dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was supported by  UK Biotechnology and Biological Sciences Research Council (BBSRC)  grants  BB/M005690/1 ,  BB/P02114X/1  and  BB/W014483/1 , and a  Volkswagen Foundation " Life? Initiative" Grant  ( Ref. 94 771 ) to YC. DS is supported by the  Max Planck Society  in the framework of the  MaxGENESYS  project. RSKW, at the  University of Edinburgh , was supported by  EPSRC  studentship ( 1419736 ) and the  Bill and Melinda Gates Foundation. RSKW, as part the Yeast 2. 0 initiative at  Macquarie University , is financially supported by  Bioplatforms Australia , the  New South Wales (NSW) Chief Scientist and Engineer , and the  Australian Research Council (ARC) Centre of Excellence in Synthetic Biology. FA is supported by a grant from the  Spanish Ministry of Science and Innovation  ( PID2020-118423GB-I00 ).
Data and code availability • WGS data, tRNAseq data, RNAseq data, nucleosome positioning data and Hi-C data is deposited at SRA with BioProject number PRJNA884518 and are publicly available as of the date of publication. • Proteome data are deposited at PRIDE and will be publicly available as of the date of publication. • Code for assigning tRNA genes to flanking sequences is deposited at GitHub and will be publicly available as of the date of publication. • Any additional information required to reanalyze the data reported in this study is available from the lead contact upon request.
Coverage analysis indicate a -1 aneupoidy for chrI in YCy2676 and a +1 aneuploidy for chrXIII in YCy2677. Notably, the strains containing the tRNA neochromosome linearized at suboptimal positions (YCy2674 to YCy2678) show alterations in tRNA neochromosome coverage, however no size alteration were detected during size analysis (cf. Figure3). There are no segmental coverage alterations specific for an individual strain. Observed peaks correspond to known variations such as CUP1-1/CUP1-2 in chrVIII, known to have variable number of tandem repeats and can further be present as extrachromosomal circular DNA (eccDNA). Plots are generated using Circos (Krzywinski et al. , 2009). Sequencing data is available at Bioproject PRJNA884518.
)" in the table) and 4. The numerical polynomial systems solver Bertini  (Bates et al. , 2013)  which we ran on each system at hand by requesting a witness set decomposition into irreducible components with fixed precision set to Bertini's default value. The implementation of our algorithms is itself done in Oscar which is written in the programming language Julia  (Bezanson et al. , 2017). Its source code is available at https://github. com/RafaelDavidMohr/Decomp. jl For all necessary Gröbner basis computations we employ the library msolve  (Berthomieu et al. , 2021)  for which Oscar offers an interface. Our suite of example systems is comprised as follows: 1. Cyclic(8), coming from the classical Cyclic(n) benchmark. 2. The systems P4L 1 to 3 come from the perspective-four line problem in robotics, see  García Fontán et al.
8. All remaining examples are part of the BPAS library  (Asadi et al. , 2021). The BPAS library offers an alternative to the RegularChains library in Maple with special emphasis on paralellism and it will be interesting to compare it to our algorithm in the future. To obtain the timings in the table below we almost exclusively used the witness set based data structure for affine cells. Every polynomial system was computed with in characteristic 65521 with the exception of Bertini which, as a numerical piece of software, computes over the complex numbers. Due to this difference a comparison between Bertini's and our timings needs to be considered carefully. We tried to indicate this in the table below by coloring the Bertini column in grey. All computations except for Magma were done on an single core of an Intel Xeon Gold 6244 CPU @ 3.
Acknowledgements The authors wish to thank  Marc Moreno Maza  for providing feedback on the BPAS library and its benchmark examples. Additionally the authors thank the two anonymous reviewers for their helpful comments and careful reading of this manuscript. This work has been supported by the  Agence nationale de la recherche (ANR) , grant agreements  ANR-18-CE33-0011  ( SESAME ),  ANR-19-CE40-0018  ( Re Rerum Natura ); by the  joint   ANR-Austrian Science Fund FWF  grant agreements  ANR-19-CE48-0015  ( ECARP ) and  ANR-22-CE91-0007  (EAGLES); by the  EOARD-AFOSR  grant agreement  FA8665-20-1-7029 ; by the  DFG  Sonderforschungsbereich  TRR 195 ; the Forschungsinitiative Rheinland-Pfalz; and by the  European Research Council (ERC)  under the  European Union 's  Horizon Europe research and innovation programme , grant agreement  101040794  ( 10000 DIGITS ).
They are capable of representing tens to hundreds of human haplotypes simultaneously. Variations graphs use a sequence graph and a list of paths to store input haplotypes, while de Bruijn graphs store all haplotype k-mers annotated by their haplotype(s) of origin. Scaling pangenome graph data structures to store hundreds of genomes is a challenge that requires significant computational resources and engineering efforts. Many software tools have been created, here we briefly describe major ones. Pantools  [10]  and Bifrost  [11]  are two methods that have been developed to generate pangenomes for analysis on large collections of genomes, mostly for applications in phylogenetics and bacterial genomics. The PanGenome Graph Builder (pggb)  [12] , Minigraph-Cactus and TwoPaCo  [13]  are methods for building general-purpose pangenome graphs.
Results In this article, we provide a comprehensive view of whole-genome human pangenomics through the lens of five methods that each implement a different graph data structure: Bifrost, mdbg, Minigraph, Minigraph-Cactus, and pggb. We examine several features of pangenome graphs, in particular their scalability and how they represent genetic diversity. To this end, we collected all publicly available high-quality human haplotypes and attempted to construct pangenomes of various complexity with each selected tool. Although vg has been widely used as the basis of relevant pangenomebased discoveries, for example on fast and accurate short read mapping  [4] , we decided to not consider it in our analysis for two main reasons: the bias introduced by the reference sequence that is used as the backbone of the graph (and associated to the vcf ) together with the limited capacity of this method to integrate structural variations from many genomes.
Scalability and characteristics of pangenome graph construction tools We ran the above five tools on three datasets consisting of 2, 10, and 104 human haplotypes, respectively (Table  3 ). We compared the computational performance of construction algorithms as well as characteristics of the produced pangenome graphs. The goal is to assess the ability of each method to scale to data available in the near future, i. e. , thousands or even millions of human genomes  [5]. The performance of each tool is evaluated in terms of running time, peak memory, disk space required by the output data structure (graph and annotations). We also compared the number of nodes, edges and connected components as indicators of the complexity of the graph. Results are displayed in Table  1. In terms of running time, mdbg is two orders of magnitude faster than other tools on all considered datasets, taking around two minutes on the H2 dataset and half an hour on H104.
Different tools yield different pangenome graphs topologies Graph metrics such as the number of nodes, edges and connected components provide useful insights on the level of detail of the represented variations and on the complexity and accessibility of the information inside the pangenome. The number of graph nodes varies between 17,000 and 11 millions for the H2 dataset across all tools. In all cases, the number of nodes is at least 3 orders of magnitude smaller than the number of bases in the haplotypes, indicating that pangenome graphs are effective at compressing linear parts of the haplotypes. Tools which discard variations (Minigraph and mdbg) yield in the order of 10 4 -10 5 nodes across all datasets, while tools which retain all variation (Bifrost, Minigraph-Cactus and pggb) yield in the order of 10 6 -10 7 nodes.
In all cases going from the H10 dataset to the H104 dataset increases the number of nodes by 5x, indicating that graph complexity grows sublinearly with the number of added haplotypes. The number of connected components varies between 2 and 1402 across all methods and datasets, and the number of large components (i. e. , those with more than 1% of total base pairs) varies between 1 and 30. If chromosomes were separated perfectly, pangenome graphs should contain exactly 24 connected components (one per nuclear chromosome, excluding mitochondria). Minigraph produces 24 large connected components as the number of chromosomes in the reference CHM13 v2. 0 (25 including mitochondria). Bifrost and Minigraph-Cactus yield graphs with less than 25 connected components while mdbg and pggb have more than 25.
In the Bifrost dBG, the vast majority of sequences (> 99. 99%) are in a single giant component, as chromosomes are joined because they share common k-mers. In mdbg, such joining does not occur on dataset H2, which has 24 large enough components (each containing > 1% of bases), possibly due to the absence of long and similar enough regions between chromosomes. Minigraph does not map any mitochondrial sequence from the input haplotypes to the reference, while they do get included into Minigraph-Cactus graphs. Even if it is common practice to analyze pangenomes chromosome by chromosome  [8, 17] , in this analysis we purposely used entire genomes as input instead. This was done for two reasons: i) to highlight the scalability of the tools, and ii) because separating chromosomes prevents the identification of inter-chromosomal inversions, translocations, and transposable elements, even if most of the generated inter-chromosomal events are probably alignment artifacts.
We extracted these two regions from pangenome graphs using a custom script that yields a subgraph corresponding to a given set of sequences and their variation. The script uses a different recommended method for each of the pangenome graph types. In a nutshell, we extracted regions using exact coordinates when possible and resorted to sequence-to-graph alignment otherwise (see Appendix "Loci extraction method" section for details). While on variation graphs and mDBGs nearby nodes of an aligned region correspond to variations of the locus, this is not always true for standard dBGs. Extracting accurate and complete loci representation is an unsolved challenge for dBGs.
HLA-E: a low complexity region Figure  2  shows how the different tools represent HLA-E over datasets H2, H10, and H104. As expected, Minigraph does not detect any variation, since the SNPs that characterize the region are too small to be considered in the construction steps of their algorithm. pggb, on the contrary, has 2 SNPs in H2 and 3 in H10. Bifrost detects the same SNPs as pggb in H2 and H10. Both of them represent the exact same variations and render the same haplotype paths. mdbg captures the heterozygosity of a large region containing the HLA-E locus as the number of samples grows. As the mdbg graph is built in minimizer space, nodes represent long genomic segments (in the order of hundreds of thousand of base-pairs). In H10 and H104, the minimizer-space representations of the haplotypes are identical; however, differences in flanking regions of the graph create variations that are captured in extra nodes that are also extracted in this region.
On H2, Minigraph-Cactus detects 3 variations as the dataset used is different, containing the CHM13 reference and just one haplotype of HG006 (as in Minigraph), as discussed in the "Datasets and haplotypes collection" section. Figure  2  also illustrates how pangenome complexity grows with the number of genomes: the Bifrost H104 subgraph has the most variation across all methods, highlighting that dBGs represent variations exhaustively in large graphs. On the other hand, pggb has the most straightforward method for extracting subgraphs, and also represents variants exhaustively in datasets H2 and H10, but could not scale to the H104 dataset.
HLA complex locus: high complexity region Figure  3  is the counterpart of Fig. 2  for the complex locus part. In this case, the overall interpretability of the region is more challenging, as the number and the structure of the variations is different than in HLA-E. It is also more difficult to compare across tools. Base-level variations, e. g. , SNPs, are not visually recognizable in Fig. 3  in the methods that retain them (i. e. , pggb, Minigraph-Cactus, and Bifrost) due to the large sizes of graphs. There are notable differences in how tools represent the variation, which is well-illustrated in the H2 dataset. While Minigraph renders H2 as a single sequence plus a large structural variant (SV) of ≈ 52 kbp, pggb separates it into two paths that differ by ≈ 54 kbp in length. Bifrost represents a detailed bubble that contains many variations inside each path.
In mdbg, even extracting the complete locus is a challenge as many of the subgraph nodes were not selected by our procedure. Minigraph-Cactus adds base level divergences between haplotypes on top of Minigraph SV graph. These differences between representations are further accentuated in the H10 dataset. For it, pggb tends to separate the haplotypes into different paths, Bifrost renders consistently the same compacted representation and Minigraph neglects most of the small differences but is able to display accurately the general picture, and Minigraph-Cactus, as in H2, adds small variations on top of Minigraph structure.
Stability Counter-intuitively, a pangenome graph construction tool may in some cases generate different outputs when executed multiple times with the same haplotypes as input. This unstability could be due to a permutation in the order of the sequences given as input, or non-determinism in the construction algorithm. Yet in order to facilitate the reproducibility of results, pangenome building tools should generate an unchanged output from the same set of input sequences, independently of the particular run or the order in which these are given. We performed two tests to evaluate tool stability: (i) we run the tools 3 times using as input the same H10 dataset and ii) we run the tools twice on shuffled input sequences, i. e. , changing the order of the haplotypes of H10. Bifrost and mdbg constructed exactly the same pangenome on every test, as by definition, de Bruijn graphs are stable.
Haplotype compression Building a graph pangenome can be seen also as a way to store, compact and retrieve the input haplotypes. As the number of new assemblies is growing faster than the data storing capacity, pangenomes can potentially help save storage space. This is highlighted by the disk space reported in Table  1 , which is consistently smaller than the sum of haplotype sizes for all methods and datasets. In order to losslessly retrieve the input genomes from a pangenome, the representation has to store all variations from the original haplotype sequences as paths in the graph. pggb and Minigraph-Cactus fall into this category while the other three considered tools do not store paths, or do not consider all variations, thus they are lossy. Of note, the GBZ tool  [9]  enables graph pangenomes that store paths in the GFA file format to be stored in a lossless compressed form.
Discussion Five state-of-the-art pangenome graphs construction tools were compared on the representation of up to 104 human haplotypes. The approaches significantly differ in terms of speed, graph size, and representation of variations. We find that it remains computationally prohibitive to generate human pangenome graphs for hundreds of haplotypes, especially while retaining all variations. Each approach has its own set of strengths, and ultimately the choice of the method depends on the downstream application. In addition, several takeaway points emerged from our analysis. Table  2  Relative strengths of five pangenome graph construction tools. Explanation of rows: (1) efficacy of construction algorithm, measuring wall-clock time; (2) degree to which variants (e. g. , SNPs) are retained; (3) ability of a tool to perform well on large datasets, both in comparison to other tools but also compared to smaller datasets; (4) ability to modify the produced data structure to add or remove haplotypes; (5) property of producing the same result irrespective of perturbations, such as permutation of the input order, and repeating the same run; (6) existence of tools (and operations) that can be applied to the resulting graphs;  (7)  whether input haplotypes information is retained by the tools, and if so, its space efficiency; (8) whether the entire graph can be directly visualized and interpreted; (9) easiness of "zooming in" a specific genomic region and interpret variants;  (10)  summarizes the functionalities provided by the tools to annotate the pangenomes with genomic data;  (11)  ability to share information between the graph and a linear reference Metric Bifrost pggb Minigraph- Cactus Minigraph mdbg 1) Construction speed • • • • • • • • • • • • • • • 2) Variations • • • • • • • • • • • • • • • 3) Scalablilty • • • • • • • • • • • • • • • 4) Editability • • • • • • • • • • • • • • • 5) Stability • • • • • • • • • • • • • • • 6) Accessibility by downstream applications • • • • • • • • • • • • • • • 7) Haplotype compression performance • • • • • • • • • • • • • • • 8) Ease of visualization • • • • • • • • • • • • • • • 9) Loci visualization and interpretability • • • • • • • • • • • • • • • 10) Metadata and annotation • • • • • • • • • • • • • • • 11) Compatibility with a linear reference coordi- nates • • • • • • • • • • • • • • • First, our focused analysis of HLA loci revealed that de Bruijn graphs and variation graphs represent genomic variations equally well as pangenomes.
Datasets and haplotypes collection In order to evaluate the state of current human pangenome representations, we sought to build a human pangenome that contains all publicly available high-quality human haplotypes. We collected from two different sources 102 different haplotypes from the genome of 51 individuals, and also used the two reference genomes, GRCh38 from the Genome Reference Consortium (GRC)  [24]  and CHM13 v2. 0 cell line of the T2T Consortium  [25]. Five haplotypes correspond to Google Brain Genomics DeepConsensus  [26]  assembly dataset: they are hifiasm assemblies of PacBio Hi-Fi reads corrected with DeepConsensus. The average of their N50 is 37. 99 Mbp. The remaining haplotype assemblies as well as the T2T reference are from the Human Pangenome Reference Consortium (HPRC) year-1 freeze  [6] , and GRCh38 is from the GRC.
Their average N50 is 40. 3 Mbp. Since HG002 is contained in the DeepConsensus data, the HPRC HG002 haplotypes were not used. The origin and the sex of the individuals are diverse and provide a fair representation of the diversity in human population: out of 51 total individuals, 21 are males and 30 are females and they represent 14 different ethnic groups, from USA to Africa and Asia. We did not perform any additional selection, regarding sex and ethnicity, on these public datasets as our main goal was to use as many genomes as possible. However, the HPRC stated that the genomes were selected to represent genetic diversity in humans  [8]  To evaluate the scalability of pangenome construction tools, we created three datasets of increasing size: (1) 2 haplotypes from the same individual, HG006; (2) 10 haplotypes from 5 different individuals (HG002, HG003, HG004, HG006, and HG00735); and finally, (3) all of the 104 haplotypes.
To test whether the order of the input sequences matters, we considered various random orderings for the 10 haplotypes in Dataset 2. Since Minigraph needs a reference sequence as the first haplotype in order to correctly build the graph, we generated specific 2 and 10 haplotype datasets with the first haplotype replaced by the reference genome CHM13. This was applied to the Minigraph-Cactus pipeline as well as it uses Minigraph variation graphs (Table  3 ).
Pangenome graph construction tools We evaluated tools that generate graph pangenomes as variation graphs and colored compacted de Bruijn graphs. Variation graphs are generally locally acyclic while de Table  3  Description of the three datasets generated to test the scalability of the tools. Data sources: 1 Google Brain Genomics  [27] ; 2 Human Pangenome Reference Consortium  [28] ; 3 1000 Genomes Project  [28] ; 4 Telomere to Telomere Consortium  [28]  Haplotypes Project Bases Bruijn graphs have cycles. In variation graphs, nodes represent sequences and edges represent immediate sequence adjacency without overlap. Variation graphs are generally easier to visualize and to interpret while challenging to construct at scale and, apart from pggb, require a reference genome. In de Bruijn graphs (dBG), nodes are k-mers (string of length k) and edges are (k-1)-overlaps between nodes.
TwoPaCo We did not consider TwoPaCo as it is redundant with Bifrost. Both methods construct the same de Bruijn graphs. TwoPaCo is a method for constructing ccdBG by finding junction k-mers at the boundaries of unitigs or in branching nodes. It consists of two main steps in which it approximates the dBG with a Bloom filter in order to reduce the size of the problem and then runs a two pass highly parallel algorithm on it. It constructs ccdBGs similarly to Bifrost. Bifrost is faster, supports edit operations, and accepts also reads other than assemblies as input. We tested both tools on NCBI datasets from three different known human variation regions part of the human leukocyte antigen (HLA) complex: HLA-A, MICB and TAP1. These loci have different number of sequences and have complexity and length.
Loci extraction method For Bifrost and mdbg graphs, nodes corresponding to the input sequences are identified with GraphAligner  [33]  and the subgraph is extracted using the Bandage reduce function. As the aligned nodes are not expected to represent the full diversity of the population in the pangenomes, the considered portion of the graph contains also nodes up to a certain distance from the aligned ones: 1 for mdbg and 3 for Bifrost. This number is based on the size of the sequences spelled by the nodes and on the considered variations. Artifacts, mostly tips, that are not part of the locus of interest are removed with a custom python script. For Minigraph generated graphs, the Minigraph own alignment function has been used to identify the nodes and then Bandage is used to extract the subgraph.
, 2004) , and such declines can be exacerbated by local stressors such as overfishing  (Hughes et al. , 2017)  and pollution (e. g. , agricultural or industrial runoff;  Wenger et al. , 2015). Now more than ever, it is of paramount importance to characterize how the environment and human disturbances shape the dynamics of the reef ecosystem, such as shifts in species abundance, community changes, or the emergence of locally adapted populations. Here, we present the Reef Environment Centralized Information System (RECIFS), an online repository of datasets describing the reef environment worldwide over the past few decades. The datasets provided through RECIFS originate from the public domain, and characterize various facets of the reef environment including water chemistry (e. g. , chlorophyll concentration, salinity, etc.