dataesr/scientific-paragraphs-categorization huggingface dataset:
Phasing using sequence reads We finally assessed the ability of SHAPEIT4 to leverage additional phase information when processing genotypes derived from high-coverage sequencing data. To do so, we merged the GIAB high coverage genotype data on chromosome 20 with 502 unrelated European individuals that have been sequenced as a part of the 1,000 Genomes project  24. We then phased the resulting dataset using SHAPEIT4 and two different layers of phase information. First, we performed haplotype assembly (using WhatsHap; Figure  1C ) for the GIAB genotype data using five types of sequencing data 22 : SOLiD5500W, Complete Genomics, Illumina HiSeq, PacBio and 10x Genomics which informed the phase at 9. 8%, 35. 1%, 57. 9%, 90. 1% and 97. 7% of the heterozygous genotypes, respectively (table  1 ). Second, we built two different haplotype scaffolds for GIAB: (i) one derived from a larger set of samples genotyped on Illumina OMNI2.
23% and 0. 07% for PacBio and 10x Genomics, respectively (Figure  4A ). Third, sparse haplotype scaffolds derived either from family information or large reference panels provide appreciable boosts in accuracy, particularly at common variants (MAF>1%; Figure  4B ). This demonstrates the benefit of using haplotype scaffolds when phasing sequencing data. Fourth, SHAPEIT4 allows correcting many switch errors introduced at the haplotype assembly step (Figure  4C ). For instance, in our data, it corrects 26% and 79% of the switch errors obtained from Illumina HiSeq and PacBio, respectively. Finally, the value of integrating data using SHAPEIT4 can be summarized by comparing three phasing scenarios of interest (Supplementary Figure  4 ): (i) the default situation in which the data was directly phased without using any additional information (SER=0.
82%), (ii) the situation that can often be implemented in which both Illumina HiSeq data and a UKB based haplotype scaffold are used in the estimation (SER=0. 42%, 49% decrease in error rate) and (iii) the most accurate situation in which both 10x Genomics data and a trio based haplotype scaffold are used (SER=0. 07%, 91% decrease in error rate).
Discussion We present here a new method for haplotype estimation, SHAPEIT4, that substantially improves upon existing methods in terms of speed and accuracy. One of the key improvements resides in its ability to quickly select a variable number of informative haplotypes to condition on through a PBWT based approach. The resulting method exhibits sub-linear scaling with sample size and provides highly accurate haplotype estimates for large scale data sets. We demonstrated this on the largest genotype dataset available so far, the UK Biobank, in which SHAPEIT4 offers the best trade-off between speed and accuracy across all tested methods. We predict SHAPEIT4 to be particularly well suited to process datasets reaching a million individuals and above. Of note, Eagle2 3,4 also uses PBWT in order to build compact representations of a fixed number of conditioning haplotypes (called HapHedge structures).
We notably showed that SHAPEIT4 is able to deliver very accurate haplotypes for high coverage sequenced data when leveraging long or barcoded reads through a first step of haplotype assembly (e. g. 10x genomics). In addition, its ability to use haplotype scaffolds offers an interesting framework in which the high accuracy delivered by very large SNP array datasets can be propagated into high coverage sequencing datasets of smaller sample sizes. We predict that these two functionalities will help improving phasing servers dedicated to whole genome sequenced samples  28. It is also important to mention here that SHAPEIT4 is not designed to perform genotype calling from low coverage sequencing and adapting the model for such data would need some additional work. Overall, we believe SHAPEIT4 to be particularly well suited to fully exploit the potential of the large datasets being generated using either SNP arrays or new sequencing technologies.
MCMC Iteration scheme In SHAPEIT2 and 3, we start from a random haplotype resolution for all individuals and then perform 35 MCMC iterations to converge towards a reasonable haplotype resolution. In SHAPEIT4, we implemented three new features to improve the mixing of the MCMC: -Initialization. We use a 'quick-and-dirty' initialization of the haplotypes so that the MCMC does not start from a random resolution. To do so, we use a simplified version of the phasing approach implemented in the PBWT software package  29  which provides initial haplotype estimates very quickly in O(NL) with switch error rates in the range of 8% to 10% in our data. Briefly, we implemented a recursive procedure: at a given variant site l, we build a vector of all 2N alleles carried by the N individuals and set as missing all alleles at heterozygous/missing genotypes.
Haplotype scaffold SHAPEIT4 also allows for some heterozygous genotypes to be phased a priori. This approach has been previously introduced in SHAPEIT2 in the context of the 1000 Genomes project to perform genotype calling from low coverage sequencing data  19. This 'scaffold' of pre-phased heterozygous genotypes can originate for instance from pedigrees in which many of the children' genotypes can be accurately phased using Mendel inheritance logic. In practice, we implemented this functionality by simply pruning out all haplotype configurations in the genotype graphs that are inconsistent with the available scaffold of haplotypes (Supplementary Figure  5 ). Of note, SHAPEIT4 does not have any requirements in terms of variant overlap between the main genotype data and the scaffold haplotype data and therefore allows the latter to be derived from SNP array data while the former from high coverage sequencing data.
The UKB datasets We generated genotype data sets with large sample sizes from the full release of the UK biobank (UKB) data set containing 488,377 individuals genotyped by SNP arrays at 805,426 genetic variants. To do so, we proceeded according to the five following steps. First, we filtered out all genetic variants with more than 5% missing genotypes. Second, we extracted data only for chromosome 20, resulting in 18477 variants in total. Third, we build trio candidates (two parents and 1 child) from the pairwise kinship and IBS0 estimates between individuals that have been measured as part of the UKB study  2   variants. All reference panels have been compressed to speed up I/O of the downstream phasing runs: we used BCF formats for both SHAPEIT4 and Eagle2 (using bcftools v1. 4) and bref3 format for Beagle5 (using bref3.
Phasing runs on the UKB datasets We phased the UKB datasets using SHAPEIT4 and three other widely used phasing methods: has been run using the options --states 200, --cluster-size 4000 and --early-stopping. All methods were run using 10 threads to speed up computations. The reported running times and memory usages were obtained using the GNU time v1. 9 command. The running times are computed as the sum of the User and System times and the memory usage as the Maximum resident set size. The error rates of each method was measured using the switch error rate (SER) on the 500 trio children. SHAPET3 Specifically, we enumerated all heterozygous genotypes (i. e. hets) in the 500 trio children that can be phased using Mendel inheritance logic (i. e. no triple hets and no Mendel inconsistencies). Then, we computed the SER as the fraction of successive pairs of hets that are correctly phased over all possible pairs.
The GIAB datasets To assess performance of SHAPEIT4 on sequencing data, we use the high quality phased genotype data generated for the NA12878 individual by the Genome In A Bottle consortium  22. In order to get population scale data, we merged the high coverage genotype data of the GIAB with 502 unrelated European individuals sequenced as part of the phase 3 of the 1000 Genomes project phase 3 (KGP3)  24. To do so, we proceeded as follows. First, we only used variants on chromosome 20. Second, since NA12878 has also been sequenced in KGP3, we only retained variants for which GIAB and KGP3 genotypes are concordant. Of note, NA12878 is not included in the 502 KGP samples we used for merging. Third, we only kept variants that were phased by GIAB thanks to some family data (i. e. multiple sequenced individuals from the NA12878 family).
Fourth, for all variants present in KGP3 and not in GIAB, we assume NA12878 to be homozygous reference allele. In total, this procedure gave us a data set on chromosome 20 comprising genotype data for 503 European individuals across 507181 genetic variants amongst which 478581 are SNPs (the rest being a mixture of short indels and large SVs). Of note, all data management was done using bcftools v1. 4  31. The GIAB consortium has generated sequence data using multiple sequencing technologies for NA12878. In the context of this work, we notably used: (a) SOLiD5500W, (b) Complete Genomics, (c) Illumina HiSeq, (d) PacBio and (e) 10x Genomics. Summary statistics for all this sequence data is given table  1. We used the 5 sets of sequencing data to phase as much as possible of the NA12878 genotype data.
Scaffolding the GIAB data We generated two haplotype scaffolds for the dataset described above either using family or large reference haplotype panel. For the first scaffold, we used genotype data derived from Illumina OMNI2. 5M for a larger set of individuals containing multiple trios and duos. Out of the 503 individuals with sequence data, 190 of them (~37. 8%) are present in the OMNI data together with parents or children so that we could fix the phase at 6. 59% of the heterozygous genotypes in the sequence data using Mendel inheritance logic. For the second scaffold, we phased the 16805 variants in the overlap with UKB data on chromosome 20 using 795678 UKB haplotypes as reference panel. This allowed fixing the phase at 6. 25% of the heterozygous genotypes in the sequence data.
Phasing runs on the GIAB datasets Using SHAPEIT4, we phased the 503 individuals with sequence data across all possible combinations of 6 types of sequencing reads (i. e. no reads, SOLiD5500W, Complete Genomics, Illumina HiSeq, PacBio and 10x Genomics) and 3 scaffolds (no scaffold, OMNI and UKB scaffolds). Each run was repeated 5 times using different random number generator seeds to assess variability of the results. Accuracy of phasing was measured using the switch error rate as described above on NA12878 using the phase provided by GIAB as reference.
(https://jmarchini. org/shapeit3/), Eagle v2. 4 (https://github. com/poruloh/Eagle) and Beagle5 (https://faculty. washington. edu/browning/beagle/beagle. html). All runs were done on a Tb of RAM. Eagle v2. 4 and Beagle5 have been run with default parameters. SHAPEIT3.
Acknowledgement This work was conducted under UK  Biobank  project  35520. OD is funded by a  Swiss National Science Foundation (SNSF)  project grant ( PP00P3_176977 ). MRR is funded by a  Swiss National Science Foundation (SNSF)  project grant ( 31003A-179380 ) and by core funding from the  University of Lausanne. JM acknowledges funding for this work from the  European Research Council  (ERC; grant  617306 ). ETD is funded by the  Louis-Jeantet Foundation ,  European Research Council and Swiss National Science Foundation.
Data availability We used the full release of the UK biobank (http://www. ukbiobank. ac. uk/). This work was conducted under UK Biobank project 35520. We used the following release of the GIAB (ftp://ftptrace. ncbi. nlm. nih. gov/giab/) and this release of the phase 3 of the 1000 Genomes project (ftp://ftp. 1000genomes. ebi. ac. uk). We downloaded this version of the larger set of 1000 Genomes samples genotyped on Illumina OMNI2. 5M (ftp://ftp. 1000genomes. ebi. ac. uk). The sequencing data we used for GIAB are available from these locations: PacBio (BAM), Illumina HiSeq (BAM), SOLiD5500W (BAM), Complete Genomics (BAM) and 10X Genomics VCF populated with PS field (BAM).
(https://jmarchini. org/shapeit3/), Eagle v2. 4 (https://github. com/poruloh/Eagle) and Beagle5 (https://faculty. washington. edu/browning/beagle/beagle. html). All runs were done on a Tb of RAM. Eagle v2. 4 and Beagle5 have been run with default parameters. SHAPEIT3 RedHat server with Intel(R) Xeon(R) CPU E7-8870 v4 @ 2. 10GHz (80 physical cores and 160 logical cores) and 3.
Pharmacological modelling Relevance: Emphasis is on the estimation of physiologically relevant information characterising the subject dependent drug exposure and relating it to a (un)wanted pharmacodynamical effect Duration: 1 d (8 h) F2F Topics: Pharmacometrics being the field closest related to biostatistics, the communalities and differences between the 2 expertises are highlighted. Noncompartmental methods are illustrated and explained. These are extended to compartmental pharmacokinetic models and pharmacokinetic models with differential equations. The connection with pharmacodynamics is explored, with the Emax model for physiological effects derived from first principles, and the importance of pharmacological modelling to drug development in general is illustrated. More broadly, the course serves as an introduction to non-linear mixed effects modelling and its implementation in SAS Skills acquired: Understanding of pharmacokinetics-and physiology-inspired modelling.
VAMDC bridged the gap between atomic and molecular (A&M) producers and users through providing an interoperable e-infrastructure connecting A&M databases, as well as tools to extract and manipulate those data. The current paper highlights the usage of the VAMDC Portal, recalls how data citation is implemented within VAMDC and provides insights about usage of VAMDC that will increase the impact factor of A&M producers and will offer a more reliable citation of A&M datasets included in application fields.
Introduction: State-of-the-Art The "Virtual Atomic and Molecular Data Centre Consortium" (VAMDC Consortium, http:// www. vamdc. eu) [1]  is a worldwide consortium which federates Atomic and Molecular databases through an e-science infrastructure and an organisation to support this activity (http://www. vamdc. org/structure/how-to-join-us/). About 90% of the inter-connected databases handle data that are used for the interpretation of astronomical spectra and for the modeling in media of many fields of astrophysics. Other application fields include atmospheric physics, plasmas, fusion, radiation damage. The current VAMDC e-infrastructure interconnects about 30 atomic and molecular databases that cover atomic and molecular spectroscopy and processes. VAMDC offers a common entry point to all encorporated databases through the VAMDC portal (http://portal.
vamdc. eu) and VAMDC develops also standalone tools in order to retrieve and handle the data, the SPECTCOL tool  [2, 3]  is an example (http://www. vamdc. eu/software). VAMDC provides also software  [4]  and support in order to include new databases within the VAMDC e-infrastructure. One feature of VAMDC e-infrastructure is the constrained environment for the description of data, in particular the VAMDC-XSAMS, a standard XML file (XML Schema for Atomic Molecular and Solid Data) 1 , and other standardized protocols (http: //www. vamdc. eu/standards) that ensure a higher quality for the distribution of data. VAMDC-XSAMS is an evolved version of the XSAMS schema presented by  [5]  and it should be used together with the description of molecular quantum numbers provided by what we call the case-by-case schema  2.
Our recent publication  [1]  provides details about VAMDC-XSAMS and about the main databases included in the VAMDC e-infrastructure. By 2016 the VAMDC Consortium started to collaborate with the Research Data Alliance (RDA) 3  and to work in its Data Citation Working Group  4. Indeed the VAMDC Consortium intended to find a way for users to cite the datasets that the infrastructure provides. The RDA Data Citation Working Group provided the researchers and data centres communities with a recommendation to identify and cite dynamic data  [6] : the proposed solution relies on a query centric view and the set-up of a Query Store. Data should be stored in a versioned time-stamped manner and accessed through queries. The Query Store will store all the identified and time-stamped queries, together with relevant metadata and availability to recover the data as it existed at the time when a given query was executed.
Recently VAMDC, commissioned by the Research Data Alliance, has implemented the recommendations  [6]  of the RDA data citation group. Indeed VAMDC provides an interesting science use case of a typical distributed infrastracture with geographically distributed databases, registries (meaning "yellow pages") and access tools. Within this context a first work has been done on provenance of datasets  [7] , meaning that versioning and data-timestamping has been included in the VAMDC-XSAMS schema. The second work, for which RDA provided funding to the VAMDC Consortium, has implemented the concept of Query Store which impacts both the node software  [4] , the registries and has been implemented in the VAMDC Portal (http://portal. vamdc. eu). The technical description of the Query Store that stores timestamped queries submitted to the VAMDC infrastructure, can be found in  [8].
Results: VAMDC Portal and Query Store The VAMDC portal uses the VAMDC standards and technological developments, it provides a seamless access to the inter-connected VAMDC databases. Through this unique interface as displayed on Figure  1 , a user can query any database member of the VAMDC infrastructure, and can retrieve data in the common shared file format VAMDC-XSAMS  5. In order to be visible from the portal, as from any VAMDC tool or user, each database must provide a VAMDC compatible access thanks to the node software (as described in Section 2. 1).
Connecting Heterogeneous Databases into the VAMDC Interoperable Infrastructure-The Role of the Node Software The e-infrastructure connects in an interoperable way about 30 heterogeneous atomic and molecular databases. By providing data producers and compilers a large dissemination platform for their works, VAMDC is successful in removing the bottleneck between data producers and the wide body of A&M data users. The "V" of VAMDC stands for "virtual" in the sense that the e-infrastructure does not contain data: it is a wrapping for exposing in a unified way a set of heterogeneous databases. An ad hoc generic wrapping software, called the node-software  [4]  transforms an autonomous database into a VAMDC federated database, called data-node. Each data-node accepts queries submitted in a standard grammar (cf.
Section 2. 3) and provides output formatted into VAMDC-XSAMS  6. Then each database must be registered into a central repository called a registry that provides a standardized application programming interface (API) to explore its content and to discover the available VAMDC resources (cf. Section 2. 2). Figure  1. VAMDC Portal welcome page: Userssee they may access data using a simplified or advanced interface, they may save queries, have access to tools, and must accept the citation policy and the condition of the disclaimer. Information are provided to users in real-time about the number of databases interconnected by the VAMDC infrastructure.
Query Language Another key element used by the VAMDC portal, and central to the VAMDC infrastructure, is the query language which we chose to be a subset of SQL, called VAMDC QSL Subset 9  , and that allows the user to query multiple databases simultaneously. This query language is understood by the data access protocol VAMDC-TAP 10  implemented on each database. The protocol accepts such queries and return files in the VAMDC-XSAMS format. VAMDC-TAP is a simplified version of the TAP protocol from the IVOA  [12]. VAMDC-TAP exposes only one table, simplifying the query by removing the join part. To achieve this result, a dictionary has been defined  11. Each quantity that any database can potentially return is defined in the dictionary with a given keyword. Then the node software can do the mapping between this quantity and an actual column in the database.
Species Database In order to provide an efficient search environment, being able to search by species name is a key element. However, as necessary as it might be, it is has been proven to be a complex matter. Looking for an atom is simple as it is efficiently identified with its name or its symbol. For a molecule though, things become more complex. A molecule's formula can be written in several ways, it can also be searched by its stoichiometric formula or by one of the many standards or classification that have been developed ( SMILE, Inchi, CAS number. ). To overcome this difficulty, the VAMDC infrastructure created a centralized chemical species repository, called the species database. Updated daily, it contains the list of all the species in each of the VAMDC databases. Each species is identified uniquely by an InChiKey, an identifier generated from an InChi description  12.
In the species database each InChiKey is associated to the different ways to identify a species, e. g. , their chemical names, formula, stoechiometic formula, CAS number. By adding a REST API  13  and a web graphical interface to this species database (https://species. vamdc. eu), we provide a versatile tool to explore the species content of the atomic and molecular VAMDC connected databases. In addition to relying on this REST API of the species database, the VAMDC portal provides both an auto-completion possibility and an isotopologues discovery feature. So it becomes possible to specify very precisely which species is the most relevant to your search.
Portal Search Interface There are two search interfaces available as displayed on Figure  1. Both of them are graphical tools that build VAMDC-TAP requests, and thus that mask the complexity of the internal language to the users. The first graphical interface follows a step by step approach as displayed on Figure  2. Each time a user chooses an option, a limited set of new options appears. It is particularly recommended for the people discovering the portal as the user is guided throughout the selection process. The second interface, which is called "advanced", lets the users choose by themselves their search criteria. They can compose their own request by combining "Species", "Processes" and "Environment" characteristics, as it is displayed on the left hand side of Figure  3. Each time the content of the query is updated, the list of databases that can answer is displayed in green on the right of Figure  3.
Portal Results Display, Query Store and Data Citation Once a request has been completely parameterized by the user, it is converted into a VAMDC-TAP process, and is sent to each database that knows about the sent dictionary keywords (c. f. Query section). The results are displayed in a table where each line is the answer from a database, as shown on Figure  4. For each database we provide a summary of the returned content, i. e. , the number of species, states, of processes that can be described as collisional transitions, radiative or non-radiative transitions following the VAMDC-XSAMS schema. Then the user can click on the "XSAMS File" button in order to download the data in a VAMDC-XSAMS format. The VAMDC-XSAMS format can be uploaded in other tools such as the SPECTCOL tool  [2, 3]  for collisional and radiative transition, or can be viewed by any editor.