dataesr/scientific-paragraphs-categorization huggingface dataset:
Our conservative approach defined genes differentially expressed as those found to be in common between the results of at least two of the three software programs mentioned above. The log-2 fold change in gene expression presented in Fig. 2d  and Fig. 4e  correspond to the log-2 fold change returned by DeSeq2. DeSeq2 and edgeR used as an input raw read counts produced by featureCounts from the Bioconductor (version 3. 3) Rsubread package (14) in R version 3. 3. 1. For the DeSeq2 and edgeR analyzes, we filtered out genes that had 0 or 1 read support across all samples. In the DeSeq2 differential expression analysis, we selected genes that were up or down regulated at a FDR lower than 0. 1. In edgeR differential expression analysis, we selected genes up or down regulated with a p-value less than 0.
Acknowledgements We thank all members of the  Steve Jackson laboratory  for help and support-particularly  Kate Dry  for editing the manuscript-and  Dr Carlos Lopez-Otin  for sharing his Lmna G609G mouse model with us. Research in the Jackson laboratory is funded by  Cancer Research UK (CRUK) program  grant  C6/ A18796  and a  Wellcome Trust   Investigator Award  ( 206388/Z/17/Z ). Institute core funding is provided by  CRUK  ( C6946/A24843 ) and the  Wellcome Trust  ( WT203144 ). D. L was funded by a Project Grant from the  Medical Research Council ,  UK MR/L019116/1. Research in the D. J. A. laboratory is supported by  CRUK  and the  Wellcome Trust. Research in the R. R. laboratory is supported by the  European Research Council  (Grant  N°647973 ), and the  Emergence Ville de Paris Program.
Gene discovery For gene discovery, a variation of the pipeline described by  Haas et al. (2011)  was used to annotate protein-coding genes in the assembled genome. This pipeline integrates evidences from different sources to create a consensus gene prediction, including ab initio protein and transcript alignments, which are integrated with EVidenceModeler  (Haas et al. 2008). The repetitive and transposable elements of the assembled genome were identified using RepeatModeler  (Price et al. 2005)  and TransposonPSI (transposonpsi. sourceforge. net). RepeatModeler identifies de novo repeat families using k-mer frequency count. These de novo repeats plus the latest giriRepbase  (Jurka et al. 2005)  were then masked using RepeatMasker  (Tempel 2012). The resulting hardmasked genome was further processed using TransposonPSI which identifies transposable elements by aligning the DNA sequences to a transposon-protein database using PSI-BLAST, which were then masked using BEDtools  (Quinlan and Hall 2010).
A match was considered evidence if the GeneWise alignment represented at least 80% of the original target protein. The consensus gene model for each locus was produced by source-weighted integration using EVidenceModeler. Weights of 5 and 1 were assigned to GeneWise and ab initio predictions, respectively. The final gene models were updated using RNA-seq data with Program to Assemble Spliced Alignments (PASA)  (Haas et al. 2003)  to include splicing-isoforms, 5'-UTR and 3'-UTR. In addition, the number of genes supported by RNA-seq data was counted.
Functional annotation Alignments were carried out using BLASTp version 2. 2. 29+ and were filtered for the first 10 hits with a cut-off e-value of 10 -5 as suggested in the literature  (Clarke et al. 2013; Liu et al. 2013). BLASTp alignments against the non-redundant RefSeq protein database (22 January 2014)  (Ye et al. 2006; Pruitt et al. 2012) , UniProt (22 January 2014)  (Consortium 2014 ) (including Swiss-Prot, fungal taxonomic division and uniref90) and KEGG (Release 69. 0, 1 January 2014)  (Kanehisa et al. 2002)  were performed to assign general protein function profiles. Protein domains were assigned using InterProScan 5. 2-45. 0 (Quevillon et al. 2005 ) (including Pfam 27. 0  (Punta et al. 2012) , SUPERFAMILY 1. 75  (Wilson et al. 2009 ), SMART 7  (Letunic et al. 2012 ), TIGRFAMs 13.
0  (Haft et al. 2013) , TMHMM 2. 0c  (Krogh et al. 2001) , PROSITE 20. 99  (Sigrist et al. 2013 ) and PANTHER 8. 1  (Mi et al. 2013 ) databases). Gene ontology (GO) annotations were determined using InterProScan and TransporterTP  (Li et al. 2009). Enzyme Commission codes (EC numbers) and KEGG Orthology (KO) numbers were assigned using the KEGG database and BLASTp. PRI enzyme functions were assigned using PRIAM (Enzyme rel. of 6 March 2013)  (Claudel-Renard et al. 2003). Targeting and transmembrane signals were obtained from SignalP4. 1  (Petersen et al. 2011 ), NetNES1. 1 (la Cour et al. 2004 ), TargetP1. 1 (Emanuelsson et al. 2000)  and SecretomeP2. 0 (Bendtsen et al. 2004 ). Transporter signals were determined using TransporterTP. Carbohydrate-activating enzymes (CAZymes) were predicted using dbCAN  (Yin et al.
Comparative genomics and phylogenetic analysis The annotated genes from P. purpurogenum were compared with 21 previously annotated genomes from members of the phylum Ascomycota (listed in Table  S1 ). Twenty are from the family Trichocomaceae (12 Penicillium, 5 Aspergillus and 3 Talaromyces). A cellulolytic-enzyme secreting fungus from a different family, Trichoderma reesei, was used as outgroup. The assembled and annotated genomes from the Ascomycota reference genomes were downloaded from the JGI database (http://genome. jgi. doe. gov/). All species were assessed and compared for the number of CAZy proteins and InterPro families. Orthologous genes between the Ascomycota members and P. purpurogenum were predicted using the OrthoMCL program  (Li et al. 2003). A total of 252,277 proteins were clustered with OrthoMCL using an e-value cut-off of 10 -5 and a moderate inflation value of 2.
Manual annotation of CAZymes dBCAN searches for genes coding for glycosyl hydrolases (GH), glycosyl transferases (GT), polysaccharide lyases (PL) and carbohydrate esterases (CE) from the CAZy database were performed. From the results obtained, all CAZy genes with an e-value of less than 10 -40 were curated with the help of Apollo  (Lewis et al. 2002). In addition, each entry was subjected to BLASTp and CD-search (Marchler-Bauer and Bryant 2004) to manually confirm or improve the automatic annotation.
Accession numbers Genome and RNASeq sequences were submitted to the sequence read archive (SRA) and can be downloaded from BioProject SRP055745. SRA numbers for Illuminaoverlapping, Illumina-jumping, PacBio and RNA-seq reads are SRR1823665, SRR1823673, SRR1823957 and SRR1824012, respectively. Assembled genome and related annotation files can be downloaded from http://ppurdb. cmm. uchile. cl/material.
No significant difference on the average protein length, number of exons and average intron length is observed when comparing genomes, supporting the aforementioned observation. A total of 9585 genes (87%) were confirmed by at least 10 reads from RNA-seq data, indicating that our The N90 and N50 scaffolds are the length of the scaffolds covering 90 and 50% of the genome, respectively. The N90 and N50 were calculated using 39 Mb as genome size. In parenthesis are the numbers of contigs/ scaffolds required to cover 90% of the genome length. gene models are well supported and in agreement with transcriptome data. Out of the 11,057 genes predicted, 94% (10,422) were successfully annotated (Table  S3 ) using standard functional protein databases. We were able to assign InterPro Number, GO Numbers and CAZy IDs to 80%, 63% and 8% of the gene models (Table  S3 ), respectively.
However, we observe in P. purpurogenum a higher  capacity in terms of gene dosage for functions related to pectin lyase, major facilitator superfamily and glycoside hydrolases (involved in plant cell-wall degradation), general substrate transport, gene regulation and degradation of biomass. The overrepresented terms suggest that P. purpurogenum is more specialised in the transport of elements across the cellular membrane (IPR016196 and IPR005828). This is a signal that the fungus has a stronger performance in the secretion of extracellular proteins and import of extracellular sugars. In addition, it is interesting that P. purpurogenum has an elevated number of proteins with IPR011050 domain, a domain associated to pectin lyases; this is consistent with the good growth of the fungus when cultivated on rich pectin carbon sources such as sugar beet pulp.
In addition, this genome is a valuable tool for the analysis and understanding of other fungal genomes. A preprint of this article has been uploaded in the biorxiv database. DI-478-14/R and DI-31-12/R] to Jaime Eyzaguirre, "Fondo de Financiamiento en Areas Prioritarias" (CRG-Fondap) [grant number 1509007] to Alejandro Maass and a "Mejoramiento de la calidad de la Educación Superior" (MECESUP) Fellowship [grant number UAB 0802] to Wladimir Mardones. We acknowledge the contribution of the National Laboratory for High Performance Computing at the Center for Mathematical Modeling, Project PIA ECM-02 -"Comisión Nacional de Investigación Científica y Tecnológica" (CONICYT).
Acknowledgements The authors thank  Dr Shahina Maqbool  ( Albert Einstein College of Medicine of Yeshiva University, New York, USA ) for performing the Illumina DNA sequencing;  Dr Rodrigo Gutiérrez  and  Dr Tatiana Kraiser  ( Pontificia Universidad Católica de Chile, Santiago, Chile ) for the RNASeq sequence and  Dr Olivier Fedrigo  ( Duke University, USA ) for the PacBio sequencing.
Its main objective is to show that, if we focus on strongly related bacterial chromosomes, the reconstruction of their most recent common ancestors is possible in practice. In order to do so, we propose a pragmatic approach that mix already published reconstruction algorithms with new original scripts and a human cross-validation. As an illustrative example, we provide the ancestral reconstruction of 65 genomes of the Mycobacterium tuberculosis complex, and of the 47 Brucella genomes that are available on the NCBI database. The dynamics of the evolution process in DNA sequences results from local evolutionary events that consist in SNPs or indels. Genomic rearrangements, which are larger alterations of the genetic organization, can take the form of inversions and transpositions, or occur by chromosome fusion and fission.
Data acquisition and processing A python script has firstly been written to automatically download all the complete genomes of Mycobacterium genus available on the NCBI database, encompassing 2 africanum, 15 bovis, 5 canettii, 1 microti, and 42 tuberculosis. Note that canettii and tuberculosis are well represented in this dataset, which is helpful to study how virulence has appeared in the first species, and if the second one is at the origin of the MTBC complex 40,000 years ago. Details about these 65 genomes are provided in Table  1. After the data acquisition stage, the next step is to align the downloaded sequences  [28, 29]. Prior to the Multiple Sequence Alignment (MSA), genomes must be operated such that each sequence starts to the same location and is read in the same direction: we deal with circular genomes.
This is why a sequence of reference (200 bp from M. tuberculosis H37Rv) and its reverse complement have been blasted locally. Then, a circular shift and/or a reverse complement of the whole sequence have been applied when required. Most of the well-known alignment tools have failed to align these genomes, due to their size, while we do not want to split the sequences, to reduce the complexity of the alignment, as this multiplies the intermediate steps, increasing by doing so the risks of errors. It was not the case of AlignSeqs, available in the R module called decipher  [30]. This latter achieved to perform the MSA in an accurate and rapid way. With this tool, multiple  sequence alignments are done by aligning 2 genomes first, and then adds a third genome, etc. , until all the sequences are aligned  [31].
Using this alignment, a very well supported phylogenetic tree has been obtained. For the sake of illustration, a subtree corresponding to the phylogeny of the Brucella abortus species is depicted in Fig. 13 , and in Fig. 14  for B. melitensis. It has been obtained using the entire genome sequences with RaxML, GTR Gamma  At this stage, all the material required to attack the ancestral reconstruction of Brucella genomes are on hand. We first have focused on the abortus and metilensis reference species, to investigate the potential origin and the history of the global spread of these Brucellas. We have considered the global alignment of both chromosomes 1 and 2 of the available complete strains, using decipher R package  [42] , and the tree depicted in Figs. 13  and 14. We firstly achieved a comparative whole-genome single nucleotide polymorphism analysis of these strains  Figure  12  shows homologous regions among many Brucella abortus genomes, as identified by FindSynteny (R).
As a proof of concept, all ancestral genomes of all M. canettii available on the NCBI database have been reconstructed, as well as all the ancestors of the available M. tuberculosis complete genomes. At each time, the single nucleotide polymorphism level has first been investigated, before considering the cases of indels and large scale recombination. Obtained results show that a concrete and accurate reconstruction can be achieved by coupling human decisions on problematic situations with automatic inference Fig. 19  Brucella abortus phylogenetic tree: estimation of the CRISPRs length and locations by using the CRISPRFinder web server  [36]  of ancestral states in easy to resolve ones, at least for some non recombinant bacteria. With such a reconstruction, it may be possible to deeply investigate the evolution of genomes over time, and possibly to predict their future modifications.
Samples were then deposited on a MiSeq flowcell 15 M and sequenced using the MiSeq Illumina ® sequencer at the Genome Transcriptome facility of the University of Bordeaux, generating paired-end reads of 2 × 250 bp. Raw data have been deposited in the ENA sequence read archive (ENA accession number PRJEB23107). 16S bioinformatic sequence analysis. Raw reads were quality filtered using the NGS QC toolkit (version 2. 3. 3)  24 and kept if both of the paired reads passed the filter criteria. The cutoff for quality score was >20 Q30 and >70% of the total read length should have high-quality bases. Possible human contamination was filtered out by mapping the remaining high quality reads against the human genome (Homo sapiens alternate assembly HuRef GCF_000002125. 1) using BWA (version 0. 7. 12)  25  with default parameters.
Possible chimeric sequences were further identified and eliminated using DECIPHER (version 2. 14. 1)  26. Remaining reads were aligned with BWA against the 16S sequences contained in the GreenGenes database (v13. 5)  27 keeping all the hits (Supplemental Table  2 ). Two complementary methods for analyzing microbiota composition were used: operational taxonomic units (OTU) and taxonomic assignment with tango. First remaining reads after filtering were classified using USEARCH_global method of VSEARCH v2. 3. 0 28 against 16S OTUs from GreenGenes database with a 97% identity threshold. We have further analysed microbiota richness of our samples by building rarefaction curves based on the OTU computation with the R package vegan  29. Microbiota alpha diversity as expressed by Shannon and Simpson indexes was computed from the OTU occurrence matrix.
To calculate phylogenetic beta diversity, we used a weighted UniFrac metric  30  implemented by R package phyloseq. 31 by inputting OTU table (Supplemental Table  3 ) and GreenGenes OTU tree. Then, to robustly deal with the reads mapped to multiple taxa, we used Tango for taxonomic assignment with q-value parameter set to 0. 5  32. This approach allows using ambiguous reads by assigning them to higher taxonomic ranks. Consequently, the number of reads assigned to the root was equal to the total number of high quality reads of the sample. Number of reads assigned at every taxonomic node was normalized by the total number of reads in each sample. Normalized counts of all samples were combined in a global occurrence matrix (Supplemental Table  4 ).
Similarly, at M3 many taxonomic levels appear to be differentially present when R and NR were compared (Supplementary Table  6 ). Again, after multiple test correction these results became not significant. LEfSe analysis confirmed the results of the Wilcoxon test and indicated genus Dialister as the most strongly correlated with the responding patients at M3 in our population. Non-responders were characterized by the presence of genus Salmonella (Fig. 4B ). Prediction of bacterial function. Additionally, we used PICRUSt  33  to infer functions performed by microbiota using KEGG pathways database. This computational analysis allows to predict the metagenome functional content, matching genes of species found in our samples with a database that links gene sequences to metabolic functions. We found that at M0, microbiota of half of NR patients presented higher proportion of bacteria that have predicted genes involved in synthesis of lipopolysaccharides, ubiquinone and phenylpropanoids (Fig.
Acknowledgements We thank  ACRONIM FHU ,  Bordeaux Bioinformatics Center and Genome Transcriptome Facility of Bordeaux. This study was funded by  Association Aquitaine pour la Recherche Clinique en Rhumatologie. KH was supported by  Agence Nationale de la Recherche  ( ANR-10-IDEX-03-02 ) awarded to  Maureen O'Malley.
Background: MUC4 is a membrane-bound mucin that promotes carcinogenetic progression and is often proposed as a promising biomarker for various carcinomas. In this manuscript, we analyzed large scale genomic datasets in order to evaluate MUC4 expression, identify genes that are correlated with MUC4 and propose new signatures as a prognostic marker of epithelial cancers. Methods: Using cBioportal or SurvExpress tools, we studied MUC4 expression in large-scale genomic public datasets of human cancer (the cancer genome atlas, TCGA) and cancer cell line encyclopedia (CCLE).
Background The cancer genome atlas (TCGA) was developed by National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI) in order to provide comprehensive mapping of the key genomic changes that occur during carcinogenesis. Datasets of more than 11,000 patients of 33 different types of tumors are publically available. In parallel, cancer cell line encyclopedia (CCLE), a large-scale genomic dataset of human cancer cell lines, was generated by the Broad Institute and Novartis in order to reflect the genomic diversity of human cancers and provide complete preclinical datasets for mutation, copy number variation and mRNA expression studies  [1]. In order to analyse this kind of large scale datasets, several useful online tools have been created. cBioportal is an open-access database analysis tool developed at the Memorial Sloan-Kettering Cancer Centre (MSKCC) to analyze large-scale cancer genomics data sets  [2, 3].
SurvExpress is another online tool for biomarker validation using 225 datasets available and therefore provide key information linking gene expression and the impact on cancer outcome  [4]. Mucins are large high molecular weight glycoproteins that are classified in two sub groups: (i) the secreted mucins that are responsible of rheologic properties of mucus and (ii) the membrane-bound mucins that include MUC4, MUC16 and MUC20  [5, 6]. MUC4 was first discovered in our laboratory 25 years ago from a tracheobronchial cDNA library  [7]. MUC4 is characterized by a long hyper-glycosylated extracellular domain, Epidermal Growth Factor (EGF)-like domains, a hydrophobic transmembrane domain, and a short cytoplasmic tail. MUC4 also contains NIDO, AMOP and vWF-D domains  [8]. A direct interaction between MUC4 and its membrane partner, the oncogenic receptor ErbB2, alters downstream signaling pathways  [9].
Expression analysis from public datasets MUC4 z-score expressions were extracted from databases available at cBioPortal for Cancer Genomics  [2, 3]. This portal stores expression data and clinical attributes. The z-score for MUC4 mRNA expression is determined for each sample by comparing mRNA expression to the distribution in a reference population harboring typical expression for the gene. The query "MUC4" was realized in CCLE (881 samples, Broad Institute, Novartis Institutes for Biomedical Research)  [1]  and in all TCGA datasets available  (13, 489  human samples, TCGA Research Network (http://cance rgeno me. nih. gov/)). The mRNA expression from selected data was plotted in relation to the clinical attribute (tumor type and histology) in each sample. MUC4 expression was analyzed in normal tissues by using the Genome Tissue Expression (GTEX) tool  [19, 20].
DAVID6. 8 identification and gene ontology of genes correlated with MUC4 We established a list of 187 genes that are correlated with MUC4 expression in CCLE dataset out of 16208 genes analyzed with cBioportal tool on co-expression tab. These genes harbor a correlation with both Pearson's and Spearman's higher than 0. 3 or lower than -0. 3. Functional annotation and ontology clustering of the complete list of genes were performed using David Functional Annotation Tool (https ://david. ncifc rf. gov/) and Homo sapiens background  [21, 22]. Enrichment scores of ontology clusters are provided by the online tool. Interaction of proteins correlated with MUC4 was determined using String 10 tool (https ://strin g-db. org/) [23]. Edges represent protein-protein associations such as known interactions (from curated databases or experimentally determined), predicted interactions (from gene neighborhood, gene fusion or co-occurrence), text-mining, co-expression or protein homology.
Gene Expression Omnibus microarray GSE28735 and GSE16515 pancreatic cancer microarrays were analysed from the NCBI Gene Expression Omnibus (GEO) database (http://www. ncbi. nml. nih. gov/ geo/). GSE28735 is a dataset containing 45 normal pancreas (adjacent non tumoral, ANT) and 45 tumor (T) tissues from pancreatic ductal adenocarcinoma (PDAC) cases. GSE16515 contains 52 samples (16 had both tumor and normal expression data, and 20 only had tumor data. Data were analysed using GEO2R software. The dataset GSE28735 used Affymetrix GeneChip Human Gene 1. 0 ST array. The dataset GSE16515 used the Affymetrix Human Genome U133 Plus 2. 0 Array. GSE13507 contains 165 bladder cancer and 58 ANT samples. GSE30219 contains 14 normal lung, 85 adenocarcinomas and 61 squamous cancer samples. GSE40967 contains 566 colorectal cancers and 19 normal mucosae.
MUC4 expression analysis in databases MUC4 expression was analyzed from databases available at cBioPortal for Cancer Genomics  [2, 3]. We queried for MUC4 mRNA expression in the 881 samples from CCLE  [1]  (Fig. 1 ). The oncoprint showed that MUC4 was altered in 195 samples out of 881 (22%). 188 were amplification (n = 120) or mRNA upregulation (n = 88) (Additional file 1: Figure  S1 ). Results were sorted depending on the tumor type. We mainly observed an important z-score expression of MUC4 in carcinoma samples (n = 538 samples, p = 0. 001) (Fig. 2a ). MUC4 Expression scores were subsequently sorted depending on the organ (Fig. 2b ). As expected, pancreatic cancer cell lines harbor the highest MUC4 expression (n = 35, z-score = 2. 166, p = 0. 0006 against theoretical control median = 0).
MUC4 co-regulated genes Using the co-expression tool on expression data extracted from the 881 samples of CCLE  [1] , we obtained Fig. 1  Strategy of analysis of genes correlated with MUC4 expression in Cancer Cell Line Encyclopedia. a Flowchart of MUC4 analysis. MUC4 mRNA expression z-scores were extracted from Cancer Cell Line Encyclopedia using cBioportal. The list of gene correlated with MUC4 expression was determined by using the co-expression tool. Genes presenting a Pearson's correlation higher than 0. 3 or lower than -0. 3 were selected. Spearman analysis was performed subsequently. Gene ontology annotation and clustering were performed using DAVID 6. a list of genes that are co-expressed with MUC4. Genes that harbor a correlation with both Pearson's and Spearman's higher than 0. 3 or lower than -0.