dataesr/scientific-paragraphs-categorization huggingface dataset:
Erroneously annotated alternative splice sites were found at the exons 3/4 and exons 14/15 junctions due to the presence of homopolymers and/or sequence homologies that led to misalignment (Supplementary Table  S1 ). These alternative junctions were thus excluded from our analysis. With the exception of the PE1a, the 6 other reported events (2 pseudoexons, and 4 alternative 3′ ss) are newly described in this study (Table  2 ). The strenght of these alternative splice sites was evaluated by maximum entropy scores (MaxEntScan, MES) and the Human Splicing Finder (HSF) program (Fig. 3 ). All three pseudoexons displayed high 5′ and 3′ splice site scores, thus supporting their use to some extent in the DMD transcripts. Regarding the detected 3′ alternative ss, all are exonic and their calculated scores were lower than the scores of the adjacent natural splices in all cases except for the exon 76 + 59 alternative 3′ ss, which exhibits scores (HSF: 89.
Discussion Over half of the multi-exonic human genes are believed to have splice variants  23. Due to its large number of exons, the DMD gene was a priori a good candidate for the occurrence of multiple alternative splicing events. A literature survey supports this common assumption with many reports describing alternative splicing events in the DMD gene since its discovery in 1986 (Supplementary Table  S2 ). However these data derive from studies that are very heterogeneous in terms of the techniques used (northern blot, PCR, nested PCR), specificity of the transcript isoform analysed (Dp427m and/or other isoforms), biological samples analysed (human versus animal tissues, skeletal muscle versus other tissues or lymphocytes, patients versus normal controls), and gene regions explored (most studies focused on specific regions), that makes qualitative and quantitative comparisons between these different datasets difficult.
Construction of 454 libraries and sequencing. For each library, around 1 μ g of purified PCR product were fragmented by nebulization (2. 1 bars of nitrogen for 1 min) and subjected to library preparation with the Rapid Library Preparation Kit (Roche) according to manufacturer's instructions. The different samples were labeled via ligation of Multiplex Identifiers (MID) oligonucleotide adaptors to allow multiplexing. Three MID-containing libraries were quantified using the Qubit ® Fluorometer and pooled in equimolar amounts into a single sample prior to emulsion PCR amplification and sequencing in parallel on the Roche GS Junior 454 sequencer giving a mean total number of 69,060 reads (387 bp mean length) per library. The data obtained were then sorted according to their tag sequences. Bioinformatics.
Raw reads were edited and filtered prior to analysis. A dedicated analysis pipeline was developed using the Galaxy framework (http://galaxyproject. org)  42. First, relevant adapter sequences were removed with Cutadapt (v. 1. 3, default parameters), and quality-based trimming at the 3′ ends of reads was performed using the Qtrim tool (v. 1. 1, parameters: mean quality = 25, window size = 20, minimum read length = 40 nt). Cleaned reads were then mapped to the Human X chromosome reference sequence (hg19, UCSC) with STAR (v. 2. 3, annotations from UCSC, parameters: -sjdbOverhang 29 -outFilterMismatchNoverLmax 0. 05 -outSJfilterReads Unique, outFilterMultimapNmax 1)  20. A script developed in-house was developed to annotate identified splice junctions, which can be obtained upon request. The output data is processed to obtain DMD mRNA coverage, as well as a list of identified splice junctions and their counts.
Only new junctions covered by a minimum of 5 reads in at least two out of the four biological replicates were considered for further analysis. The Percent-Spliced-In (PSI) was calculated for each splicing event using intron-centric metrics  43  , with the SJPIPE pipeline, from the ipsa package (parameters: margin = 0, deltaSS = 0, mincount = 0, https://github. com/pervouchine/ipsa). For the comparison of the DMD-targeted versus publicly available total mRNA sequencing data from the Illumina Human Body Map project 2. 0 (skeletal muscle tissue, 2 × 50 bp, GEO sample GSM759515), raw reads were mapped to the Human X chromosome sequence with Bowtie2 (v2. default parameters)  44  and exon junctions detection and count was performed by TopHat2 45 (v2. 0. 2, Gene Model Annotations option: chrX_GTF downloaded from UCSC).
8% agarose gels and spliced products were quantified with the Quantity One (v. 4. 6. 9) software (Bio-Rad). For semi-quantitative PCR, fluorescein-labeled forward primers were used and 26 cycles were done. Capillary electrophoresis analysis was performed using 1 μ l of PCR product added to 18 μ l of formamide and 0. 5 μ l of ROX 400 HD fluorescent size standards (Applied Biosystems). Amplified products were separated on an ABI 3130 XL DNA analyzer and the peaks areas were measured by the GeneMapper v4. 0 software. Ratios of splicing isoforms were determined as the peak area of one specific isoform divided by the total peak areas for the two or three detected isoforms. Data represent the mean + /-SD of at least three independent assays.
Current three-dimensional (3D) genome modeling platforms are limited by their inability to account for radial placement of loci in the nucleus. We present Chrom3D, a user-friendly whole-genome 3D computational modeling framework that simulates positions of topologically-associated domains (TADs) relative to each other and to the nuclear periphery. Chrom3D integrates chromosome conformation capture (Hi-C) and lamin-associated domain (LAD) datasets to generate structure ensembles that recapitulate radial distributions of TADs detected in single cells. Chrom3D reveals unexpected spatial features of LAD regulation in cells from patients with a laminopathy-causing lamin mutation. Chrom3D is freely available on github.
However, TAD positioning in the 3D nucleus space has not been examined because there are currently no means of assessing spatial mammalian genome conformation using chromatin anchor-point information. This limits our understanding of principles of genome dynamics. Chromatin connections with intranuclear structures such as the nuclear lamina  [9]  contribute to spatial genome organization and regulation of gene expression. In yeast, attachment of centromeres to the spindle pole body and tethering of telomeres to the NP  [12] [13] [14]  provide constraints on chromosome movement which have proven useful to generate 3D genome structures  [15, 16]. These observations suggest that integrating positional constraints from various genomic datasets, such as LAD information from chromatin immunoprecipitation sequencing (ChIP-seq) of nuclear lamins, in addition to Hi-C, would provide more realistic structures of the mammalian genome.
github. io/TADbit). However, IMP has not been designed for genome modeling and requires advanced programming skills. Another constrained optimization approach (BACH-MIX) designed for local genome modeling, relies on Bayesian inference of 3D chromosome arrangements to assess variations in genome structures in a cell population  [35]. BACH-MIX, however, is not designed to incorporate positional constraints for loci in the nucleus. There is therefore no user-friendly framework that models the 3D genome over a wide range of scales and that incorporates chromosome positional constraints. We introduce Chrom3D, a genome 3D modeling platform designed to integrate positional constraints based on association of loci with intranuclear anchors. The combination of Hi-C and LAD information enables genome-wide radial positioning of TADs in ensembles of 3D structures.
Features of LADs estimated from the structures concur with lamin-genome contact patterns observed in single cells We next compared our three radial placement categories (center, intermediate, periphery; see Fig. 2a ) with singlecell NP-genome contact frequencies. These were defined by association of chromatin with the nuclear lamina observed in a previous single-cell LMNB1 DamID study in the near-haploid KBM7 cell line, the only cell type for which to our knowledge LADs have been mapped at the single-cell level  [44]. Despite the difference in ploidy between HeLa and KBM7 cells, our structure ensembles reveal features of genome organization inferred from the single-cell observations. Indeed, TAD sequences assigned to the periphery in our structures show the highest peripheral contact frequency calculated from the single-cell LMNB1 DamID data and, conversely, the central category shows the lowest peripheral contact frequency in the single-cell dataset (Fig.
Genome browser views of LMNA enrichment and detected LADs, and LAD overlap analyses, show that LMNA LADs in control and FPLD2 cells are overall conserved (Fig. 6a ) but also show differences in genome coverage (Fig. 6b, c ; Additional file 1: Figure  S11a-c ). Differential LMNA-chromatin association was corroborated by ChIP-quantitative polymerase chain reaction (qPCR) analysis of 16 genic and ten intergenic loci in fibroblasts from three patients and two controls (Additional file 1: Figure  S11d, e ). Using Chrom3D, we generated 100 structures for each of the control and patient fibroblasts by integrating control and FPLD2 LMNA LAD datasets with TAD information from published Hi-C data for IMR90 human fibroblasts  [38]. We find that strikingly, LADs specific to FPLD2 patients (LADs "gained" in FPLD2 fibroblasts) are more centrally located than all LADs in these cells (Fig.
Discussion We present Chrom3D, a software for 3D genome modeling based on the inclusion of positional input constraints from chromosomal interactions (Hi-C data) and nuclear lamin-chromatin associations (lamin ChIP-seq data). Several key features of our modeled structures are shown: inclusion of radial positional constraints, scalability from a single locus to the whole human genome, and predictive value of radial placement of TADs. Chrom3D is versatile in that other positional constraints can be integrated. Finally, we show an application of Chrom3D to the study of disease mechanisms using FPLD2 patient-specific positional constraints imposed by a LMNA mutant displaying alterations in its association with the genome. Incorporation of radial positional constraints provides new insight into the placement of genomic regions in the 3D mammalian nucleus space with respect to the NP, which has not been possible from current genome modeling platforms.
ChIP of LMNA and LAD identification ChIP of Flag-LMNA proteins in HeLa cells was done using anti-Flag antibodies (20 μg/10 7 cells) as described  [57]. ChIP of LMNA from fibroblasts was done using anti-lamin A/C antibodies  [57]. Illumina libraries were sequenced on a HiSeq2500. DNA was also used as template for qPCR (Additional file 1: Table  S3 ), with 95 °C for 3 min and 40 cycles of 95 °C for 30 s, 60 °C for 30 s, and 72 °C for 30 s. Sequence reads were aligned to hg19 genome using Bowtie2 with default parameters and option -best enabled. LADs were called using Enriched Domain Detector  [57]  using a 1-kb bin size and default parameters. Browser files were generated from the ratio of ChIP/input for each 1-kb bin with input normalized to ratio of [total ChIP reads/total input reads]. Scripts were written in Perl  [62]  or R  [61].
TAD and bead definition Genomic positions of TADs were based on contact domains identified from Hi-C data  [38]  accessed under GEO GSE63525. Overlapping TADs were merged into single domains and regions not covered by a TAD were assigned a bead of size proportional to the corresponding genomic region. Bead sizes were scaled so that total bead volume constituted 15% of the volume of a 10-μm diameter modeled nuclei  [63] , using a previous scaling function  [24].
Modeling of the α-globin gene locus The ENm008 ENCODE region containing the α-globin locus was modeled using Chrom3D based on published 5C chromosome conformation capture data  [18]. For each restriction fragment, we created beads (n = 70) of diameter corresponding to the genomic length of the fragment multiplied by 0. 005  [18]. Preprocessing and distance conversion rules for 5C data were as described  [18]. In contrast to the input file used for earlier 3D reconstruction  [18] , we did not include distance constraints between neighboring beads since our modeling framework represents each chromosome as a chain of connected beads. Thus, distance constraints included non-interaction constraints (two beads should not get closer than a given distance) and interaction constraints (two beads should not get further from each other than a given distance).
For all bead pairs with zero contacts detected in the 5C contact matrix, we used noninteraction constraints. Thresholds for non-interaction and interaction distances were cell type-specific (K562 and GM12878 cells)  [18]. We ran 1000 simulations for each cell line using 40,000 iterations and a cooling rate of 0. 000125, excluding whole chromosome "Translation" and "Rotation" moves. Final structures were aligned using Procrustes analysis (procrustes method in the vegan R package; default parameters with no scaling) and clustered using agglomerative hierarchical clustering (agnes method in the cluster R package; metric = "manhattan"). We extracted the cluster containing highest proportion of simulated structures for each dataset and plotted these on top of each other with high transparency so that the most common positions for each bead are the most visible in the plot.
FISH probe design The model nucleus was divided into two compartments at r half_v = 3. 97 μm distance from nuclear center (considering a 5 μm radius), each compartment being of equal volume. This provided two regions for bead placement: beads with centers located < r half_v from the nucleus center were classified as central, and peripheral otherwise. Proportions of each bead placed in peripheral or central region across 400 structures were used to identify the most stable beads in the periphery or center. Beads were further filtered to select beads associated with LMNA  [7]  (GEO GSE57149; track GSM1376181). We also designed probes to beads that were neither stable in the periphery nor center ("intermediate" area). Additionally, due to the variable copy number of genomic segments in HeLa cells, probes were designed to avoid areas with high copy number variations.
Slides were mounted with 0. 2 μg/mL DAPI in Dako Fluorescent Mounting Medium. A total of 484 FISH images were analyzed using FISHfinder  [66]  to detect probes and calculate their position relative to the nucleus edge (n = 1105 FISH signals). Images were taken in DeltaVision image stack format (. dv). Significance of FISH signal localization in central, intermediate, and peripheral regions was tested by Mann-Whitney-Wilcoxon tests. HeLa cells. Henrietta Lacks, and the HeLa cell line established from her tumor cells without her knowledge or consent in 1951, have made significant contributions to scientific progress and advances in human health. We are grateful to Henrietta Lacks, now deceased, and to her family members for their contributions to biomedical research. This study was approved by the NIH HeLa Genome Data Access Working Group.
Availability of data and materials Chrom3D software and documentation can be accessed under an MIT license at https://github. com/CollasLab/Chrom3D. The version of the Chrom3D source code used in the manuscript has been assigned doi. org/ 10. 5281/zenodo. 168212. ChIP-seq data for LMNA and LMNB1 from HeLa cells  [7]  were downloaded from NCBI GEO GSE57149 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE57149). We note that in this dataset, two tracks have been inadvertently swapped; correct tracks are for LMNA_sonication: GSM1376181, and for LMNA_digestion: GSM1376179. ChIP-seq data for fibroblasts and HeLa cells expressing LMNA wt, R388P, and L647R are available in NCBI GEO GSE81671 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE81671). RNA-seq for HeLa cells were downloaded from NCBI GEO GSE33480 (https://www.
ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE33480). RNA-seq data for control and FPLD fibroblasts are available in NCBI GEO GSE81671 (https://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc=GSE81671). HeLa Hi-C data were accessed under dbGap number phs000640 after permission. Hi-C data for IMR90 fibroblasts  [38]  were from NCBI GEO GSE63525 (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE63525). IMP code used in the Chrom3D-IMP comparison is available at http://folk. uio. no/jonaspau/imp-lamin.
In addition, XACML is an XML language which is not known to be user-friendly for non-technical people. Thus, the whole process demands lots of technical skills and cannot be performed by owners of smartphones. Arena et al. [17]  have proposed an XACML-based extension of the Android's security framework called SecureDroid. This tool allows users to define situations and specify which permissions are accepted or denied in these situations. Users can also be prompted when a permission is requested. This approach do not use abstraction so scaling up is still a problem. Stepien et al. [18]  have worked on a graphical editor to help nontechnical users write XACML rules. With this editor, it is possible to choose an attribute, an operator, and a value to compare to. It makes writing rules possible without using an XML format.
As a consequence, the PEP transfers the request to the DSS. To gather information about the user's preferences, the DSS interacts with him. Kapuer has two sorts of interactions with the user: 1) When a request is sent by the PEP to the DSS (step 3), Kapuer informs the user that an application wants to access one of his private data. If possible, details about the request are also given such as similar applications asking for the same permission or indications about the permission asked. The user just needs to accept or decline that request. Making a decision in the appropriate context (i. e. , when the user runs the application) is easier than doing it out of the context of use; it limits the cognitive load. Once the user has made his decision, the DSS creates the corresponding XACML V3 rule for that specific couple (application, permission) and puts it in the policy database (step 4).
, 2015; Schmied et al. , 2014). Automated approaches for cell segmentation and tracking have also been developed  (Amat et al. , 2014) , however they do not yet reach the precision required for unsupervised extraction of cell lineages. To address this issue, we describe here the Massive Multi-view Tracker (MaMuT) software that allows the visualization, annotation, and accurate lineage reconstruction of large multi-dimensional microscopy data. not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was this version posted February 28, 2017. ; https://doi. org/10. 1101/112623 doi: bioRxiv preprint 5 We quantitatively followed cell dynamics with the MaMuT software on the Parhyale multi-view LSFM acquisitions in order to understand the cellular basis of arthropod appendage morphogenesis.
MaMuT: a platform for cell tracking in multi-view and multi-terabyte datasets In order to examine the cellular basis of morphogenesis, we developed a novel Fiji software application to extract cell lineages from multi-view and multi-terabyte datasets. This tool was dubbed MaMuT for Massive Multi-view Tracker and is a hybrid and extension of two existing Fiji plugins: the BigDataViewer visualization engine  (Pietzsch et al. , 2015) ; http://imagej. net/BigDataViewer) and the TrackMate annotation engine  (Tinevez et al. , 2016) ; http://imagej. net/TrackMate). MaMuT is an interactive, userfriendly tool for visualization, annotation, object tracking and lineage reconstruction of large multi-dimensional microscopy data. Within the Fiji ecosystem, MaMuT is tightly integrated with the plugins for multi-view LSFM data processing (Figure  3A ).
Documentation, training datasets and tutorials on MaMuT can be found at the dedicated link http://imagej. net/MaMuT. MaMuT can handle multiple data sources but was developed primarily to enable the analysis of large and complex LSFM image datasets (Figure  3  and Figure  S1 ). Its unique feature is the ability to annotate an image volume synergistically from all available input views without the need to fuse these into a single volume. All raw views were first registered and then imported into MaMuT (Figure  3A ). The software allowed users to open the raw image data in as many Viewer windows as required and visualize each zstack in any desired orientation, scale and time-point (Figures  3B-3D ). Most importantly, all Viewer windows were synced based on the calculated registration parameters and not certified by peer review) is the author/funder.
Selected nuclei were tracked over time in user-defined time intervals, and the reconstructed trajectories and lineages were also displayed in two additional synced windows, the TrackScheme and 3D Viewer. The TrackScheme lineage browser and editor displayed the reconstructed cell lineage tree with tracked nuclei represented as nodes connected by edges over time and cell divisions depicted as split branches in the tree (Figure  3E ). The 3D Viewer window displayed interactive animations of the spots depicted as spheres and their tracks over time. The spots and the tracks in the Viewer, TrackScheme and 3D Viewer windows could be color-coded by lineage, position and other numerical features to assist visual analysis and interpretation of the data (Figures  3F-3H  and Figures 4 to 7 ). In addition, all these windows were synced to simultaneously highlight selected spots of interest at the selected time-point, greatly facilitating the cell lineaging process (Figures  3B-3H ).
Although, all lineage reconstructions presented in this article were generated manually, the latest MaMuT architecture also offers two functionalities for automated tracking: i) a semi-automated option where individual nuclei can be selected by the user and segmented and tracked computationally over time, and ii) the option to import into MaMuT fully automated annotations generated by the Tracking with Gaussian Mixture Models (TGMM) software  (Amat et al. , 2014) , which is one of the most accurate and computationally efficient methods for segmentation and tracking of fluorescently labeled nuclei (Figure  3A ). Therefore, MaMuT is a versatile platform that can be used either for fully manual or semi-automated tracking of selected populations of objects of interest, or for visualization and editing of fully automated computational predictions for systemslevel lineage reconstructions.
Cellular basis of the elaboration of the limb proximal-distal axis We then asked whether our dataset could help resolve the patterning mechanisms operating during appendage segmentation. Appendage segmentation is a gradual process as the elongating proximal-distal axis becomes progressively subdivided into an increasing number of elements (Figure  6 )  (Rauskolb, 2001). At the tissue level, the forming Parhyale T2 limb was made of a single bud up to 103 hours AEL (Figures  6G-6H ), while a second element bulged out distally from the original bud at around 114 hours AEL (Figure  6I ). Over the next 40 hours, a number of consecutive circumferential constrictions subdivided the limb into the final pattern of 7 segments (Figures  6J-6L ) that are called (from proximal to distal) coxa, basis, ischium, merus, carpus, propodus, and dactylus.