dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, several leads could be investigated to overcome the limitations of the translation from the abstract query language to MongoDB. -Our method generates the RDF triples resulting from each atomic queries and subsequently performs joins (INNER JOIN, LEFT OUTER JOIN). In some cases though, joins may rule out many of the triples that were just materialized. Hence, it should be studied when joins can be evaluated on the database documents. This would typically rule out unnecessary documents earlier in the process, thus saving the useless generation of RDF triples. -Our implementation of xR2RML for MongoDB relies on JSONPath to extract data elements from MongoDB results. In turn, the SPARQL rewriting process must handle conditions on JSONPath expressions. Consequently, we have to cope with the expressiveness discrepancy between SPARQL and MongoDB, and between JSONPath and MongoDB alike.
While we must cope with the earlier (our goal is specifically to access heterogeneous databases with SPARQL), the latter is somewhat more an implementation choice. Hence, an investigation should figure out whether considering a restricted subset of JSONPath may produce a simpler solution while still enabling to address most mapping situations. -Beyond this, another promising lead is to determine what type of MongoDB query should be used preferably: find or aggregate queries. We address this question in section 7, as part of a broader discussion about the similarities and discrepancies between our approach and that of ontop's authors. Comparison with the MongoDB-enabled ontop. To the best of our knowledge, the only other approach meant to access arbitrary MongoDB documents with SPARQL has been proposed by the authors of ontop, Botoeva et al.
[8]. This approach starts with deriving a set of type constraints (literal, object, array) from the mapping assertions, called the MongoDB database schema. Then, a relational view over the database is defined with respect to that schema, notably by flattening array fields. A SPARQL query is rewritten into relational algebra (RA) query, and RA expressions over the relational view are translated into MongoDB aggregate queries. Similarly, we translate a SPARQL query into an abstract representation (that is not relational algebra) under xR2RML mappings. To deal with the tree structure of JSON documents we use JSONPath expressions. On the one hand, this avoids the definition of a relational view over the database, but this comes with additional complexity in the translation process, as translating conditions on JSONPath expressions is not straightforward.
On the other hand, the advantage of our method is that the query evaluation relies on existing database indexes, whereas in the case of Botoeva et al. , the flattening step prevents from exploiting these indexes. The mappings are quite similar in both approaches although xR2RML is more flexible: (i) class names (in triples ?x rdf:type A) and predicates can be built from database values whereas they are constant in the approach proposed by  Botoeva et al. , and (ii)  xR2RML allows to turn an array field into an RDF collection or container, while the latter approach only supports the multipletriples strategy. Finally, the main differences pertain to the type of target query. Botoeva et al. produce MongoDB aggregate queries, with the major advantage of ensuring a semantics-preserving SPARQL-to-MongoDB query translation, thus delegating the whole processing to MongoDB and making the query translation simpler.
In practice however, aggregate pipelines may perform poorly. To optimize them, an option suggested by the authors is to decompose the pipeline into smaller queries and have the query-processing engine perform the remaining steps. Our approach works the other way around: it produces less-expressive MongoDB find queries, leaving much more work to the query-processing engine. Nevertheless, having the job done outside of the database engine allows to leverage extensive works about smart query optimizations  [17, 33, 16, 21] , whereas this is not possible when the database performs an aggregate query in a black-box manner. Typically though, in situations involving large joins, aggregate queries perform faster than find queries as they can leverage database indexes. In the future, it would be interesting to assess whether we could characterize mappings with respect to the type of query that shall perform best: single vs.
Conclusion The method proposed in this paper aims at fostering the development of SPARQL interfaces to heterogeneous databases, as we believe this is a key to push the Web of Data forward. In particular, we think that this should help to bridge the gap between the Semantic Web and the NoSQL family of databases. To achieve this goal without defining yet another SPARQL translation method for each and every database, we proposed a two-phase approach. First, we defined an abstract query language deriving from the syntax and semantics of SPARQL. Utilizing the xR2RML mapping language and leveraging R2RMLbased SPARQL-to-SQL works, we introduced a generic method to translate a SPARQL 1. 0 graph pattern into an abstract query. We showed how optimizations can be beneficially enforced at this abstract level, saving subsequent work at the level of a target database language.
In a second phase, the abstract query is translated into the query language of a target database. To demonstrate the effectiveness of our approach, we applied it to the MongoDB NoSQL document store. We devised a method to translate an abstract query into MongoDB find queries, and we showed that this translation is challenged by the expressiveness discrepancy between SPARQL and the MongoDB query language. Finally, we conducted an experimentation based on the real-world use case of a taxonomical reference stored in a MongoDB database. Utilizing a mapping of this database to a SKOS thesaurus, we first measured performances in the case of single SPARQL triple patterns that translate into single MongoDB queries. Then, we measured the performances of richer SPARQL queries and we demonstrated the effectiveness of some of the optimizations performed at the level of the abstract query language.
We underlined some limitations of the translation from the abstract query language to MongoDB, that can impair performances. In section 7 we discuss several improvement leads that could be investigated. From a broader perspective, we have shown that translating a SPARQL query into efficient concrete queries can be challenging when it comes to address data sources such as NoSQL databases. These systems are generally optimized for fast storage and retrieval of vast collections of documents. They favor scalability, high throughput and availability over consistency and query language expressiveness. As a consequence, they often come with denormalized data models where redundancy is common, and barely support joins. This is the case of other document stores such as CouchDB that are designed in a way very similar to MongoDB.
Column family stores usually allow for a richer data model and provide a more expressive query language. But although their columnar data model makes them easily compared with relational systems, they often suffer the same limitations as document stores with respect to the limited support of joins. Key-value stores are designed for fast retrieval of data e. g. accessed by key. They are typically used to implement cache systems, for which a very simple query language (consisting essentially of put and retrieve by key operations) covers most use cases. Consequently, it is likely that the hurdles we encountered with MongoDB will be encountered with other NoSQL databases alike. The situation may not be so much different for the last category of NoSQL databases, namely graph stores. By nature, their data models are closer to RDF.
In a single command line, ALFA can compute and display distribution of reads by categories (exon, intron, UTR, etc. ) and biotypes (protein coding, miRNA, etc. ) for a given aligned dataset with nucleotide precision. We present applications of ALFA to Ribo-Seq and RNA-Seq on Homo sapiens, CLIP-Seq on Mus musculus, RNA-Seq on Saccharomyces cerevisiae, Bisulfite sequencing on Arabidopsis thaliana and ChIP-Seq on Caenorhabditis elegans. Conclusions: We show that ALFA provides a powerful and broadly applicable approach for post mapping quality control and to produce a global overview using common or dedicated annotations. It is made available to the community as an easy to install command line tool and from the Galaxy Tool Shed.
However, combining those is not straightforward and the overall process is often largely sub-optimal  [13]. To the best of our knowledge, there is no available tool that provides a broadly applicable and fast quantitative overview of several samples with nucleotide precision. Here, we introduce ALFA (Annotation Lanscape For Aligned reads), a simple and broadly applicable tool that produces a global overview of the distribution of mapped reads, both in terms of genomic categories (stop codon, 5′-UTR, CDS, intergenic, etc. ) and biotypes (protein coding, miRNA, ncRNA, etc. ) with nucleotide precision. ALFA turned out to be a very useful systematic post-mapping quality control tool for a broad range of NGS applications we have been dealing with. We describe here the results obtained with ALFA using datasets produced by several types of NGS experiments on various model organisms.
Implementation Common genomic databases (e. g. Ensembl) mostly propose two types of functional annotations: genomic categories (5 ́-UTR, CDS, intron, intergenic region, etc. ) and gene biotypes (protein-coding, miRNA, IG-gene, etc. ). An annotation file with both the category and biotype information is required to run ALFA. In order to compute the proportions of reads from a given NGS dataset falling into these annotated regions, ALFA proceeds in three steps. In a first step, ALFA generates reusable index files from genomic annotations. In a second step, ALFA computes the actual nucleotide count per category from a given set of NGS samples using those index files. Finally, ALFA can display bar plots of the raw and normalized distributions. Importantly, the three steps described in detail below can be processed at once with a single command line call.
ALFA can detect inconsistencies between replicates Comparing feature distributions right after mapping is a simple and powerful way to easily detect possible inconsistencies between replicates. To illustrate this point, we used ALFA to evaluate the consistency of three replicates in a CLIP-Seq experiment on Mus musculus samples. CLIP-Seq, for Cross-Linking ImmunoPrecipitation followed by high throughput sequencing, is used to identify the binding sites of RNA associated proteins. In Fig. 1 , the category plots produced by ALFA distinctly highlight that one of the replicates behaves differently from the others, with an Fig. 2  ALFA category plots (raw and normalized) for Bisulfite Sequencing (BS-Seq) data on Arabidopsis thaliana samples (public data available on NCBI: SRA035939 and EBI: ERA051872).
Datasets were gathered from two studies performed on the same model but in two different laboratories: Lab1-Rep1 (SRR342381) and Lab1-Rep2 (SRR342391) from  [14]  and Lab2-Rep1 (ERR046552) and Lab2-Rep2 (ERR046553) from  [15]. ALFA highlights laboratory dependent differences between reads falling in CDS (t-test significant at a 5% level with a p-value of 4 × 10 -2 ). enrichment of reads mapping within intergenic regions. In this case, ALFA allowed to quickly point out, after the mapping step, that a replicate should be further investigated, treated with special care or even discarded.
ALFA can be used to validate a protocol Feature distributions can also be used to check if a technique is working as expected. To illustrate this point we used ALFA to validate the difference between two Ribosome Profiling protocols on Mus musculus samples. The Ribosome Profiling technique, which consists of the deep sequencing of ribosomeprotected mRNA fragments, produces a global snapshot of the translatome. Alongside two replicates using the standard protocol, we produced two extra replicates with an additional harringtonine treatment. Harringtonine is a drug that inhibits the elongation phase of translation, after initiation. In Fig. 3 , the Fig. 3  ALFA category plots (raw and normalized) for Ribosome Profiling (Ribo-Seq) data on Mus musculus samples (unpublished data from HLH available on demand).
ALFA can detect contaminations Comparing feature distributions enables to quickly detect a contamination. For instance, ALFA was used to identify a mitochondrial ribosomal RNA (rRNA) contamination in a RNA-Seq experiment on Homo sapiens samples. Contamination by rRNA can reach a few percents to the vast majority of reads depending on the experiment. In Fig. 5 , a biotype plot produced by ALFA confirms a substantial Fig. 4  ALFA category plots (raw and normalized) for Ribosome Profiling (Ribo-Seq) data on Homo sapiens (unpublished data from AL available on demand) performed with two different procedures for footprinting: treatment with MNase as in  [16]  or treatment with RNase I as in  [17]. As a preprocessing step, rRNA and mtRNA reads were computationally filtered out. The enrichment of signal in intergenic and 3′-UTR regions shows that treatment with MNase seems to produce a substantial increase of the non-protein coding reads compared to RNase I.
ALFA is fast One of the compelling advantages of ALFA, as a single integrated tool, is that it was optimized to be fast even when run on a regular desktop computer. Furthermore, it can also take advantage of multi-core machines. ALFA needs approximately 7 min to build the Homo sapiens genome index and 6 min to compute distributions of four samples of approximately 40 M reads using four CPUs on a regular standalone computer. Note that computing the genome index needs to be performed only once and can be reused to compute additional sample distributions. Fig. 5  ALFA biotype plots (raw and normalized) for RNA-Seq data on Homo sapiens samples (public data available on NCBI: SRP058036). This dataset is part of a research work where ribosomal RNA depletion is compared between adult and fetal tissues  [18].
Conclusions In functional genomics, a vast majority of NGS experiments have the two first data analysis steps in common: a quality control of the raw sequencing reads and a mapping to the corresponding organism reference genome. We introduced ALFA, a third step that can be easily inserted into a pipeline after mapping in order to reveal genome scale possible artefacts. ALFA produces a global overview of the distribution of the mapped reads across genomic categories and biotypes at nucleotide resolution. ALFA was designed to process data from a wide variety of NGS experiments. In order to illustrate its broad usability, we applied ALFA on 6 sequencing datasets that used 5 different methods on 5 different organisms. Those examples highlighted the effectiveness of ALFA to provide a powerful post mapping quality control step on various Fig.
6  ALFA biotype plots (raw and normalized) for Chromatin Immuno-Precipitation sequencing (ChIP-Seq) of NPP-13 from Caenorhabditis elegans samples (public data available SRA062428). This dataset originates from a study  [19]  where snoRNA and tRNA genetic loci were found to be enriched in the IP (SRR628901) compared to the Inputs (SRR628899 and SRR628900). Here, ALFA can retrieve this result in a simple call as highlighted by this plot. Moreover, by providing a global overview without additional work, ALFA seems to denote enrichments in the IP for other biotypes such as miRNA, ncRNA genetic loci. type of NGS experiments, together with, in some cases a useful post mapping global overview. ALFA is open source and freely available as a Python script and as a Galaxy tool. Fig. 7  ALFA biotype plots (raw and normalized) for RNA sequencing (RNA-Seq) data from a Saccharomyces cerevisiae sample (public data available on NCBI: SRA030505 -SRR927165).
In this example, a customized GTF annotation file was created to highlight the flexibility of ALFA. Dedicated biotypes characterizing various Saccharomyces cerevisiae stable transcripts (SUTs  [24] ) and unstable transcripts (CUTs  [21] , NUTs  [22] , XUTS  [23] ) were converted from a BED file. and sets up the script ALFA. py along with all required dependencies. ○ Conda: ALFA can be installed using the command "conda install -c biocomp alfa" which also provides alfa as a standalone executable directly available from the command prompt ○ Galaxy: ALFA can also be installed on Galaxy easily by just searching for "ALFA" in the main Galaxy Tool Shed (https://toolshed. g2. bx. psu. edu/).
This work has received support under the program "Investissements d'Avenir" implemented by ANR (ANR-10-LABX-54 MEMOLIFE and ANR-10-IDEX-0001-02 PSL University), Fondation pour la Recherche Médicale (FRM-AJE20131128944), Inserm ATIP-Avenir and Mairie de Paris (Programme Émergences -Recherche médicale). The funders were not involved in study design, and collection, analysis, and interpretation of data and in writing the manuscript. Availability of data and materialsAll data presented in the figures are available from the specified data repositories corresponding to the cited publications. A few figures contain unpublished data from the authors' laboratories that are available on demand. All datasets are listed below. SRR342381 https://trace. ncbi. nlm. nih. gov/Traces/sra/?run= SRR342381 ○ SRR342391 https://trace.
ncbi. nlm. nih. gov/Traces/sra/?run= SRR927165 Some unpublished data from HLH and AL (specified in some of the figure captions) are available on demand. Author's contributions MB, BFN and VM conceived and implemented ALFA, LB tested ALFA, HLH and AL generated datasets and suggested typical use cases. CB wrote the Galaxy wrapper. AG, BFN and MB wrote the manuscript. All authors have read, edited and approved the manuscript.
Availability and requirements Project name: ALFA Project home page: https://github. com/ biocompibens/ALFA Operating systems: Linux, Windows, MAC Programming language: Python (version 3) Other requirements: Python libraries: os, sys, re, numpy, collections, copy, argparse, pysam, pybedtools, matplotlib, progressbar and multiprocessing Licence: MIT Any restrictions to use by non-academics: N/A Availability ○ GitHub: the Python code and additional information on how to use ALFA can be retrieved from the developers' code repository at https:// github. com/biocompibens/ALFA. ○ PyPI: ALFA can be installed by typing the command "pip install ALFA" which downloads.
ncbi. nlm. nih. gov/Traces/sra/?run= SRR927165 Some unpublished data from HLH and AL (specified in some of the figure captions) are available on demand. Author's contributions MB, BFN and VM conceived and implemented ALFA, LB tested ALFA, HLH and AL generated datasets and suggested typical use cases. CB wrote the Galaxy wrapper. AG, BFN and MB wrote the manuscript. All authors have read, edited and approved the manuscript. 1: alfa -a Mus_musculus. GRCm38. 91. chr. gtf -g M_musculus --bam Rep1. bam Rep1 Rep2. bam Rep2 Rep3. bam Rep3 -s forward NCBI: SRA035939 ○ Figure 2: alfa -a Arabidopsis_thaliana. TAIR10. 38. gtf -g A_thaliana --bam SRR342381. bam Lab1 -Rep1 SRR342391. bam Lab1 -Rep2 ERR046552. bam Lab2 -Rep1 ERR046553. bam Lab2 -Rep2 Figure 3: alfa -a Mus_musculus. GRCm38. 91. chr. gtf -g M_musculus --bam HA -Rep1.
It is not yet clear which model appears to be the best, but the truncated model seems to be very accurate and the constant offset model very robust even for high iron levels  [30]. The complex fit has been postulated as the best approach to avoid noise-related biases  [37]. Nevertheless, as this is not a solution for daily clinical practice, we suggest that you adhere to two important aspects: decide on a decay model and stay with it, especially at follow-up examinations. Freely available software is available in which the group from Rennes recently launched, MRQuantif (http://mrquantif. org). This DICOM-dedicated solution automatically selects the best method and the preferable algorithm depending on the data provided but gives also the opportunity to select a fitting method and visualize the matching of curve to the data points (Fig.
They produce T2* or R2* but also fat fraction maps by doing a pixelwise fitting. An advantage that results from this is the possibility of calculating the proton density fat fraction. Some of these sequences have already been evaluated in literature with promising results at 1. 5 T  [40]. Comparison between 3D multi-echo Dixon approaches and the conventional, already-approved 2D GRE technique has shown excellent correlation  [41]. Nevertheless, studies have shown some outliners or individual limitations such as reconstruction errors with fat-water swap. The first TE should be short Fig. 4  Example of a MR evaluation using the MRQuantif software. Selected ROIs are placed manually, three in the liver, one in the spleen, two in paraspinal muscles, and one in the background noise (a). The software then automatically calculates R2* (and T2*) values and further provides results calculated with the selected SIR method (b).
Multivariate time series with long-dependence are observed in many applications such as finance, geophysics or neuroscience. Many packages provide estimation tools for univariate settings but few are addressing the problem of long-dependence estimation for multivariate settings. The package multiwave is providing efficient estimation procedures for multivariate time series. Two semi-parametric estimation methods of the long-memory exponents and long-run covariance matrix of time series are implemented. The first one is the Fourier-based estimation proposed by  [18]  and the second one is a wavelet-based estimation described in  [4]. The objective of this paper is to provide an overview of the R package multiwave with its practical application perspectives.
Hence it is crucial to take into account the presence of longmemory for defining good estimators  [5]. In the case of univariate time series, several very efficient approaches have been developed and validated. A web page entitled "Time Series Analysis" 1 from the R software is providing a very exhaustive list of methods and softwares dealing with long-memory time series. Among others, we can cite the packages fracdiff  [7] , arfima and FGN  [23] , longmemo  [5]  and forecast  [8]. For example, fracdiff is dedicated to simulation of fractional ARIMA time series and to estimation using regression of the periodogram. longmemo provides real data examples of time series with long-memory properties. Approaches for multivariate long-memory time series are less developed. When dealing with multivariate time series, an important quantity to estimate is the covariance or correlation between pairs of time series.
The effect of the presence of long-memory on this estimation is obvious, as stated by  [14]. One R package, waveslim, is dedicated to the wavelet correlation analysis for pairs of random variables  [24]  but long-range dependence properties are not considered. [17]  provide R code 2  for bivariate longrange dependent time series with parametric estimations. The objective of this paper is to provide an efficient R package, called multiwave, to estimate the long-memory parameters and covariance matrices for multivariate time series. The estimation procedures are based on a semi-parametric approach, which is robust to model misspecification. The procedures are also suited for dealing with more than two dimensional data. Indeed they are based on Whittle approximation which provides a simple function to optimize.