dataesr/scientific-paragraphs-categorization huggingface dataset:
are selected by a new function of the PyRSM python package 5  , which regroups the different functions of the Auto-RSM framework. This function studies the evolution of the contrast at different angular separations when modifying the number of principal components, ranks, or tolerance. The upper boundary of the considered ranges is defined as the value for which the contrast, averaged over the different angular separations, reaches a peak. In the case of APCA, NMF, LLSG, and KLIP, the obtained ranges were divided in two equal size ranges, to form two separate models. This should provide more diversity to the RSM algorithm and increase the framework's performance as planetary signals and residual speckle noise evolve differently with the number of principal components used to generate the reference PSF.
We also observed a higher degree of dissimilarity between the parametrisations of these 15 targets, especially for the PSF-subtraction techniques parameters. These results highlight the limits of a clustering approach based solely on the parameters characterising the ADI sequence, when facing noisier samples. They also demonstrate the necessity to adopt an empirical approach, such as the Auto-RSM optimisation framework, to optimise the parametrisation when the samples noise structure cannot be well captured by the set of ADI sequence characteristics. However, the low residual noise level in the detection maps shown in Figs. B. 1-B. 3, as well as the large fraction of the survey dataset (70%) that did not require the use of Auto-RSM, still favour the use of a limited number of optimal parameter sets computed for well chosen targets.
Identification of planetary candidates Figure  3  presents the two ADI sequences containing a signal above the previously defined threshold of 0. 05, after having applied Auto-RSM on the 16 sequences for which a signal was previously detected. The two ADI sequences contain already known targets, with HD 206893 B identified in  Milli et al. (2016) , and the debris disk from HD 114082 in  Wahhaj et al. (2016)  which includes also two background stars. In the rest of the section, we propose a new way to extract the photometry and astrometry of point-like sources based on the RSM framework, and apply it to these two datasets.
5 FWHM on either sides of the expected target position. We did not consider the entire annulus, as local features may be observed in the detection map, leading to a potential bias. We also considered replacing the PSO minimisation by a Bayesian optimisation. We tested these different versions along with the NEGFC function provided by the VIP package (Gomez  Gonzalez et al. 2017) , which relies on Nelder-Mead minimisation. We based our performance comparison on the ADI sequence obtained on HD 3003, considering an intermediate angular separation of 4λ/D. We injected fake companions at eight different azimuths and considered eight different contrasts ranging from 1 × 10 -5 to 8 × 10 -5. This range goes from a non-detection in a traditional S/N map (a detection just above the background with the RSM map) to a very bright planetary signal.
Influence of environmental parameters We perform a similar correlation analysis as the one made in Appendix A, but focusing here on the relationships existing between the parameters characterising the ADI sequences and the performance in terms of achievable contrast. We start by re-expressing every contrast curve in terms of magnitude and average these magnitudes over the set of considered angular distances. We then compute the Pearson correlations between the parameters characterising the ADI sequences and the median contrast, considering the entire SHARDDS dataset. As can be seen from Fig. 10 , the raw contrast at 500 mas, the Strehl and the WDH asymmetry show relatively high correlations and have the expected sign. A higher asymmetry of the WDH is indeed more difficult to treat by the PSF-subtraction techniques, which do not cope well with anisotropy in the speckle field.
2021b) , we decided to rely on clustering to reduce the number of required optimisations. We divided our dataset into eight clusters using K-means clustering algorithm, based on parameters characterising the ADI sequence itself and the related observing conditions. For each cluster, the most representative ADI sequence was selected and the Auto-RSM framework was applied on it. The generated set of optimal parameters for both the PSF-subtraction techniques and the RSM algorithm was then used to generate detection maps for all the ADI sequences contained in the cluster. The analysis of the obtained detection maps showed the presence of a higher number of bright speckles when reversing the parallactic angles, providing an important reminder that care should be taken when computing detection thresholds based on reversed parallactic angles.
(2021b). The impact of the dissimilarities in the optimal parametrisations on the performance in terms of achievable contrast is further investigated in Section5. We now turn to the analysis of the relationship existing between the parameters that we selected in Section 3. 3 to describe our dataset and the parametrisations of the PSFsubtraction techniques 15. We computed the Pearson correlation between the ten parameters characterising our sample and the PSF-subtraction techniques parameters, considering the eight cluster centroids as data-points. The raw correlations show a significant correlation between these sets of parameters, with overall, around 25% of the obtained values over 0. 5. Table  A. 1 gives the absolute values of 13 For centroid A with 20 principal components for APCA and centroid B with 24 principal components, the normalised distance is equal to 4/22 = 0.
Acknowledgements. We dedicate this work to our friend an colleague  Matthew Willson  who tragically passed away on 18 January 2022. He will be missed by his family, friends, and colleagues. We thank  Tim Pearce  for sharing his planet mass and orbital parameters dataset. This work was supported by the  Fonds de la Recherche Scientifique -FNRS  under Grant n • F. 4504. 18 and by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement n •  819155 ). This work has made use of the SPHERE Data Centre, jointly operated by  OSUG/IPAG (Grenoble), PYTHEAS/LAM/CeSAM (Marseille) ,  OCA/Lagrange (Nice) ,  Observatoire de Paris/LESIA (Paris) , and  Observatoire de Lyon/CRAL , and supported by a grant from  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ).
Meiotic recombination is an essential biological process that ensures faithful chromosome segregation and promotes parental allele shuffling. Tetrad analysis is a powerful approach to quantify the genetic makeups and recombination landscapes of meiotic products. Here we present RecombineX (https://github. com/yjx1217/RecombineX), a generalized computational framework that automates the full workflow of marker identification, gamete genotyping, and tetrad-based recombination profiling based on any organism or genetic background with batch processing capability. Aside from conventional reference-based analysis, RecombineX can also perform analysis based on parental genome assemblies, which facilitates analyzing meiotic recombination landscapes in their native genomic contexts. Additional features such as copy number variation profiling and missing genotype inference further enhance downstream analysis.
RecombineX also includes a dedicate module for simulating the genomes and reads of recombinant tetrads, which enables finetuned simulation-based hypothesis testing. This simulation module revealed the power and accuracy of RecombineX even when analyzing tetrads with very low sequencing depths (e. g. , 1-2X). Tetrad sequencing data from the budding yeast Saccharomyces cerevisiae and green alga Chlamydomonas reinhardtii were further used to demonstrate the accuracy and robustness of RecombineX for organisms with both small and large genomes, manifesting RecombineX as an all-around one stop solution for future tetrad analysis. Interestingly, our re-analysis of the budding yeast tetrad sequencing data with RecombineX and Oxford Nanopore sequencing revealed two unusual structural rearrangement events that were not noticed before, which exemplify the occasional genome instability triggered by meiosis.
While powerful statistical inferences regarding recombination can be made in this way with existing genomic data, additional factors such as demographic histories and selection schemes might perplex the result interpretation. In contrast, meiotic recombination can also be studied by directly examining the makeup of gamete genotypes in terms of parental genetic backgrounds. One way of doing this is to perform bulk genotyping analysis for random gametes  [5] [6] [7] [8]. Although this approach allows for detailed delineation of cumulative recombination landscapes across a large number of gametes, it lacks the power and resolution for analyzing individual meioses, which prevents an indepth view of the meiotic recombination process. Alternatively, at least for a selection of corresponding genome assemblies of the gametes AND1702-8:a and AND1702-12:a have been deposited to the SRA database under the accession number of PRJNA698424.
In addition, another copy of the genome assembly of AND1702-8:a and AND1702-12:a together with the corresponding genome annotations have been uploaded to RecombineX's GitHub depository under the data subfolder (https://github. com/yjx1217/ RecombineX/tree/master/data). The key raw data are uploaded to the Research Deposit public platform (www. researchdata. org. cn), with the approval RDD number of RDDB2021509656. The underlying numerical data for all the graphs presented in the manuscript is in the Supporting Information. model systems, it is feasible to isolate and genotype all four meiotic products (i. e. , tetrad) derived from a single meiosis. This approach is called "tetrad analysis", which remains the most straightforward and powerful way of studying meiotic recombination and its modulators at fine scales.
The general design of RecombineX RecombineX is a Linux-based computational framework designed for automated highthroughput tetrad analysis. It is self-contained by design and can be automatically installed and configured via a pre-shipped installer script. RecombineX comes with a series of task-specific modules handling different workflow phases: genome and read preparation -> parental marker identification -> gamete read mapping and genotyping -> tetrad-based recombination event profiling (Fig  1 ). Depending on the available input data, RecombineX can be executed in two modes: 1) the reference-based mode and 2) the parent-based mode. The reference-based mode requires a reference genome assembly as well as the short reads of the two crossing parents. The parent-based mode requires the native genome assemblies of both crossing parents, while the short reads of the two crossing parents can be further used when available (S1 Fig).
Both reference-based mode and parent-based mode further need short reads of individual gametes, ideally labeled based on their original tetrad contexts. For each mode, we numbered the corresponding modules based on their execution orders, ensuring a wellorganized data analysis workflow. Within each module, a task-specific executable bash script is provided for invoking the corresponding module. A template directory with these modules pre-configured is further provided as a testing example, which can be easily adapted for users' own project (S2 Fig). In the reference-based mode, parent reads are mapped to the reference genome for reference-based parental marker identification (①), based on which gamete genotyping is further performed by evaluating the gamete-to-reference read mapping support at each marker position (④).
Simulation-based validation for RecombineX's gamete genotyping module To assess RecombineX's gamete genotyping accuracy, we used RecombineX's built-in simulation module to simulate one recombinant tetrad derived from two hypothetical crossing parents: P1 and P2. The inputs of this simulation module include a reference or parent genome to set up the coordinate system and a list of parental markers projected to the same coordinate system. Here we used the S. cerevisiae reference genome as the coordinate system and the above identified 49,153 reference-based markers based on 100X parent reads (Fig  2  and S1  Table). For the simulated recombinant tetrads, we introduced CO and NCO events based on the count and size parameters estimated from real yeast tetrads  [9]  (Fig  3A ). For the four resulting recombinant gametes, sequencing reads of varied depths (1X, 2X, 4X, 8X, 16X, 32X, 64X) were further simulated.
With this simulated dataset, we evaluated RecombineX's genotyping performance for mimicked scenarios of both fully and partially viable tetrads. For the scenario of fully viable tetrad, the simulated reads of all four gametes were used for genotyping  (Fig 3B). For the scenario of a partially viable tetrad, the simulated reads of only 3 gametes were used, leaving the remaining one to be inferred by RecombineX (Fig 3C  and 3D ). Recom-bineX infers missing genotypes by assuming a tetrad-wide 2:2 segregation ratio between the two parental genotypes across the whole genome. While such ratio can deviate from 2:2 in genomic regions with GC tracts, the cumulative size of all GC tracts is typically orders of magnitude smaller in comparison to the genome size, making this assumption hold in general.
Additional features such as copy number variation profiling and missing genotype inference further extends its usage for various downstream analyses. Finally, RecombineX also ships with a dedicate module for simulating the genomes and reads of recombinant tetrads, which enables fine-tuned simulation-based hypothesis testing. This simulation module revealed the power and accuracy of RecombineX even when analyzing tetrads with very low sequencing depths (e. g. , 1-2X). Tetrad sequencing data from the budding yeast Saccharomyces cerevisiae and green alga Chlamydomonas reinhardtii were further used to demonstrate the accuracy and robustness of RecombineX in tetrad analysis for organisms with both small and large genomes. As demonstrated in these examples, RecombineX unifies different functional modules under an integrated framework and provides a generalized one-stop solution for tetrad analysis.
Software Prerequisites RecombineX is designed for a desktop or computing server running an x86-64-bit Linux operating system. Multithreaded processors are preferred to speed up the process since some timeconsuming steps can be configured to use multiple threads in parallel. A stable internet connection is required for its installation. A number of standard Linux software compilation prerequisites are listed as below. • bash (https://www. gnu. org/software/bash/) • bzip2 and libbz2-dev (http://www. bzip. org/) • gcc and g++ (https://gcc. gnu. org/) • git (https://git-scm. com/) • GNU make (https://www. gnu. org/software/make/) • gzip (https://www. gnu. org/software/gzip/) • libopenssl-devel • libcurl-devel • java runtime environment (JRE) v1. 8. 0 (https://www. java. com) • perl v5. 12 or newer (https://www.
perl. org/) • tar (https://www. gnu. org/software/tar/) • unzip (http://infozip. sourceforge. net/UnZip. html) • wget (https://www. gnu. org/software/wget/) • zlib and zlib-devel (https://zlib. net/) • xz and xz-devel (https://tukaani. org/xz/) features are further indicated. The local genotype landscapes of the AND1702-8:a genome are shown on the left along the Y-axis, with the red and blue colors denoting the S288C and SK1 genotypes respectively. The duplicated region is colored in light grey and indicated with the "dup" label. (C) The gene synteny correspondence between AND1702-8:a and its crossing parents (S288C in red and SK1 in blue), with the red and blue shades representing different degrees of sequence similarity. https://doi. org/10. 1371/journal. pgen. 1010047. g007.
38; options: PE -phred 33 ILLUMINACLIP:Tru-Seq3-PE-2. fa:2:30:10 SLIDINGWINDOW:5:20 MINLEN:36) to trim off adapter contamination and low quality bases. The trimmed reads are mapped to the preprocessed reference genome with bwa  [38]  (version: 0. 7. 17; option: mem). The resulting read mapping bam file is further filtered with samtools  [33]  (options: -q 30 -F 3340 -f 2) to only retain uniquely aligned and properly paired reads. Based on the filtered bam file, Picard Tools (https:// broadinstitute. github. io/picard/) (version: 2. 19. 0) is used for sorting, mate information fixing, PCR duplicates removal, and indexing. Afterwards, GATK3  [39]  (version: 3. 6-6) is used for performing local read mapping around Indels for better accuracy. Based on the GATK3-realigned bam file, samtools is used again for mpileup file production (options: mpileup -C 0 -q 30), mapping depth calculation (options: depth -aa), and mapping summary statistics calculation (option: flagstat).
The filtered SNVs derived from both parents are compared to each other to generate a candidate reference-based marker set comprising SNVs segregated between the two parents. Such candidate marker set is further leveraged with the mpileup file generated during read mapping to control for potential false negative and false positive from SNV calling. Finally, RecombineX pre-shipped with several plotting scripts in R to generate graphic reports on CNV and marker distribution along the reference genome coordinates. Parent-based parental marker identification. Parent-based parental marker identification can be performed in two strategies: the genome-alignment-based marker identification and consensus-based marker identification. For the genome-alignment-based marker identification, RecombineX employs mummer3  [44]  (version: 3.
For both raw and inferred genotyping results, RecombineX will make both tetrad-based and batch-based genotyping plots with pre-shipped R scripts to visualize the composition and segregation of two parental genetic backgrounds across the genome. Parent-based gamete read mapping and genotyping. The protocol used by Recombi-neX for parent-based gamete read mapping and genotyping is largely the same as that used for reference-based gamete read mapping and genotyping, except for now the analyses are separately performed based on both parental genome assemblies. Therefore, after reads mapping, two genotyping results will be obtained separately, each based on the genome space of a single parent. These two genotyping calls will be cross validated with each other. Those markers with conflicted genotyping calls will be ignored for downstream analysis (i.
Simulation based analysis Genome and reads simulation for parental marker identification. To evaluate the performance of parental marker identification, a pair of hypothetical crossing parents, P1 and P2, were simulated for this study. The genome of P1 is an exact copy of the budding yeast S. cerevisiae reference genome (version: R64-2-1_20150113) retrieved from Saccharomyces Genome Database (SGD) with the mitochondrial genome excluded. Based on the same reference genome, the genome of P2 was further generated by simuG  [46]  (GitHub commit version: 212ea1f) in a two-pass manner to randomly introduce 60,000 SNVs and 6,000 Indels (options: -refseq SGDref. genome. fa -snp_count 60000 -titv_ratio 2. 0 -indel_count 6000 -seed 20190518 -prefix yeast_60kSNP_6kINDEL) as well as six CNVs (options: -refseq yeast_60kSNP_6kIN-DEL.
Real tetrad-based analyses Two real tetrad sequencing datasets retrieved from previous studies  [16, 26]  were used to run the full workflow of RecombineX. The first dataset includes five tetrads derived from the budding yeast S. cerevisiae cross S288C x SK1, for which both reference-based and parent-based analyses were performed (S9 and S10 Tables). The SGD yeast reference genome (version: R64-2-1_20150113) was used for the reference-based analysis, while our previously generated longread-based genome assemblies for S288C and SK1  [27]  were used for the parent-based analysis. The second dataset includes five tetrads derived from the green alga C. reinhardtii cross: CC408 x CC2936 (S9 and S10 Tables), for which only reference-based analysis was performed. For this we retrieved the alga C.
reinhardtii (v5. 5) reference genome from Ensembl Plants (https://plants. ensembl. org). For both yeast and green alga datasets, a Q net cutoff of 50 was used and no adjacent recombination event merging was applied. The RecombineX profiled recombination events were compared with the events reported in the original studies, which were retrieved via the following links respectively: yeast tetrads: http://dx. doi. org/10. 5061/dryad. g6s2k green alga tetrads: https://figshare. com/s/a95156f0ed5272b9109e In our comparison, two events were considered "match" only if they completely agreed with each other in event types, genomic locations, and involved gametes. For events that were identified by RecombineX or the original studies alone, we further examined the gamete read alignment in the IGV browser  [28]  (version: 2.
Three successive rounds of long-read-based polishing are performed by Racon  [48]  (version: 1. 4. 7) and Medaka (https://github. com/nanoporetech/medaka) (version: 0. 8. 1; options: -m r941_flip235). Another three successive rounds of short-read-based polishing are performed by Pilon  [49]  (version:1. 23;-fix snps,indels). The polished assembly is further processed with Ragout  [50]  (version: 2. 2) and circulator  [51]  (version: 1. 5. 5; option: fixstart-genes_fa ATP6. cds. fa-min_id 90) for reference-based scaffolding and mitochondrial assembly improvement. The resulting final nuclear and mitochondrial genome assemblies are further annotated by Maker3  [52]  (version: 3. 00. 0-beta) and Mfannot (https://github. com/BFL-lab/Mfannot) (version: 1. 35) respectively, with additional reference-based gene orthology identification performed by Proteinortho  [53]  (version: 5.
Acknowledgments We thank  Dr. Johan Hallin  ( Gothenburg University, Gothenburg, Sweden ) and  Ms. Jessica Chevallier  ( CRCM, Marseille, France ) for beta testing and valuable discussion. We thank  Dr. Alain Nicolas  ( Institut Curie, Paris, France ) and  Ms. Sophie Loeillet  ( Institut Curie, Paris, France ) for locating and sharing the original gamete samples of AND1702-8:a and  AND1702-12 :a.
This work is supported by  National Natural Science Foundation of China  ( 32070592  to J. -X. Y. and  32000395  to J. L. ),  Guangdong Basic and Applied Basic Research Foundation  ( 2019A1515110762  to J. -X. Y),  Guangdong Pearl River Talents Program  ( 2019QN01Y183  to J. -X. Y),  Microsoft   Azure Research Award  (CRM: 074871  to J. -X. Y. ),  ANR  ( ANR-15-IDEX-01  and  ANR-20-CE13-0010  to G. L. ;  ANR-18-CE12-0013  to B. L. ),  Fondation pour la Recherche Medicale  ( EQU202003010413  to G. L), and  Guangzhou Municipal Science and Technology Bureau  ( 202102020938  to J. L. ), respectively. The funders have not played any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Pre-messenger RNA splicing is initiated with the recognition of a single-nucleotide intronic branchpoint (BP) within a BP motif by spliceosome elements. Forty-eight rare variants in 43 human genes have been reported to alter splicing and cause disease by disrupting BP. However, until now, no computational approach was available to efficiently detect such variants in massively parallel sequencing data. We established a comprehensive human genome-wide BP database by integrating existing BP data and generating new BP data from RNA sequencing of lariat debranching enzyme DBR1mutated patients and from machine-learning predictions. We characterized multiple features of BP in major and minor introns and found that BP and BP-2 (two nucleotides upstream of BP) positions exhibit a lower rate of variation in human populations and higher evolutionary conservation than the intronic background, while being comparable to the exonic background.