dataesr/scientific-paragraphs-categorization huggingface dataset:
It is worth mentioning though, that the majority of the transcripts inside these loci corresponded to subparts of introns or UTRs, and not to full introns or exons, and therefore could not describe AS events. In order to assess the precision and the sensitivity of our method, we compared our set of found AS events to the set of true AS events. Following the definition of Astalavista, an AS event is composed of two sets of transcripts, the inclusion/exclusion isoforms respectively. We consider that an AS event is true if at least one transcript among the inclusion isoforms and one among the exclusion isoforms is present in the simulated dataset with at least 5 reads per kilobase (RPK). The rationale for adding this threshold is that very rare events are considerably hard, or even impossible, to detect by all methods.
To compare the results of KisSplice with the true AS events, we propose that a true AS event is a true positive (TP) if there is a bubble such that one path matches the inclusion isoform and the other the exclusion isoform. If there is no such bubble among the results of KisSplice, the event is counted as a false negative (FN). If a bubble does not correspond to any true AS event, it is counted as a false positive (FP). To align the paths of the bubbles to transcript sequences, we used the Blat aligner  [21]  with 95% identity and a constraint of 95% of each bubble path length to be aligned (to account for the sequencing errors simulated by FluxSimulator). We computed the sensitivity TP/(TP+FN) and precision TP/(TP+FP) for each simulation case and we report their values for various classes of expression of the minor isoform.
Local assembly: results The overall sensitivity and precision of Ks_2. 2. 0, Ks_2. 1. 0, Trinity and Oases can be found in Fig. 7a , b, respectively. A first look reveals that Ks_2. 2. 0 outperforms the other three methods in both measures and datasets. A closer look at Fig. 7a  shows that both versions of KisSplice had better sensitivity than both transcriptome assemblers in the 5% pre-mRNA dataset. However, due to its inability to deal with repeat-associated regions, the performance of Ks_2. 33%, when a higher rate of 15% of pre-mRNA is present in the data. The same happened with Oases. Ks_2. 2. 0 and Trinity, on the other hand, were able to slightly improve their sensitivity from the 5 to the 15% pre-mRNA dataset. It is however worth mentioning that the sensitivity of Ks_2. 2. 0 is substantially higher than the one of Trinity in the 15% pre-mRNA dataset.
In summary, we can say that Ks_2. 2. 0 is substantially more sensitive than all the other three methods. This reflects the fact that most problematic repeats are in introns. A small unspliced mRNA rate leads to few repeat-associated subgraphs, so there are not many AS events drowned in them. In this case, the advantage of using Ks_2. 2. 0 is less obvious, whereas a large proportion of pre-mRNA leads to more AS events drowned in repeat-associated subgraphs which are identified by Ks_2. 2. 0 and usually missed by the other methods. In Fig. 7b  we can see that Ks_2. 2. 0 and Trinity presented the highest precision (98%) of all methods in the 5% pre-mRNA dataset. Although Ks_2. 1. 0 is ranked third, it still presents a very high precision (95%), while Oases presented a moderate value (80%). Nevertheless, the most important aspect to be observed in Fig.
Those mostly correspond to repeatinduced bubbles mistakenly identified as AS events. Finally, Fig. 7c , d present the detailed sensitivity by expression levels of the four methods on both datasets, allowing for a better understanding of their performance. As we can see, Ks_2. 2. 0 presented the best sensitivity in practically all expression levels in both datasets, while the other three methods were worse, but comparable between themselves. We can also observe that the gap between the sensitivity of Ks_2. 2. 0 and the sensitivity of the other methods tends to increase as the expression levels of the genes increase, especially in the 15% pre-mRNA dataset. Since highly-expressed genes tend to present higher levels of pre-mRNA content, they bring several repeat copies in their introns, and thus some parts of their associated graphs are complex and repeat-induced.
Therefore, AS events inside such genes tend to be trapped in troublesome regions of the graph, making them harder to find. As Ks_2. 2. 0 is able to avoid such complex regions and retrieve the AS events deeply plunged into them, it presents better sensitivity than the other methods, especially in highly-expressed genes and datasets with high rate of pre-mRNAs. As was already reported in  [12] , KisSplice (i. e. both Ks_2. 2. 0 and Ks_2. 1. 0) is faster and uses considerably less memory than Trinity and Oases. For instance, on these datasets, KisSplice uses around 5 GB of RAM, while Trinity uses more than 20 GB, and Oases, around 18 GB. However, it should be noted that both these latter methods try to solve a more general problem than KisSplice, that is reconstructing the full-length transcripts.
On the usefulness of Ks_2. 2. 0 on real data In order to give an indication of the usefulness of our repeat-avoiding bubble enumeration algorithm with real data, we also ran Ks_2. 2. 0 and Ks_2. 1. 0 on the SK-N-SH Human neuroblastoma cell line RNA-seq dataset (wgEncodeEH000169, total RNA). In Fig. 8 , we have an example of a non-annotated exon skipping event not found by Ks_2. 1. 0. Observe that the intronic region contains several transposable elements (many of which are Alu sequences), while the exons contain none. This is a good example of a bubble (exon skipping event) drowned in a complex region of the de Bruijn graph. The bubble (composed by the two alternative paths) itself contains no repeated elements, but is surrounded by them. In other words, this is a bubble with few branching vertices that is surrounded by repeat-associated subgraphs.
Global assembly To test our hypothesis that the Branching Measure is able to identify problematic transcripts, we evaluated it on the transcripts assembled by Trinity on the two simulated RNA-seq datasets described in "Local assembly: results" section, and on two other real RNA-seq datasets: one from the GEUVADIS project  [18]  1 and one from a neuro-blastoma SK-N-SH cell line (ENCODE) differentiated or not using retinoic acid. 2 Even though our method is reference-free, we have chosen to evaluate it under a model species so that we could make use of annotated reference genomes to assess if our predictions are correct. We compared our measure against two state-of-the-art methods for de novo transcriptome evaluation, Rsem-Eval  [4]  and TransRate  [5] , on the specific task of identifying chimeric transcripts in Trinity's assemblies on all four described datasets.
In all our tests, we used the contig impact score of Rsem-Eval as a measure of contig correctness. Formally, the contig impact score is a statistical measure that compares the hypothesis that a particular contig (i. e. transcript) is a true contig with the null hypothesis that the reads composing the contig actually represent the background noise  [4]. In other words, the contig impact score determines the relative contribution of each transcript to explaining the assembly. Wellassembled transcripts should therefore have a high contig impact score, and badly assembled transcripts, including chimeras, should have a low contig impact score. Tran-sRate  [5] , on the other hand, allowed us to work with a specific metric representing the probability that a contig is derived from a single transcript.
This metric denotes the probability that the read coverage of a transcript is best modelled by a single Dirichlet distribution, rather than two or more distributions, and it corresponds to the field sCseq of TransRate's output file contigs. csv. As was shown before, one of the main errors that transcriptome assemblers do is to build chimeric transcripts. We compared the performances of the Branching 2 This dataset can be found at http://genome. crg. es/encode_RNA_dashboard/hg19/, and is also accessible with the following accession numbers: ENCSR000CPN-SRA: SRR315315, SRR315316 and ENCSR000CTT-SRA: SRR534309, SRR534310. For cell lines treated by retinoic acid, the reads were 76nt long, while they were 100nt long for the non treated cells. Hence we trimmed all reads to 76nt. Measure, Rsem-Eval, and TransRate on identifying chimeric transcripts.
In order to have our ground truth, we first identified which assembled transcripts are chimeric with respect to a reference genome by using Algorithm 1. In total, 253 out of 18,706 transcripts (1. 3%) in the 5% pre-mRNA dataset, 376 out of 26,407 transcripts (1. 4%) in the 15% pre-mRNA dataset, 375 out of 99,591 transcripts (0. 3%) in the GEUVADIS dataset, and 2830 out of 457,383 transcripts (0. 6%) in the SKNSH dataset were classified as chimeric. Figure  9  depicts four ROC curves showing the performance of the three methods on all datasets. We can observe that the Branching Measure outperforms both Rsem-Eval and TransRate by a large margin in all tests and, with a high-value threshold, is also able to flag a majority of the chimeric transcripts while keeping a low false positive rate. These experiments show that, in the provided datasets, chimeric transcripts could be well captured by the Branching Measure.
Our false positives include well-assembled transcripts traversing high copy-number low divergence repeats, and our false negatives include chimeric transcripts that did not go through a complex region. The main issue with Rsem-Eval and TransRate, on the other hand, is that both methods failed to find chimeric transcripts assembled from genes with similar expression levels. These chimeras had low scores and corresponded to the false negatives at the end of the ROC curves for Rsem-Eval and TransRate. As a side effect of this misclassification, many well-assembled transcripts had higher scores than several real chimeras, and were mistakenly flagged as chimeras. the design of an algorithm for efficiently identifying AS events that are not included in repeated regions. The new algorithm was implemented in KisSplice version 2.
2. 0, and by using simulated RNA-seq data, we showed that it improves significantly the sensitivity of the previous version of KisSplice, while also improving its precision. In addition, we compared our algorithm to Trinity and Oases, and showed that for the specific task of calling AS events, our algorithm is significantly more sensitive while also being more precise. Our results also showed that the advantage of using KisSplice version 2. 2. 0 is more evident when the input data contains high pre-mRNA content or the AS events of interest stem from highly-expressed genes. Moreover, we gave an indication of the usefulness of our method on real data. Finally, we explored the proposed model in the context of full-length transcriptome assembly by introducing the Branching Measure, which is able to flag the transcripts that go through a complex region in the de Bruijn graph.
Concluding remarks and perspectives Although transcriptome assemblers are now commonly used, their way to handle repeats is not satisfactory, arguably because the presence of repeats in transcriptomes has been underestimated so far. Given that most RNAseq datasets correspond to total mRNA extractions, many introns are still present in the data and their repeat content cannot be simply ignored. Although repeats in transcriptomic and genomic data cause similar problems to assemblers, the specificities of each mean that a successful strategy in one context may fail in the other. It is thus essential for transcriptome assemblers to formally address the repeats problem. In this paper, we first proposed a simple formal model for representing high copy-number repeats in RNA-seq data. Exploiting the properties of this model, we established that the number of compressible arcs is a relevant quantitative characteristic of repeat-associated subgraphs.
We proved that the problem of identifying in a de Bruijn graph a subgraph with this characteristic is NP-complete. However, this characteristic drove measure is useful from a user's point-of-view since it enables to flag the transcripts that result from a "difficult" choice during the assembly, no matter which assembler is used. We showed that this measure can indeed capture assembly errors in real cases and, when compared to Rsem-Eval  [4]  and to TransRate  [5]  on the specific task of identifying chimeric transcripts, the measure we propose outperformed the ones used by Rsem-Eval and TransRate by a large margin. The originality of our work, when compared to other methods for transcriptome assembly evaluation, is that we use only the topology of the graph. Despite the successful application of the Branching Measure in global transcriptome assembly, it remains a simple method, and in particular, we would like to emphasise that it must be seen as a proof of concept that exploring the topology of the subgraph around a transcript can give some hints about its confidence level, quality, assembly hardness, etc.
The method proposed is not a full-fledged one for assessing transcripts in a de novo context. It could however be a promising direction to improve transcriptome assembly evaluation, especially when combined with statistical and read-mapping approaches (e. g. Rsem-Eval  [4]  or TransRate  [5] ). As concerns the local transcriptome assembly of variations, the most interesting open problem which currently remains is how to efficiently enumerate AS events whose variable region (e. g. skipped exon, retained intron) traverses repeats. Although the application of the proposed model enabled to retrieve several AS events that were previously missed, the current algorithm is still only able to avoid repeats, not to solve them. The presence of repeats in RNA-seq data shows that transcriptome assemblers should formally address the repeats issue, as is generally the case of genome assemblers, preferably by solving them.
Another example would be to explicitly model sequencing errors in Theorem 1. Although, in practice, assemblers like KisSplice  [1]  employ a sequencing error removal module, it remains unclear how to distinguish the structures created by sequencing errors from the ones induced by a lowly-expressed member of a highlyexpressed family of repeats, or by infrequent allelic differences in pool-seq. The difficulty increases in regions that are highly expressed or that present sequencing error bias. In practice, error removal strategies may be too stringent and erroneously remove SNPs and repeats. Taking into account the sequencing errors in the model would make it applicable even without any pre-processing of the data, and would thus be more sensitive for finding repeats if such errors are correctly modeled.
Histology Human subcutaneous adipose tissue samples were collected and prepared as described previously  (27). Mouse subcutaneous adipose tissue samples were obtained after sacrifice. Samples were placed in 10% neutral buffered formalin for 48 h, embedded in paraffin, and sectioned at 5 m. Adipose tissue sections were deparaffinized and stained with Sirius red for 1 h to detect collagen fibrils. The surface area stained with Sirius red was quantified with ImageJ software on three randomly chosen regions for each section, and all values were normalized to an arbitrary control section.
RT-quantitative PCR Total RNA was extracted from human and mouse tissue using the RNeasy Lipid Tissue Isolation Kit (Qiagen) following the manufacturer's instructions. Mouse adipose tissue was collected at 40 weeks of age. For primary fibroblast cultures, RNA was extracted using RNeasy Isolation Kit (Qiagen). Quality and concentrations of RNA were measured using a Nanodrop spectrophotometer (Thermo Fisher Scientific). The cDNA was synthesized using Superscript First Strand Synthesis System (Thermo Fisher Scientific), according to the manufacturer's instructions. Quantitative real-time PCR was performed on an ABI 7300 Real-Time PCR System (Applied Biosystems) using HotStart-IT SYBR green qPCR Master Mix (Affymetrix). Relative levels of mRNA expression were calculated using the CT method  (29).
To quantify signals, we scanned immunoblots and densities of the bands quantified by using ImageJ software. The TGF-, Smad4, and   We used primers recognizing sequences present in both human and mouse lamin A/C cDNA (forward: 5′-GGACCTGCAGGAGCTCAATG-3′; reverse: 5′-TCCTCACGCACTTTGCTCAG-3′). Nontransgenic mice (Control) are shown as a reference, in which lamin A/C mRNA is normalized to 1. 00. Values are means ± SEs (control n = 6; WT n = 6; R482Q n = 6; R482Q "Wojtanik" n = 6) and P values given for various comparisons; ns, not significant. at subconfluency and were fixed in cold methanol for 10 min at 20°C. Rabbit antibody directed against type I collagen (Abcam, #ab-292) was revealed by anti-rabbit immunoglobulin G coupled to Texas Red (Thermo Fisher Scientific). Cell nuclei were visualized by diamidino-2-phenylindole hydrochloride staining.
The FLAG-tagged proteins were selectively expressed in extracts of adipose tissue but not liver from the transgenic mice (Fig. 2A ). Immunofluorescence microscopic analysis of adipose tissue sections confirmed that FLAG-tagged wild-type and R482Q lamin A were correctly localized to the nuclear envelope, where they colocalized with lamin B1 in cells expressing the Fab4 promoter-containing transgene (Fig. 2B ). However, R482Q lamin A transgenic mice did not develop gross anatomic or metabolic signs of partial lipodystrophy. In female mice fed a high-fat diet for 28 weeks, the ratios of adipose tissue mass to total body mass did not show significant differences compared with either transgenic mice expressing wild-type human lamin A or nontransgenic controls (Fig. 2C ). Female R482Q lamin A transgenic mice demonstrated a trend toward impaired glucose tolerance; however, the results did not reach statistical significance compared with transgenic mice expressing wild-type human lamin A or nontransgenic collagen I staining per unit area was quantified using ImageJ software, and all values normalized to an arbitrary measurement from a control human fibroblast.
Negative image of the gel is shown so that MMP2 and MMP9 activity appears as dark bands against a light background. Migrations of molecular mass standards are indicated at the right and migration of MMP9 and MMP2 at the left. D: Bar graphs showing quantification of MMP9 activity assessed by gelatin zymography in protein supernatants from three consecutive passages of dermal fibroblast cultures of control individuals (Control 1, Control 2) and patients with FPLD2 and the indicated LMNA mutations. Values were calculated using ImageJ software and are means ± SEs (n = 3). **P < 0. 01; ***P < 0. 001; ns, not significant compared with Control 1. Results are presented as fold change over Control 1. E: Immunoblots using antibody against MMP9 on protein extracts from fibroblasts of control individuals (Control 1, Control 2) and patients FPLD2 and the indicated LMNA mutations.
The top 50 high-depth contig lengths ranged between 200 and 2103 bp, and read depth ranged between 139 and 13 366 copies. The average contig length of B. oleracea was much larger than that of B. rapa, and the contigs were annotated based on a sequence similarity searches against the Repbase and National Center for Biotechnology Information (NCBI) databases (Supplementary Table  3 ). Twelve contigs represented slightly different forms of B. oleracea centromeric tandem repeats (CentBo), 9 sub-telomeric repeats (BoSTR), 8 nrDNAs, and 14 known TEs. The remaining 7 contigs were unknown repeats. Deep investigation and grouping of these major contigs based on sequence similarity led to the identification of 10 major repeat families, including nine well-known repeats and a new B. oleracea-specific Copia retrotransposon (BoCop-1) (Supplementary Table  4 ).
Genomic datasets. Approximately 5 ng total genomic DNA from each B. oleracea accession was utilized to decode genomic information using an Illumina genome analyzer (Hiseq 2000) at Macrogen, (Seoul, South Korea). Randomly sheared genomic libraries were prepared, with an insert size of 300 bp, following the 101 bp paired-end approach recommended by the manufacturer. Genome sequences of 11 B. rapa accessions belonging to eight phenotypic groups, and nine B. napus, were obtained from previous studies  40, 65. Raw reads were preprocessed using the CLC-quality trim tool to remove any remaining linker, adapter or low quality sequences. Sequences of 0. 8x to 4. 4x genome coverage from 64 Brassica accessions were utilized for further analyses (Supplementary Table  11 ). De novo assembly and identification of highly abundant genomic components.
The top 50 contigs of greatest depth were retrieved according to highest genomic representation (RD x LC). These were then annotated by BLASTn (best hit) against the Repbase database 66 and BrassicaTED internal database, using previously reported Brassica REs, and Genbank. REs were classified as known repeats if contigs shared 80% similarity and 80% sequence alignment according the 80:80 rule  67. Partial or truncated repeats containing contigs were manually analyzed and characterized with a complete structure based on reference sequence information or manual reads. Likewise, the B. oleracea (Bo-1-1) genome sequence was independently analyzed to identify the major REs using the abovementioned approach. Sequences of the individual repeat families for each species were stored in the BrassicaTED  48.
In addition, graph-based clustering and characterization of repetitive elements by RepeatExplorer was performed using the 0. 1x WGS reads from each B. rapa, B. oleracea, and B. napus genome  68. Clustering was performed with the criteria of more than 90% sequences similarity and at least 55% sequence length cover were likely to be grouped into a single cluster. Clusters were characterized against Repbase database. Quantification of repeat proportion in the three Brassica genome assemblies. Whole-genome pseudo-chromosome assemblies with unanchored scaffold sequences were obtained for B. rapa (v2. 1), B. oleracea (v1. 0), and B. napus (v4. 1) from Genbank and turned into an in-house database. Total copies of the MRs were identified based on BLASTn searches against the corresponding Brassica reference assembly.
Acknowledgements This work was supported by  Golden Seed Project (Centre for Horticultural Seed Development , No. 213003-04-4-SB430 ),  Ministry of Agriculture, Food and Rural Affairs (MAFRA)  and the  Next Biogreen 21 program  ( PJ01101101 ) of the  Rural Development Administration in the Republic of Korea. We also grateful to  Minkyu Park ,  University of Georgia , and  Yasuhito Sakuraba ,  University of Tokyo , for their critical comments.
3% sequence similarity with Actinomyces odontolyticus strain ATCC 17929 T (Genbank accession number AJ234040), the phylogenetically closest species with standing in nomenclature  [5]. Strain Marseille-P3257 exhibited a 93. 9% sequence identity with Actinomyces hyovaginalis strain ATCC 51367 T (X69616). As the minimum 16S rRNA sequence similarity observed among Actinomyces species genera is 85. 0%  [6] , the similarity values that we obtained putatively classify them as new species within this genus (Fig. 1 ). The genus Actinomyces was discovered in 1877  [7]  and currently contains 47 species with validly published names. We thus propose the creation of two new species: Actinomyces bouchesdurhonensis sp. nov. (bou. ches. du. rho. nen'sis, N. L. masc. adj. Bouchesdurhonensis pertaining to Bouches-du-Rhône, the department in Southern France) and Actinomyces mediterranea sp.
nov. (me. di. ter. ra'ne. a, L. fem. adj. Mediterranea of Mediterranea, the Latin name of the Mediterranean sea). Strain Marseille-P2825 T is the type strain of A. bouchesdurhonensis sp. nov. and strain Marseille-P3257 T is the type strain of A. mediterranea sp. nov. The MALDI-TOF Spectrum of A. bouchesdurhonensis strain Marseille-P2825 T and A. mediterranea strain Marseille-P3257 T are available at http://www. mediterranee-infection. com/article. php? laref=256&titre=urms-database.