dataesr/scientific-paragraphs-categorization huggingface dataset:
■ RESULTS AND DISCUSSION Binding affinity calculations performed by means of molecular dynamics simulations (using NAMD) depend on an extensive set of parameters. Exhaustively listing all possible parameters, we gathered 175 variables. However, not all these parameters should be included in the UQ procedure, and we use expert opinion to reduce this set. Dimension Reduction. A large number of parameters in the listing are configurational parameters; they control aspects such as I/O data flow but do not influence the behavior of the model simulation. Some parameters are also redundant between different equilibration and simulation phases of the affinity calculation. After eliminating these inputs, the listing was reduced down to 25 parameters. These remaining parameters can be classified into two groups: • Group 1: "Physical parameters" which control the thermodynamics of the equilibration and binding processes; these essentially refer to the duration, the temperature and the pressure of the simulations (e.
ASSOCIATED CONTENT * sı Supporting Information The Supporting Information is available free of charge at https://pubs. acs. org/doi/10. 1021/acs. jctc. 1c00526. Data used to generate the figures in the results section (ZIP) Data used to generate the figures in the results section (ZIP) Additional results which provide further information on aspects of the uncertainty in the binding affinity calculation workflow based on NAMD and on the parameter refinement performed; code used to generate our results available at a GitHub branch (https://github. com/UCL-CCS/UQ_NAMD/releases/tag/0. 0. 1); and scripts to perform the UQ campaign pushed to GitHub (PDF) Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, United Kingdom.
■ ASSOCIATED CONTENT * sı Supporting Information The Supporting Information is available free of charge at https://pubs. acs. org/doi/10. 1021/acs. jctc. 1c00526. Data used to generate the figures in the results section (ZIP) Data used to generate the figures in the results section (ZIP) Additional results which provide further information on aspects of the uncertainty in the binding affinity calculation workflow based on NAMD and on the parameter refinement performed; code used to generate our results available at a GitHub branch (https://github. com/UCL-CCS/UQ_NAMD/releases/tag/0. 0. 1); and scripts to perform the UQ campaign pushed to GitHub (PDF) Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, United Kingdom ■ AUTHOR INFORMATION Corresponding Author Peter V.
Viruses that infect bacteria (phages) are increasingly recognized for their importance in diverse ecosystems but identifying and annotating them in large-scale sequence datasets is still challenging. Although efficient scalable virus identification tools are emerging, defining the exact ends (termini) of phage genomes is still particularly difficult. The proper identification of termini is crucial, as it helps in characterizing the packaging mechanism of bacteriophages and provides information on various aspects of phage biology. Here, we introduce PhageTermVirome (PTV) as a tool for the easy and rapid high-throughput determination of phage termini and packaging mechanisms using modern largescale metagenomics datasets. We successfully tested the PTV algorithm on a mock virome dataset and then used it on two real virome datasets to achieve the rapid identification of more than 100 phage termini and packaging mechanisms, with just a few hours of computing time.
Because PTV allows the identification of free fully formed viral particles (by recognition of termini present only in encapsidated DNA), it can also complement other virus identification softwares to predict the true viral origin of contigs in viral metagenomics datasets. PTV is a novel and unique tool for high-throughput characterization of phage genomes, including phage termini identification and characterization of genome packaging mechanisms. This software should help researchers better visualize, map and study the virosphere. PTV is freely available for downloading and installation at https:// gitlab. paste ur. fr/ vlegr and/ ptv. Viruses play key roles in diverse microbial ecosystems. They are recognized to influence biogeochemical cycling, modulate microbial populations and metabolism, and act as a driving force in gene flow in soil and the oceans 1-6.
Similarly, datasets from the deeply sequenced Global Ocean Viromes show that only a minimal fraction of reads (≈ 10-20%) can be mapped to large de novo assembled viral reference genome databases, such as GOV1 and GOV2 databases  13, 14. The term "viral dark matter" has been used to describe this vast unknown sequence space  [19] [20] [21]. Recent efforts to establish viral clusters in gene-sharing networks have revealed structures that will greatly help in virus identification and classification, but a large OPEN.
number of unknown clusters likely remain to be discovered  22, 23. Despite the continuous rise in the number of sequences compiled in these databases, researchers will undoubtedly keep finding unknown sequences in abundance for the foreseeable future. It is thus still important to ascertain the viral origin of sequences discovered in metagenome analysis with higher confidence. Phage and virus biologists have therefore focused their efforts on developing orthogonal complementary prediction approaches, such as the analysis of k-mer frequencies and viral genome features, also recently implemented through machine-learning algorithms  24, 25. It has been previously shown that the ends of linear DNA, a hallmark of genomic DNA (gDNA) packaged in the capsid of free phage particles, can be easily detected from shotgun sequencing data if the sequencing library was prepared in a way that preserves such DNA ends (i.
Here, we introduce PhageTermVirome (PTV), an easy-to-use bioinformatics software that efficiently identifies critical information such as phage genome termini and packaging mechanisms in modern large-scale viral metagenomics datasets. Due to its ability to detect termini present in capsid-packaged linear DNA, PTV also offers an entirely orthogonal and complementary approach to predict the true viral origin of metagenomic contigs, along with other existing phage prediction tools  [28] [29] [30] [31] [32] [33] [34] [35] [36].
Results and discussion Overview of the PhageTermVirome workflow. The key steps of the PTV analysis workflow are depicted in Fig. 1. They consist of: (i) mapping all viral metagenomic reads on contigs assembled from the same read dataset, (ii) calculation of the starting position coverage (SPC) and identification of positions on the contig for which the SPC is significantly higher, which may represent genome ends (termini) and secondary terminase cutting sites, (iii) if applicable, characterization of the corresponding packaging mechanism by analysis of the coverage patterns near the identified termini, and (iv) aggregation of the prediction results and coverage plots for Figure  1. Overview of the different steps involved in the PhageTermVirome workflow. Virome sequencing reads are simultaneously mapped to numerous contigs assembled from the virome dataset.
For contigs with successful predictions, PTV also outputs a new fasta file containing the contigs reorganized to start at the predicted termini. The PTV algorithm was developed to allow the analysis of multiple contigs in a single workflow (i. e. , users do not have to re-run the script for each contig they want to analyze). The mandatory inputs into PTV required for analysis are (i) a multifasta (. fasta) file containing the assembled contigs and (ii) a fastq file (. fastq) containing the reads used to assemble the contigs found in the fasta file. When available, the user can also provide the corresponding paired-end fastq read file. Although not required, the use of paired-end reads is recommended, as it has been shown to improve termini and packaging mechanism prediction  27. The PTV algorithm is largely based on the mapping of all reads of a dataset to each contig provided, which can represent a daunting task for ultra-deeply sequenced viromes containing a vast amount of reads and a large number of contigs.
Additional options and parameters have thus been added to the PTV scripts to accelerate and scale the process. For example, in addition to the multi-threading option initially implemented in the original PhageTerm software  27  (using the command-line option --core), a new multi-machine functionality has also been deployed in PTV (using the command-line option --mm). The multi-machine option can be used to go over the limit of the number of cores available on a machine when processing large datasets and thus accelerate the analysis. It is intended for advanced users who have the possibility to perform analyses on multi-machine computer clusters. As PTV can still rapidly process average-sized virome datasets using only multi-threading options, the multi-machine option becomes more attractive for extra-large datasets (e.
g. , ≳ 100 million reads with ≳ 1000 contigs). Many factors can influence the required time to complete a PTV analysis, such as the total number of reads, the total number and length of contigs to analyze, the speed of the processors, and the number of cores available for parallelization. Detection of phage termini and packaging mode in a control mock virome dataset. We first assessed the ability of PTV to correctly identify termini and packaging modes in metagenomics datasets using a mock viral metagenomics dataset. This dataset was built by pooling 1 × 10 7 paired-end reads from five reference phages, using 2 × 10 6 paired-end reads from each phage previously sequenced individually  27. HK97, T4, T7, P1, and Lambda phage paired-end reads were used, as these phages harbor a diversity of well-described termini and packaging mechanisms.
After running the PTV workflow on the mock read dataset and the reference phage genomes, the expected termini positions were identified and corresponding packaging mechanisms successfully defined by PTV for all five reference phages (Fig. 2 ): Lambda (5′cos), HK97 (3′cos), P1 (headful with pac site), and T7 (direct terminal repeat, DTR). As expected, PTV returned no signal for T4 (headful without precise pac site). It is known that T4 type phages generate no precise or detectable termini, as their DNA is randomly packaged inside the capsid. This type of phage packages full genomes with redundant ends, starting and ending at random positions, generating no observable over-represented sequencing biases after sequencing. Our results on the mock virome dataset suggest that PTV algorithms, coverage analyses, and statistical models are sufficiently robust (assuming sufficient coverage) to accurately detect termini and classify packaging mechanisms when the reads of different phages are merged, as is the case for real virome metagenomic datasets.
Characterization of phage termini and packaging mode in two real virome metagenomics datasets. Given the successful characterization of phage termini and packaging mechanisms in the mock virome dataset, we next applied PTV to two real virome datasets. The first dataset selected to test PTV was a human gut virome previously sequenced and published in the study by Manrique et al. 37. The second dataset was generated during the course of this study and also corresponds to a human gut virome (Fig. 3 ). In addition to analyzing the two virome datasets with PTV, we also performed a phage prediction analysis with three widely used software programs, MetaPhinder  30  , VirSorter  29  and Phaster  31. Concomitant analysis of the assembled contigs was performed with these software programs and PTV to verify whether our tool performs better on contigs that are typically predicted to be phages.
Thus, we produced Venn diagrams showing contigs individually and commonly predicted to be phage by each tool and compared the results with the successful termini PTV predictions. For the Manrique dataset, PTV predicted termini and the packaging mode for 44 contigs from a total of 155 assembled contigs. Among the 44 contigs, 28 were predicted to be phage by the other softwares, meaning that PTV could determine termini and the packaging mode on contigs (N = 16) that were not declared to be phage by any other prediction tools. Detailed analysis of these 16 contigs showed a diversity of predicted packaging modes (4 cos phages, 10 pac phages, and 2 DTR phages). In the second human virome dataset, generated for this study, PTV was able to predict termini and the packaging mode for 72 contigs (from a total of 596), among which 50 were also predicted to be phage by the other phage prediction tools.
Thus, PTV obtained predictions for a number of contigs (N = 22) in this dataset that were not identified as phage by any of the other tools. The predicted packaging modes for these 22 contigs were also diverse, with 8 cos phages, 8 pac phages, and 6 DTR phages. One finding that arises from this comparative analysis is that each phage prediction software performs unevenly on different datasets and that it is useful, even necessary, to use a combination of different tools to obtain a more complete and precise vision of the landscape of the phages in a virome dataset. In light of these results, we wished to understand why certain contigs with successful PTV predictions could not be established as phage by any of the three prediction tools used in this study. Thus, the 38 contigs (16 + 22)  were annotated with PROKKA using the latest PHASTER phage protein database (as of December 2020).
For each contig, if an ORF was not annotated with a known protein in the database, a second annotation was performed using PROKKA's default bacterial protein database. Proteins of phage origin could be found on most of the contigs but, in many cases, represented a minority of the total predicted ORFs. The high number of hypothetical proteins found on these contigs likely explains, at least partially, why they were not identified as phage by the other prediction tools used here. Another hypothesis is that these contigs may be fragments of a partially  assembled phage genome, as is often the case in virome metagenomic datasets  38. In such fragmented assemblies, hallmark phage genes required by certain tools to declare the contig as phage may be lacking. However, given sufficient read coverage, PTV is still able to determine termini and packaging modes on a contig, even if a large part of the corresponding phage genome is missing from the assembly.
4 ). In this analysis, 12 of 16 contigs from the Manrique dataset clustered with at least one contig from the reference viral databases included here. It also showed that 17 of 22 contigs from the dataset generated for the study also clustered with contigs from the reference viral databases. Further in-depth analyses of the contigs found to be singletons will help determine the nature of these sequences and whether they represent unknown and distant phages or other entities capable of packaging DNA, such as phage-inducible chromosomal islands (PICIs), genomic islands, or other classes of transducing agents  40. Limitations and perspectives for PhageTermVirome. The capacity of PTV to detect termini and identify packaging mechanisms depends on the protocol used to prepare the nucleic-acid libraries prior to sequencing.
Although only Illumina sequencing was assessed here, other sequencing technologies or approaches, such as SMRT PacBio sequencing, Nanopore sequencing, and the recently developed. Viral cluster analysis with vConTACT2 using a gene-sharing network. The large red dots represent phage contigs (Manrique dataset) for which PTV predicted a terminus and packaging mode but not predicted to be viral by MetaPhinder, Phaster, or VirSorter. The large black dots represent phage contigs (dataset from this study) for which PTV predicted a terminus and packaging mode but not predicted to be viral by MetaPhinder, Phaster, or VirSorter. Network analysis was performed using three reference contig collections: GVD 8 , ViralRefSeq V. 97  41  , and a database of curated virus-like particles from mice  42. VirION2 approach  43  , are theoretically also well suited to work with PTV.
g. , around 30 ×). Coverage depth can be variable from one contig to another in virome datasets and can be very low for viral particles that are scarce in the sample. PTV can often identify the position of potential termini for contigs with very low coverage, but may have difficulty in properly determining the packaging mechanism. PTV issues a warning for contigs exhibiting very low coverage and invites users to manually inspect the results and consult the coverage-pattern plots to confirm ambiguous results. For extra-large virome datasets (a massive number of reads and a very large quantity of contigs), PTV analysis may require long processing times, especially if the analysis is being performed with very few computing cores. In this first version of PTV, a multi-threading option is available, as well as a newly implemented multi-machine option.
7-1. 5 kb and group 2 single-stranded satRNAs typically package linear RNA fragments < 700 bp (21,994,595). Thus, we recommend carefully choosing the metagenomics contigs excluded from the analysis. Another recommendation, also left to the discretion of the user, is to preselect contigs with a minimal coverage threshold (e. g. , reject contigs ≤ 10 × mean coverage), which will reduce the processing time and also help to generate more robust results. The PhageTerm approach is naturally restricted to the detection of viruses that package linear nucleic acids. In viral metagenomic samples, dsDNA phages of the caudovirales order represent most sequences known to date, but a large number of diverse phages are yet to be discovered. Recent studies have shed light on the previously underestimated prevalence of single-stranded DNA (ssDNA) phages, including Innoviridae and Microviridae phages  [45] [46] [47]  , but the ssDNA viruses discovered thus far appear to package circular DNA and therefore escape detection and characterization by PTV.
Until recently, gene transfer agents (GTA), which are phage-like particles, were thought to package bacterial DNA in a random fashion only, but recent studies have shown that certain GTAs (i. e. , those in the dinoflagellate-associated bacterium Dinoroseobacter shibae) can package non-random DNA fragments, presumably also using a headful packaging mechanism  56. Like phages, these entities are also of great interest, as they are increasingly alleged to be involved in horizontal gene transfer, host adaptation, and bacterial virulence  57, 58. The ability to define the exact start and end of these sequences by identifying the termini should help in more precisely defining and characterizing these particles and their role in various ecosystems. Going forward, the detection and thorough characterization of viral contigs in metagenomic datasets will benefit from the use of a combination of tools based on various approaches  59, 60.
Materials and methods Origin of samples, library preparation, sequencing, and contig assembly. Three sequence datasets were used in this study: (i) a simulated mock virome containing reads collected from five reference phages sequenced in our previous study (Lambda, HK97, T7, P1, Mu)  27  , in which the mock virome was generated by randomly subsampling 2 × 10 6 paired-end reads from each phage dataset and grouping them in a single forward and reverse read file (1 × 10 7 paired-end reads in total), (ii) a human gut virome sequencing dataset (SRR4295172) from a previously published study  37  , and (iii) a human gut virome sequencing dataset generated for this study. For this study, viral particles were separated and purified as follows: 500 mg of fecal matter was resuspended in sodium citrate solution (50 mL) and centrifuged at 300×g for 10 min at 4 °C.
DNA was extracted using phenol chloroform, precipitated with ethanol, and resuspended in 10 mM Tris-HCl. DNA was sonicated using a Covaris S220 apparatus. The sequencing library was prepared using a TruSeq Illumina kit, 2 × 75 bp paired end, and sequenced on a NextSeq550 Illumina sequencer. The run generated 102,581,131 paired-end reads (15. 6 Gbases total). The sequence dataset was deposited under SRR11177648.
Read quality control and virome assembly. To generate a proper assembly of all virome datasets, raw reads were trimmed and cleaned using AlienTrimmer v. 0. 4. 0  61  and the NextFlex PCR Free adapter list, with the following options: -l 30. Cleaned reads were then assembled using metaSPAdes v. 3. 10. 0  62  with the following parameters: --meta -k 21,33,55,77,99,127 --threads 12. Contigs ≥ 10 kbp were retained for further analyses. Prediction and detection of phage contigs. Assembled contigs were analyzed for viral/phage detection using three different popular user-friendly software programs: MetaPhinder v. 2. 1 30 , VirSorter v. 1. 0. 3  29  , and PHASTER (using multiple separate contigs option)  31. All software was used with the default parameters. PhageTermVirome analyses. PTV v. 4. 0.
Phage contig annotation. Selected contigs were annotated with PROKKA v. 1. 14. 0  63  , run locally, using the PHASTER curated database as the primary source of trusted phage proteins (as of December 22, 2020), with an E-value threshold of 10 -3. Protein network analysis with vConTACT2. Network analysis with vConTACT2 v. 0. 9. 22 was performed using three reference contig databases (GVD 8 , ViralRefSeq V. 97  41  , and a database of curated virus-like particles from the mouse from Duerkop et al. 42. The visualization of the protein-sharing network was done using Cytoscape software v. 3. 1. 1; http:// cytos cape. org/)  64. The edge-weighted spring-embedded model was used to position genomes sharing most protein clusters in proximity to one another. Nucleotide sequence accession numbers. The raw read data of the human fecal virome from this study was deposited in the sequence read archive (SRA) under the accession number SRR11177648.
Funding This work was supported by the  French Government's program Investissement d' Avenir program ;  Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases ' [ ANR-10-LABX-62-IBEID ] and the  Natural Sciences and Engineering Research Council of Canada  (NSERC # 341450-2010 ). Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France , is supported by  France Génomique  ( ANR-10-INBS-09-09 ) and the  Infrastructures de recherche en biologie , santé et agronomie (IBISA).
PTV analysis of human virome dataset from Manrique et al. B A PTV analysis of human virome dataset from this study SRR4295172 (≈ 6 million PE reads) Assembly with metaSPAdes (155 contigs ≥ 10 kb) SRR11177648 (≈ Phage detection Phage detection Phage detection with with with PTV analysis VirSorter MetaPhinder PHASTER 28 contigs predicted as phage 55 contigs predicted as phage 28 contigs predicted as phage 44 contigs with termini + packaging mode predictions.
As such, in the present study, we aimed to investigate structural changes in associative and sensorimotor striatal areas that could affect information conduction. We used AxonDeepSeg, an open-source software to automatically segment and measure myelin thickness and axon caliber, and found that axon caliber, the main contributor for changes in conduction speed, is specifically reduced in the associative but not the sensorimotor striatum of the Sapap3-KO mouse. This smaller axon caliber in Sapap3-KO mice is not a general neuronal feature of this region, but specific to a subpopulation of axons with large caliber. These results point to a primary structural deficit in the associative striatum, affecting signal conduction and consequent connectivity.