicken genome assembly with its associated reference genome annotations have been released, leading us to update and improve this gene atlas. Consequently, we included new databases such as FAANG multi-tissue resources and the NONCODE database, and provided an extensive functional annotation for the 24,102 PCGs and 44,428 lncRNAs using 1,400 RNA-seq samples from 47 tissues or cell types (available at www.
Overview of the different databases used to generate the chicken gene-enriched atlas. Six databases containing lncRNAs and PCGs -for five of them -have been selected to create an enriched genome annotation. This set includes: i) "NCBI-RefSeq" (abbreviated in "RefSeq") and "EMBL-EBI Ensembl/GENCODE" (abbreviated in "Ensembl") databases, that are frequently updated and widely considered as references; ii) two databases from FAANG multi-tissue projects, namely the Fr-AgENCODE annotation ("FrAg") and the UC Davis annotation ("Davis"); iii) the INRAE annotation ("Inrae") previously used in Jehl et al. , 2020  13  , for the gene enriched-atlas according to the GRCg6a assembly; iv) the "Noncode" database dedicated to non-coding RNAs. Comparison of the content of the gene models in the databases (Figure  1A ) shows that PCGs overlap more with each other between databases than lncRNAs do.
Thus, for PCGs, the "RefSeq"/"Ensembl"/"FrAg" dataset trio shows a high overlap rate around 95% globally, while consistency drops with the other annotations (75% for "Davis" and around 50% for "Inrae"). For lncRNAs, the overlap rate ranges from 50% for "RefSeq"/"FrAg" to 7% for "Ensembl"/"Davis". Note that despite their reference status, the overlap rate does not exceed 37% between both "RefSeq" and "Ensembl" reference databases. Consequently, as indicated by the low percentage of lncRNA overlapping, but also by the admittedly high but lower than 100% for PCGs, these resources appear complementary. As shown in Figure  1B -top (and Sup. Table  2 ), while PCG numbers are quite constant globally, with 18,022, 17,007, 14,078 and 18,341 for "RefSeq", "Ensembl, "FrAg" and "Davis" respectively, the number of lncRNAs is more variable, ranging from 5,789 at minimum for "RefSeq" to over 10,000 for the other databases, with, interestingly, a higher proportion of mono-exonic lncRNAs for "Inrae" and "Davis" (more than 65% against less than 24% for the other databases).
This gene model variability between the six databases is also observed at the transcript level through the number of transcripts, which supports gene models (Figure  1B -bottom). Overall, the number of transcripts per gene is higher for PCGs than for lncRNAs and shows a greater variability. While the median number of transcripts is between 1 to 3 across the databases for PCGs, it does not exceed 1 for lncRNAs. As the number of transcripts supporting gene locus still low, regardless the PCG or lncRNA biotype, we chose to focus more on gene loci, level at which expression analyses are mostly performed, than on transcripts. Based on these observations, we integrated the various annotations by sequentially adding gene loci from each database, keeping only gene loci that had no overlapping transcripts with transcripts already present in the growing catalog (see Mat.
& Meth. for more details). Since the conserved gene models in the enriched genome annotation -with their associated transcript models -are the ones that appear first during the successive additions of annotations, the gathering order is crucial. To better characterize the precision of transcripts models from each database, we computed the concordance between the annotated transcription start sites (TSS) and CAGE peaks from the FANTOM project (see Mat. & Meth. ) (Figure  1C ). The resulting support was higher for PCG promoters than for lncRNAs: the overlap rate between TSS and CAGE peaks varies between 60% for "RefSeq" and 40% for the other databases for PCGs (except for "Davis" which reaches 15%) whereas this overlap rate do not exceed 15% for lncRNAs. However, the rank of each database with respect to CAGE peaks is preserved, except for "Ensembl" that is lower for lncRNAs with only 5% of concordance.
Considering gene model quality characteristics (i. e. , number of gene loci and transcripts, biotypes, mono-exonicity), the concordance with CAGE peaks, and the popularity of each databases, the following order was chosen: 1-"RefSeq", 2-"Ensembl", 3-"FrAg", 4-"Davis", 5-"Inrae", 6-"Noncode". Consequently ad by construction, "RefSeq" gene models are fully included in the enriched genome annotation. Finally, this enriched gene atlas contains respectively 24,102 PCGs and 44,428 lncRNAs. Similarly, 991 miRNAs and a total of 78,323 gene models of various biotypes are annotated (Figure  1D  & Sup. Table  3 ). This enriched gene atlas is available as a. gtf file on the Fr-AgENCODE website (www. fragencode. org/lnchickenatlas. html). Interestingly, the PCG and lncRNA gene density per chromosome is correlated (R = 0.
Gene expression across 47 chicken tissues. In order to functionally characterize the 78,323 gene models, especially PCGs and lncRNAs, their expressions were quantified through 47 tissues (40 tissues stricto sensu and 7 cell types) coming from 36 datasets for a total of 1,400 individuals (see Mat. & Meth. ), as presented in the Figure  2A  and Sup. Table  4. This whole dataset is not exhaustive but tends to represent an important part of the physiology of the chicken by including tissues representing different specific systems such as the nervous (shades of grey), digestive (shades of green), respiratory (shades of purple), sexual (shades of pink), circulatory (shades of brown), immune (shades of blue), or metabolic/energetic systems (shades of red). A total of 63,513 (81%) genes are considered as expressed (Figure  2B -top), considering inter alia, a normalized expression threshold of 0.
1 TPM and TMM (see Mat. & Meth. ). This includes 22,468 (93%) PCGs and 35,257 (79%) lncRNAs. Interestingly, among the 6,238 genes with no defined biotype, identified as "other", 4,490 (72%) are also considered as expressed. The number of expressed genes per source (Figure  2B -bottom) averaged 75% but varied from 91% for "RefSeq" to 49% for "Noncode", which is below the other databases due to older gene models and its addition as a final step in the sequential aggregation of gene models. Regardless of the biotype, the PCAs performed on the expression data (Figure  2C  and Sup. Figure  1 ), resulted in a tissue-dependent clustering across all datasets, validating the consistency of the expression data. Interestingly, lncRNAs clustered first the data according to the tissues with the most tissue-specific genes, i.
More precisely, Figure  3C (top)  indicates that the proportion of TS genes of each categories was very variable across tissues. As an example, 74% of the 4,905 genes which are TS for "testis" are mono-specific. In contrast, 0. 8% of the 510 TS genes for the "duodenum" are mono-specific (all numbers per tissue are provided in Sup. Table  5 ). This variability in proportion is related to the other tissues present in the dataset and their common functions. Thus, tissues belonging to a common function tend to be express concomitantly for poly-specific genes. For example, tissues associated to the intestinal system as the duodenum, jejunum, ileum, cecum and colon, tend to express genes concomitantly as do the tissues associated to the brain system (Figure  3C-bottom ). Thus, it should be noted that a gene can be considered as TS despite that no break in its expression pattern is observed.
An intriguing case is the "LOC430486" gene (iv) responsible for chicken epilepsy  27, 28  and encoding the synaptic vesicle glycoprotein 2A (SV2A) acting in the brain-related tissues. This gene was initially identified in 2011 in the galgal2 assembly (chr25:776,500-777,079 -1 exon  29  ) before being removed in subsequent releases because it was no longer predicted. It then reappeared in the galgal5 assembly both in "RefSeq" (LOC101748017) and in "Ensembl" (ENSGALG00000044909) but with a different gene structure and notably on a scaffold (KQ759566. 1:4,207-4,692). Gene models were later harmonized between the two databases in the GRCg6a assembly (LOC101748017/ENSGALG00000050830) and the gene returned to its original position (chr25:1,854,812-1,880,902). It is also present in the GRCg7b assembly (LOC101748017/ENSGALG00010028753) for which, interestingly, the originally predicted sequence has a unique hit with 100% identity to the gene.
Differential expression between sexes. We also provide a list of 4,206 differentially expressed genes (DEG) between sexes. These genes were identified in six tissues for which at least eight birds per sex were available from the same dataset: 2,475, 1,003, 768, 759, 659, and 233 DEGs were identified for liver, adipose tissue, bone marrow-derived macrophages, bursa of Fabricius, feathers, and the Harderian gland respectively (Sup. 4D , the incomplete sex chromosome dosage compensation known in chicken was observed with a median of log(fold-change "male/female") reaching 0. 76. As for autosomal genes, the majority (419/438 genes, 95. 7%) of Z-linked genes with sex biased expression in more than one tissue exhibit consistent effect directions across tissues. LncRNAs host miRNA genes. Using FEELnc, we classified the 991 chicken miRNAs into positional categories relatively to their closest lncRNA or PCG.
This phenomenon can lead to erroneous co-expression and is expected to be more intense for downstream/3'UTR that are not well defined for PCG transcript models in our livestock species and can be much longer compared to the upstream/5'UTR. In line with this, we observed that lncRNAs in downstream/3'UTR of a PCG (noted "SS. down" -12. 6%) are more co expressed with it compared to other intergenic configurations, especially lncRNAs in upstream/5'UTR (i. e. , "SS. up" -5. 2%) of a PCG. To illustrate these possible erroneous lncRNA model in downstream/3'UTR of a PCG, some lncRNA:PCG pairs in same strand, coming from different databases were tested by PCR for reliability. Three lncRNA:PCG pairs (LOC121113202/VSIG10L; NONGGAG001811/SARDH; FRAGALG000000006896/PA2G4) were identified in which the lncRNA was in the downstream/3'UTR of the PCG and can be considered as an extension of it.
Overlap with the previous enriched annotation galgal5 and GRCg6a. This work proposes a genome annotation (. gtf) and a gene annotation (. tsv) built on the GRCg7b assembly, that is considered as the new reference since April 2021 and July 2022 for "RefSeq" and "Ensembl" respectively  10. This change in assembly and its coexistence with the previous GRCg6a and the alternate one GRCg7w, has led to a significant change in gene identifiers in some databases -particularly for "Ensembl" -which can complicate the transition and lead to uncertainties between studies performed on variable assemblies and annotations. For example, the SLC27A4 well-known protein coding gene is known as LOC417220 in "RefSeq" for galgal5, GRCg6a, GRCg7b and GRCg7w assemblies but in "Ensembl", the associated gene ID is ENSGALG00000004965 for galgal5 and GRCg6a, ENSGALG00010027394 for GRCg7b, and ENSGALG00015027711 for GRCg7w.
To enhance the comparison between studies and different genome assemblies, we provide an equivalence table for i) the "Refseq" and "Ensembl" gene identifiers of GRCg7b for genes referenced in both databases, ii) the "Ensembl" gene identifiers of GRCg7b and GRCg7w, iii) the gene identifiers from our previous annotation in galgal5 and GRCg6a to the one in GRCg7b (Sup. Table  8 ).