dataesr/scientific-paragraphs-categorization huggingface dataset:
Use case datasets We created three other datasets to validate the performance of our approach on several use cases (see Fig. 6B ). The CONTIG dataset was created by using all the 1328 bacterial genomes classified as 'contig' that were released on RefSeq between September 1st and 30th, 2020. This dataset gathered 151,634 contigs (see Additional file 2: List S1 for the list of genome identifiers). The COMGENOME dataset was created by using all the 653 bacterial genomes classified as 'complete' that were released on RefSeq between September 1st and October 31st, 2020. These genomes were thus released after PlasForest was trained, and none of their sequences were used to create either the training and testing sets or the plasmid database. To simulate draft genome assemblies, the sequences from these genomes were randomly cut into contigs following the same size distribution as used to create the training and testing sets (Additional file 4: Table  S1 ), but no correction was applied to the frequency of contigs of plasmid origin.
This process resulted in 57,605 contigs (see Additional file 7: Table  S3  for the list of artificial contigs). The METAGENOME dataset was created by using two subsamples of draft genomes assembled from metagenomic short reads. 500 genomes were randomly sampled from the Human Gastrointestinal Bacteria Genome collection  [14] , and 500 genomes from the Genomes from Earth's Microbiomes catalog  [15] , thus bringing together 143,663 contigs (see Additional file: List S2 for the list of genome identifiers).
Extraction of the features All contigs were compared against the plasmid database using BLASTn  [21]. Pair-alignments with homologous sequences (hereafter referred to as "hits") were kept if their e-value was below 10 -3. For each contig and homologous sequence, we computed overlap as the fraction of the query contig aligning to the homologous sequence hit. The G + C content of all contigs was computed with the function SeqUtils. GC from the Biopython library in Python 3. 6  [23]. Our aim is not to assemble plasmids (or to assign contigs to precise replicons), but to identify contigs that originate from plasmids. This motivates for a distinct design from other homology-based approaches. By combining both homology search and measures of nucleotide composition, we aim to obtain a strong distinction between plasmids and Fig.
6  Datasets and application of a hold-out method for supervised learning. Schematic representation of the processes that allow to generate the datasets used to build PlasForest and to benchmark its performances. A 10,152 bacterial genomes from NCBI Refseq Genomes FTP server were randomly cut into contigs, and were distributed into the following datasets: the (balanced) training set contains 70% of the initial 10,152 genomes assemblies and it is used to train the random forest classifier; the testing set contains 30% of the genomes. B Other genome assemblies were drawn from more recent releases of NCBI Refseq Genomes FTP or from other sources to build the COMGENOME, CONTIG, and METAGENOME datasets. With the testing set, they are used to benchmark the performance of PlasForest compared to other plasmid identification methods chromosomes.
We therefore selected features as follows. (1) Maximal overlap was measured among hits in the subject database, because we expect that query plasmid contigs should form longer alignments with sequences from the plasmid database than query chromosomes. (2) Contig size was included as short contigs align more often than large contigs with the subject database. (3)  The number of hits, the average overlap, the median overlap, and the variance of overlaps provide other parameters of the distribution of overlaps among hits, that may help distinguish between chromosomes and plasmids. Indeed, due to recombination events, one may expect that query chromosome contigs will align with subject plasmids, but more rarely than query plasmid contigs. (4) Finally, the G + C content was also included, as the nucleotide composition of plasmids are most often different from those of chromosomes  [24].
Training of the classifier We extracted the differences in the features of plasmid contigs and chromosome contigs thanks to a random forest classifier. This approach relies on a multitude of independent decision trees, which allows for a reduction of individual error  [25]. The aim was therefore to build a model able to predict, from the extracted features, whether a contig comes from a plasmid or a chromosome. The random forest classifier algorithm was trained with the RandomForestClassifier function from scikit-learn library  [26]  in Python 3. 6, using the seven features described above. The number of random decision trees was kept to 500, as out-of-bag error estimate (i. e. , the internal error of individual decision trees during the training process) did not significantly decrease when using more trees.
Sensitivity of the classifier We tested the sensitivity of PlasForest (1) to the composition of the plasmid database and (2) to the composition of the balanced training set, by performing two independent bootstrap analyses. To assess the importance of the composition of the plasmid database, we resampled the plasmid database with 50 different seeds. We then computed new features for each contig of the balanced training set and testing set. A classifier was trained on the balanced training set for each resampled plasmid database, and its performance was measured on the testing set. To test the sensitivity to the composition of the balanced training set, we resampled the balanced training set 50 times, while the testing set did not change. We trained classifiers on the resulting balanced training sets, and measured their performances on the testing set.
Comparison with other softwares We ran 4 other plasmid identification softwares on the same datasets as PlasForest, and compared their predictions and performances to those of PlasForest. The version 1. 1 of PlasFlow was downloaded from https:// github. com/ smaeg ol/ PlasF low. Taxonomic assignments were not taken into account to assess the performance of the classification. To avoid the algorithm assigning sequences as "unclassified", we used a threshold value of 0. 5. MOB-suite was downloaded from https:// github. com/ phac-nml/ mob-suite, and MOB-recon was run with default values. MOB-recon clustering algorithm requires draft genome assemblies as inputs, so contigs on which MOB-recon was tested were gathered by their genome of origin. PlasClass was downloaded from https:// github. com/ Shamir-Lab/ PlasC lass and the program was run with default values.
The virtual machine version of PPR-Meta was downloaded from http:// cqb. pku. edu. cn/ ZhuLab/ PPR_ Meta, and the program was run in VirtualBox v. 5. 2. 42 with default values. When the algorithm assigned sequences as "phage", they were considered as negative predictions. • fast, convenient online submission • thorough peer review by experienced researchers in your field • rapid publication on acceptance • support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year.
Availability of data and materials PlasForest was released on GitHub (https:// github. com/ leaem iliep radier/ PlasF orest) and is available under GPLv3 license. The NCBI accession numbers of sequences used in this study can be retrieved in the Additional file 5: Table  S2A  for the balanced training set, Additional file 6: Table  S2B  for the testing set, Additional file 7: Table  S3  for the COMGENOME dataset, and List S1 for the CONTIG dataset. The list of NCBI accession numbers of sequences used for the plasmid database is available on the PlasForest GitHub repository (https:// github. com/ leaem iliep radier/ PlasF orest/ blob/ master/ list_ ids. txt). The sequences used for the METAGENOME dataset can be retrieved from HGG collection (ftp:// ftp. ebi. ac. uk/ pub/ datab ases/ metag enomi cs/ hgg_ mags.
tar. gz) and GEMs catalog (https:// genome. jgi. doe. gov/ portal/ GEMs/ GEMs. home. html), with accession numbers available in Additional file: List S2. All the sequence coordinates necessary to cut complete genomes into contigs (for the balanced training set, the testing set, and the COMGENOME dataset) are available in Additional file 5: Table  S2A , Additional file 6: Table  S2B  and Additional file 7: Table  S3.
Genotyping-by-sequencing (GBS) provides the marker density required for genomic predictions (GP). However, GBS gives a high proportion of missing SNP data which, for species without a chromosome-level genome assembly, must be imputed without knowing the SNP physical positions. Here, we compared GP accuracy with seven map-independent and two map-dependent imputation approaches, and when using all SNPs against the subset of genetically mapped SNPs. We used two rubber tree (Hevea brasiliensis) datasets with three traits. The results showed that the best imputation approaches were LinkImputeR, Beagle and FImpute. Using the genetically mapped SNPs increased GP accuracy by 4. 3%. Using LinkImputeR on all the markers allowed avoiding genetic mapping, with a slight decrease in GP accuracy. LinkImputeR gave the highest level of correctly imputed genotypes and its performances were further improved by its ability to define a subset of SNPs imputed optimally.
However, GBS also comes with a higher proportion of missing data and sequencing errors as compared to SNP arrays  [9]. This is problematic for GS, as genomic prediction models cannot handle incomplete molecular datasets, making imputation of missing genotypes a requirement before GBS data can be used for genomic predictions. A number of imputation algorithms have been developed to infer missing marker genotypes but the majority of them require ordered markers to perform imputation  [10]. For species lacking a reference genome and without the construction of a reliable genetic linkage map, the majority of markers from GBS are unordered. To deal with this situation, imputation approaches that do not use marker positions were developed, but existing comparisons among them and with approaches using marker positions are not comprehensive (i.
e. they focus on just a few imputation methods)  [10] [11] [12] [13]. Another solution for non-model species without a reference genome would be to make a genetic map of SNPs obtained from GBS and use the genetic positions for imputation. A number of software such as JoinMap  [14] , Lep-MAP3  [15] , OneMap  [16]  and HighMap  [17]  have been developed for the construction of genetic linkage maps, especially for biparental populations. Using SNP array genotyping in wheat, map-dependent imputation methods led to higher GS accuracy than map-independent methods  [18]. However, to the best of our knowledge, this was not investigated in the case of GBS data, and the interest of using genetic positions when physical positions are not available has not been evaluated. Therefore, in the context of genomic predictions with SNPs obtained by GBS, in particular for non-model species without a reference genome, a comparison of SNP imputation approaches using (i) map-independent algorithms and (ii) map-dependent algorithms and SNP genetic positions is necessary.
For this purpose, we compared the effect of nine imputation approaches on GS accuracy using rubber data (Hevea brasiliensis). Despite the great economic importance of this species, which is the only economically viable source of natural rubber, covering around 14 million hectares worldwide and producing around 14 Mt. of natural rubber per year  [19] , so far only two articles on rubber GS were published  [20, 21]  and a reference genome with 18 chromosomes was published very recently  [22]. In this study, we used two rubber datasets, i. e. dataset 1 comprising of 304 full-sib clones of cross PB260 × RRIM600 genotyped with 3420 SNP markers and phenotyped for rubber yield and sucrose content, and dataset 2 consisting of 251 full-sib clones of cross PR255 × PB217 genotyped with 9088 SNP markers and phenotyped for trunk circumference.
Plant material and field phenotyping Dataset 1 comprised of a panel of 304 F1 rubber clones that originated from a cross between clones PB260 and RRIM600, both from Malaysia. A detailed description of the two parents is available in Cros et al. [21]. The clones were phenotyped for rubber yield and sucrose content in two different small-scale clonal trials (SSCTs) in the South-Western parts of Côte d'Ivoire. Sucrose content is a trait of interest for rubber breeding as sucrose is a precursor of rubber synthesis and an indicator of the clone ability to withstand latex production intensification. A total of 179 clones were evaluated at HR46 (Site 1) which is located at Société des Caoutchoucs de Grand-Béréby (SOGB) estate, and the remaining 125 clones were phenotyped at Sapest13 (Site 2) which is found at Société Africaine de Plantations d'Hévéas (SAPH) estate.
Dataset 2 consisted of 251 F1 clonal hybrids from a cross between PR255 and PB217. As described in Souza et al. [20] , the field trial was laid out in a randomized block design over four replications and with each plot consisting of four grafted trees from the same individual. The individuals were phenotyped for trunk circumference in a single site trial in Itiquira (Mato Grosso, Brazil) from March 2006 to March 2007. Trunk circumference is a major trait for rubber breeding as growth speed determines the earliness of tapping initiation and the onset of rubber production, at around 6 years old. Trunk circumference data were collected before tapping initiation on 6. 5 years old trees grown under low-water conditions by measuring stem diameter at 50 cm from the ground using a tape measure.
Estimation of clone genetic values The raw phenotypic data collected on the ramets were used to estimate the clone genetic values (i. e. phenotypes adjusted for effects related to the trial experimental design), which were used to train and to validate the GS models. The clone genetic values were obtained using univariate linear mixed models and best linear unbiased predictor (BLUP) analysis implemented with ASReml-R version 3. 0 statistical package  [31]. In dataset 1, the clone genetic values for rubber yield and the clonal mean heritability (H 2 ) were taken from Cros et al. [21]. To get clone genetic values using raw sucrose data from the two sites, the model was adjusted for block effects. The clonal mean heritability (H 2 ) for sucrose content in site 1 and site 2 was 0. 66 and 0. 85, respectively.
The H 2 was calculated as per equation below: H 2 = σ 2 G /( σ 2 G + σ 2 E h r ) Where σ G 2 is the genetic variance of clones, σ E 2 is the residual error variance and h r is the trial harmonic mean number of ramets per clone, with σ E 2 and σ G 2 obtained from the linear mixed model  [21]. In dataset2, to get clone genetic values for stem circumference data, the model was adjusted for block, year and age effect. The clonal mean heritability (H 2 ) for trunk circumference was 0. 9 and was calculated using the same approach used for sucrose content. For a more detailed description of this dataset, refer to Souza et al. [20].
Molecular data 2. 3. 1. Genotyping-by-sequencing For dataset 1, high-quality genomic DNA was extracted from young and healthy leaflets on Macherey-Nagel NucleoMag magnetic beads, using the Beckman robot  [32]. After DNA extraction, the DNA was digested by two enzymes (Pst1 and Mse1), in league with the barcode and adapters, and multiplexed to 96 individuals per library. The libraries were sent to Genewiz (USA) for next-generation sequencing. In brief, the GBS protocol followed the following steps: normalization of genomic DNA, digestion with Pst1 and Mse1, ligation of barcoded adapter sequences, direct pooling of PCR products (pooling of 96 genotypes to get one GBS library), DNA purification on column, PCR amplification with adapter specific primers, double purification of DNA and sequencing on Hiseq 4000 platform  [6, 32, 33] , generating two opposite reads of 150 nucleotides for each analyzed fragment.
The SNPs were called from the sequence reads using VcfHunter, a pipeline available at https://github. com/SouthGreenPlatform/VcfHunter/ [34]. The reference genome used for SNP calling was the one published by Tang et al. [35] , which was the best sequence available at the time of the study. It contains 7453 scaffolds with a median length of 1. 28 Mbp. For dataset 2, genomic DNA was extracted using the approaches of Conson et al. [36]  and Souza et al. [37] , and GBS library preparation and sequencing were done according to Elshire et al. [6]. Genomic DNA samples were digested by EcoT22I to reduce genome complexity and SNP calling was performed using TASSEL 5 GBS version 2 pipeline  [38]  and the reference genome of Tang et al. [35]. Refer to Souza et al. [20]  for a more detailed description of genomic DNA sequencing and SNP calling on dataset 2.
Marker filtering and quality checking For dataset 1, the following criteria were used to filter SNPs: indels and SNPs that were not biallelic were discarded using VCFtools  [39]. All SNPs with a MAF (minor allele frequency) of less than 15% were also removed, as this was not compatible with the possible segregation patterns expected with biallelic markers in a biparental cross. In addition, SNP data points with a read depth of less than eight were set as missing and SNPs with more than 50% missing data were removed. The remaining markers were thinned to keep only one SNP per window of 500 bp using VCFtools. All individuals with more than 50% missing data were removed and all SNPs with a mean read depth greater than 400 were removed, as it was assumed that they were found in duplicated regions of the genome.
All SNPs with a heterozygosity percentage greater than 80% and less than 20% were discarded as this was not compatible with the possible values expected with biallelic markers in a biparental cross. Furthermore, all SNPs with a segregation pattern that significantly differed from the segregation expected from the parental genotypes were discarded. This was assessed with the Monte Carlo exact multinomial test (p-value <0. 05), using the function multinomial. test in the EMT R package  [40]. This resulted in a final marker dataset of 3420 SNPs, with the percentage of missing data per SNP averaging at 11% and ranging from 0 to 50% (see Fig. 1 ) and percentage of missing data per individual ranging from 0 to 30%. The depth per SNP in dataset 1 was on average 58. 8 and ranged from 8 to 401. 3. For dataset 2, SNPs that were not biallelic were removed.
Only SNPs with a MAF greater than or equal to 5% were kept. All SNP data points with a read depth of less than 9 were set to missing and only SNPs with less than 60% missing data were kept. If a genotype was present in less than 10% of the non-missing data of a given SNP, it was set as missing (i. e. not compatible with the possible values expected with biallelic markers in a biparental cross). All SNPs which showed inconsistencies between parents and offsprings, i. e. with offsprings showing genotypes that were not possible given the parental genotypes, were discarded. All SNPs with unknown parental genotypes were removed. This resulted in Fig. 1. Distribution of mean depth per SNP (top), and percentage of missing data per SNP (bottom) in dataset 1 (left) and 2 (right). N. Munyengwa et al.
Genomics 113 (2021)   [655] [656] [657] [658] [659] [660] [661] [662] [663] [664] [665] [666] [667] [668]  an SNP dataset with 9088 markers. The 9088 SNPs had an average percentage of missing data of 15. 2% which ranged from 0 to 60%. The depth per SNP was on average 27. 7 and ranged from 7. 7 to 162. 6.
Construction of genetic linkage maps For dataset 1, the 302 F1 individuals of the cross PB260 × RRIM600 and 3750 markers (3420 SNPs and 330 SSRs) were used to construct the genetic linkage map. For dataset 2, 253 F1 individuals from the PR255 × PB217 cross and 9253 markers (9088 SNPs and 165 SSRs used in Rosa et al. [41] ) were used to construct the genetic map. The two genetic linkage maps for the two populations were constructed using the Join-Map 5. 0 software  [14]  using parameters set for cross-pollinated population types and the regression mapping procedure. Assignment to linkage groups was done using a high logarithm of the odds (LOD) threshold value of 5. 0. We used linkages with a recombination rate of <0. 4, a map LOD value of 0. 05, and a goodness-of-fit jump threshold set at five for inclusion into the linkage map and for calculating the linear order of markers within a linkage group.
The Kosambi mapping function  [42]  was used to estimate map distance and to convert the recombination fractions between markers to map distances in centiMorgans (cM). We integrated SSRs into the genetic maps in order to identify linkage groups according to those already described in previous studies such as  Lespinasse et al. [43]  and Pootakham et al. [44]. The SSR data were not used in the rest of the study, i. e. imputation and genomic predictions, as the practical GS application is expected to rely only on SNPs, for cost efficiency and practical reasons. With dataset 2, for computational reasons, a first map was made using maximum likelihood. This allowed subsetting the SNPs by removing the ones that mapped very closely, and the final map was then made on this subset of SNPs by regression mapping.
Marker imputation methods Imputation algorithms Beagle, LinkImputeR, RFI, kNNI, EMI, PPCAI, SVDI, FImpute and MNI were used to impute the sporadic missing genotypes. Beagle and FImpute were applied only to the subset of SNPs located on the genetic map (1769 SNPs on dataset 1 and 3111 SNPs on dataset 2, see results), while the other methods were applied on both markers located on the genetic map and all the markers (3420 SNPs on dataset 1, and 9088 SNPs on dataset 2). In the rest of the article, the M matrix refers to the matrix of genotypes, with m rows corresponding to individuals, n columns of SNP markers and data points presented in (0, 1, 2, NA) format, representing the three possible genotypes coded in number of copies of the reference allele and the missing value (NA), respectively.
In order to obtain the highest accuracy, LinkImputeR optimizes the k and l values. LinkImputeR allows the user to run the algorithm in accuracy mode to test different read depth thresholds and additional data quality filters such as MAF, percentage of missing data per SNP and per sample, thus allowing for selection of the best parameters for imputation. After testing different MAF and read depth levels, for dataset 1 we used a MAF value of 0. 17 and read depth of 10. In addition to the MAF and read depth thresholds, we used a max depth = 500, min depth = 40 and numbermasked = 10,000. For dataset 2, a MAF value of 0. 26 and a read depth of 40 was used, with max depth = 500, min depth = 40 and numbermasked = 25,000.
Random forest imputation (RFI) We included RFI among the map-independent imputation methods as it gave the highest prediction accuracy in a GS study with GBS data in perennial ryegrass  [47]. RFI was also the best imputation approach in winter wheat with GBS data where it was compared to EMI  [48]. In addition, RFI gave the highest imputation accuracy in a study comparing four map-independent imputation methods with markers from GBS in wheat, maize and barley  [10]. The RFI algorithm was implemented in R using the package Mis-sForest  [26]  and the function missForest, using 15 iterations and 300 regression trees. The RFI procedure as described by Rutkoski et al. [10]  was implemented as follows: 1. For the marker matrix M, SNP markers were first sorted in ascending order according to the percentage of missing data and MNI was used to impute missing values.