dataesr/scientific-paragraphs-categorization huggingface dataset:
007 by a permutation test; S5 Table ). The resulting changes seen in our analysis of the microbial communities that vary by tumor stage were similar to those found in previous studies, including one using a Chinese patient cohort  [4, 41]. In both cases, there were significant changes among several taxa within the phylum Bacteroidetes, including Porphyromonadaceae, and Cyclobacteriaceae (Fig  1   ). We applied the model generated from our data to two independent datasets that were analyses of the CRC microbiome and that also reported tumor staging  [10, 42]   The comparisons of interest here are between tumor samples at high stage and tumor samples at low stage. This study utilized patient-matched normal samples, but these are not true normals, as they came from the same cancer patients who themselves have low or high stage cancer.
g. diet, family history, immune profile, etc. ), without which it is possible there may be a confounding variable that explains some of our correlations. Similarly, using whole-exome sequencing does not allow us to include non-coding mutations and larger tumor structural variants and chromosomal abnormalities. Moreover, the study sample was relatively small (n = 88 samples from 44 patients). Nevertheless, the sample size was sufficient to detect significant patterns. As ours is the first study, as far as we know, to present simultaneous exome sequencing of tumors alongside assessment of the microbial communities associated with these tumors, no validation datasets are available. In summary, we present an association between tumor genetic profiles and the proximal microbiome, and identify tumor genes and pathways that correlate with specific microbial taxa.
The criteria for selection were limited to the availability of patient-matched normal and tumor tissue specimens. In all cases, normal tissue was confirmed by the pathologist to be tumor free. The tumor stages were classified using a modified stage grouping based on the TNM scale (collapsing the letter-scales into single numberseg. IIIA, IIIB, and IIIC are reported as stage 3. Additional patient metadata are provided in S4 Table and in the indicated work  [75]. Microbial community data from 16S rRNA gene sequencing of an independent cohort of healthy patients (n = 12) who underwent routine colonoscopies was acquired from a separate, unpublished dataset and used in the comparison of low-stage, high-stage, and normal microbial communities.
Microbiome characterization The microbiome data used in the study was generated previously and is described exhaustively in  [75]. Briefly, microbial DNA was extracted from patient-matched normal and tumor tissue samples using sonication for lysis and the AllPrep nucleic acid extraction kit (Qiagen, Valencia, CA). The V5-V6 regions of the 16S rRNA gene were PCR amplified with the addition of barcodes for multiplexing using the forward and reverse primer sets V5F and V6R from Cai, et al. [76]. The barcoded amplicons were pooled and Illumina adapters were ligated to the reads. A single lane on an Illumina MiSeq instrument was used (250 cycles, paired-end) to generate 16S rRNA gene sequences. The sequencing resulted in approximately 10. 7 million total reads passing quality filtering in total, with a mean value of 121,470 quality reads per sample.
The forward and reverse read pairs were merged using the USEARCH v7 program 'fas-tq_mergepairs', allowing stagger, with no mismatches allowed  [77]. OTUs were picked using the closed-reference picking script in QIIME v1. 7. 0 using the Greengenes database (August 2013 release)  [78] [79] [80]. The similarity threshold was set at 97%, reverse-read matching was enabled, and reference-based chimera calling was disabled.
Exome sequence data generation Genomic DNA samples were quantified using a fluorometric assay, the Quant-iT PicoGreen dsDNA Assay Kit (Life Technologies, Grand Island, NY). Samples were considered passing quality control (QC) if they contained greater than 300 nanograms (ng) of DNA and display an A260:280 ratio above 1. 7. Full workflow details for library preparation are outlined in the Nextera Rapid Capture Enrichment Protocol Guide (Illumina, Inc. , San Diego, CA). In brief, libraries for Illumina next-generation sequencing were generated using Nextera library creation reagents (Illumina, Inc. , San Diego, CA). A total of 50 ng of genomic DNA per sample were used as input for the library preparation. The DNA was tagmented (simultaneously tagged and fragmented) using Nextera transposome based fragmentation and transposition as part of the Nextera Rapid Capture Enrichment kit (Illumina, Inc.
The total mutations group The total mutations group is simply the sum of all the mutations that were found in each tumor. The non-synonymous mutations included all the mutations in the total mutations group that were non-silent. The LoF group only included those mutations that resulted in a premature stop codon, a loss of a stop codon, or a frameshift mutation. Mutations in genes were collapsed to pathways (PID and KEGG) based on pathway membership as defined by the relevant database authors  [31, 33, 34]. Specifically, we relied upon Uniprot annotations to link genes to pathways with the database file, idmapping. dat. gz, available from Uniprot  [88, 89]. This, three column database file is used by the web-based mapping interface for cross-referencing between genes and pathway membership datasets, including KEGG and PID  [88].
In our case, we annotated each gene in our dataset with pathway membership information by first reducing the size of the idmapping. dat file by including only entries for KEGG and PID. For each LoF mutation in our dataset, we found the corresponding gene in the reduced database file with a matching ENSEMBL Gene ID (S1 Table ) and recorded which grouping it was found in (KEGG or PID) and what the name of the defined pathway was (e. g. Canonical Wnt signaling). Many genes had multiple annotations as they are members of more than one of the defined pathways. These annotations were used to identify which tumor samples had mutations in KEGG and PID pathways, defined as having at least one gene harboring a LoF mutation that was a member of the pathway.
0. 4 was used as a test for method robustness to alternative statistical parameters  [43]. Without performing LOO iterations, the same sets of genes, KEGG pathways, and PID pathways that passed mutation prevalence filtering as for the LEfSe-based pipeline were used as metadata input for MaAsLin. Patient age and sex were also included in the model as covariates. The unrarefied OTU table collapsed to the genus level was used as abundance input, with prevalence filters set to only include taxa found at relative abundances of more than 0 in at least 5 tumors and an abundance filter of 0. 001, relative abundance. Other parameters in MaAsLin were defaults. The p-values generated by MaAslin were used to identify sets of taxa that had altered abundances in tumors as a function of mutational status.
These taxa were used to generate risk indices for each of the samples, as above, but in a non-iterative fashion as the LOO procedure was not used. The RIs for the samples harboring specific LoF mutations were tested against those without a detected LoF mutation, as above. The p-values generated were corrected for multiple testing by FDR. These values are reported in S10 Table. Receiving Operating Characteristic (ROC) curves were plotted and the area under the curve (AUC) values computed on a dataset containing 10 sets of predictions and corresponding labels obtained from 10-fold cross-validation using ROCR package in R  [91]. A risk index threshold was also obtained that best predicts the membership in a stage group or the presence or absence of LoF mutation with a leave-one-out cross-validation on the risk index.
This work is supported in part by funds from the  University of Minnesota College of Biological Sciences (RB) , The  Randy Shaver Cancer Research and Community Fund (RB) ,  Institutional Research Grant  # 124166-IRG-58-001-55-IRG53  from the  American Cancer Society (RB) , a  Research Fellowship  from  The Alfred P. Sloan Foundation  (RB), a  Translational Research Development Grant  from the  University of Minnesota Clinical and Translational Science Institute (MBB) , the  Robert Tournut Grant  from the  French Society of Gastroenterology (EM) , the  Mezin-Koats Colorectal Cancer Research Fund  Grant (TKS), a  MN Colorectal Cancer Research Foundation  Grant (TKS),  NIH  Grant  R01AI121383 (DK ), the  Minnesota Partnership for Biotechnology and Genomics Grant  # 13. 28 (DK ),  NCI/NIH  Project grant ( P30-CA77598 ), and a grant from the  National Center for Advancing Translational Sciences  of the  NIH  ( UL1TR000114 ).
Results Construction of RNA:Ψ-synthases and H/ACA RNP components gene deletion mutants. We performed sequence analysis of the T. kodakarensis genome  56  in the aim of identifying ORFs for the PUS Cbf5 and Pus10, and for protein components L7Ae, Nop10, and Gar1 of the H/ACA RNPs. The protein-protein BLAST program using the amino acid sequences of P. abyssi PUS and H/ACA sRNP protein components as queries identified a single ortholog of Cbf5 (TK1509), Pus10 (TK0903), L7Ae (TK1311), Nop10 (TK1101), and Gar1 (TK2286) in T. kodakarensis genome (Fig. 1A  and Supplementary data, Tables  S1  and S2 ). To analyze how those proteins contribute to pseudouridylation of RNAs in vivo, we disrupted their genes using the gene targeting method (Supplementary data, Fig. S1 )  57, 58. The gene disruption vectors harboring the selectable marker pyrF (TK2276), which encodes orotidine-5′-monophosphate decarboxylase, and 1 kb of the 5′ and 3′ flanking regions of each target gene were constructed as described in Methods.
More detailed information on the deletions is given in Supplementary data (% of covered positions by gene and sequencing reads aligned in the regions of expected deletions in Fig. S2 ). Altogether these data unambiguously confirm that only one ORF in each T. kodakarensis mutant was affected by deletion and no undesired off-target integration/popping out of pyrF gene occurred. Since in archaeal genomes, many genes are organized in clusters or in operons  60  , it was possible that the individual ORF disruption affected the structure of the operon or of the cluster. As a consequence, the transcription of downstream ORFs or genes may also be affected. To check for this possibility, we isolated total RNAs from the null mutant cells and performed RT-PCR to detect mRNA produced from the downstream gene.
Sequence analysis of T. kodakarensis was performed using the NCBI genomic database (http://www. ncbi. nlm. nih. gov) and the BAGET sequence exploration tool (http://archaea. u-psud. fr/bin/baget. dll/EXEC). To ascertain the identity of the identified ORFs, sequences of T. kodakarensis proteins were compared with already known and biochemically characterized PUS and H/ACA sRNP proteins from P. abyssi, P. furiosus, or Haloferax volcanii. Only Pus10/Cbf5 showed slight similarity, and Nop10 and L7Ae shared limited similarity with IF2-gamma and L30e, respectively (see Supplementary data, Table  S1 ). Inspection of the entire genome of T. kodakarensis revealed no orthologs of any other potential proteins (Supplementary data, Table  S2 ).
The radioactivity of the 3′-monophosphate nucleotides was quantified with the Phosphorimager and quantified using the ImageQuant software Version 5. 2 (Molecular Dynamics). The quantities of Ψs formed were determined by taking into account the total number of U nucleotides in the substrate RNA. The obtained values in moles of Ψ per mole (mol. mol -  ) of substrate RNA were analyzed with Prism software Version 5. 04 (GraphPad).
Acknowledgements We thank  Drs Haruyuki Atomi  and  Rie Matsumi  at  Kyoto University  for their kind gift of KU216 strain and vector, and for technical advices on the gene-targeting method. Dr. Virginie Marchand  ( UMS2008   IBSLor ,  NGS core facility ) for supervising library preparation and performing Illumina sequencing. This work was supported by the  Agence Nationale de la Recherche  ( ANR-08-BLAN-0036-01 ), the  French Ministère de l'Enseignement.
Cysticercosis is caused by the larvae of the cestode Taenia solium. Few data are available on the prevalence of this disease in pigs and humans in West African countries. The aim of this study was to provide an overview of existing data concerning the spread of this parasitosis in the countries of the Economic Community of West African States (ECOWAS) on the basis of the literature published over the last five decades. Systematic searches for publications were carried out on PubMed and Google Scholar, as well as in certain regional and local journals. From a total of 501 articles initially retrieved concerning T. solium cysticercosis in West African countries, only 120 articles were relevant for this review and therefore finally retained. For pigs, only eight out of sixteen countries of the region have reported porcine cysticercosis.
Methods This review covered all the 16 West African countries. The data on cysticercosis in West Africa were collected from: (1) peer-reviewed articles on T. solium cysticercosis in Africa, (2) grey literature consisting of written materials such as theses and dissertations obtained from Google Scholar, and (3) published reports regarding T. solium posted by United Nationsrelated agencies such as WHO, FAO, or OIE, and the Centers for Disease Control and Prevention (CDC). The following search strategy was applied: (1) in PubMed, using the Boolean operator AND, the terms ''cysticercosis'', ''Taenia solium'' and ''West Africa'' (and each individual country name); (2) in Google Scholar (ahttp://scholar. google. com), the terms ''cysticercosis Taenia solium'' were screened in the full core texts and not only in titles and keywords.
Thereafter, several Google searches were performed in respect of the various countries of the region and key words and expressions such as ''porcine cysticercosis'', ''human cysticercosis'', ''neurocysticercosis'', ''Taenia solium'', ''T. solium'', and ''cysticercosis'' were searched in order to retrieve scientific publications on cysticercosis relating to each country. Additionally, some other internet websites, such as The Journal of Infection in Developing Countries (https://www. jidc. org), Société de Pathologie Exotique (http://www. pathexo. fr) and African Journals Online (https://www. ajol. info) were consulted for the purpose of gathering information and data which are published in local journals only.
Data extraction Based on the online researches in databases, a total of 501 articles were identified. Redundant articles were removed (Fig. 1 ). All abstracts were collected, and titles and abstracts were analyzed manually. Accordingly, 146 articles were retained and full texts were obtained. Subsequently, certain articles were excluded based on the following criteria: (1) stud-ies carried out to assess laboratory test performance (unidentified samples); (2) studies focusing on taeniasis only and/or on T. saginata infection; (3) studies performed outside of the  study area; and (4) reviews without original data. Case reports were retained. Overall, 120 articles were included (Table  1 ). These articles, which cover only 11 out of the 16 countries of the region, concern the following topics: porcine cysticerco-sis  (39) , human cysticercosis  (36) , and neurocysticercosis  (34).
Some data were obtained through epidemiological studies using Ag-ELISA, highlighting infection between 5% (The Gambia) and 40% (Burkina Faso)  [53, 108]. In Nigeria, some chronological data are also available over the period between 1980 and 2014  [9]. During this time, prevalence increased from 1. 76% in 1980 to 20. 5% in 1995, fell to 3. 2% in 2010 and 2012, but increased again to 9. 3% in 2014. No data are available for 1991-1994, 1996 and 2009  [46]. Records were obtained through postmortem examinations without a clear description of the protocol, with the consequence that the relevance of these fluctuations may be questioned. In Burkina Faso, the first records (years 2000), based on meat inspection, showed a very low prevalence rate of 0. 57%; thereafter, no data were provided until 2011, when, based on the Ag-ELISA diagnostic technique, seroprevalence reached 39.
Here we chose to develop an innovative integration strategy combining state-of-the-art statistical and computational methods using proteomic and phenotypic data, with the incorporation of transcriptomic information, to observe and identify some important proteins with possible transcriptional regulation. One of the advantages of our strategy is to avoid the scale issues that may arise when different types of data are combined. Networks are increasingly used as tools for analysis and for the visualization of data in biology and genetics  [10] [11] [12] [13]. A complex network architecture into clusters of functionally related genes/proteins can be explored and genes/proteins with high connectivity (called hubs) can be identified. In our study, integrated network analysis was first performed to explore relationships between co-expression network models, 6 built from a proteomic dataset, and phenotypes of interest that would enable identification of important molecular signatures underlying late fetal muscle development.
Correlations with a transcriptomic dataset were then investigated to complete and combine different layers of expression. Here we report the results of multi-omics analyses of muscle during the last three weeks of gestation in pigs. The objective was to identify proteins, with possible transcriptional regulation, and related biological mechanisms, specially those involved in differences in the muscle maturation process in late gestation between two extreme pig breeds: Large White and Meishan, and reciprocal crosses. The Large White (LW) breed is a highly selected breed for lean growth with a high rate of mortality at birth, whereas the Chinese Meishan (MS) is a fatter and more robust breed that produces piglets with an extremely low mortality rate  [14, 15]. Physiological muscle maturity, which occurs at the end of gestation, has already been shown to improve early survival after birth  [16] [17] [18].
Study design Details regarding animal resources and experimental designs can be found in  [18]. Briefly, longissimus skeletal muscle mRNA, protein and phenotypic data were acquired at two developmental time points (90 and 110 days of gestation (dg)) from four fetal genotypes. These genotypes consisted in two extreme breeds concerning mortality at birth (Meishan (MS) and Large White (LW)) and two crosses (MSLW from LW sows and LWMS from MS sows). MS and LW sows were inseminated with mixed semen so that each litter was composed of purebred and crossbred fetuses. The two developmental time points correspond to the end of gestation when intense maturation of muscle fibers occurs from 90 dg to birth (around 114 dg)  [16, 17, 22]. Therefore, eight conditions were considered according to the two development time points and the four fetal genotypes (n = 64 fetuses exhibiting birth weight close to the average birth weight within litter and genotype, n d90.
0; Swiss Institute of Bioinformatics, Lausanne, Switzerland). Artefacts and saturated spots were removed from the analysis. Spots were matched across all 64 samples and 1,025 valid spots were successfully matched between gels. For each gel, each spot volume was expressed as a percentage of the volume of all matched spots on a given gel. Data were further log2 transformed. Representative 2D electrophoresis gels are included in Additional file 1 and Additional file 2.
This allowed us to assess how often a given spot, selected with our parameters/criteria, is selected when running several RF procedures. In sPLS-DA, we chose to keep two axes and to keep 25 spots by axis. The first axis discriminated the fetuses according to the gestational age, whereas the second one discriminated fetuses according to the fetal genotype. No parameter was tuned using the mixOmics R functions because we wanted to keep about 50 spots from this specific analysis and only two axes based on the PCA obtained using the normalized proteome dataset. Finally, several statistical multivariate methods (sparse partial least square (sPLS)  [34]  and sparse canonical correlation analysis (sCCA)  [35] ) were also performed to find additional spots correlated with phenotypic biological characters of interest such as MyHC profiles, plasmatic values and muscle glycogen content.
In sPLS analysis, 20 and 30 spots were kept according to axis 1 and 2, respectively. In sCCA, about 50 spots were also kept to get as many spots as with sPLS analysis. Like in sPLS-DA, the first axis discriminated the fetuses according to the gestational age and the second one according to the fetal genotype. In total, 179 spots were selected and manually excised from preparative gels loaded with 600 µg of proteins from pooled samples for further identification by nano-LC-MS/MS, as described in  [36]. An R script is provided as additional file in order to allow outside labs to reproduce all these analyses (Additional File 3 Results were scored using the probability-based Mowse algorithm, where the protein score is -10*log(P) and P is the probability that the observed match is a random event.
Protein identifications were considered valid if at least two peptides with a statistically significant Mascot score > 36 were assigned, and at least 20% sequence coverage was required. The accuracy of the experimental to theoretical isoelectric point and molecular weight were also considered. Among the 179 selected spots, 120 spots, corresponding to 89 unique proteins, were successfully identified (Additional Files 4 and 5). Raw mass spectrometry data have been deposited in the publicly accessible repository MassIVE site (ftp://MSV000081117@massive. ucsd. edu/ with id0181 as the password).
Integration of proteome network data Network analysis was performed using a three step approach as illustrated in Figure  1  and at the two developmental time points separately (90 and 110 dg (32 individuals per condition)), resulting in two global networks. We chose to infer two networks to avoid the high gestational age effect already observed in a previous study analyzing a transcriptomic dataset with the same individuals  [18]. All analyses were performed using the R computing environment  [39]. Figure  1  about here.
Inference of the proteome co-expression network The first step consisted in inferring a proteome co-expression network using the PCIT (partial correlation and information theory) algorithm  [40]  (Figure  1A ). PCIT has already been successfully used in transcriptomic analyses  [41, 42]. In the present study, we performed PCIT on a proteomic data set. PCIT belongs to the family of weighted network algorithms and is based on the combination of the concept of partial correlation coefficient and the information theory to identify meaningful associations. Briefly, the co-expression arrangements for all triplets are compared, with all triplets being exhaustively explored. PCIT estimates the correlation for each pair of proteins taking the presence of a third protein into account. Significant correlations establish an edge in the reconstruction of the network.
A significant result (FDR < 0. 05) indicated a node was more central in the network than expected. Therefore, betweenness centrality is a good measure of the importance of the node in the network. A node with a high betweenness (or centrality) value has a marked influence on the structure of the network. All these analyses were performed using the R package igraph  [50]. If no node with significant betweenness was found, the nodes with the highest betweenness were highlighted. In an integrative strategy, phenotypic information was also added to the co-expression network. Links between sub-networks and biological phenotypes of interest were investigated (Figure  1C ). To this end, methods coming from spatial statistics were used as described in  [13, 51]. First, the correlation between each protein expression community and the phenotype of interest was calculated.