dataesr/scientific-paragraphs-categorization huggingface dataset:
The low percentage at class level comes in part from the incomplete taxonomy of cyanobacteria in NCBI. Indeed, almost all of the sequences assigned to cyanobacteria (933/938) do not have a class name in NCBI database. The other sequences with an unnamed rank at the class level (440/1373) are distributed across different phyla in photosynthetic microeukaryotes. We also noticed diverging rankings between the PR 2 /SILVA, NCBI and AlgaeBase databases. For example, the cryptomonads group was ranked at the class level by NCBI and at the phylum level by AlgaeBase. The classification of this particular group remains widely debated; this is why we chose to propose both affiliations, and leave it up to the user to decide. Indeed, it was not until very recently that the consensus classification for eukaryotes  61  started using any ranks at all.
Thus, cryptomonads are just listed as 'Cryptophyceae' (not the phylum/division Cryptophyceae or the class Cryptophyceae) but should now be classified down to the order level  43. Another example is the Phaeophyceae group that is associated at the phylum level by NCBI but at the class level by AlgaeBase. As stated on the NCBI website, their taxonomy database is not an authoritative source for nomenclature or classification. For this reason, we recommend using AlgaeBase taxonomy because it provides manual curation, and offers a very complete bibliography for each taxon  61, 62. Analysis of the environmental soil samples validated the power of µgreen-db to characterise the taxonomic composition of indigenous phototrophic microbial communities. We were able to assign 98. 5% of the sequences at the phylum level and 96% at the genus level, highlighting good coverage of phototrophic diversity in the database based on AlgaeBase taxonomy.
In conclusion, our results demonstrate that µgreen-db is a powerful tool to assign the 23S rRNA genes of photosynthetic microeukaryotes and cyanobacteria of soil environments to different taxonomic levels. Future improvements to the database will consist in (i) setting up regular routines (once a year) to enrich this open-access database by adding new sequences (e. g. , SILVA r132), and (ii) assimilating any accession changes by updating NCBI accession numbers and taxonomy from various sources. We also encourage the future community of users to contact the curators of the database to report any errors found in the database or on the website, or via the website portal or directly by email to the corresponding author.
Methods Retrieval of 23S rDNA sequences from public databases. We developed several strategies to recover the maximum and diversity of sequences possible (Fig. 5 ). 23S rRNA sequences in cyanobacteria, photosynthetic microeukaryotes and bryophytes were retrieved from SILVA r123 (June 2016)  51. We also retrieved 23S chloroplast sequences from various organisms (photosynthetic microeukaryotes, bryophytes, angiosperms) from a Comparative RNA Web Site and Project led by the Gutell Lab at the University of Texas (Austin, USA) (www. rna. ccbb. utexas. edu/DAT/3C/Alignment/)  72. Another set of sequences was also recovered from NCBI with the Gene database (the list of different queries is available in Supp. data file 1). We also used various BLAST (with a megablast approach with a maximum target parameter of 1,000) to improve sequence recovery.
We first performed a BLAST with a 23S rRNA sequence from a phylogenetically close organism on plastid genomes. We then performed a second BLAST by taking a sequence query in the nr/ nt database and retrieved all the returned sequences. Based on these sequences, we built a phylogenetic tree recursively to know which sequence was furthest away every time. Then we aligned the sequences using Muscle (Mega7)  73  and reconstructed the phylogenetic tree using a maximum-likelihood method  74. To improve µgreen-db exhaustivity, we performed another BLAST against the NCBI WGS database, selecting sequences with a bit score greater than 1,000 and belonging to the targeted organisms, and performed a final BLAST from these sequences against the 23S rRNA sequence file. Sequences corresponding to taxa not present in the 23S rRNA sequence file were then selected and added to the sequence dataset based on less than 97% identity to increase the diversity of the sequences affiliated at the genus level in µgreen-db.
For all BLAST strategies we did not use the "mask" option at all and we requested BLAST to perform a global alignment of the retrieved sequences with the reference sequences. Only sequences presenting the best alignment score (based on percent of identity; >97% to find new species and <97% to find new genera or families) were finally kept. This minimised the possible bias of BLAST alignment due to the variability of sequence lengths in the dataset, with sequences possibly including conserved and/or variable regions.
Sequence verification. According to the origins of the sequences, we applied a series of different filters to retain only plastid sequences (Fig. 5 ). Regarding SILVA sequences, we only kept sequences with a length higher than 700 bp and a quality ≥75%. For the sequences recovered from other databases, we also verified the secondary structure, using the INFERNAL tool  75. Finally, we checked the non-redundancy of the sequences to retain only unique sequences. For each sequence found in both the SILVA and BLAST databases, we checked whether the sequence was included in the 'BLAST' sequence (i. e. , at the identity level). If such was not the case, we aligned them and kept the least fragmented sequence. We also removed the sequences assigned to Angiosperms from the CRW database (Supplementary Fig.
S2 ). Long sequences (more than 5,000 bp) were also deleted. Taxonomic validation -The taxonomic framework of µgreen-db. PR 2 /SILVA, NCBI and AlgaeBase taxonomies were all retrieved to provide users the choice for further analyses (Fig. 5 ). To obtain a standardised taxonomy in the form of phylum, class, order, family, genus and species, we recovered the taxonID from the NCBI accession number (ftp://ftp. ncbi. nih. gov/pub/taxonomy/accession2taxid/) and used the taxonkit tool (http:// github. com/shenwei356/taxonkit) to retrieve the full lineage. AlgaeBase taxonomy was also used to obtain more information at the kingdom level. When no ranking information was available, we ascribed the abbreviation rank followed by two underscores plus Unnamed_rank (e. g. , p__Unnamed_rank). As non-vascular land plants are not represented in AlgaeBase, we assigned the Plantae Kingdom from NCBI taxonomy to these sequences and made modifications at the phylum level.
All these sequences were assigned to the phylum Streptophyta by NCBI taxonomy. However, as Streptophyta is an infrakingdom subdivided into three phyla in AlgaeBase, we assigned the classes Bryopsida, Polytrichopsida, Sphagnopsida, Tetraphidopsida, Takakiopsida, Andreaeobryopsida, Andreaeopsida, Oedipodiopsida to the Bryophyta phylum, Jungermanniopsida, Marchantiopsida, Haplomitriopsida to the Marchantiophyta phylum, and Anthocerotopsida and Leiosporocerotopsida to the Anthocerotophyta phylum. To format the PR 2 /SILVA taxonomy, the full lineage was constructed with the NCBI genus name by searching the PR 2 database (https://github. com/pr2database/pr2database) and SILVA taxonomy archive (https://www. arb-silva. de/no_cache/download/archive/current/Exports/taxonomy/). Database finalisation -construction of the µgreen-db database.
The database is available in two forms: from tabular flat files, and from a website (http://microgreen-23sdatabase. ea. inra. fr) (Fig. 5 ). The tabular flat files were formatted with a custom homemade script. The web interface was built using Bulma (https://bulma. io), a modern and open-source CSS framework based on Flexbox with a custom template. The website uses PHP (v7. 2. 7) to communicate with the MySQL database, providing back-end storage of sequences and taxonomy by using queries and Javascript to make it more dynamic and user-friendly. We estimated the hypothetical coverage of primers conventionally used to study the diversity of photosynthetic microeukaryotes and cyanobacteria  44, 57  by performing an in silico PCR amplification. Illustration of the application of µgreen-db for studying complex soil phototrophic microbial communities.
At the end of the seven cycles, the PCR products were purified using a MinElute PCR purification kit (Qiagen), and quantified using a QuantiFluor staining kit (Promega). For all libraries, an equimolar mix was obtained by pooling equal amounts from each of the 6 samples. The mix was then cleaned using the Agencourt AMPure XP system (Beckman Coulter Genomics). TE buffer (100 µl) (Roche) was used for elution. Sequencing was then carried out on an Illumina MiSeq system (GenoScreen, France). Bioinformatics sequence analysis. To perform the raw data analysis of the 23S plastid rDNA amplicons generated from the soil samples, we used the GnS-PIPE pipeline available at: https://zenodo. org/record/1123425#. W82vmDVR2OE  78. The different steps were described previously  79. After preprocessing, filtering and chimera checking, all samples were normalised at 31.
650 sequences. Taxonomic affiliation was performed using μgreen-db and the USEARCH program (v6. 0. 307; www. drive5. com/usearch) with specific parameters (-maxhits 15, -maxaccepts 0, and maxrejects 0). For alpha diversity analysis, we calculated various indices (Chao1, Shannon, Simpson)  80. A Shannon index-based measure of evenness was also calculated (corresponding to the Evenness column in Supplementary Table  S1 ). The microbial DNA sequence datasets supporting the results provided in this article are available at the EBI ENA under accession No. PRJEB30252. To access the putative number of amplifications and the coverage of the different taxa, we performed an in silico PCR from μgreen-db. We used mothur software (v. 1. 40. 5) with the pcr. seqs command and allowed zero mismatch between each of the primer pairs.
Acknowledgements This project received funding from the  Agence Nationale de la Recherche  ( ANR ,  ORCA  project  award  no. ANR-13-BS06-0005-01 ). JS was jointly funded by a PhD scholarship from the  INRA departments EFPA  and  EA  and the  ANR  project  ORCA. This project also received funding from the  European Research Council (ERC)  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013 ) (grant agreement No. 338264 ). Thanks are also extended to  Annie Buchwalter  for correction and improvement of English language in the manuscript.
Herein, we describe an open-source, Python-based, script to treat the output of differential scanning calorimetry (DSC) experiments, called pyDSC, available free of charge for download at https ://githu b. com/leona rdo-chiap pisi/pyDSC under a GNU General Public License v3. 0. The main aim of this program is to provide the community with a simple program to analyze raw DSC data. Key features include the correction from spurious signals, and, most importantly, the baseline is computed with a robust, physically consistent approach. We also show that the baseline correction routine implemented in the script is significantly more reproducible than different standard ones proposed by proprietary instrument control software provided with the microcalorimeter used in this work. Finally, the program can be easily applied to large amount of data, improving the reliability and reproducibility of DSC experiments.
C A P and C B P will be considered to be temperature independent within the investigated range in this work. A physical consistent baseline subtraction, associated with a correction of residual signals, is simple steps which may significantly improve the data quality and the amount of information which can be extracted from the experiment. Herein, we describe a simple, freely accessible and modifiable tool, pyDSC, to treat DSC experiments. The program, which will be constantly updated, can be freely downloaded from the GitHub repository https ://githu b. com/leona rdochiap pisi/pyDSC. At the same link, the up-to-date manual of the program can be found. In the following, we provide a few experimental notes on a typical DSC experiment, a concise description of the operations performed by pyDSC, together with some examples.
Materials and methods The DSC curves used to describe the functionalities of pyDSC were recorded on a micro-DSC III from Setaram, France. The instrument is provided with the SetSoft2000 software suite. To test the program, we have investigated a 8 mass% solution of the EO 26 -PO 40 -EO 26 triblock copoly- mer Pluronic P85, bought from Sigma-Aldrich. The solutions were prepared using water of Millipore grade. Furthermore, pyDSC was applied to analyze DSC curves recorded on a Nano DSC, on a Multicell DSC and on a DSC 2920 from TA-Instruments.
Experimental In order to work with the apparent heat capacity of the investigated compound, the DSC raw data need to be corrected for the contribution of the empty cells and of the buffer. Accordingly, three sets of measurements can be provided to the script: • The standard sample versus buffer run. For a proper normalization of the data, information such as the mass of (1) C P (T) = (T)C B P + (1 -)C A P sample and the concentration of the investigated compound needs to be known. Moreover, to minimize the contribution of the buffer to the baseline, the mass of buffer in the sample cell and in the reference cell should be as close as possible. We define the difference of mass of buffer in the sample cell and in the reference cell as m BS. • The buffer versus buffer run. This run is used to determine the heat capacity of the buffer solution and will be used to correct the DSC data for the contribution of the buffer to the recorded signal.
Program description The script is written in Python 3, it makes use of the SciPy  [19]  and matplotlib  [15]  packages and is platform independent. Several operations on the data are performed, provided the required information is available. The following operations are executed on the raw data files: 1. Data averaging and resizing The DSC raw data of the sample and, if provided, of the empty cell and bufferbuffer runs are read by the program and the points which fall outside the temperature region of interest are discarded. The reference measurements are averaged. If requested by the user, in order to facilitate any further data treatment, the number of points is reduced by a factor N, defined by the user.
Program usage pyDSC performs all the aforementioned steps on the DSC raw data provided by the user, located in the folder rawdata. In addition, the user will provide all the necessary information to perform the corrections within the Files. txt and Input_params. txt text files. Full details are provided in the user manual and exemplary files at https :// githu b. com/leona rdo-chiap pisi/DSC_corre ction. Finally, the pyDSC script can be executed within a terminal by invoking the Python3 interpreter as: In addition to an output ASCII file for each sample run, the script will generate five output figures: Alpha. pdf, Baseline_ data. pdf, Corrected_data. pdf, Final_data. pdf, and Rawdata. pdf. In Rawdata. pdf, the recorded heat flows of the different runs are reported as a function of temperature.
Results and discussion The script is tested using a semidiluted solution of the triblock copolymer poly-(ethyleneoxide)-poly-(propyleneoxide)-poly-(ethyleneoxide) Pluronic P85 ( EO 26 -PO 40 -EO 26 ). DSC was extensively employed to reveal the self-assembly behavior of this class of polymers in solutions  [1-3, 10, 21] , and the system was chosen due to its good signal-to-noise ratio, non-negligible baseline dependence from the temperature, and the large availability of reference data to compare with. The proprietary software commonly used to perform a first analysis of the microcalorimetric data provide different routines for the baseline subtraction and the blank correction, which typically consists in a simple subtraction of the raw heat flow data. For what concerns the baseline evaluation and subtraction, different algorithms are available, depending on the vendor and on the software version.
However, in all cases, the baseline under the transition peak is evaluated from, somehow arbitrary chosen, regions prior and after the process signal. To test the robustness of the pyDSC script, the enthalpy of micellization recorded for a solution of the triblock copolymer EO 26 -PO 40 -EO 26 was evaluated using pyDSC and two different algorithms provided by the Setaram data analysis software: "linear"-which uses a line from the starting to the end point arbitrarily selected by the user-and "curve"which generates a spline curve from the selected initial/final points that are selected by user-using different regions for the baseline evaluation. For the sake of the comparison, we also report data analyzed with pyDSC without the correction for the buffer and empty cell contributions. The results are shown in Table  1  and two clear results stand out: the arbitrary choice of the baseline region has a non-negligible effect on the obtained values of H m , with a variation of approx.
2 , further demonstrating that the arbitrary choice of the integration region as only a minimal effect on the results. pyDSC was also applied to correct the calorimetric output from DSC experiments performed on very diverse systems on other instruments. In particular, in Fig. 3 , pyDSC was applied for the correction of data stemming from the micro-DSC III from Setaram and from the DSC 2920, nano DSC, and Multicell DSC from TA-Instruments. The curves refer to the melting of isopropylpalmitate in a mixture of surfactant and alcohols  [8] , to the melting of Indium, to the thermal transition within self-assembled fibrils  [5] , and to the thermal denaturation of a protein  [23]  (See figure caption and corresponding references for further details). Finally, it is useful to recall that the trustworthiness of the microcalorimetric data does not depend only on the data correction and baseline subtraction procedures, but a regular instrument maintenance and calibration are equally essential  [6, 9].
Transposable elements (TEs) are non-static genomic units capable of moving indistinctly from one chromosomal location to another. Their insertion polymorphisms may cause beneficial mutations, such as the creation of new gene function, or deleterious in eukaryotes, e. g. , different types of cancer in humans. A particular type of TE called LTR-retrotransposons comprises almost 8% of the human genome. Among LTR retrotransposons, human endogenous retroviruses (HERVs) bear structural and functional similarities to retroviruses. Several tools allow the detection of transposon insertion polymorphisms (TIPs) but fail to efficiently analyze large genomes or large datasets. Here, we developed a computational tool, named TIP_finder, able to detect mobile element insertions in very large genomes, through high-performance computing (HPC) and parallel programming, using the inference of discordant read pair analysis.
TIP_finder inputs are (i) short pair reads such as those obtained by Illumina, (ii) a chromosome-level reference genome sequence, and (iii) a database of consensus TE sequences. The HPC strategy we propose adds scalability and provides a useful tool to analyze huge genomic datasets in a decent running time. TIP_finder accelerates the detection of transposon insertion polymorphisms (TIPs) by up to 55 times in breast cancer datasets and 46 times in cancer-free datasets compared to the fastest available algorithms. TIP_finder applies a validated strategy to find TIPs, accelerates the process through HPC, and addresses the issues of runtime for large-scale analyses in the post-genomic era.
The latter was the most recently developed pipeline for the detection of TIPs in gene pools and it was designed to unravel the transpositional activity of TEs in genomic datasets by applying a faster and validated methodology. Although the bioinformatic programs used by TRACKPOSON can be executed in multiple processors, this pipeline does not use any parallel strategies and as a consequence BLAST is executed in a non-efficient way and the BLAST output is processed serially (i. e. , on one processor). In addition, this program cannot be run in more than one computational node (or computer) and has issues with the amount of disk space required. Thus, this pipeline is not scalable to current supercomputers (with hundreds of servers) and remains suboptimal for analyzing large genomes with a substantial amount of resequencing data, such as Arabica coffee with 1.
3 Gb  [42, 43] , maize (Zea mays) with 2. 3 Gb  [44] , pine tree (Pinus taeda) with 22 billion base pairs  [45] , and the human genome with 3. 3 Gb  [46] , as well as large datasets (e. g. , 10K plant genomes  [47]  and the Earth BioGenome  [48] ) and huge databases such as the NCBI genome collection with sequence assemblies from more than 13,000 different organisms  [49]. Therefore, it is important to take advantage of supercomputing, parallel programming  [50] , and high-performance computing (HPC) approaches to speed up the bioinformatics analyses of large genomes and huge datasets  [51] [52] [53]. These techniques have been applied in several tasks, such as the hierarchical clustering of nucleotide sequences  [54] , scaled BLAST using CPU  [52]  or Xeon Phi  [53, 55]  architectures, and a pipeline to analyze LTR-retrotransposons  [2].
Message passing interface (MPI)  [56]  is a well-known library for parallel programming  [57] , which is used to run many sub-problems that are previously divided given different focuses  [58]. Here, we present TIP_finder, a pipeline that can be applied to analyze large genomes and large resequencing datasets to discover TIPs using the classical methodology of inference of discordant read pairs as proposed by several pipelines. TIP_finder, written in python, proposes different BLAST engines (NCBI or MagicBLAST) and works under HPC techniques and parallel programming. It can be scaled on many computational nodes (or servers) and multi-core architectures, which makes it especially functional for applications in massive sequencing projects in the current (post) genomic era  [24, 34, 59 ]. To test the performance of TIP_finder in a challenging way, we use the very large human genome and available breast cancer datasets, for detecting the insertion of HERVs in relation with this disease.
Implementation of TIP_finder TIP_finder follows the strategy of the analysis of discordant read pair as proposed by several algorithms such as by  [30]  to detect TIPs using (i) short pair reads such as those obtained by Illumina, (ii) a reference genome sequence assembled at the chromosome level, and a database of consensus TE sequences (or other mobile elements like HERVs). With available programs, we identified several bottlenecks dramatically reducing the speed of the analysis and increasing the size of the output files such as: the execution time of the NCBI-BLAST program, the writing time and format of the BLAST results, the absence of parallelization or the non-use of a robust programming language. The first step consisted of mapping paired reads to a mobile elements database and filtering only the reads showing one of the paired reads mapped.
Align unmapped reads against reference genome TIP_finder used the following bioinformatic software: Bowtie2 (v. 2. 3. 4. 1) to map the paired reads of genomic data to the indexed consensus sequence of each TE/HERV family  [30, 60] , Samtools (v. 1. 9) to process the bowtie2 output and keep only the unmapped reads  [61] , bedtools (v. 2. 26. 0) to split the reference genome into 10 Kb windows and count reads in these windows  [62] , and NCBI-BLASTn (v. 2. 10. 0) [63]  or Magic-BLAST  [64]  to align the unmapped reads. Although Magic-BLAST is significantly faster than NCBI-BLASTn, it requires more disk space. TIP_finder was developed using Python3 (3. 8) and the following libraries: sys, time, os, subprocess, argparse, and MPI4py  [65]. Furthermore, TIP_finder_utils. py requires the additional libraries: math, Pandas  [66] , matplotlib  [67] , Seaborn  [68] , and SciPy [69].