dataesr/scientific-paragraphs-categorization huggingface dataset:
Individual 6 and his unaffected parents underwent WGS as part of the 100,000 Genomes Project. Genomic DNA was processed using a kit (TruSeq DNA PCR-Free Sample Preparation; Illumina) and sequenced using a high-throughput sequencing platform (HiSeq X Ten; Illumina), generating minimum coverage of 15 times for more than 97% of the callable autosomal genome. Readings were aligned to build GRCh37 of the human genome using the Isaac aligner (Illumina). Singlenucleotide variants and indels (insertions or deletions) were identified using Platypus software (v. 0. 8. 1; Wellcome Trust Centre for Human Genetics) and annotated using Cellbase software. Variant filtering was performed using minor allele frequency in publicly available and in-house datasets, predicted protein effect, and familial segregation.
SWIM domains are present in diverse archaeal, bacterial, and eukaryotic proteins, including bacterial ATPases of the SWI2/SNF2 family and vertebrate MEK kinase-1, and have putative roles in DNA and protein binding. 16 In addition to the SWIM domain (246-283 aa), ZSWIM6 (GenBank: NP_065979. 1) contains a BC-box (72-79 aa), Cul2-box (99-107 aa), a Cut8/STS1-like domain (285-482 aa), and a C-terminal Sin-3 like domain (1,148-1,215 aa)  4  (Figure  1A ). The c. 2737C>T variant lies within the penultimate exon 13, 48 base pairs (bp) upstream of the last exon/exon junction (Figure  1A ), suggesting that the ZSWIM6 PTC-encoding mRNA may escape nonsense-mediated decay (NMD). We therefore sought to confirm this in cells from the affected individuals. Lymphoblastoid cell lines (LCLs) from affected individual 1 and control subjects were generated using standard methods.
We cultured LCLs from affected individual 1 in the presence or absence of cycloheximide, a translational inhibitor and suppressor of NMD. Sanger sequencing of ZSWIM6 cDNA revealed that the c. 2737C>T transcript is not subjected to NMD (Figure  1B ). This was consistent with real-time quantitative PCR data that showed no significant difference in ZSWIM6 expression between the affected individual and his unaffected brother and four unrelated male control LCLs (Figure  1C ). Sequencing of cDNA generated from peripheral blood of individual 2 also suggested that the variant does not result in NMD (Figure  S1 ). We could not detect the full-length (in LCLs of unaffected brother of individual 1) or truncated (in affected individual 1) ZSWIM6 protein using an anti-ZSWIM6 antibody raised against amino acids 634-696 (Sigma HPA035938).
Hypothesizing that the recurrent c. 2737C>T (p. Arg913Ter) variant is not a simple haploinsufficiency allele, we examined the frequency of ZSWIM6 loss-of-function (LoF) variants or chromosomal deletions in case and control populations. In the Database of Genomic Variants and the Copy Number Variation Morbidity map available through the UCSC genome browser, the only deletions affecting coding sequence correspond to a small, highly GC-rich region containing exon 1 and are therefore likely false positives (Figures  S3A  and S3B ). The only LoF variants listed in the gnomAD database  18  fall within a region of very poor coverage in a highly GC-rich and repetitive region of exon 1 (Figure  S3C ); all are indels and visual inspection suggests these are also artifactual. No individuals in our in-house cohorts, DECIPHER, or the literature have chromosomal deletions confined to ZSWIM6.
2620C>T (p. Gln874Ter) (Figure  1A ), in a proband who died of an immunodeficiency phenotype caused by a homozygous recessive mutation in IFNGR2 (MIM: 147569; GenBank: NM_005534. 3; c. 679G>A [p. Gly227Arg]). Sanger sequencing indicated that this heterozygous ZSWIM6 variant was inherited from the proband's mother; neither individual had significant neurological deficits. c. 2620C>T, falling within exon 12, is predicted to result in NMD of its mRNA. Unfortunately, this could not be tested experimentally. Available data suggest that a heterozygous loss of ZSWIM6 does not cause ID. As the c. 2737C>T ZSWIM6 variant mRNA escapes NMD, cellular consequences may be due to a dominant-negative effect caused by the presence of truncated ZSWIM6 protein. Several human diseases are caused by clustered C-terminal protein-truncating mutations.
23 The biochemical function of the ZSWIM6 Sin3-like domain and the divergent molecular consequences of its truncation (p. Arg913Ter) or local surface alteration (p. Arg1163Trp) will be an important area for future investigations. Frequency data were available from the Baylor Genetics and DDD cohorts: the recurrent p. Arg913Ter variant was found in 3/6,100 case subjects with a neurological phenotype who had exome sequencing through Baylor Genetics, and 1/5,873 case subjects with pediatric-onset intellectual disability or global developmental delay who had exome sequencing through the DDD cohort, suggesting a frequency of 1. 7-4. 9 3 10 À4 in individuals with neurocognitive/neurological disorders. The variant was not present in any of the 123,136 exomes and 15,496 genomes listed in the gnomAD database, which is depleted of individuals and their first-degree relatives with severe pediatric disease.
Acknowledgments The authors thank the affected individuals and their families for participation in this study. We would like to thank the  Greenwood Centre, South Carolina, USA , and in particular the  Director of Molecular Studies Professor Charles Schwartz  for arranging the sending of DNA of individuals with a Coffin-Lowry phenotype for investigation of the recurrent ZSWIM6 variant. J. G. was supported by  NHMRC  Program Grant 1091593, Senior Research Fellowship 1041920, and  Channel 7 Children's Research Foundation. This research was made possible through access to the data and findings generated by the 100,000  Genomes  Project. The  100 , 000   Genomes  Project is managed by  Genomics England Limited  (a wholly owned company of the  United Kingdom Department of Health). The 100,000  Genomes  Project is funded by the  National Institute for Health Research and NHS England.
The detection and identification of bacterial pathogens involved in acts of bio-and agroterrorism are essential to avoid pathogen dispersal in the environment and propagation within the population. Conventional molecular methods, such as PCR amplification, DNA microarrays or shotgun sequencing, are subject to various limitations when assessing environmental samples, which can lead to inaccurate findings. We developed a hybridization capture strategy that uses a set of oligonucleotide probes to target and enrich biomarkers of interest in environmental samples. Here, we present Oligonucleotide Capture Probes for Pathogen Identification Database (OCaPPI-Db), an online capture probe database containing a set of 1,685 oligonucleotide probes allowing for the detection and identification of 30 biothreat agents up to the species level.
Here, we present the Oligonucleotide Capture Probes for Pathogen Identification Database (OCaPPI-Db), which is an oligonucleotide hybridization capture probe database targeting virulence factors and genes used to discriminate the NIAID's priority list for bacterial pathogens. The use of this probe set coupled with a hybridization capture strategy could be used as a sensitive and efficient alternative diagnostic tool to detect and identify bioterrorism agents from various environmental samples to prevent propagation of these agents within the population.
Sequence collection To target the NIAID's 30 priority bacterial genera or species through hybridization capture (Table  1 ), previously published studies were reviewed to obtain information regarding biomarkers used to detect and identify each of the pathogens. Specific genes from existing databases of pathogen virulence factors such as VFDB  (12)  were also utilized to complete the list of biomarkers. Finally, a list of specific biomarkers was defined for each of the bacteria (Figure  1 ). These biomarkers can be virulence factors, broadly defined, functional genes, antibiotic resistance genes or any other genes allowing for the specific enrichment of the pathogen at the genus level, and whenever possible at the species level. If no genes were species-specific, biomarkers were selected so that all, or a subsection, of the species from the corresponding genus are enriched in a manner where species can be discriminated based on small biomarker sequence variations.
A total of 245 biomarkers were thus selected to target the 30 bacterial genera or species of interest (Table  1 ). Fasta sequences of the selected genes were collected from GenBank using the WWW-Query research tool (http://doua. prabi. fr/search/query_fam. php) with the names of the organism and the gene targeted as keywords (Figure  1 ). Collected sequences for each gene were then aligned using ClustalW  (13)  to visually ensure that all sequences correspond to the gene of interest and to remove from the sequence data set any sequences arising from erroneous gene annotation (Figure  1 ).
Probe design Probes were designed using KASpOD (K-mer based Algorithm for high-Specific Oligonucleotide Design) software (14) (Figure  1 ). KASpOD is a fast k-mer-based software that allows for the selection of highly specific and explorative probes based on large sequence data sets. KASpOD first extracts group-specific k-mers from targeted and non-targeted datasets if provided, and removes k-mers found in both groups to keep only targeted data setspecific probes. Fully overlapping k-mers are then clustered using CD-HIT (15) at an 88% identity threshold. For each cluster, a degenerate consensus probe is constructed taking into account sequence variability at each position. Coverage of each degenerate consensus probe specific for the targeted data set is then measured using PatMaN  (16)  and specificity is assessed in the same way by comparing degenerate probes against the non-target group sequences.
Probe validation The quality and specificity of each probe were evaluated using HiSpoD (High Specific Oligo Design) (17) (Figure  1 ), a functional microarray probe design algorithm that uses individual nucleic acid sequences or consensus degenerate sequences to design highly specific probes. For each sequence, the algorithm selects all probes of a given size along the sequence by incrementing the constant defined probe size in a window. Probes are then filtered according to their degeneracy, melting temperature (T m ) and complexity stretch (maximum authorized number of identical consecutive bases). Probes that successfully pass the first step are then assessed for similarity using the BLASTN program against the dedicated EnvExBase database containing approximately 10 million microbial coding data sequences (CDSs) from the EMBL databank.
Web interface To make all the pathogen-targeting probes easily available, we developed a web interface to freely access and download the 1,685 probes that compose our database (Figure  2 ). The website, freely available online at http:// ocappidb. uca. works, was implemented using Framework Symfony 3 (PHP 7), ORM Doctrine 2 (MySQL 5) and HTML 5/CSS 3/java-script for UI. Based on the NIAID's pathogen categories A, B and C, the OCaPPI-Db web interface provides a hierarchical browse of the database content. When a pathogen is selected, the probes designed for all the genes of this genus or species are then displayed. Associated information is provided for each probe: category, phylogenetic assignment of the pathogen, gene name, gene function, gene length, gene IDs, probe ID, probe sequence, probe length, probe degeneracy, start and end position, T m , GC%, gene sequences coverage, sequences covered and design tool.
Therefore, we constructed OCaPPI-Db, a capture probe database containing a set of 1,685 80-mer oligonucleotide probes allowing for the specific enrichment of the NIAID's 30 category A, B and C bacterial genera or species (Table  1 ). These probes target 245 different biomarkers correspond to virulence factors but also any other genes allowing for the specific enrichment of the pathogens of interest. This probe set can be used in its entirety in hybridization capture experiments to simultaneously detect and identify all the above-mentioned pathogens with potential for use as agents of bio-or agroterrorism due to the enrichment of their associated biomarkers. Alternatively, probes targeting genes belonging to a particular genus can be selected to obtain a genus-specific diagnostic tool; probes targeting toxin-coding genes from all pathogens can be selected to evaluate the potential pathogenicity of the present species; or probes targeting one biomarker, specific for one bacterial genus, can be selected to enrich this specific gene and to identify gene variants potentially responsible for enhancement of a pathogen's virulence.
Thus, a desired set of probes can be selected from the complete database to adapt this scalable tool to the diagnostic requirements. Probe design has been performed so that probes are gene-specific, and at least genus-specific. Nevertheless, based on selected genus biomarker sequences or a combination of detected genes, species discrimination is possible. Moreover, due to the enrichment of complete biomarkers and their flanking regions over several tens of kbp by hybridization capture, reconstruction of very long subsets of genomes or even complete genomes is possible. Based on this sequence data, identification of pathogens can reach the strain-or biovar-level and provide insight regarding organism origin and evolution. Furthermore, due to the explorative design of probes, all variants of the targeted biomarkers are captured, allowing for the possibility of detecting all pathogens, including modified and unknown bacterial agents.
Other sets of probes targeting the viral and fungal pathogens of the NIAID's priority list will be designed to provide a comprehensive diagnostic tool capable of detecting and identifying any biothreat agent. Additionally, the enrichment of the database with probes targeting pathogens of clinical importance or targeting an exhaustive list of antibiotic resistance genes may also be a future endeavor. OCaPPI-Db will be updated annually adding newly designed probes and re-evaluating probe specificity considering the new sequences published in the databases.
Louis, MO) for C. andremenezi (specimen 1), C. praecellens (specimen 1), and C. geographus. For C. praecellens (specimen 2), strand-specific Illumina libraries with a mean insert size of 220 bp were prepared at the University of Utah High-Throughput and Genomics Core Facility. For C. andremenezi (specimen 2), mRNA was extracted from the venom gland using Dynabeads mRNA DIRECT kit (Invitrogen Dynal AS, Oslo, Norway) and a cDNA library was constructed with an average insert size of 200 bp. All libraries were sequenced on an Illumina HiSeq 2000 platform. Prior to assembly, quality filtering and adaptor trimming were performed using Fqtrim software (version 0. 9. 4, http://ccb. jhu. edu/software/fqtrim/) and Prinseq (version 0. 20. 4) (Schmieder and Edwards 2011). After preprocessing, reads shorter than 70 bp and those containing more than 5% ambiguous bases (Ns) were discarded.
Phylogenetic Analysis Phylogenetic analysis was performed using a partial sequence of the COI gene segment of mitochondrial DNA, corresponding to the standard barcode region. COI nucleotide sequences for specimens used in this study were retrieved from the assembled venom gland transcriptomes, and those from additional members of the Turriconus subgenus were obtained from GenBank (KJ550384, KJ550243, KJ550048, KJ549906, KJ549742, KJ550110, KJ550109, KJ550108, KJ550107, KJ550106, KJ550105, KJ550104, KJ550103, KJ550102, KJ550101, KJ549855, KJ550425, KJ549743, KJ550125, KJ549858, KJ550424, KJ550423, KJ550124, KJ550123). Phylogenetic analyses were performed using MrBayes v. 3. 2. 6  (Huelsenbeck and Ronquist 2001) , running two parallel analyses, consisting each of six Markov chains of 15,000,000 generations with a sampling frequency of one tree each 1,000 generations, and chain temperature set to 0.
02. Parameters of the substitution model were estimated during the analysis (six substitution categories, a gammadistributed rate variation across sites, and a proportion of invariable sites approximated in four discrete categories). Each codon position of the COI gene was treated as a separate partition. The first 25% of trees were omitted as burn in prior to the construction of consensus trees. Convergence of the analysis was evaluated using Tracer 1. 4. 1 (http://tree. bio. ed. ac. uk/software/tracer/) to ensure that effective sample size values were all greater than 200 (default burning). Bootstrap branch support values were obtained for an equivalent maximum-likelihood tree estimated by RaxML  (Stamatakis 2006)  with each codon position treated as an independent partition. Nodal support of the obtained tree was estimated using the thorough bootstrapping algorithm  (Felsenstein 1985)  with 1,000 iterations.
2011). Trinity settings were optimized for sequence read length and library type according to simulation results; RNAseq reads were simulated using the published Conus victoriae venom gland transcriptome  (Robinson et al. 2014)  with the same read length and quality obtained for RNAseq data sets sequenced in this study. Systematic, serial modifications of the assembly parameters and batch assemblies were performed to optimize assemblies. The true positive rate, defined as the percentage of C. victoriae toxins that were correctly assembled, was used as the criteria of optimality. Assembly parameters resulting in the highest true positive rate were used. A bash script for this procedure is available online (git@github. com/qingl0331/turri conus. git). Because the second specimen of C. praecellens (specimen 2) was sequenced with longer (125 nt instead of 101 nt) and stranded reads, different assembly parameters were used in Trinity (version 2.
0. 5): Paired-end mode, wherein the kmer size used to build De Bruijn Graphs was 31, sequence library type was set to reverse forward, minimum kmer coverage was 10, and the minimum glue was 10. Transcriptome assemblies were annotated using NCBI-BLASTX against a combined ConoServer (  Kaas et al. 2012)  and UniProtKB database (release April 2015). Recently published conotoxin sequences that were not yet uploaded into Uniprot at the time of analysis  (Hu et al. 2012; Barghi et al. 2015a Barghi et al. , 2015b;; Phuong et al. 2016)  were also added to our inhouse database. Contigs with significant similarity (e-value 1e À4 ) to known conotoxins were designated as putative conotoxin sequences. Contigs with no blast annotation were translated into six frames and predicted open reading frames that contained a start and stop codon and a total length of 50-200 amino acids were selected for further analysis.
Read Counts for Expression Analysis To determine approximate relative expression levels, reads were mapped back to the final conotoxin data set with Bowtie2  (Langmead and Salzberg 2012). Mapped reads that were shared between > 2 sequences were divided equally. For comparative analysis with members of the Splinoconus clade, Illumina reads for the venom glands of Conus tribblei and Conus lenavati were obtained from GenBank (SRR1803937, SRR1803938, SRR1803939, SRR1803940, SRR1803941, SRR1803942) and mapped back to conotoxin sequences expressed in these two species  (Barghi et al. 2015a ) with Bowtie2 as described above.
Comparative Analysis All-by-all NCBI-BLASTN searches were carried out to identify reciprocal best-hit (rbh) pairs among the inter-/intraspecific conotoxin nucleotide sequences. The rbh pairs were compared using NCBI-BLASTX to our in-house conotoxin database in order to classify rbh pairs as either conotoxin or nonconotoxin rbhs. ClustalW alignment was then used to globally align the rbh pairs  (Larkin et al. 2007). The alignment results were parsed to obtain the percent identity of each aligned rbh pair. The program codeml from PAML 4. 7  (Yang 2007 ) was used to estimate synonymous and nonsynonymous substitutions with model ¼ 2 (fix omega), over all codon positions in an alignment of rbh pairs of both the conotoxin and nonconotoxin sequences. Analyses were conducted in runmode ¼ -2. Rbh pairs with branches showing dS < 0.
Data Availability All C. andremenezi and C. praecellens conotoxin prepropeptide sequences from this study are provided in the Supplementary Material online and have been deposited at GenBank (accession numbers MF576542-MF576988). The mean K2P genetic distance among specimens of C. andremenezi is 1. 9% and among specimens of C. praecellens is 0. 9%. The mean K2P genetic distance between specimens of C. praecellens and C. andremenezi is 5. 4%. Within-group K2P genetic distance in the analyzed data set ranges from 0% to 3. 4%, whereas the minimal K2P value for between-group comparison is 4. 8%. This difference corresponds to the socalled "barcode gap" marking the difference in within-and among-species comparisons, and is overall consistent with within-and among-species genetic distances in other groups of Conoidea  (Puillandre et al.
Similarities in Toxin Number and Superfamily Expression Patterns within the Turriconus Clade We analyzed venom component transcripts from five venom gland transcriptomes: Two specimens of C. andremenezi and C. praecellens, from the Turriconus subgenus, and one specimen of C. geographus from the distantly related subgenus Gastridium that served as a reference. An in-depth analysis of the venom components of C. geographus has been provided elsewhere  (Hu et al. 2012; Dutertre et al. 2014; Safavi-Hemami et al. 2014). Information on RNA sequencing and assembly statistics for these transcriptomes is provided in supplementary table 1, Supplementary Material online. Additionally, published data for two species of the Splinoconus clade (C. tribblei and C. lenavati) were interrogated. Comparison between the two specimens of the same species within Turriconus allowed us to estimate intraspecific variation in venom composition, whereas comparison between C.
Interspecific Differences within Turriconus: The P-and O1d-Superfamilies In all four Turriconus individuals analyzed, P-superfamily conotoxins were both diverse and highly expressed. In contrast, P-superfamily conotoxin expression was lower in the two species of Splinoconus and not detected in the venom gland transcriptome of the fish-hunter C. geographus. In C. praecellens, the P-superfamily is by far the most highly expressed conotoxin superfamily (41-44%), with 16-27 variants identified. In both specimens of C. andremenezi P-superfamily expression levels, although generally high   (Langmead and Salzberg 2012). Expression levels were normalized to total read counts after trimming. Principal components were calculated using the prcomp package in R (R Core Team 2013). O1@: @-like O1 superfamily; *Superfamilies that did not contribute to separation on PC1 and PC2.
Acknowledgments The specimens used in this study were obtained in conjunction with a collection trip supported in part by an  ICBG  Grant No. 1U01TW008163. We would like to thank  Noel Saguil  for help with sample collection. This work was supported in part by  National Institutes of Health Grants GM 48677  (to B. M. O. ) and  GM 099939  (to M. Y. and P. K. B. ), an  International Outgoing Fellowship Grant  from the  European Commission  ( CONBIOS 330486  to H. S. -H. ), and the  National Natural Science Foundation of China  (Grant No. 31500626  to A. L. ).
As in the case of the time series models, we predict the output class ̂ ( ) = 1 if > , and 0 otherwise to compare the actual and predicted directions of change. Results are obtained using the R package nnet. Models parameters are tuned through cross-validation by performing a grid search over the parameter ranges using the tune function from the R package e1071. For details of these packages, see https://cran. rproject. org/web/packages/nnet/nnet. pdf and: https:// cran. r-project. org/web/packages/e1071/e1071. pdf).