dataesr/scientific-paragraphs-categorization huggingface dataset:
Neutrino interaction model and uncertainties A framework for considering the impact of neutrino interaction model uncertainties on the oscillation analysis has been developed. The default interaction model is implemented in v2. 12. 10 of the GENIE generator  [27, 28]. Variations in the cross sections are implemented in various ways: using GENIE reweighting parameters (sometimes referred to as "GENIE knobs"); with ad hoc weights of events that are designed to parameterize uncertainties or cross-section corrections currently not implemented within GENIE; or through discrete alternative model comparisons. The latter are achieved through alternative generators, alternative GENIE configurations, or custom weightings, which made extensive use of the NUISANCE package  [29]  in their development. The interaction model components and uncertainties can be divided into seven groups: (1) initial state, (2) hard scattering and nuclear modifications to the quasielastic, or one-particle one-hole (1p1h) process, (3) multinucleon, or two-particle two-hole (2p2h), hard scattering processes, (4) hard scattering in pion production processes, (5) higher invariant mass (W ) and neutral current (NC) processes, (6) final-state interactions (FSI),  (7)  neutrino flavor dependent differences.
Low W single pion production Axial mass for CC resonance in GENIE 0. 94 Normalization of CC1π non-resonant interaction 0. 43 Table  3  Neutrino interaction cross-section systematic parameters that receive a central-value tune and modify the nominal event rate predictions. -Category 2: implemented in the same way for ND and FD, but on-axis ND data alone is not sufficient to constrain these parameters. They may be constrained by additional ND samples in future analyses, such as off-axis measurements. -Category 3: implemented only in the FD. Examples are parameters which only affect ν e and ν e rates which are small and difficult to precisely isolate from background at the ND. All GENIE uncertainties (original or modified), given in Tables  1  and 2 , are all treated as Category 1. Table 4, which describes the uncertainties beyond those available within GENIE, includes a column identifying which of these categories describes the treatment of each additional uncertainty.
Expected Far Detector Event Rate and Oscillation Parameters In this work, FD event rates are calculated assuming the following nominal deployment plan, which is based on a technically limited schedule: -Start of beam run: two FD module volumes for total fiducial mass of 20 kt, 1. 2 MW beam -After one year: add one FD module volume for total fiducial mass of 30 kt -After three years: add one FD module volume for total fiducial mass of 40 kt -After six years: upgrade to 2. 4 MW beam Table  5  shows the conversion between number of years under the nominal staging plan, and kt-MW-years, which are used to indicate the exposure in this analysis. For all studies shown in this work, a 50%/50% ratio of FHC to RHC data was assumed, based on studies which showed a roughly equal mix of running produced a nearly optimal δ CP and mass ordering sensitivity.
In order to avoid falling into a false minimum, all fits are repeated for four different δ CP values (-π, -π/2, 0, π/2), both mass orderings, and in both octants, and the lowest χ 2 value is taken as the minimum. Two approaches are used for the sensitivity studies presented in this work. First, Asimov studies  [98]  are carried out in which the fake (Asimov) dataset is the same as the nominal MC. In these, the true value of  Table  10  Treatment of the oscillation parameters for the simulated data set studies. Note that for some studies θ 13 has a Gaussian penalty term applied based on the NuFIT 4. 0 value, and for others it is thrown uniformly within a range determined from the NuFIT 4. 0 3σ allowed range. |∆m 2 32 | (×10 -3 eV 2 ) Uniform |[2. 3; 2. 7]| δ CP (π) Uniform [-1;1] all systematic uncertainties and oscillation parameters except those of interest (which are fixed at a test point) remain unchanged, and can vary in the fit, but are constrained by their pre-fit uncertainty.
One of the primary physics goals for DUNE is the simultaneous measurement of all oscillation parameters governing long-baseline neutrino oscillation, without a need for external constraints. Figure  24  shows the 90% constant ∆χ 2 allowed regions in the sin 2 2θ 13 -δ CP and sin 2 θ 23 -∆m 2  32 planes for seven, ten, and fifteen years of running, when no external constraints are applied, compared to the current measurements from world data. An additional degenerate lobe visible at higher values of sin 2 2θ 13 and in the wrong sin 2 θ 23 octant is present in the seven and ten year exposures, but is resolved after long exposures. The time to resolve the degeneracy with DUNE data alone depends on the true oscillation parameter values. For shorter exposures, the degeneracy observed in Figure  24  can be resolved by introducing an external constraint on the value of θ 13.
Acknowledgements This document was prepared by the DUNE collaboration using the resources of the  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359. This work was supported by  CNPq ,  FAPERJ ,  FAPEG  and  FAPESP, Brazil ;  CFI ,  IPP  and  NSERC ,  Canada ; CERN; M ŠMT, Czech Republic; ERDF,  H2020-EU  and  MSCA ,  European Union ;  CNRS/IN2P3  and  CEA, France ;  INFN, Italy ; FCT, Portugal; NRF, South Korea;  CAM, Fundación "La Caixa " and  MICINN, Spain ;  SERI  and  SNSF, Switzerland ;  T ÜB İTAK ,  Turkey ;  The Royal Society  and  UKRI/STFC ,  United Kingdom ;  DOE  and  NSF ,  United States of America. This research used resources of the  National Energy Research Scientific Computing Center (NERSC) , a  U.
Background: In [Prezza et al. , AMB 2019], a new reference-free and alignment-free framework for the detection of SNPs was suggested and tested. The framework, based on the Burrows-Wheeler Transform (BWT), significantly improves sensitivity and precision of previous de Bruijn graphs based tools by overcoming several of their limitations, namely: (i) the need to establish a fixed value, usually small, for the order k, (ii) the loss of important information such as k-mer coverage and adjacency of k-mers within the same read, and (iii) bad performance in repeated regions longer than k bases. The preliminary tool, however, was able to identify only SNPs and it was too slow and memory consuming due to the use of additional heavy data structures (namely, the Suffix and LCP arrays), besides the BWT.
Results: In this paper, we introduce a new algorithm and the corresponding tool ebwt2InDel that (i) extend the framework of [Prezza et al. , AMB 2019]  to detect also INDELs, and (ii) implements recent algorithmic findings that allow to perform the whole analysis using just the BWT, thus reducing the working space by one order of magnitude and allowing the analysis of full genomes. Finally, we describe a simple strategy for effectively parallelizing our tool for SNP detection only. On a 24-cores machine, the parallel version of our tool is one order of magnitude faster than the sequential one. The tool ebwt2InDel is available at github. com/nicolaprezza/ebwt2InDel. Conclusions: Results on a synthetic dataset covered at 30x (Human chromosome 1) show that our tool is indeed able to find up to 83% of the SNPs and 72% of the existing INDELs.
In this scenario, there has been a growing interest in reference-free (and assembly-free) variant calling methods, that perform their task directly on the raw reads data  [1] [2] [3] [4] [5] [6] [7]. In the literature one can find reference-free methods and tools for detecting SNPs  [1, 4, 7, 8] , small INDELs  [4, 8, 9] , sequencing errors  [10] [11] [12] , rearrangement breakpoints  [13]  in genomic data, haplotype assembly  [14] [15] [16] , as well as alternative splicing events  [2]  on RNA-Seq data. Most of the above mentioned effective tools (e. g. [1-4, 13, 17, 18] ) in variant discovery use a de Bruijn graph (dBG), i. e. a directed graph in which the set of nodes corresponds to the set of k-mers contained in the reads and two k-mers are connected by an edge if (i) they perfectly overlap on k-1 nucleotides, and (ii) the corresponding (k+1)-mer obtained by concatenating them occurs in the dataset (usually, with a fixed minimum frequency).
The identification of variants is carried out by detecting and analyzing bubbles in dBG, i. e. pairs of disjoint paths sharing the same source node and target node. The advantages of using dBG include the formalization of several biologically interesting features through specific properties of paths in the graph. The drawback of these dBG representations is the need to set in advance the value of the parameter k and the lossy constraint of considering k-mers rather than representing the actual whole collection of reads. In particular, coverage information of each k-mer, as well as the fact that two or more k-mers might belong to the same read (and are thus adjacent in the genome), are important pieces of information that are not usually stored in the de Bruijn graph. The limitation of fixing k is also present in  [19, 20]  where k-mers are indexed, and in the methods of  [21, 22]  that use a Gk array, while other methods that use suffix array based data structures (like PgSA  [23] ), instead, do not have a fixed k-mer size.
The eBWT of a read set can be built by using e. g. [43] [44] [45] [46] [47]. In  [48]  the eBWT was used to index reads from the 1000 Genomes Project  [49]  in order to support k-mer search queries. An eBWT-based compressed index of sets of reads has also been suggested as a basis for both RNA-Seq  [50]  and metagenomic  [51, 52]  analyses. In this paper, we present a new eBWT based strategy for SNPs and INDELs detection in a dataset of "raw reads". Unlike the usual methods, our tool takes as input the (extended) Burrows-Wheeler Transform of the read collection. The strength of BWT-based indexing for variant calling stems from the facts that the BWT tends to group symbols that share the same right context into runs of letters and, remarkably, that the original text can be reconstructed from it (thus allowing the BWT to represent the original string without loss of information), see  [24, 26, [53] [54] [55].
In  [6, 7]  we exploited this property and introduced the Positional Clustering framework via a series of theoretical results that overall led to the characterization of SNPs as clusters in the BWT of the reads collection, and to detect such clusters using two additional data structures: the Longest Common Prefix Array (LCP) and the Suffix Array (SA) of the dataset. We thus implemented a SNPs calling tool that we experimentally validated: its accuracy in terms of sensitivity and precision was very promising and competitive, with running time and memory usage comparable when limited to the actual search phase. However, when taking into account the preprocessing phase, and thus the cost of computing LCP array and SA (in addition to the eBWT), the positional clustering based tool for SNPs detection resulted to be overall computationally inefficient.
More formally, given as input the eBWT of a read set S, our task is to output frequent enough (i. e. covered-enough in the reads dataset) SNPs and INDELs T 1 → T 2 , with T 1 , T 2 ∈ * DNA (e. g. T 1 = CGT and T 2 = C means that the INDEL CGT → C is present in the dataset) surrounded by a user-defined number of bases (in the example above, we may output the pair (ATCGTCT, ATCCT), where the INDEL is underlined). Such procedure can be interpreted as a filtering process that isolates statistically-significant fragments containing variants (note that the output is considerably smaller than the input). This could be useful, e. g. as a pre-processing step to speed up subsequent alignment against a database containing known variants, or simply as a direct quantification of the number of variants in the input dataset (the output's size is proportional to this number).
Positional clustering A crucial property of the eBWT is that the symbols preceding suffixes that begin with some context w (a suitable w will be defined later) will result in a contiguous substring of ebwt(S). We call this substring a cluster. A similar concept has been used in  [60]  for the reference-free compression of sequence quality scores and in  [52]  for an alignment-free and assembly-free framework for metagenomic classification. The general idea is that if all symbols in a cluster associated to w are equal to a symbol c, then every occurrence of w in S is preceded by c. The opposite situation is more interesting for our purposes: if a cluster contains at least two distinct symbols and w is long enough, then those symbols may reflect a variant (e. g. a SNP or the right end of an INDEL) in the input dataset.
Data structures In  [56] , the authors showed that the LCP of a collection S of total size n on alphabet [ 1, σ ] can be computed from ebwt(S) in O(n log σ ) time using o(n log σ ) bits of working space on top of the input and output size. The algorithm works by simulating a visit of the generalized suffix tree of the collection using just its eBWT. For each visited suffix tree node x, the algorithm induces the LCP values whose coordinates stand between the eBWT ranges of the children of x. At the end of the visit, all LCP values have been filled. Describing this algorithm is out of the scope of this article; the interested reader can find all the algorithmic details in  [56]. We note that, in order to implement the positional clustering strategy, we do not require the whole LCP array: we just need to know which of its entries are larger than k min and which are local minima, i.
e. our goal is to compute two bitvectors K min  [ 1. n] and localMin  [ 1. n] defined as K min [ i] = 1 if and only if lcp[ i] ≥ k min and localMin[ i] = 1 if and only if lcp[ i -1] ≥ lcp[ i] < lcp[ i + 1]. Similarly, for a fixed parameter k right (by default, k right = 30) we compute an additional bitvector K right  [ 1. n] defined as K right [ i] = 1 if and only if lcp[ i] ≥ k right. This bitvector will be used to know from which suffix to extract the k right characters that will form the right-context of our output events (for more details, read the next section). It is easy to modify the algorithm of  [56]  to compute these three bitvectors instead of the LCP array, and we do not describe the technical details here. By using the same data structures described in  [56]  (a cacheefficient packed string on alphabet DNA ), the total space used in RAM by our variant calling algorithm (which we will describe in the next subsection) is just 7n bits (including the bitvectors K min , K right , and localMin), i.
Variant calling algorithm The eBWT and arrays K min , K right , and localMin are all we need to find variants (i. e. SNPs and INDELs) in the input dataset. Figure  1  illustrates the process of finding an INDEL (SNPs are found similarly, read below). As discussed above, an eBWT cluster is a maximal substring ebwt[ i, j] such that K min [ r] = 1 and localMin[ r] = 0 for all i ≤ r ≤ j. For each eBWT cluster ebwt[ i, j] (in Fig. 1 , the cluster is highlighted in gray: TCCTC), we proceed as follows. (i) If the cluster does not contain at least two distinct letters, each occurring at least min cov times (by default, min cov = 6; this is a parameter that can be modified using option -m), then we discard it. Otherwise, we proceed with the next step. (ii) For each pair (c 1 , c 2 ) of distinct letters occurring at least min cov times in the cluster we do the following: • We compute a consensus L t , for values t = 1, 2, of length at most M = k left + max indel -where by default k left = 31 (this is a parameter that can be modified using option -L) and max indel = 10 (this is a parameter that can be modified using option -g) -among suffixes of strings preceding and including character c t in the eBWT (in Fig.
In the example of Fig. 1 , the optimal alignment is the one that deletes GC from L 2 = ATGC. • We output the sequences L 1 • R and L 2 • R, as well as information useful to localize the SNP/INDEL (i. e. length and position in the two fragments), and the coverage of the two sequences in the input dataset. Note that, by definition of our procedure, INDELs necessarily end with two distinct characters. For example, in Fig. 1  the output INDEL is TGC → T. This is due to the fact and following (RIGHT) each eBWT character. In bold: LCP minima. In gray: eBWT cluster. Note that we explicitly compute only column eBWT (the other columns are shown only for illustrative purposes). LCP minima are computed on-the-fly, whereas contexts LEFT and RIGHT are reconstructed using backward search and the FL mapping, respectively.
VCF creation If a reference genome is available, then the variants called by our tool can be aligned against it and converted to. vcf format. This can be useful, for example, to compare our output against a database of known variants and, in particular, to validate the output of the tool (see next section). In the next paragraph we describe a pipeline based on the aligner bwa-mem  2. First of all, variants can be filtered by minimum coverage using our executable filter_snp. In general, a higher minimum coverage will increase precision and decrease sensitivity (see "Results" section). The next step is to convert our calls to fastq format. This can be achieved, for example, using the tool seqtk 3  (with the command seqtk seq -F 'I' in. fa > out. fq) that creates a. fastq file with one entry per output fragment and with dummy high base qualities.
Then, the. fastq file can be aligned against the reference genome using bwa-mem. The resulting. sam file can finally be directly converted to. vcf format using our executable sam2vcf. This tool converts every mismatch and INDEL contained in the alignments into a. vcf entry. In our repository we provide a script (snp2vcf. sh) that automatizes this process. Finally, as an optional stage (that was performed in our experiments) one can remove duplicated variations by sorting the. vcf by coordinate and then removing duplicate lines.
Validation We used hap. py (https://github. com/Illumina/hap. py) to validate the variations output by the tools. This tool is among the gold standards solutions to compute sensitivity = TP TP+FN , precision = TP TP+FP , and F1 score = 2TP 2TP+FP+FN (TP, FP, FN being true positives, false positives, and false negatives, respectively) of a given. vcf against a ground truth. vcf.
Results We compared our EBWT2INDEL with DISCOSNP++  [8] , that is an improvement of the DISCOSNP  [1, 5]  algorithm: while DISCOSNP only detects (both heterozygous and homozygous) isolated SNPs from any number of read datasets without a reference genome, DISCOSNP++ detects and ranks all kinds of SNPs as well as small INDELs. As shown in  [8] , DISCOSNP++ performs better than state-of-the-art methods in terms of both computational performances and quality of the results. DISCOSNP++ is a pipeline of several independent tools. In the first step, DISCOSNP++ builds the dBG of the input datasets taking into account both the size of the k-mers and the minimum coverage c (DBGH5 module), and presumed erroneous k-mers are removed based on their frequency. Then, DISCOSNP++ detects bubbles  [17]  generated in the dBG by the presence of SNPs (isolated or not) and INDELs, and it outputs a.
fasta file containing the variant sequences (KISSNP2 module). A final step (KISSREADS2) maps back the reads from all input read sets on the variant sequences, mainly in order to determine the read coverage per allele and per read set of each variant. This module also computes a rank per variant, indicating whether it exhibits discriminant allele frequencies in the datasets. The last module generates a. vcf of the predicted variants. If no reference genome is provided, this step is simply a change of format from. fasta to. vcf (VCFCREATOR module). We performed three experiments: (i) on synthetic data, Human chromosome 1, (ii) on real data, Human chromosome 1, and (iii) on whole-genome sequencing real Human data. We added the reverse-complement of the reads to each dataset in order to improve the sensitivity of our tool (note that DISCOSNP++ performs implicitly this step by adding the reverse-complement of the k-mers to the de Bruijn graph as well).
We validated the results of experiments (i) and (ii) at different coverages to assess the effect of coverage on the tools' performance. In the simulated experiment (i), the ground truth, represented by a. vcf file, was known with certainty and thus the experiment had the goal to assess the precision and sensitivity of the tools. The experiments on real data allowed us to assess the sensitivity and speed of the tools in real-case scenarios. The goal of experiments (i) and (ii) was to reconstruct the genotype of individual HG00096 (from the 1000 Genomes Project's database), Chromosome 1, by detecting heterozygous sites from the raw reads. Precision and sensitivity of the tools were calculated by comparing the ground truth. vcf with the. vcf generated from the tools' outputs, by using the pipeline described above.
In experiment (iii), we analyzed variations contained in the union of the two whole-genome datasets NA12892 and NA12878 (mother and son, respectively). The goal of this experiment was to compare the number of variations reported by the two tools in a more realistic (and large) scenario. Both tools allow the user to filter out variants covered less than a fixed threshold. In our case, this is done at post-processing time (using the tool filter_snp) by simply discarding low-covered variants from the output of EBWT2INDEL. In the case of DIS-COSNP++, this requires re-building the de Bruijn graph with a different value of parameter -c. For both tools, precision is proportional to this threshold, while sensitivity is inversely proportional. In experiments (i) and (ii), we therefore ran both tools varying this threshold in the range  [ 2, 26]  and selected the value yielding the best average between the F1 scores of INDELs and SNPs.
Synthetic experiment (i) -human chromosome 1 In the synthetic experiment (i), we generated two variants of human Chromosome 1 by applying to it the two alleles of each heterozygous variation contained in the. vcf file downloaded from ftp://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/release/20130502/. The modified bi-allelic chromosome was used to simulate synthetic reads with the tool SimSeq  [63] , uniformly distributing the coverage among the two chromosome's variants and using the HiSeq error profile 5  publicly available in the SimSeq's repository. We simulated 100bp synthetic reads with total coverage ranging from 10x to 50x in order to assess the effect of coverage on the sensitivity and precision of the two tools. The results on the synthetic dataset are reported in Figs. 2  and 3.
Experiment on real data (ii) -human chromosome 1 In the real data experiment (ii) we ran the two tools on a reads dataset (HG00096, Chromosome 1) downloaded from ftp://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/phase3/data/. We randomly sampled reads from the original dataset keeping a coverage ranging from 10x to the 48x full-dataset coverage. For the ground truth, we used again the. vcf file used in the synthetic dataset. Unlike in the first experiment, we note that in this case such. vcf was only an approximation of the real ground truth (that is, the individual's unknown genotype) since it had been created by the 1000 Genomes Project consortium starting from the raw read dataset that we also used for our experiment. It is therefore expected that the differences between this and the. vcfs created by EBWT2INDEL and DISCOSNP++ are larger with respect to those in the synthetic dataset, with consequent drops in precision for both tools.