dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 31 (evalue = 1e-10) and removed loci that did not blast to itself with sequence identity >90%. The final set of target loci for phylogenetic inference included 1749 loci, with a total length of 470,435 bp. To recover conotoxin loci, we targeted sequences generated from both the previous targeted sequencing dataset  (Phuong & Mahardika 2017)  and the transcriptome dataset  (Phuong et al. 2016). For conotoxin sequences discovered from the targeted sequencing dataset  (Phuong & Mahardika 2017) , we performed the following to generate our target sequences: (1) we trimmed each sequence to only retain the coding region and included 100bp flanking the exon, (2) merged sequences using cd-hit v4. 6. 4  (Li & Godzik 2006 ) at 95% sequence similarity to reduce redundancy among conotoxin loci (3) masked repeats using the RepeatMasker v4.
0. 6 web server  (Smit et al. 2015) , and (4) retained loci >120bp to ensure that the locus was longer than our desired bait sequence length. We concatenated all sequences below 120bp to create a single, chimeric sequence for capture. The final set of target sequences from the previous targeted sequencing dataset consisted of 12,652 unique loci totaling 3,113,904 bp and a single concatenated sequence representing 351 merged loci with a total length of 37,936 bp. We also targeted conotoxin loci from the transcriptomes described in  (Phuong et al. 2016 ) to obtain conotoxin loci from gene superfamilies that were not targeted in  (Phuong & Mahardika 2017)  or performed poorly. We performed the following to generate a set of conotoxin loci from the transcriptome data: (1) we trimmed sequences from  (Phuong et al.
2016)  to only include the coding region and 100bp of the untranslated regions (UTRs), (2) merged sequences using cd-hit v4. 6. 4  (Li & Godzik 2006 ) at 97% sequence similarity to reduce redundancy among conotoxin. CC-BY-NC 4. 0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint (which. http://dx. doi. org/10. 1101/359976 doi: bioRxiv preprint first posted online Jul. 1, 2018; loci, and (3) masked repeats using the RepeatMasker v4. 0. 6 web server  (Smit et al. 2015). This filtered dataset contained 395 conotoxin loci with a total length of 171,317 bp. We submitted the following datasets to MYcroarray (Ann Arbor, Michigan, USA) for bait synthesis: (1) 1749 loci for phylogenetic inference, (2) 12652 conotoxin loci using data from  (Phuong & Mahardika 2017) , (3) a single concatenated sequence using data from  (Phuong & Mahardika 2017) , and (4) 395 additional conotoxin loci using transcriptome data from  (Phuong et al.
2016). We chose to synthesize a MYbaits-3 kit, which included 60,000 bait sequences to accommodate all the targeted loci. Because our aim was to recover sequences from species throughout Conidae, each bait sequence was 120bp in length, which increases the efficiency of recovering divergent fragments. We used a 2X tiling density strategy (a new probe every 60bp) across the sequences from datasets (1) and (  2 ) and used a 4X tiling density strategy (a new probe every 30bp) across datasets (3) and (4). We chose to increase the tiling density for datasets (3) and (4) because the boundaries between exons were not denoted and we wanted to ensure effective capture of the conotoxin loci. The set of probe sequences will be made available on DRYAD following publication.
Phylogenetic data processing and filtering To associate assembled contigs with the target sequences for phylogenetic inference, we used blastn v2. 2. 31 (word size = 11, evalue = 1e-10). For the set of target sequences that originated from the transcriptome dataset, we redefined exon/intron boundaries using EXONERATE v2. 2. 0  (Slater & Birney 2005)  using the est2genome model because we found that several predicted exons actually consisted of several smaller exons. For each sample, we mapped reads using bowtie2 (very sensitive local and no discordant options enabled) to a reference that contained only sequences associated with the targeted phylogenetic markers. We marked duplicates using picard-tools v2. 0. 1 and masked all regions below 4X coverage and removed the entire sequence if more than 30% of the sequence was below 4X coverage.
Conotoxin assembly, processing, and filtering Commonly used assembly programs are known to poorly reconstruct all copies of multilocus gene families  (Lavergne et al. 2015; Phuong et al. 2016). To address this issue, we followed the conotoxin assembly workflow outlined in  (Phuong & Mahardika 2017). Briefly, we first mapped reads back to our assembled contigs using the 'very sensitive local' and no discordant' options. Then, we identified conotoxins within our dataset by using blastn v2. 2. 31 (word size = 11, evalue = 1e-10) to associate our assembled contigs (from SPAdes) with conotoxins we targeted in the bait design. We generated a set of unique conotoxin 'seed. CC-BY-NC 4. 0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which. http://dx. doi. org/10. 1101/359976 doi: bioRxiv preprint first posted online Jul. 1, 2018; sequences' (a short stretch [~100bp] of conotoxin-blasted sequence) using a combination of of the pysam module (https://github. com/pysam-developers/pysam), cd-hit v4. 6 (percent identity = 98%), cap3 (overlap percent identity cutoff = 99%), blastn v2. 2. 31 (word size = 11, evalue=1e-10), and Tandem Repeats Finder v4. 09 (Benson 1999, minscore = 12, maxperiod = 2). We mapped reads to these seed sequences using bowtie2 v2. 2. 6 (very sensitive local and no discordant options enabled) and built out the conotoxin sequences using the PRICE v1. 2 algorithm, which uses an iterative mapping and extension strategy to build out contigs from initial seed sequences  (Ruby et al.
2013). We ran price on each seed sequence at 5 minimum percentage identity (MPI) values (90%, 92%, 94%, 96%, 98%) with a minimum overlap length value of 40 and a threshold value of 20 for scaling overlap for contig-edge assemblies. A reassembled sequence was retained if it shared 90% identity with the original seed sequence and we reduced redundancy by only retaining the longest sequence per seed sequence out of the 5 MPI assembly iterations. This approach is described in further detail in  (Phuong & Mahardika 2017). We note that the final conotoxin sequences per sample consisted of exon fragments, where each sequence represents a single conotoxin exon flanked by any adjacent noncoding region. We updated our conotoxin reference database because we targeted additional conotoxin transcripts from  (Phuong et al.
2016). We used blastn v2. 2. 31 (word size = 11, evalue =1e-10) and EXONERATE v2. 2. 0 to define exon/intron boundaries for these additional conotoxin transcripts and added them to our conotoxin reference database. The final conotoxin reference database consisted of conotoxin sequences with the coding regions denoted and gene superfamily annotated. We also annotated the conotoxin sequences for functional region (e. g. , signal, pre,. CC-BY-NC 4. 0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint (which. http://dx. doi. org/10. 1101/359976 doi: bioRxiv preprint first posted online Jul. 1, 2018; mature, post) using blastn v2. 2. 31 (word size = 11, evalue = 1e-10) with a conotoxin reference database that was previously categorized by functional region  (Phuong & Mahardika 2017).
With the final conotoxin reference database, we performed blastn v2. 2. 31 (word size = 11, evalue = 1e-10) searches between the conotoxin reference and every sample's re-assembled conotoxin sequences. We retained sequences if they could align across the entire coding region of the reference sequence. We guessed the coding region for each retained sequence by aligning the query sequence with the reference conotoxin using mafft v7. 222 and denoting the coding region as the region of overlap with the exon in the reference conotoxin. We fixed misassemblies by mapping reads with bowtie2 (very sensitive local and no discordant options enabled, score min = L, 70, 1) back to each conotoxin assembly and marked duplicates using picard-tools v2. 0. 1. We masked regions below 5X coverage and discarded sequences if coverage was below 5X across the entire predicted coding region.
1. 1  (Grabherr et al. 2011 ) reduced redundancy in these transcriptomes with cap3 and cd-hit (percent identity = 99%). We used blastn (word size = 11, evalue=1e-10) to associate contigs with the phylogenetic markers present in our dataset. We used bowtie2 v2. 2. 7 (very sensitive local and no discordant enabled), samtools v1. 3, and bcftools 1. 3 to map reads and call SNPs. We removed sequences if they were below 4X coverage for > 30% of the sequence and masked bases if they were below 4X coverage. We also removed sequences if they had a heterozygosity value two standard deviations away from. CC-BY-NC 4. 0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint (which.
CC-BY-NC 4. 0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint (which. http://dx. doi. org/10. 1101/359976 doi: bioRxiv preprint first posted online Jul. 1, 2018;  (Kohn 1990). We assigned 12 additional fossils (Table  S2 , Fig. S1  (Duda Jr. et al. 2001; Hendricks 2009 Hendricks , 2015 Hendricks , 2018)) ) to nodes throughout the phylogeny as minimum age constraints, which MCMCtree treats as soft bounds on the minimum age  (Yang 2007). Further information on fossil placement on nodes can be found in the Supplementary. A recent paper showed that the number of species in Lautoconus may be overestimated  (Abalde et al. 2017 ). To account for potential artificial inflation in the species richness of this clade, we artificially removed half the unique species in Lautoconus from our dataset and ran all dating analyses and downstream diversification analyses on this secondary dataset.
Characterizing diversification patterns To visualize lineage accumulation patterns, we generated a log-lineage through time plot using the R package APE  (Paradis et al. 2018). We estimated diversification rates and identified rate shifts using BAMM (Bayesian Analysis of Macroevolutionary Mixtures)  (Rabosky 2014) , which uses reversible jump Markov chain Mone Carlo to explore potential lineage diversification models. To account for non-randomness in species sampling across Conidae genera, we applied generic-specific sampling fractions. Using the number of valid Conidae names on WoRMS as estimates of total species diversity in each genus (Worms Editorial Board 2017), we applied a sampling fraction of 32. 1% to Profundiconus, 50% to Lilliconus, 100% to Californiconus, 16. 7% to Pygmaeconus, 28% to Conasprella, and 33.
This is in contrast to other genera in Conidae, such as Conasprella or Profundiconus, where low conotoxin diversity values are likely the result of poor knowledge of the venom repertoire of these genera (Fig. S2 ) First, we used BiSSE (binary state speciation and extinction,  (Maddison et al. 2007))  implemeneted in the R package diversitree (FitzJohn 2012), which employs a maximum likelihood approach to estimate the impact of a binary trait on speciation, extinction, and transition rates between character states. We coded the conotoxin gene diversity data as 'low' or 'high' across several thresholds (i. e. , 250, 300, 350, 400, 500, 550, or 600 estimated conotoxin genes per species) and compared BiSSE models where speciation rates were allowed to vary or remain equal between traits. We applied a sampling fraction of 33.
7%, taking the maximum number of Conus species to be the number of valid names on WoRMS (World Register of Marine Species, (Worms Editorial Board 2017)). We determined the best-fitting model using Akaike Information Criterion (AIC). Second, we used FiSSE (Fast, intuitive State-dependent Speciation-Extinction analysis), a non-parametric statistical test that assesses the effects of a binary character on lineage diversification rates  (Rabosky & Goldberg 2017). We followed the same coding strategy as in the BiSSE analyses to convert conotoxin gene diversity counts to binary character states. Finally, we used STRAPP (Structured Rate Permutations on Phylogenies,  (Rabosky & Huang 2016 ) implemented in the R package BAMMtools  (Rabosky et al. 2014). CC-BY-NC 4. 0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
Targeted sequencing data We sequenced an average of 9,548,342 reads (range: 1,693,918 -29,888,444) across the 363 samples (Table  S1 ). After redefining exon/intron boundaries in the phylogenetic marker reference, we ultimately targeted 2210 loci. On average, we recovered 1388 of these loci per sample (range: 30 -1849, Table  S1 ) at an average coverage of 12. 39X (range: 3. 08X -27. 87X, Table  S1 ). For the conotoxin dataset, each sequence we re-assembled contained a single conotoxin exon with any associated noncoding regions (referred to here as 'conotoxin fragments'). We recovered on average 3416 conotoxin fragments per sample (range: 74 -11535 fragments, Table  S1 ) at an average coverage of 32. 3X (range: 5. 06X -65. 77X, Table  S1 ). When mapped to a reference containing both the phylogenetic markers and conotoxin genes, the % reads mapped to our targets was on average 14.
S3 ). CC-BY-NC 4. 0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint (which. http://dx. doi. org/10. 1101/359976 doi: bioRxiv preprint first posted online Jul. 1, 2018; Our final conotoxin sequence dataset consists of exon fragments and we do not have information on exon coherence (which exons pair together on the same gene). We were unable to assemble full conotoxin genes because conotoxin introns are long (>1 kilobases,  (Wu et al. 2013) ) and exceed the average insert size of our sequencing experiment (~350bp). We recovered fragments from all 58 gene superfamilies we targeted and obtained 159,670 sequences containing some or all of the mature toxin region (Table  S3 ).
Phylogeny The amount of missing data from the alignments was 15. 4% when a minimum of 80% of the taxa were present in each locus, 26. 8% when 50% of the taxa were present, and 38. 6% when 20% of the taxa were present. The number of loci retained in the alignment was 387 (107,011 bp) when a minimum of 80% of the taxa were present in each locus, 976 (237,027 bp) when 50% of the taxa were present, and 1476 loci (336,557 bp) when 20% of the loci were present. Across all methods and datasets, we recovered phylogenies with a moderate level of resolution (average number of nodes resolved = 71. 1%, range = 61. 4 -79. 2%, Table  S6 ). In general, as increased amounts of sequence data was given to the phylogenetic programs, more nodes became resolved (Table  S6 ). While we recovered all 6 genera within Conidae with high confidence, relationships among subgenera were less supported (bootstrap and PP = 100%, Fig.
Diversification patterns. CC-BY-NC 4. 0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint (which. http://dx. doi. org/10. 1101/359976 doi: bioRxiv preprint first posted online Jul. 1, 2018; We found that most branching events within each genus began to occur in the Miocene and continued until the present (Fig. 1 ). When analyzing the entire dataset, we found support for diversification rate heterogeneity, where BAMM identified at least one rate shift across Conidae (Fig. 1, S10 ). Across the 95% credible set of distinct shift configurations, BAMM detected an increase in diversification rates on the branch leading to Lautoconus, a clade consisting mainly of species endemic to the Cape Verde islands (Fig.
Trait dependent diversification Across all thresholds for the BiSSE analysis, we found that diversification rates were not influenced by conotoxin gene diversity. In all cases, the null model was either preferred (delta AIC > 2, Table  S7 ) or was indistinguishable from a model where speciation and extinction were allowed to vary (delta AIC < 2, Table  S7 ). Both the FiSSE and STRAPP analyses revealed that speciation rates were not correlated with conotoxin gene diversity (p > 0. 05). These results were consistent across both the full dataset and the reduced dataset. We found that diversification rates were not dependent on diet when analyzing the full dataset (Table  S8 ). However, in the reduced dataset, we found a signal for diet-dependent speciation rates (delta AIC > 2, Table  S8 ). We found that species with mollusk-feeding diets had the fastest speciation rates (0.
33), followed by piscivory (0. 24), and vermivory (0. 16). For the larval dispersal trait, we found support for trait-dependent speciation rates in the full dataset (delta AIC > 2, Table  S9 ), where species with short-lived larvae had higher speciation rates (0. 27. CC-BY-NC 4. 0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint (which. http://dx. doi. org/10. 1101/359976 doi: bioRxiv preprint first posted online Jul. 1, 2018; vs. 0. 16). However, this result was not significant when examining the reduced dataset (Table  S9 ).
2014b). Our results do not support the monophyly of Elisaconus, as the sister relationship between C. moncuri and C. pergrandis was not supported in 5/6 trees. Additional data is required to classify C. moncuri and C. pergrandis into the appropriate subgenus. (11) C. cocceus was placed into Floraconus based on morphological characters in  (Puillandre et al. 2014b). With sequence data, we found that it was actually nested within Phasmoconus. Therefore, we transfer C. cocceus to the subgenus, Phasmoconus. Classification within Conidae is known to be highly unstable (Jiménez-Tenorio & Tucker 2013;  Puillandre et al. 2014a; b; Puillandre & Tenorio 2018). Although the phylogeny presented here improved understanding of subgeneric relationships and monophyly of subgenera, resolving relationships within Conidae still remains a significant challenge.
Given the underperformance of our capture experiment (Table  S1 ), it is unclear if the reason for the moderate power in resolving relationships is due to insufficient data/incomplete data or due to short internal branches during the origination of Conidae subgenera that are extremely difficult to resolve. Overall, our results suggest that both additional data and increased sampling of Conidae species are reasonable pursuits to continue attempting to resolve the phylogeny and classification of this family of marine snails.
The Conidae fossil record and analyses of several molecular phylogenetic studies suggest a major radiation of Conus during the Miocene  (Kohn 1990; Duda Jr. et al. 2001; Uribe et al. 2017). While we noted that many branching events within Conus ocurred during the Miocene into the present, we did not detect an increase in diversification on the branch leading to the origin of Conus (Fig. 1, S10, S11 ). This is congruent with diversification rates estimated from the fossil record  (Kohn 1990) , suggesting that the accumulation of species during the Miocene may have been a function of an increased number of lineages present rather than an increase in diversification rates. The number of species we included in the subgenus Lautoconus had an impact on the BAMM diversification analyses. On the full dataset, BAMM detected an increase in diversification rates leading to Lautoconus (Fig.
However, we did find some signal for the impact of diet and larval dispersal strategy on diversification rates when using the BiSSe and MuSSE methods (Table  S8, S9 ). Further work is needed to be fully confident in this signal given high false positive rates in these methods (Abosky 2017; Rabosky & Goldberg 2017) and given that our results depended on which dataset was used. What is remarkable about these results is the lack of any signal on the impact of venom gene diversity on diversification rates in cone snails, even as we found some signal for trait-. CC-BY-NC 4. 0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint (which. http://dx.
Although we do not detect a strong signal of conotoxin gene diversity shaping speciation rates in Conidae, it does not refute the importance of venom evolution in adaptation and prey specialization as venom may be necessary, but not sufficient, to promote speciation  (Duda et al. 2009; Safavi-Hemami et al. 2015; Chang & Duda 2016; Phuong et al. 2016; Phuong & Mahardika 2018). Future work in other venomous animal systems may shed light on whether or not the ability to adapt to different prey through venom evolution translates to the long-term evolutionary success of taxa. Table  S1. Sample information and capture efficiency metrics. We first list the species name listed in all phylogenetic analyses ("species") and the accepted taxonomic classification in the WoRMS database at the genus, subgenus, and species level ("WoRMS genus", "WoRMS subgenus", "WoRMS species").
Phylogenetic trees were inferred using either RAxML or ASTRAL-II. The "% taxa per locus was" the percent of samples needed per locus in order to retain the locus for phylogenetic inference. Table  S7. Venom gene diversity BiSSE AIC results. "Dataset" represents whether the full dataset was used or the reduced dataset. "Threshold" represents the conotoxin gene diversity value used to decide between "high" and "low" conotoxin diversity. Values above the threshold value were categorized as "high" and values below were categorized as "low". "AICvariable rates" shows AIC values for a model where speciation and extinction rates were allowed to vary depending on a trait. "AIC -equal rates" represents AIC values for the null model, where rates were not allowed to vary by trait. Table  S8. Diet BiSSE AIC results.
Acknowledgements. CC-BY-NC 4. 0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint (which. http://dx. doi. org/10. 1101/359976doi: bioRxiv preprint first posted online Jul. 1, 2018;.
Recent data suggest a new therapeutic drug candidate, β-cyclodextrin  [40] , which may resolubilize cholesterol within cells and induce the release of cholesterol-rich membrane inclusions  [41]. A third autosomal recessive disease with direct links to cholesterol is desmosterolosis (OMIM 602398), which is caused by mutations in the 24-dehydrocholesterol reductase (DHCR24) (Figure  1 ). This enzyme catalyzes the reduction of desmosterol to cholesterol, a crucial step in the Bloch branch of cholesterol synthesis (Figure  1 )  [42]. Patients with desmosterolosis present microcephalia, hydrocephalia, ventricular enlargement, defects in the corpus callosum, and thinning of white matter and seizures. As for SLOS, the symptoms of this disease may be attributed to reduced cholesterol levels or to accumulation of a precursor, in this case desmosterol.