dataesr/scientific-paragraphs-categorization huggingface dataset:
We sought to develop a faster and more efficient method, based on Nanopore sequencing of PCR products. We amplified the same 1. 3 kb fragment encompassing the variable regions of each gene and sequenced the PCR products by Nanopore sequencing. Then, we set up a bioinformatics pipeline to identify all the different variants present in the Nanopore reads. This pipeline relies upon the 30 references previously identified through cloning and Sanger sequencing, and on previous knowledge of the genes mutation distribution. It assigns each read to its closest known reference through mapping, then uses a combination of SNP calling and haplotype phasing to identify new variants. The pipeline was validated and fine-tuned using a combination of read simulations and molecular biology approaches. Read simulations were used to (i) confirm that the pipeline properly recovered variants in spite of Nanopore sequencing errors and (ii) ensure that it was possible to identify genetically distant new variants which were absent in the existing database.
The DNA amplicons were quantified using a Tecan infinite 500 Fluorometer (Tecan, Switzerland) with a dsDNA High Sensitivity Assay Kit (Thermo Fisher Scientific, Massachusetts, USA). The fragments size distribution was checked using a 5200 Fragment analyzer (Agilent, USA) system with a Sandard NGS kit (Agilent, USA). The amplicons libraries construction was done according to the Nanopore protocole NBA_9102_V109_re-vA_09Jul2020. Two hundred nanograms of DNA are end repaired and dA-tailed using NEBNext End repair/dA-tailing Module (E7546, New England Biolabs, Ipswich, Massachusetts, USA). The samples were then barcoded using EXP-NBD196 (barcodes 1-96) kits (Oxford Nanopore Technologies, Oxford, UK) and ligation Sequencing Kit 1D SQK-LSK109, (Oxford Nanopore Technologies, Oxford, UK). Up to 96 samples were pooled per run.
Barcoded samples were pooled and purified using 0. 4 vol of AMPure XP magnetic beads. The AMII sequencing adapter (Oxford Nanopore Technologies, Oxford, UK) was ligated to barcoded pools using Quick T4 DNA ligase (New England Biolabs, Ipswich, Massachusetts, USA) and the sequencing libraries were purified using 0. 4 vol of AMPure XP magnetic beads. MinION sequencing was performed as per manufacturer's guidelines using R9. 4. 1 flow cells FLO-MIN106, ONT and controlled using Oxford Nanopore Technologies MinKNOW software version v20. 06. 5. Flow cells were then transferred to Nanopore MinION Mk1b (Oxford Nanopore Technologies, UK) for Nanopore single molecular sequencing. Base calling was performed after sequencing using the GPU-enabled guppy basecaller in high accuracy mode for 96 samples (version 6.
Pipeline details Throughout the whole pipeline, mapping was done using minimap2  [30] , with parameters -ax map-ont, unless specified otherwise. We first separated cidA and cidB reads by mapping all reads on a short monomorphic sequence of each gene, corresponding to positions 228-327 and 1211-1309 for cidA and cidB respectively. After this step, there were around 15,000-20,000 reads per gene. The pipeline was then run separately for cidA and cidB reads, the following steps of the pipeline being identical for both genes. For each gene, reads were mapped on the full reference database (the making of this database is described in Section 2. 5) and secondary alignments were removed. Samtools coverage was then used (samtools 1. 15,  [31] ) to obtain the coverage of each reference (Fig. 1 ). Using specific PCRs, we determined that selecting references which had a coverage above 10% of the total number of reads enabled to get all the true references, and excluded artifactual chimeric reads (detailed in Section 3.
2). References above that threshold were extracted, along with reads mapping on these references. Since highly similar references artificially introduce INDELs, and decrease coverage in SNP calling when using bcftools mpileup, we looked for pairs of references differing by less than 3 SNPs and kept a single representative for each pair (Fig. 1 ). Exclusion was done by computing the raw distance among sequences using the dist. dna function in the R package ape  [32]. Reads which had mapped on at least a reference were then mapped on the reference subset, and SNPs were called using bcftools mpileup (with the ont config, a minimum mapping quality of 10, a disabled BAQ and a max depth of 75,000) and bcftools call (multiallelic caller, with an expected substitution rate of 0. 5). If no SNPs were called, variants present and their respective coverage were extracted to assemble the repertoire.
Reference database: true and in silico references The above-described pipeline is based on mapping on a cid (cidA or cidB) reference database. This database was made of two distinct sets of references: (i) references previously acquired through cloning and Nanopore sequencing of cid genes  [19] , following the protocol described in 2. 2. ; and (ii) references built in silico, using knowledge on the polymorphism profile in cid genes. cid genes are composed of two variable regions separated by a monomorphic region, and recombination occurs between those two regions (Fig. 1 ,  [19] ). We thus completed the pool of references already sequenced by creating an in silico pool of references for each of the five wPip group  [34]  by combining all the previously sequenced upstream and downstream regions within each group.
The base is assigned the Phred score and we introduce an error at that position with a probability corresponding to the phred score. The error types are either deletion, substitution, or insertion with probabilities of 0. 45, 0. 35, and 0. 2, respectively, which roughly mimics the error profile of ONT data although nanopore base calling algorithms changes rapidly. Our script is able to produce reads with a mean error of ~3. 9%, 7, and ~11. 4% error rate through different ratios of phred quality values.
DNA was added in a quantity sufficient to get enough positive droplets (for full mosquitoes, DNA amount could not be quantified as infection levels of the endosymbiotic Wolbachia vary and extracted DNA is always mixed with host DNA). The reaction mixtures were loaded into wells of Sapphire chip and were subsequently emulsified (20,000-30,000 droplets/sample) and amplified in a geode thermocycler (Stilla technologies). The ddPCR conditions used were 10 min initial denaturation at 95 • C, followed by 45 cycles of 95 • C for 30 s, 58 • C for 15 s and 72 • C for 30 s. After template amplification, the chips were transferred to the reader. Extracted fluorescence values for each droplet were analyzed using the Crystal Miner software (Stilla Technologies).
Code availability All scripts used for simulating datasets, to run the pipeline and its evaluation are found at https://github. com/alnam3/nano_seq. Specifically, the code to simulate Nanopore reads under https://github. com/ alnam3/nano_seq/Nano-read-simulator, and the suggested awk script to test for missed SNPs under https://github. com/alnam3/nano_seq/ Missed_SNPs.
Simulations confirm pipeline's ability to recover variants Although Nanopore sequencing quality has drastically improved in the last years, the rate of sequencing errors was still estimated to be around 7% in 2021  [12]. We tested, using simulated reads, the ability of the pipeline to recover all variants present in spite of Nanopore errors. To our knowledge, Nanopore read simulators such as NanoSim  [35] , DeepSimulator  [36] , SimLord  [37] , and SNaReSim  [38]  are designed for genomic data and do not mimic targeted sequencing where the majority of reads covers the whole amplicon. We therefore wrote our own read simulator for targeted data based on the full-length transcriptome read simulation pipeline in  [17]  (isONcorrect) (details of the simulation script in the method section Nanopore read simulation).
SNP calling and haplotype calling enable to properly recover new variants We also tested the ability of the pipeline to recover true new variants (i. e. not yet present in the complete database). To that extent, we used our Nanopore read simulator, which enables to create simulated mutants with a user-set mutation rate. We created new variants by introducing a fixed number of SNPs in the existing ones. Nanopore reads were then simulated from (i) simulated variants only and (ii) a mixture of simulated and existing variants. For this experiment, reads were simulated with a 7% error rate, as we showed above that the pipeline correctly recovered the references with error rates up to 12%, and because 7% is close to recent estimates of the realistic Nanopore sequencing error rates  [12]. Simulated SNPs were introduced in the existing variants in such a way that the new simulated variants differed from the existing ones by a distance equal to the median pairwise raw (Hamming) distance among true references (the database being already wide, finding a new variant differing from all others by more than this distance was highly unlikely).
No INDELs were introduced since previous work showed they were unlikely in cid genes  [19, 26, 28] , even if the read simulation script used enables to introduce INDELs. Simulating sequences enabled us to know exactly where the SNPs were located and thus to check our pipeline's performance in a controlled scenario. Using 100 different simulations, we computed the true number of SNPs (ranging from 41 to 71), along with the number of SNPs recovered by SNP calling. We found that on average, 3. 4 ± 3. 1 SNPs were missed (mean ± standard deviation, ranging from 0 to 18). There were two distinct cases for missed SNPs: (i) SNPs located in repeated regions (e. g. AATTAATA -> AATTTAAT, likely considered by the caller as a spurious SNP resulting from a sequencing error); (ii) SNPs not called in spite of a high coverage of the alternative base (e.
Discussion Within a genome, there can be multiple highly similar copies of a multi-copy gene family. For cid genes, our focal example here, we called these different copies 'variants', and the full set of copies per genome is called a 'repertoire'. Sequencing and assembling such families, with highly similar copies and copy number variations represents an issue. Indeed, long reads are highly error-prone, making it difficult to differentiate true polymorphism from sequencing errors, and short reads fail to reconstruct haplotypes. Here, we developed a bioinformatics pipeline enabling to reconstruct the full repertoire of a gene family within a single genome through long read Nanopore sequencing of PCR products, based on an existing reference database of Sanger sequences. We validated the pipeline using a combination of read simulation and molecular biology approaches.
Conclusion While the method presented here requires a good pre-existing reference database, the recent development of methods to work on multigene families based on less error-prone PacBio sequencing data  [13] , along with the improvement of both Nanopore basecalling  [15, 49]  and error-correction  [17]  suggest that de novo assembly of multigene families will be possible with Nanopore sequencing in the coming years. Overall, in spite of some limitations due to the PCR step itself, this method enables a quick acquisition of numerous variants, which could be further increased by acquiring more than two genes at the same time. This paves the way to wide scale multigene family acquisitions and, with the present study, to a better understanding of links between cid genes and cytoplasmic incompatibility phenotypes.
Acknowledgements We thank the  MBB platform  for their help on code-related issues. Digital PCRs were run on the ISEM qPCR platform. Qubit were run using GenSeq platform facilities. We thank the MGX platform for Nanopore sequencing, and especially  Dany Severac  and  Anaïs Louis. This project was funded by the  French MUSE  project with the reference  ANR-16-IDEX-0006. Kristoffer Sahlin  was supported by the  Swedish Research Council (SRC, Vetenskapsrådet)  under Grant No. 2021-04000. We thank  Nicole Pasteur  and three anonymous reviewers for thorough reading and helpful comments on previous versions of this manuscript.
After assessing the role of secondary processes such as volatile degassing and fractional crystallization on the fO 2 of melts and reconstructing primary melt compositions, we show that (1) fO 2 values derived from Fe 3+ /ΣFe and D v Ol/Melt are comparable and (2) arc and Mount Etna primary melts are more oxidized than mid-ocean ridge and Reunion Island primary melts. We then demonstrate, from Zr/Nb, that the observed variability in primary melt fO 2 is not due to chemical variability of the mantle source. Finally, the correlations between incompatible trace element ratios such as Th/La, Ba/La, Ba/Th and La/Yb and the fO 2 of primary melts reveal a link between the oxidized nature of arc and Mount Etna primary magmas to slab fluid and/or sediment melt influence. Our arc dataset displays a variety of subduction influences, from fluid-dominated (Aoba and Mount Meager) to sediment melt-dominated (La Sommata) influences.
Samples To investigate the redox state of primitive magmas we selected glassy primary melt inclusions trapped in high-Mg olivines (Fo > 80 %). Our dataset is mainly composed of melt inclusions that were previously studied by  Gaborieau et al. (2020)  and  Laubier et al. (2007 Laubier et al. ( , 2012)). Our melt inclusions from mid-ocean ridge basalts come from the FA-MOUS zone (Northern Mid-Atlantic Ridge; ARP73-10-03, ALV519-2-3, ALV519-4-1, ALV527-1;  Kamenetsky, 1996; Laubier et al. , 2007 Laubier et al. , , 2012) )  and the Gakkel Ridge. Our oceanic island basalt (OIB) melt inclusions come from Piton de Caille on the Northwest rift-zone of Piton de la Fournaise, Reunion Island (CAI;  Bureau et al. , 1998a; Bureau et al. , 1998b; Laubier, 2006). The arc melt inclusions were selected from a picritic lapilli layer collected on the Torgil tuff ring and from the Red Cliff pyroclastic sequence (Ao8, Ao15, Ao17, Ao2T-T, Ao-T, Ao-C;  Sorbadere et al.
Raman and FTIR spectroscopy. The water contents of thirty melt inclusions, determined by Raman spectroscopy and by Fourier Transform infrared spectroscopy at LMV, were reported in  Gaborieau et al. (2020). A few melt inclusions were analyzed by both Raman and FTIR spectroscopy. We concluded that both methods gave consistent results but that uncertainties on the water contents derived from FTIR spectroscopy were larger due to the error on the thickness of melt inclusions and the uncertainty resulting from the range in published values for the molar absorption coefficient, which is highly dependent on glass composition  (Mercier et al. , 2010). Therefore, we preferentially use the water contents derived from Raman spectroscopy when both types of data are available. New measurements of the water content of twenty-two melt inclusions are reported here.
, 2000; Gaetani and Watson, 2000, 2002). Melt inclusion compositions were therefore corrected for olivine crystallization (this correction is usually referred to as post-entrapment crystallization correction -PEC -in the literature) and/or "Fe loss". In order to have a consistent dataset, the previously published melt inclusion compositions from  Gaborieau et al. (2020) ,  Laubier (2006) ,  Laubier et al. (2007 Laubier et al. ( , 2012)) , that were initially corrected using the Petrolog3 software  (Danyushevsky and Plechov, 2011) , were recorrected in the same way as our new data. Here, major, volatile and trace element (including V) contents were corrected using the MIMiC program  (Rasmussen et al. , 2020). This program allows PEC correction by adding olivine into the melt inclusion composition until the olivine-melt equilibrium (i.
Results As mentioned in the previous section, previously published major element data were recorrected for PEC processes with a different correction scheme to generate a consistent PEC-corrected dataset (Table  S1  of the Supplementary material). However, the new melt inclusions display major element compositions that are very similar to the ones that were previously published  (Gaborieau et al. , 2020; Laubier, 2006; Laubier et al. , 2007 Laubier et al. , , 2012)). As the new melt inclusions added to this study are coming from the same samples, their major element compositions (Table  S1  of the Supplementary material) are not further described here and readers are invited to refer to  Gaborieau et al. (2020) ,  Laubier (2006)  and  Laubier et al. (2007 Laubier et al. ( , 2012)).
Volatile element concentrations in melt inclusions Within our dataset, the volatile element concentrations in the melt inclusions from subduction zones generally show the largest variations, as shown in Fig. S2  of the Supplementary material. Aoba melt inclusions display water contents ranging from 1. 1 to 3. 2 wt% H 2 O which overlaps previous data from  Métrich and Deloule (2014). They also show a wide range in chlorine from 0. 08 to 0. 56 wt% Cl as already observed by  Bouvier et al. (2019) , and a narrow range of sulphur contents from 0. 04 to 0. 16 with one outlier at 0. 26 wt% S. La Sommata melt inclusions display variable water contents ranging from 1. 3 to 4. 4 wt% H 2 O in agreement with those measured by  Chen et al. (2013) , Cl contents from 0. 17 to 0. 42 wt% Cl (in agreement with  Bouvier et al.
Discussion Our results show that arc magmas are more oxidized than those from mid-ocean ridges and Reunion Island, which is in good agreement with previously published data (e. g. ,  Behrens and Gaillard, 2006; Berry et al. , 2018; Bézos and Humler, 2005; Birner et al. , 2018; Carmichael, 1991; Christie et al. , 1986; Cottrell and Kelley, 2011, 2013; Kelley and Cottrell, 2009, 2012; O'Neill et al. , 2018; Zhang et al. , 2018b). In an attempt to determine whether the mantle wedge is more oxidized than the mantle generating MORBs and Reunion Island basalts, we review the different processes that may play a role in the variability of magmatic fO 2.
, 2002). The authors suggest, instead, that the modification of the Fe 3+ /ΣFe of melt inclusions due to a combination of diffusive reequilibration of fO 2 and H 2 O between melt inclusions and the degassed external melt could explain the discrepancy. In our case, as discussed in section 3. 3, the ƒO 2 values calculated from D V Ol/Melt are generally higher than the values given by Fe 3+ /∑Fe but the offset depends on the method used for calculating ƒO 2 from Fe 3+ /∑Fe  (Borisov et al. , 2018; Jayasuriya et al. , 2004; Kress and Carmichael, 1991; O'Neill et al. , 2018). The offset is systematically the smallest when using O'Neill et al. (2018)'s parameterization and some data points from our dataset and  Humphreys et al. (2022) 's melt inclusions fall on the one to one line in Fig. 1c. The relatively good agreement between the fO 2 derived from D V Ol/Melt and that calculated using O'Neill et al.
(2018)'s Fe 3+ /∑Fe proxy suggests that post-entrapment fO 2 re-equilibration is not responsible for the observed variability in fO 2. However, because of the variability in the fO 2 values given by the different methods, and since our current dataset does not provide another way of determining whether Fe 3+ /ΣFe re-equilibration occurred in our melt inclusions, it is not possible to completely rule out post-entrapment fO 2 re-equilibration.
We modelled fractional crystallization using the alphaMELTS 1. 9 software  (Smith and Asimow, 2005; Ghiorso and Sack, 1995)  for temperatures and pressures ranging from 1400 to 1050 °C and 1 to 10 kbar, respectively. The starting compositions used in our modelling correspond to those of melt inclusions that display the highest MgO contents and for which we had measured the H 2 O content. fO 2 was fixed according to the values derived from their Fe 3+ /ΣFe using the O'Neill et al. (2018)'s parameterization. H 2 O was allowed to degas. Our crystallization models for Aoba indicate that olivine is likely to be the first phase to crystallize, followed by clinopyroxene and spinel. The models lead to the appearance of biotite and apatite at the end of the crystallization path for pressures ≥ 4 kbar, which has not been reported in the literature  (Eggins, 1993; Sorbadere et al.
Reconstruction of primary melt compositions and fO 2 To reconstruct the compositions of primary melts, we had to correct the melt inclusion compositions for the potential effects of preentrapment fractional crystallization. Most of them required correction for the crystallization of olivine only, and twenty for olivine ± clinopyroxene ± spinel crystallization. According to our crystallization models (see section 4. 2. 2), the amount of clinopyroxene and/or spinel does not affect melt compositions significantly. As a consequence, we applied a simple correction using the MIMiC program  (Rasmussen et al. , 2020)  to incrementally add olivine back to the compositions of melt inclusions until they reached equilibrium with a Fo 90 olivine. Reference values for the distribution coefficient were calculated using the Toplis (2005)'s Fe-Mg exchange model.
k : Average Fe 3+ /ΣFe per locality calculated using a and b only. 2) show that arc primary melts are indeed more oxidized than those from mid-ocean ridges and Reunion Island. The variability in fO 2 that we observed within the population of arc melt inclusions is still visible within arc primary melts: i. e. , primary melts from Stromboli and Mount Meager are the least oxidized melts whereas those from La Sommata are the most oxidized. Primary melts from Aoba record intermediate redox conditions. The fO 2 recorded by Reunion island primary melts (ΔQFM -1. 2 to +0. 3 depending on the Fe 3+ /ΣFe proxy) is particularly reduced compared to other OIB primary melts, which is in agreement with the recent data from  Brounce et al. (2022).
In our dataset, melt inclusions from Reunion Island and Etna, with Dy/Yb of 2. 5 ± 0. 1 (2σ; n = 3) and 2. 9 ± 1. 7 (2σ; n = 6) respectively, clearly show evidence of residual garnet in their mantle source (i. e. , Dy/Yb >2), which precludes the use of the V/Yb and V/Y proxies  (Laubier et al. , 2014; Lee et al. , 2005; Li and Lee, 2004; Mallmann and O'Neill, 2009). If we restrict our comparison to melts derived from a spinel lherzolite source, we find a distinct difference in V/Yb between MORB and Aoba melt inclusions (both clearly representative of melts derived from a spinel-bearing source with Dy/Yb < 2) with average ratios of 92 ± 56 (2σ; n = 16) and 238 ± 58 (2σ; n = 12) respectively. Melt inclusions from La Sommata and Mount Meager (with Dy/Yb close to 2) also display higher average V/Yb values than MORBs: 207 ± 65 (2σ; n = 6) for La Sommata and 139 ± 26 (2σ; n = 4) for Mount Meager.
Acknowledgments We are very grateful for thorough and thoughtful reviews from  R. W. Nicklas  and an anonymous reviewer, which improved this paper significantly. MG was supported by the  Région Auvergne ( Nouveau chercheur fellowship  awarded to ML). This research was founded by the  French Government Laboratory of Excellence initiative (ClerVolc LabEx)  and the  Région Auvergne ( Nouveau chercheur fellowship  awarded to ML). This is ClerVolc contribution number 615.
DEM-PFV coupled model We briefly describe the adopted numerical framework in what follows. The interested reader can refer to  [6]  and  [7]  for further details. The solid phase is modelled through a soft-sphere approach  [8]  and we assume having a viscous flow of a Newtonian fluid within the pore space. In order to account for the interaction between the two phases, i. e. poromechanical coupling, we use the DEM-PFV numerical scheme implemented in the open-source code YADE  [9]. It should be noted that in this work, we assume the two phases to be incompressible and we do not consider lubrication forces. The domain is discretized in tetrahedral regions by using a regular triangulation, in which the vertices of the tetrahedra are the soil particles centres; we refer to the pore space within a tetrahedron as a pore.