dataesr/scientific-paragraphs-categorization huggingface dataset:
Coolpup. py implementation Coolpup. py is a versatile tool that uses. cool files as the main input together with a bed (chrom, start, end) or pair bed ("bedpe": chrom1, start1, end1, chrom2, start2, end2) file to define the regions under investigation. The tool is implemented as a python package which parses all arguments via argparse, performs the computation and saves the output file(s). It leverages the scientific python environment, taking advantage of numpy (van der Walt et al. , 2011), scipy  (Virtanen et al. , 2020)   and pandas (McKinney,   2010). A separate CLI tool included in the package (plotpup. py) can be used to visualize the results and uses matplotlib  (Hunter, 2007). The code is available on GitHub (https://github. com/Phlya/ coolpuppy) and the package can be installed using pip, which then makes coolpup.
py and plotpup. py available in the command line. Alternatively, all main functions can be accessed directly from python. The overall procedure for piling up a lot of small regions is the following. To minimize the number of file reads (at the cost of required computer memory), a sparse representation of each chromosome's Hi-C contact matrix is loaded into memory. Then, using an iterator, each required location (on-or off-diagonal) is individually retrieved to generate a corresponding submatrix from the data [with some specified padding around the centre of the region of interest (ROI)], and added to the matrix of the same shape, initialized with zeros, while keeping track of the number of summed-up regions. If specified, coverage of the window on each side is recorded. Similarly, if needed, the window (and the coverage) is rescaled to a required shape.
All expected matrices are averaged to generate a normalizing matrix. Alternatively, if the expected values are not available, for example, for single-cell Hi-C data, this normalization can be performed using randomly shifted control regions. In that case, to generate the normalizing matrix, the whole pile-up procedure is repeated, but the coordinates are randomly shifted. In the end, the resulting matrix of averaged ROIs is divided element-wise by the normalizing matrix to remove effects of distance. If not specified, balanced data with chromosome-wide expected normalization were used when creating pile-ups, except for the zygote and single-cell Hi-C datasets, where randomly shifted controls and coverage normalization were used instead. For the single-cell Hi-C  (Nagano et al. , 2017)  analysis, we only used pairs of convergent CTCF peaks within 100-800 kb of each other, since previous analysis (data not shown) indicated this as the distance range where CTCF-associated loops are most prominent; to reduce noise, RING1B-associated interactions were analysed for all distances above 100 kbp.
Performance profiling Coolpup. py performance was tested on the University of Edinburgh Open Grid Scheduler cluster (Eddie3) using the Hi-C datasets for mouse ES cells  (Bonev et al. , 2017; Nora et al. , 2017). To generate the required large number of coordinates for testing, we used coordinates of the B3 repeat from the RepeatMasker track available from UCSC Genome Browser. For coordinate pairs, we used all pairs of convergent CTCF sites, described above. A separate job was submitted for each measurement, and the runtime of the coolpup. py call was recorded. Subsets of different sizes were generated using coolpup. py's -subset argument. Where not specified, one compute core was utilized. The same procedure was performed for HiCExplorer hicAggregateContacts, except shuf was used to generate a random sample of required size.
py. However, because of the averaging of multiple regions during the pile-up procedure, the effect of biases can be partially mitigated by normalizing the matrix by the coverage (i. e. the total number of contacts of the bins in the chromosome) of the averaged regions  (Flyamer et al. , 2017). To illustrate this, we integrated ChIP-seq datasets with Hi-C and analysed CTCF and polycomb (RING1B)-associated loops, and all potential intersections between high RING1B peaks, in mouse ES cell Hi-C data  (Bonev et al. , 2017). This approach visibly reduces coverage variability between bins and removes sharp crosses from the central bin that is present with unbalanced data. This normalization seems to slightly over-correct, i. e. the value of the central pixel is consistently somewhat lower than when using balanced data.
Applications of pile-ups As well as the basic pile-up procedure, there are multiple variations built in to coolpup. py which are tailored to answer different biological questions. The following ones are trivial, but worth mentioning. For example, often it is desirable to restrict the minimal and/or maximal separation of analysed sites, either to remove short-range artefacts, or to analyse the distribution of enrichment signal across different distance scales. Only certain chromosomes might need to be included, or, with too many regions of interest, a random subset can be taken to speed up the computation. A popular variation of the pile-up approach is 'local' pile-up: an analysis which focuses on near-diagonal features. For example, we averaged regions of high insulation annotated in the deep ES cell Hi-C dataset to visualize insulation strength after auxin-induced degradation of CTCF  (Nora et al.
, 2017)  (Fig. 2A ). In this case, the pile-ups are performed in the same way as previous off-diagonal pile-ups; however, the regions that are averaged lie on the main diagonal of the Hi-C map. A variation of this approach is local pileups with rescaling to analyse features of different size, for example, TADs  (Flyamer et al. , 2017). As an example, TADs, based on aforementioned regions of high insulation annotated in data from (ref. Bonev et al. , 2017) , were averaged to visualize changes in local interaction strength upon CTCF degradation  (Nora et al. , 2017)  (Fig. 2B ). Here, all windows centred on regions of interest are rescaled to the same size, and then averaged. Pile-ups are a particularly important approach to analysing very low-depth datasets to uncover genome-wide average patterns, which are indiscernible when looking at individual regions in such sparse data.
Here, we apply coolpup. py to reproduce results from a dataset comprising pooled data from a few single cells, to show a loss of loops and TADs in mouse zygotes lacking SCC1 (RAD21), the kleisin subunit of cohesin  (Gassler et al. , 2017). Since the material is so limiting and data are based on single cells, the total number of contacts in this dataset is very low: 4. 8 and 9. 2 million contacts in Scc1 þ/þ and Scc1 -/-, respectively. However, we successfully performed pile-ups, both with 'traditional' averaging of loops, and local pile-ups of TADs with rescaling, and observe the loss of both loops and TADs upon deletion of cohesin, comparable to the original study (Fig. 2C ). All pile-up approaches include averaging of multiple regions, a drawback of which is the loss of locus-specific information.
, 2017). By splitting the CpG islands (data from  Illingworth et al. , 2010)  -the main targets of polycomb binding in ES cells -in the mouse genome into RING1B (a core component of Polycomb Repressive Complex 1 -PRC1) negative, RING1B positive, and RING1B and H3K27me3 positive sets (data from  Illingworth et al. , 2015) , we observe high 'loop-ability' values for the two latter groups, while the RING1B negative CpG islands have close to no enrichment (Fig. 2E ). We perform more detailed analysis of such loop-ability measurements in our recent report  (Boyle et al. , 2019). Pile-ups are an invaluable tool when analysing Hi-C data from single cells, since averaging features across the whole genome helps to circumvent the sparsity of the data. Here, we apply coolpup. py to analyse the looping interactions across the cell cycle using a published single-cell Hi-C dataset from hundreds of mouse ES cells  (Nagano et al.
, 2018). Creating pile-ups from intersections of genomic regions can require averaging a huge number of 2D windows: the number of two combinations grows quickly with the number of regions. For example, with $1000 regions per chromosome (which is approximately equivalent to the number of genes), requires averaging of $10 000 000 regions for the whole genome, several orders of magnitude more than the number of regions usually averaged, such as number of annotated loops ($10 000). Therefore, it is important for a general-purpose tool for creating pile-ups to scale well with the number of averaged 2D windows. To facilitate this, coolpup. py performs a very low number of read operations on the Hi-C dataonly once per chromosome (or twice, when using randomly shifted controls). Whilst this necessitates that the whole Hi-C matrix of a chromosome has to be loaded into memory, it is only stored in a sparse format, and so conventional Hi-C datasets can be analysed on a regular desktop (although multi-billion contact datasets might require a high-memory machine; data not shown).
To test the performance of coolpup. py and how this depends on number of regions of interest, we measured the runtime with varying number of two-sided coordinate pairs (mimicking loop annotation) (Fig. 4A ), and varying the number of one-sided coordinate interactions being averaged (Fig. 4B ). We used both deep  (Bonev et al. , 2017) , and 'regular depth' Hi-C data  (Nora et al. , 2017)  from mouse ES cells. With both datasets, the runtime was almost constant (probably due to file system read speed limitations) up to a certain number of 'loops' ($1-2 Â 10 5 ), where it starts quickly increasing (Fig. 4A ). Notably, the best annotations that exist to date only contain <40 000 loops  (Krietenstein et al. , 2019) , and therefore this would fall within the flat part of the curve. Similarly, in the latter analysis, runtime did not increase up to 1600 and 3200 regions of interest for the  Nora et al.
and Bonev et al. datasets, respectively. Importantly, in both analyses the difference in time between datasets with almost 10-fold sequencing depth difference is not very large, and probably mostly driven by differences in time required to read the data from disk. When similar analysis was performed using HiCExplorer hicAggregateContacts, the runtime was >10-fold longer for each dataset with low numbers of regions (Fig. 4B ), and the analysis required much more memory since the algorithm uses dense data structures and stores each submatrix of interest in memory (required >100 Gb for the Bonev et al. dataset; the longest timepoint required over 512 Gb of RAM and was not computed, while coolpup. py only needed $8 Gb for any calculation). HiCExplorer implementation computes observed/expected matrix for every calculation and cannot use pre-computed expected values, which at least partially accounts for much longer runtimes.
Acknowledgements We are grateful to  Maksim Imakaev ,  Nezar Abdennur ,  Anton Goloborodko ,  Hugo Brandão  and  Sergey Venev  for discussions, help and advice about the cooler ecosystem, and to  Aleksandra Galitsyna  for sharing. coolfiles for the Nagano et al. (2017) dataset. This work has made use of the resources provided by the  Edinburgh Compute and Data Facility (ECDF)  (http://www. ecdf. ed. ac. uk/). We also thank  Sergey Ulianov  for critically reading the manuscript. Funding I. M. F. is funded by a PhD studentship from the  Darwin Trust of Edinburgh. W. A. B. is funded by a  Medical Research Council University Unit  programme grant [ MC_ UU_00007/2 ]. R. S. I. is supported an  MRC Career Development Award  ( MR/S007644/1 ) and by a seed award from the  University of Edinburgh Simons Initiative for the Developing Brain  (SIDB; a  Simons Foundation Autism Research Initiative -SFARI).
Extracellular matrix (ECM) gene expression profile analysis was performed using the SAM (Significance Analysis of Microarrays) software, and protein expression was confirmed by western blotting. Results: hISCs kept an unlimited proliferative potential. They exhibited several properties of MSCs such as CD73, CD90, and CD105 expression and differentiation capacity. From a functional point of view, hISCs inhibited the proliferation of activated peripheral blood mononuclear cells. The transcriptomic profile of hISCs highly clusterized with bone marrow (BM)-MSCs and revealed a differential enrichment of genes involved in the organization of the ECM. Indeed, the expression and secretion profiles of ECM proteins including collagens I, IV, and VI, fibronectin, and laminins, known to be expressed in abundance around and within the islets, were different between hISCs and BM-MSCs.
Flow cytometry analysis For immunophenotyping hISC were trypsinized and resuspended in PBS containing 0. 1% BSA and 2 mM EDTA. Then, cells were incubated for 30 min at 4 °C with monoclonal antibodies against CD13, CD73, CD90, CD105, CD11b, CD14, CD31, CD34, CD45, and HLA-DR conjugated with either allophycocyanin (APC), phycoerythrin (PE), BV421, BV605, Alexa 647, peridinin chloryophyll-protein complex (PerCP), V500, or matched isotype control (BD Biosciences). Data were acquired and analyzed through a multiparameter Gallio flow cytometer with the Kaluza software (Beckman Coulter).
Microarray hybridization and gene expression profiling analysis RNA from hISCs (n = 5) and human pancreatic islets (n = 1) was extracted using the RNeasy kit (Qiagen). cRNA preparation and hybridization to the HG-U133 2. 0 plus microarray (Affymetrix, Santa Clara, USA) were done as previously described  [26]. Public microarray datasets were obtained with Gene Expression Omnibus (GEO, http:// www. ncbi. nlm. nih. gov/geo/) analysis from NCBI and selected based on the same platform (Affymetrix HG-U133 2. 0 plus). Gene expression data of human BM-MSCs (GSE 9894  [27]  GSE6460  [28] ), human ASCs (GSE61302) expanded pancreatic islet cells, and human pancreatic islets (GSE15543  [22] ) were downloaded from GEO. Normalization was completed using GC-RMA. Gene expression data were analyzed using the SAM (Significance Analysis of Microarrays) software  [29]  as published  [30].
All these analyses have been done with R. 2. 10. 1 (http:// www. r-project. org/), bioconductor version 2. 5, and Genomicscape (http://www. genomicscape. com) [31]. Genes differentially expressed between cell populations were identified using the SAM package on R software (fold change ≥ 2, false discovery rate (FDR) ≤ 0. 05, 1000 permutations). Significantly enriched pathways were identified using Reactome Functional Interaction Cytoscape plugin (http://www. cytoscape. org/). Clustering was performed and visualized with Cluster and TreeView  [32]. Mesenchymal stem cell and endocrine gene lists were generated using published transcriptomic data  [22]  and literature data.
Although GFP detection by flow cytometry constituted a good evidence for vector insertion, we confirmed the expression of hTERT by RT-PCR. The amount of hTERT mRNA in hISCs was 6000-fold higher than hematopoietic stem cell line known to express hTERT (data not shown). During the first 40 days, hTERT-transduced cells as well as pEGN-MCStransduced or Mock cells proliferated with a population doubling rate of about 3 days. However, after 50 days, non-hTERT-transduced cells gradually stopped proliferating and died at about 80 days (about 20 doubling times) whereas hTERT-transduced cells continued to proliferate (Fig. 1e ).
However, the absence of chondrocyte gene upregulation (type II collagen, Sox9, and aggrecan) and positive Safranin O staining indicated no chondrogenic differentiation (data not shown). As hISCs share several phenotypic and some functional characteristics with MSCs, we aimed to analyze their immunomodulatory properties by looking at their ability to inhibit PBMCs proliferation. After 96 h co-culture of hISCs with PHA-activated PBMCs at different ratios, we measured the percentage of proliferative PBMCs. BM-MSCs were used as a positive control. PHA-activated PBMCs proliferated and formed clusters as depicted on Fig. 5a. The addition of hISCs or BM-MSCs led to a dose-dependent decrease of PBMC clusters (Fig. 5a ). This dose-dependent inhibitory effect of hISCs or BM-MSCs on PBMCs was quantified by FACS.
8 Mb -2% of the assembly). The maximum number of sequence gaps is four (on chromosomes 10 and 11) and ten chromosomes contain only a single sequence gap, illustrating the high and homogenous contiguity across chromosomes (Supplementary Note 1, Supplementary Data 1, Supplementary Tables  1 2 3 ). We generated RNA-seq data from ten different organs, widely covering gene expression in WL (entire root system in +Pi and -Pi conditions, lateral roots, primary roots, cluster roots, nodulated root system, leaves, flowers, pods, and seeds). The assembled reads were mapped using EuGene-EP pipeline  18  and protein and non-protein coding gene models were predicted. Three protein databases (Swiss-Prot, a plant subset of Uniprot proteins and the proteome of Medicago truncatula) were aligned to contribute to translated regions detection.
P27174 assembly has a total length of 18. 67 Mb of structural variations (SVs) affected and the GRAECUS accession was similarly affected by SVs (18. 08 MB -Fig. 2d , Supplementary Data 3). The majority of the SVs in both GRAECUS and P27174 are located in intergenic regions (62 and 53%, respectively). Considering a promoter region of 2 Kb upstream of 5′-UTR, a total of 8166 genes are impacted by SVs in the GRAECUS genome, whereas only 6524 genes are impacted in P27174. A total of 3463 common genes are altered in both accessions and 671 of these genes have common exons impacted (Supplementary Fig. 7 ). These SVs highlight genomic regions that may help understand major events associated with WL domestication. White lupin genome evolution. We retraced the paleohistory of 12 legume genomes including WL and covering the Genistoid, Dalbergioid, Galegoid, and Millettoid clades.
These blocks have an average size of 65 kb and the largest duplication consists of a 4. 1-Mb block shared between Chr18 and Chr20 (Fig. 4a , Supplementary Data 5). Reciprocal pairwise comparisons  33  of the 38,258 WL genes with 104,607 genes from its closest relative NLL, the model legume Medicago truncatula  34  and Arabidopsis thaliana identified 25,615 orthologs clusters (Fig. 4b ). 473 out of these groups contain only WL paralog genes (1242 in total), probably as a result of the predicted genome triplication event (Supplementary Data 6). Gene Ontology  35  terms representation revealed an enriched annotation of serine-type carboxypeptidase activity proteins (GO:0004185), however most of the clusters have no GO term associated (58%, Supplementary Data 6). The WL genome shared highly conserved syntenic blocks with the genome of NLL and Medicago truncatula, the reference genome within this family (Supplementary Fig.
9 ). Soil exploration. Most terrestrial plants can form mycorrhizal symbioses that greatly improve mineral nutrition. Lupins however, lost the ability to form such associations (est. 12-14 My) and the ability to form cluster roots appeared ca. 2. 5 My ago  36. The former was accompanied by the loss of all mycorrhizal specific genes in the WL genome whereas common symbiotic genes remained functional (Supplementary Fig. 10 , Supplementary Note 6 and Supplementary Data 7 and 8). This suggests that WL favored a new type of root adaptive mechanism towards nutrient acquisition  11. Despite the importance of cluster roots, no gene controlling their development has been described to date. We therefore generated a detailed transcriptomic dataset of WL cluster root developmental zones. Our RNA-seq survey (mRNA and miRNA) covered 8 sections of mature clusters that mimic the temporal stages of their development (Fig.
Methods Genome assembly and annotation of L. albus L. cv. AMIGA. A meta-assembly strategy similar to the one developed to assemble the Rosa genome  52  was applied. The Supplementary Data 1 provides details of the different steps of the process including data, software and the evolution of the metrics of the assembly. Firstly, three assemblies were performed with CANU 15 using different level of stringency (errorRate = default, 0. 015 and 0. 025 respectively). Corrected reads generated by CANU  15  were also used to run FALCON  16. The graph of overlaps of FALCON was filtered using three different sets of parameters of the program til-r  52  , in order again to generate alternative assemblies with different level of stringency. The N50 metrics of the primary assemblies ranged from 1. 6 to 7.
1 Mb. The sequences of these six primary assemblies were first transformed in pseudo long reads of 100 kb with an overlap of 50 kb. Then, the pseudo long reads were assembled with CANU 1. 6 in the mode -trim-assemble to enable the trimming of sequence ends specific to a single primary assembly. The meta-assembly result displays a N50 of 8. 9 Mb in only 129 contigs. The Bionano hybridScaffold. pl software was run in order to scaffold the contigs of the meta-assembly using the Bionano Optical map (N50 2. 3 Mb). In all, 15 putative breakpoints were identified and corrected by the scaffolder. The scaffolds were polished twice, firstly using arrow and the pacbio raw data mapped with blasr, then with Pilon 53 using 100x of illumina data mapped with glint software (http://lipmbioinfo. toulouse. inra.
Reads with a length lower than 35 have been discarded. We used BWA-MEM version 0. 7. 17  56  to map the resequencing reads to the white lupin reference genome. Picard tools (https://github. com/broadinstitute/picard/issues) have been used to detect and remove PCR and Optical duplicates. We then used GATK 4. 0  57  HaplotypeCaller tool to call variants. This identified ca. 300,000 SNPs without filtering the data. All the SNPs are evenly distributed on the 25 chromosomes and contigs. We generated a VCF file with this information, available in the white lupin Genome Browser. Assembly of mitochondrial and chloroplastic genomes. A de novo assembly protocol was used to assemble both cytoplasmic genomes. They were generated using NOVOPlasty 3. 2  58  , by using the aforementioned Illumina reads, after adapter-removing step.
Assembly of chloroplastic genome (plastome) was performed using as reference a publicly available L. albus plastome (GenBank accession NC_026681) and mitochondrial genome (mitogenome) was assembled using the Vicia faba mitogenome (GenBank accession KC189947) as reference. The assemblies were checked with Geneious v. 9. 1. 9 mapper tool by mapping Illumina and PacBio reads. For the mitogenome annotation we used as reference other legume species with available annotated mitogenomes on NCBI, whereas for the plastome annotation we used as a reference the available L. albus plastome. The assembly of the cytoplasmic genomes resulted in single circularized contigs of 151,915 bp for the plastome (Supplementary Fig. 22 ) and 405,575 bp for the mitogenome (Supplementary Fig. 23 ).
vectorlabs. com) was applied. Microscopic images were recorded using a Zeiss Axiovert 200 M microscope equipped with a Zeiss AxioCam CCD. Images were analyzed using the ZEN software (Carl Zeiss GmbH). Primer and oligo-probes information is presented in Supplementary Note 3, Supplementary Table  4. Labeling of tandem repeat and retroelement fragments. Fragments for probe labeling were amplified using genomic DNA from L. albus using the forward and reverse primers as supplied on Supplementary Table  4. Eight PCR reactions for each target repeat were performed in 50 μL reaction volume containing 100 ng of gDNA, 1 μM primers, 1 × PCR buffer, 0. 2 mM dNTPs, and 1U of Taq polymerase (Qiagen). Thirty-five amplification cycles with proper conditions for each set of primers were run. PCR reactions were sampled, purified and concentrated using Wizard® SV Gel and PCR Clean-Up System (Promega).