dataesr/scientific-paragraphs-categorization huggingface dataset:
It also contains a summary table and a graph which traces the different steps (assembly, vector cleaning and final contig building steps) in a user-friendly visual way. The report includes a table with links enabling to download the files generated in the different steps either for each initial DNA insert or for all DNA inserts as an archive. The HTML report file includes all graphics and download-able results files which makes it very easy to distribute. The mintia_assemble. fasta file contains contigs in fasta format and serves as the input of the annotation module. The second module (MINTIA Annotate: Fig. 3 ) first detects CDS in contigs using prokka  (Seemann, 2014). Then, CDS and contigs are aligned versus RefSeq non redundant (nr)  (OLeary et al. , 2015)  and Uniprot/SwissProt (The UniProt Consortium, 2016) using diamond  (Buchfink, Xie & Huson, 2014).
It has been decided to align contigs (and not only CDS) versus nr and swissprot in order to be able to check if there were issues in the detection of CDS starting position. Results of these steps can be viewed in the HTML report as a table or in an igv. js based browser  (Robinson et al. , 2020). CDS are also aligned using rpsblast  (Camacho et al. , 2009)  versus the COG database  (Tatusov et al. , 2003)  and they can additionally be aligned with blast versus an in-house database provided by the user as a fasta file. MINTIA can also run MEGAN5  (Huson et al. , 2007)  in order to obtain taxonomic affiliations and can generate GENBANK compliant submission files. The Annotate module also produces a dynamic HTML report (Fig. 4 ) presenting the selected options and a table describing annotation results including links to individual or global result files.
Simulations First, 40,000 base pair long genomic chunks were randomly extracted from the genomes of Bifidobacterium adolescentis ATCC 15703 (refered as Bifido later in the text -NCBI NC_008618. 1 -complete genome of 2089645 bp), Bacteroides stercoris ATCC 43183 scaffold 12 (refered as Sterco12 -NCBI NC_DS499673. 1 -587129 bp) and scaffold 16 (refered as Sterco16 -NCBI NZ_DS499677. 1 -468184 bp). Fifteen chunks were extracted for each genome. Then the cloning vector (PCC1FOS of Epicentre Technologies) was inserted into those sequences, at a random position either at one of the extremities or inside the sequence. When the vector was inserted inside the sequence an overlap of 500 bp was created at the fosmid end to mimic circularity. This was performed using an in-house Python program. Then, 150 base pair long paired reads (using art_illumina with parameters -ss HS25 -p -l 150 -f 1000 -m 200 -s 10) and 150 base pair long single reads (using art_454 with parameters -s and coverage 500) were generated for each sequence (called reference fosmid sequence later) using the ART software package  (Huang et al.
Mis-assemblies were manually checked to understand which genomic feature could explain them. RAST and MINTIA were run on MINTIA assembled contigs to compare annotation results. RAST and MINTIA detected CDS were aligned with blat  (Kent, 2002)  version 36 using default parameters against the annotated CDS of the corresponding parts of the reference genome. True positive CDS are those aligning from start to end positions on their reference. The annotation precision corresponds to the ratio of CDS present in the genome detected by the software package over CDS present in the genome. The annotation recall corresponds to the ratio of CDS in the genome and detected by the software package over CDS detected by the software package. The quality and relevance of assembly and annotation were analyzed using R software package (version 4.
Data Availability The following information was supplied regarding data availability: The software package is available at GitHub: https://github. com/Bios4Biol/MINTIA. The simulated reads sets are available at the INRAE network: Laguerre, Sandrine, 2020, ''MINTIA paper Simulated data'', https://doi. org/10. 15454/TJC6SP, Portail Data INRAE, V1.
DISCUSSION In order to evaluate MINTIA assembly results, we compared them to those of fabFos and Shims2. fabFos produced much more contigs than Shims2 and MINTIA because it does not include circularization and filtering steps and therefore produced several contigs where both other software packages only generated one contig as expected. fabFos precision is much lower than both others because it does did not produce vector cleaned contigs. fabFos includes a vector search step but does not output a vector cleaned contig fasta file which is the classical input for the annotation step. fabFos recall was close to the ones observed for the two other software packages but still lower. This could originate from the larger kmer size used in the SPAdes assembly step. Shims2 produced mainly one contig per assembly but only for paired reads.
Three cohorts (N = 795, EDEN, GENEIDA, and INMA) contributed to the EWAS of sustained MSDP, defined as maternal smoking throughout pregnancy, among which 163 (20. 5%) mothers reported sustained MSDP. Distributions of covariates by cohort are provided in the Supplementary Materials (Supplementary Data 1). The comparison group for models of any or sustained MSDP included all mothers that did not report smoking cigarettes during any trimester. Genome-wide DNAm meta-analyses. We produced four statistical models for each CpG site, regressing DNAm on both any and sustained MSDP, with and without adjustment for putative cellular heterogeneity, which was estimated with a reference-free deconvolution algorithm, RefFreeCellMix  22. All models were adjusted for maternal age, parity, and maternal education.
4 ). Among the top 20 CpGs from our meta-analysis, 15 mapped to eQTM genes (Table  3 ) and 5 of which were significant at the Bonferroni-adjusted threshold: SH3D21, TBC1D8, USP46, CRAT, and TGFB1. Functional and regulatory enrichment analyses. Enrichment analyses were performed to gain insights into the biological processes in placenta that may be impacted by MSDP through altered epigenetic regulation. We performed gene-set enrichment analyses using two different gene sets (Supplementary Data 10), first using the genes annotated to the 443 MSDP-associated CpGs according to the Illumina annotation file (284 genes), and second using the genes mapped to these CpGs via the eQTM analyses (211 genes). The first gene list leverages as much of the data as possible that was generated from our most well-powered analysis.
Since DNAm is a relatively stable epigenetic feature, this could represent a record of the genes whose methylation levels were perturbed by MSDP across the pregnancy period. On the other hand, gene expression is more dynamic than DNAm and is affected by multiple stimuli. Thus, the second eQTM-mapped gene set likely represents the genes that are influenced by DNAm at these MSDP-associated CpGs at, or close to, the time of birth. We found that 46 and 9 biological pathways were significantly (q value < 0. 05) enriched among the Illumina annotated and eQTM-mapped genes, respectively (Supplementary Data 11 and 12). Overall, the eQTM genes were involved in inflammatory activity (aryl hydrocarbon receptor (Ahr) pathway, Th17 cell differentiation, neutrophil degranulation, and platelet degranulation), tyrosine kinase signaling (TYROBP causal network and signaling by receptor tyrosine kinases), carcinogenesis (pathways in clear cell renal cell carcinoma), adipogenesis, and platelets.
e. , 2p22. 2). β 1 coefficient of the association between DNAm and MSDP, which represent the estimated difference in the proportion of DNAm at each CpG when comparing mothers that smoked during pregnancy (MSDP), to those that did not smoke during pregnancy; p values from inverse-variance fixed-effect meta-analyses, % Diff. the percent difference in effect size when comparing the β 1 for sustained MSDP to the β 1 for any MSDP (% Diff. ), MSDP maternal smoking during pregnancy. To further understand the regulatory landscape for these differentially methylated CpG sites, we used EnrichR to test for enrichment of transcription factor (TF) targets from ENCODE/ ChEA databases  25. The genes annotated to our MSDP-associated CpGs were targets of GATA1 and GATA2, the androgen receptor, TP63, SMAD4, RUNX1, and ZBTB7A (Supplementary Data 13).
While the eQTM gene list was not significantly enriched for TF binding, the top TF was RUNX1 (Supplementary Data 14). We then examined whether the MSDP-associated CpG sites were enriched for allele-specific germline differentially methylated regions (gDMR)  26  , regulatory features from the placenta-specific 15-chromatin state annotation from ROADMAP  27  , or placentaspecific partially methylated domains (PMD)  28  , which contain placenta-specific repressed genes (annotated to the results files in Supplementary Data 8). Most notably, the MSDP-associated CpGs were substantially depleted for PMDs (Supplementary Fig. 5 ), and highly enriched in placental enhancers (Supplementary Fig. 6 ); both of these are indicators that our set of placental MSDP-associated CpGs occur within highly active regions of the placental methylome.
While we also explored whether our findings were enriched for allele-specific gDMRs, only one of the 443 MSDP-associated CpGs was within a candidate maternal gDMR (cg05211790 annotated to RAI14), suggesting that gDMRs are not substantially affected by MSDP. Phenotype enrichment analyses. We tested for enrichment for phenotypes within the database of Genotypes and Phenotypes (dbGAP) using EnrichR, to understand the types of health outcomes that have been associated with these genes. We again tested the gene lists based on those annotated to our CpGs (n = 284 genes) and those mapped to eQTM genes (n = 211 genes). The Illumina annotated genes were enriched for cell adhesion molecules (CAM), asthma, body mass index (BMI), blood pressure, and antipsychotic agents; while the gene list based on eQTM mapping did not yield significantly enriched phenotypes (Supplementary Data 15 and 16).
35 ) and 4342 (ref. 36 ) placental mQTLs. Our findings did not appear to be biased by genetic variation as only 5 of the 443 MSDP-associated CpGs were previously characterized placental mQTLs. DNAm associated with smoking-related birth outcomes. We then performed secondary meta-analyses to examine the relationships between DNAm with GA at birth, preterm birth, BW, BL, and HC z-scores. Of the 443 CpGs tested, 142 (32. 1%) were related to at least one birth outcome after Bonferroni adjustment (0. 05/443). The majority of birth outcome associations were related to GA at birth (121 CpGs) (Supplementary Data 18). Preterm delivery, produced similar associations, although fewer CpGs were statistically significant (Supplementary Data 19). We also found that numerous CpGs were associated with birth size z-scores, with the majority of these being associated with BW (25 CpGs; Supplementary Data 20), followed by BL (11 CpGs; Supplementary Data 21) and HC (2 CpGs; Supplementary Data 22).
Methods Participating cohorts. Cohorts that are members of the PACE consortium were identified for participation in the current study, if they had existing DNAm data quantified from placental tissue via the Illumina Infinium HumanMethylation450 BeadChip, and if they had obtained information on self-reported smoking during pregnancy. The seven cohorts that contributed to the meta-analysis of any MSDP included AQUA  16  , EDEN  17  , Gen3G  18  , GENEIDA, INMA  19  , NHBCS  20  , and RICHS  21. EDEN, GENEIDA, and INMA also contributed to the sustained MSDP stratified analyses. RICHS contributed RNA-seq data for analyses with mRNA expression. All cohorts acquired ethics approval and informed consent from participants prior to data collection through local ethics committees. Exclusion criteria for this study were non-singleton births, preeclampsia, and DNAm not assessed in the fetal side of the placenta.
All participants in the study were of European ancestry, except 1. 85% of EDEN mothers. Detailed methods for each cohort are provided in the Supplementary Material (Supplementary Methods File). Tobacco smoking definitions. Any MSDP was defined as mothers reporting smoking cigarettes at any time during pregnancy. Sustained MSDP was defined as mothers reporting smoking cigarettes in the first and third trimester of pregnancy. For both exposure variables, the comparison group was defined as the mothers that reported no smoking during any of the pregnancy. DNAm data quality control and normalization. All DNAm data processing and analyses were conducted in R, with the exception of the meta-analyses, which were performed with METAL. Placental DNAm from the fetal side was assessed with the Infinium HumanMethylation450 array (Illumina, San Diego, CA USA).
Models for differential DNAm were corrected for the number of surrogate variables minus one to reduce multi-collinearity. Genome-wide differential DNAm analyses. Within each cohort, robust linear regression from the MASS package 74 in R were used to account for potential heteroskedasticity, while testing the associations between normalized DNAm beta values at each CpG with any MSDP and sustained MSDP. Models were adjusted for maternal age, parity, maternal education, and cohort-specific variables first unadjusted for putative cellular heterogeneity then adjusted for RefFreeCellMix estimates of putative cellular heterogeneity. Due to the inflation and potential residual or unmeasured confounding, we applied BACON to the cohort-specific results before performing meta-analyses. BACON was specifically developed for EWAS and estimates an empirical null distribution to correct for residual bias and inflation  23.
We performed inverse-variance weighted fixed-effect meta-analyses using METAL  75. The meta-analysis was performed independently by two groups to ensure consistent results. Both groups detected some inconsistent errors within some cohort-specific results, and the cohorts were contacted to redo the analysis. After these were addressed, results between independent groups were completely consistent. A third group then used an independent R script to perform metaanalysis with the metafor package, and again successfully reproduced identical results. CpGs not retained in at least two cohorts were filtered out. We used the Bonferroni adjustment to control for multiple testing. To examine whether differential methylation associated with sustained smoking yielded stronger magnitudes of effect relative to models of any smoking, we calculated the percent change in the coefficients between the two models (|β sustained | -|β any |)/|β any | × 100.
Secondary analyses were only performed on CpGs that passed a Bonferroni-corrected threshold for associations with any or sustained MSDP in models that were adjusted for RefFreeCellMix and corrected for residual bias via BACON. Expression quantitative trait methylation. We performed eQTM  76  analyses in the RICHS cohort. Transcription was measured via RNA-seq on 194 placentas. The details of sample collection, assay, and QC for the RNA-seq data are presented in detail elsewhere  77  , and summarized in the Supplementary Material (Supplementary Methods File). In this dataset, we identified 2567 unique transcripts annotated to an Ensembl ID (GrCh37/hg19) and with a TSS within 250 kb upstream or downstream of 423 out of the 445 candidate CpGs. The association between DNAm and expression levels was assessed via 3507 linear regression models, using the MEAL package  78  in R; these models were adjusted for RNA-seq batch, DNAm batch, the RICHS selection factor (small, appropriate, or large for GA), and self-reported maternal ancestry.
ConsensusPathDB performs a hypergeometric test and corrects multiple testing with FDR. Enrichment for TFs and for phenotypes were assessed at the gene level with EnrichR, using ENCODE and ChEA consensus TFs from ChIP-X database, and dbGaP database, respectively. EnrichR results were ranked using the combined score (p value computed using Fisher exact test combined with the z-score of the deviation from the expected rank)  25. Enrichment for regulatory features was assessed with the hypergeometric test, and p values were Bonferroni corrected for 15 (placental chromatin 15 states) and 6 (relation to CpG island) tests, respectively. Overlap of MSDP-sensitive CpG sites and birth outcome SNPs. Overlapping genomic regions between MSDP-associated CpGs in placenta with previously identified BW, BL, HC, and GA SNPs from the largest GWAS to date  [29] [30] [31] [32] [33] [34]  was assessed using the GenomicRanges package in R  81.
Acknowledgements We would like to thank all the families that participated in these studies for their generous contribution. Detailed acknowledgements and funding can be found in Supplementary Material. 1 Gangarosa Department of  Environmental Health, Rollins School of Public Health at Emory University, Atlanta, GA, USA. 2 Center for Genomic Regulation (CRG ),  Barcelona Institute of Science and Technology, Barcelona, Spain. 3  Universitat Pompeu Fabra, Barcelona, Spain. 4  CIBER Epidemiología y Salud Pública (CIBERESP) ,  Madrid, Spain. 5University  Grenoble Alpes, Inserm ,  CNRS ,  IAB, Grenoble, France. 6Department of  Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA. 7Division of  Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, CA, USA.
Data availability All relevant data supporting the key findings of this study are available within the article and its Supplementary Information files, or from the corresponding author upon reasonable request. A reporting summary with additional information is also included. The complete summary statistics from these meta-analyses are available at FigShare (any MSDP [https://doi. org/10. 6084/m9. figshare. 12198471]) and (sustained MSDP [https:// doi. org/10. 6084/m9. figshare. 12198504]). The raw fastq files for placental RNA-seq measurement are available in the NCBI database for Genotypes and Phenotypes (dbGaP) under accession number phs001586. v1. p1.
To uncover biological pathways altered in the disease, we applied MOGAMUN, a multi-objective genetic algorithm that integrates multiplex complex networks of biological interactions (protein-protein interactions, co-expression, and biological pathways) and RNA Seq expression data to identify active modules. Results We identified 132 differentially expressed genes that are specific to FSHD cells (false discovery rate < 0. 05). In FSHD, the vast majority of active modules retrieved with MOGAMUN converges towards a decreased expression of genes encoding proteins involved in sarcomere organization (P value 2. 63e À12 ), actin cytoskeleton (P value 9. 4e À5 ), myofibril (P value 2. 19e À12 ), actin-myosin sliding, and calcium handling (with P values ranging from 7. 9e À35 to 7. 9e À21 ). Combined with in vivo validations and functional investigations, our data emphasize a reduction in fibre contraction (P value < 0.
11, 12 BAMS patients also display D4Z4 hypomethylation and DUX4 expression  12, 13  despite the absence of muscle phenotype in these patients, indicating that alternative genes/pathways are implicated in the two diseases. To identify FSHD-specific pathways and understand muscle alterations that are specific to FSHD cells, we used induced pluripotent stem cells differentiated into innervated muscle fibres. 14 We compared the transcriptome of FSHD-derived cells with cells from healthy donors but also cells from patients affected with BAMS by RNA Sequencing using classical pipelines but also applied MOGAMUN, a recently developed algorithm able to find active modules from multiplex biological networks by integrating protein-protein interactions, biological pathways and co-expression. 15 nteresting areas are located automatically, by finding highly deregulated and dense subnetworks.
RNA-Seq data processing and differential expression analysis We assessed fastq sequence data quality using FastQC v0. 11. 5 and trimmed the reads to remove adapter sequences and low-quality bases using DimerRemover v0. 9. 2. The resulting trimmed single-end reads were aligned using STAR v2. 5. 3a  16  to the GRCh38 human genome release. BAM files were indexed using Sambamba (v0. 6. 6) after ordering them by coordinates. Differentially expressed genes (DEGs) were extracted at the fold-change cutoff ≥2 and an FDR corrected P value of 0. 05 (named after FDR) in DESeq2 analysis. The numbers of transcripts per million (TPM) for each sample are shown in Data S1. RNA-Seq data were analysed using MOGAMUN, a multi-objective genetic algorithm that identifies active modules (i. e. highly interconnected subnetworks with an overall deregulation) in a multiplex biological network composed of different layers, where each of them represents physical and/or functional interactions.
15 We used as input for MOGAMUN the resulting FDR-corrected p-values, and a multiplex biological network composed of three layers or undirected interactions from. 17 The first layer corresponds to physical protein-protein interactions obtained by merging the CCSB Interactome database  18  and several databases from the PSICQUIC portal. 19 The second layer contains data of pathways, obtained using the R-package graphite. The third layer corresponds to absolute Spearman correlation superior or equal to 70%, calculated using the RNA-Seq expression data of 32 tissues and 45 different cell lines. 20 We ran MOGAMUN 30 times with the default parameters. The resulting accumulated Pareto front is composed of active modules, which contain different number of genes, including significant ones considering an absolute log fold change >1 and FDR <0.
Imaging and analysis of calcium transients Fluorescent Ca2+ indicator FLUO8-AM (5-μM Fluo8-AM and 0. 04% Pluronic acid, AAT Bioquest) was added to cell culture medium. After 30 min incubation at 37°C, imaging was performed on a Fast-Imaging Observer (Zeiss). For calcium handling measurements, fluorescence was excited at 488 nm and emission collected at >509 nm. Images were acquired as a time series. Different regions were tracked to record the fluorescent intensity at a 10× magnification during 30 s and an interval of 200. 0 ms corresponding to 151 frames. The amplitude of the signal was automatically determined using the Zen software (Zeiss) and reported for individual cells. For measurement of fibre contraction, images were processed with IMARIS (Bitplane) using the automatic spots tool.
573A>C; p. Q193P), BAMS (BAMS-1; c. 407A>G, p. E136G) (Table  S1 ). 13 Fibroblasts were reprogrammed into human induced pluripotent stem cells (hiPSCs), for FSHD1 mosaic cells, hiPSCs clones carrying the contracted D4Z4 or the healthy D4Z4 allele were isolated separately. Cells were described in Dion et al. 13  All hiPSCs clones were differentiated into functional muscle fibres (MFs). Gene expression analysis was performed by high-throughput RNA-sequencing. For each condition, two biological replicates corresponding to two independent differentiation experiments were used. DEGs were selected based on a minimum two-fold change and statistical significance of FDR, compared with control cells. In complement to classical RNA-Seq pipeline analysis, we applied MOGAMUN, a recently developed algorithm aimed at revealing active modules in multiplex biological networks.
Active module analysis reveals perturbed sarcomeric organization in FSHD To further identify subnetworks of interest and thus reveal pathways associated to the disease, we analysed our RNA-Seq data using MOGAMUN, a multi-objective genetic algorithm that integrates expression data into multiplex biological networks. 15 These multiplex network are composed of three layers, which correspond to protein-protein interactions (PPI, blue edges), BPs (red edges), and an absolute correlation of expression (referred to as co-expression, yellow edges) of at least 70%, calculated using the RNA-Seq expression data of 32 tissues and 45 different cell lines. Comparison between the pathological and control conditions revealed between 16 and 23 subnetworks considered as active modules. Based on the redundancy of genes in these different networks, we further selected the most similar active modules for in-depth analysis.