dataesr/scientific-paragraphs-categorization huggingface dataset:
Tools Until recently, DNA microarrays would be typically used for genotyping, and a range of state-of-the-art tools exist that can phase from the resulting data. SHAPEIT  [6]  is an example of a software for statistical, population-based phasing and Merlin  [7]  for genetic, pedigree-based phasing. Driven by the advances in third-generation sequencing  [8, 9, 10]  and the corresponding growth in read length, read-based phasing has been gaining attention  [11]  because it allows to link consecutive variant alleles directly from single reads. Read-based phasing can be formulated as the NP-hard Minimum Error Correction problem  [12]. To solve it, heuristic  [13, 14, 15]  and exact approaches  [16, 17, 18, 19]  have been suggested, often accompanied by an implementation of the algorithm. Arguably, the main focus has often been on creating proof-of-principle implementations, not on providing stable software.
Exceptions to this are GATK's  [20]  Read-BackedPhasing command, which is one of few tools that directly support standard Variant Call Format (VCF) files for both input and output. It can even be made to work with long reads when the correct command-line parameters are used. Its underlying algorithm is not published, and it is no longer maintained. HapCUT  [13]  on the other hand is Open Source and even supports phasing of indels, but requires pre-processing of input data and produces non-standard output. Recently, phASER  [21]  was published with a focus on phasing from RNA sequencing data, but it can also be used for whole genomes, even supporting standard VCF output. For production use, a software is needed that is easy to use, gives very good phasing results, works with standard formats (VCF and BAM), supports a variety of sequencing technologies and is maintained.
WhatsHap implements a fixed-parameter tractable (FPT) algorithm with read coverage as fixed parameter that solves the wMEC problem. The algorithm proceeds from left to right over the variants. At each position, it considers all k reads covering that position (for read pairs, also the middle section of the fragment without sequencing data is considered to be covering) and tests all 2 k possibilities of assigning the reads to the two haplotypes. The combinations are enumerated in Gray code order, and dynamic programing with some extra data structures is employed to avoid score recomputation, which taken together allows to update the cost for a subsolution in constant time. Unlike other phasing tools, the read length does not affect the runtime of Whats-Hap, which means it is ready for the ever-longer reads that might arrive with future generations of sequencing technology.
Software engineering The command-line front-end of WhatsHap is written in Python 3 due to its simplicity and maintainability. The core dynamic programming algorithm that solves the wMEC (or PedMEC) problem is implemented in C++. The interface between Python and C++ is written in Cython, which is a Python dialect that compiles directly to C/C++ (http://www. cython. org/). Other performance-critical parts have been implemented directly in Cython, such as the re-alignment algorithm. External libraries used include PyVCF (https://github. com/jamescasbon/ PyVCF), pysam (https://github. com/pysam-developers/pysam) and pyfaidx  [25].
Usage and file formats WhatsHap is easily installed from the Python Package Index (PyPI) or Conda's bioconda channel (https://bioconda. github. io/) with a single pip3 install or conda install command. WhatsHap is a command-line tool that expects -in the common case -a single Variant Call Format (VCF) file and one or more BAM files as input. The command whatshap phase -o phased. vcf input. vcf input. bam is typically sufficient for phasing in non-pedigree mode. Instead of BAM, phasing information can be provided to WhatsHap also as phased VCF files, such as those generated by 10X Genomics' phasing pipeline, and already phased blocks are then treated as "reads". Whatshap phases all samples it finds in a multi-sample VCF, using BAM read groups and VCF sample names to associate each read to its originating sample.
Input reads -even those from a single sample -can be distributed over several BAM (or phased VCF) files, and may use different sequencing technologies. For example, PacBio, Illumina mate-pair reads, and 10X Genomics phased blocks can be used simultaneously for phasing. WhatsHap outputs an augmented version of the input VCF file, where phasing information for those variants that could be phased has been added. The standardized "PS" tag (phase set) is used to encode phasing results by default. The "HP" tag -compatible with GATK ReadBackedPhasing output -is also supported.
Experimental setup We evaluate WhatsHap and other phasing tools on both real and simulated datasets. The setup we explain in the following is similar to the one described earlier  [5]. Real data. As evaluation sample, we chose the child (NA24385) of the Ashkenazim trio sequenced by the Genome in a Bottle Consortium (GIAB)  [26]. We downloaded a consensus genotype call set (NIST CallsIn2Technologies 05182015) for chromosome 1, containing 48 023 heterozygous SNVs and 4 265 heterozygous non-SNVs. We refer to these variants as ground truth unphased variants. We phased all these variants with the population-based phasing tool SHAPEIT (v2-r837)  [27]  with default parameters, using the reference panel provided by Phase 3 of the 1000 Genomes Project. We refer to them as ground truth phased variants.
Furthermore, we use the Pacific Biosciences (PacBio) reads from GIAB aligned to chromosome 1. They have an average coverage of 60. 2× and average mapped read-length of 8 687 bp. To analyze the effect of sequencing depth on phasing performance, we created additional datasets by downsampling the aligned reads to average coverages of 2×, 3×, 4×, 5×, 10× and 15×. Simulated data. While population-based phasing is very accurate, it has limitations with respect to low-frequency variants. Since our ground truth phasing will therefore not be completely error free, we complement our evaluation with a simulation study. To this end, we generated two haplotypes of a "virtual child" by recombining the haplotypes of the parents NA24143 and NA24149 in silico as described previously  [5]. The resulting virtual child has 45 625 heterozygous variants with known phasing (ground truth), containing 42 062 SNVs and 3 563 non-SNVs.
Next, we incorporated these true variants into the GRCh37 reference genome to obtain two FASTA files representing the true haplotypes. We simulated reads that mimic the read length profile and coverage of the real data using pbsim  [28]. The resulting reads were then aligned to GRCh37 using BWA-MEM (0. 7. 12-r1039)  [29]. Similar to the real dataset, we further downsampled the aligned reads to average coverages of 2×, 3×, 4×, 5×, 10×, and 15×.
Since hapCUT does not produce VCF, but a custom output format, we implemented a WhatsHap subcommand "hapcut2vcf" that converts the custom format to VCF. We did not use hapCUT's --longreads option since the help states that it should be set if the "program uses too much memory", which was not the case. We ran all programs both with and without requesting phasing of non-SNVs (mostly indels), except ReadBackedPhasing, which does not support indel phasing. WhatsHap was additionally run in a non-realignment mode, in which it also does not support non-SNV phasing.
Runtime We measured running time of all tools on the artificial child dataset, chromosome 1. To be able to include all tools, only SNVs were phased. For those tools supporting non-SNV phasing, the rankings are identical with only slightly higher runtimes. Results can be seen in Table  1. WhatsHap's runtime is clearly competitive with those of the other tools. At all coverages, WhatsHap or hapCUT is the fastest tool. HapCUT has an advantage at high coverages, while WhatsHap is faster at low coverages. WhatsHap runtimes in "no re-alignment" mode are also given for completeness and due to it being slower than the re-aligment variant of the algorithm, we can see that the additional optimization efforts that went into the algorithm have paid off. The two other tools hapCUT and phASER consistently have the highest runtimes.
The development of the On-Site Analysis system involves two Work Packages of the current CTA Product Breakdown Structure: the Data Management (DATA)  [7]  that has the responsibility of the On-Site Analysis software development, and the Array Control and Data Acquisition (ACTL)  [8] , that has the responsibility to provide the on-site Information and Communication Technology (ICT) infrastructure (networking/computing architecture, cost estimations) to allow the On-Site Analysis to successfully run. A deeper study for the definition of detailed use cases of the On-Site Analysis (with a special focus on the Real-Time Analysis) is in progress with the involvement of people from Data Management, Array Control and scientists expert on different fields. The starting point are the CTA science cases and the main purpose is the definition of the steps of interaction with the CTA system to maximize the science return of each observation in the management of external triggers (by other observatories/facilities) or self-triggered by the Real-Time Analysis.
The Real-Time Analysis scientific results (the RT Science Alerts) should be stored in a database (the RT Science DB) with the produced event list (RT DL3 Repo). The real-time scientific reports (RT Science Report), which are the summary of the analysis results from the data being analyzed by the Real-Time Analysis will be retrieved from a real-time dedicated DB (RT Science DB). The Level B analysis pipeline has the following modules: 1. The Level-B Data Quality (DQ) Monitoring performs a detailed data quality check to evaluate the correct execution of the observations. These results are stored into a dedicated data base (LB DQ DB), used to generate dedicated data quality reports (LB DQ Reports). 2. The Science Monitoring can analyze the data produced by the reconstruction software which runs within the Level-B Analysis pipeline (LB-RECO) to check the detection and the status of astrophysical sources with a sensitivity that is expected to be closer to the offline ones and certainly improved compared to the Level A analysis.
In this article the stability of AEs is systematically addressed for the 15 MA ELMy H-mode ITER baseline scenario  [5, 6, 7, 8]  making use of a recently introduced framework  [8]  that is based on the hybrid MHD drift-kinetic code CASTOR-K  [9, 10] , which is the key element in our suite of numerical codes. Growth rates are computed systematically for all possible eigenmodes taking into account the alpha-particle drive, the Landau damping due to the DT.
Numerical codes and modelling workflow Our workhorse is CASTOR-K, which is used to assess the stability of AEs through the computation of their linear growth-rates. Besides CASTOR-K our suite of numerical codes comprises HELENA  [13]  to obtain magnetic equilibria, and the incompressible ideal-MHD code MISHKA  [14]  to compute the eigenmodes of a given equilibrium. The extensive identification of all the AEs of an equilibrium is key to the success of our methodical stability-analysis approach. For this reason MISHKA has been preferred to the resistive-MHD code CASTOR, which we also use to calculate radiative-damping rates, as reported in section 3. 2. In a simple performance test done by executing both MHD codes in the same CPU, with the same input, MISHKA calculated an eigenmode in around 2.
Here, the same approach that has been followed in  [8]  is used to estimate γ rad , the radiative-damping contribution to the net growth rate of selected AEs from both scenario variants. The method  [27, 25, 26, 28]  relies on a formal equivalence between a non-ideal MHD model that accounts for finite parallel electric field and first-order ion-gyroradius effects, and the resistive MHD model that is implemented in the CASTOR eigenvalue code  [29]. In order to compute non-ideal eigenmodes and determine their radiativedamping rates, in place of the (usually real) resistivity we input to CASTOR the complex quantity  [26, 28] η = i4q 2 3 4 + T e T i [1 -iδ (ν e )] ω ω A 3 ρ i R m 2 , (6) and conduct a scan in δ(ν e ), a wave dissipation-rate due to collisional friction between trapped electrons and passing particles (ν e is the electron collision frequency), which leads to an imaginary frequency component and to the corresponding damping rate γ CASTOR.
Summary The linear stability of AEs in the presence of alpha particles has been analysed for two variants of the 15 MA ELMy H-mode ITER baseline scenario using a specialized workflow that is based on the hybrid MHD drift-kinetic code CASTOR-K. Our modelling results show that, considering alpha-particle drive and Landau damping on DT ions, helium ashes and electrons, the most unstable modes have toroidal mode numbers around n ≈ 30 in the scenario variant with a lower pedestal temperature, named LoPed, and n ≈ 15 in HiPed, with maximum growth rates of 1. 24% and 0. 60%, respectively. In both scenario variants these modes are LSTAEs, i. e. , they are localized in the low magnetic-shear region of the plasma core. LoPed has a higher density of alpha particles at the plasma core than HiPed, which is consistent with the larger AE growth rates that have been found for LoPed.
Acknowledgments This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No. 633053. IST activities received financial support from  "Fundação para a Ciência e Tecnologia" (FCT)  through project  UID/FIS/50010/2013. ITER is Nuclear Facility INB no. 174. The views and opinions expressed herein do not necessarily reflect those of the  European Commission, IST ,  FCT , or the  ITER Organization. All computations were carried out using the HELIOS supercomputer system at the  Computational Simulation Centre of the International Fusion Energy Research Centre (IFERC-CSC) in Aomori, Japan , under the  Broader Approach collaboration between Euratom and Japan implemented by Fusion for Energy and JAEA.
Over recent decades, substantial efforts have been made to understand the interactions between host genomes and transposable elements (TEs). The impact of TEs on the regulation of host genes is well known, with TEs acting as platforms of regulatory sequences. Nevertheless, due to their repetitive nature it is considerably hard to integrate TE analysis into genome-wide studies. Here, we developed a specific tool for the analysis of TE expression: TEtools. This tool takes into account the TE sequence diversity of the genome, it can be applied to unannotated or unassembled genomes and is freely available under the GPL3 (https://github. com/l-modolo/TEtools). TEtools performs the mapping of RNA-seq data obtained from classical mRNAs or small RNAs onto a list of TE sequences and performs differential expression analyses with statistical relevance.
This pipeline provides quantitative information for both small and messenger RNAs, performing differential expression analyses among different samples using the DESeq2 program  (16). It can be used for non-model organisms with no annotated reference genome but for which a list of TE copies is available. When this list is not available, TETOOLS can be jointly used with a dedicated tool for TE identification from raw reads, such as DnaPipeTE  (17) , RepeatExplorer  (18)  or other TE identification tools if the genome is assembled (see as a review  (19) ). The pipeline is user friendly and is available for use in Galaxy  (20). We applied TETOOLS to explore TE regulation in D. simulans wild-type strains. In this species, TE sequences belonging to the same family are very diverse and the activity of TEs depends on the strain studied  (21) (22) (23) (24).
Small RNA extraction and sequencing Small RNAs from D. simulans ovaries were manually isolated in HiTrap Q HP anion exchange columns (GE Healthcare) as described in Grentzinger and Chambeyron  (32). Library construction and 50 nt read sequencing were performed by Fasteris SA (Switzerland) on an Illumina HiSeq 2500 instrument. Libraries from the Makindu and Chicharo strains were previously published  (33). The small RNA library of the Mayotte strain is available under the accession number SRX1287860. The poly-A tails attached to the sequence before sequencing to obtain 50nt RNA were removed using UrQt (-N A) before other analysis  (34).
Gene transcript analysis D. simulans gene sequences were obtained from FlyBase (ftp. flybase. net/genomes/Drosophila simulans/dsim r1. 4 F B2014 03/fasta/dsim-all-gene-r1. 4. fasta. gz). RNA-seq reads were trimmed to remove poor quality nucleotides using UrQt (-t 25) (34) and then aligned against D. simulans genes using Tophat2  (35). Alignment counts were performed on sorted bam files using eXpress  (36) , and differential expression was assessed using DESeq2  (16). We used a 0. 05 FDR threshold value for significance. All subsequent calculations were performed on the DESeq2 normalized read counts. Genetic Euclidian distance matrices were computed on the 10 samples using the R dist() function with default parameters on normalized read counts. We retrieved D. melanogaster orthologs using the gene orthologs fb 2014 06.
The TETOOLS pipeline. To determine the read count corresponding to each TE family, we used the first module of TETOOLS (TECOUNT) with the TE (sub)family column in the rosette file as the variable (Figure  1A ). The output table from this module was used in the second module (TEDIFF) to perform the differential expression analyses (Figure  1B ). The module TEDIFF outputs a table of TE families (or any other variables specified in the rosette file) that are differentially expressed among the various conditions/strains, as well as various graphics on the quality of the analysis and the results corresponding to DESeq2 analyses. As an example, we put on Figure  1 (C to H) the graphics corresponding to an mRNA analysis of three of our strains. Figure  1C  corresponds to the model goodness of fit of the data that takes into account the within-group variability and that corresponds to the dispersion plot of the data estimates (black), the fit to a trend curve to the maximum likelihood estimates to capture the dependence of these estimates on average expression strength (red) and the maximum a posteriori estimates used in testing (blue).
A new approach for the automatic transcriptomic analysis of TEs We developed TETOOLS, which is a new pipeline to perform analyses of the differential amounts of mRNAs and piR-NAs from TE copies across different samples. This tool can be used to analyze factors such as different strains, conditions and tissues. This pipeline is implemented in two different modules. The first module (TECOUNT, Figure  1A ) is a python script that performs the mapping of all reads from the RNA-seq dataset to a large list of TE sequences representing different copies, and produces a list of read counts. The use of a list of TE copies provided by the user rather than a sequenced genome or TE consensus sequences has two advantages. First, we can work with TE families not present in the sequenced genome and with non-annotated genomes.
The third input of the TECOUNT module is a rosette file that contains the names of each TE copy. This simple tabular text file can be easily built to group the TE copies by family or any other criteria (i. e. super-family, or even according to other features, such as germline or somatic cell specificity). TECOUNT produces a list of read counts corresponding to the chosen criteria in the rosette file. We stress the fact that TETOOLS uses raw counts in contrast to other piRNA analysis pipelines, which allows the system to avoid biased normalization and to lower the number of false positives for the subsequent differential analyses  (44). An option is also available to filter by size and place read counts that could correspond to siRNAs (21 nt-long reads) into a separate file. The novelty of TEtools is that it intends to integrate the TE intra-family sequence diversity that was observed in some genomes.
Thus, the expected outcome is a higher number of aligned reads compared to the use of only consensus sequences, as already existing software do. However, in genomes that show low intra-family sequence diversity for TEs--such as D. melanogaster--we expect the outcomes of both tools not to be significantly different. We used TETOOLS on our dataset using a list of consensus sequences instead of the full set of TE insertions. The total number of TE aligned reads was then 20% lower to what we got using the full set of TE insertions (2 175 381 versus 1 780 985), reinforcing the relevance of our procedure. The second module of the TETOOLS pipeline (TEDIFF) is an R script (45) that performs a differential analysis of the read counts using DESeq2 (46) (Figure  1B ). TEDIFF requires only the list of counts computed by TECOUNT, a description of each sample (i.
To use bowtie2 on paired-end reads, the user must specify the size of the insert and the mapper is set to its most sensitive option (-verysensitive). To facilitate the use of TETOOLS and its adoption, the pipeline has been implemented as a Galaxy package  (20). All the modules of TETOOLS, which are distributed under the GNU General Public License version 3 (https://github. com/l-modolo/TEtools), can also be used with a command line interface.
Gene transcription reflects the geographical distribution of strains Our dataset was generated from five wild-type strains of D. simulans. Four strains of natural origin (Chicharo, Makindu, Mayotte and Zimbabwe) were chosen because they were known to present variable proportions of some TEs, different levels of TE transcripts and different amounts of piRNAs  (22, 24, 33, 48, 49). We also included w501, which is the most represented strain in the 2007 D. simulans sequenced genome  (37). Hierarchical clustering on the sample-to-sample distances from normalized gene counts (Figure  2 ) first clusters samples per replicate of the same strain and then groups them together with two strains from the ancestral area (Mayotte and Makindu) and strains from the derived area (w501 and Chicharo)  (50). This geographical pattern is reinforced by the significant correlation between the geographical distance (in km) and genetic distance calculated from the read counts (see 'Materials and Methods' section, Mantel test, r = 0.
piRNA amounts are positively correlated to TE transcript amounts To deepen our study of TE dynamics, we used piRNA sequencing data previously obtained for three of our wildtype strains (see  (33)  for Chicharo and Makindu) and we performed small RNA sequencing in one additional strain, Mayotte. These data were analyzed using TETOOLS and all figures and complete tables produced are available as supplemental data (Supplementary Tables  S5, 6  and Supplementary File 2). Because the piRNA data were not produced with replicates, DESeq2 could not provide a statistical result on the differential expression analysis. We compared the expression of the piRNAs based on their normalized read counts and observed that the most targeted TEs by piRNAs were the same for all strains (Supplementary Table  S7 ).
84, with more piRNAs targeting TirantC in the Mayotte strain than in the Chicharo strain. The same is true for this element in the comparison between Chicharo and Makindu, which is in agreement with our experimental knowledge of this TE  (33). The silencing of TEs depends on two distinct piRNA pathways that specifically trigger either somatic or germline-expressed TEs. Primary piRNAs are produced from genomic clusters and are implicated in the somatic regulation of TEs. Secondary piRNAs are either produced from TE transcripts that participate in the ping-pong amplification loop or are maternally transmitted from the mother to the embryo. One way to distinguish primary from secondary piRNAs is to identify the ping-pong signature. We estimated the proportion of piRNAs implicated in the ping-pong loop for 10 representative TEs with high piRNA production log fold changes (>|1|) (Supplementary Figure  S2 ).