dataesr/scientific-paragraphs-categorization huggingface dataset:
The median number of fusions per patient detected was 49. 5 fusions (range, 3-223) (Table  1 ). Of those 1,374 fusions, 787 (57. 2%) were intra-chromosomal and 588 (42. 8%) were inter-chromosomal. Only 8% (107/1,374) were already known and annotated in at least one of the databases, suggesting that the majority of fusion transcripts called here are de novo and potentially positive. We then assessed the presence of known oncogenes and TSGs from publicly available databases  [18] [19] [20]  (Figure  1 ). A total of 91/1,374 (7%) fusions were found to contain an oncogene (Table  S2 ) and 137/ 1,374 (11%) a TSG (Table  S3 ). Interestingly, every patient had at least one fusion containing either a known oncogene or TSG (Table  1 ), except patient 45 who had very few fusions detected. Nervous system tumors were the ones with the highest rate of oncogenes (ependymoma, high-grade glioma [HGG], neuroblastoma, medulloblastoma, and diffuse intrinsic pontine glioma [DIPG]), while tumors with the highest number of TSGs were of various origins.
A strong correlation between the total number of fusions and the number of fusions containing an oncogene or a TSG per patient was found (respectively, Spearman coefficient r = 0. 4637, p = 0. 001 and r = 0. 6948, p < 0. 0001). When considering the output of each software independently, deFuse predicted the largest number of fusions followed by TopHat, whereas FusionCatcher software was the most conservative. A large number of fusions were exclusively detected by either deFuse or TopHat (29. 8% and 9. 5%, respectively), and, when combined, these 2 tools shared the highest number of fusion gene overlap (46. 4%) (Figure  2A ). To define a high-confidence group of fusion transcripts, we combined the calls of the three algorithms by considering intersecting events. This identified a set of 118 fusions (9%, 118/1,374), of which 107 (90%) have not been previously reported (Table  S4 ).
Fusions of Potential Therapeutic Interest with Currently Available Drugs 19 fusions belonging to the high-confidence list and affecting 33% (16/48) of patients were identified as potentially targetable or contributing to drug resistance, being thus important for drug choice in the individual patient (Table  3 ). 15 of 19 fusions had a very high oncoscore (>0. 89). The total list contains four well-known disease-defining transcripts as well as two others that have been listed in fusion databases before. The remaining 13 transcripts constitute individual events, occurring in 8 patients. Five transcripts (5/19) occurred in patient 16 with a highly rearranged medulloblastoma, three of which concerned the ERBBsignaling axis through different mechanisms, including two via MGMT. Further, two patients with neuroblastoma (patient 26) and alveolar rhabdomyosarcoma (patient 14), presented two potentially targetable transcripts, respectively.
DISCUSSION With the aim to discover new fusion oncogenes in pediatric patients, we carried out a retrospective study in a cohort of relapsed or resistant patients included in the MOSCATO-01 clinical trial. 14 The in-house analysis package ChimComp was created to reliably detect fusion transcripts in a unique cohort of pediatric patients with solely solid tumors. We primarily concentrated on fusion candidates from the high-confidence group detected by three tools representing 118/1,374 fusion transcripts (8. 6%). To differentiate potential driving from passenger fusions, they were classified into four groups according to their oncoscore values. Interestingly, among them a misbalance in favor of high or low oncoscore (53. 8% and 37%, respectively) was observed when compared to the distribution of all detected fusions.
A total of 42 fusions found by ChimComp underwent qRT-PCR assay. As positive controls, some of the well-known fusion oncogenes detected by ChimComp were also included, namely, C11orf95-RELA, EWSR1-FLI1, and HEY1-NCOA2. A total of 38/42 fusions were successfully validated. For each fusion, the type of chromosomal rearrangement potentially involved, the number of tools detecting the fusion, the number of patients carrying the fusion and their identification numbers, the result of PCR, the oncoscore provided by ChimComp as well as the number of databases reporting the fusion also provided by ChimComp, the primer sequences used, the presence of oncogenes or tumor suppressor genes, and the functional prediction are enlisted. Intra, intrachromosomal; inter, interchromosomal; read number (mean), mean number of junction/spanning reads of all tools.
Ortiz et al. 58  further provided in vivo proof that loss of PTPRD cooperates with CDKN2A/B deletion, which was also found in our patient, and promotes tumorigenesis even if PTPRD is heterozygously inactivated. Next we extracted fusions from the high-confidence group that might be also therapeutically relevant. One-third of analyzed patients displayed a fusion transcript with potential druggability or involved in drug resistance to certain agents, three of which presented more than one of those. To which extent these fusions might have been present at diagnosis or have evolved over time under current treatment Fusion transcripts representing potential targetable alterations by existing anti-cancer drugs were found among the high-confidence list candidates and are listed with the associated patient number, the result of PCR validation, the associated oncoscore provided by ChimComp, the presumptive fusion reconstitution and biological effect, the possible drugs to target the fusion products, the mechanism involved, and the references in literature used.
2] [63]  We also validated three fusions with high oncoscore (TP53-ZNF565, UBE4B-CTNNBIP1, and NF2-KIAA0368) in one patient with osteosarcoma, because their involved genes were linked to the biology of the patient's cancer. To what extent events like these could be causative, occur due to antineoplastic treatment, occur due to progression, or are random bystanders of multiple copy number aberrations still needs to be defined. In conclusion, we established a robust pipeline called ChimComp to detect fusion transcripts in cancer patients with 90. 5% reliability and close to 100%, if fusions with a very low oncoscore were omitted. This might be especially useful in large screenings, as fusions that are found by three tools and are primarily provided with a high oncoscore most likely represent true events in a patient's tumor and can be considered for further investigation.
Patients The cohort comprised 48 pediatric patients with 21 different relapsed or refractory solid neoplasms, which had undergone RNA-seq as part of the Molecular Screening for Cancer Treatment Optimization genomic profiling program (MOSCATO-01, ClinicalTrials. gov: NCT01566019) run at Gustave Roussy. 14, 64 ole-Exome and RNA-Seq Extraction of tumor DNA, RNA, and germline DNA, as well as library preparation, capture and WES, RNA-seq (detailed in the Supplemental Materials and Methods), and CGH array, was performed as part of the MOSCATO-01 clinical trial, as previously published. 14 n the same study, the mutational burden per patient was also calculated from WES data. Bioinformatic analysis was done by IntegraGen and Gustave Roussy Bioinformatics Core. The total number of genomic breakpoints per patient was calculated from the results of the CGH array, which were converted into.
Then, for each fusion detected, an oncogenic potential score was computed by using the Oncofuse algorithm. 12 This results in a score between 0 (non-oncogenic) and 1 (highly oncogenic). In our study, it was calculated for both gene orientations and the highest oncoscore value was considered. The detected fusions were annotated with different fusion gene databases comprising Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer (https://cgap. nci. nih. gov/Chromosomes/Mitelman), TICdb,  65  COSMIC (https://cancer. sanger. ac. uk/cosmic), ChimerDB,  66  and ChiTaRS. 67 All the fusions identified among the cohort were tested for the presence of TKs, oncogenes, or TSGs by comparing our listing of genes to a set of 90 known TK genes, 803 known oncogenes, and 1,017 known human protein-coding TSGs  [18] [19] [20]  (Figure  1 ).
Reconstitution of the Fusion Sequence and Primer Design The breakpoint position in mRNA sequences of both genes was predicted in silico (see the Supplemental Materials and Methods). The sequence around the breakpoint was used to design the primers necessary for qRT-PCR validation. Primers were selected with a size of about 20 nt, a melting temperature of 60 C, and an amplicon size of 100-300 bp, using Oligo Explorer 1. 1. 0 program (Kuopio University, Kuopio, Finland).
Statistical Analysis The numbers of fusion transcripts found in patients are presented as mean ± interquartile range. To compare the number of fusions with mutational load or genomic breakpoint number per patient, Spearman correlation coefficient was determined. To compare the average number of fusions per patient between the different groups of tumor types, Kruskal-Wallis followed by Dunn's tests were used. To compare the percentage of total fusion versus fusions found by 3 tools in the 4 categories of oncoscore, the Z score test was used. A Z score < 1. 96 was considered as significant. All tests were performed by using GraphPad Prism 4 software; p values and adjusted p values < 0. 05 were considered as a statistically significant level.
ACKNOWLEDGMENTS We are grateful to all patients and their parents for participating in the trial. We acknowledge  Catherine Richon  (technical assistance),  Damien Drubay  (biostatistics platform of  Gustave Roussy Institute) , as well as  Marc Gabriel  and  Bastien Job  (bioinformatics core of  Gustave Roussy ). This study was supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( Labex NanoSaclay ,  ANR-10-LABX-0035 ), the  Fondation Gustave Roussy , the  Fondation Philantropia (ACH) , the  Université Paris-Sud (CD) , and the  CNRS.
On the contrary, the resistance of Starling resistor reacts mechanically and automatically to the pressure changes. Bench hardware can be programmed to simulate the artificially composed respiratory scenarios, which generally contain a string of repetitive disordered breathing events, i. e. obstructive and central apneas, obstructive and central hypopneas, inspiratory flow limitation, snoring. Various APAP devices are then connected to the bench simulation. Their pressure responses to the SDB events are recorded, assessed and compared. However, all these benches use airflow from a limited database of breathing airflow short segments recorded in patients and/or artificially designed. Thus, the respiratory scenarios simulated on bench cannot represent completely the physiological variability and chronology of human breathing.
There is a relationship between ΔP mus and non-obstructed breathing airflow _ V cc , dominated by the equation DPmus ¼ À Vcc C À R � _ V cc; ð1Þ in which V cc represents pulmonary instantaneous volume, thus Vcc ¼ R _ V cc dt. The flow produced on the bench ( _ V bench ) was monitored by a pneumotachograph located between the source of positive pressure 4 cmH 2 O and the Starling resistor (Fig  2 ). All signals measured on the bench were sampled via a NI USB-6210 card (data acquisition card, National Instruments, USA) and a custom-developed LabVIEW (National Instruments) V cc is directly assumed identical to _ V source , whereas during obstructive breathing events, the amplitude of _ V cc is hypothesized to be equal to _ V source amplitude of the pre-event 2-minute baseline. (d) Muscular effort pressure calculation, step 6: the respiratory muscular effort pressure (ΔP mus ) is calculated by taking into program at a rate of 20 Hz.
Polygraph signal processing The polygraph data used in our study issued from a one-night demonstration recording by a Nox T3 Sleep Monitor (Nox Medical, Reykjavik, Iceland). On our bench, we reproduced a total of 8 hours and 59 minutes of this breathing profile by integrating polygraph recordings. The corresponding apnea-hypopnea index (AHI) of this recording was 22. 6 events/h with apnea index (AI) = 15. 5 events/h and hypopnea index (HI) = 7. 1 events/h. The signals used in our study were respectively an acceleration signal sampled at 20 Hz, nasal pressure sampled at 200 Hz, thoracic and abdominal Respiratory Inductance Plethysmography (RIP thorax and RIP abdomen) sampled at 25 Hz, RIP flow at 25Hz, pulse oximetry (SpO 2 ) sampled at 3 Hz, and audio volume sampled at 100 Hz. We developed an algorithm with Matlab (MATLAB, Signal Processing Toolbox and Statistics Toolbox Release 2013b, The MathWorks, Inc.
Then nasal pressure, RIP flow, RIP thorax and RIP abdomen were smoothed by a Savitzky-Golay filter  [21]. In our conditions, the filter was constructed by fitting successive sub-sets of adjacent data points in 1 second with a third-degree polynomial function. According to AASM rules  [20] , the nasal airflow modulation with time _ V source (20 Hz) was estimated as the square-root transformation of nasal pressure. Step 2: Automatic segmentation of stable periods by using acceleration signal. The aim of this second step was to avoid body motion artifacts in the respiratory signals. We chose to exclude periods with a high probability of artifact occurrence by using the 3D accelerometer signal (Fig  4(a) ), which reflected body movement at night during recording. This signal was the calculation of the Euclidean norm of 3-axis acceleration.
These molecules can be respectively assigned to a diverse panel of structural classes, including peptides, polyketides, alkaloids, lipids, and terpenes  [10, 11]. These complex metabolites are commonly synthesized on enzymatic assembly lines of non-ribosomal and ribosomal biosynthetic pathways namely the non-ribosomal peptide synthetase (NRPS) and the polyketide synthase (PKS) enzyme systems  [12] , or the ribosomally synthesized and post-translationally modified peptide (RiPP) pathways  [13]. Genome-mining studies based upon the recent increase of genome sequences available in public databases have demonstrated unexpected diversity and greatly expanded the known distribution of these biosynthetic gene clusters across cyanobacteria  [14]. Indeed, genome mining demonstrates that these biosynthetic gene clusters typically encode a large range of auxiliary enzymes that tailor the structure of the secondary metabolites and greatly increase the chemical diversity of the products  [15, 16].
This process compares individual MS/MS fragmentation patterns of each analyte, then constitutes molecular families of structural similarity features, potentially sharing the same biosynthetic origin. This enables the comparison of a high number of samples at once aiding dereplication and tentative structural characterization and constitutes a promising tool especially when integrated with genome information  [21, 22]. Here, we investigated the metabolite chemodiversity of four monoclonal strains of Planktothrix, prominent freshwater bloom-forming cyanobacteria in lakes and reservoirs producing a large set of various bioactive metabolites  [23]. To be the most exhaustive and accurate as possible, we performed an integrated global metabolomic and genomic approach, taking advantage of gene and chemical feature analyses with publicly available databases and prediction tools.
e. , Planktothrix rubescens/agardhii) morphotypes was observed (Figure  S1 ), but the clustering confirms the separation of the planktonic from the benthic Planktothrix eco-species as previously observed  [25]. Overall, the genomic comparison between these four strains highlights their global gene content similarities (Figure  S2 ). The four Planktothrix genomes contain distinct backgrounds in terms of biosynthetic gene clusters (Table  1 ). Up to 11 gene clusters were detected and represented 1%-2% of the whole genome size. In total, three genes clusters coding for three RiPPs and eight genes cluster coding for NRPS and/or PKS were found in these four genomes (Table  1 ). Seven of these encode the synthesis of already-described metabolite families, including prenylagaramides (pag), microviridins (mdn), anabaenopeptins (apt), cyanopeptolins (oci/mcn), aeruginosins (aer), microcystins (mcy), and microginins (mic).
The shared gene clusters of these known compounds were highly conserved (between 92. 0% and 99. 4% of amino-acid sequences identity-Table  1 ). In all four strains, the apt, mic, and oci genes appeared to be co-localized in a ~70 kb genomic island, as it had been previously showed in P. rubescens NIVA CYA 98  [25, 26]. In addition to the seven gene clusters corresponding to already-described metabolite families (i. e. , pag, mdn, apt, oci, aer, mcy, and mic). The gene clusters for apt, mdn, mcn/oci were forming similar genomic islands in the strain PCC 10110 than in the three other strains, as recently described  [25]. Four other gene clusters encode enzymes for so-far unidentified products (arbitrarily called R1, PNL1, PNL2, and PNL3). Their respective sequences, although presenting the signature of RiPP, NRPS, and/or PKS genes identified by antiSMASH  [27] , do not exhibit significant similarity in the database.
Correspondences with standard molecules from cyanobacteria similarly analyzed or with components from the fragmentation pattern library available from the GNPS server are indicated by heavy red and black lines, respectively. Only clusters of at least two nodes are represented. Table  2. Distribution and diversity of metabolite produced by the four Planktothrix strains according to metabolomic dataset investigated with GNPS (Figure  2 ). Green "YES" and red "NO" indicate when the gene cluster and the corresponding metabolite are present, or not, respectively. "N. D. " indicates when no metabolite was detected despite the presence of its specific genetic information in the respective genome. In contrast, prenylagaramides were only detected in PCC 7805, PCC 7821, and PCC 10110 and microviridins in NIVA CYA 126/8 (Table  2 ), while the genome analysis predicted a potential synthesis of both families (both being cyclic peptides produced by RiPP pathways) for all the four studied strains (Table  1 ).
Microcystins Some of the cyanobacterial metabolites, such as microcystins, are subject to intensive monitoring due to their role in animal toxicoses  [3]. Microcystins are cyclic hepta-peptides that have been firstly described from Microcystis and Planktothrix  [19, 45, 48]. They are characterized by the presence of a non-proteinaceous amino acid in position 5 (Adda), two amino acids derived from Asp and Glu in position 3 and 6, respectively, and two very variable positions (2 and 4), that serve as reference to name the variant. More than 240 variants have been described so far  [49] , 215 being references in the microcystin database Toxinmasslist_com_v15b  [50]. Two main microcystin molecular clusters were highlighted in the GNPS network. Their identification as microcystin variants was confirmed by the direct matching of two of them according to microcystin standards ([Asp3]-Mcyst-LR and Mcyst-YR) (Figure  2 ).
Microcystins are cyclic hepta-peptides that have been firstly described from Microcystis and Planktothrix  [19, 45, 48]. They are characterized by the presence of a non-proteinaceous amino acid in position 5 (Adda), two amino acids derived from Asp and Glu in position 3 and 6, respectively, and two very variable positions (2 and 4), that serve as reference to name the variant. More than 240 variants have been described so far  [49] , 215 being references in the microcystin database Toxinmasslist_com_v15b  [50]. Two main microcystin molecular clusters were highlighted in the GNPS network. Their identification as microcystin variants was confirmed by the direct matching of two of them according to microcystin standards ([Asp3]-Mcyst-LR and Mcyst-YR) (Figure  2 ). In these two microcystin clusters, other various ions presented a match between their respective masses and those of microcystin variants previously described [50], while the others, not presenting any match with known microcystin variants may correspond to potential new microcystin variants (e.
Global Capability to Annotate Cyanobacterial Metabolites On the one hand, the potential of secondary metabolite production of the four Planktothrix strains was investigated by genome mining approach using RiPP, NRPS, and/or PKS gene cluster search software antiSMASH 4. 0 (Table  1 ). This investigation suggests that various metabolite families (i. e. , microcystins, anabaenopeptins, aeruginosins, cyanopeptolins, microviridins, microviridins, and prenylagaramides) could be synthetized by these different strains. On the other hand, the molecular diversity of the produced metabolites was investigated by high-resolution mass spectrometry-based analyses. In this approach, molecules could be identified according to their molecular formula (estimated according to their accurate mass and isotopic pattern), their retention time, and the presence of qualifying ions in their MS/MS fragmentation spectra.
Such direct identifications (referred here as "gold" annotations) are supported by a specific database of   A, B ). Correspondences with standard aeruginosins similarly analyzed or with components, whose fragmentation patterns are available in library of the GNPS server, are indicated by heavy red and black lines, respectively. The color code is similar to the one in Figure  1. Selection of isotopic pattern of various aeruginosins observed in the strains PCC 7805 (C), PCC 7821 (D), and PCC 10110 (E), respectively. Note that these isotopic patterns indicate the presence of mono-or di-chlorinations exclusively for aeruginosins of the PCC 7805 strain. The +34 Da mass shift between potentially non-, mono-, and di-chlorinated molecules is indicated in red.
Such direct identifications (referred here as "gold" annotations) are supported by a specific database of analytical standard molecules. The whole analysis of MS/MS spectra by GNPS molecular networking gave the opportunity to propose even more annotations for the molecules whose analytical standards are lacking (that is the case of most of the cyanobacterial metabolites), according to a logical annotation-by-propagation principle. Indeed, the analytes that belong to a molecular cluster that present a match with a standard can be annotated by extension, as "silver" annotation, when the molecule presents a mass that corresponds to an already known cyanobacterial metabolite  [41]  or as "bronze" annotation when no known metabolite corresponds, suggesting this analyte concerns a potentially new variant.
We thus recommend a first screening with genome mining in order to select the cyanobacterial strains of interest based on their secondary metabolite production potential. In a second step, the effective production of secondary metabolites can be characterized in order to verify the prediction based on genome mining. For that commercial standards, databases on fragmentation mass spectra, and molecular networking analyses can be used together in order to obtain a precise description of the secondary metabolite diversity. In the near future, we expect an even deeper global metabolome characterization thanks to chemoand bio-informatic tools that are currently in development and aim at better predicting the structure of novel analogues by in silico MS/MS fragmentation  [59, 60] , the de novo sequencing of circularized and modified peptides  [61] , or the structure prediction of biosynthetic products based on cluster gene sequences  [27].