dataesr/scientific-paragraphs-categorization huggingface dataset:
However, a recently developed transition state search using chemical dynamics simulations (TSSCDS) methodology  [134]  shows promising results for global and automatic location of stationary points of a PES with semiempirical accelerated dynamics simulations and is now implemented in the AutoMeKin code  [135]. Combination of TSSCDS with chemical dynamics simulations (implemented in the VENUS package) has recently allowed for correct prediction of fragmentation patterns involved in the Collision-Induced Dissociation of deprotonated cysteine-S-sulfate  [90]. Another recent implementation, named M 3 C  [136] , introduces entropy maximization as a procedure equivalent to an MD simulation in the infinite integration time limit. This approach has proven to be a successful tool for obtaining fragmentation breakdown curves of, e.
Concluding remarks Action spectroscopy is a very powerful method to explore spectroscopy and dynamics of gas-phase Fig. 12  Excited-state decay (upper figure) and groundstate recovery (lower figure) of GFP chromophore anions, pumped at 480 nm, and probed at 800 nm (for the excited state lifetime) or 480 nm (for the ground state recovery). The data were recorded at 300 K (room temperature) and at 100 K by pre-cooling them in an ion trap. The cooled ions show a much longer decay due to trapping behind a potential energy barrier in the first excited state. Data are taken from  [209]  molecules. We have demonstrated how pump-probe schemes may be combined with detection of timeresolved action in ion-storage rings to reveal internal dynamics of molecules. We have also shown that temperature matters for the dynamics, and we foresee that with new cooling schemes, exciting new photo-physics will be revealed at the many ion-storage rings.
Acknowledgements Work partially supported  Italy-Sweden MAECI  project " Novel molecular tools for the exploration of the nanoworld. "Acknowledgements We thank  Roland Thissen  and  Christian Alcaraz  ( Institut de Chimie Physique, Université Paris-Saclay ) for their help during manuscript elaboration. Acknowledgements This work was supported by the  MINECO  project  CTQ2016-76061-P  and  MICINN  project  PID2019-110091GB-I00. Acknowledgements JK acknowledges the support from a statutory activity subsidy (No. 25/20/B ) from the  Polish Ministry of Science and Higher Education. PLV acknowledges the  Portuguese National Funding Agency FCT  through  Research Grants CEFITEC  ( UIDB/00068/2020 ) and  PTDC/FIS-AQM/31281/2017. Acknowledgements We acknowledge the support of the  Czech Science Foundation  via grants no  19-01159S  and  20-11460S , and  Czech Ministry of Youth, Education and Sports  via grant no  LTC20067.
Acknowledgements We thank  Paul Scheier  for his invaluable support and input. This work was supported by the  EU commission ,  EFRE K-Regio FAENOME-NAL EFRE 2016-4 , the  Austrian Science Fund FWF  ( P31149  and  I4130 ) and the  Swedish Research Council  (Contract No. 2016-06625 ). Acknowledgements We want to thank all colleagues of the electrostatic ion-storage ring community for many interesting and pioneering experiments of which only a small fraction are presented in this brief report. DESIREE is a  Swedish National Infrastructure (Swedish Research Council  contract No. 2017-00621 ). The authors further acknowledge individual  Swedish Research Council  grants ( 2019-04379  and  2018-04092 ) and the support from the  Knut and Alice Wallenberg Foundation  for the project "Probing charge-and masstransfer reactions on the atomic level" ( 2018.
We used all 278,683,802 untrimmed Illumina reads (41. 8 Gb) produced by Genotoul, as recommended by  Zimin et al. (2017). We filtered Nanopore reads using Nanofilt  (De Coster et al. 2018)  to only keep reads longer than 7 kb (3,085,942 reads amounting to 45,6 Gb with an N50 of 17 kb). We then purged haplotigs and heterozygous overlaps from the assembly using the purge_dups pipeline described by  Guan et al. (2020). We used all the default parameters, except for mini-map2, for which we specified that we have ONT reads (xampont), and for get_seqs, where we used the option -e to remove duplications at the ends of the contigs only. We checked for contamination in the assembly using blobtools v1. 1  (Laetsch and Blaxter 2017) , with default parameters. Blobtools requires three inputs: (1) the assembly, (2) a hit file that we generated using our assembly as a query to perform a blastn search (-task megablast, -max_target_seqs 1, -max_hsps 1, -evalue 1e-25) against the NCBI database NT (downloaded in March 2019), and (3) an indexed BAM file that we generated by mapping the trimmed Illumina reads  (Trimmomatic v0.
38; Bolger et al. 2014 ) against the assembly with Bowtie2 v2. 3. 4. 1  (Langmead and Salzberg 2012). We also ran the module "all" of MitoZ v2. 3 in order to assemble the mitogenome, annotate it and visualize it  (Meng et al. 2019). We used the raw Illumina reads as input as recommended by  Meng et al. (2019) , we set all parameters to default and we set the genetic code and clade to invertebrate and Arthropoda, respectively. Once assembled, we used the mitogenome as a query to perform a blastn search against the assembly to identify possible nuclear mitochondrial DNA (NUMTs). We validated the largest of these NUMTs by PCR, using primers covering three nuclear-mitochondrial junctions (junction 1F: CAACACCGATGACATATTG GGT; junction 1R: CGCACACATAAACAATAACGCC; junction 2F: TGAGGGAGAAGGTAAGTCGA; junction 2R: TGAGGAGG CGTATTGAGGTT; junction 4F: GCGGCTCCTCCTAGATTAAATC; junction 4R: ACTCTCCACGACCAAACCTC).
Genome annotation We annotated genes and repeated elements of S. nonagrioides using Maker v2. 31. 10  (Holt and Yandell 2011; Campbell et al. 2014). First, we identified repeated elements de novo with RepeatModeler v2. 0. 1 (https://github. com/Dfam-consortium/ RepeatModeler). We then ran a first round of Maker to (1) mask repeated elements and (2) perform a preliminary gene annotation using the transcriptome of S. nonagrioides  (Glaser et al. 2015)  and the proteomes of three related species: Busseola fusca  (Hardwick et al. 2019) , Spodoptera litura  (Zhu et al. 2018) , and Trichoplusia ni  (Chen et al. 2019). We merged the outputs of this first round into a GFF3 file, which we used to train SNAP, a gene predictor. We then ran a second round of Maker using this first GFF3 file and SNAP. We then trained Augustus, another gene predictor, with the second GFF3 file, generated by the second round of Maker.
Functional annotation We identified putative protein functions by blastp search (-evalue 1e-6 -max_hsps 1 -max_target_seqs 1) using the predicted proteins of S. nonagrioides against the nonredundant database UniProtKB/Swiss-Prot that contains unique proteins. In addition, we identified the GO terms and the conserved domains with InterProScan v5. 46-81. 0. To do this, we ran the 16 analyses proposed by InterProScan, including Pfam.
Data availability The data associated to this article are available on NCBI under the BioProject ID PRJNA680928 and GenBank accession number JADWQK000000000. The BioProject includes the annotated nuclear and mitochondrial assemblies and the raw short and long reads. The data are also available in the DRYAD database at the following address: https://doi. org/10. 5061/dryad. dfn2z3515 and on the Bioinformatics Platform for Agrosystem Arthropods (BIPAA) at the following address: https://bipaa.
armigera genome, the third best assembly of Noctuidae to date (Table  1 ). Our search for contamination using Blobtools revealed that the amount of contaminating DNA present in our purged assembly is likely low. Among the 2552 scaffolds of our purged assembly, we assigned 2507 scaffolds to arthropods, representing 95. 127% of the assembly size. Among the remaining 45 scaffolds, we retrieved no-hit for 25 of them and we assigned the rest to Chordates (2), undefined viruses (15), undefined (2), and Proteobacteria (1). Upon submission of the purged assembly to Genbank, the Proteobacteria scaffold was the only one identified by the NCBI staff as contaminated. It contains an internal 3395bp fragment showing 95% identity to the genome of Escherichia coli (K-12 strain C3026). This fragment is not covered by any Illumina reads so we removed it from our assembly.
4-99. 4% nucleotide identity. We used this 1197-bp sequence as a query to perform a similarity search against GenBank non-redundant proteins and against a custom TE protein database, which yielded no significant alignment. However, this region yielded a 209-bp significant alignment showing 88. 7% identity to a B. mori helitron (Helitron-N1_BM, 266bp long). Given the high nucleotide identity between the wasp and moth sequences (95. 4-99. 4%) and the deep divergence time between hymenopterans and lepidopterans (>300 million years;  Misof et al. 2014) , we infer that this helitron-like sequence has been recently transferred between S. nonagrioides and C. sesamiae. This event adds up to the list of helitrons reported to have undergone HT between parasitoid wasps and lepidopterans  (Thomas et al.
Conclusions and perspectives We have assembled the complete mitochondrial genome and a draft nuclear genome of S. nonagrioides. The nuclear genome is remarkable in that it is the largest noctuid genome sequenced by far, being two to three times larger than the 10 other noctuid genomes available in GenBank as of January 2021. This difference merely stems from a higher repeat content in S. nonagrioides, in line with the known correlation between genome size and the amount of repeated sequences. It will be interesting to decipher the causes of this higher repeat content, by comparing population sizes, mutation rates and the dynamics of TE activity between the various noctuid species. We found no sign of recent HT from the bracovirus circles of C. sesamiae, which is sister to C. typhae, to S. nonagrioides.
Systematic DNA sequencing of cancer samples has highlighted the importance of two aspects of cancer genomics: intra-tumor heterogeneity (ITH) and mutational processes. These two aspects may not always be independent, as different mutational processes could be involved in different stages or regions of the tumor, but existing computational approaches to study them largely ignore this potential dependency. Here, we present CloneSig, a computational method to jointly infer ITH and mutational processes in a tumor from bulksequencing data. Extensive simulations show that CloneSig outperforms current methods for ITH inference and detection of mutational processes when the distribution of mutational signatures changes between clones. Applied to a large cohort of 8,951 tumors with wholeexome sequencing data from The Cancer Genome Atlas, and on a pan-cancer dataset of 2,632 whole-genome sequencing tumor samples from the Pan-Cancer Analysis of Whole Genomes initiative, CloneSig obtains results overall coherent with previous studies.
Second, beyond their frequency in the tumor, SNVs also record traces of the mutational processes active at the time of their occurrence through biases in the sequence patterns at which they arise, as characterized with the concept of mutational signature  11. A mutational signature is a probability distribution over possible mutation types, defined by the nature of the substitution and its trinucleotide sequence context, and reflects exogenous or endogenous causes of mutations. Forty-nine such signatures have been outlined  12  , and are referenced in the COSMIC database, with known or unknown aetiologies. They are sometimes denoted as SBS for single-based substitutions signatures, in opposition to signatures of other genomic alterations. Deciphering signature activities in a tumor sample, and their changes over time, can provide valuable insights about the causes of cancer, the dynamic of tumor evolution and driver events, and finally help us better estimate the patient prognosis and optimize the treatment strategy  2, 5.
We show through extensive simulations, and three independent simulated gold-standard datasets  2, [20] [21] [22]  that CloneSig reaches state-of-theart performance in subclonal reconstruction and signature activity deconvolution from WGS and WES data, in the presence or absence of signature activity variations between clones. We then provide a detailed analysis of 8,951 pancancer WES samples from the Cancer Genome Atlas (TCGA) with CloneSig, and on 2,632 WGS samples from the International Cancer Genome Consortium's Pan-Cancer Analysis of Whole Genomes (PCAWG) cohort  23  , where we recover results coherent with published results on WGS  2, 23, 24  as well as different findings of potential clinical relevance. CloneSig is available as a Python package at https://github. com/judithabk6/clonesig  25.
Performance for subclonal reconstruction. We first assess the ability of CloneSig to correctly reconstruct the subclonal organization of a tumor on simulated data, using four different simulators: (1) the DREAM challenge dataset devised in  20, 21  , consisting of 5 simulated WGS tumors with different sequencing depths; (2) PhylogicSim500, comprising 500 samples generated using the PhylogicNDT method, and (3) SimClone1000, comprising 972 samples generated with the simulator SimClone, both proposed by  2, 22  ; and (4) CloneSigSim, a simulator we propose which simulates data according to the probabilistic graphical model behind CloneSig. The different simulators differ in the underlying simulation models, as well as other features such as the number of mutations simulated (DREAM, PhylogicSim500, Fig.
1  CloneSig analysis of 246 SNVs obtained by WES of a sarcoma sample (patient TCGA-3B-A9HI). The main panel displays all SNVs in 2 dimensions: horizontally the mutation type, which describes the type of substitution together with the flanking nucleotides, and vertically the estimated CCF, as corrected by CloneSig with the estimated mutation multiplicity. From these data CloneSig infers the presence of 2 clones and a number of mutational signatures active in the different clones. Each mutation in the main panel is colored according to the most likely mutational signature according to CloneSig. On the right panel, the CCF histogram is represented and colored with estimated clones, and superimposed with mutational signature density. The bottom panel represents the total mutation type profile.
The changing pattern of mutation types with CCF is clearly visible, illustrating the opportunity for CloneSig to perform joint estimation of ITH and signature activity, while most methods so far explore separately those data, considering solely the CCF histogram in the right panel for ITH analysis, or the mutation profile of the bottom panel to infer mutational processes. and SimClone1000 simulate typical WGS samples while Clone-SigSim simulates both WES and WGS samples), the depth of sequencing, the presence of subclonal copy number events (in DREAM and SimClone1000). Importantly, the SNVs in the DREAM dataset were simulated with a fixed distribution of signature activities across subclones, so this is a case where we do not expect a benefit for CloneSig compared to other methods. The PhylogicSim500 and SimClone1000 datasets were generated without concern about mutational signatures, so we can add a mutation type to each simulated SNV (among the 96 possibilities) using either constant or varying signature activities across clones.
In the unfavorable "constant" scenario, PhylogicNDT has the best score, which may reflect the fact that this particular dataset is simulated according to PhylogicNDT's model and might therefore be biased towards that method by construction. As already observed in Fig. 2 , CloneSig, TrackSig, and TrackSigFreq see their performance increase in the favorable "varying" scenario, which is expected as they can leverage extra information to distinguish clones in cases where signature activity varies between distinct clones. SciClone tends to find a high number of clones, explaining its relatively good score1B for heterogeneous samples with 5 or 6 clones. However, other scores do not have the same positive evolution, Fig. 2  Subclonal reconstruction performance of CloneSig and eight ITH reconstruction methods on simulated data.
Each radar plot summarizes the performance of the nine ITH reconstruction methods according to four performance measures on one set of simulated data. In short, score1B evaluates the number of clones found by the method, score1C the resulting mutation CCF distribution, score2A the co-clustering of mutations in the defined clones, and score2C the classification of subclonal versus clonal mutations. All scores are normalized such that the best performing method lies on the outer circle, and the worst near the center, to enhance visual distinction between methods. We have ensured that all scores are comparable by averaging them only on simulations where all methods successfully produced an estimate under reasonable computation time and memory limits; PyClone results are not shown on the DREAM, PhylogicSim500 and SimClone1000 datasets because it failed to produce an output too often.
We compare CloneSig to the three other methods that perform both ITH and mutational process estimation, namely, TrackSig, TrackSigFreq, and Palimpsest, and add also deconstructSigs  13  in the benchmark, a method that optimizes the mixture of mutational signatures of a sample through multiple linear regressions without performing subclonal reconstruction. Figure  5  shows the performance of the different methods according to the different metrics on the PhylogicSim500 dataset. For Score_sig_1A and Score_sig_1B, there is a clear advantage for CloneSig, TrackSig, and TrackSigFreq over Palimpsest and deconstructSigs in all scenarios except when the number of mutation is the smallest, in which case all methods behave similarly. For Score_sig_1C, CloneSig, TrackSig, and TrackSig-Freq exhibit the best AUC to detect present signatures in all scenarios.
We also present a thorough evaluation of signature activity deconvolution on the CloneSig-Sim, SimClone1000 and DREAM simulated datasets in Supplementary Note 2, which overall confirm the results observed on the PhylogicSim500 dataset. Pan-cancer overview of signature changes. We now use Clo-neSig on real pan-cancer data, to analyze ITH and mutational process changes in 8,951 tumor WES samples from the TCGA cohort spanning 31 cancer types, and in 2,632 WGS samples from 32 cancer types analyzed by the PCAWG initiative which represents the largest dataset of cancer WGS data to date. For each sample in each cohort, we estimate with CloneSig the number of subclones present in the tumor, the signatures active in each subclone, and test for the presence of a significant signature change between clones.
We retained only signatures found variant in more than 10% of each cohort samples. Complete results for the PCAWG samples are presented in a similar way in Supplementary Note 4. Overall, CloneSig detects a significant change in Fig. 5  Comparison of CloneSig, deconstructSigs, Palimpsest, TrackSig, and TrackSigFreq for signature activity deconvolution on the PhylogicSim500 dataset. Several metrics have been implemented, and are detailed in the main text. Scores_sig_1A and 1B are distances between the estimated mutation type profile (as defined by the signature activity proportions) to the true mutation profile (defined using the parameters used for simulations, 1A), and the empirical observed mutation profile (defined using available observed mutations, 1B), and is better when close to 0. Score_sig_1C is the area under the ROC curve for the classification of signatures as active or inactive in the sample, and is better when close to 1.
In prostate cancers, CloneSig identifies both increases and decreases of signatures 3 and 5 activities in the TCGA (PRAD), while only in one direction in the PCAWG (Prost-AdenoCA). Both increases and decreases of those two signature activities were previously reported in an independent cohort of 293 whole-genome sequenced localized prostate tumors  24. In short, despite a few minor differences, the agreement between CloneSig's results on the two cohorts is excellent, and in case of discrepancy the signals found on the larger TCGA cohort seem to be more coherent with current literature than those found on PCAWG. This illustrates the ability of CloneSig to detect patterns of ITH and signature activity change using WES or WGS data, and to benefit from the availability of larger WES cohorts. To further explore the reliability of CloneSig's results on real data, we now compare our findings in the PCAWG to results obtained on the same dataset with two different methods: TrackSig 2 , and the careful distinction of early and late (and clonal and subclonal mutations) in  23.
In most types, like CESC (Supplementary Fig. 41 ), HNSC (Supplementary Fig. 47 ) and others, we observe groups of patients with different patterns of signature activity. The clinical significance of such groups remains to be further explored. Comparable clinical information is not available for the PCAWG cohort. Altogether, we note a good agreement between findings on the TCGA WES and PCAWG WGS datasets using CloneSig, and previous similar analyses of the PCAWG WGS cohort, but also some differences which may be due to several factors including (i) the fact that both cohorts differ in size, patient clinical profiles and treatments, (ii) the fact that the algorithms used for the analysis are different, (iii) the fact that the TCGA cohort focuses on exonic mutations while PCAWG is based on WGS, and (iv) the fact that we and 2 focus on signatures with large activity changes in absolute difference while  23  focuses more on a general description of the cohort, report changes in log-fold change, and go beyond SNVs by integrating doublet-based substitutions (DBS) and small insertions and deletions (ID) signature changes.
CloneSig is a generative probabilistic graphical model that considers somatic mutations as derived from a mixture of clones where different mutational signatures are active. We demonstrated with a thorough simulation study the benefits of the joint inference in detecting ITH, both in WES and WGS samples. We included four different datasets to obtain a thorough evaluation of CloneSig and other ITH methods. Resorting to several datasets is necessary to obtain a complete and fair evaluation of the algorithms in a variety of situations. First, two of those datasets are generated using exactly some of the reconstruction methods: CloneSigSim and PhylogicSim500, respectively, follow the models of CloneSig and PhylogicNDT. The two other datasets do not explicitly follow the model of an evaluated method, but might also be biased towards one or more methods.
Second, the DREAM and Sim-Clone1000 have subclonal copy number events, while the other two datasets have only clonal events, which allows us to further assess the effect of neglecting them in CloneSig and other evaluated methods. Third, the simulated samples from the different datasets highly vary in number of observed SNVs, encompassing characteristics from WES and WGS data. Finally, although some relevant characteristics of our benchmark such as a very low number of observed SNVs are only covered by the CloneSigSim dataset, integration of gold-standard datasets allow us to contribute to establishing evaluation standards for ITH methods, and to facilitate comparison of CloneSig with (future) methods not considered in our study. We showed that CloneSig is competitive with or outperforms state-of-the art ITH methods, even in the absence of signature activity change between the clones, and is particularly efficient for the detection of samples with one or a few subclones.