dataesr/scientific-paragraphs-categorization huggingface dataset:
The comprehensive dataset we reported in this study also included 3,623 genes that upon deletion did not show a significant BMD phenotype (S2 Table ). A preliminary assessment of these genes showed that the false negative rate was about 2%, further supporting the robustness of our data (S2 Table ). Within the pool of BMD negative genes, two genes illustrated principle restrictions in our approach. First, we did not detect a change in BMD upon deletion of Daam2. Yet, using GWAS for both BMD and bone fracture as well as mouse and in vitro studies, Morris et al. identified Daam2 as a potential genetic factor in osteoporosis  [10]. In fact, they investigated the same Daam2 KO mouse as we did, however they relied on the OBCD pipeline for skeletal analyses. Consistent with our findings, no BMD alteration was seen, but bones of KO mice showed reduced mechanical stability.
This example highlights the previously discussed limitation of sole BMD measures for the detection of osteoporosis genes  [39]. Second, based on the IMPC data release 6. 0, which we explored in this study, we observed no BMD phenotype after deletion of Slc20A2. However, the ongoing IMPC program constantly adds data on new mouse KO lines, and thus over time the pool of control mice may expand or changes to the PhenStat statistical package introduced. This can affect data output. In fact, in IMPC data release 11 the gene Slc20A2 has been assigned a BMD phenotype. From a comparison between different IMPC data releases, we estimate that such shifts in BMD phenotype hits may occur in the order of 0. 5% of genes per release. Regardless, we recommend that investigators interested in skeletal studies using IMPC mouse models (a) re-examine the mutant mice and matching wt controls using in vivo DXA and standard laboratory and animal group sizes, and (b) perform in vitro microCT at least including parameters measured under the OBCD pipeline, such as femur bone volume/tissue volume, BMC, and BMD as well as several structural measures derived from cancellous and cortical bone.
The 141 class 2 genes constitute an important stand-alone data set, as they provide experimental in vivo evidence justifying the molecular and cellular investigation of a comparably large number of gene products previously not known to affect BMD. In this study, however, we sought to address further questions. We asked which of the 200 BMD genes caused both BMD alterations and skeletal abnormalities. A set of 19 class 2 genes was found (Fig  4C ). To assess a potentially compromised skeletal stability in mice lacking one of these genes, we initiated and executed the import of data from the OBCD program into IMPC, a step necessary for proper data analysis. Despite the currently limited overlap (around 10%) of genes in the IMPC and OBCD programs, Hbs1l could be analyzed and was found to yield an altered, reduced skeletal stability after ablation.
Another question we asked was whether evidence existed for functions of BMD genes in osteoclasts or osteoblasts. Our experimental approach was to use available gene expression data for assignment of the 200 BMD genes to osteoclasts and osteoblasts. For high confidence, curated data acquired during bone cell differentiation was used. We utilized this data for two purposes. First, we simply probed for cellular expression (Fig  5 ). Here, we noted that several class 1 osteoclast genes, like Ckb, Ndrg1 and Sat1b, were also detected in osteoblasts (Fig  5 ). Because of a lack of verification of their biological function in osteoblasts, we did not further consider these genes. Second, gene expression data was included in the more sophisticated calculations of protein interaction probabilities (Fig  6 ).
Mouse phenotyping Data was derived from postnatal mice that were phenotyped under the adult and embryonic phenotype pipeline (https://www. mousephenotype. org/impress). Briefly, this pipeline conducted 16 tests, each with a set of phenotyping procedures. Phenotyping recorded appearance, behavior, or organ function across a spectrum of organs and tissues. All IMPC phenotyping data is shared with the public through the IMPC website. Experimental procedures are detailed also under IMPRESS (https://www. mousephenotype. org/impress). Although we mined the entire phenotyping data when investigating co-phenotypes, our study primarily investigated the X-ray phenotyping data obtained in postnatal week 14. The skeletal X-ray exams encompass radiographs and DXA-based measures of BA and BMC. Radiography and DXA are mandatory and voluntary IMPC measures, respectively.
Specifically, for the data reported in this study, they were carried out using the UltraFocus DXA (Faxitron Bioptics LLC), the Lunar Piximus II (GE Medical Systems), or the pDexa sabre (Norland Stratec) instruments. For DXA, a minimum of 7 male and 7 female mutants were phenotyped, giving a minimum of 14 mice per line, while for radiography phenotyping was performed on a minimum of 4 male and 4 female mutants. In all experiments, mutants were matched with wild-type (wt) animals, with matching genetic background. Mutant and wt mice were phenotyped in the same manner based on the IMPReSS protocol, and detailed experiment characteristics captured in the procedure metadata. BMD was calculated from the measures of BA and BMC. For data analysis, mutants were matched with wt animals from the same center that used the same metadata.
IMPC data analysis The IMPC uses a bespoke statistical package, implemented in R, called PhenStat, which was developed for identification of abnormal phenotypes from high-throughput pipelines  [54]. The bone mineral data was analyzed by PhenStat using linear, mixed models, which take factors such as sex and body weight into account. S1 Table details the number of wt control mice analyzed for each of the 200 BMD genes. PhenStat identifies phenotype hits by sex to enable identification of sex-specific phenotypes which have been previously shown to be relevant in a large proportion of IMPC lines across many procedures  [17]. Phenotype hits were identified by searching the IMPC database (version 6. 0, released November 10 th , 2017, data access: https://www. mousephenotype. org/data/previous-releases/6.
Mapping genes to gene ontology terms GO terms for the genes of interest were extracted from the MGI database (www. informatics. jax. org), which contains information on genes and their annotated phenotypes. In order to avoid circular findings, as the MGI database includes IMPC phenotype data, IMPC entries were removed using MGI's internal filters. We analyzed terms of the "molecular function" and "biological processes" domains as they provide information on the biological activity of genes of interest. To identify genes involved in skeletal function based on gene ontology term annotation, we selected all gene ontologies that included the terms "bone", "skeletal", "ossification", "osteoclast" and "osteoblast". MGI's Gene eXpression Database www. informatics. jax. org/expression. shtml) was also searched to determine the known expression profiles of the BMD phenotype genes.
Genome-wide association studies (GWAS) Orthologous/paralogous genes of mouse metabolism genes mapping to the human genome were used for analysis. GWAS analyses on human BMD were based on data acquired in the UK Biobank (www. ukbiobank. ac. uk)  [9, 10, 55]  and GEFOS (www. gefos. org) [8]  studies. We used the Ensembl BioMart online tool to adjust all genomic data to the latest Assembly (GRCh38/hg38). The UCSC genome browser was used to analyze genes in detail. We searched for SNPs in a ±2 kb region in the SNiPA database. For each SNP occurring in or around genes, we evaluated the extent of sharing based on the three bone density phenotypes recorded by the GEFOS consortium, and used metadata without any link to individual IDs or data only. We used cross phenotype meta-analysis (CPMA), which detects association of a SNP to multiple, but not necessarily all, phenotypes  [56].
Gene expression analysis The NCBI Gene Expression Omnibus was used to analyze gene expression in osteoblasts and osteoclasts. To assure robustness of the expression data, the analysis was restricted to the curated data available under https://www. ncbi. nlm. nih. gov/sites/GDSbrowser/, and only data on mouse-derived cells was considered. To identify genes expressed in osteoclasts or osteoblasts, we assessed expression data acquired during differentiation of these cells. Genes significantly (p<0. 05) increased during differentiation were considered. For osteoclastogenesis, data sets GSE43811 and GSE57468 (series GDS5398 and GDS5422, respectively) were used. GSE43811 compared osteoclast differentiation at 48 hrs between osteoclast precursor cells (GSM 1071626, 1071627, 1071628) and control cells (GSM 1071629, 1071630, 1071631).
In Additionally, we selected GSE57468, which compared differentiation of primary osteoclast precursors between days 0 (GSM1383260, 1383262) and 3 (GSM1383261, 1383263). For osteoblastogenesis, the data set GSE2332 (series GDS1631) was selected because it permitted for the study of highly enriched primary pre-osteoblasts. Differentiation days 7 (GSM43188, 43202, 43209) and 17 (GSM43195, 43216, 43225) were included. All data was analyzed using the dedicated NCBI Geo2R suite (https://www. ncbi. nlm. nih. gov/geo/geo2r/).
To experimentally validate the in silico model, we used gene expression data together with the R package GeneNet 1. 2. 13 (http://www. strimmerlab. org/software/genenet/). Based on gene expression data this package enabled the statistical assessment of the reliability of a given network (nodes and edges) and to compute q-values and posterior probabilities (= 1-local FDR) for each potential edge connecting in a pairwise manner the nodes of the network (S9 Table ). We captured the bone resorption-formation sequence characteristic for bone turnover with the following expression data sets (1) GSE43811 (GSM 1071626, 1071627, 1071628 and GSM 1071629, 1071630, 1071631), (2) GSE57468 (GSM1383261, 1383263 and GSM1383260, 1383262), (3) GSE2332 (GSM 43191, 43212, 43219 and GSM 43184, 43205, 431198), (4) GSE37676 (GSM 25432, 25433, 25434 and GSM 25429, 25430, 25431), (  5 ) GSE2332 (GSM43195, 43216, 43225 and GSM43188, 43202, 43209).
Promoter analysis To identify TFs that may regulate genes implicated in bone phenotypes, we surveyed for the bone transcriptional targets, which contain common TF binding sites in their cis-regulatory control elements. Using i-CisTarget and two datasets as input; genes with increased bone mineral density and genes with decreased bone mineral density, we detected motifs within 10Kb around the transcription start site. Every gene was scanned for motifs using a library of approx. 10k PWMs and 1120 Chip-seq tracks, each motif is scored with an algorithm called Clusterbuster and results in a subset of genes that are predicted as direct targets.
(C) Analysis of the IMPC phenotyping data showed that 19 of the 141 class 2 genes were also associated with skeletal abnormalities in addition to changes in BMD. Vertebrae abnormalities were the most frequent skeletal abnormalities associated with the deletion of one of the 19 genes. (D) OBCD data showed a difference in vertebra stiffness for Hbs1l compared to wt. (E) OBCD data detected a difference in vertebra yield load for Hbs1l compared to wt. (F) OBCD data measured a difference in vertebral height for Hbs1l compared to wt. (G) Dorsal images were taken from the IMPC x-ray screen. Representative female Hbs1l-deficient mice and wild type (wt) control mice are shown. In the upper panels, the position of the pubic body was denoted by a black line. In Hbs1l -/-mice, a distal shift of the pubic body intersection relative to the S1 sacral vertebra was seen (p<0.
Together, these proteins (red elliptic nodes) formed the PPI networks for osteoclastic resorption and osteoblastic formation during bone turnover. Connecting proteins distinct from the bone turnover genes are represented as pink elliptic nodes, while white nodes marked genes with an undefined role in bone. Black lines represent PPIs as determined by STRING or REACTOME analysis. Arrows indicated REACTOME-curated data. For validation, we used experimental gene expression data from osteoclasts and osteoblasts in combination with statistical testing. The resulting gaussian probability of interactions, i. e. FDR-adjusted p-values (q-values) were superimposed on the STRING PPIs as follows: large line width, q<0. 0005; medium line width, q<0. 05; small line width, q>0. 05 or no q data. (B) Based on their cellular expression, the three class 2 low BMD candidate genes Rab31p, Ncald, and Arl4d were probed in the osteoclastic and osteoblastic compartments of bone turnover.
Acknowledgments Hands-on mouse IMPC phenotyping has been carried out by a large number of laboratory staff with superb experimental skills and unsurpassed dedication. The authors profoundly apologize for not be able to name all these individuals due to space limitations. MHA, VGD, HF thank all past and present GMC technicians and animal caretakers for their outstanding work. JH would like to thank the  French National Centre for Scientific Research (CNRS) , the  French National Institute of Health and Medical Research (INSERM) , the  University of Strasbourg , and all members of the  Mouse Clinical institute (MCI-ICS)  for their help and helpful discussion during the project. REB, SAM, JKW, KLS thank the members of the  Jackson Laboratory Research Animal Facility and Laboratory Informatics Team  for their outstanding support.
opvavpi. cz),  Biotechnology and Biomedicine Centre of the CAS and Charles University in Vestec (BIOCEV)   CZ. 1. 05/1. 1. 00/02. 0109and  Higher Quality and Capacity for Transgenic Models   CZ. 1. 05/2. 1. 00/19. 0395  by  MEYS  and  ERDF (RS) ;  MEYS  and  ESIF -European Structural Investment Fund  (https:// opvvv. msmt. cz)Upgrade of the  Czech Centre for Phenogenomics : developing towards translation research by  MEYS  and  ESIF CZ. 02. 1. 01/0. 0/0. 0/16_013/0001789  (RS);  Agence Nationale de la Recherche  ( ANR , https://anr. fr)grants  ANR-10-IDEX-0002-02 ,  ANR-10-LABX-0030-INRT ,  ANR-10-INBS-07 PHENOMIN (YH ); the  Wellcome Trust  (https://wellcome. ac. uk)Strategic Award number  101123  (GRW, JHDB);  Wellcome Trust   Joint Investigator Award number   110141  (GRW, JHDB); Project Grant number  094134  (GRW, JHDB);  European Union   Horizon 2020 Program  (https://ec.
Gut Microbiota Composition Analysis Gut microbiota were analyzed in two cohorts (15 WT and 16 CAMLCK Tg ) from 8 different WT dams. At sacrifice, colonic contents were stored at -80 C. DNA was extracted using the ZR fecal DNA MiniPrep kit (Zymo Research, Irvine, CA) and adjusted to 1 ng/mL. 6] [7]  A universal bacterial primer set was included as the reference gene. quantitative reversetranscription PCR was performed in duplicate on a ViiA7 (Applied Biosystems, Foster City, CA). Fluorescence data were imported into LinRegPCR to perform baseline corrections, calculate mean PCR efficiency per amplicon group. and calculate initial quantities. Among the 24 targeted amplicon groups, 6 were not detected in any fecal samples and were removed from the analysis (Bacteroides vulgatus, Alistipes spp, Parabacteroidetes distasonis, Roseburia spp, Escherichia coli, and Akkermansia muciniphila).
Funding This work was supported by an institutional grant from  INRA  and by  National Institutes of Health  grants  R01DK61931  and  R01DK68271  (to  Jerrold R. Turner ). Muriel Darnaudéry was supported by  Bordeaux University, the FFAS (Fond Français Alimentation Santé) , and the  ANR (Agence Nationale de la Recherche). Marion Rincel  was supported by the  French ministry of research and education and Labex Brain. Julie Thomas  was a recipient of a fellowship from the  French Society of Paediatric Research. Orsolya Inczefi  was a recipient of a fellowship from the  Nutrition, Chemical Food Safety and Consumer Behaviour Division of INRA.
A challenge in this field is choosing methods, software and databases fitting the data and the questions of the study. In this review, we provide an updated guideline for the analyses of WGS and marker gene sequencing data. We also discuss recent comparisons of the available methods, software and databases to perform those metagenomic analyses. The quality filtering and preprocessing of the reads include trimming the sequencing adapters, discarding short and lowquality reads, removing low-quality extremes, or removing reads with 'N' characters based on quality. Different software has been developed to achieve these tasks, such as the FASTX-Toolkit (http:// hannonlab. cshl. edu/ fastx_ toolkit/), PRINSEQ  [25]  and Trimmomatic  [26]. However, the determination of the required quality filtering set that needs to be applied to a specific data set is challenging.
More recent tools are the R package findGSE  [39]  or the web application GenomeScope  [40]. GenomeScope is a user-friendly tool allowing the user to estimate the necessary parameters to choose the right k-mer easily. The new version of Geno-meScope 2 is also applicable to polyploid genomes. Finally, some assemblers, such as IDBA  [41] , IDBA-UD  [42]  and subsequently SPAdes, SOAPdenovo2, or the recently developed ScalaDBG  [43] , have implemented innovative ways to deal with the choice of the best k-mer by using a multi-k-mer approach. Genome assembly of one organism is already a complicated step. However, it is even more challenging in metagenomics, since this requires the simultaneous assembly of many genomes contained in the analysed sample. Most of the assemblers used are previously developed single-genome assemblers that have been adapted to metagenomic samples: MetaVelvet, MetaVelvetSL, MEGAHIT, metaSPAdes, Ray Meta, IDBA-UD, or SOAPdenovo2  [36, 37, 42, [44] [45] [46].
MEGAHIT requires less computational resources and is more efficient than metaSPAdes, but it is biased towards lowcoverage genomes  [49, 50, 55]. Therefore, when the degree of captured diversity is more important than contig lengths, or computational resources become limited, MEGAHIT  [54]  is the most attractive option  [49]. In contrast, if the main aim of the study is to represent the largest fraction of the diversity of the analysed sample accurately, then metaSPAdes should be the assembler of choice. Whereas for low-complexity databases, such as samples with low species richness, MaSuRCA  [46]  is the best option  [51]. According to Critical Assessment of Metagenome Interpretation (CAMI), a community-driven initiative for the critical assessment of metagenome interpretation  [56] , MEGAHIT, Meraga (MEGAHIT combined with Meraculous  [57] ) and Minia  [47]  recovered the most substantial fraction of all genomes when compared to Ray Meta  [36] , Velvet  [58]  and OperaMS Scaffolder  [56].
Other supervised methods are composition-based, and these rely on characteristics that can be extracted directly from the nucleotide sequences (e. g. oligonucleotide frequencies, GC content). Examples are TACOA  [67] , PhyloPythiaS+  [68] , or Phymm  [63]. The third subcategory of supervised methods (e. g. PhymmBL, a combination of Phymm and blast  [63] ) is based on a hybrid approach that is alignment-and composition-based, but their use has gradually declined. Some pipelines, such as IMG/M v. 5. 0  [69] , MG-RAST v. 4  [70]  and MEGAN6  [71] , integrate similarity-based binning algorithms. One of the drawbacks of supervised methods is the limited number of available sequenced genomes in the current databases and the long computing time necessary for aligning contigs to a reference. Unsupervised methods do not have these limitations, since they do not rely on a reference genome.
5 and MaxBin 2. 0 appeared to recover the largest number of genomes with high purity and completeness. However, there is no guarantee that the best-performing binners on CAMI-analysed datasets are the most appropriate for another dataset. In addition, new tools integrating new and different clustering approaches for guiding binning continue to appear. Some examples are the recently developed BMC3  [90] , MetaBMF  [91]  and Solidbin  [92]. BMC3C is an unsupervised ensemble-clustering method based on codon usage, composition and coverage information. MetaBMF is a fast reference-free binning method that can be used for large-scale metagenomic applications and allows the binning of DNA fragments accurately at both species and strain level. Finally, SolidBin is a semi-supervised method that uses sequence feature similarity and/or additional biological information to construct the bins.
Identification of coding regions: annotation After the classification of sequences in taxonomic bins, the next step is to identify and annotate genes and regulatory elements. The MetaGene  [102]  gene-finding program was the first one designed to predict genes from fragmented genomic sequences. It uses the GC content of genome fragments to approximate codon frequencies and estimate the original codon usage of the genome, allowing one to predict genes. MetageneAnnotator is an upgraded version of MetaGene adapted for metagenomic data  [103]. It allows the prediction of typical prokaryotic genes, but also atypical genes, such as horizontally transferred and prophage genes, as well as new ribosomal binding sites. Later, the heuristic model integrated in MetageneAnnotator was improved by Zhu and colleagues  [104]  and implemented in the software GeneMark.
Glimmer-MG carries out taxonomic classifications using Phymm  [110]  to find closer reference genomes that are used to train models for gene prediction, and the first annotations are done based on this result and then unsupervised clustering using SCIMM  [76]  is employed to complete the annotations. A gene predictor developed to identify genes directly from both genomes and short reads is FragGeneScan  [111] , a program that uses both sequencing error models and codon usage in a hidden Markov model for gene calling. Several comparative studies have also been undertaken to help in selecting the best calling method. For example, Kelley and colleagues compared Glimmer-MG, MetaGeneAnnotator and MetaGeneMark  [109] , and concluded that although it is computationally more demanding, Glimmer-MG shows the best performance for simulated metagenomes.
g. the JGI annotation pipeline  [114] , which uses GeneMark. hmm, MetaGeneAnnotator, Prodigal and FragGeneScan. If one is interested in the eukaryotic sequences present in metagenomic samples, then gene calling is a more complex problem than in prokaryotes. GeneMark offers specific software for the annotation of eukaryotic genes, GeneMark. hmm-E and GeneMark. hmm-EIS (http:// exon. gatech. edu/ GeneMark/ gmhmme. cgi)  [115, 116]. In parallel, many tools that are specific for gene calling in eukaryotes have been developed, such us AUGUSTUS  [117] , Gnomon  [118] , or SNAP  [119]. Others, such as EuGene  [120] , have been developed for both eukaryotic and prokaryotic genomes. Moreover, annotation pipelines such as MAKER2  [121]  combine multiple annotation tools that run three different gene prediction programs (SNAP, GeneMark-ES and AUGUSTUS).
Functional annotation and taxonomic profiling Functional annotation of WGS metagenomics allows one to answer the question, which functional capacities are encoded in a microbial community? Once genome assembly, binning and gene calling have been done, many tools allow one to carry out functional annotations. The most common way to identify gene function is through similarity searches using classical tools such as blast. However, additional databases of broad scope such as Pfam  [122] , Interpro  [123] , PRIAM  [124] , or Metacyc  [125]  should be used to refine the predicted function. If instead of obtaining a broad functional overview, one is interested in identifying specific functions, specialized databases assembled for identifying e. g. metal detoxification genes, antibiotic resistance genes, or virulence factors can be used.