dataesr/scientific-paragraphs-categorization huggingface dataset:
Code availability Data pre-processing was done using automagic Matlab toolbox https://github. com/methlabUZH/automagic ( Pedroni et al. , 2019 )  for dataset 1. Brainstorm toolbox  ( Tadel et al. , 2011 )  was used to pre-process the signals of dataset 2, and to reconstruct the regional time series using wMNE. To estimate the head model, OpenMEEG  ( Gramfort et al. , 2010 )  software was used. Brain Networks estimation of EEG data (datasets 1,2) was done using Matlab. Beamforming construction and networks estimation of MEG data (dataset 3) was performed using the megconnectome pipeline package https://www. humanconnectome. org/software/hcp-meg-pipelines. The Matlab code developed to extract the modular brain states is publicly available at https://github. com/ librteam/Modularity _ algorithm _ NN.
Even if the final assembly is very contiguous, there are still several gaps, and the complete X chromosome sequence was obtained by manual curation. This huge effort is not possible for all genome projects because it is far too expensive and time-consuming. It is clear that these multilayer assemblies reveal the architecture of complex regions as well as the complete organization of chromosomes, which remained complicated before. Long-range technologies make it possible to organize contigs based on long-reads but they are not able to fill the gaps between these contigs. Indeed, usually complex regions like centromeres or telomeres still contain many gaps, depending on their repetitive content. We selected the banana genome, a medium-size genome in the plant lineage (~500 Mbp), and hypothesized that recent improvement of the ONT technology, coupled with dedicated DNA extraction protocol and efficient software enables the reconstruction of gapless and Telomere-to-Telomere chromosome sequences.
Two optical maps were generated using DLS with the DLE-1 enzyme and NLRS with the BspQI enzyme. The resulting DLE-1 and BspQI optical maps were 469 and 474 Mbp lengths, respectively, and had a N50 of 35 and 16 Mbp, respectively. We used these two optical maps to first validate the contigs, and then order and orient them. As a result, only one contig of 380 kbp, composed of tandem repeated elements, was flagged as conflictual with the optical maps and split into two contigs (Supplementary Fig. 3 ). All other contigs were in accordance with the maps, which strongly validate the accuracy of the NECAT assembler. The 124 contigs were ordered in 96 scaffolds using the Bionano Solve workflow and the BiscoT 15 software (88 scaffolds correspond each to one contig). In the end, eight of the eleven chromosomes are represented by a single scaffold and the other four remain in two scaffolds.
In addition, chromosome reciprocal translocations were recently shown to have accompanied subspecies evolution in Musa 6 , and some of them have their breakpoints in centromeric regions. Having access to the sequence of these centromeric regions will permit investigating the mechanism and sequences involved in the origin of these translocations. Finally, comparison with the Musa acuminata V2 assembly highlights a higher proportion of each class of transposable elements, and a large amount of additional sequences in the centromeric regions, like Nanica elements, or large retro-transposon derivatives. It is often mistakenly thought that short-read assemblies are complete at the gene level. This hypothesis is mainly based on the results given by the BUSCO software, which only focuses on single-copy genes.
The libraries were sequenced on an Illumina HiSeq2500 instrument (Illumina, San Diego, CA, USA) using 250 base-length read chemistry in pairedend mode. After the Illumina sequencing, an in-house quality control process was applied to the reads that passed the Illumina quality filters. The first step discards lowquality nucleotides (Q < 20) from both ends of the reads. Next, Illumina sequencing adapters and primer sequences were removed from the reads. Then, reads shorter than 30 nucleotides after trimming were discarded. These trimming and removal steps were achieved using in-house-designed software based on the FastX package  39. The last step identifies and discards read pairs that are mapped to the phage phiX genome, using SOAP aligner  40  and the Enterobacteria phage PhiX174 reference sequence (GenBank: NC_001422.
Assembly validation. The DLE-1 map was generated using the Direct Label and Stain (DLS) technology and the BspQI map using the Nick Label Repair and Stain (NLRS) technology. Genome map assemblies were performed using Bionano Solve Pipeline version 3. 3 and Bionano Access version 1. 3. 0. We used the parameter "Add Pre-Assembly" which produced a rough assembly. This first result was used as a reference for a second assembly, using the parameters "non-haplotype without extend and split". We filtered out molecules smaller than 150 kbp and molecules with less than nine labeling sites (Supplementary Tables  9  and 10 ). The nanopore contigs were then validated using the two Bionano maps and organized with the scaffolding procedure provided by Bionano Genomics (Supplementary Fig. 13 ). Negative gap sizes were checked and corrected using the BiscoT software  15  to avoid artifactual genomic duplications (Supplementary Table  1 ).
As recommended by the BiscoT authors, we performed a last iteration of Hapo-G  14  to polish merged regions (Supplementary Table  11 ). In order to obtain a quality score used to compare the different versions of the assembly, we downloaded and used merqury  16  version 1. 3 (git commit 6b5405e). We first used the included best_k. sh script with a tolerable collision rate of 0. 0001 and a genome size of 500 Mbp, which gave us an estimated best k-mer size of 21. Then, we used meryl 46 version 1. 3 (git commit 3400615) to compute the reads k-mer counts via the meryl count command with default parameters. Merqury was then launched on two sets of sequences. The first one consists of the V1 and V4 assemblies, in order to compare them in their globality. As the V4 assembly is larger than V1, we aligned the V1 assembly to the V4 assembly using minimap2 and kept alignments that were larger than 50 kbp.
Then, transcript sequences of M. acuminata predicted genes were aligned by BLAT (default parameters) on the masked genome assembly. Only the alignments with an identity percent greater or equal to 90% were kept. For each transcript, the best match was selected based on the alignment score. Finally, alignments were recomputed in the previously identified genomic regions by Est2Genome 58 in order to define precisely intron boundaries. Alignments were kept if more than 80% of the length of the transcript was aligned to the genome with a minimal identity percent of 95%. To proceed to the gene prediction, we integrated the protein homologies and transcript mapping using a combiner called Gmove  59. This tool can find CDSs based on genome located evidence without any calibration step. Briefly, putative exons and introns, extracted from the alignments, were used to build a simplified graph by removing redundancies.
Then, Gmove extracted all paths from the graph and searched for open reading frames (ORFs) consistent with the protein evidence. A selection step was applied to all candidate genes, essentially based on gene structure. Also, all gene predictions included in a genomic region tagged as transposable elements were removed from the gene set (Table  2 ). The completeness of the predicted genes was assessed with BUSCO 60 version 5 (embryophyta dataset odb10). The search for NLR loci was performed using NLR-annotator tools  26  that scan specifically the 6 reading frames of the nucleotide sequence for the presence of 19 NLR-associated motifs and reconstruct a potential NLR locus which might correspond to a complete or partial gene and might also be a pseudogene. Transposable element detection. Transposable elements were detected using RepeatMasker  53  associated with the TE Musa library  9  and CR sequences  18.
Assemblytics was launched on the nucmer delta file (unique_length_required = 10000). Figures  1  and 2b  were generated using the Circos software  65. In the same way, each chromosome sequence of the Musa acuminata V4 was aligned against its relative chromosome sequence of Musa acuminata V2 using nucmer version 3. 23 (-r -1 -l 10000) and dot plots were generated using the mummerplot command. Detection of specific regions of the V4 assembly. New regions of the Musa acuminata V4 were determined using blast 66 (ncbi-tools/6. 1. 20120620) alignment between the chromosomes of each assembly version. Regions of the V4 assembly larger than 100 kbp without any alignment to the V2 assembly were considered new. In addition, the Musa acuminata V2 gene predictions were aligned (see Gene prediction section) on the V4 assembly, and the positions of the genes were compared with the V4 gene catalog using bedtools  61  (version bedtools-2.
Acknowledgements This work was supported by the  Genoscope , the  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), France Génomique  ( ANR-10-INBS-09-08 ), the  Center de coopération Internationale en Recherche Agronomique pour le Développement (CIRAD)  and  Agropolis Fondation  ( ID 1504-006 ) ' GenomeHarvest ' project through the  French Investissements d'avenir program  ( Labex Agro :  ANR-10-LABX-0001-01 ). EH and JD were supported by  ERDF  project ' Plants as a tool for sustainable global development'  (No. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000827 ). The authors thank the staff of  Oxford Nanopore Technology Ltd  for technical help,  Jitka Weiserová ,  Eva Jahnová  and  Dr. Jan Vrána  for their help with the material preparation, the  CRB Plantes Tropicales Antilles CIRAD-INRA Guadeloupe France  for providing the plant materials and the  CIRAD -UMR AGAP HPC Data Center of the South Green Bioinformatics platform  (http:// www.
We present Clinica (www. clinica. run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines.
It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e. g. , tissue probability maps), meshes, surface-based scalar fields (e. g. , cortical thickness maps), or scalar outputs (e. g. , regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.
The first issue met when working on clinical studies concerns the organization of neuroimaging datasets within or between institutions. The lack of a consistent structure makes arduous the sharing or reuse of data. This is true for in-house, but also for publicly available neuroimaging datasets. Another consequence of the lack of standard is the difficulty to apply automatic pipelines (e. g. , extraction of neuroimaging features, statistical analysis, or machine learning) and to perform quality assurance. The second issue faced by researchers processing data from clinical studies is related to the high number of software packages, such as FreeSurfer 1   (Fischl, 2012) , FMRIB Software Library (FSL) 2   (Jenkinson et al. , 2012) , or Statistical Parametric Mapping 3  (SPM)  (Friston et al. , 2007) , that exist in the community.
, 2011). Nipype is an open-source Python project that provides a uniform environment facilitating interaction between neuroimaging software tools or algorithms, regardless of their programming language, within a single workflow. Later, the issues related to the organization of the clinical and imaging data have been tackled by the Brain Imaging Data Structure (BIDS)  (Gorgolewski et al. , 2016) , a new standard from the community for the community. The BIDS standard is based on a file hierarchy rather than on a database management system, thus facilitating its deployment in any environment. Thanks to its clear and simple way to describe neuroimaging and behavioral data, the BIDS standard has been easily adopted by the neuroimaging community. Organizing a dataset following the BIDS hierarchy simplifies the execution of neuroimaging software tools, resulting in the development of user-friendly software.
For instance, BIDS Apps  (Gorgolewski et al. , 2017)  provides a set of pipelines for the processing of neuroimaging data that follow a BIDS hierarchy. Currently, it mainly wraps neuroimaging software packages from the community into a Docker image and is used via a command line interface. Moreover, the Nilearn 5   (Abraham et al. , 2014)  package facilitates the application of advanced machine learning approaches to neuroimaging data. To that purpose, it leverages the scikit-learn library 6   (Pedregosa et al. , 2011)  and provides tools for handling and visualizing different types of neuroimaging data and building predictive models. Nevertheless, carrying out a multimodal neuroimaging study remains challenging due to the know-how necessary to grasp each modality and tool involved. While technical implementation has been facilitated by Nipype, the development of a pipeline still requires substantial programming skills and time to master both the neuroimaging software tools and Nipype.
While the BIDS standard is being adopted by the scientific community, not all public neuroimaging datasets provide a BIDS version of their data. Besides, performing a single or multimodal neuroimaging study will also require methodological expertise. For instance, a classification study of healthy subjects and patients with a neurodegenerative disease using 18 F-fluorodeoxyglucose positron emission tomography (FDG PET) could involve notions of multimodal registration between FDG PET and T1-weighted magnetic resonance imaging (MRI), tissue segmentation of T1weighted (T1w) MRI, PET partial volume correction (PETPVC), normalization into a standard space, and machine learningbased classification, as well as know-how of the tools used to perform these steps. Moreover, the image processing steps need to be chained from one to the other and the absence of data organization for processed neuroimages makes data analysis more complex.
Finally, the neuroimaging features generated by the pipelines need to be correctly connected to statistical or machine learning frameworks. Clinica (www. clinica. run) aims to make clinical research studies easier and pursues the community effort of reproducibility. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1w MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. Clinica relies on tools written by the scientific community and provides converters of public neuroimaging datasets to BIDS, processing pipelines, and organization for processed files, statistical analysis, and machine learning algorithms. The target audience is mainly of two types. First, neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, typically not experts in image processing for all of the involved imaging modalities or in statistical analysis.
They will benefit from a unified set of tools covering the complete set of steps involved in a study (from raw data to statistical analysis). Second, researchers developing advanced machine learning algorithms, typically not experts in brain image analysis. They will benefit from tools to convert public datasets into BIDS, fully automatic feature extraction methods, and baseline classification algorithms to which they could compare their results. Overall, we hope that Clinica will allow users to spend less time on data management and processing, to perform reproducible evaluations of their methods, and to easily share data and results within their institution and with external collaborators.
CLINICA OVERVIEW Clinica is an open-source software platform for reproducible clinical neuroimaging studies. It can take as inputs different neuroimaging modalities, currently anatomical MRI, diffusion MRI, and PET. Clinica provides processing pipelines that involve the combination of different software packages. It currently relies on FreeSurfer  (Fischl, 2012) , FSL  (Jenkinson et al. , 2012) ,  SPM (Frackowiak et al. , 1997) , Advanced Normalization Tools (ANTs) 7  (Avants et al. , 2014) , MRtrix3 8  (Tournier et al. , 2012) , and the PET Partial Volume Correction (PETPVC) toolbox 9  (Thomas et al. , 2016). The pipelines are written using Nipype  (Gorgolewski et al. , 2011). Features extracted with the different pipelines can be used as inputs to statistical analysis, which relies on SPM  (Frackowiak et al.
, 1997)  and SurfStat 10 (Worsley et al. , 7 https://stnava. github. io/ANTs/ 8 http://mrtrix. org 9 https://github. com/UCL/PETPVC 10 http://www. math. mcgill. ca/keith/surfstat/ 2009), or machine learning analysis, which relies on scikit-learn  (Pedregosa et al. , 2011)  and PyTorch  (Paszke et al. , 2019). Input neuroimaging data are expected to follow the BIDS data structure  (Gorgolewski et al. , 2016) , as explained in section Input Data With the BIDS Standard. Since this new standard has only recently been adopted by the community, not all public neuroimaging datasets are yet proposed in BIDS format. To facilitate the adoption of BIDS, Clinica curates several publicly available neuroimaging datasets and provides tools to convert them into the BIDS format. Processed data are organized following the ClinicA Processed Structure (CAPS) format, detailed in section Input/Output Data With the CAPS Structure, which shares the same philosophy as BIDS.
, 2001) , scikit-learn  (Pedregosa et al. , 2011 ), scikit-image (van der Walt et al. , 2011) , nilearn  (Abraham et al. , 2014) , and PyTorch  (Paszke et al. , 2019). Clinica is provided to the end user in the form of a Python package distributed through Python Package Index (PyPI) and can simply be installed by typing pip install clinica through the terminal, within a virtual environment. The main usage of Clinica is through the command line, which is facilitated by the support of autocompletion. The commands are gathered into four main categories. The first category of command line (clinica run) allows the user to run the different pipelines on neuroimaging datasets following a BIDS or CAPS hierarchy. The clinica convert category allows the conversion of publicly available neuroimaging datasets into a BIDS hierarchy.
Input Data With the BIDS Standard When dealing with multiple datasets, it is difficult to automate the execution of neuroimaging pipelines since their organization may vary from each other or even within each individual dataset. If we consider neuroimaging datasets involving many participants, the lack of a clear structure will necessitate a large amount of time to curate these databases and make them easily usable. Besides, large databases are often associated with database management systems, which involve additional technical and financial resources to be maintained. Brain imaging data structure  (Gorgolewski et al. , 2016)  is a community standard enabling the storage of multiple neuroimaging modalities and behavioral data. The BIDS standard provides a unified structure and makes easier the development and distribution of code that uses neuroimaging datasets.
Moreover, the BIDS format is based on a file hierarchy rather than on a database management system, thus avoiding the installation and maintenance of additional software. As a result, BIDS can be easily deployed in any environment. The specification is intentionally based on simple file formats and folder structures to reflect current laboratory practices, which makes it accessible to a wide range of scientists coming from different backgrounds. People unfamiliar with the BIDS format can see an example of a BIDS folder in Figure  3. For these reasons, we also adopted this standard and Clinica expects that the input data are BIDS-compliant for the execution of pipelines. Note that if a cross-sectional dataset (i. e. , with no session folder) is provided, Clinica will interactively propose to convert the cross-sectional dataset into a longitudinal dataset with a unique session.
a modality will be inputs for another pipeline, but it is also true for studies involving a single modality: features extracted from one or several pipelines are usually connected to statistical or machine learning frameworks. Finally, a structured organization for processed data will ease the access and sharing of data, thus improving the reproducibility of neuroimaging studies. The CAPS format defines a hierarchy for the Clinica processed data. The idea is to include in a single folder all the results generated by the different pipelines and to organize the data following the main patterns of the BIDS specification. CAPS folders are kept separate from the raw data. Indeed, when processing data, it is very common to have the raw dataset located on a separated storage or read-only storage, while ongoing processed data are located on a separate location or on a faster data storage.
Clinica Command Line Arguments For each pipeline, the command line interface will require a set of arguments which can be compulsory or optional. The number of mandatory arguments is kept as small as possible, to ease its use. This set of arguments is gathered into four categories. First, the user will be asked to provide the Clinica mandatory arguments. These arguments are in general the BIDS directory, the CAPS directory, and/or the Group label, which were described in the Clinica environment section (sections Input Data With the BIDS Standard and Input/Output Data With the CAPS Structure). Then, several options are common to every pipeline: the Clinica standard options. For instance, we can run a pipeline on a subset of participants and sessions by specifying a TSV file. Moreover, it is possible to specify the number of cores of your machine used to run pipelines in parallel thanks to the Nipype engine  (Gorgolewski et al.
Continuous Integration, Testing, and Package Distribution The source code of the Clinica's platform follows the most standard current practices for software development. The code is hosted in a publicly available platform  16  and it uses a version control system. A rigorous code review is performed for every contribution. The project has adopted a commonly used workflow for development and the code is tested at different stages under controlled conditions. In order to do this, several pipelines are executed by the continuous integration setup at different levels. For each contribution proposal: -The most recent commit pushed to the repository triggers a first iteration of the test suite. This first round validates the package environment, the installation process, and the correct instantiation of the main tools proposed by Clinica.