dataesr/scientific-paragraphs-categorization huggingface dataset:
The de novo approaches are usually based on the repetitiveness of TE sequences or their structural motifs (e. g. , the presence of LTRs for LTR-retrotransposons). Several bioinformatics tools are available based on these different approaches  [7, 98] , and there are also pipelines that combine the different tools to be able to make a comprehensive annotation of TEs, such as the REPET package  [100]  (Table  1 ). Another tool called PiRATE has been developed recently, which includes several available packages for TE identification and annotation like similarity based and de novo structural based tools, repeat based and TE reconstruction from short reads  [101]. Although these tools are in general very efficient, it is still challenging to compare the TE landscapes of different genomes only based on their TE annotations, even when they have been produced using the same tools and parameters.
The annotation of TEs in reference genomes is only a snapshot at a given time of a particular individual which may not be representative of all individuals of the species. Although a majority of TEs are no longer able to transpose, and most insertions are likely to be fixed, mobile TEs are at the origin of an important part of the genetic variability within populations. For example, in human, several thousand polymorphic TE insertions were identified when comparing 185 samples in three major populations  [102]. It is therefore essential to be able to identify non-reference TE insertions. There are different bioinformatic methods available to use NGS data on individuals or varieties to identify TE insertion variability. Most of these methods use a reference genome of the species to map de NGS paired-end short reads and identify those that map discordantly, selecting those for which one of the reads maps to a previously identified TE.
Examples of these include software packages such as Jitterbug  [103] , TEMP  [104] , MELT  [105] , PopoolationTE2  [106]  or TEFLoN  [107]. Others on the contrary map first to the consensus sequence of previously identified TEs and map afterwards the paired read onto the genome, such as TRACKPOSON  [108]. A more recent approach using long Pacbio reads has also been reported, but it still relies on the good annotation of reference TE sequences  [109]. Another approach infers insertion events using the misalignment and split alignment of short reads without relying on a database of previously identified TEs. Several of these tools have been reviewed in  [110]. For non-model species, due to the general lack of a good quality genome assembly or in the case that no reference genome exists at all, the search for TEs is especially challenging.
Similarity or motif-based searches will identify older elements in the assembled draft genome or transcriptome, but new elements with high sequence conservation will not be assembled and the data will be hidden in the unassembled reads. In this case, TEs have to be reconstructed from the non-assembled short reads by using software packages such as RepARK  [111] , Tedna  [112] , RepeatExplorer  [113] , or dnaPipeTE  [41].
Additional parameters may be implemented to keep uniquely mapped reads, which in this case will remove almost all information concerning TEs. Alternatively, it is possible to get all possible positions of reads or to return up to a given number of alignments. The mapping may be performed on consensus TE sequences, which represent a given TE family. However, the variation of quality among copies of a given family may have a direct impact in the process of building the sequence reference (consensus) of the considered family. These consensus sequences are usually supposed to correspond to full-length and active sequences or to represent the ancestral element at the origin of all copies currently present in a genome. Such type of sequences is available in the TE database Repbase  [115] , a resource that is widely used for TE annotation.
Normally, only nearly full-length and thus potentially active copies are considered in the process of consensus construction. However, a consensus sequence may be quite divergent from older copies. Moreover, not all species are represented in this database, which may lead to consider consensus TE sequences from related species instead of real TE representative of the analyzed species. This may lead to a wrong assessment in TE recognition by mapping approaches but also to a loss of information from the unmapped reads. Thus, the crucial step of mapping reads on TE sequences remains a challenge that will become easier as the sequenced reads become longer.
LoRTE Uses PacBio long read sequences to identify TE deletions and insertions between a reference genome and other sampled genomes  [109]  TE annotation in unassembled genomes RepARK Produces the assembly of de novo repeat sequences from detected repeat k-mer directly from raw reads. [111]  Tedna Assembles TEs directly from the reads  [112]  RepeatExplorer Pipeline to identify and characterize repetitive DNA elements from NGS data using a graph-based clustering approaches to identify repeats, and additional programs to annotate and quantify them. [113]  dnaPipeTE Pipeline to de novo assemble, annotate and quantify repeats from NGS low coverage genomic datasets. [41]  Epigenetic analysis of TEs Repeat Enrichment Estimator Histone modifications associated to TE families; Web interface developed to analyze human, mouse and Drosophila data.
| Variants pathogenicity classification ACMG guidelines  (Richards et al. , 2015)  were followed for variant pathogenicity classification. Pathogenic (Class 5) and likely pathogenic (Class 4) variants were considered as Mendelizing pathogenic variants. Familial segregation of the Mendelizing variants was checked using Sanger sequencing to demonstrate trans configuration in the autosomal recessive cases and de novo inheritance in the autosomal dominant cases. All variants reported in this manuscript have been submitted to the ClinVar database (https://www. ncbi. nlm. nih. gov/ clinvar/; ClinVar accessions SCV000998479-SCV000998508).
| Mutation burden tests Variants were filtered for quality criteria (pass GATK  (DePristo et al. , 2011)  standard filter, read depth ≥10), allelic frequency (based on the maximum minor allele frequency found in ExAC  (Lek et al. , 2016) , 1,000 G (TheGenomes Project Consortium, 2015 1000), ESP6500 (https://evs. gs. washington. edu/EVS/; source: dbNSFP2. 8), GoNL r5 (Genome of the Netherlands Consortium, 2014), ARIC5606 (https://sites. cscc. unc. edu/ aric/), and our in-house database), and for functional impact (nonsynonymous or splice junction effect, using snpeff_effect from SnpEff  (Cingolani et al. , 2012) ). Five different exome DNA capture kits were used for exome sequencing (see above). The intersection of the five different kits was examined, and only those variants included in the intersection of the bed files were considered for mutation burden testing.
ACKNOWLEDGMENTS We thank  Christelle Golzio  for guidance in zebrafish phenotyping, and  Raphael Helaers  for help in variant filtering. We thank all the patients and families for their participation in this study. This work was supported by  ERA-net E-Rare Euromicro , the  Action de Recherche Concertée (ARC) of the Belgian Fédération Wallonie Bruxelles , the  Fonds de la Recherche Scientifique FRS-FNRS Belgium , the  Convention Jean Van Damme of the Fonds Erasme  and the  Fonds de soutien Marguerite-Marie Delacroix. It was facilitated by the European Reference Network on Rare Congenital Malformations and Rare Intellectual Disability (ERN-ITHACA; EU Framework Partnership Agreement ID:  3HP-HP-FPA ERN-01-2016/739516 ).
Thus, there is the potential for interspecific competition between these two types of ectoparasites. Previous studies have examined the consequences of ectoparasitism on common lizards, such as the effect of host density on host-parasite interactions  [26] , the effect of maternal parasite load on offspring life-history traits  [27] , and the impact of maternal infestation on offspring performance and dispersal  [28]. However, whether different environments may influence the prevalence of mites and ticks and their competitive interactions remains unknown in this species. The aim of this study is to investigate the organismal and environmental factors associated with parasite prevalence and the potential for competition between mites and ticks. We collected data on body size, body mass, and parasite infestation on common lizards from twelve populations along an altitudinal and human-induced perturbation gradient.
Environmental correlates of parasite infestation At each capture site, we recorded the altitude, the maximum temperature during June (T max6 ), an index of vegetation coverage, an index of anthropogenic disturbance, and the presence or absence of livestock (grazing). Altitude was the mean altitude of each site. The maximum temperature of June was based on measurements from the nearest meteorological station. However, as there was no unique meteorological station for each study site, we measured the local temperature of all sites by using temperature data loggers (Thermochron iButtons©, Waranet Solution, Auch, France, see Rutschmann et al. [55] ). The final maximum June temperature in the analysis was predicted by the coefficients of a linear regression between temperature estimated by data loggers and those from the nearest meteorological station  [55].
Statistical analysis Influence of the environment on parasite infestation The variables we used to characterize the environment of each capture site have different scales of measurement. Thus, we used a principal components analysis (PCA) on four environmental factors (altitude, vegetation index, human disturbance and grazing condition) to generate new axes for describing differences among sites (Table  4 ,  [55] ). We extracted principal components from a correlation matrix using the function principal in the 'psych' package in R  [56]. We determined the number of PC axes to retain based on the broken-stick method  [57]. We used the PC axes to characterize the environmental features of each sample site. We used the PC scores to investigate the relative roles of host phenotypic characteristics and environmental factors in structuring the susceptibility to parasitism on common lizards.
Following the method of hurdle models  [58] , we used generalized linear models (GLM) to examine the probability of parasite infestation (Binomial distribution, uninfested vs. infested) and parasite load (zero-truncated Poisson distribution, the number of parasites found on an infested individual). We included the following predictor variables: the first PCA axis based on the environmental variables, T max6 , year, sex, and body condition. We also performed a generalized linear mixed model (GLMM) analysis that used capture site as the random factor. When including all 3 years in the analysis, the GLMM model failed to converge. We attribute this to unbalanced sampling of some sites during 2015. We repeated the mixed model analysis, but excluded the data for these sites (110 lizards, accounting for 14% of total sample size), and the model yielded similar results to the GLM.
Environments and competition between mites and ticks We used piecewise structural equation modeling (SEM) to explore the causal relationships between environmental factors, host traits, and potential competition between two parasites. We built models either for infestation probability or parasite load based on a priori observations and results, using the 'piecewiseSEM' package in R  [65]. Compared to the traditional SEM, the piecewise SEM can account for the non-normal distribution of variables and random structure in models. Furthermore, the method allows a test of whether any paths are missing from the model by using Shipley's test of d-separation  [66]. The goodness-of-fit of piecewise SEM was also evaluated with the akaike information criterion (AIC). The adequacy of overall fit is indicated by a non-significant P value based on a Chi square test (P > 0.
Funding Monitoring was financially supported by the 'Agence Nationale de la Recherche' (ANR 319 MOBIGEN), the 'Agence Nationale de la Recherche' (ANR MOBIGEN) and the Laboratoire d'Excellence TULIP (ANR-10-LABX-41). Q. Wu is financially supported by China Scholarship Council (CSC) for his PhD study. D. B. Miles was supported by NSF Grant (EF128428). The funding body had no roles in the design of the experiment and collection, analysis, and interpretation of the data or the writing of the manuscript.
To date, secreted single-domain ShK toxins have been only reported from the Mediterranean vampire snail Cumia reticulata (Gastropoda, Colubrariidae), and identified as salivary gland (SG)-specific products based on transcriptomic evidence  [24]. However, the same study also highlighted the presence of multidomain proteins where ShKT domains are associated either with  (cysteine-rich secretory proteins, antigen 5, and pathogenesis-related 1 proteins) or with M12 (astacin metalloprotease) domains. The association between CAP and ShkT domains is relatively common in nature, and it is found in more than 600 proteins currently deposited in public sequence databases, both from vertebrates and invertebrate organisms. Most notably, the ShKT motif is often embedded in the larger cysteine array of the CRISP domain, which takes its name from vertebrate cysteine-rich secretory proteins (CRISPs).
These multifunctional proteins, which have been linked with the proper functioning of the mammalian reproductive system  [25]  are most commonly known for their role as cyclic nucleotide-gated ion channel inhibitors in the venom of snakes  [26, 27] , lizards  [28]  and lampreys  [29]. The MEROPS peptidase family M12 domain (InterPro: IPR001506) is the characterizing signature of astacins, a very large family of metalloendoproteases with widespread taxonomic distribution and diverse biological functions  [30]. Astacin-like proteases often combine the M12 domain with several other N-terminal or C-terminal modules, with CUB being the most prevalent. M12-CUB proteases are found in a wide range of organisms, from vertebrates to the nematode Caenorhabditis elegans, where they form a largely expanded gene family  [31].
M12-ShKT Proteins We could identify seven transcripts encoding full-length M12-ShK proteins in C. reticulata (henceforth referred to as CreM12-ShK), but the size of this gene family is likely larger, as suggested by the detection of a number of partial contigs denoting fragmentation. The identified proteins shared an N-terminal signal peptide for secretion, followed by a 50-80 aa long uncharacterized region, and the M12 domain, which was always recognizable with respect to the Pfam astacin domain consensus (PF01400). This zinc-dependent functional domain displayed a good level of overall conservation in all seven sequences, in particular for the ion-binding active site exemplified by the consensus sequence HEXGHXXGLXHEH (Figure  4 , panel E). The precursor proteins sequences of CreM12-ShK varied from 512 (CreM12-ShK 4) to 725 (CreM12-ShK 2) aa residues.
Discussion Although the ShKT domain is a rather common structural fold in the animal kingdom, it is often combined with other functional modules in large multidomain proteins. Based on the analysis available for fully sequenced genomes, mollusk genomes typically possess multiple genes encoding ShKT-domain containing proteins, for a global number that appears to be quite uniform across the three major molluscan classes, ranging from 15 in the limpet Lottia gigantea to nearly 50 in the oyster Magallana gigas. However, only a very few of these proteins has been functionally characterized so far as most of the sequences deposited in public databases derive from automated gene prediction. The two exceptions are Pf-ALMP, an astacin-like metalloproteinase highly expressed in the haemocytes of pearl oysters and implicated in proliferation processes  [38] , and a myc-like protein from Crassostrea virginica, tentatively linked to the control of cell cycle progression  [39].
Identification and Characterization of ShKT Domain Containing Protein Sequences The translated ORFs derived from C. reticulata salivary, mid-oesophageal and whole-body transcriptomes  [24] , assembled de novo using different methodologies as described in  Gerdol et al. 2018 [35] , were analyzed with Hmmer v. 3. 1b2 [77]  to detect significant matches with the ShKT domain. Compared to the previous work  [35] , protein predictions were obtained using a lower minimum ORF length threshold, equal to 50 codons, taking into account the average expected length of single-domain ShK-like proteins. ShkT-domain containing proteins were identified by the detection of the conserved domain based on the Pfam PF01549 signature with e-value < 0. 05. The completeness and correctness of the obtained protein sequences and their encoding mRNAs were evaluated by the assessment of a uniform sequencing coverage, as previously described  [35].
Calculation of Gene Expression Values To ensure broad comparability both within and between samples, the expression level of C. reticulata ShKT-domain containing transcripts are here reported as transcripts per million (TPM), calculated using the formula described by Wagner and colleagues  [81]. Namely, sequencing data from Modica et al. , 2015, linked to three tissues (salivary gland, mid-oesophageal gland and the whole body except for the two aforementioned organs), with three biological replicates each, were used to infer gene expression values. Trimmed reads were mapped on the non-redundant C. reticulata transcriptome with high stringency (length fraction = 0. 75, similarity fraction = 0. 98) using the map reads to reference tool included in the CLC Genomics Workbench v. 11 (Qiagen, Hilden, Germany).
We used Log-transformed TPM values to generate heat maps for graphically depicting the transcriptional profiles of ShKT-domain containing proteins across tissues. The expression levels of target orthologous genes were computed with the same strategy in Pomacea canaliculata  [42]  (in gills, lung, ovary, testis, haemocytes, stomach, albumen gland, kidney, mantle and digestive gland) and Magallana gigas  [60]  (inner and outer mantle, male and female gonad, digestive gland, gills, labial palps, posterior adductor muscle and hamocytes). The RNA-seq datasets analyzed were retrieved from the NCBI SRA database (BioProjects PRJNA427478 and PRJNA423172 for P. canaliculata, PRJNA146329).
Phylogeny of C. reticulata ShKT-Domain Containing Proteins The protein sequences identified as described above, were divided into three different subgroups: (i) single domain ShK-like proteins; (ii) proteins with an ShKT domain, associated with cysteine-rich secretory proteins, antigen 5, and pathogenesis-related 1 proteins (CAP) domains, identified by the presence of the IPR014044 signature; and (iii) proteins with an ShKT domain associated with a Peptidase M12A domain, identified by the presence of a IPR001506 signature. The three sequence sets were independently subjected to multiple sequence alignment with MUSCLE  [82] , together with other metazoan sequences chosen in order to maximize both functional and taxonomic coverage (see below for details). In all analyses, sequences derived from the genomes of Pomacea canaliculata  [42] , Lottia gigantea  [43]  and Biomphalaria glabrata  [44] , were selected as representatives for Caenogastropoda, Patellogastropoda and Heterobranchia, respectively.
Multiple alignments used for phylogenetic reconstruction are available as Supplementary Material (Text S1). The phylogeny of CreCAP-ShK proteins was built using the region corresponding to the CAP domain only, due to the heterogeneous organization of the C-terminal cysteine-rich domain. Representative vertebrate CRISPs, ecdysozoan venom allergens and salivary gland secretory proteins, the Conus proteins Mr30 and Tex31 and a putative venom protein from the blue ringed octopus Hapalochlaena maculosa  [37]  were also included in the multiple sequence alignment. We evaluated the WAG model of molecular evolution, with a proportion of invariable sites and a gamma-distributed rate variation across sites, as the best-fitting one for this dataset  [83] , using ModelTest-NG (https: //github. com/ddarriba/modeltest/).
For M12-ShK proteins, phylogenetic inference was carried out on the region corresponding to the astacin domain only. Representative sequences from cnidarians (based on the N. vectensis genome), nematodes (including those derived from the genome of C. elegans) and Platyhelminthes were included in the MSA, together with Pf-ALMP from the pearl oyster P. fucata  [38]. All the sequences included displayed the characterizing M12 domain accompanied by one or more ShKT domains. In this case, the selected model for molecular evolution was VT+I+G. Phylogenetic inference was performed in both cases with MrBayes 3. 2. 1  [84]  running two independent MCMC analysis in parallel with four chains each, for 200,000 generations. Run convergence was evaluated with Tracer v. 1. 7. 1 (https://github. com/beast-dev/tracer/) based on achieving an effective sample size value >= 200 for all the estimated parameters.
Chaperone mobility analysis by FLIP and FCS We used FLIP and FCS to analyze chaperone mobility in a Zeiss LSM780 confocal microscope. FLIP was used as a qualitative assay to determine Ssa1-GFP and Ydj1-GFP mobility in the whole cell. A small circular region of the (3. 6 μm 2 ) was repetitively photobleached at full laser power, whereas the cell was imaged at low intensity every 0. 5 s to record fluorescence loss. After background subtraction, fluorescence data were corrected to discard the small loss of fluorescence caused by image acquisition as measured in control cells in the same field that were not purposely bleached. Then, fluorescence data from an unbleached cell region were made relative to the initial time point, and a mobility index was calculated as the inverse of the fluorescence half-life obtained by fitting an exponential function to fluorescence emitted as a function of time.
9-fold (Fig S7 ). Stress effects were simulated on steady states by transferring different fractions (20-80%) of the folded protein (Prot F and Cln3 F ) to the unfolded population (Prot U and Cln3 U , respectively). To simulate transient effects in Cln3F by stress, experimental data of Ydj1-GFP mobility were used to fit the model time variable at different percentages of protein unfolding  (Fig 5C and D) , resulting in 60% for salt stress and 80% for heat stress. We took into account that heat, but not salt, stress increases Ydj1 mRNA levels by 2. 9-fold  (Gasch et al, 2000). Then, the resulting fitted parameters were applied to the model to simulate the evolution of Cln3F after stress, assuming that Prot U and Cln3 U compete or not for Ydj1 A  (Fig 5G and H). The model was deposited in the BioModels  (Chelliah et al, 2015)  database as MODEL1808310001 in SBML format and a COPASI  (Hoops et al, 2006)  file to reproduce simulations with THE parameter set 3114.
Acknowledgements We thank  A Cornadó  and  E Rebollo  for technical assistance,  T Zimmermann  for technical advice in FCS experiments, and  B Futcher  and  J Skotheim  for providing strains. We also thank  C Rose  for editing the manuscript and  F Antequera ,  Y Barral ,  C Gallego ,  JC Igual ,  S Oliferenko , and  F Posas  for helpful comments. This work was funded by the  Spanish Ministry of Science, Consolider-Ingenio 2010 , and the  European Union (FEDER)  to  M Aldea. DF Moreno  received an  FI fellowship  from  Generalitat de Catalunya.