dataesr/scientific-paragraphs-categorization huggingface dataset:
A distinguishing feature of neoENCODE is how, with the right strategic investments that expand existing database and information sharing infrastructure, its organization is permitted to evolve with the number neoENCODEs and the scale of their projects. As described below, we need road maps and exchanges between neo-ENCODEs communities to improve each of the individual neoENCODE projects. Another distinguishing feature of neoENCODE is the point at which a project is completed, or the results deemed to be comprehensive. ENCODE and modENCODE had/ have the ambition to screen a number of organs, tissues, cells and biotic and abiotic challenges, mainly in laboratory conditions, for empowering biomedical research. By contrast, NeoENCODE will endeavor to largely replicate many ecological conditions across a wide array of non-model species either under controlled conditions or directly from natural populations.
We argue that the most important contribution of consortium scale biology, particularly of the ENCODE Consortium, has been the establishment of uniform data quality standards and assay conditions, which have rendered thousands of experiments directly comparable in several systems. We thus have the opportunity here to rally such projects through a proposition of guidelines for data generation and data exchange. This again can be done utilizing some of the ENCODE consortia principles  [24]. Before going into a more detailed presentation of a putative road-map for neoENCODE, we propose to keep in mind four technical differences between a neoENCODE initiative and the now-concluded modENCODE projects: -For most non-model organisms, the size of the scientific community will be much smaller than the communities working on model systems or human.
Build the computational pipelines Bioinformatics begins with a genome database: gene expression data is best analyzed in reference to a genome annotation. This means that a dedicated database is required. Usually, raw data are cleaned and mapped to a reference genome. These steps are now pretty welldefined and do not cause difficulties for trained scientists. In all cases, the multiplicity of samples requires access to substantial computational and storage capacities. Bioinformatic platforms and/or data centers are necessary for accomplishing these analyses. Of course, the neoENCODE project needs the involvement of trained computer scientists, and trained genomicists in analysts and programmers. Systems such as Galaxy  [28]  allow biologists, after brief training, to handle large quantities of data and perform some analyses.
If my organism of interest were for example, a scale insect, what would a non-mod-ENCODE initiative look like? What data would be collected, how and what kind of biological questions such a complex catalogue would serve to address. How many non-model organisms should this be done for? How should they be selected? Since there are so many potential non-model organisms, these are relevant and complicated questions that should be addressed, especially when the relative value of the effort is very difficult to appreciate ahead of time. It would seem given the limited resources we have to work with that we would be adding a few more organisms to the list of model organisms rather than really expanding into (many) nonmodel organisms. I suppose if resources were unlimited then, non-modENCODE (I'd prefer beyond-modENCODE) would be great, but resources are never unlimited.
We still have a very long way to go to interpret mod-ENCODE data in that context. Most of the data collected to date, for example, do not even consider the contribution of genetic variation or genotype-byenvironmental interactions, which could not be ignored for non-model organisms. It would appear that what the authors are arguing for is not a non-mod-ENCODE project (and all that it entailsneed for controlled environmental conditions, assay of developmental stages, in different tissues, etc. …). Instead they advocate the development of genomic resources that would enable the application of system-biology and system-genetics to investigate complex biological problems that are specific to certain groups of organisms. I do understand the appeal of following in the footsteps of a successful initiative, but this paper would be much improved if realistic and more specific goals were outlined clearly.
Mark Blaxter School of Biological Sciences, The University of Edinburgh This is an interesting opinion piece that suggests 'upscaling' our genomics approaches on non-model organisms, and building ENCODE-like programmes to deliver 'systems biology' level understanding of a wider range of organisms than those currently studied. The point of models is that they are taken to represent the core biology of all species in a particular realm: so we can have model species that model humans, or that model development in a particular group, or that model genetic processes. It is perhaps necessary to revisit the model when a phenotype of interest is not represented in the core set, but analysis of these phenotypes can be achieved in new or satellite models that do have the relevant biology. The thesis is intriguing, but is poorly focussed, and perhaps oversells the knowledge that can be mined from the ENCODE data systems.
The Dolittle critique is measured and reasonable; the Graur critique is more detailed and, points out a number of the pitfalls of BIG SCIENCE (inconsistent definitions, methods, and database procedures). Both critiques point out the glaring absence and need for evolutionary analyses in ENCODE studies. The present manuscript contains a single sentence acknowledging only the existence of these criticisms; it doesn't grapple with them but rather charges forth to a "Let's do it!" rallying cry at the end. There are a number of things about this perspective that don't sit well with me. First, I am a typical maverick scientist; I run a small lab and prefer to pose my own questions and design my own approaches. I don't like to be told what to do and my default state is to distrust and question calls for topdown approaches to scientific endeavors.
It is only the worst genomic approaches with non-model species that aim only to obtain a large amount of sequence data without a compelling reason to do so and with little knowledge of what to do with the data. Much of the latter does get published (including in this journal) but that in no way motivates my thinking about the best ways to approach new research. That said, there is clearly great importance in building and maintaining public databases of sequence data and associated functional annotation, but this paper is not about databases per se. Gaining insights about the genetic mechanisms underlying interesting features of a particular species is often (but not always) a goal of the best genomic research in non-model species. Clearly there will be cases where non-coding regulatory elements are key parts of the answer, but for the vast majority of nonmodel species research, there will not be a big enough team to undertake an Encode-style approach.
New variants of previously described Salmonella serovars are provided in Table  3  For the first time, the multilocus sequence type has been indicated for all the 63 new serovars as well as for the 25 new variants of previously described Salmonella serovars. Multilocus sequence typing (MLST) has been performed both at the WHO-CC or at the PHE according to Achtman et al. [6]. The data have been submitted to the Salmonella enterica MLST database (http://mlst. warwick. ac. uk/mlst/dbs/Senterica). The present number of serovars per species and subspecies is given in Table  4. The next issue of the White-Kauffmann-Le Minor scheme, which will contain MLST data, is expected in December 2015. (5'-GGTTACAACTTCGARGAYTC-3') with conditions (annealing at 50°C) described previously  [5]. The different S.
Acknowledgements We thank  P. Dunker  ( Institut für Hygiene and Umwelt ),  S. Simington  ( CDC ), and  M. S. Van Duyne (CDC) ,  J. Bale  ( PHE ) and  D. Meunier  ( PHE ) for their technical assistance. We thank all the National Reference Centres that sent the new serovars or variants to the  WHO-Salm: Institut für Medizinische Mikrobiologie und Hygiene (IMED), Graz, Austria (C. Kornschober) ;  Institut scientifique de Santé Publique (ISSP), Brussels, Belgium (S. Bertrand) ;  Institut National des Laboratoires de la Santé (INLASA) ,  La Paz ,  Bolivia  ( R. M. Ruiz );  Public Health Agency of Canada (PHAC) ,  Winnipeg ,  Canada  (H. Tabor),  Korean CDC (KCDC) ,  Seoul ,  South Korea  ( D-Y. Lee );  National Public Health Institute (NPHI) ,  Helsinki ,  Finland  ( S. Lukinmaa & A. Siitonen );  Institut Pasteur (IP), Paris, France  ( F.
Statistical analysis Statistical analyzes were performed with R 3. 0. 2 software  [49]. To analyze jointly differences between breeds and gestational ages, the following mixed linear model was fitted to each probe (R nlme package, lme function  [50] ): y ijk = μ + A i + FG j + A. FG ij + S k + ijk (1) with i ∈ {d90, d110}, j ∈ {LW , MS, LWMS, MSLW }, k = 1,. 18, S k ∼ N(0, σ 2 S ) independent and identically distributed (iid) and ijk ∼ N(0, σ 2 e ) iid. S k and ijk are mutually independent. y ijk is the expression of the probe (gene) being studied, μ a general mean of the considered gene expression and ijk is a residual. This model includes two fixed effects and their interaction: A i is the effect of fetal gestational age i, FG j the effect of fetal genotype j and A. FG ij the interaction effect between gestational age i and genotype j.
S k represents the random sow effect. To identify differentially expressed probes (DEPs) for gestational age and/or genotype, the mixed linear model (1) was fitted to the microarray data. A F-type test was performed by comparing the complete model (  1 ) and the reduced model y ijk = μ + S k + ijk. A correction for multiple testing was then implemented using Bonferroni  [51]  or False Discovery Rate (FDR)  [51, 52]  using the multtest R package  [53]. The list of DEPs was then partitioned into 4 sub-models. Sub-model 1 combined the two fixed effects and their interaction. Sub-model 2 involved the two fixed effects in an additive manner. Sub-model 3 included only the fetal gestational age effect whereas sub-model 4 included only the fetal genotype effect. All models included the random sow effect.
Gene Ontology functional enrichment analysis Functional annotation of genes from sub-model 1 based on Gene Ontology (GO) was provided by GeneCoDis 3. 0 software  [54]. Enrichment analysis was applied to lists of genes selected for an absolute log2-fold change greater than 1  2 between both gestational ages. This threshold of a 1  2 -log2-fold change was used to obtain two values: upregulated genes at day 90 or up-regulated genes at day 110. This threshold was applied to ensure that only the genes with a minimal change between gestational ages were retained for the GO functional enrichment analysis. The two lists contained up-regulated genes at 90 days and up-regulated at 110 days respectively. To set the statistical enrichment of a particular biological function, a hypergeometric test was used.
Relevance network A relevance network is a graphical model displaying genes as edges and relationships (correlations here) between genes as vertexes  [55]. In our study, a network building pipeline, using the igraph R package  [56] , was decomposed into three steps. In a first step, a similarity matrix S was calculated using the Pearson correlation coefficient between pairs of genes. In a second step, S was transformed into a binary adjacency matrix A using hard thresholding. This matrix A was composed of 0 and 1 depending on whether the correlation coefficient was lower or greater than 0. 98 (in absolute value), respectively. The final step consisted in a graph representation of A. An edge was present between two nodes (genes) i and j if the value a ij in A was 1. To determine communities in the graph, a fast-greedy algorithm was used to optimize the modularity of a partition of the network  [57].
The efficiency of PCR amplification was determined specifically for each gene, by serially diluting (1, 1:2; 1:2; 1:2) the muscle cDNA pool. Data was then analyzed using Fluidigm Digital PCR Analysis software with the Linear (Derivative) Baseline Correction Method. After determination of the threshold cycle (Ct), the Pfaffl method  [60]  was applied as described in  [59]  to calculate the relative expression of each gene. HPRT, which was not regulated during the maturation process, was used as the reference gene. Pearson's correlations were calculated between microarray expression and qPCR values. with help of MSC and LL. LL supervised the project. All authors read and approved the final manuscript.
Acknowledgments This project received financial support from  French National Agency of Research  ( PORCINET  project,  ANR-09-GENM005 ). VV is a PhD fellow supported by the  INRA GA (Génétique Animale) , the  INRA PHASE (Physiologie Animale et Systémes d'Elevage)  and the  région Midi-Pyrénées. Microarray annotations were managed by  SIGENAE (Système d'Information des GENomes des Animaux d'Elevage , http://www. sigenae. org). The authors would like to thank  Jasha Leenhouwers ,  Agnès Bonnet  for help, and also  Helen Mundutéguy-Hutchings  for the English revision. The authors thank the reviewers and the editors for their useful comments and suggestions that helped improve the quality of this paper.
The two rRNA operons presented a high number of interoperon polymorphisms distributed along the entire operon sequences, including the 23S rRNA gene. This feature, already observed in the 16S rRNA gene, has been used for molecular epidemiology studies (4). Complete circular annotated genomes are now available for all Mycoplasma species belonging to the Mycoplasma mycoides cluster  (5). This provides the foundation for future studies on genome organization plasticity and comprehensive comparative genomics both intra-and interspecifically. Nucleotide sequence accession number. This whole-genome sequence has been deposited in DDBJ/ENA/GenBank under the accession no. LM995445.
Results Copy number dynamics define three distinct groups of TEs within the genome While surveying copy numbers of TEs within B73, we observed an interesting phenomenon. The observation began by mapping 18,689,555 paired-end (PE) reads of B73 genomic data to the published Unique TE (UTE) database. The UTE consisted of 1514 TEs that was built by filtering the exemplar database of 1526 TEs (TEdb)  [16, 22]  to reduce cross-homologies between TE exemplars and thereby improve mapping resolution  [17]. Plots of the RPKM (Reads per Kilobase per Million mapped, see Methods) values for individual TE subfamilies (RPKM TE ) yielded different distributions between DNA transposons and RNA transposons. The DNA transposons had a unimodal distribution of RPKM TE , while the RNA transposons had a bimodal distribution (Figure  1a ).
The presence and consistency of these TE groups was also assessed in two genetically distant maize landraces with contrasting genome sizes. The variation in siRNA targeting across different TE groups and families, as well as the lack of correlation between TE and siRNA abundances, argues for the existence of multiple mechanisms and strategies for TE silencing. distributional properties, we defined three TE groups: group D, which consisted of 841 exemplar DNA elements; group R1, which included 365 exemplar RNA elements with relatively low abundances; and group R2, the set of 198 high abundance class I retroelements (Figure  1ab ; Table  S1 ). Note that these three groups do not include 110 exemplar elements for which the RPKM TE data suggested fewer than 2 copies in B73. Among the three groups, it may not be surprising that the 'high copy' R2 group contained retroelements known to be common throughout the maize genome, including Ji and Opie Copia elements and the Cinful, Huck and Prem1 Gypsy elements (Table  1 )  [22] [23] [24].
Expression of the R1 group is suppressed in mop1 mutants Given dramatic differences in age and siRNA targeting among groups, we also determined whether the groups differ in expression dynamics. To assess expression, we examined existing RNAseq data from B73 leaf tissue (see Methods). The data indicate that total expression of R2 elements is highest among the three groups, with similar levels of expression for the D and R1 groups (Figure  3a ). However when corrected for abundance, the R2 TEs have the lowest expression on a per-copy basis (Figure  3b ), consistent with the possibility of copy-number repression  [29, 30]. In contrast, R1 elements exhibit the highest expression on a percopy basis (Figure  3b ). We found similar expression patterns based on germline (immature tassel) tissue (data not shown).
There were clear trends among groups. On average, expression of the D group was enhanced in the mop1 mutant; for the 109 TE subfamilies in the data set expression increased slightly, ,0. 29 log 2 units or ,1. 2-fold on average (Figure  3c ). The 144 members of the R2 group in the dataset exhibited no strong tendency, with an average 1. 03-fold shift in expression. In contrast, the R1 group experienced an average 21. 6-fold decrease in expression in the mop1 mutant, with 80% (70 of 87) exemplars exhibiting a decrease. The effect of decreased expression was particularly prominent for TE exemplars targeted by high ratios of 24:22 siRNA, based on our B73 leaf data (Figure  3d ). Thus, the puzzling phenomenon of decreased TE expression in a maize RDR2 mutant is due to R1 elements.
The three TE groups are evident in other maize genomes We questioned whether the three TE groups were unique to the reference genome or a consistent genomic feature across maize sensu lato. To assess TE copy numbers across individuals, we sequenced one lane of genomic DNA from each of the landraces Palomero Toluquen ˜o (PT) and Olote Colorado (OAXA). Recall that PT has a genome size of 5. 58 pg/2C, which is similar to that of the 5. 64 pg/2C B73 genome, whereas OAXA genome is 7. 11 pg/2C  [20]. Our Illumina sequencing yielded a total of 53,535,615 and 54,318,379 paired-end reads, respectively, for the two accessions (Table  S1 ). These genomic HTS data were mapped to three databases: i) the Filtered Gene Set (FGS)  [16] , ii) the KnobC database and iii) the UTE. Briefly, the percentage of reads that mapped to the FGS and UTE was similar across accessions: 15.
0% and 61. 7%, respectively, for B73; 17. 0% and 62. 4% for PT; and 16. 8% and 55. 2% for OAXA. The largest difference between accessions was in the percentage of genomic HTS reads that mapped to the KnobC database (at 6. 12% for B73, 1. 26% for PT and 11. 14% for OAXA). Thus, the most obvious difference between accessions was in heterochromatic sequences, consistent with previous studies suggesting that knob DNA is the primary determinant of GS differences within the genus Zea  [18]. Given HTS data, we determined whether the R1 and R2 groups were consistent across accessions or simply a property of the B73 genome. We therefore calculated the RPKM TE values based on reads from PT and OAXA (Figure  S2 ). For both landraces, the retroelements had a bimodal distribution of copy number, consistent with the B73 analyses (Figure  1ab ).
No significant correlations were detected. For example in the B73:PT comparison, FC TE was uncorrelated with FC 24 (r 2 = 0. 002; p = 0. 10) and FC 22 (r 2 = 5610 26 ; p = 0. 94) across all of the TE exemplars in the R2 group. The second approach was to formulate and conduct statistical test of the hypothesis that TE copy number and siRNA expression change proportionally between individuals. We devised such a test (x 2 Prop ) and applied it to all TE exemplars between accession pairs (see Methods and Text S1). Based on the x 2 Prop test, data from up to 917 TE subfamilies rejected the null hypothesis of proportionality between TE copy number (RPKM TE ) and 24 nt siRNAs (Figure  4bc ; Table  2 ). There were fewer rejections between TE copy number and 22 nt siRNAs, but up to 506 between B73 and PT.
We chose to harvest these leaf tissues based on precedent in the literature  [50]  and the ease of establishing developmental homology. Genomic and siRNA libraries. Leaf tissue from 10 different seedlings per landrace were pooled and ground in liquid nitrogen. Although the plants were not genetically identical, the distribution of genome sizes between the two landraces was not overlapping  [20]  and hence pooled samples give insights into average genomes of contrasting sizes. Genomic DNA was extracted from 1 g of pooled tissue using the Qiagen DNeasy plant mini kit. A pairedend library was built for each landrace using 1 mg of genomic DNA with the kit TruSeq Paired-End Cluster Kit v2. 5 (Illumina PE-401-2510). Sequencing was performed in one lane on an IlluminaHiSeq 2000 sequencer. The genomic data are archived at NCBI Sequence Read Archive (SRA) under accession numbers SRX476038 (OAXA) and SRX476570 (PT).
We also included genomic paired-end read data from B73 in our analyses  [17]  (SRA-SRP004910). For all three accessions, total RNA was isolated from 1 g of pooled tissue using TRIzol reagent (Invitrogen) following the manufacturer's instructions. siRNA was extracted by running total RNA on a 15% PAGE gel and selecting bands in the 20 to 30 nt size range. Libraries for siRNAs were prepared from 100 ng of siRNA using the Illumina Truseq Small RNA Sample Prep Kit, according to the manufacturer's protocol. siRNA sequencing was performed in one lane on an IlluminaHiSeq 2000 sequencer per genotype using the Truseq SR cluster kit v. 2 for B73 and v. 3 for PT and OAXA libraries. The siRNA data have been archived at the GEO database (GSE55730).
Mapping procedures Reference data sets. We mapped our genomic libraries to three reference databases: i) the filtered gene set (FGS) from RefGen_v2 (Release 5b. 60) of the maize genome sequence  [16] ; ii) a custom-made database of knob and centromeric sequences (hereafter the KnobC database; Table  S3 ) including 32 knob and 73 CentC maize sequences; and iii) the unique transposable element database (UTE) developed by Tenaillon et al. [17]. The siRNA libraries were also mapped against the FGS and the KnobC databases but also against the full TE exemplar database (TEdb) of 1526 elements  [16]. Mapping genomic data. The pair-end datasets from PT, OAXA and B73 were mapped against all three reference sets separately. To map genomic reads to the UTE, we employed SSAHA2 version 0. 1  [51]  with default parameters, the ''best'' option and 80% homology, the criterion generally accepted as the level of similarity of reads within a single TE subfamily  [25].
Only alignments. 30 bp were counted, and each aligning read was counted as a ''hit''. When multiple best-mapping reads were found for a single TE, we counted them as a single hit for that TE. The UTE virtually eliminates hits to multiple TEs, but reads that mapped to multiple TEs with the same score were discarded. The genomic data were mapped against the FGS and KnobC databases by the same procedure, except applying a 90% homology criterion for the FGS  [17, 52]. For each accession, we recorded the total number of UTE, FGS and Knob hits. Because knob and centromeric sequences contain portions of TEs  [53] , we preferentially considered reads that mapped to both the Knob and UTE database as hits to the Knob database. We also considered reads mapping to both the UTE and FGS databases as TEs because the FGS may have not been filtered completely for the presence of TE-derived sequences  [54].