dataesr/scientific-paragraphs-categorization huggingface dataset:
ROC Curve Areas The distributions of integration sites datasets (Table  S1 ) were compared with the distributions of 10 different epigenetic marks or bound DNA binding proteins. 49 Each of these was mapped by chromatin immunoprecipitation sequencing (ChIP-seq), in which each protein was covalently cross-linked to DNA, and bound DNA fragments were recovered by immunoprecipitation, sequenced, and then mapped to the human genome to identify relative density. Densities of mapped ChIP-seq annotations were compared with distributions of integration sites within 10 kb windows, and the collection of values was used to generate ROC areas. For the gene therapy specimens made by infection of stem cells with a gammaretroviral vector (patient 1 [P1]), the distribution mostly favored marks associated with active transcription (H3K9me1, H3K4me1, H4K20me1, and H3K27me1).
Heatmaps are generated as scalable vector graphics (SVG), which can be opened in an Internet browser. Users can click on a row or column, and statistical results appear on the heatmap tiles documenting whether re-sults in other rows or columns differ from the query. Users can also click on a button to the right of the maps to allow comparison of all tiles with the random control. Results of statistical comparisons are reported as asterisks on each tile. Some examples are shown in Figure  3 , illustrating comparisons among random (Figure  3A ), the leftmost HAP1 dataset (Figure  3B ), or the Pol II ChIP-seq distribution (Figure  3C ). Figure  4  presents another form of the heatmap that queries the results of multiple additional features, including mapped DNase I cleavage site (which reports DNA accessibility), CpG islands (important in gene regulation), guanine/cytosine (GC) percentage, gene counts as documented in the refSeq dataset, and proximity to gene boundaries.
For those features that are mapped in intervals (GC percentage over 1 Mb, 100 kb, 10 kb, and so on), it is often unknown a priori what width is the most relevant to the biological question at hand. Thus, for these features, results for a number of different interval sizes are shown. All three datasets are compared over their four replicates against these features (Figure  4 ). High densities of DNase I hypersensitive sites and high densities of CpG islands are associated with favored integration for both lentiviral and gammaretroviral vectors (red coloration). High GC content is also favored, likely because high GC content is characteristic of gene-rich regions, although for lentiviruses, the preference switches to local high adenine/thymine (AT), possibly associated with binding of LEDGF/p75, the tethering cofactor, or wrapping of integration target site DNA on nucleosomes.
11 Paralleling the favored high GC content, direct measures of gene richness are also positively  associated. Integration is disfavored relative to regions with long gene widths or long intergenic distances, because these are indicative of gene-sparse regions. The gammaretroviral vector sites are disfavored relative to long gene boundary distances because they favor integration near transcription start sites. Integration is favored within genes (as annotated by the refSeq dataset) for lentiviral vectors,  3  but only weakly favored for gammaretroviral vectors. Thus, numerous relationships between integration site datasets and genomic features can be explored statistically using these interactive heatmaps.
Reports on Integration Site Sample Sets for Tracking Outcome in Human Gene Therapy An important application of integration site analysis is tracking outcome in human gene therapy. For this we have developed a standardized patient report template that rests on top of the INSPIIRED pipeline. Use of a reproducible report format allows tracking of datasets used in each study and version control of code (which are specified by dates). The report software takes in integration site and break point positional information, annotates the sites using hiAnnotator, and outputs targeted analyses of integration site distributions. An example of a patient report is provided in Data S2, showing two recent time points monitored for patient 1 (P1) treated for SCID-X1. Earlier time points were analyzed by 454 Roche pyrosequencing and were previously reported.
25 me excerpts from the report are presented in Figure  5. The software generates a summary table (Figure  5A ) that reports the patient, time   summarizing  sample metadata, including the trial, internal tracking number (GTSP), number of replicates, patient, time point queried, cell type, total number of sequence reads (TotalReads), inferred number of cells queried from SonicAbundance (InferredCells), the number of integration sites recovered after dereplication (UniqueSites), the method used to break the DNA (shearing in this case), the vector copy number if available (VCN) determined from qPCR, the minimum population size inferred from sharing among replicates (S. chao1), the asymmetry of clonal distribution (Gini), the diversity summarized as the Shannon index (Shannon), and the number of unique clones making up the top 50% of the sample abundance (UC50).
(B) Stacked bar graph showing the most abundant clones, named after the nearest gene. Genes are annotated by whether the site is within a transcription unit (*), whether the site is within 50 kb of a cancer-related gene ($), or whether the site is associated with a gene strongly associated with human lymphoma (!). (C) Graph indicating the position of integration sites near CCND2, and their proportions as inferred by SonicAbundance. (D) Word bubbles summarizing the proportions of integration sites near each named gene. The size of the gene name in the word bubble is a function of the SonicAbundance of that site. Note that there is an antisense transcript upstream of the CCND2 transcription start site; thus, the integration site upstream is reported as CCND2-AS1 because it is within the DNA transcribed in the antisense transcript.
Here, we introduce a new metric, called the UC50 (unique cell progenitors contributing the most to the expanded 50% of progeny cell clones). To generate the number, progenitor cell clones (reported as unique integration sites) are first ranked by the relative abundance of progeny cells using SonicAbundance (reported by linker ligation site data). The UC50 then reports the number of unique clones (integration sites) responsible for making up the top 50% of all cells sampled. Thus, if a single clone comprises more than 50% of the sample, the UC50 value will be 1. In contrast, for efficient lentiviral infections of cells in short-term tissue culture, the UC50 values can be in the thousands (data not shown). Finally, where available, the vector copy number per cell (VCN), determined separately by qPCR, is added to allow assessment of the efficiency of gene marking in the cell population.
DISCUSSION Here, we describe a collection of tools for the analysis and visualization of integration site distributions. This tool set takes advantage of the INSPIIRED pipeline described in the accompanying paper. 9 Integration sites are mapped to the human genome, the abundances of their host cells tabulated using the SonicAbundance method, and results stored in a database, allowing flexible downstream analysis. Integration site distributions can then be compared with genomic features and sites of epigenetic modification as ROC areas. These data are summarized as interactive heatmaps, allowing comparison with random distributions, other integration site datasets, or genomic annotation by simply clicking on a row or column, which outputs comprehensive statistical tests. For applications to human gene therapy, interest often focuses on longitudinal behavior of expanded clones and proximity of integration sites to cancer-associated genes.
A standardized report format was developed, allowing interactive comparison among patient datasets and querying multiple aspects of longitudinal behavior. For this, we introduce the UC50 metric, which is generated by ranking progenitors (integration sites) from most to fewest daughter cells produced (linker ligation sites) and counting the number of progenitors contributing to the top 50% of the distribution. Thus, clonal expansion yields low UC50 numbers and highly polyclonal samples high UC50 numbers. These tools were used to query recent clonal behavior in a patient from the first SCID-X1 gene therapy trial. 29, 47, 48 The patient studied has an expanded clone with an integration site near the proto-oncogene CCND2. The analysis of integration sites from month 177 to month 189. 5 post-treatment revealed stability of this clone, with possible slow expansion.
Integration Site Analysis As explained in the companion paper,  9  integration sites are identified by sequencing the LTR-host junctions from genomic DNA after linker-mediated PCR amplification. Genomic DNA is randomly sheared by ultrasonication, after which linkers are ligated to the repaired DNA for amplification. Nested PCR is used to amplify the LTR-host DNA junctions by priming from the viral LTR and the linkers, appending the sequences needed for sequencing. Samples are sequenced using the Illumina paired-end platform, and the output sequencing files are processed by intSiteCaller to yield integration site positions on a host draft genome. Integration site data and ChIP-seq data were mapped onto the hg18 genome draft, to match the original draft genome used for analysis of the ChIP-seq data.
ACKNOWLEDGMENTS We are grateful to members of the F. D. B. laboratory for help and suggestions. All authors were supported by grants  AI 052845 ,  AI 104400 ,  AI 082020 ,  AI 045008 ,  AI 117950 , and  HL 113252  and  ERC Regenerative Therapy  grant  269037  from the  European Research Council , and an award from the  French ANRS. We also acknowledge support from the  Penn Center for AIDS Research  (grant  P30 AI 045008 ) and the  PennCHOP Microbiome Program.
Mobile genetic elements (MGEs) are an important feature of prokaryote genomes but are seldom well annotated and, consequently, are often underestimated. MGEs include transposons (Tn), insertion sequences (ISs), prophages, genomic islands (GEIs), integrons, and integrative and conjugative elements (ICEs). They are intimately involved in genome evolution and promote phenomena such as genomic expansion and rearrangement, emergence of virulence and pathogenicity, and symbiosis. In spite of the annotation bottleneck, there are so far at least 75 different programs and databases dedicated to prokaryotic MGE analysis and annotation, and this number is rapidly growing. Here, we present a practical guide to explore, compare, and visualize prokaryote MGEs using a combination of available software and databases tailored to small scale genome analyses.
These elements are known to promote genomic expansion and rearrangements, and the emergence of virulence, pathogenicity, and symbiosis. Over the last 15 years, several reviews have focused on the impact of LGT mediated by MGEs  [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]. It is now generally recognized that MGEs are central to bacterial and archaeal genome evolution. To begin an analysis of the MGE content of a given genome, it is first necessary to identify and annotate ordinary genome features such as coding sequences (CDSs), rRNAs, and tRNAs. There are several useful genome annotation pipelines that use a combination of distinct methods based on computer predictions for CDS detection and product assignment. Putative gene product attribution for each identified CDS is generally based on sequence similarity obtained by using BLAST  [14]  and Hidden Markov Model  [15]  searches against trusted databases such as RefSeq  [16] , Interpro  [17] , Pfam  [18] , and TIGRFAMs  [19].
However, few sequencing projects include manual curation and validation, mostly relying on the annotation results of automatic computer predictions. Indeed, this generally results in quite variable annotation qualities  [20] , impacting downstream analyses. For MGE detection and annotation it is essential to use a variety of approaches including additional specialized pipelines and software which are normally not implemented in a regular annotation pipeline. Most MGE de novo-detection software are not integrated into the most popular genome annotation pipelines, such as Prokka  [21] , the NCBI Prokaryotic Genome Annotation Pipeline (PGAP), the Rapid Annotation using Subsystem Technology (RAST)  [22, 23] , or the Integrated Microbial Genomes System/Expert Review (IMG/ER)  [24]. The absence of MGE predictions is therefore an important example of the deficiencies in expert genome annotation.
The resulting incomplete annotation files are regularly deposited in public databases and used by the scientific community for detailed evolutionary and comparative genomic analyses. Identification, annotation, and classification of each MGE type are becoming central in the prokaryote genomics field. A correct MGE annotation will ultimately lead to new insights into how prokaryotes are genetically tailored to their lifestyles  [25]. A considerable number of software and databases have been developed for MGE detection and analysis in the last decades. This makes it tiresome and time consuming to evaluate the programs that are most suited for a given work-flow. Some MGE software and databases provide very precise and curated information, together with gold standard methods and protocols for annotation.
These include the ISfinder Database  [26]  and ISsaga2  [25] , for IS analysis and annotation, respectively, while other databases such as ICEberg  [27] , INTEGRALL  [28] , and PHAST  [29]  for ICEs, integrons and prophages, respectively, provide valuable information concerning other MGEs. Conversely, there are a number of predictors that provide de novo methods for MGE detection, giving the user the opportunity to investigate the detailed role of MGE in novel complete and draft genomes. Tables  1  and 2  provide a list of software and databases published and currently available for MGE research and some of their features. There are several software and/or databases for each MGE, and therefore there is no single solution to deal with all MGE types at once. This makes it difficult to choose the most reliable and appropriate software and databases.
In view of the number of possibilities available (as shown in Table  1 ), a novice in this research area will probably spend a considerable time switching between programs and deciding which should be included in their projects and methodologies. Figure  1  illustrates the canonical mechanisms of known lateral gene transfer events (transduction, conjugation, and transformation) in a prokaryotic cell, and the databases and software for detection and analysis of each MGE type used by this protocol. It is worth mentioning that it is not unusual to find chimeras between different MGE types (Fig. 1 , dashed boxes), making it difficult to distinguish their boundaries. These concepts are discussed in this protocol. To provide an easier introduction to this subject, we will present a standard MGE bioinformatics work-flow for prediction and annotation using established methods and tools and provide tips for MGE investigation and analysis.
Materials For de novo MGE prediction and comparative genomics analysis, a modern computer containing a 64-bit processor, with at least 4 GB of RAM, a 500 GB hard drive, and broadband Internet access are usually enough for meeting the minimum requirements of most software. Additionally, as presented in Table  1 , a Unix-like operating system (OS) is required by the majority of the available MGE analysis software. In these examples, we use Ubuntu 16. 04, a free and open source Linux distribution that can be downloaded from https:// www. ubuntu. com. In most hardware, the 64-bit version of Ubuntu can be installed either as your sole OS, alongside a preexisting OS (such as Microsoft Windows or Apple OS X) or in a virtual machine.
MUST Transposable elements  [94]  Profile HMM search Transposable elements  [93]  Repeats search Transposable elements  [93]  Transposon express Transposable elements  [95]  following instructions from the Ubuntu community help wiki (https://help. ubuntu. com/community/installation). However, for better performance, it is best to avoid virtual machines and install Ubuntu directly on the computer instead. To follow this guide a basic knowledge in management and analysis of directories, files and genomic data in a command line interface is also necessary. We used 21 genomes from different bacterial and archaeal classes bearing distinct MGE types and numbers as examples for the methods described in this protocol. Final results for the analyses of these genomes can be obtained from https://github.
Preparing Your Dataset: Obtaining Prokaryote Genomes For most MGE software and databases, either unannotated genome sequences in the plain fasta format (". fasta", ". fas", ". fa", ". fna", ". fnt" and ". fnn" file extensions) or annotated sequences in the GenBank format (". gbk" and ". gb" file extensions) or the new GenBank flatfile format (". gbff" file extension) are mandatory (seeNotes 1 and 2). Thus, target genomes should be available in both formats so that a broad range of programs can be used. For novel genomes, SPAdes  [98]  is currently among the best options for assembling prokaryotic genomes from Illumina and Ion Torrent short reads, and Platanus  [99]  can be used as an additional step for scaffolding and gap-closing sequences. To generate GenBank files there are several available genome annotation pipelines capable of providing CDS assignment and their putative products, signal peptides, non-coding RNAs, and other features.
We recommend Prokka  [21] , an automated pipeline for annotating prokaryotic genomes, which runs on local hardware and can completely annotate a typical bacterial genome in under 10-15 min on a desktop or laptop computer. Conversely, three other satisfactory annotation pipelines are available on the following web-servers: RAST, IMG/ER, and NCBI Pipeline PGAP (for which further details are found in the NCBI handbook chapter at https://www. ncbi. nlm. nih. gov/books/NBK174280). All these pipelines generate standard-compliant output files. If the user is not interested in analyzing novel genomes or wants to compare them to available genomes, two useful sources of bacterial and/or archeal genomes are the NCBI ftp server and the Genomes OnLine Database (GOLD)  [100]. Available genomes can be retrieved by visiting ftp://ftp.
Preparing Your Computer for MGE Analysis: Installing Dependencies While some MGE analysis software provides web servers with browser-based user interfaces or graphical user interfaces, most available programs are based on command-line interfaces and need to be installed on a local computer running a Linux-based OS before analysis can be carried out (seeNote 3). In some cases this can be difficult even with bioinformatics experience. Out-of-thebox Ubuntu lacks a few software elements and libraries on which these programs depend. Nevertheless, several of these dependencies can be easily installed by typing a few commands in a terminal emulator, a program that allows users to interact with computer programs by a text-based interface. Ubuntu's terminal emulator can be accessed by typing "terminal" in the dash (the first button from top on the left-sided launcher bar) or pressing the <ctrl>þ<alt>þ<T> key combination.
Software written in the Java programming language is commonly distributed as. jar files. These files depend on a runtime environment that does not come pre-installed in Ubuntu systems. To run Java-based applications, install the Open Java Development Kit: sudo apt-get install openjdk-7-jdk. R is an open-source computing language widely used for statistics on which some bioinformatics tools rely. Most R packages are included in the Comprehensive R Archive Network (CRAN), but the Ubuntu repositories include some of these packages, which can also be installed with the apt-get manager for an easier process. Install the basic R files and the randomForest package with the following command: sudo apt-get install r-base r-cranrandomforest. Bioinformatics software written in the Python programming language may sometimes depend on Biopython, a library containing several bioinformatics tools, or TkInter, an interface to the GUI toolkit Tk.
Insertion Sequence Prediction ISs are the smallest and simplest autonomous mobile genetic elements, ranging from 0. 7 to 3. 5 kbp, and generally include a transposase gene encoding the enzyme that catalyzes IS movement. They are classified into about 28 different families on the basis of the relatedness of transposases and overall genetic organization  [13]. The ISfinder database (https://www-is. biotoul. fr/) is the reference center for IS analysis, providing a gold standard method and protocol for IS identification, name attribution, annotation, and classification  [25]. We strongly recommend following the ISfinder annotation protocol, using a manual expert annotation of each predicted IS, with the ISsaga web-service. These instructions can be found on the ISsaga Manual in the "About" section of the ISsaga main webpage (http://issaga.
csv") and save this new file in your genome folder. 9. If the ISfinder/ISsaga gold standards for IS annotation were followed to the end, extract the final annotation results in the "Annotation" tab, and choose "Extract Annotation" option. On the new webpage, click on the "GenBank annotation file", and use this new GenBank file to visualize the results in the GView or Artemis genome viewers (optional). Temperate or lysogenic phages generally integrate their genomes into the bacterial chromosome as prophages, which replicate passively with the host chromosome until conditions favor their reactivation. Integration occurs by site-specific recombination often into tRNA/tmRNA genes  [46]  (see  Note 11). Prophages can encode virulence-related genes converting non-pathogenic bacteria into pathogens.
It is well known that prophage identification in bacterial genomes is a difficult process  [46]. Indeed, the longer and more complex the MGE the more difficult it is to correctly identify it. Prophages can also be confounded with Gene Transfer Agents (GTAs), which also resemble phages in a subtle way  [101]. GTAs are widespread in bacteria and may also be involved in virulence and adaptation  [102]. CRISPRs are specific loci which provide acquired immunity against viruses and plasmids  [103]. Therefore, in addition to the prophage prediction and analysis, CRISPR detection also provides valuable information concerning the history of phage infection of the analyzed genome (see  Note 12). CRISPR analysis should use both de novo approaches, such as MinCED and CRISPRdb (http://crispr. u-psud. fr/crispr/), one of the most complete databases for CRISPR analysis.
Genomic Island Prediction GEIs are central to the dissemination and evolution of a broad spectrum of bacteria  [8]. GEIs can be correlated with pathogenicity, virulence, fitness, symbiosis, and cell metabolism, depending on the gene composition of the given GEIs. Therefore, the better the CDS annotation, the better the GEI classification. There are several available GEI detection software packages. Genomic Island Prediction software (GIPSy)  [65]  is one of the most complete and userfriendly GEI detection software currently available, and it is capable of distinguishing pathogenicity islands, metabolic islands, resistance islands, and symbiotic islands. We recommend downloading and following the GIPSy annotation through their interface (http://www. bioinformatics. org/groups/?group_id¼1180).