dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, we concluded that neither the expression of the U7-E53 RNA nor the transduction with the rAAV3b vector significantly disturbed the expression of genes in the transduced samples. Among these differentially expressed genes, we identified the transcripts harboring a predicted binding site (i. e. , a sequence at least partially complementary) for the antisense sequence expressed through the U7-E53. We used the alignment tool miRanda, specif-ically developed for miRNA target design,  42  and subsequently crossed the results with those of the differential analysis described above (Table  S3 ). Among the 38 differentially expressed genes containing a putative degenerated U7-E53 binding site, only 8 genes (2 lincRNAs and 5 coding genes in myotubes and 1 coding gene in hepatocytes) exhibited a more than 2-fold variation in gene expression between transduced and NT samples.
Among these genes, we performed a qRT-PCR assay on TOP2A, BIRC5, and NCAPG genes from primary myotubes and the MDM2 gene from primary hepatocytes to validate the quantification using an alternative method. As presented in Figure  5 , we compared the gene expression between NT samples, samples transduced with the rAAV3-U7-E53 vector, and samples transduced with the rAAV3-U7-Scr vector carrying a scrambled antisense sequence. As explained above, this latter supplemental control was included to confirm the U7-E53-related specificity of the potential off-target events. After assessing whether U7-E53 and U7-Scr were expressed in all samples (data not shown), we confirmed the differential expression for the four tested genes, with the underexpression of TOP2A, BIRC5, and NCAPG and the overexpression of MDM2 (Figure  5 ).
Among all transcripts deemed differentially spliced (in human primary myotubes and hepatocytes), we used in silico predictions to highlight those that had a predicted binding site for the antisense sequence harbored in U7-E53. Alterations in splicing could reflect the interaction of the antisense sequence with regulatory elements located in exons or introns up to 100 bp from the exon. 45 We therefore searched for sequences at least partially complementary to U7-E53 antisense sequence in "extended exons" comprising the exon plus 100 bp of flanking intronic sequences on both sides. We used miRanda  42  and Blast (NCBI), which provided complementary results with the notion of contiguous identical bases between the antisense sequence and the pre-mRNA target. After comparing these in silico data with the differentially used junctions, we identified a small number of off-target events potentially altered through U7-E53 binding (Table  S4 ).
Moreover, it has been described that siRNA and nuclease off-targets were dependent on the site of hybridization, the nature per se of the antisense sequence, and the number of mismatches tolerated between the antisense sequence and its target. 29, 48, 49 It has also been shown that AONs of only 15 nucleotides could effectively induce exon skipping. 50 We think that in silico analyses alone could not be pertinent enough because the results are heavily dependent on: (1) the database or reference genome used, (2) the software or algorithm used, and (3) the cutoff values applied to identify complementary sequences. Particularly, off-target analyses should be preferentially conducted in a highly sensitive and comprehensive manner with high-throughput strategies. 35, 49, 51 Therefore, in the present study, we designed a methodology based on RNA-seq to identify, at the transcriptomic level, all the potential off-target effects induced through the antisense sequence harbored in our clinical product, U7-E53.
These results are consistent with previous studies showing that rAAV transduction did not lead to a massive deregulation of gene expression, contrary to adenovirus. 55, 56 milarly, the analysis of splicing junction's usage alterations through DEX-seq identified few differentially used junctions, but the vast majority exhibited low FC values, with the exception of the DMD gene and of some poorly expressed junctions. Overall, weakly expressed junctions are more differentially used than junctions that are highly expressed. This phenomenon, giving a typical triangular shape to MAplots, is commonly observed in RNA-seq datasets as a direct consequence of the use of reads count data in which the results are biased when the number of reads is low. 39 oreover, MAplots showed a symmetric representation of junctions, meaning an equivalent number of junctions statistically lower and more used in transduced samples, likely reflecting background splicing noise.
Filtrations through in silico software (miRanda and/or Blast) following RNA-seq analysis facilitated the selection, among identified potential off-target candidates, of transcripts harboring a potential fixation site for the antisense sequence harbored in the U7-E53. To minimize biases and false-negative results potentially induced through miRanda and/or Blast programs,  46, 57  we did not apply cutoff values regarding the percentages of identity or the number of mismatches tolerated between the antisense sequence harbored in the U7-E53 and candidates transcripts. Notably, such a strategy does not enable the identification of potential indirect off-target effects (i. e. , on transcripts that do not share any complementarity with U7-E53). The supplemental control samples transduced with a scrambled antisense sequence (U7-Scr) could address this last issue, but these samples were not analyzed through RNA-seq.
Data Preprocessing The human genome sequence primary assembly, transcript sequences, and comprehensive gene annotation files were all retrieved from the Gencode consortium website (Release23-GRCh38. p3 accessible at https://www. gencodegenes. org/releases/23. html). Raw BCL data for the samples were de-multiplexed with CASAVA (Illumina) according to their barcodes and were stored in independent fastq files (data available at the European Nucleotide Archive website PRJEB13980). A subset of reads was aligned to select genomes/ DNA sequences to assess the presence of contamination within the sample using FastQScreen 0. 5. 0 (http://www. bioinformatics. babraham. ac. uk/projects/fastq_screen/). Following a general sequence quality control using FastQC v0. 10. 1 (http://www. bioinformatics. babraham.
ac. uk/projects/fastqc/), fastq files were trimmed with Cutadapt 1. 8. 1 (https://github. com/marcelm/ cutadapt) in paired-end mode to clean up sequences with low-quality extremities (-q 30,30) and those containing sequencing adapters, polyA/T (-a file:adapters. fa -A file:adapters. fa) or N bases (-trim-n). The fasta sequences contained in the adapters. fa file are as follows.
Alignment against Human Genome The short read alignment was performed with STAR 2. 4. 2a. 38 irst an index was built using recommended options for the human genome with a gff annotation file (STAR-runMode genomeGenerate-genomeDir. /-genomeFastaFiles GRCh38. primary_ assembly. genome. fa-sjdbGTFfile gencode. v23. primary_assembly. annotation. gff3-sjdbOverhang 100-sjdbGTFtagExonParentTranscript Parent). The alignment step, the post-processing of reads with samstat 1. 5 and bedtools 2. 17. 0, and the read count per gene with HTSeq 0. 6. 1 were automatized with a dedicated bash script available at https://github. com/a-slide/BASH_NGS_Tools/blob/ master/RNAseq_U7_pipeline. sh. Except for the minimal mapping quality (MAPQ) that was set up to 30, all of the other options are hard-coded directly in the script.
Differential Splicing Analysis Differential splicing analysis was based on the use of counting the reads overlapping a junction, as described in Noiret et al. 64  This analysis was called junction-centric  41  in opposition to the exon-centric analysis more commonly used, where the relative abundance of exons is compared. The DEX-seq package initially developed for the exon-centric approach was used without modification (https:// bioconductor. org/packages/release/bioc/html/DEXSeq. html). In brief, the junctions identified by RNA-seq were quantified during the mapping step with STAR. Then junctions were annotated according to their chromosomal positions (chromosome name, start/end of genomic coordinates, and strand information) and assigned to a gene. Only unambiguous junctions (i. e. , not assigned to two different genes) were used.
In Silico Prediction of U7-E53 Binding Sites All the bioinformatic tools used for prediction of U7-E53 binding sites are available as part of the TargetPredict package, which is available at http://a-slide. github. io/TargetPredict/. As stated previously, all reference files were obtained from the Gencode consortium website (Release23-GRCh38. p3). For the differential expression analyses (DESeq2), the reference human transcript fasta file was enriched with additional information gathered from the GFF reference annotation file with the FastaEnhanceFromGff script. This fasta dataset is referred as "gencode_v23_transcripts_enhanced". Regarding the differential splicing analyses (DEX-seq), extended exon sequences were extracted from the primary human genome assembly sequence (GRCh38 primary assembly), based on the gff files (Gencode v23) using GffFastaExtractor.
Exons sequences were extended upstream and downstream by 100 bp to include intronic splicing regulatory elements. Overlapping extended exonic regions were fused so as to avoid multiple reports of the same genomic targets. This fasta dataset is referred to as "GRCh38_gencode_Offset-100_Features-exon_Chr-all_fused. " From these two datasets, imperfect targets of the recognition sequence (U7-E53) were found in the subject sequences using TargetPredict based on hits found by Miranda v3. 3a and NCBI Blast 2. 2. 28. TargetPredict reports containing the parameters used to run the program and raw results are available in Table  S5  for the gencode_v23_transcripts_enhanced dataset and in Table  S6  for the GRCh38_gencode_Offset-100_Features-exon_Chr-all_fused dataset.
ACKNOWLEDGMENTS We would like to acknowledge  Vincent Mouly  and  Anne Bigot  ( Institute of Myology, Paris, France ) for providing us with human primary myoblasts. We also thank all the personnel of the Atlantic Gene Therapies institute vector core for rAAV3b productions. We thank  France Pietri-Rouxel  ( Institute of Myology ) for providing us the U7-Scr plasmid. This project was supported by  FRM  ( Fondation pour la Recherche Médicale ; grant  FDT20140931046 ), by  AFM-Téléthon  ( Association Française contre les Myopathies ), and by  ADNA (Advanced Diagnostics for New Therapeutic Approaches) , a program dedicated to personalized medicine, coordinated by  Institut Mérieux , and supported by research and innovation aid from the  French public agency, Bpifrance.
CRISPR (clustered regularly interspaced short palindromic repeats) arrays and their associated (Cas) proteins confer bacteria and archaea adaptive immunity against exogenous mobile genetic elements, such as phages or plasmids. CRISPRCasFinder allows the identification of both CRISPR arrays and Cas proteins. The program includes: (i) an improved CRISPR array detection tool facilitating expert validation based on a rating system, (ii) prediction of CRISPR orientation and (iii) a Cas protein detection and typing tool updated to match the latest classification scheme of these systems. CRISPRCasFinder can either be used online or as a standalone tool compatible with Linux operating system. All thirdparty software packages employed by the program are freely available.
The rapid rate of evolution of certain CRISPR arrays also allows their effective use in typing bacterial isolates  (6). Several programs have been developed to identify CRISPR arrays in genomic sequences, the most frequently cited being CRISPRFinder  (7) , CRT  (8)  and PILER-CR  (9). Additional programs such as CRISPRDetect  (10) , CRISPRdigger  (11)  and CRF  (12)  are also available. Three programs have been proposed for CRISPR array strand prediction based on the characteristics of the CRISPR repeat and the leader: CRISPRDirection using CRISPRDetect  (10, 13) , CRISPRstrand  (14, 15)  and CRISPRleader  (16). Cas proteins and systems can be identified using the program Macromolecular System Finder (MacSyFinder), which has a dedicated module (Cas-Finder)  (17). The program is based on the search of protein similarity using Hidden Markov Models (HMM) and a model of genetic composition and organization of the identified components.
HMMCAS is a web tool that can be queried online to identify Cas proteins  (18). The search for CRISPRs and Cas in user-submitted data can be done on the web using CRISPRone  (19)  or locally using the CRISPRdisco pipeline  (20). Here, we present CRISPRCasFinder, which is an updated, improved, and integrated version of CRISPRFinder and CasFinder with freely available third-party software dependencies. CRISPRCasFinder now includes a standalone version, and presents enhanced CRISPR detection performance. Research, 2018, Vol. 46 , Web Server issue W247.
Availability and implementation The CRISPRCasFinder web server is based on independent front and back ends. The front end was implemented as a user-friendly web application using. NET Core (dotnet core) development platform and C# (C sharp) programming language. The Bootstrap framework was used to design the web application. CRISPRCasFinder is also available as a standalone program compatible with Linux (including Windows Subsystem for Linux) and MacOS systems. The program was written in Perl. A workflow is shown on Figure  1  and Supplementary Figure  S1 , and details on dependencies are provided in Supplementary Material.
Arrays showing repeats heterogeneity often correspond to coding sequences with a repeated element and are rarely real CRISPRs  (23). In contrast, spacers are not expected to show a significant degree of similarity, except in the case of rare recombination or duplication events  (24). Therefore, the degree of similarity between spacers is expected to be very low in bona fide CRISPRs. We thus implemented an algorithm to measure CRISPR repeat conservation based on Shannon's entropy (Supplementary Material, Table  S1 , Figures  S2-4 ) and produce an EBcons (entropy-based conservation) index. We empirically determined EBcons thresholds based on the analysis of 128 CRISPR arrays from 128 genomes in CRISPRdb (  23 ) (See Supplementary Material and Supplementary dataset 1). Putative CRISPR arrays with at least four spacers are assigned to levels 2-4 as follows: repeats EBcons < 70 (level 2), repeats EBcons ≥ 70 and spacers overall percentage identity > 8% (level 3); repeats EBcons ≥ 70 and spacers overall percentage identity ≤ 8% (level 4).
CRISPR arrays having evidence-levels 3 and 4 may be considered as highly likely candidates, whereas evidence-levels 1 and 2 indicate potentially invalid CRISPR arrays. The ambiguous notion of 'confirmed' or 'hypothetical' CRISPR array (associated with CRISPRFinder v1. 0) is no longer used in CRISPRFinder v4. 2. We used a panel of 400 genomic sequences (260 bacteria and 140 archaea) from different species (Supplementary dataset 2) taken in alphabetical order, to evaluate the distribution of CRISPR arrays in the four different evidence-level groups. Out of 3251 arrays, there were respectively 1969, 63, 76 and 1143 arrays with evidence level 1, 2, 3 or 4. The identification of false-positive arrays when they possess less than four spacers is not an easy task and some of the evidence-level 1 arrays may in fact be real CRISPRs (see Supplementary Material for an example).
Therefore we give the possibility either to view all the detected CRISPR arrays or to hide those with evidence-level 1. When a short CRISPR array has the same consensus repeat as an evidence-level 4 array, it can be considered as a level 4 CRISPR. Applying this rule would upgrade about 5% of the level 1-3 arrays in the test panel to level 4 (163 arrays out of 2108). This scoring correction will be automatically applied in the future when the CRISPR database is integrated in the system. In addition, evidence-level 1 arrays which are not associated to cas genes will be deleted (42 arrays in 26 genomes out of the 400 test genomes).
Orientation of the CRISPR array CRISPRFinder provides two indicators of CRISPR arrays orientation. First, orientation was predicted by CRISPRDirection for a curated dataset of consensus CRISPR repeats and this result is shown for CRISPR arrays with a matching repeat. We provide an additional method that does not require the existence of previously oriented homologous systems and is based on the AT% in the 100 bp region flanking the array on both sides. As the 5 region of an oriented array is often AT-rich  (25) , the flanking side showing the higher AT% is used as a second indicator of orientation. An option in the standalone program allows users to determine the length of the flanking region to be analyzed. The result of both tests is sometimes different as illustrated on Figure  2  with the genome of Bacillus halodurans, showing that additional developments are still necessary to orientate CRISPR arrays with accuracy.
The last update (CasFinder version 2. 0) fits with the new community-based classification of CRISPR-Cas systems  (2, 3). The program distinguishes class 1 from class 2 systems, detects three new types and nine new subtypes of CRISPR-Cas systems (a description of the sub-typing models is provided in Supplementary Figure  S5 ). Moreover, as 394 new profiles were published with the new classification, previously existing models and protein profiles were revised to improve speed, limit the redundancy between protein profiles, and enhance subtyping accuracy (Supplementary Figure  S6 ). As a result, the new ver-W250 Nucleic Acids Research, 2018, Vol. 46, Web Server issue sion contains 43 additional protein profiles, for a total of 120 profiles (Supplementary dataset 3).
Evaluation of CRISPR and Cas detection by CRISPRCas-Finder To test the accuracy of the novel CRISPR detection method we used the test panel of 400 genomic sequences. Predictions were compared to the expert-curated annotations in CRISPRdb (used as a reference-set) and to three CRISPR detection programs PILER-CR, CRT and CRISPRDetect. Only CRISPR arrays having at least four spacers were taken into account in the evaluation. In CRISPRdb, 1263 arrays were displayed for the above mentioned set of 400 genomes after manual curation (Supplementary dataset 4). Precision, recall and F-measure metrics were used to compare the three programs with the reference-set (Supplementary Tables S2 and 3). These metrics showed that the four programs performed similarly, with precision from 0. 921 to 0. 982, recall from 0.
935 to 0. 987, and F-measure from 0. 932 to 0. 9776. Detailed validation procedures are provided in Supplementary Material. Predictions of CRISPRCasFinder are visible in Supplementary dataset 5. A comparison between the detection of CasFinder v. 2. 0 and the summary presented with the new classification in (2) revealed few differences (Jacquard coefficients between 77 and 96%, Supplementary Figure  S7 ). Overall, CasFinder v2. 0 is more conservative, finds fewer systems, because it requires at least three genes in the Class 1 Cas system, whereas the previous study only required two genes. We opted for the conservative approach because, to the best of our knowledge, no study identified a fully functional Class 1 Cas system capable of adaptation and interference with only two genes. Expert users can change the underlying models to identify Cas systems in the standalone version and lower the threshold.
However the use of the 'General' model already allows the identification of relevant small clusters such as in Melioribacter roseus (NC 018178) which possesses an evidence-level 4 CRISPR with a 46-bp repeat located immediately adjacent to two class 2 cas genes (cas2 TypeI-II-III, cas1 TypeII). We evaluated the performance of CRISPRCasFinder, CRISPRDetect and CRISPRone online using a set of 30 genomes (Supplementary dataset 6) selected because they possess particular sets of CRISPRs observed while curating CRISPRdb (Supplementary Table  S5 ). As compared to CRISPRCasFinder and CRISPRone, CRISPRDetect proposes options to edit the CRISPR array and provides a directional analysis based on seven characteristics of the leader and the CRISPR repeat  (10) , however it relies on NCBI annotations to identify Cas protein and therefore often fails to produce a result.
DISCUSSION AND CONCLUSION The updated CRISPRCasFinder shows enhanced performance and capabilities to identify both CRISPR arrays and Cas proteins, improving the previously existing separate tools CRISPRFinder and CasFinder. In addition CRISPRCasFinder and CRISPRCasViewer (Supplementary Figure  S11 ) are available as standalone programs for users willing to analyze large volumes of sequences (see Supplementary Material for details). We are developing a dedicated tool for the analysis of large metagenomic datasets, allowing a simpler and faster CRISPR array and Cas protein detection. CRISPRCasFinder will continue to evolve, notably by providing a better prediction of CRISPR array orientation using curated data from the currently developed database. Key criteria for array orientation are the presence of a leader/promoter sequence immediately before the first repeat, the existence of a diverged/truncated repeat at the 3 end, the nature of the repeat sequence and its secondary structure, and the position of the cas genes cluster.
ACKNOWLEDGEMENTS We thank  Stefan Kurtz  for making Vmatch an open source project without licensing requirements. We thank  Christine Drevet ,  Maud Pupin ,  Luis M. Rodriguez  and  Lee S. Katz  for their help in the previous developments of CRISPRFinder. We are particularly grateful to the  SICS team  ( I2BC ), and especially to  Cyrille Petat  and  Pierre-Albert Charbit  for the development of the web application. We also acknowledge the constant help and support by  Arnaud Martel  and  Anne-Pascale Jaudier. We thank  Mélina Gallopin ,  David Christiany ,  Nicolas Villeriot ,  Nicolas Maillet ,  Fabrice Leclerc  and  Emilie Drouineau  for helpful comments and for testing the standalone software.
g. different Copia-like, Gypsy-like or Bel-Pao families. It is a generic tool that uses sequence clusters to find family-specific patterns, based on the LTRDIGEST detected features. These patterns are then used as evidence for family discrimination. TECLASS  [13]  classifies TE sequences into Class I and Class II TEs. The Class I elements can further be classified into LTRs and non-LTRs, and the non-LTRs are classified into the SINE or LINE orders. This classification is obtained by a hierarchy of binary classifiers based on machine learning support vector machines, using oligomer frequencies as features. REPCLASS  [14]  consists of three independent classification modules: a module based on homology information, a module that searches for structural characteristics such as LTRs or TIRs, and a module that searches for target site duplication.
The three modules provide classifications at different levels of granularity, typically at the subclass or order level, sometimes at the superfamily level. Finally, an integration module aims to compare, rank, and combine the results of the three modules providing a single tentative classification. PASTEC  [15]  also uses multiple features of TEs to classify TE sequences: structural features (TE length, presence of LTRs or TIRs, presence of simple sequence repeats, etc. ), sequence similarities to known TEs, and conserved functional domains found in HMM profile databases. It provides classifications on the order level, including all orders from the classification hierarchy defined by Wicker et al. [2] , whereas TECLASS and REPCLASS only consider a subset of the orders. Importantly, none of the above classification systems is able to provide classifications for LTR retrotransposons at the superfamily level.