dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS We developed the SeqTailor webserver to provide a userfriendly and efficient approach for the rapid extraction of DNA and protein sequences for genomic variants (single nucleotide variants and indels) in VCF format, with userdefined window sizes. Additionally, users can choose to annotate the nearest splice sites and to consider the neighbor-  ) and mutated alternative (alt. ) DNA sequences, with user-defined up/downstream (±) window sizes (in base pairs), on the selected strand(s). When the users opt to annotate the nearest splice sites, SeqTailor will compute the distance between each given variant and its closest splice site, check if the distance is within the user-defined window sizes, followed by appending the nearest splice site information (distance, gene symbol, transcript ID, exon number and acceptor/donor site) to the header line of the output FASTA file.
Moreover, SeqTailor offers an option to consider the neighboring variants within the given window of sequence extraction, thereby making multiple changes to the extracted reference sequence accordingly. The neighboring variants are then appended in the header line of the output FASTA file, and also provided in the report file. To demonstrate the input, output and functionality of this module, we selected five variants in the gene IF-NAR1 (on forward strand) from the gnomAD database. In this example (Figure  3 ), SeqTailor extracts the ref. /alt. DNA sequences on the forward strand, with window size ±25 base pairs on Human GRCh37 assembly, considering the neighboring variants within the given window, and annotating the nearest splice sites. In the output sequence shown below, the three neighboring variants (chr21-34713283-A-G, chr21-34713304-G-T, and chr21-34713315-G-T) were identified, and the multiple resulting nucleotide changes were indicated in the reference sequence accordingly.
Protein sequence extraction For protein sequence extraction, SeqTailor firstly inspects all input data, and generates a report file, before sending for built-in SnpEff variant annotation. It retains 13 types of variant consequences for protein sequence extraction and alteration. For missense variants, SeqTailor directly alters the reference protein sequences based on the annotated amino acid changes. For stop-gained variants, SeqTailor terminates the reference protein sequences on the annotated position, and appends a '*' character to the new last amino acid. For synonymous variants, the protein sequences are unchanged. For variants annotated as frameshift, in-frame deletion or in-frame insertion, SeqTailor alters the reference CDS sequences, followed by running amino acid translation according to the genetic code table (Supplementary Tables S4-S7) to generate the alternative protein sequences.
The translation stops when a stop codon is encountered, and a '*' character is appended to the new last amino acid. If no stop codon is encountered in the altered CDS sequence, Se-qTailor will output the altered protein sequence ending with '. '. This is based on the assumption that no aberrant splicing events will result from this variation. For variants annotated as feature/transcript/gene ablation, SeqTailor out- puts '!' as the alternative protein sequence. SeqTailor offers the option to extract the entire protein sequence, or the partial protein sequence within a customized window size (in amino acids). Finally, it provides the reference and altered protein sequences in FASTA format, with gene symbols, transcript IDs and amino-acid changes given in the header line. The SnpEff annotation file is also available for download in the result page.
SeqTailor standalone programs We also provided standalone programs for users who wish to run SeqTailor DNA sequence extraction in a commandline based manner. The programs are coded in Python 2. 7, and users should have Biopython library installed  (31). Currently, we provided three standalone programs: (i) 'Seqtailor DNA VCF independent. py' for extracting DNA sequences for genomic variants independently in VCF files; (ii) 'SeqTailor DNA VCF neighborhood. py' for extracting DNA sequences for genomic variants in VCF files with the consideration of the neighboring variants falling inside the given window size and (iii) 'SeqTailor DNA BED. py' for ex-.
SeqTailor bridges genomic variants with sequence-based tools We further exhibited a case study on pathogenic genetic variants with different effects and different clinical consequences identified in five human genes (MSH2, BRAF, GJB2, BRCA2 and IL2RG) by both the HGMD professional database (3) and the ClinVar database (4) (Table  2 ). We used SeqTailor to extract the DNA sequences and protein sequences for these variants, and directly applied the output sequences with a number of tools for splicing prediction, protein domain search, and functional prediction. The details of the case studies and their bridged applications are described in the Supplementary Data. These examples demonstrated the practical power of SeqTailor to bridge the gap between genomic variant data and sequencebased tools for downstream analyses and predictions.
SeqTailor runtime evaluation The SeqTailor webserver is designed for the rapid extraction of DNA and protein sequences from genomic variant data in VCF files, in a user-friendly manner. The runtime evaluation was performed by applying 50 VCF files for each of the input data sizes (10, 100, 1000 and 10 000 genomic variants) on human GRCh37 genome. The online version was tested on the computer server hosting SeqTailor (8 CPUs and 48GB RAM), and the standalone version was tested on a desktop (20 CPUs and 128 RAM). The SeqTailor runtime performance is shown in Figure  5. To extract DNA sequences from genomic variants independently, the SeqTailor webserver takes 6, 11, 12 and 13 seconds on average for 10, 100, 1000 and 10 000 genomic variants, respectively. When the option to consider the neighboring variants within the window is enabled, the average runtime is 6, 12, 13 and 105 seconds for 10, 100, 1000 and 10 000 genomic variants.
However, BEDTools-getfasta is intended mainly for extracting reference sequences within the given genomic ranges in BED files. If the inputs are VCF files, BEDTools only outputs the reference alleles on the given genomic positions. Users have to write scripts to generate genomic ranges for genomic variants, before extracting the reference sequence around each variant. SAMtools-faidx does not accept BED or VCF format directly, so users have to transform data into the 'CHROM:START-END' format for sequence extraction. Importantly, these tools do not generate the mutated alternative DNA sequences based on the variation data, and they do not support the extraction of protein sequences encoded by the DNA sequences around genomic variants. For users who have script knowledge and who wish to extract reference sequences only, BEDTools and SAMtools are recommended.
For non-programmers, SeqTailor webserver can serve for this purpose. Alternatively, IGV software  (28)  can be used to locate the position of the variant on the reference genome, by manually copying and pasting to extract the desired DNA sequences or the corresponding protein sequences one at a time, which would be very time-consuming, error-prone, W630 Nucleic Acids Research, 2019, Vol. 47, Web Server issue and impractical in high-throughput data applications. The popular GALAXY online platform  (32)  and Biopython library  (31)  do not offer such options for DNA or protein sequence extraction. Moreover, none of the existing tools provide a function to annotate the nearest splice sites for genomic variants, or to consider the neighboring variants within the given window for sequence alteration.
Untargeted analysis of a composite house dust sample has been performed as part of a collaborative effort to evaluate the progress in the field of suspect and nontarget screening and build an extensive database of organic indoor environment contaminants. Twentyone participants reported results that were curated by the organizers of the collaborative trial. In total, nearly 2350 compounds were identified (18%) or tentatively identified (25% at confidence level 2 and 58% at confidence level 3), making the collaborative trial a success. However, a relatively small share (37%) of all compounds were reported by more than one participant, which shows that there is plenty of room for improvement in the field of suspect and nontarget screening. An even a smaller share (5%) of the total number of compounds were detected using both liquid chromatography-mass spectrometry (LC-MS) and gas chromatographymass spectrometry (GC-MS).
The study aimed to (a) evaluate progress in the field of suspect and nontarget screening of dust, (b) build an extensive database of semivolatile and nonvolatile organic contaminants that could be applied in future screening of the indoor environment, (c) assess the degree of complementarity of instrumental analytical techniques, and (d) present recommendations for successful suspect and nontarget screening. To our knowledge, this is the first collaborative effort on untargeted analysis of indoor dust.
In addition, participants were later requested to analyze two additional standard mixtures, for negative electrospray ionization (ESI) and positive ESI, respectively, each of them containing 18 substances (ESM Table  S1b ) to facilitate quantitative structure-retention relationship (QSRR)-based prediction of retention times of unknown compounds. All participants were requested to measure these standards and report the results by June 2016. The reporting was done using data collection templates that included details related to the chromatographic and mass spectrometric methods and related to the reported compounds, e. g. , retention time (RT), m/ z, intensity, intensity of blank, MS/MS data, type of workflow, proposed ID, molecular formula, CAS, and identification confidence level. Twenty-seven participants from 26 organizations representing 15 countries registered to participate in the study.
Seventeen participants registered for LC-MS and GC-MS techniques, three only for the GC-MS, and seven for LC-MS only. Out of these, 20 datasets were received for the LC-MS techniques and 14 for the GC-MS techniques. One participant officially withdrew from the trial for both techniques and one for the GC-MS technique only. The participants' list included institutions with various levels of experience in performing suspect and nontarget methods (i. e. , some were performing nontarget analysis for the first time, while others were more experienced). Table  1  shows a summary of the contributions received.
LC-HRMS An overview of LC-HRMS(MS) methods is presented in ESM Table ESI was mainly used with different collision-induced dissociation (CID) or higher-energy CID (HCD) energies. Some participants submitted MS data only. One participant used atmospheric pressure chemical ionization (APCI) and atmosphere pressure photoionization (APPI) in addition to ESI. All participants who measured in both positive and negative ESI modes did so in separate runs. Some participants used Ball-ion^data-independent acquisition approaches (fragmentation without precursor ion selection). Most participants used time-of-flight (TOF), quadrupole TOF (QTOF), or ion mobility QTOF mass analyzers. Three used Orbitrap mass analyzers. The resolution of the TOFbased systems ranged from 10,000 to 42,000 and the resolution of the Orbitrap systems from 70,000 to 120,000.
In general, the most commonly used workflows consisted of peak-picking and deconvolution by instrument vendor software and spectra matching to commercially available or inhouse mass spectral libraries. One participant used opensource XCMS  [23]  and R-packages, while another used the STOFF-IDENT open source platform  [24]. MetFrag  [25]  was a popular tool used by many participants for prediction of MS/ MS fragmentation. Two participants used correlation RT vs. logD to facilitate molecule identification. S4  and Table  S5. Most participants used nonpolar capillary columns coated with 5% phenyl polydimethylsiloxane or 5% phenyl dimethylarylene siloxane. One was using a 100% polymethylsiloxane column, and the remaining were using selectivity-tuned nonpolar columns (PAH, volatiles, EPA 1614).
GC-MS An overview of GC-MS methods is presented in ESM Table A range of MS analyzers were used. The most popular were (Q)TOF analyzers, used by half of the participants, followed by triple-quadrupole (QQQ) and single-quadrupole analyzers, used by four and three participants, respectively. One data contributor used a GC-Orbitrap system. Most of the GC-(Q)TOFs were HRMS systems, ranging widely in age and performance. The reported mass resolution ranged from 5000 to 60,000 and the mass accuracy from 5 to 20 ppm, with one exception at 200 ppm. The remaining instruments were low-resolution mass spectrometers (LRMS) that provide unit mass resolution. Electron ionization (EI) was by far the most common ionization technique, used by all but one participant. Complementary data were generated in chemical ionization (CI) and APCI mode by four and two participants, respectively.
The most common suspect and nontarget screening workflow was peak-picking, EI library search (NIST and inhouse libraries), manual spectra review, and (sometimes) interpretation. Several participants used peak and spectra deconvolution algorithms (PARAFAC, AMDIS, and similar) to enhance spectra quality. Some of these labs also used RI matching (NIST or in-house databases) to increase confidence in their identification. In the absence of RI data, at least one participant utilized RT correlation (RT vs. molecular weight) to check the plausibility of proposed structures. Additional information on the workflows and libraries used by each laboratory is given in Tables  S3  and S5  of the ESM.
For example, one participant reported sorbitol monostearate, tri-xylenyl phosphate, ethyltriacetoxysilane, 2,2-dihydroxy-4,4-dimethoxy-benzophenone, acrylic acid, acrylic acid 2-ethylhexyl ester copolymer, phtalic acid divinyl ester, gallic acid propyl ester, tert-butyl-4-hydroxyanisole, and diacetoxy-di-tert-butoxysilane at level 4 confidence. However, the reported MS/MS spectra did not match the library or in silico spectra, and thus, those structures were likely incorrectly assigned and therefore were not included in the list of dust contaminants. In the next step, data from participants that did not report MS/MS data were evaluated. Those results stem from suspect screening using exact mass (pseudo-molecular ion) information, which is error prone (i. e. , high false-positive rate), unless carefully evaluated.
To support such curation, calculated RTIs were employed. Two sets of calibration standards were used for indexing the LC-MS data. The first set was used as calibrants for QSRR models for RTI prediction, as described by Aalizadeh et al. [29]. The second set was used to establish a RTI/logD correlation within the FOR-IDENT platform (hosted at the Technical University of Munich, Germany) for compounds in the STOFF-IDENT database (Bavarian Environment Agency and the University of Applied Sciences Weihenstephan-Triesdorf, Germany). These resources may be found at https://www. lfu. bayern. de/stoffident and https://water. for-ident. org [24, 30]. In the FOR-IDENT processing workflow, the RTs of the candidate compounds are used to estimate their normalized retention time and thus correlated logD values (at a specific pH value).
These observed logD values are then compared with the logD values for those molecules stored in the compound database STOFF-IDENT (ΔlogD). In addition, in cases where alternative compounds with the same empirical formula are present in the STOFF-IDENT database, their ΔlogD will also be calculated and all compounds will be ranked (original candidate and alternative compounds) in FOR-IDENT. Table  S6  (see ESM) illustrates the application of RTI in the identification of some emerging contaminants reported by 12 participants, while ESM Table  S7  shows an example of using ΔlogD in FOR-IDENT platform for data curation. A useful application of RTI is to remove false positives from the list of identified compounds. A compound is considered as false positive if it gives high residuals (error between predicted and experimental RTIs), while its structure belongs inside the application domain of the models, and the experimental RTI does not overlap with other participants or measurements.
This is illustrated by some of the surfactants, pentaethylene glycol, hexaethylene glycol, heptaethylene glycol, and octaethylene glycol that were reported by four participants. Although the structures were outside of the application domain of the RTI models, the RTIs on different LC systems were statistically equivalent and two participants reported the surfactants at identification confidence level 2a (mass spectra available in the library). Thus, those compounds were likely present in the dust. Less strict acceptance criteria should therefore be applied to this class of compounds. Curation of GC-MS data Alkanes were used for indexing of GC data. All participants used linear temperature programming, and therefore, the original Kovats equation  (1958)  was not applicable and the modified method by Van den Dool and Kratz  [31]  was used for calculation of linear retention indices (LRIs).
8). An Abraham general solubility model was therefore developed using Abraham constants from ACD labs Percenta software, with Absolve add-on (Toronto, Canada), and multiple linear regression (MLR) in Microsoft Excel. This model produced satisfactory results (r 2 > 0. 9) to allow recognition of suspected outliers. It proved difficult to set strict elimination criteria, mainly because some compound classes were poorly represented in the dataset and therefore likely outside the model domain. Potential outliers were therefore manually reviewed. A conservative approach was used, and compounds were only removed if there were strong reasons to do so, e. g. , there was a large difference between the experimental and predicted LRIs and the compound was likely to be within the model domain. A new Abraham model was constructed after elimination of compounds that were suspected to be misassigned.
It exhibited a strong linear relationship with a slope close to 1 and an r 2 of 0. 96 (see Fig. 1 ). As can be seen, there were still compounds with LRIs deviating from the predicted LRIs, but those were generally target analytes or compounds that were reported by multiple laboratories (piperine, two glycols, HBCDD, and one organophosphate ester). There were some systematic deviations, e. g. , cholesteryl benzoates were generally overestimated, and polyethylene glycols underestimated. In addition, all deviating compounds are likely to appear in indoor environments because they are constituents of food, personal care products, or building materials. The spread in the remaining LRI data is regarded as normal considering that the data was generated by multiple laboratories using a range of different nonpolar GC columns.
Compounds identified in house dust Database of identified compounds A compilation of the individual compounds that were identified or tentatively identified in house dust by the participants in the collaborative trial and that passed the data curation is provided in Microsoft Excel format as part of the ESM. The identification confidence level of each compound is given in the Excel file. Overall, nearly 2350 compounds were identified (18%) or tentatively identified (25% at confidence level 2 and 58% at confidence level 3). The compounds have been manually grouped based on origin (biogenic or anthropogenic), use category, or chemical class to aid a more detailed contaminant discussion. Figure  2  summarizes the major groups of contaminants found in house dust. Compounds identified using LC-MS techniques All participants using LC-MS techniques reported in total 969 compounds within the level of confidence 1-3 using the scale proposed earlier  [26].
Of the nearly 2400 tentatively identified compounds overall, only 5% were found by both techniques. The substances common to both platforms were predominantly fatty acids, glycols and polyethylene glycols, phthalates, and organophosphate esters. The remaining (minor) fraction measured by both platforms was as follows (in order of importance): amine/amides, pesticides, pharmaceuticals and illicit drugs, (nonphthalate) plasticizers, UV screens, PACs, and fragrances. Taken together, these data demonstrate that in order to comprehensively and holistically characterize contaminants in a complex matrix, such as house dust, it is necessary to use multiple LC-MS and GC-MS-based complementary analytical techniques.
GC-LRMS with complementary full-scan EI and methane PCI and NCI The main advantage of using a GC coupled with a single quadrupole mass spectrometer (in addition to the relatively low cost and availability) is that there are several easily exchangeable ionization techniques available that provide complementary information and increase the number of compounds that can be tentatively identified with a high level of confidence. One participant used three types of ionization EI, and methane-positive ion chemical ionization (PCI) and positive ion chemical ionization (NCI), all in full-scan mode. The identification workflow was based on the full-scan EI data and the use of mass spectral and LRI databases (allowing a ± 50 RI unit tolerance). Often this was sufficient for a tentative identification; however, many compounds displayed chimeric (mixed) spectra or spectra without a clear molecular ion.
In such cases, the PCI and NCI data provided useful complementary information, PCI provided molecular ion or adduct ion information for compounds with high proton affinity, and NCI provided molecular ion information for compounds with high electron affinity and high molecular ion stability. NCI can also be used to verify the presence of electronegative atoms, mainly halogens, through inspection of chlorine and bromine EICs. These compounds can sometimes be tentatively identified by matching with entries in a Bhome-made^NCI library. Three examples of the complementary use of EI, CI, and LRI information are given in the following sections (i) verification of candidate structure, (ii) correction of proposed structure, and (iii) tentative identification based solely on NCI data. In the first example, a NIST library search indicated the presence of two isomers of cyhalothrin (MW = 449) in the dust extract.
On the contrary, several environmental contaminants respond well in negative ion ESI, APCI, or APPI, but poorly in positive ion ESI  [47]. Thus, there is likely a bias in the results toward compounds with high proton affinity, which respond well in positive ion ESI. This may explain the high percentage of compounds with high basicity, such as amines, amides, nitriles, and polyethers (incl. glycols), among the LC-MS datasets. Among the GC-MS techniques, the use of 2D separations and complementary soft (CI) and traditional (EI) ionization proved valuable. Two participants using GC×GC-APCI-MS and GC×GC-EI-MS, respectively, collectively found more than 1000 compounds. The use of CI did not, in isolation, lead to identification of many compounds, mainly due to lack of commercial CI spectral libraries.
g. , a HILIC column, which was only used by one participant. Consequently, only a few compounds with log K ow values below zero were reported (ESM Fig. S3 ). Similarly, large nonpolar compounds with molecular weight above 600 Da were also insufficiently covered. The largest hydrocarbon nalkane detected was n-hexatriacontane (506. 6 Da), the largest nonhalogenated compound was tri(2-ethylhexyl) trimellitate (546. 4 Da), and the largest of all nonpolar compounds was decabromodiphenyl ether (961. 2 Da). However, analysis of the latter was done by targeted methods aimed at brominated flame retardants, utilizing short thin-film GC columns. Lack of conformity among reported datasets Almost half of the compounds reported from LC-MS analyses and most of the compounds reported from GC-MS analysis were only reported once.