dataesr/scientific-paragraphs-categorization huggingface dataset:
The atmospheric profiles were obtained from the ECMWF ERA-Interim full-resolution database. For each Landsat image, a MARS request was sent to the ECMWF server to access the ERA-Interim database. The atmospheric vertical composition of the required variables (air temperature, pressure, relative humidity and ozone concentration) was downloaded on the useful's footprint of image at the uniform lat/long grids resolution of 0. 125 • and for the heights between the Earth's surface and 100 km in atmosphere. Calculating the three atmospheric correction parameters with this grid resolution results in a more relevant spatial variability than found in previous studies and thereby improves the spatial precision of LST estimations compared to local scale approaches. To complete the correction process, surface emissivity was obtained through the NDVI empirical relationship (Equation (  8 )) in accordance with Wittich  [37]  and adapted by Olioso  [39].
At the scale of a Landsat scene, the effect may attain an RMSE of ±2 • C. Moreover, the variability of the atmospheric correction parameters observed for each scene changes very little over time, at least for the sites considered here. To conclude, we propose to the community a correction tool developed in the Python programming language and based on Barsi's Atmocorr methodology. This tool implements the commercially-available radiative transfer reference model MODTRAN and the freely-available European Weather reanalysis ERA-Interim. The primary purpose of generating LST outputs with LANDARTs is to provide values that are representative of surface measurements and that offer consistent quality on the scale of an entire Landsat tile.
Ancient DNA has emerged as a remarkable tool to infer the history of extinct species and past populations. However, many of its characteristics, such as extensive fragmentation, damage and contamination, can influence downstream analyses. To help investigators measure how these could impact their analyses in silico, we have developed gargammel, a package that simulates ancient DNA fragments given a set of known reference genomes. Our package simulates the entire molecular process from post-mortem DNA fragmentation and DNA damage to experimental sequencing errors, and reproduces most common bias observed in ancient DNA datasets.
In the case of aDNA retrieved from hominin species, the DNA from present-day humans, which can be introduced at any stage including during the excavation and in the laboratory, is particularly problematic as it mixes unrelated population histories within a single sample. Ancient DNA researchers often use simulations to test the robustness of summary statistics aimed at inferring population parameters. While some packages have simulated platform-specific errors due to sequencing, no packages are currently available to properly simulate aDNA sequence datasets, including their most prominent characteristics, such as such as damage, fragmentation, human and microbial contamination. Here, we present gargammel, a package that simulates aDNA sequence datasets from a set of genome references representing the microbial fraction, the endogenous fraction and the present-day human contamination.
Features We tested gargammel for its ability to reproduce empirical features found in six previously released aDNA datasets (see Supplementary Results). These include: (i) size distribution, (ii) base composition, (iii) GC-bias due to the DNA polymerase used for library amplification and; (iv) DNA misincorporation. The results presented in the Supplementary Results show a high consistency between observed and simulated distributions to show the applicability of gargammel as a sequence simulator for aDNA. Gargammel provides researchers with the opportunity to perform various inquiries to evaluate the robustness of various analyses to aDNA properties. In the Suppl. Results, we present two such types of analyses. First, we evaluated the potential impact of present-day contamination on admixture tests based on the D-statistics  (Durand et al.
, 2011). Simulated sequences were obtained from coalescence models without any admixture. We find that amounts of present-day human contamination in ancient human datasets can create spurious signals of admixture depending on the coalescent model, especially when a distant outgroup is used in the test and when the modern contamination source originates from a population coalescing deeply with the endogenous individual. Second, we evaluated whether microbial fragments of increasing size could impact the false positive alignment rate against the human reference genome (see  Schubert et al. , 2012  for an evaluation of aDNA mapping). We identified a size threshold (35 bp) that reduces such impact for the microbial community identified in the 12. 8 kyr-old Clovis individual  (Rasmussen et al. , 2014).
Acquisition of PCMRI Data All nine patients underwent morphological and CSF flow MRI of the brain (Table  1 ). All acquisitions were performed prospectively with a 3 Tesla MRI system (Signa, General Electric Medical Systems, Milwaukee, WI, USA). Conventional anatomic sequences were acquired for each patient. A sagittal T2-weighted image was used to select two PCMRI flow acquisitions (one perpendicular to the presumed direction of CSF flow through the C2-C3 subarachnoid space and the other through the cerebral aqueduct; Figure  1 ). Retrospective peripheral gating was used to measure 32 different time frames over the entire CC for each of the two flow acquisitions. The main PCMRI parameters were as follows: echo time: 6-9 ms; repetition time: 20 ms; flip angle: 20 • ; field-of-view: 16 × 12 mm 2 ; pixel resolution: 0.
Analysis of PCMRI Data Data were analyzed using dedicated PCMRI image processing software  (Balédent et al. , 2001)  with an optimized CSF flow segmentation algorithm. The software automatically extracts the anatomic regions of interest and calculates the corresponding flow curves over the 32 CC segments (Figure  1 ). The CSF flow curves were integrated to provide the CSF SVs, which correspond to the CSF volumes displaced in both directions through the cerebral aqueduct (SV aqu ) and the spinal canal (SV spine ) over the CC  (Nitz et al. , 1992; Enzmann and Pelc, 1993). SVs are quoted in µL/CC. An overall index of CSF dynamics (''I dyn '') was derived as a guide to the CSF volume displaced in the ventricles and the spinal canal during the CC. It was defined as the product of the two CSF SVs and the two CC times (CCT aqu and CCT spine ); I dyn = SV aqu × SV spine × CCT aqu × CCT spine.
Background The first two plant genomes to be sequenced were Arabidopsis and rice. Their sequences were obtained by sequencing a minimum tiling path of bacterial artificial chromosome (BAC) clones selected from physical maps. Since then, the number of sequenced plant genomes has increased steadily each year, thanks to considerable decrease in costs and increase in throughput of sequencing technologies  [1] [2] [3]. Nowadays, most genome assemblies are produced after whole genome shotgun sequencing (WGS) using Next Generation Sequencing (NGS). WGS is based on three main steps: i) assembling raw sequence reads into larger sequences called contigs; ii) building bridges between contigs using end-sequenced DNA fragments of various lengths (e. g BACs, fosmids, plasmids, large insert size libraries) to generate scaffolds; iii) anchoring scaffolds to chromosomes using genetic mapping data to produce pseudo-molecules.
Sequence data The first draft reference sequence of banana (Musa acuminata)  [27]  was produced from DNA of a doubledhaploid plant of cv. 'Pahang' (DH-Pahang) using reads obtained by 454 sequencing (ERX166948 to ERX167027), Sanger 10 kb fosmid paired-reads (available on the Banana Genome Hub, http://banana-genome. cirad. fr/download), Sanger BAC-end reads (available on the Banana Genome Hub, http://banana-genome. cirad. fr/download) and 330 bp pair-end illumina sequences (ERX179491 to ERX179503). In the present work a 5 kb mate-pair library of DH-Pahang was created and sequenced using illumina HiSeq 2000 to 40x genome coverage. The reads obtained were trimmed and filtered following three criteria: (1) trimming of both read ends until base quality is higher or equal to 20; (2) read trimming at the second unknown base in the sequence; and (3) read larger or equal to 30 bases were conserved.
Bioinformatics pipeline An overview of the pipeline used to improve the banana draft genome assembly is shown in Fig. 1. It is divided into 8 distinct modules corresponding to different and optional operations. This pipeline exploited several tools that we have developed and which are available under Scaffhunter and Scaffremodler toolboxes. The first one exploits genetic mapping data and the second one Large insert size Paired Reads (LPR). They are described in details in the Additional file 1.
Module 2: identification and splitting of scaffold/contig misassemblies This module identified and split misassembled contigs/ scaffolds using a combination of GBS genetic mapping data and LPR data. Genetic markers were grouped into linkage groups using JoinMap4. 1 software  [39]. No marker ordering was performed at this stage. In parallel, marker sequences were aligned to scaffolds using a consensus of BWA, bowtie2  [40]  and BLAST  [41]  and only single hits markers were conserved. Scaffolds harboring markers attributed to more than one linkage group were identified. LPR aligning (using bowtie2 in -very-sensitive mode) in these scaffolds were inspected to precisely locate the misassembly boundaries. The misassembled boundaries were identified based on the absence of overlap of read-pairs in the area and an increased proportion of discordant reads.
Module 3: scaffold fusions/junctions This module used LPR to identify scaffolds that should be inserted into larger ones (hereafter referred to as fusion) and scaffolds that should be end-joined (hereafter referred to as junction). LPR were aligned to the scaffolds using bowtie2 in -very-sensitive mode. Only single hit LPR were conserved. Redundant LPR were filtered out using MarkDuplicates tool of Picard (http:// broadinstitute. github. io/picard/). Filtered LPR were then used to identify discordant read clusters, which were used to identify potential scaffold fusions and scaffold junctions. Potential scaffold fusions and junctions were then manually validated by inspecting circos  [42]  picture showing paired reads position in these regions. Fusion and junction performed were validated by aligning LPR along the corrected scaffolds using bowtie2 (in -verysensitive mode) and mapped reads were inspected to ensure that the newly created junctions are spanned with reads mapped in the correct orientation.
Module 4: scaffold gap re-estimation In this module, the size of all remaining gaps (region composed of N) were re-estimated using all paired-reads (i. e. LPR, BAC-ends sequences and fosmid paired reads). Paired reads were aligned against scaffolds using bowtie2 in -very-sensitive mode for illumina reads and BWA with mem algorithm for Sanger reads. For each paired read library, gaps were re-estimated so that correctly orientated paired read overlapping a gap have an insert size corresponding to the expected median insert size of the library. For the 5 kb mate-pair library (illumina), at least 30 pairs were required to re-estimate a gap while for the 10 kb and BAC-end Sanger reads at least 2 and 1 pairs were required respectively. The complete process and tools used for this module are described in Additional file 1.
Module 5: super scaffold construction This module exploits genome map to arrange scaffolds into super scaffolds. First, the sequence assembly fasta file was converted into the BioNano Irys map format by running an "in silico digest with the Nt. BspQI nicking endonuclease" of the sequence assembly using Knickers (http://www. bnxinstall. com/knickers/Knickers. htm). Only scaffolds larger than 20 kb with more than five sites were used, representing 613 scaffolds for a cumulative size of 437 Mb. Then, using BioNano's proprietary alignment tool RefAligner  [43, 44] , the sequence maps were compared with Irys genome maps to find their best alignments; here only sequence maps with more than 5 labels (i. e. Nt. BspQI nicking endonuclease site) were used for comparison. The sequence-Irys map pairs with significant discordance were flagged and removed, with discordance defined as more than 5 consecutive labels not unaligned on both the sequence map and the Irys map.
Module 7: scaffold anchoring This module used genetic markers obtained from a genetic mapping population to group, order and assemble scaffolds into pseudo-molecules. Our approach avoided the step of genetic map construction and a subsequent conciliation between genetic map and scaffolds. We used blocks of already ordered markers based on their position on scaffolds and first ordered them relative to each other, using UPGMA-like based approach. Then this first order was improved with permutation testing. The process can be decomposed into 4 steps: 1) Marker location on scaffolds using a consensus of BWA, bowtie2 and BLAST, 2) Pairwise linkage LOD calculation between markers using JoinMap4. 1, 3) Calculation of a first order using an UPGMA like approach on mean pairwise linkage LOD calculated between scaffolds, 4) Scaffold ordering and orientation optimization by performing scaffold permutations and re-orientations leading to maximization of a score calculated as follows: score ¼ X n i¼1;j¼1;x i <x j 1- x j -x i À Á n LOD ij with n the number of markers in the LG to order, x i and x j are the position of markers i and j in the tested order, and LOD ij the LOD score between markers i and j.
Module 8: annotation transposition This module consisted of transposing annotations from the first draft genome sequence to the new assembly. Gene annotations (consisting in fasta putative transcripts) were transferred to the new assembly using Exonerate software  [46]  with the cdna2genome model and a maximum allowed intron size of 30 kb. Exonerate performed genomic searches and spliced alignments in a single run. Using a custom Perl script, based on the exonerate output, we transferred the annotation on a new GFF3 files, and generated a file of sequence identifier equivalence between the two releases. The script performed some quality checks by comparing protein-coding sequences before and after the transfer as some discrepancies may occur. In such case, the script used Blastp to align genes exons by exons.
Each pseudo-molecule comprised from 16 to 57 scaffolds and N50 in pseudomolecules varied between 1. 4 Mb to 9. 9 Mb. The mean a b Fig. 3  Example of a clue leading to scaffold fusion. a Graphical representation of paired read leading to the identification of fusion of scaffold1112 into scaffold24. This representation is drawn using Scaffremodler's tools. In the inner circle, links between read pairs are drawn with the color code described in Fig. 2 : grey for concordant pairs; red and orange for discordant in size; purple, green and blue for orientation discordant pairs. The second circle represents scaffold in blue with gaps as black regions. The next represents the proportion of discordant reads and the last circle represents read coverage as in Fig. 2. Red and blue beams linking scaffold1112 and scaffold24 allowed identifying scaffold fusion schematized in (b).
Based on the analysis of several manually curated genes, the NCBI RefSeq genome annotation proved to be generally of better quality than the first published annotation in particular because the first annotation over predicted introns. In addition, the NCBI RefSeq genome annotation integrated RNAseq data and predicted alternative transcripts. We thus created a consensus annotation that combined all the manually curated 5 Dot plot comparison of gene order between the and the new version of Musa acuminata genome sequence assembly. A dot represents the position of a gene in the two assembly versions with the initial assembly on x axis and the new one on the y axis. Ruptures in the diagonal indicate differences of gene order. Red circles indicate the main differences and green circles indicate the variations resulting from the approximate scaffold order in the peri-centromeric regions.
Conclusion The significant improvements made on the banana reference genome sequence will have important impact on the quality of future genetic and comparative genomic analysis. The bioinformatics methods and tools described in this work can be useful to improve draft genome assemblies in other plant species. The pipeline comprises independent modules adaptable to various datatypes. It can be used to improve existing assemblies or in combination with existing automated programs during de novo assembly. The improved version of the Musa acuminata genome assembly is accessible and can be downloaded in the new version of the Banana Genome Hub at http:// banana-genome. cirad. fr/ [53]. Tools are available in command line version on GitHub (https://github. com/ SouthGreenPlatform). Most of the options (Modules 2, 3, 4 and 7) are also available on the South Green Galaxy platform under Scaffhunter and Scaffremodler toolboxes (http://galaxy.
Acknowledgments The authors thank the  Diversity Arrays Technology Pty Ltd  for DArTSeq genotyping,  Jan Vrána  and  Hana Šimková  for preparation of HMW DNA, and  CGIAR Research Program on Roots, Tubers and Bananas (RTB)  for financial support for sequencing data acquisition. We also thank the  South Green Bioinformatics Platform  (http://southgreen. cirad. fr)for providing us with computational resources. We thank  Christophe Jenny  for providing the Pahang segregating population from the  CIRAD research station in Guadeloupe, French West Indies.
MINERVA provides automated content annotation and verification for improved quality control. The end users can explore and interact with hosted networks, and provide direct feedback to content curators. MINERVA enables mapping drug targets or overlaying experimental data on the visualized networks. Extensive export functions enable downloading areas of the visualized networks as SBGN-compliant models for efficient reuse of hosted networks. The software is available under Affero GPL 3. 0 as a Virtual Machine snapshot, Debian package and Docker instance at http://r3lab. uni. lu/web/minerva-website/. We believe that MINERVA is an important contribution to systems biology community, as its architecture enables set-up of locally or globally accessible SBGN-oriented repositories of molecular interaction networks.
INTRODUCTION Biomedical knowledge is rapidly growing in size and complexity. Many repositories have been established to complement the literature body of knowledge by encoding and providing access to the curated information on molecular interactions. Both the literature and repositories support the effort of knowledge curation-the process of gathering relevant information pieces and assembling them into a contextualized molecular interaction network. Such curated networks may greatly help in the analysis and interpretation of highly dimensional datasets produced by modern biomedical assays. 1  Experts creating a mechanistic description of molecular networks face the challenges of format standardization, and need support in quality control of generated networks, their exploration, use and sharing.
The first challenge is well addressed by existing and continuously updated process description languages, such as Systems Biology Graphical Notation. 2 In order to tackle the remaining issues, we have developed the MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform. The management of biomedical knowledge in the form of molecular networks requires a number of components. First, easy access to the repository is needed, as network content is often assembled by entire groups of researchers over time, like in WikiPathways or Parkinson's disease map. 3, 4 Second, comprehensive visualization and exploration of complex content are required in order to grasp high-level associations and access single mechanism-level details, even for large networks. Third, capabilities for detailed content curation and extraction, aided by automatic annotation and verification, are mandatory in case of large, longitudinally maintained repositories.
Currently available solutions address these challenges to a significant extent, but have certain limitations regarding their integration as a user-centric service. The MINERVA platform integrates the functionalities of pathway databases and user-centric content hosting into a standalone web-based platform for knowledge repositories of molecular interaction networks. It features extensive interactivity with the content, and straightforward and automated annotation of hosted networks. MINERVA enables highlighting targets of drugs, and the upload and visualization of user-provided experimental datasets, including multi-omic entries and custom color coding. Importantly, our platform supports content management and re-use. It handles both CellDesigner and SBGN formats and gives users the ability to directly download chosen areas of the visualized diagram.
A commenting system enables users to directly annotate the explored content, providing direct feedback to the curators of MINERVA repositories. MINERVA allows web management of hosted maps, registered users, recorded comments and interfaces to external bioinformatic databases. In this article, we describe the MINERVA platform and its functionalities. We briefly review repositories of molecular interactions and their supporting technologies. Next, we provide details on the architecture and important functions of our solution. Then, we focus on use-case description for knowledge repositories supported by the MINERVA platform. Finally, we describe further steps foreseen in the development of the platform.
RELATED WORK Repositories of molecular interaction networks vary in size and scope. Large-scale databases of molecular interactions, such as PSICQUIC  5  or IntAct,  6  offer a broad insight into the function of a given molecule. Construction of networks from such sources provides an excellent basis for network analysis and unbiased hypothesis generation. In order to construct detailed hypotheses on the mechanisms of biological systems, further refinement of such networks is required. One of the possible approaches is to assemble contextualized groups of interactions into focused, diagrammatically represented graphs, called pathways. Pathway repositories, such as Reactome,  7  PANTHER, 8 KEGG 9 and WikiPathways,  3  enable easy access to carefully curated molecular interaction networks and provide search engines allowing to explore it.
The curation process, ensuring high-quality content, can be driven by dedicated curators, like in the case of Reactome or KEGG, or involve community experts, like WikiPathways. Remarkably, the content offered by pathway databases becomes more and more interoperable, allowing its efficient re-use, with SBGN becoming a common denominator in many cases. For instance, Reactome and PANTHER follow SBGN in its visual layout, and provide export to this format. Similarly, WikiPathwayssupporting pathway editor PathVisio 10 offers SBGN export among many others. Finally, a workflow for translation of KEGG pathways into SBGN format was recently proposed. 11 Such pathway data interoperability greatly facilitates development of molecular interaction networks aimed for specific research project, or a focused knowledge domain.