dataesr/scientific-paragraphs-categorization huggingface dataset:
In line with this, a higher fraction of circRNA relative to linear RNA has been observed in a wide range of human biofluids, which makes them interesting biomarker candidates, with the potential to be used for minimally-invasive tests for diagnosis or response monitoring  (2). Wang et al. reviewed 112 differentially expressed circRNAs in various biofluids from patients with different cancer types  (3). Furthermore, 15 clinical trials incorporating circRNAs as disease biomarkers have been initiated (ClinicalTrials. gov). Eukaryotic circRNAs are formed through a process called back-splicing, where the 5' end of an RNA molecule forms a covalent bond with its own 3' end, forming a circular molecule with a characteristic back-spliced junction (BSJ) sequence (Figure  1B )  (1). CircRNAs comprise of one or multiple exons, and analogous to linear RNA, there is alternative splicing of circRNAs, where circRNAs with the same BSJ sequence may have a different exon (and/or intron) composition  (1).
In our benchmarking study, we aimed to evaluate all currently available circRNA detection tools with an orthogonal approach using RT-qPCR, RNase R, and amplicon sequencing (Figure  2A ). Our study highlights that although the precision of the tools is generally excellent, their sensitivities are highly variable. Black boxes highlight the BSJ in the circRNA isoforms. C. CircRNAs can be detected with RT-qPCR using a BSJ-specific primer pair. The primer pair can only bind in a divergent manner (facing away from each other) to linear RNA, where no amplification will be possible, yet binds the circRNA in a convergent manner (facing towards each other), amplifying the BSJ sequence. D. Large-scale circRNA detection is typically performed using total RNA sequencing datasets and specialized computational tools.
The number of detected circRNAs differs greatly between tools A total of 315,312 unique circRNA predictions (corresponding to 1,137,099 unique circRNA/strand/tool/sample tuples) were detected using 16 different tools based on deeply sequenced total RNA from three human cancer cell lines (Supplementary Table  2 , because of large size, only available on https://github. com/OncoRNALab/circRNA_benchmarking). There is a striking almost 40-fold difference between the tool with the highest number of predicted circRNAs (circseq_cup with 58,032 circRNAs) and the tool with the lowest number of predicted circRNAs (segemehl with 1,372 circRNAs) for one of the cell lines (Figure  2B  shows results for HLF cells, similar results for the other cell lines are shown in Supplementary Figure  1 ). Tools were further compared based on different metrics, including predicted circRNA length, strand information, correspondence to linear annotation, and predicted exon composition (Supplementary Data 1-4, Supplementary Figure  2 3 4 5 ).
Most circRNAs are characterized by low BSJ counts CircRNA abundance is reflected by the BSJ count, which is the number of reads uniquely assigned to a given circRNA. The majority of circRNAs (86. 6%) are detected with a BSJ count below 5 (Figure  2B ), with only 46. 1% of the detected circRNAs being observed with at least 2 BSJ counts (detailed distribution in Supplementary Figure  6 ). To increase confidence, circRNA_finder and segemehl filtered their results to report only circRNAs with a BSJ count of at least 5, and CirComPara2 and KNIFE filtered for circRNAs with a BSJ count of at least 2. Circtools filtered circRNAs with at least 2 counts in at least 2 samples. Of note, Sailfish-cir does not report raw BSJ counts, but transcripts per million (TPM) instead. The similarity of circRNA BSJ counts between tool pairs is reasonable, according to correlation analysis (median r 2 = 0.
Comparing precision values in function of circRNA annotation To compare precision values in function of circRNA annotation, we restrict the analyses to high-abundance circRNAs with information for all validation techniques. Furthermore, a strict validation definition was used, where all circRNAs failing for at least one technique were classified as unvalidated. CircRNAs previously described in databases have higher chances of getting validated (Chi-squared test with p-value < 0. 001, odds ratio (OR) = 13. 6). Nevertheless, false-positive circRNAs according to our data are still present in multiple published databases (Supplementary Figure  23 ). For example, false-positive circRNA chr6:47526627-47554766 (hg38, 0-based) is present in CircAtlas (as hsa-CD2AP_0048) and in exoRBase (as exo_circ_65199).
Discussion Multimodal orthogonal validation of bioinformatics tools that predict circular RNAs from total RNA sequencing data is currently lacking. Hence, their precision and sensitivities are unknown and scientific data is confounded with false positive and false negative predictions. To accommodate this lacune, we set up a large-scale international collaborative circRNA detection tool benchmarking study. First, a deeply sequenced total RNA sequencing dataset was processed by the developers of 16 different circRNA detection tools. Next, 3 empirical validation strategies were used to evaluate a random selection of 1,560 circRNAs representing each tool: 1) RT-qPCR to determine if the circRNA was detectable; 2) RNase R treatment to confirm that the detected RNA was most likely circular and not linear; and 3) amplicon sequencing to confirm the BSJ sequence.
This underscores the importance of using different validation methods to compensate for their intrinsic limitations and to increase the validation status confidence (as previously suggested in (  7 )). Although long-read sequencing has been implemented to study full-length circRNAs  (29) (30) (31) (32) , the bulk of currently available data remains short-read sequencing. Therefore, this benchmarking study evaluated circRNA detection tools for short-read sequencing data, which typically report circRNAs by their BSJ position (chr, start, end, strand). However, it remains unknown if the detected BSJ corresponds to one circRNA, or multiple alternatively spliced circRNAs with different exon/intron compositions. Henceforth, the prediction precision values reported here might be influenced by more than one circRNA with the same BSJ.
As this study is focused on circRNA detection in short-read sequencing data, the internal circRNA composition was not evaluated. Furthermore, no distinction can be made between circRNAs on the positive strand or negative strand using RT-qPCR and amplicon sequencing (9. 4% of circRNAs were reported to originate from different strands according to different tools). Based on a pilot study (Supplementary Data 5, Supplementary Figure  11 ), a cut-off was set at BSJ count 5, as circRNAs under this cut-off approached the qPCR limit to reliably detect RNase R based degradation of falsely predicted circRNAs. While very deep sequencing of a large RNA input amount was performed, it is beyond the scope of this study to evaluate if the BSJ count should be reconsidered in function of sequencing depth. However, as the majority of predicted circRNAs have a BSJ count below 5, we decided to include at least a subset of these low-abundance circRNAs to calculate the corresponding prediction precision.
It is no surprise that the precision values for low-abundance circRNAs are significantly lower compared to high-abundance circRNAs (Chi-squared test with p-value < 0. 001, OR = 2. 8). This difference is likely due to the detection limits of the applied validation strategies in conjunction with the sampling bias of low-abundance analytes, and not due to inherently more false positive predictions for circRNAs with a lower count. Of note, it can be presumed that weakly expressed circRNAs are less relevant for both functional studies and biomarker research. Focusing on high-abundance circRNAs, interesting links were found between circRNA annotation and validation rates. As such, circRNAs had higher validation rates when they were detected by multiple tools, when they were previously reported in a circRNA database, when they were surrounded by canonical splice sites, and when they originate from a region with an annotated linear transcript.
Study set-up As this study includes executing and evaluating circRNA detection tools, the co-authors can be divided into two groups 1) a first independent group (with no circRNA detection tool of their own) which initiated and designed the study and performed all the wet-lab work and data analysis (the validation co-author group), and 2) a second group of tool developer coauthors, which detected circRNAs using their own circRNA detection tools according to their expertise (the circRNA prediction co-author group) (details in Author Contribution section). During the study, meetings and emails were used to share the results (first anonymously) and discuss the final manuscript with the circRNA prediction co-authors. Cell culture Three cancer cell lines were randomly chosen as biological replicates.
CircRNA detection In November 2020, a comprehensive list of all published circRNA detection tools was compiled, and all developers were invited to collaborate. Upon consent, they were asked to detect circRNAs using their own circRNA detection tool as they seemed fit for the data that was provided. The circRNA detection steps for each tool are detailed in the Supplementary Notes. We were unable to get into contact with the authors of find_circ  (23)  and decided to run this tool ourselves, as it is one of the most frequently cited and broadly used circRNA detection tools. After collecting all circRNA detection results, a uniform list of circRNAs defined by their BSJ position (chr, start, end, strand) and the BSJ count for each tool was compiled (Hg38, 0-based). CircRNA selection and primer design Guided by a pilot experiment assessing circRNA RT-qPCR detectability in function of abundance and RNA input amount (Supplementary Data 5, Supplementary Figure  11 ), for each tool, 80 high-abundance circRNAs (with a BSJ count of at least 5), and 20 lowabundance circRNAs (with a BSJ count below 5) were selected (as two separate count bins).
Primer pairs were designed using our primer design tool CIRCprimerXL  (28). All primer sequences are available in Supplementary Table  3. If no primer pair could be designed for a given circRNA, a substitution was randomly selected from the complete dataset, considering the BSJ count bin. In total, 1,560 circRNA/tool/cell line tuples were selected. As some circRNAs were selected more than once (for different tools) the total number of unique circRNA/cell line pairs is 1,516, and the number of unique circRNAs (not taking into account the strand) is 1,457 (Supplementary Figure  14 ). Additionally, most of the selected circRNAs are detected by multiple tools (for which they were not selected). For this study, only the 20 + 80 selected circRNAs for a specific tool were used to evaluate that tool to keep the number of observations equal for each tool, even though more of its predicted circRNAs might have been validated.
To retain all amplicons, no size selection was performed after adaptor ligation, and 1. 0x AMPure XP beads (#A63881, Beckman Coulter) in a 1:1 sample:beads ratio was used instead. After library preparation, the samples were pooled equimolarly. The pool was sequenced on a NextSeq 500 instrument using a Mid Output Kit v2. (38)  were used. The human reference transcriptome was downloaded as a GTF file from Ensembl  (39). All data analysis scripts are available at https://github. com/OncoRNALab/circRNA_benchmarking.
Amplicon sequencing data analysis First, a custom Python script matches the primer sequences with the first 16-mer of each read (forward and reversed) and generates a separate FASTQ file per primer pair, containing all reads starting with that primer sequence. The FASTQ reads are then clipped to remove the primer sequences. Next, all FASTQ files are mapped against the reference genome (Ensembl version GRCh38. 101) supplemented with the theoretical BSJ amplicon sequences using Bowtie2 with default settings. Lastly, the Bowtie2 BAM files are converted to counts using another custom Python script and the percentage on-target amplification was calculated for each primer pair.
Filtering and determination of orthogonal precision values Several strategies to filter the data prior to precision value calculation were explored. For RT-qPCR, a circRNA was considered validated when at least one of the untreated RNA samples had a Cq value above 10. Multiple variations of this threshold and a potential upper Cq threshold were evaluated. For RNase R validation, a subset of circRNAs with at least one untreated replicate with a Cq value below 32 was selected to ensure that the enzymatic degradation of a false-positive circRNA could be measured. A circRNA was considered validated upon RNase R treatment if the difference in Cq between the untreated and treated RNA sample was equal to or less than 3 cycles, based on a previous study (4). As there were two qPCR replicates available for each (un)treated sample, the 'best-case scenario' was used to calculate the difference in Cq by subtracting the maximum untreated Cq replicate from the minimum treated Cq replicate.
Annotation of circRNAs To obtain the circRNA splice site information, the BSJ-flanking nucleotides were extracted from the reference genome using fastahack (Ensembl version GRCh38. 104). To compare BSJ positions with known linear annotation, BEDtools intersect was used with a list of canonical transcripts from Ensembl ('canonical tsv') with their positions based on the corresponding Ensembl GTF file (Ensembl version GRCh38. 103). When a circRNA mapped to multiple isoforms, the annotation was labeled as 'ambiguous' and the circRNA was not taken into account for further annotation-based calculations and figures. The annotation was used to compute the length of each circRNA excluding introns, and the number of exons per circRNA. CircRNAs smaller than their host gene exon were labeled 'single-exon' circRNAs.
Acknowledgments We thank  Steve Lefever  for his contribution to primer design in the early stage of this project. The computational resources ( Stevin Supercomputer Infrastructure ) and services used in this work were provided by the  VSC (Flemish Supercomputer Center) , funded by  Ghent University, FWO , and the  Flemish Government -department EWI. Stefania Bortoluzzi  would like to acknowledge support of  Fondazione AIRC per la Ricerca sul Cancro, Milan, Italy  ( Investigator Grant   2017 20052 ),  Italian Ministry of Education, Universities, and Research  ( PRIN 2017 2017PPS2X4_003 ),  EU  within the  MUR PNRR "National Center for Gene Therapy and Drugs based on RNA Technology " (Project no. CN00000041 CN3 RNA ) and " HPC, big data and quantum computing " ( CN1 HPC ) and the  Department of Molecular Medicine of the University of Padova.
This mirrors incomplete knowledge on species-level tooth morphology of extant taxa that complicates species delimitations in the recent fossil record, coupled with weaker sampling of post-Miocene (< 5 Ma) geological formations, which departs from the 'pull of the Recent'  51. Consequently, although the carcharhiniform fossil record allowed species-level estimates of diversification events, the probable post-Paleocene diversification of the clade is partly hidden when only considering fossil data  52. Here we aim to provide a comprehensive estimation of carcharhiniform diversification history by combining palaeontological and neontological data (Fig. 2 ; see "Methods" below). First, we compiled available molecular data (14 gene fragments) to reconstruct a Bayesian species-level time-calibrated phylogeny of Carcharhiniformes, from which we extracted divergence times of extant species taking into account age uncertainties.
Second, we compiled species-level fossil occurrences (> 1300) of the whole clade, spanning the entire existence of the order, to estimate fossil occurrence-based diversification rates with Bayesian process-based birth-death models incorporating the temporal variation of the preservation processes and uncertainties associated with the age of fossil occurrences as implemented in PyRate  24. Third, we combined the phylogeny-based and fossil occurrence-based estimates of extant and extinct species' lifespans into a single Bayesian framework implemented in PyRate to provide inclusive estimates of the carcharhiniform diversification processes through time. Fourth, we assessed whether the speciation and extinction rates of carcharhiniforms responded to major crises such as the Cretaceous-Paleogene (K-Pg) event and Eocene-Oligocene transition (EOT), to environmental changes in  and molecular data (orange colour) at the species level for Carcharhiniformes that were analysed separately and jointly (blue colour).
The fossil occurrence data are first analysed under a birth-death with constrained shifts (BDCS-Fossils) model, using PyRate, while jointly modelling the preservation process to estimate speciation (λ), extinction (μ), and preservation (q) rates. This also estimates the times of speciation (Ts) and times of extinction (Te) of all species with a fossil record. The DNA sequences were analysed with Bayesian phylogenetic inferences, using BEAST, while simultaneously estimating the tree topology and divergence times using fossil calibrations retrieved from the fossil compilation. The divergence times provide Ts for extant species that have no fossil record (see Fig. 3  for details on this step). We then combined the Ts and Te estimated with PyRate and the Ts and Te inferred with molecular dating in BEAST to generate the most comprehensive species-level dataset incorporating Ts and Te as well as the age uncertainties.
This dataset is used for the subsequent analyses under the BDCS model (BDCS-Combined) to compare diversification dynamics with the fossil-only estimates, and the multivariate birth-death (MBD) model to assess the role of global environmental change, as well as clade competition on speciation and extinction rates. Earth history, and to intra-clade biotic interactions  53. We also performed a comparison of the macroevolutionary history of carcharhiniforms between an inference drawn from the fossil record only and an inference made with the combined dataset. We argue that combining extinct and extant taxa based on different types of datasets (fossil occurrences and molecular phylogenies) offers an opportunity to provide more reliable estimates of the evolutionary history of clades experiencing recent radiations that correspond to periods of undersampled fossil record.
We established a list of 284 species that we consider as valid and provided a list of species considered as non-valid or requiring revision (Supplementary Data S1). The latest comprehensive phylogeny of sharks  43  generated a molecular supermatrix composed of two nonprotein-coding mitochondrial loci (12S and 16S ribosomal DNA), 11 protein-coding mitochondrial loci (CO1, CO2, CO3, Cyt b, ND1, ND2, ND3, ND4, ND4L, ND5 and ND6) and two nuclear protein-coding loci (RAG1 and SCFD2). We collected all molecular data of Carcharhiniformes available on GenBank and BOLD Systems (last accessed on 18 June 2020) and retrieved DNA sequences from the same 13 mitochondrial genes and one nuclear locus (RAG1). We did not gather the data for the nuclear gene SCFD2 because the species sampling was too low (< 5%) for Carcharhiniformes.
The complete mitochondrial genome was recovered for 44 species from which we extracted the 13 mitochondrial genes. Data completion was variable among loci from 18% of the species for RAG1 to 87% of the species for COI. All families of Carcharhiniformes were included, as well as 50 genera and 195 species, representing 96. 2% of the generic diversity and 68. 7% of the species diversity of the order, respectively. DNA sequences for some carcharhiniform species could not be included in our analyses, although they were used in previously published works  56, 60, 61. These sequences were not available through public repositories and were not made available to us despite formal requests to the authors. The resulting dataset was substantially improved compared with the last shark supermatrix published  43  , which included 55.
63% of the total extant carcharhiniform species diversity. We selected all 15 extant species of the order Lamniformes, a clade constantly found in sister position to the order Carcharhiniformes  43, 55  , as outgroup. The accession numbers for all the DNA sequences of the dataset are available in the Supplementary Data S2. Phylogeny and dating. Each locus was aligned with MAFFT 7. 3  62  with default options (E-INS-i algorithm) and alignments were checked and refined by eyes. Reading frames of coding genes were checked in Mesquite 3. 61. The final molecular matrices are made available (Supplementary Data S3). The supermatrix of 15,948 nucleotides was divided into 14 partitions (one per locus), and we identified the best molecular partitioning strategy with PartitionFinder 2. 1. 1  63  using the greedy search algorithm and the Bayesian Information Criterion.
We simultaneously estimated the phylogenetic tree and divergence times of Carcharhiniformes and Lamniformes using a Bayesian relaxed-clock approach accounting for rate variation across lineages  64  as implemented in BEAST 1. 10. 4  65. We set the following settings and priors: a partitioned dataset (after the best-fitting Partition-Finder scheme) was analysed using the uncorrelated lognormal distribution clock model, with the mean set to a uniform prior between 0 and 1, and an exponential prior (lambda = 0. 333) for the standard deviation. The branching process prior was set to a birth-death process  66  , using the following uniform priors: the birth-death mean growth rate ranged between 0 and 10 with a starting value at 0. 1, and the birth-death relative death rate ranged between 0 and 2 (starting value = 0.
Partitioning the molecular clocks has been shown to influence the divergence times  [68] [69] [70]  , and we assessed its impact by comparing dating analyses made with four molecular clocks (three for the mitochondrial markers and one for the nuclear marker) versus dating analyses made with seven molecular clocks (one clock per molecular partition). Likewise, the maximum age of the tree root is known as a key prior for molecular dating  71, 72. We evaluated the effect of maximum age at the root by setting it to 208. 5 Ma or 251. 9 Ma. The age of 208. 5 Ma corresponds to the oldest fossil of Galeomorphii, the shark superorder including Carcharhiniformes, Heterodontiformes, Lamniformes, and Orectolobiformes (see Supplementary Data S4). The age of 251. 9 Ma corresponds to a safe maximum age for the divergence between Carcharhiniformes and Lamniformes given that there are no galeomorph fossils in the Permian.
Median ages were obtained for each node using 20 trees randomly taken from the post-burn-in trees distribution of the Bayesian dating analysis (Fig. 3 ). According to several authors  27, 49  , divergence times of extant species are equivalent to the times of species origination (Ts), and by definition the time of species extinction (Te) is equal to zero for extant species (i. e. not extinct today). This approach allows the inclusion in a single Ts-Te dataset of all extinct and extant species represented in the fossil record as well as extant species that are not represented in the fossil record. However, it is important to keep in mind that molecular divergence times are not the exact Ts because divergence from the sister species does not equate to the origination of the species (see Discussion).