dataesr/scientific-paragraphs-categorization huggingface dataset:
Construction of Pt-DPH activity reporter lines The 931-nt upstream region of the HSF4. 6a gene (Phatr3_J49557) and the YFP:FcpAt fragment were amplified by PCR using the Phusion High-Fidelity DNA polymerase (Thermo Fisher, USA) from genomic DNA and the primer pairs : Hsf4. 6ap_Fw0 and Hsf4. 6ap-YFP_Rv, and from the pDEST-C-HA vector and the primer pairs : YFP-Hsf4. 6ap_Fw and FcpAT_Rv0, respectively (Extended Data Table  5 ). The two fragments were assembled by PCR using the Hsf4. 6ap_Fw0 and FcpAT_Rv0, and the final product cloned into pGEM-T (Promega). Transgenic lines were obtained by biolistic cotransformation with the Phleomycin resistance plasmid pAF6 as described in  43  , in WT, WT (Tc) (Transformation control) and PtDPH-KO mutants obtained in  6.
RNA extraction and gene expression analysis WT and PtDPH KO mutant lines were grown as described for the induction spectra experiments, and exposed for 30 min of saturating blue (445 nm, 30 µmol photon. m -2. s -1 ) or far-red (800 nm, 200 µmol photon. m -2. s -1 ) light, or placed in the dark. About 1. 5x 10 8 cells were harvested by filtration and total RNA was extracted as in  39. For qRT-PCR, 600 ng of total RNA were reverse-transcribed using the QuantiTect Reverse Transcription Kit (Qiagen, USA) and the qPCR reaction accomplished with 12. 5 ng cDNA as template, following the SsoAdvanced Universal SYBR Green Supermix instructions (Bio-Rad, USA), in a CFX 96 Real-Time Detection System (Bio-Rad). Primer sequences are indicated in Extended Data Table  5. Histone H4 (H4, Phatr3_J34971) was used as a reference gene as in  44  and normalization was performed to the values before specific exposure (i.
Modeled and real environmental light spectra We used TUV v5. 3  47  to compute sea-levels light spectra (direct solar and diffuse) for different sun zenith angles (downwelling irradiance only). Underwater attenuation coefficients were calculated with the formula from Morel and Maritonema, 2001  48. Light spectra were then calculated down to 100m deep for different solar zenith angles and Chlorophyll a (Chl a) concentration hypothesizing that Chl a concentration would be homogeneous in the water column. CDOM and particles  49, 50  in addition to the model of Morel and Maritonema were also considered to mimick coastal and turbid light attenuation, but with the same effect as Chl a concentration (data not shown). In situ measured environmental spectra were retrieved from  15  , and 2 representative stations are shown in Fig.
Only patients with a PNH clone size larger than 20% in granulocytes and/or monocytes were included. This study was approved by the Research Ethics Committee of the Federal University of Sao Paulo and Research Ethics Committee of Hospital Sirio Libanes. All patients and healthy controls agreed to participate in the study and were included after signing a written informed consent form. We confirm that all the experimental protocols were in accordance with guidelines of the Declaration of Helsinki. Data collection included detailed information on the symptoms and signs of hemolysis (abdominal pain, hemoglobinuria, dysphagia, and fatigue), venous and/or arterial thromboembolism, transfusions, hospitalizations, hepatic and renal functions, PNH clone size, and on the treatment received for PNH. Peripheral blood samples (10 ml) were collected using a syringe in tubes containing EDTA.
, 2020)  and at https://github. com/hzuber67/RACEseq_virus, respectively. The Illumina-based 3'RACE-seq protocol allows for in-depth mapping of RNA 3' extremities with single nucleotide resolution, including the identification of any untemplated nucleotides. Of note, Unique Molecular Identifiers (UMIs) are incorporated by ligating the 3' ends of RNAs to an adapter that contains a 15-nucleotide degenerate sequence (see Supplemental Data Set S1 for information on all primers used in this study). This molecular barcoding allows for a deduplication step during data analysis and therefore each final read corresponds to a single original RNA molecule. 3'RACE-seq has a few technical limitations that are worth to note in the context of this study. The first one is that 3'RACE-seq interrogates 3' extremities independently of 5' extremities and therefore, full-length viral RNAs and subgenomic RNAs sharing identical 3' extremities are not discriminated here.
This indicates that both GT RNA1 and RNA2's termini have been optimized when the GT isolate was multiplied in planta. We independently verified this observation by using another synthetic isolate called K30. The in vitro transcribed RNA1 and RNA2 of the synthetic K30 isolate end by AUUUU(A) 31 and UUUAU(A) 22 G, respectively (Supplemental Data Set S2b)  (Vigne et al. , 2013). We analyzed by 3'RACE-seq GFLV RNAs from plants infected by virions that were originally produced from quinoa plants infected with K30 infectious transcripts and propagated multiple times. As a control, we also analyzed plants infected with the B844 isolate which originates from infected grapevine and was also propagated multiple times in quinoa plants. 3'RACE-seq analysis revealed similar levels of RNA1 and RNA2 mono-uridylation for the K30 and B844 isolates (Figure  9 ).
PCR cycles were as follows: a step at 94°C for 1 min; 25, 20 or 5 cycles (for PCR1, PCR2 or PCR3, respectively) at 94°C for 30 s, 55-65°C for 30 s and 72°C for 40 s; a final step at 72°C for 40 s. All used primers are listed in Supplemental Data Set S1a. All PCR3 products were purified using one volume of magnetic beads (AMPure XP Reagent for PCR purification, Beckman Coulter). Libraries were paired-end sequenced with MiSeq (v3 chemistry) with 41x111 bp cycle settings.
Data processing for 3' and 5' RACE-seq. Fastq files were analyzed by a homemade pipeline adapted from  (Scheer et al. , 2021)  and composed of scripts using Python (v2. 7), Biopython (v1. 63)  (Cock et al. , 2009) and RegEX (v2. 4)  libraries. Most steps of data processing are common between 3' and 5' RACE-seq data analyses. Bioinformatic protocols are detailed in Supplementary Methods and are available in Github.
RT-qPCR was then performed using primers specific for the TuMV RNA or of the housekeeping gene ACTIN2 of Arabidopsis (AT3G18780) (see primer sequences in Supplemental Data Set S1c). RT-qPCR was performed in a LightCycler® 480 II (Roche) in a 10 µl reaction volume containing 0. 1 µL of cDNA, 2. 5 µM of forward and reverse primers and 1X of SYBR green (Applied Biosystems). PCR cycles were as follows: a 5 min step of denaturation at 95°C, followed by 45 cycles of 10 s denaturation at 95 °C, 15 s annealing at 58 °C, 15 s elongation at 72 °C, and a final denaturation step of 5 s at 95 °C and a final elongation step of 1 min at 55°C. Cycle threshold (Ct) values were exported using the LightCycler 480 Software, (v 1. 5. 1). For each sample and primer pair, RT-qPCR reactions were performed in triplicates and triplicate CT values were averaged.
Phylogenetic analysis The phylogenetic tree in Supplemental Figure  S2  show relationships among species of the Secoviridae family. The tree was calculated from the aligned amino acid sequences of the conserved protease-polymerase (Pro-Pol) region, from the protease CG motif to the polymerase GDD motif. Evolutionary analyses were conducted in MEGA X  (Kumar et al. 2018). Alignments were performed using the MUSCLE program with default parameters. The evolutionary history was inferred by using the Maximum Likelihood method with the best fit model (LG) including a discrete Gamma distribution to model evolutionary rate differences among sites (5 categories, +G parameter = 3. 53). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 0. 05% sites). 1000 bootstrap replicates were performed.
The tree was drawn to scale with branch lengths indicating the number of substitutions per site. The analyzed Secoviridae species and sequence accession numbers are as follows: GFLV (GQ332372, NC_003615, MF804979, JN391442, KC900162), ArMV (MW380904, BK059316, CQ369527, MN5999884, MH802018), RpRSV (MN384981, MZ220963, MZ291909, MW057710, AY310444), TRSV (MT210150, MT563078, MN504762, U50869, KJ556849), TBRV (MW961144, MZ291911, MW057704, KX977560, MT992604), CLRV (NC_015414, KC937021, GU167974, LT883167, KU215412), CPMV (X00206, MT682297, MT815984, MT723988), BBWV1 (MN216348, AY781171, MF770979, AB084450, MZ202340), CNDV (MW848523, NC_038320, MW080951) and SLRSV (MF797011, MF796997, MF797001, NC-006964, MF796999).
Statistics and reproducibility All plots were generated using R (v. 3. 6. 1) and the R package ggplot2 (v. 3. 3. 5) on  RStudio (v. 1. 4. 1106). To compare uridylation percentages of viral RNAs (Figure  6A  and Figure  7A ), RNA fragment percentages (Figure  6F ) and the frequency of each nucleotide before tail (Figure  6E ) across genotypes, we used the R package car (v3. 0-5) applying a generalized linear model for proportions with a quasibinomial distribution. To test the difference of U-tail sizes between genotype (Figure  7B ), tail size medians were calculated for each infected plant and genotype and a non-parametric test was applied using Pairwise Wilcoxon Rank Sum Tests with data considered as unpaired (two-tailed). For Figure  3 , Figure  7C  and Supplemental Figure  S7 , a linear regression (R package stats, v3.
Accession numbers NGS datasets generated during this study have been deposited in NCBI's Gene Expression Omnibus  (Edgar et al. , 2002)  and are accessible through the GEO Series accession number GSE212358 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE212358). GEO Series accession numbers for individual datasets are GSE212292 (for Figure  1 , Figure  2 , Figure  3  and Figure  5A , and Supplemental Figure  1 , Supplemental Figure  3  and Supplemental Figure  4A ), GSE212354 (for Figure  4 ), GSE212293 (for Figure  6 , Figure  7  and Figure  8A , and Supplemental Figure  4A  and Supplemental Figure  5 ), GSE212355 (for Figure  8B  and Supplemental Figure  S6B ), and GSE212356 (for Figure  9 ). Source data for all figures are included as Supplemental Data Sets. Bioinformatic pipelines including python and bash source code for 3'RACE-seq are available in Github (https://github.
Figure4: High uridylation levels are restricted to GFLV and ArMV. A, Uridylation percentages among the Secoviridae family. Each bar represents an infected plant, the host plant is indicated below and virus isolates are indicated in Supplemental Data Set S2b. The percentages of long (> 1 U) and 1 U-tails are indicated by dark gray and light gray, respectively. Percentages shown for GFLV in Vitis spp. and CNDV were calculated from the same dataset used in Figure2. No data were obtained for the BBWV-1 RNA2 for unknown technical reasons. The diagram below barplots illustrates the phylogenetic distances between the Secoviridae viruses analyzed in this study. The phylogenetic tree is shown in Supplemental FigureS6B. B, Proportion of the different U-tail sizes from 1 to 30 nt. The percentages were calculated using the number of U-tails as denominator.
Acknowledgments The authors gratefully acknowledge  Salah Bouzoubaah ,  Véronique Brault ,  Marc Fuchs ,  David Gilmer ,  Anthony Gobert ,  Manfred Heinlein ,  Jean-Sébastien Reynard ,  Christophe Ritzenthaler ,  Corinne Schmitt-Keichinger ,  Claire Villeroy  and  Véronique Ziegler-Graff  for providing viruses or infected plant material analyzed in this study. The authors also thank  Peter Brodersen  for providing the heso1-4 urt1-1 line,  Anthony Gobert  for sharing the experimental procedure for ligation of structured RNA and expressed their gratitude to  David Gilmer ,  Heike Lange  and  Corinne Schmitt-Keichinger  for helpful discussions. We also acknowledge the respective plant facility staff at IBMP and the experimental unit of  INRAE Grand Est-Colmar  for plant maintenance.
Funding This work was supported by  Centre National de la Recherche Scientifique (CNRS) ,  Institut national de recherche pour l'agriculture , l'alimentation et l'environnement (INRAE),  Université de Strasbourg  and by a research grant from the  French National Research Agency   ANR-20-CE20-0010. Some of the sequencing experiments were supported by a funding from the state managed by the  French National Research Agency  as part of the  "Investments for the Future" program  under the framework of the  LABEX :  ANR-10-LABX-0036_NETRNA  and  ANR-17-EURE-0023. of recombinant Flaviviridae RNA-dependent RNA polymerases: implication for viral RNA synthesis. J Virol 75: 8615-8623 Ren G, Chen X, Yu B (2012) Uridylation of miRNAs by hen1 suppressor1 in Arabidopsis. Curr Biol 22: 695-700 Ritzenthaler C, Viry M, Pinck M,  Margis R , Fuchs M, Pinck LY 1991 (1991) Complete nucleotide sequence and genetic organization of grapevine fanleaf nepovirus RNA1.
PAD2: interactive exploration of transcription factor genomic colocalization using ChIPseq data Characterizing transcription factor (TF) genomic colocalization is essential for identifying cooperative binding of TFs in controlling gene expression. Here, we introduce a protocol for using PAD2, an interactive web application that enables the investigation of colocalization of various TFs and chromatin-regulating proteins from mouse embryonic stem cells at various functional genomic regions. We describe steps for accessing and searching the PAD2 database and selecting and submitting genomic regions. We then detail protein colocalization analysis using heatmap and ranked correlation plot.
SUMMARY Characterizing transcription factor (TF) genomic colocalization is essential for identifying cooperative binding of TFs in controlling gene expression. Here, we introduce a protocol for using PAD2, an interactive web application that enables the investigation of colocalization of various TFs and chromatin-regulating proteins from mouse embryonic stem cells at various functional genomic regions. We describe steps for accessing and searching the PAD2 database and selecting and submitting genomic regions. We then detail protein colocalization analysis using heatmap and ranked correlation plot. For complete details on the use and execution of this protocol, please refer to Kim et al. (2022). 1.
BEFORE YOU BEGIN ChIP-seq data preparation and user's data format (optional) PAD2 accepts user provided ChIP-seq data for comparison with those in the database. Note that this is optional and is only applicable when a user would like to compare their own ChIP-seq data with those in the PAD2 database. To process the user's data for comparison, reads from a ChIP-seq dataset should be aligned to the mouse genome (mm9 assembly) using Bowtie alignment tool or similar alternatives and first saved in BED (Browser Extensible Data) format. To generate an input file for PAD2, the following steps are required from the BED file, 1. for each DNA binding or interacting factors, mapped reads in a BED file should be binned across the mouse genome with 1 kb bin size and quantified as count and reads per base/kilobase per million reads (RPKM).
KEY RESOURCES TABLE MATERIALS AND EQUIPMENT PAD2 is a web application accessible at http://pad2. maths. usyd. edu. au/. PAD2 was developed in Python 3. 9 (v3. 9. 2) based on the Django (v2. 0. 8) web framework and the interactive plots in PAD2 were rendered using Plotly. js (v2. 16. 1), an extension from PAD clustering  12  implementation. The mapping of signal files to genomic regions are performed with pybedtools (v0. 8. 2), a python wrapper around BEDTools (v2. 30. 0). The signal files in the database of PAD2 consist of transcription factors, cofactors, histone marks and chromatin remodellers sourced from studies listed in the key resources table and processed as described in ChIP-seq data preparation at before you begin section. Submitting addition file for analysis is optional (see ''ChIP-seq data preparation and user's data format'' subsection in ''before you begin'' section) but if provided must be processed the same way with alignment to the mouse genome, mm9 assembly for compatibility.
Timing: <5 min Here we describe the steps to begin the analysis from PAD2, starting with selecting a set of signal files from the database. The selected proteins will be used in targeted colocalization analysis within 12 functional genomic regions identified by ChromHMM. 13 Open PAD2, from http://pad2. maths. usyd. edu. au/. 2. Click the ''Select signals from the database'' button on the side panel (Figure  1A ) to open a modal page with an interactive data table of files in the database (Figure  1B ). 3. Select the signals of TFs and/or histone marks of interest from the database. a. You can sort the table by clicking the column names and search for a file using the search bar on the top right corner of the modal page. b. Click anywhere on the row of the table to select. The selected rows will be highlighted.
c. Once all the files of interest are selected, click the ''Save'' button. 4. User's signal file upload (optional). a. Click the ''Browse. '' button on the side panel. b. Browse your local computer directory to select all the files you wish to upload. c. Click the submit button. Note: The user's own signal data submission is optional. If the user only wishes to analyze the files in our database, action 4 can be skipped. CRITICAL: Access to the website may not be successful in the event of server maintenance or power outages. Please send an email to the administrator for assistance. Troubleshooting 1. CRITICAL: The user's input file must be aligned to mm9 assembly to be compatible with those in the database and in the required format shown in Table  1  above (See ChIP-seq data preparation from before you begin.
EXPECTED OUTCOMES PAD2 is an interactive, easy-to-use online bioinformatics software tool to investigate the colocalization of chromatin binding or interacting proteins on chromatin based on their genomic binding profiles in mouse embryonic stem cells (mESCs). It extends from PAD clustering  12  (http://pad2. maths. usyd. edu. au), which was previously developed to characterize the colocalization of TFs and epigenomic marks at proximal and distal genomic binding sites. Here, we increase the number of partitions for genomic binding sites and histone modifications to 12 functional genomic regions defined by 1 using ChromHMM 13 in mouse ESCs. In addition, we also provide the controls including low signal/repetitive elements regions and heterochromatin regions, and other functional regions including intragenic RNA Pol II sites (IRS) and promoter-proximal RNA Pol II sites (PRS) as defined in Cinghu et al.
LIMITATIONS PAD2 is only available as a web platform through http://pad2. maths. usyd. edu. au. The application may not be available during server maintenance or updates. The server is usually back online within a business day. Refer to Troubleshooting 1 if the server is down. The current version of PAD2 is limited to mouse ESCs, and the files in the database are limited to the mm9 assembly. The database consists of curated 462 signal files and is expected to be updated in the future. Unlike PAD,  12  where we show a comparison between the clustering of proximal and distal regions on the page, to highlight the colocalization at specific functional genomic regions, we only show a specific region of interest selected by the user. We have compared a set of similarity metrics to asssess the similarity of the signals and found that Pearson's correlation was the best metric.
6 GEO: GSE27841 Mouse ESCs CDK9 ChIP-seq Whyte et al. 3 GEO: GSE44286 Mouse ESCs TBP ChIP-seq https://www. ncbi. nlm. nih. gov/geo/ GEO: GSE22303 Mouse ESCs CoREST ChIP-seq Whyte et al. 6 GEO: GSE27841 Mouse ESCs HDAC1-2 ChIP-seq Whyte et al. 6 GEO: GSE27841 Mouse ESCs H3K27ac ChIP-seq Yang et al. 7 GEO: GSE117896 Mouse ESCs H3K4me3 ChIP-seq Yang et al. 7 GEO: GSE117896 Mouse ESCs H3K27me3 ChIP-seq Yang et al. 7 GEO: GSE117896 Mouse ESCs GRO-seq Williams et al. 8 GEO: GSE43390 Mouse ESCs LSD1 KD microarray Forster et al. 9 GEO: GSE21131 Mouse ESCs cMYC KD RNA-seq Seruggia et al. 10 GEO: GSE113329 Human ESCs LSD1 KD microarray Adamo et al. 11 GEO: GSE24844 Mouse ESCs 0 h RNA-seq Yang et al. 7 GEO: GSE117896 Software and algorithms PAD2 Kim et al. 1 http://pad2. maths. usyd. edu. au.
Reduce: Purchase • Buy in bulk when possible, and consider combining orders with other groups. • Choose products with reduced plastic content and/or packaging. • Reduce the number of suppliers and prioritize local shipments, thereby reducing the number of deliveries (which reduces packing material as well as shippingrelated CO2 emission). • When ordering, ask for a Life Cycle Assessment (LCA) and check the provided 'ACT. Label' by My Green Labs to buy the most sustainable piece of equipment (https://actdatabase. mygreenlab. org/).
• When equipment is replaced, make sure to choose sustainable new equipment: ○ Choose electrical equipment with low energy demand. Especially freezers and fridges have become much more energy friendly in recent years. Leverage the information contained in the energy labels to determine whether an appliance has good sustainability properties (https://www. energielabel. nl/apparaten). Ask for a Life cycle assessment (LCA) and check the provided 'ACT. Label' by My Green Labs to buy the most sustainable piece of equipment (https://actdatabase. mygreenlab. org/). 62  ○ Replace oil baths with metal heating blocks. • https://us. vwr. com/store/product/4698629/vwr-modularheating-blocks-for-standard-test-tubes • https://www. coleparmer. com/p/accessories-for-ikasynthesis-packages/64812 • https://www.
Concrete actions: • Implement an electronic lab journal: Mbook: https://mestrelab. com/software/mbook/ ELabJournal: https://www. elabnext. com/products/elabjournal/ • Opt for energy efficient devices. • Use the search engine Ecosia (https://www. ecosia. org/), which is powered by renewables only, to support planting trees while looking up information on the internet. • Switch online meetings to audio only to reduce emissions by up to 96%. 64  • Evaluate the necessity for online meetings and replace with asynchronous alternatives such as project management tools or email where appropriate. • Clean out your digital inbox on a regular basis, to avoid unnecessary storage in data centers and thus energy consumption. • Sign up for the newsletter of the Green Office or Green Labs team of your institution.
70 • Create an inventory of all large datasets stored and used by the group and their corresponding storage ○ If the large dataset can be regenerated readily, consider only storing the information required for its regeneration. ○ Consider whether alternative storage locations are more efficient for the dataset. • Quantify the carbon footprint of computations: ○ Utilize: https://www. green-algorithms. org/ ○ Optimize the use of computing resources needed (e. g. number of cores, running time, and data center efficiency). ○ Evaluate if the temperature of the air conditioning (i. e. set point temperature) in server rooms can be increased, to reduce active cooling. ○ Reduce the carbon intensity by considering the location and production methods of the energy (e. g. wind and solar vs. nuclear, gas or coal).
Acknowledgements We thank the entire  Green Labs RUG team , the  Green Office of the University of Groningen and Green Labs NL  for supporting sustainable lab practices. In particular we thank  G. P. R. Schaeffer ,  D. Lijdsman ,  E. W. Zinkstok ,  E. M. Klop ,  T. Canrinus ,  N. N. H. M. Eisink ,  F. Trigka ,  R. Tarozo ,  D. Gerlach ,  J. L. Sneep ,  P. Nauw ,  S. R. Bonsing-Vedelaar ,  E. Zhemchuzhnikova ,  K. Horlings ,  L. A. A. Giannini ,  M. Farley ,  S. B. Beil ,  M. Heinemann , and  V. Andrikopoulos. We thank  M. B. Blikmans-Middel  for guidance and advice in the context of Open Educational Resources. We thank  Maaike M. Weber  and  Francine Nijp  for designing logos, posters and stickers. Furthermore we thank the  Health Safety and Environment department  for critical assessment and discussions.