dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, whole automatic genome annotation generates numbers of errors, notably with gene merging, splicing problems or tandem duplications  [15]. These problems are exacerbated in the case of multigenic families like most proteins already included in our database. The guarantee of a high-quality sequence input is a prerequisite for performing reliable analyses, especially phylogeny. Efforts to provide only expert annotation derived sequences, in opposition to automated ones, exist elsewhere, but are still rather marginal. Since its creation in 2004, the PeroxiBase has been a very active database with new sequences and new organisms daily added together with constant update of the interface with new tools and functionalities. Then, the RedoxiBase will take advantage of this existing dynamics to go further and pursue increase of available contents and features.
Data available for each entry and tools In April 2019, the database contains more than 15 000 sequences distributed over 2599 organisms. This brings an important biodiversity aspect and can grow further with availability of genomes from novel organisms. In addition to protein, cDNA, CDS, genomic, 2000 bp upstream and downstream sequences, the gene structure information (intron/exon structure), in Genbank format, is displayed along with a schematic representation. The main challenge concerning large multigenic families is to obtain a comprehensive and reliable image of their evolution. To help establishing an evolutionary scenario, our interface provides many tools either to analyse the database entries or to compare them with input sequences. A regular BLAST including usual options (such as the nature of query and subject sequences and the choice of organism(s)) allows the users to search for sequences similar to their query in the database.
1B ) of the orthogroup is available directly from one entry or from the tab "Browse the database by orthogroup" and "Analysis from input/Orthogroup search". The green displayed the species and their ancestors, which possess sequences from the visualized orthogroup, while gray showed species that do not have sequences from the visualized orthogroup. The lack of sequence inside a visualized orthogroup can result from the absence of data or to the loss of sequence in a given species.
New web interface and new code As described above, the availability of a set of tools -some developed by our team -directly executable through the database website, facilitates evolutionary analysis. In addition, to improve the management of the database, as well as the speed of script execution and the database querying, the web application has been implemented in an open-source PHP framework (Codeigniter). This framework uses the Model-View-Controller concept and allows faster development, best security, better maintenance of the code and a reusability of applications developed in the laboratory with the same framework. Since 2008, the database is hosted by the GenoToul bioinformatics facility (http://bioinfo. genopole-toulouse. prd. fr). Recently, a new powerful computing cluster is available and can be used for local phylogenetic and clustering analysis.
Discussion and future prospects With the accumulation of available genomes, the number of sequences included in the database was largely increased (from 6026 in 2008  [17]  to 10710 in 2012  [2]  and 15136 in 2019). Although, the numbers of organisms within each kingdom are in the same range, the RedoxiBase (formerly PeroxiBase) is still mainly composed of sequences originated from Viridiplantae (64%) and from fungi (22%). This is mainly due to the larger size of the red-ox proteins families found in plants and fungi which are subjected to large duplication events. Then, a particular effort needs to be done to increase the representation of ROS-related proteins from other kingdoms (mainly Protista and Animalia) and within them from exotic and poorly represented organisms. Special attention must be paid to genes from those species threatened with global extinction as reported recently by IPBES (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services Paris 2019).
Regularly updating RedoxiBase with manually annotated sequences will allow to perform robust evolutionary analyses also for concatenated sequences. The quality of the annotation, which is our main concern since the creation of the database, has been maintained, but manual annotation does not allow an efficient coverage of all the available sequences. The semi-automatic protocols developed will facilitate the upload of peroxidase-encoding sequences from already annotated proteomes while maintaining our high-quality standard. In addition, the annotation procedure relying on Scipio which has already demonstrated its effectiveness for gene prediction based on homology with closely related already annotated organisms  [25] , will be improved. Indeed, a new strategy that will take advantage of our specific profiles defined with controlled batches of sequences need to be developed for the prediction in more divergent genomes.
Many red-ox proteins families included in the RedoxiBase belong to multigenic families and result from tandem, segmental and chromosomal duplication events, which complicates global phylogenetic analysis and the understanding of their evolutionary history. The visualization of inter-or intra-species sequence orthogroup belonging and their chromosomal localization is very helpful in this context. This requires the availability of genomic localization for larger number of organisms. In addition, we have recently developed ExpressWeb, an online tool to perform gene clustering using personal or selected expressed value sets in order to construct co-expression gene networks  [26]. ExpressWeb is available directly from the RedoxiBase and a current priority is to set up a pipeline to load publicly available expression data in order to perform expression clustering with our favorite genes.
Acknowledgments The authors are thankful to the  Paul Sabatier-Toulouse 3 University  and to the  Centre National de la Recherche Scientifique (CNRS)  for granting their work. We thank all the past contributors and curators of the PeroxiBase, and  Sylvain Picard  and  Raphael Taris  for their contributions to the development of the RedoxiBase. The RedoxiBase is hosted by the  Toulouse Midi-Pyrénées bioinformatics platform. We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul)  for providing help and/or computing and/ or storage resources. This work has been done in the  Plant Science Research Laboratory (LRSV). MZ was supported by the  Austrian Science Fund (FWF , project P  31707-B32 ) and by the  Slovak Grant Agency VEGA  (grant  2/0061/18 ).
We note, however, that we do not model the intricacies of geographic speciation at the nodes and assume that allopatric speciation does not occur; we leave the modeling of this important speciational process to forthcoming work. Our method fits the model using data augmentation within a Bayesian framework to perform parameter inference, enabling accurate propagation of uncertainty in the posterior distributions by integrating over all trait and biogeographic scenarios found likely by the model. This algorithm has the added advantage of returning joint posterior reconstructions of trait and biogeographic histories, which can be used in post hoc analyses and visualizations. To assess the behavior of our model and to validate our method, we first measure how well it fits a variety of datasets that were simulated under a breadth of evolutionary scenarios.
Software. -We denote this model as "TRIBE" (which stands for "Trait and Range Interspecific Biogeographic Evolution") and implement it in a new open source package named "Tapestree" (https://github. com/ignacioq/Tapestree. jl) that we wrote in Julia  (Bezanson et al. 2017). This software makes available the tribe() function for inference and the simulate tribe() for simulations given a fixed tree. We note that, in the software, we allow the user to fix to 0 any or all of the parameters governing the effect of biotic interactions (i. e. , ω x , ω 0 , & ω 1 ).
Simulations We use simulations to explore model behavior. To simulate biogeographic histories under this model, we take advantage of the following approximation. Let V be a random variable denoting the time of an event and λ(t) be the event rate at time t, then given a small enough time step δt, we have P(t ≤ V < t + δt|t ≤ V ) ≈ λ(t)δt. Thus for a given lineage and timepoint, we use the above time step size for all areas across the geographic range. If there is more than one event within one time step as defined by our time discretization scheme, we reject and sample again. Similarly, to simulate trait evolution under the competition model, we, again, take advantage of the Euler-Murayama method detailed in the Appendix. Simulation code, given a phylogenetic tree, can be found at https://github.
2 in  Grant and Grant (2011)  and phenotypic measurements from  Clarke et al. (2017) , originally compiled in  Harmon et al. (2010)  from which we obtained the data for Certhidea olivacea. Specifically, we used three beak measurements: length (culmen), width and depth (gonys), and tarsus and wing length, all with natural logarithmic transformations. Given the high correlation between the three beak measurements, we used the first and second principal components (which together explained > 99. 6% of the variance). The first component mostly corresponds to size, while the second corresponds to overall shape (Supplementary Figure  2 ;  Grant and Grant 2002).
Other circRNAs and additional regulatory transcriptional roles have subsequently been described in cancer  (Salzman et al. 2013; Guo et al. 2014; Li et al. 2015b; Kristensen et al. 2018). Because circRNAs lack a free 5 ′ or 3 ′ end, such molecules escape exonucleic acid degrading enzymes, making them more stable  (Memczak et al. 2013)  than their linear counterparts. Therefore, circRNAs represent potentially useful biomarker candidates for diagnosis and therapy-monitoring; indeed, cell-free circRNAs are present in exosomes  (Li et al. 2015a ) and saliva  (Bahn et al. 2015). In breast cancer, little has been described except for one study  (Nair et al. 2016 ) using the The Cancer Genome Atlas (TCGA) data bank. However, this cohort has a huge limitation because the RNA-seq data were prepared using a poly(A) selection step, thereby omitting the majority of circRNAs (as these lack a poly(A) tail).
2013). To avoid spurious correlations, only those circRNAs found in at least 50 samples were considered (n = 1625), and data were first normalized using the trimmed mean of M-values (TMM)  (Robinson and Oshlack 2010). Correlations are listed in Supplemental Table  S1  and ranged between -0. 34 and 0. 97, with 210 circRNAs showing a negative correlation to the linear gene in which the circRNA is located. When considering the average and standard deviation of the distribution of all correlation coefficients, 30 circRNAs are at the low end of the distribution (P < 0. 05), showing a correlation below R = -0. 182. Finally, by using GENCODE information, the position of annotated start codons was matched to the circRNA positions. circRNAs recurring in at least 50 samples showed an almost threefold higher than expected presence of a start codon compared with circRNAs that were not recurring (χ 2 , P < 0.
0001; 557 circRNAs; expected 207. 3 circRNAs). Are circRNAs distinct molecules, specifically regulated, or splicing residues? Many of the circRNAs that are positively correlated with the expression level of the full-length linear transcript are thought to be a residue of splicing. Figure  3A  shows an example; the circRNA of exons 3, 4, and 5 of the WDR1 gene (Chr 4: 10,097,711-10,103,986, with a correlation coefficient of R = 0. 66 to overall gene expression) exactly matches the difference between the known linear isoforms of this gene (Ensembl transcript ENST00000499869 includes exons 3, 4, and 5; ENST00000502702 lacks these exons). As current quantification methods do not take circRNAs into account, reads originating from the circular molecule are erroneously included in the read count of a linear isoform, resulting in an overestimation of the overall expression level.
However, we observed circESR1 exon 2-3 in 110 patients and circESR1 exon 3-4 in 64 patients, with 29 patients showing both these circRNAs. These must be derived from separate RNA molecules. To investigate whether or not in general circRNAs should be considered splicing residues, we systematically evaluated how many of the identified circRNAs exactly match those exons that make up the difference between known linear isoforms of a gene. By using the GENCODE annotation for each gene, every possible known combination of spliced exons was matched to our circRNA catalog. Of the 25,783 circRNAs that matched to exons of the same gene, only 2193 (8. 5%) exactly matched exons known to differ between described isoforms of a gene. This was 16. 9% for the circRNAs with a correlation coefficient of R > 0.
5 to linear gene expression. This suggests that the vast majority of circRNAs that matched to exons of the same gene are generated by yet-unknown splicing events of the gene. Because the majority of circRNAs did not match to known spliced exons, we manually inspected several highly recurrent circRNAs in the UCSC Genome Browser. For example, two isoforms of CREBBP are described (ENST00000262367 and ENST00000 382070) that differ in the presence of exon 5 (of note, there are 10 additional known transcripts, but all of these transcripts start downstream from exon 5). However, we observed exon 2 as circRNA (Chr 16: 3,850,297-3,851,009) that was present in 160 patients (Fig. 3C ), indicating that this circRNA is either specifically generated or is a splicing residue of a yet-undescribed isoform of CREBBP that skips this exon.
1). Annotation shows that exon 1 is untranslated and both exon 2 and exon 4 start with the methionine codon (ATG). Thus, the linear transcript wherein exon 1 is ligated to exon 4 lacks the start codon from exon 2 and may use the ATG in exon 4 for its translation. Third, we observed that circRNAs matching exon boundaries of the same gene rarely (8. 5%) overlap with known spliced exons. It could be that our analysis overlooked possible splice variants from the GENCODE annotation (both HAVANA and Ensembl exon annotations were included), but if the concordance is indeed this low, two scenarios may be applicable: Either circRNAs are still mostly a remnant of splicing, implying that many more transcript isoforms of genes exist, or otherwise circRNAs are specifically generated, implying that they do have a biological role.
Sequencing Internal review boards of each participating institution approved collection and use of samples of all patients in this study. RNAseq data were generated by our consortium  (Nik-Zainal et al. 2016; Smid et al. 2016)  for 348 primary breast cancer tumors that are available through the European Genome-phenome Archive under accession number EGAS00001001178. Sequence protocols of the samples were previously described in detail  (Nik-Zainal et al. 2016) ; in short, total RNA after gDNA removal, clean-up, and depletion of ribosomal RNA using duplex-specific nuclease (DSN) treatment was used as input for random-primed cDNA synthesis. Paired-end (75 bases) sequencing was performed on an Illumina HiSeq 2000. The resulting FASTQ files were mapped to GRCh38 using STAR (version 2. 4. 2a) (Dobin et al.
Identification of circRNAs A detailed explanation of the methodology to identify circRNA reads, including the Perl script, is stated in the Supplemental Methods. The script is also available at https://bitbucket. org/ snippets/MSmid/Le949d/identify-circularrna-reads. In short, the method developed here uses sequence reads that have a "secondary alignment" (SA) tag. When using paired-end sequence data and assuming a circRNA molecule is present (Fig. 1 , top), the sequence read that aligns over the crossing of the junction (green arrows) would "point toward" its read-mate (orange arrow) somewhere in the circle. Aligning these reads to the linear reference (Fig. 1 , middle), the junction read will get an SA tag and will be assigned to two locations if and only if this is the one and unique alignment configuration the STAR software can find.
The read-mate aligns somewhere in between these two locations. Finding additional read-pairs showing this configuration, with a breakpoint at the exact same location, strengthens the evidence for circular transcripts. Only regions with at least five reads crossing the circular junction were included. After filtering (for details, see Supplemental Methods), GENCODE annotation was used to obtain the exon locations of genes that exactly matched to the circular region. For each sample, STAR also gives the raw read counts for all genes. These were normalized (TMM implemented in edgeR)  (Robinson and Oshlack 2010)  and used to correlate with the number of junction reads of the circular transcripts.
Multiple correspondence analysis Because many genes only show a linear transcript in many samples, standard cluster analysis to identify groups of samples with similar circRNA-related biology is problematic because of the many missing values. Thus, the circRNA data were considered categorical using "circular" or "not circular" if a circRNA was present or absent in a sample. These categorical data are suitable for an MCA, a generalization of a principle component analysis. The MCA generates a combined plot that shows both patients and circRNAs such that patients/circRNAs that have similar patterns are closer together. R-packages "ade4," "canceracm," and "cluster" were used to perform the MCA and determine the optimum number of clusters. The latter was determined using the clusGap option (k-means to partition the samples) in the cluster package.
Gene expression signatures We used several signatures: a TIL and proliferation signature  (Smid et al. 2016) , a hypoxia signature  (Winter et al. 2007) , and a stromaspecific signature using public data GSE5847 (Gene Expression Omnibus)  (Boersma et al. 2008). We performed a paired t-test to obtain genes significantly higher expressed in microdissected stroma (FDR < 0. 05 and fold-change >1. 7). For all signatures, genes that were up-regulated in the category of interest were matched to our data set, and the average expression of the signature genes was calculated per sample. Samples were labeled as high-TIL (or stroma, proliferation, hypoxia) if the average expression of the signature genes fell into the top quartile. To identify significantly differentially expressed circRNAs, we compared the top quartile of samples versus the rest, per ER-group.
Acknowledgments We thank the  Erasmus MC Cancer Computational Biology Center  for giving access to their IT infrastructure and the software that was used for the computations and data analysis in this study. We thank  Sandra Albassam  for her help with the first versions of the script to identify circular regions. We thank  Maurice P. H. M. Jansen ,  Jean C. Helmijr ,  Inge de Kruijff , and  Manouk K. Bos  for their help in evaluating plasma samples that were gathered in the EU- FP7  CareMore (nr 601760) project. We thank for technical support  Miriam Ragle Aure  and  Anita Langerød  of the  Oslo University Hospital, Norway ;  Ewan Birney  of the  European Bioinformatics Institute, UK ; and  Stefania Tommasi  of the  IRCCS Istituto Tumori "Giovanni Paolo II," Bari, Italy. We thank the  Oslo Breast Cancer Research Consortium (OSBREAC) ,  Norway  (https://www.
ous-research. no/home/kgjebsen/home/14105) for contributing patient samples and  Sabine Linn  and  Marleen Kok  of  The Netherlands Cancer Institute  for contributing samples for the AI cohort. Finally, we thank all members of the  ICGC Breast Cancer Working Group. This work has been funded through the  ICGC Breast Cancer Working group  by the  Breast Cancer Somatic Genetics Study  (a European research project funded by the  European Community 's  Seventh Framework Programme  ( FP7/2010-2014 ) under the grant agreement number  242006 ) and the  Triple Negative  project funded by the  Wellcome Trust  (grant reference  077012/Z/05/Z ). F. G. R. -G. and S. M. were funded by  BASIS. J. A. F. was funded through an  ERC Advanced Grant  ( ERC-2012-AdG-322737 ) and  ERC   Proof-of-Concept Grant  ( ERC-2017-PoC-767854 ).
K. U. was funded by the  Daniel den Hoed Foundation. S. N. -Z. is a  Wellcome Beit Fellow  and personally funded by a  Wellcome Trust   Intermediate Fellowship  ( WT100183MA ). A. L. R. is partially supported by the  Dana-Farber/Harvard Cancer Center SPORE in Breast Cancer  ( NIH/NCI 5 P50 CA16 8504-02 ). A. M. S. was supported by  Cancer Genomics Netherlands  (CGC. nl) through a grant from the  Netherlands Organization of Scientific research (NWO). M. Smid was supported by the  EU-FP7 -DDR response project. C. D. was supported by a grant from the  Breast Cancer Research Foundation. J. E. was funded by The  Icelandic Centre for Research (RANNIS).
INTRODUCTION The assessment and monitoring of biodiversity are fundamental tasks for conservation management and ecosystem preservation. Both suffer heavily from their strong reliance on a too scarce taxonomic knowledge  (Giangrande, 2003)  and on the general absence of comprehensive, inexpensive, and user-friendly tools for species identification. This is especially critical for insects  (Green, 1998; Stork et al. , 2015) , which are massively impacted by environmental changes, with cascade effects on the functioning of ecosystems  (Hallmann et al. , 2017). One recent methodological development that can reduce this shortfall in insect species identification is the use of a short and standardized DNA fragment, termed "DNA barcode"  (Hebert et al. , 2003a). This approach relies on the use of a 658 base pair (bp) fragment of the mitochondrial gene cytochrome c oxidase subunit 1 (COI) and on an online centralized database and workbench, the Barcode of Life Datasystems (BOLD)  (Ratnasingham and Hebert, 2007, www.
boldsystems. org) as a reference library ensuring the link between reference specimens identified by experts and these DNA barcode sequences. DNA barcoding is now a widely adopted tool for species delimitation and identification. As of today (10/09/2019) BOLD holds as many as 7480K DNA barcodes for 210K named species, and 645K BINs (Barcode Index Numbers, an automatic classification system of DNA barcode sequences that can be used as a proxy to species when records are unnamed; see  Ratnasingham and Hebert, 2013). Such rapid-and ongoing-development has created a completely new and efficient access to taxonomic expertise for whoever can retrieve this short DNA snippet from specimens or their parts. Furthermore, it is now the ground for developing high-throughput approaches such as environmental sequencing or DNA metabarcoding  (Deiner et al.
for which an abdomen was taken after genitalia removal, due to the lack of significant diagnostic characters for taxonomic identification and the higher amount of tissue it provides. Sampling was done using sterilized forceps. Collecting data were compiled into a standard Darwin Code spreadsheet and vouchers were photographed using either a 14MP 1/2. 3 ′′ APTINA CMOS Sensor U3CMOS mounted on a stereomicroscope, or a Nikon D7200 with an AF-S DX NIKKOR 18-300MM F/3. 5-5. 6G ED VR Lens for the biggest individuals.