dataesr/scientific-paragraphs-categorization huggingface dataset:
Detection of CNV regions In order to define regions that are likely copy number variable in any given sample, we searched for regions where there are overlapping contig alignments in respect to the T2T-CHM13 (v1. 1) reference. In this analysis we considered only autosomes and we filtered out regions that overlap centromeric satellites. We opt to validate putative CNV regions using short-read-based copy number profiles obtained for 44/47 HPRC samples. Short Illumina reads were computationally parsed into 36 bp segments and aligned to a hardmasked T2T-CHM13 (v1. 1) reference using mrsFAST  (Hach et al. , 2010)  allowing an edit distance of 2. Read-depth-based copy number estimates were generated using the  FastCN (Pendleton et al. , 2018)  software package, which uses known copy number stable regions to correct for Illumina sequencing GC bias and convert read depth to diploid copy number over windows containing 500 unmasked base-pairs.
Analysis of pangenome brnn regions Genomic regions that have been excluded from the CHM13-based pangenome graph construction were obtained from https://github. com/human-pangenomics/hpp_pangenome_resources#masked-seque nc. Detailed description of how these regions were defined is reported in the link above. We next took the file 'hprc-v1. 0-mc-chm13. clipped-intervals. bed. gz' and for each genomic region we extracted the FASTA sequence from a corresponding phased assembly. We next aligned these to T2T-CHM13 (v1. 1) reference using minimap2 (version 2. 24) with the following parameters: --secondary=no --eqx -ax asm20 -r 100,1k -z 10000,50. Next we kept only alignments of minimum mapping quality of ≥10 and also we excluded any alignments from mitochondrial DNA.
Generation of DeepVariant SNP calls for false LOH detection Alignments of raw PacBio HiFi reads (from eight samples: HG02486, HG02572, HG02622, HG02886, HG03516, HG03540, HG03579) to T2T-CHM13 (v1. 1) were made with pbmm2 (https://github. com/PacificBiosciences/pbmm2) using the 'CCS' preset. DeepVariant calls were generated using DeepVariant  (Poplin et al. 2018)  version 1. 4. 0 and the 'PACBIO' pretrained model.
ACKNOWLEDGEMENTS We thank  Tonia Brown  for assistance in editing this manuscript. T. M. and P. E. acknowledge the support and computational infrastructure provided by the  Centre for Information and Media Technology at Heinrich Heine University Düsseldorf. This article is subject to  HHMI's Open Access  to Publications policy. HHMI lab heads have previously granted a nonexclusive CC BY 4. 0 license to the public and a sublicensable license to HHMI in their research articles. Pursuant to those licenses, the author-accepted manuscript of this article can be made freely available under a CC BY 4. 0 license immediately upon publication.
DATA AND CODE AVAILABILITY PacBio HiFi, ONT and Strand-seq data have been deposited into NCBI Sequence Read Archive (SRA) under the following bioproject IDs: PRJNA850430, PRJNA731524 and PRJEB54100. The T2T-CHM13 (v1. 1) assembly can be found on NCBI (GCA_009914755. 3). DeepVariant callset for selected samples (HG02486, HG02572, HG02622, HG02886, HG03516, HG03540, HG03579) is available at Zenodo DOI: 10. 5281/zenodo. 6762544 Dipcall callset for selected samples (HG02486, HG02572, HG02622, HG02886, HG03516, HG03540, HG03579) was obtained from: https://s3-us-west-2. amazonaws. com/human-pangenomics/index. html?prefix=workin g/HPRC/{sample. id}/assemblies/year1_f1_assembly_v2_genbank/assembly_qc/dipc all/{sample. id}. f1_assembly_v2_genbank. dip. vcf. gz Download location of ONT data used in this study are reported in Table  S9.
Download location of Strand-seq data for eight samples (HG01123, HG01258, HG01358, HG01361, HG01891, HG02257, HG02486, HG02559) are reported in Table  S9. Custom R scripts used in this study to process assembly alignments and report assembly gaps can be obtained from Zenodo data repository, DOI: 10. 5281/zenodo. 6762544 FASTA sequences from selected low complexity regions (n=27) can be obtained from Zenodo data repository, DOI: 10. 5281/zenodo. 6762544.
, 2021). Though both regions are able to provide a similar picture of eukaryotic phytoplankton diversity at higher taxonomic levels (division, class, see  Tragin and Vaulot, 2018) , at lower taxonomic levels (e. g. species) specific groups may be better resolved using a specific region. For example, V9 distinguish better between phytoplankton species of the class Chloropicophyceae (Lopes dos  Santos et al. , 2017) , whereas it has poor resolution among the order Dolichomastigales (class Mamiellophyceae,  (Tragin and Vaulot, 2018) ). One drawback of the V9 region is the limited number of reference sequences in public databases. In aquatic metabarcoding studies, the V4 region is clearly the most often used, probably because of the better coverage of reference databases (Table  11. 1). Other marker genes have been used (Tables 11.
One problem is that the reverse primer has one mismatch to Haptophyta, a very important phytoplankton group, at the 3' end of the primer, which is particularly unfavorable, resulting in datasets where Haptophyta are absent. A small modification of this primer (V4 18S Next. Rev, Table  11. 2) removes this bias, allowing more complete datasets  (Piredda et al. , 2017). For the V9 region, two primer sets, 1380F/1510R and 1389F/1510R, are the ones most often used. Some primer sets are designed to target specific groups, enhancing, for example, the coverage of haptophytes or diatoms  (Gran-Stadniczeñko et al. , 2017; Rynearson et al. , 2020).
Qiime, a suite of programs written in Python is the most used to date, despite its initial versions being complicated to install, whereas dada2 running under R is becoming increasingly popular (Table  11. 4). Another new option is PEMA, which groups a number of tools, such as VSEARCH and Swarm in a ready-to-use Docker container  (Zafeiropoulos et al. , 2020). It is also possible to use web services to process datasets. VAMPS (https://vamps2. mbl. edu/) is one of the oldest such site and offers, besides data processing, the possibility to download and visualize public datasets  (Huse et al. , 2014). It is superseded by a service, such as SILVAngs (https:// ngs. arb-silva. de/), which is linked to the SILVA sequence database (see below) or FROGS (http://frogs. toulouse. inra. fr/). Such services suffer from having to upload your data on a web server, which can be slow, and from offering only fixed processing options (e.
g. , clustering or reference database). Some web sites also facilitate downloading processed data from large scale projects such as Tara or Malaspina, (see below) and the Ocean Barcode Atlas (http://oba. mio. osupytheas. fr/ocean-atlas/,  Vernette et al. , 2021). Read processing. Many samples are usually sequenced together, and usually the demultiplexing step is performed by the sequencing software, but programs such as cutadapt can also perform this step. It is always necessary to remove the primers if this is not performed before, which can be done with cutadapt, and eventually combined with demultiplexing. The next step is to remove reads that are either too short or of bad quality. Quality can be checked using, for example, the function plotQualityProfile in the R dada2 package, allowing for the adjustment of parameters using the FilterandTrim function.
Therefore in recent years, more "natural" clustering algorithms have been developed, which includes swarm  (Mahé et al. , 2014)  and dada2  (Callahan et al. , 2016). These approaches are more computationally efficient and yield data that can be easily compared between datasets. The clusters produced by these methods are called either "swarms" or "ASVs" (amplicon sequence variants). The processing software assigns sample reads to each cluster, forming what is known as an ASV or OTU table, with one line per OTU/ASV and one column per sample, the cells representing the number of reads for a given combination of OTU/ASV and sample. One important source of error is linked to chimera formation during PCR amplification. Chimeric sequences are two or more fragments from distinct species that are combined during PCR to form a sequence, which is further amplified and could be inaccurately detected as a novel species  (Meyerhans et al.
, 1990). If undetected, the resulting diversity estimates will be falsely skewed  (Porazinska et al. , 2012; Wang and Wang, 1997). A variety of tools have been developed to detect and remove these sequences, such as UCHIME  (Edgar et al. , 2011)  or the removeBimeraDenovo function in dada2. However, this process is often not satisfactory and chimera removal may have to be done by visual inspection of an alignment of OTU/ASV and reference sequences. Taxonomic assignation. The taxonomic assignation of the OTU/ASV sequences is at the heart of the metabarcoding analysis. OTU/ASV representative sequences are assigned according to their similarity to those from a reference database, for which the taxonomy has been curated by experts. The database should cover the whole targeted taxonomic domain, e.
g. , eukaryotes when using the 18S rRNA gene. If some important groups are missing, the corresponding OTU/ASV will not be correctly annotated. The sequences from the reference database should also be correctly annotated: for example, if a reference sequence is annotated as a diatom, whereas it is in reality a green alga, this will result in errors in the final community structure. For eukaryotes, three general databases can been used. Genbank nr contains all published sequences and is therefore the most comprehensive in terms of coverage. Its main handicap is the taxonomic annotation is not curated and can be very minimal, in particular for environmental sequences (e. g. , many sequences are simply annotated as "eukaryotes"). SILVA  (Quast et al. , 2013)  is better annotated than Genbank, and is periodically updated (https://www.
arb-silva. de/). For protists, however, the best choice is the Protist Ribosomal Reference database (PR 2 , https:// pr2-database. org), which contains fewer sequences (∼180,000), but these sequences are periodically re-annotated by experts from each group using 8 taxonomic levels, from the kingdom to the species  (Guillou et al. , 2013). Some specialized databases have also been developed for specific taxonomic groups, such as dinoflagellates (DINOREF,  Mordret et al. , 2018) , or specific genes, such as the plastidial 16S rRNA (PHYTOREF,  Decelle et al. , 2015). Once a reference database is selected, the taxonomic assignment can be done in several different ways. The best strategy is to use alignmentindependent approaches, such as the naive Bayesian classifier, also known as the RDP (Ribossomal Database Project) classifier  (Wang et al.
, 2007). RDP is implemented in pipelines such as mothur or dada2, and provides bootstrap values for the confidence of the assignment at each taxonomic level. Similar approaches are used by the SINTAX algorithm  (Edgar, 2016) , as implemented in VSEARCH or the IdTaxa function from the R DECI-PHER package  (Wright Erik, 2016). Other methods rely on alignments such as a LCAClassifier of Crest  (Lanzén et al. , 2012) , which uses the aligned SILVA database as a reference, or pplacer  (Matsen et al. , 2010) , which places sequences on a fixed reference tree. A simple  BLAST (Altschul et al. , 1990)  search can also be used to obtain the closest matching sequences from either Genbank or a reference database, but it is difficult to implement for a large number of OTU/ASV, and does not provide bootstrap values.
Abundance normalization. Metabarcoding data are used in general to calculate changes in relative taxa abundance between different samples. A first issue is that the total number of reads vary between samples. There are roughly two ways of normalizing samples  (Pérez-Cobas et al. , 2020). One approach is to rarefy all samples to the least abundant sample, which may lead to the loss of rare OTU/ASV using, for example, the rarefy function from the R vegan package  (Oksanen et al. , 2016). Alternatively, one can compute the relative abundance of a given OTU/ASV in each sample, or normalize each sample to a fixed number of reads, e. g. , the mean or median of all samples. More sophisticated approaches can be taken  (Love et al. , 2014). Another concern is that the rRNA gene copy number varies between species.
Downstream analyses. Once an OTU/ASV table has been constructed with the taxonomy of each sequence assigned and abundance normalized, many different analyses can be performed. One very useful R package is phyloseq  (McMurdie and Holmes, 2013). It combines a large number of tools in a single package, providing ways to filter (for a sample type) and group the data (e. g. , at higher taxonomic level), to display the data under a variety of graphic formats (barplots, heatmaps), to perform alpha and beta diversity analyses, such as NMDS (nonmetric multidimensional scaling), or to visualize networks. Another useful tool is the DESeq2 package  (Love et al. , 2014) , which allows detecting OTU/ASV that are specific of a given set of samples. Making data available: a critical but overlooked step. Data availability is key for scientific reproducibility.
Researchers should be able to start from the original data and reproduce all steps to reach the final conclusions, following the FAIR Data Principles  (Wilkinson et al. , 2016). Moreover, the primary data from any paper should be available to the community, so they can be reused in new studies. Therefore it is imperative that raw sequences are deposited into public repositories, preferably the NCBI/EBI/DDBJ SRA (Sequence Read Archive), with standardized environmental metadata and technical information  (Yilmaz et al. , 2011). The accession number provided should be stated in the publication. Today very few journals request explicitly that authors provide primary sequence files, and even when it is stated in the instructions to authors, this is only partially enforced. It is very revealing that only 80% of the HTS studies listed here (Table  S1 ) have deposited raw sequences into public repositories, a situation that has been also pointed out for 16S metabarcoding data  (Jurburg et al.
e. , organisms in very low abundance  (Logares et al. , 2015). Moreover, samples were also analyzed by metagenomics and metatranscriptomics approaches, complementing the metabarcoding data. A final key element was the quick release of the primary and secondary data (raw and processed sequences), as well as the associated environmental data. This led besides the initial papers (e. g. ,  de Vargas et al. , 2015)  to a very large number of papers making use of the data, for example, to look at the distribution of specific plankton groups (e. g. , giant protists,  Biard et al. , 2016; Malviya et al. , 2016) , or at global diversity trends (Lopes dos  Santos et al. , 2017; Ibarbalz et al. , 2019). For more details on Tara Oceans expeditions, please refer to Chapter 15. The Malaspina ex-pedition  (2010 ( -2011 ( , Duarte, 2015) )  performed round-the-world sampling (Fig.
For the ITS, the presence of inserts of variable size between members of Synechococcus SC 5. 1, 5. 2, and 5. 3 makes it impossible to automatically align the sequences from different SC  (Mella-Flores et al. , 2012; Rocap et al. , 2002). The higher the sequence variability of the gene marker is, the harder will be the identification of conserved primer binding areas, leading to the design of degenerated primers. Finally, the use of new (or not widely used) markers also suffers from databases with a low number of reference sequences, which can lead to primer amplification biases. For example, metabarcoding petB primer set is heavily biased towards marine Synechococcus SC 5. 1  (Mazard et al. , 2012) , although petB gene is an excellent marker to reliably assign both Prochlorococcus and Synechococcus with a high taxonomical resolution  (Farrant et al.
mi tags as an alternative to picocyanobacteria metabarcoding Metagenomics approaches are particularly suitable for diversity and functional studies of marine picocyanobacteria. Their relative high abundance and ubiquity in situ compared to other taxa  (Biller et al. , 2015; Partensky et al. , 1999)  results in the dominance of their gene sequences in oceanic metagenomics datasets  (DeLong et al. , 2006; Rusch et al. , 2007; Venter et al. , 2004). The availability of several complete or near-complete genomes can be used as references to annotate taxonomically and functionally environmental reads  (Biller et al. , 2015; Doré et al. , 2020; Dufresne et al. , 2008; Kettler et al. , 2007; Scanlan et al. , 2009). These characteristics allowed the mining off Prochlorococcus and Synechococcus sequences in early environmental metagenomic libraries, such as the whole-genome shotgun datasets from the Sargasso Sea  (Mühling et al.
, 2006; Venter et al. , 2004) , or the Global Ocean Sampling (GOS)  (Biers et al. , 2009; Huang et al. , 2012; Rusch et al. , 2007)  projects. The mi tags approach is a non-PCRbased strategy to retrieve 16S rRNA sequences from metagenome datasets to access the taxonomic diversity and structure of prokaryotic communities  (Logares et al. , 2014). The mi tags approach recovers sufficient reads for community taxonomic profiling, as well as for richness, evenness, and beta diversity estimations. Using a reference database of the petB gene,  Farrant et al. (2016)  recruited reads out of 109 metagenomes from the Tara Oceans expedition (Fig. 11. 5). This work revealed novel genetic diversity within both Prochlorococcus and Synechococcus, even among the most abundant and well-characterized clades.
In addition, 136 completely new petB sequences were successfully assembled from metagenomic reads, enriching the reference database. By combining the genetic information with environmental parameters, "ecologically significant taxonomic units (ESTUs)" were further defined, representing organisms belonging to the same phylogenetic clade defined with petB and occupying a common ecological niche. For example, within clade II, ESTU IIB was found to co-occur with clades I and IV in cold waters, whereas ESTU IIA dominated in warm, (sub)tropical waters. With respect to clade III, although the presence of the two ESTUs defined within this clade was confirmed in warm, oligotrophic waters, they seem to be particularly thriving in phosphorus-depleted waters, a previously unknown characteristic for members of this clade.
This was later confirmed by comparative genomics  (Doré et al. , 2020). The CRD1 and EnvB Synechococcus genotypes were shown to dominate the Pacific stations analyzed between 33°S and 35°N, and to be locally abundant in the South and North Atlantic, as well as in the Indian Ocean, all corresponding to Fe-limited ocean regions. Moreover, each of these clades could be split into three distinct ESTUs, showing different distribution patterns: CRD1B and EnvBB thrive in cold waters, CRD1C and EnvBC in warm areas, whereas CRD1A and EnvBA were found in both types of ecological niches, suggesting that these latter populations are able to acclimate to a wide range of temperatures. Although the metagenomic recruitment of marker genes has not been popular among studies focusing on eukaryotic phytoplankton (in particular due to the low representation of eukaryotic reads in environmental metagenome datasets), the mi tags approach seem to constitute a powerful alternative to the metabarcoding for picocyanobacteria community, allowing to have access to the still undescribed lineages and microdiversity within existing lineages and to outline the physicochemical parameters driving the distribution of these lineages using the numerous metagenomics datasets now available.
Several considerations should be addressed before applying functional diversity frameworks to the analysis of communities. For example, which functional information or traits about the organism are relevant and how different traits should be weighted according to their impact in the role of the organisms in the ecosystem. Given the complexity of plankton communities, both in terms of function and diversity, the understanding of the relationship between functional and taxonomic diversity remains a major challenge for plankton ecologists  (Litchman and Klausmeier, 2008). Few studies have attempted to correlate plankton molecular diversity with functional traits  (Ramond et al. , 2019; Schneider et al. , 2020). Their results point to a promising field in plankton ecology. A global effort to create a consensus list of plankton traits, combined with an open curated reference sequence database, is needed to advance our understanding of crucial ecosystem services provided to the biosphere by planktonic microbes.
, 2020; Thompson et al. , 2020). There are still some limitations that need to be addressed. At the time of writing this chapter, the error rate of long-read amplicon sequencing can be up to 12%, much higher than HTS at 0. 1%  (Goodwin et al. , 2016). The use of alternative long-read sequencing strategies (such as PacBio circular consensus sequencing) with specific pipelines was reported to reduce error rates near to zero  (Callahan et al. , 2019) , and along with the continuous development of sequencing technologies, might be changing the long-read metabarcoding scenario soon. Moreover, new reference sequence databases containing longer gene fragments would need to be developed.
Segmentation of structural parts of 3D models of plants is an important step for plant phenotyping, especially for monitoring architectural and morphological traits. This work introduces a benchmark for assessing the performance of 3D point-based deep learning methods on organ segmentation of 3D plant models, specifically rosebush models. Six recent deep learning architectures that segment 3D point clouds into semantic parts were adapted and compared. The methods were tested on the ROSE-X data set, containing fully annotated 3D models of real rosebush plants. The contribution of incorporating synthetic 3D models generated through Lindenmayer systems into training data was also investigated.
In most of the previous studies, local surface features were extracted to segment 3D point clouds acquired from plants. Such features included eigenvalues of local covariance matrix  [45]  or the second tensor  [4] , Fast Point Feature Histograms (FPFH)  [6, 8, 46, 47] , and surface curvature  [7]. There are also methods that operate on volumetric 3D data of plants. Extraction of eigenvalues of the second-moments tensor of the 3D neighbourhood  [48] , a breath-first flood-fill algorithm with a 26-connected neighbourhood  [10] , extraction of multi-scale texture and edge features  [27]  are examples to volumetric approaches. In some studies, supervised learning techniques such as Support Vector Machines (SVM)  [8, 45, 47]  and Random Forests  [27]  were applied to classify 3D plant points to semantic classes.
Synthetic Rosebush Models To create synthetic rosebush (Rosa x hybrida) models, we used a simulation procedure developed by Favre et al. [67]. The procedure is implemented with the L-studio software  [41] , which is based on L-systems  [24, 25]. It simulates plant growth in 3D in stress-free environments. The simulation model uses the rules of growing found in the literature and observations from real plants. Morphometric measurements (i. e. diameter and length of organs), architectural structures (i. e. leaf formation order) and physiological data were taken from real plants, analyzed and integrated into the model. Using this simulation procedure, we generated 48 different rosebush models in the triangle mesh structure. The triangle mesh model and point cloud model of some rosebush are given in Fig.