dataesr/scientific-paragraphs-categorization huggingface dataset:
We developed a suite of complementary ontologies to support the application of systems biology tools and their integration: (1) AdaLab-meta, an ontology for the description of metadata about datasets; (2) AdaLab, a domain ontology to represent relevant biological entities in systems biology; and (3) Eve-CV, a controlled vocabulary that defines typical Eve experiments and experimental conditions (SI Appendix 15). When combined these ontologies consist of ∼20,000 RDF (Resource Description Framework) triples. We collected and formalized in RDF all of the bioinformatic data used for this study to form a knowledge  base of 1,301,017 RDF triples grouped in five separate RDF graphs: imported genes, genes annotations, genes expressions, Eve strains, and relevant metadata. The data are accessible via the linked data web interface (SI Appendix 15).
We have successfully combined multiple systems biology software tools and laboratory robotics to execute three cycles of improvement for a model of the yeast diauxic shift. The cycles were not fully automated, as in the Robot Scientists Adam  (12)  and Eve  (15) , as the automation of systems biology is very much more complicated. However, full automation will be necessary to execute the hundreds or thousands of model improvement cycles required. The achievement of this full automation will require the software tools to be more robust and more modularly designed. Many of the software tools we have used are based on techniques originating in artificial intelligence (AI), especially machine learning: CoRegNet, CoRegMine, ELSA, ActiveFB, MinerLC*, Adarev, and Adana (Fig. 2 ). However, more advanced ideas from AI will be required to improve performance  (21).
The described model in our work has been implemented in COMSOL Multiphysics and Matlab software for five different configuration tests (Insulated roof, Homogeneous wall, Light weight wall, Response analysis and Capillary active inside insulation) and for different materials. The results showed a good agreement between the used model and the other numerical solutions for all the studied cases. In conclusion, we can apply this model for our study using a single layer homogenous wall under temperature and relative humidity variations.
Simulation The model was implemented in COMSOL Multiphysics, a simulation software and partial differential equations solver based on the finite element numerical method. The implementation has been done through the PDE modes under the time dependent coefficient form PDE's, details on implementation can be found in  [12, 13]. The mesh was generated for default parameters according to  [10]. The mesh sensitivity analysis has been carried out and discussed in section 3. 1. The same wall and boundary conditions were simulated (Fig. 4 ). The climatic conditions were exported from the experiment and applied directly to the simulated wall.
Conclusion In this work, we performed a validation of a mathematical model for heat and moisture transfer of bio-based building wall. We compared the thermal and hygric behaviors of the model with experimental data obtained from a wall based on date palm fibers concrete. Heat and mass conservation equations were presented as well as boundary conditions and material properties. To solve the equation system, they were implemented in COMSOL Multiphysics, that is adopted for partial differential equation systems. The same experimental conditions were regenerated in the software. A global good agreement was found between the experimental and numerical results through temperature and relative humidity behavior. Better concordance was obtained during temperature rise, and at the deeper points of the wall for moisture transport.
Introduction Motivations Fast and accurate pre-filters are key to many bioinformatics applications such as assembly (to remove spurious nucleotides and propose more contiguous assemblies), variant calling, genotyping or mapping (to help reduce the noise ratio and increase the power of these methods). Removing errors is also determinant to reduce the quantity of data to index, in particular for k-mer approaches. Thus, a good read correction should allow to index less, though more meaningful, data. While numerous and very efficient methods exist for genomic correction, less methods were dedicated to short-reads datasets presenting uneven coverage 1 such as transcriptomics or metagenomics. Short read sequencing (shotgun and single-cell) remain the widest way to access transcriptomics, metagenomics and metatranscriptomics data despite the growing applications of long reads (PacBio, Oxford Nanopore).
Background k-mer spectrum methods These correction methods assume a uniform sequencing coverage. With the second assumption that sequencing errors are uniformly distributed along read sequences, then there must exist a coverage level for k-mers (the set of read substrings of length k) at which it becomes highly unlikely to observe errors. Thus, they compute distribution of the k-mers' number of occurrences (spectrum) in the dataset, and rely on an abundance threshold that is chosen to distinguish "weak" (error-prone) k-mers from "solid" k-mers. "Weak" k-mers are then corrected by converting them to corresponding solid ones. Because they are conceptually simple and only rely on computationally efficient structures  [15, 18, 28] , these methods have been preferred to other approaches relying on suffix structures  [25] ,  [21] ,  [13]  and probabilistic models or multiple sequence alignment  [23].
BayesHammer (designed for single-cell datasets) proposes a clustering technique based on bayesian statistics derived from the k-means clustering technique to find groups of close k-mers according to the Hamming distance. Rcorrector is designed for transcriptomic datasets, and proposes to choose among sets of k-mers from possible alternative paths in the De Bruijn graph to correct an error. The path chosen must not contain k-mers counted less than a fixed threshold, and must not be incoherent with the read's k-mer counts. Finally, to our knowledge, there exist no method specifically dedicated to the correction of shotgun metagenomics data.
Preliminaries Let Σ be an alphabet of fixed size, in this work we assume Σ={A,C,G,T }. A k-mer is a sequence s∈Σ k (i. e of length |s|=k). We consider a read dataset as a multiset R⊂Σ * and an integer k, and define the De Bruijn graph as a directed graph G k (R)=(N,E) where N is the set of distinct k-mers that appear in R, and for u,v ∈N,(u,v)∈E if and only if u[2,k]=v[1,k-1] (see  (1)  in Figure  1 ). The out-neighborhood (respectively in-neighborhood) of a node u is {v ∈ N|(u,v) ∈ E} = N + (respectively {v ∈ N|(v,u) ∈ E}=N -and its in-degree d + (u) (respectively out-degree d -(u)) is |N + | (respectively |N -|). A node u is called a branching node if d + (u)>1 or d -(u)>1. We call simple path in G k a set of nodes s = u 0 ,. u n such that, for each 0 ≤ i ≤ n, (u i ,ui+1) ∈ E, d + (u 0 ) = 1,d -(u n ) = 1 and for each 1≤i≤n-1,d + (u i )=d -(u i )=1.
Graph construction and hard filters To build a reference De Bruijn graph, we rely on Bcalm2  [5]  to build a raw De Bruijn graph from all non-unique k-mers of the dataset. k-mers are extracted and counted, then Bcalm2 constructs the unitigs and computes their average coverage. Optionally, we can apply a filter on this coverage, removing low coverage unitigs (useful in case (5), Figure  2 ). While this kind of filtering has been shown to be efficient in genomics  [17] , such a threshold could suppress poorly represented but relevant k-mers. Unless said otherwise we did not applied this filter. In genomics, since most sequencing errors create bulges (pattern (3) in Figure  1 ) and tips (pattern (1) in Figure  2 ), their removal is done by a very efficient cleaning  [1]. However, those patterns are commonly found as variants in transcriptomic De Bruijn graph  [11] , thus we introduce more advanced strategy to distinguish sequencing errors from actual variants in the next paragraph.
Differences with the other correction strategies As BCT, Bcool and Rcorrector rely on De Bruijn graphs for the reads correction. Bcool was designed to correct genomic data with high uniform sequencing coverage. Its approach, based on a global alignment of the read to the graph is not adapted to RNA-seq data. Indeed, the reference graph may not contain all rare alternative events. Furthermore the graph cleaning step proposed by Bcool strongly relies on a uniform coverage. Rcorrector uses paths of the De Bruijn graphs built from the reads' k-mers for the correction. However, it does not perform a graph cleaning step. It selects k-mer nodes that are more covered than a threshold to correct reads in a greedy way. The correction step highly relies on the coverage estimation of the read to be corrected.
Transcriptomic De Bruijn graph cleaning In this section we assess the behavior of our proposed method to remove erroneous k-mers from a De Bruijn graph constructed from non uniform dataset as RNA-seq data. To assess our ability to correct sequencing errors, we simulated RNA-seq dataset of more than 100 millions reads with FluxSimulator  [12]. The reads were generated from RefSeq reference genome v19 and represent 107 millions reads, and 19 billions bases and were simulated with a one percent error rate. We selected rnaSPAdes  [3]  as it shares similarities with the presented approach (graph topology and hamming-based cleaning), the graph correction module of Bcool  [17]  to benchmark our own BCT implementation. We also included how a simple k-mer spectrum technique would perform for comparison.
Results on simulated transcriptomic data We kept the previously described dataset to assess our ability to correct sequencing errors. We corrected the read set using BFC  [15]  and Musket  [18] , that are correctors among the best performers according to recent benchmarks  [17] , Bcool and Rcorrector  [27]. In Table  2 , we report the recall and precision of our correction, the F1-score, the correction ratio (by which factor the error rate was divided after correction) and the proportion of reads that still contain at least one error after correction. 2 : Evaluation of the correction of simulated RNA-seq data. After correction we reported the number of true positive (TP),errors corrected to the right nucleotide, false positive (FP), nucleotides modified by the corrector to a wrong nucleotide and false negatives (FN), errors not corrected by the correctors.
Precision, recall and F1-score are defined as in Table  1. The correction ration is defined as F N+F P T P +F N. All corrector were used with default parameters as experimental evaluations showed a low impact of the parameters on the performances (See Appendix). We observe that BCT is able to correct more errors than Rcorrector at the cost of being slightly less precise. We also observe that k-mer spectrum techniques developed for genomics data does not perform well. Because of low expression variants, we expect the correction to be harder with a lower coverage as it become more challenging to distinguish erroneous from rare k-mer. To assess this effect we constructed smaller datasets by random sub-sampling on the one presented. We build a "medium" dataset of 53 millions reads and 9 billions bases, a"small" dataset of 17 millions reads and 5 millions bases and a "shallow" dataset of 13 millions reads and 2 billions bases.
We kept Rcorrector as the most competing tool for this benchmark and report it in Table  3. We show that BCT is able to increase its correction performances when a higher coverage is available. The correction ratio going from 35 from the 13 millions reads dataset to 52 with the dataset containing four time more reads. This can be explained that a higher coverage leads to the construction of a better De Bruijn graph as the sequencing errors and the variant become easier to distinguish. Per contra the performances of Rcorrector remained very similar despite an order of magnitude coverage change.
Results on simulated metagenomic data To evaluate the capacity of BCT to correct sequences from metagenomic samples, we created 2 synthetic datasets. First, we created a highly covered dataset (more than 100X) of 25 mixed bacterial genomes using CAMISIM  [8]   Results are presented in Table  4. A study of the influence of BCT's parameters is available in Appendix. While BCT, BFC and Rcorrector could handle 25 genomes correction, only BFC and BCT's performances continued to keep up with the number of genomes. With default parameters, BCT had the best results on all metrics on the 100 genomes dataset.
Results on real datasets We tested our correction on more realistic scenarios from human sequencing: 2 RNA-seq files and 5 single cell RNA-seq (scRNA) files, presenting various read sizes and coverages. Since no ground truth is available when working on real data, we used read alignment on the reference genome as a proxy to correctors' performances. All read files (raw and corrected) were mapped to the human reference genome using STAR  [7]  with default parameters. Since BayesHammer uses different information (FASTQ qualities) than our algorithm, we do not report direct comparison. However, BayesHammer's results can be found in Appendix, showing that it performed best on scRNA-seq, with BCT ranking second in terms of mismatches. Results are presented in Tables  5  and 6 , and the complete results are available in Appendix.
Conclusion and future works In this work we presented a method able to construct a De Bruijn graph containing a low amount of erroneous k-mers while preserving most of the diversity present in dataset, even if in presence of a high discrepancy in the observed coverage. We shown that this graph could be used as a reference for read correction using De Bruijn graph local read alignment. We benchmarked the implementation of the proposed method, dubbed BCT, against state-of-the-art correctors on RNA-seq and metagenomic data and showed the interest of such a method to efficiently raise the signal to noise ratio in such datasets. Since our proposition is designed as a workflow, a natural extension of this work could be to test different graph alignment tools as graphAligner  [19]  or vg  [10]  to assess if they could be adapted to perform read correction.
2  was used. The perfect dataset refers to reads without errors. The corrected dataset was generated with BCT. We filtered k-mers with abundance ¡ 2 to obtain solid k-mers for these two datasets. We show k-mers from raw reads, with abundance filters ¡ 2 and ¡3 for solid k -mers. On the right, the proportion of solid corrected k-mers that intersect real k-mers is presented. An ongoing application of this work is the hybrid correction of long reads. Transcriptomic long reads (Iso-seq and Nanopore) are increasingly used for full-length isoform identification and de novo transcript and alternative splicing discovery. For these data, hindered by high error rates and especially insertion/deletion, a primary correction step is often required. The mixture of short and long reads was shown to provide sequences with higher accuracy  [6] , in particular for technologies prone to systematic errors such as Oxford Nanopore.
(genomes included in CAMISIM). Second, we created a low coverage dataset (20 in average) with high diversity of bacterial genomes (100 genomes). Datasets used for the reads generation can be found in Appendix. For both of the datasets, we added 1% of uniformly distributed errors. Evaluation of the correction of simulated metagenomic data. CAMISIM25 genomes simulation contains 22,222,221,750 nucleotides split into 148,148,145 reads. 100 genomes simulation contains 7,654,414,260 nucleotides split into 51,023,830 reads. The average genome coverage is 20 with a lognormal distribution.
Introduction The discovery of position effect variegation in Drosophila melanogaster paved the way towards revealing the importance of chromatin contexts in the regulation of gene expression  (Wang et al, 2014; Muller, 1930). Since then, cytogenetic and molecular profiling of the epigenome, as well as topological analyses of chromatin architecture, have allowed the mechanisms involved in partitioning the gene-rich euchromatic fraction from the repeat-rich heterochromatic fraction to be elucidated. Large protein complexes specific to insulator DNA sequences contribute to partitioning chromatin domains with distinct identity at multiple scales. These complexes maintain the identity of adjacent genomic loci with antagonistic chromatin marks and functionality, and more globally influence the formation of long-range chromosomal interactions  (Ali et al, 2016).
Overall, these observations suggested that caa39 downregulated genes might be part of bona fide euchromatic islands (EIs). To generate a comprehensive view of their structural features in the genome, we systematically investigated the pericentromeric and hk4S heterochromatic regions defined in Figure  S6B. We designed a script to extract all EIs surrounded by chromatin states 8 and 9 and identified 232 EIs containing 540 EI genes this way, among which 6 correspond to unsequenced gaps (https://apps. araport. org/jbrowse) and were discarded in subsequent analyses (Appendix Table  S3 ). S3 ). A majority of EIs are short (S-EIs) and contain 1 or 2 genes (Fig. 3E ), and the 78 state 8-containing EIs were in average significantly longer (Fig 3F ; Appendix Table  S4 ) and contained more genes (Fig 3E ,.
4% in replicate 2, 70. 5% in replicate 3, 21. 7% in replicate 1; Appendix Table  S3 ). The limited number of EIs identified this way in replicate 1 can be attributed to the fact that it was less deeply sequenced than replicates 2 and 3 (GSE103924). Despite that, there was a large overlap between replicates (Fig  S7C ). We then extracted the number of significantly (g test, P < 0. 01) up or down H3K9me2 peaks in caa39 versus wt, in each individual replicate (Appendix Table  S5 , sheet 6). A similar percentage of significantly up or down H3K9me2 peaks was observed at the genome scale by this method  (Fig 4D) , in agreement with the fact that the global level of H3K9me2 is not overtly affected in caa39. In contrast, the proportion of peaks was significantly shifted toward gains of H3K9me2 within EIs (Fig  4D ), here again confirming the specific role of Topo VI in safeguarding EI genes from ectopic H3K9me2 marking.
RNA-seq bioinformatic treatment and analysis To facilitate comparisons, each RNA-Seq sample followed the same workflow from trimming to transcript abundance quantification, as follows. Read preprocessing criteria included trimming of library adapters and performing quality control checks using FastQC (v0. 10. 1). The raw data (fastq) were trimmed for Phred Quality Score > 20, read length > 30 bases and sort by Trimming Modified homemade fastx_toolkit-0. 0. 13. 2 software for rRNA sequences. Bowtie v2  (Langmead & Salzberg, 2012)  was used to align reads against the Arabidopsis thaliana transcriptome (with --local option). The 33602 mRNAs were extracted from TAIRv10 database  (Lamesch et al, 2012)  with one isoform per gene to avoid multi-hits. The abundance of mRNAs was calculated by a local script which parses SAM files and counts only paired-end reads for which both reads map unambiguously to the same gene.
Whole-genome bisulfite sequencing and DNA methylation analysis Seeds of wt and caa39 were sowed with an interval of one week to generate biological replicates. For each replicate, DNA samples were obtained from 400 mg of fresh aerial parts of 6 day-old plants (developmental growth stages  (Boyes et al, 2001) ) crushed in liquid nitrogen. Genomic DNA was prepared using the NucleoSpin® Plant II Midi kit (Macherey-Nagel), with the addition of Poly(vinylpyrrolidone) to the powdered sample. Libraries were prepared and sequenced at the BGI (China). Quality control was performed on sequencing files using FASTQC (v0. 11. 5) and adapter trimming was performed at BGI. Methylation analysis has been performed with Bismark (v0. 20. 0) (Krueger & Andrews, 2011)  using default parameters, except -CX option, to get data for all the three contexts (CG, CHH and CHG).
The bedgraph output file has been filtered into 3 files, one for each context. DMRfinder  (Gaspar & Hart, 2017)  was used to identify differentially methylated regions and the "combine_CpG_sites. py" script was used to cluster CpG (and other context) sites into regions with data_bismark. cov files as input files and default parameters. findDMRs. r was used with default parameters to conduct pairwise tests of sample groups in order to identify DMRs.
Extraction of euchromatic island sequences Those islands harbor characteristic chromatin states of expressed gene and are surrounded by state 9 and 8. Using a homemade perl script, we screened for all regions within heterochromatic regions that are surrounded by chromatin state 8 in the proximal region and state 9 in the distal region, as defined by Sequeira-Mendes et al. , 2014. The length threshold of state 9 was set at 450 bp and of the state 8, 150 bp. The list of coordinates generated by this script was then converted to a bed file and used to define the region of interest in the bigwig files. In order to map read density along islands, we used the aggregate command of the bwtool (v1. 0)  (Pohl & Beato, 2014)  with the functionality up:meta:down where meta is set to the median length (in bp) of the element studied.
Statistical analysis of the Eukaryotic island profiles The matrix program of Bwtools (https://github. com/CRG-Barcelona/bwtool/wiki/matrix) was used to extract the data from bigwig files. Using the up:meta:down option, the data was extracted in the same way as the aggregate program but instead of returning a mean for each position, it creates a matrix containing each replicate value for each position. The H3K9me2 or H3K27me3 matrix were normalized by the H3 matrix, replicates were concatenated in one matrix which was then treated as the aggregate file. A Shapiro test was first performed to check for normal distribution. The nonparametric Mann-Whitney test was used to compare the value for WT and caa39 at each position.
Firstly, becauseH3K9me2 and non-CG (CHG and CHH) DNA methylation are strongly inter-dependent (Stroud et al, 2014) , we tested whether H3K9me2 spreading into EIs might in turn, or reciprocally, affect DNA methylation. Whole-genome bisulfite sequencing revealed that DNA methylation levels in all sequence contexts were unaltered in caa39 relative to wt in EIs(Fig 4F and I, Fig S8C). Differentially methylated region (DMR) analyses confirmed that there was no global significant increase of DNA methylation in EIs (Dataset S1). Surprisingly, despite CHGs being known to be methylated through a feedback loop with H3K9me2, DMR analyses rather revealed a decrease of CHG, with 51 hypo-CHG DMRs and 16 hyper-CHG DMRs observed in EIs of caa39 (Dataset S1). Therefore, DNA methylation does not seem to contribute to the down-regulation of gene expression in the EIs and.
Acknowledgments We thank  Imen Mestiri  ( IBENS, Paris ) for her expertise with cytogenetics. We also want to express our gratitude to students who contributed to this work, especially  Justine Quillet ,  Julien Vieu  and  César Botella. We thank  Ben Field  for critical reading of the manuscript. This work was supported by the  French National Research Agency  ( ANR 2010-JCJC-1205-01  and  ANR-14-CE02-0010  to CL). Work by FB was supported by the  Investissements d'Avenir LabexMemory in Living Systems (MEMOLIFE)  grant  ANR-10-LABX-54. LD was supported by  CEA  and  Région PACA. High -throughput RNAsequencing was performed at the POPS plateform, supported by the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). MS analysis was performed at the IMM platform supported by a grant from  GIS IBiSA.