dataesr/scientific-paragraphs-categorization huggingface dataset:
Filter size fractions were 0. 1 to ,0. 8 mm, 0. 8 to ,3 mm, and 3 to ,20 mm. This graph is only intended to give a snapshot of one marine sample; relative distributions vary based on distance from shore and depth, and several studies provide more detailed reviews of available SSU rRNA gene sequence surveys, see e. g. ,  [21, 32]. (B) Taxonomic diversity of eukaryotes with complete genome sequences, as summarized in the Genomes Online Database (GOLD: http://genomesonline. org). Note that multicellular organisms are included (unlike in A or C); animals, land plants, and multicellular rhodophytes are included in the opisthokont, viridiplantae, and rhodophyte categories, respectively. (C) Taxon breakdown of the MMETSP sequencing project, collapsed at the strain level (for some strains, cells were grown under multiple conditions and these have been counted only once).
In addition, all data is in the Sequence Read Archive (SRA) under BioProject PRJNA231566, giving access to the raw trace data through GenBank. Given that library construction is not as robustly consistent as one might hope and that Illumina RNAseq assembly (in the absence of a sequenced genome) is not a completely solved problem, it is helpful that all of this work occurred at a single sequencing center where the protocols used for the. 650 transcriptomes were similar (see Text S1 for a full description of methods). This approach not only broadened the types of participating labs (i. e. , not just those with experience in genomics) but also maximized comparability of the datasets without the user feeling obliged to reassemble contigs, or to re-predict protein sequences for consistency. At the same time, the availability through the SRA allows for re-analysis of particular datasets.
More Than a Reference Database The more than 650 transcriptomes will have far-reaching impact beyond the field of marine science. The diversity of taxa represented in the database is impressive, even when held up to the enormous diversity of microbial eukaryotes as a whole (Figure  2 ). In some cases, these data provide the first glimpse of the genome of an important group of microbial eukaryotes, such as parasitic haplosporidia, several amoebozoans, and the enigmatic heterotrophic flagellate Palpitomonas. In other cases, they provide genomic data from a diverse selection of taxa within a lineage where only sparse genomic data previously existed from a few distant relatives (such as the ciliates  [18] [19] [20] ). Experience has shown that such data can transform our understanding of the basic biology and function of these organisms.
In the past, we have described a protistan lineage for which there is a single genome sequence as being ''well studied. '' Thus, even for those that are comparatively ''well studied,'' the MMETSP data facilitates new directions. It opens the door to comparative genomics within lineages and between related lineages in major. A schematic of the major lineages in the eukaryotic tree of life, showing the relationships between lineages for which genomic resources are currently available and those that have been targeted by the MMETSP. Lineages with complete genomes according to the GOLD database, as summarized in  [3] , are indicated by a solid line leading to that group, whereas lineages with no complete genome are represented by a dashed line. Lineages where at least one MMETSP transcriptome is complete or underway are indicated with a red dot by the name.
A Fast Start and a Long Way to Go The MMETSP is a significant step in recognizing that purpose-built reference databases from ecologically key biomes are essential for all domains of life. Nevertheless, it is only the beginning, and important biases remain that should be addressed. The MMETSP relies primarily on cultured organisms, and this introduces a different set of biases, most obviously, favoring organisms that are photosynthetic. Eukaryotic heterotrophs have critical ecological roles but are under-represented. Indeed, the natural diversity of eukaryotic heterotrophs is huge in general (Figure  1A ), and the four most commonly recovered sequences retrieved in environmental surveys of marine samples worldwide correspond to lineages for which most members are uncultivated (e. g. ,Marine Stramenopiles (MAST) and Marine Alveolates (MALV)  [22] [23] [24] ).
The MMETSP program foreshadows this development by sequencing a small set of culture-independent samples. The MMETSP dataset serves as an example of how purpose-built reference databases focused on a particular niche or environment can be established relatively quickly and efficiently. This database will allow us to address eukaryotic sequences from nature in a robust manner for the first time. Because the strength of the MMETSP project is precisely its focus on the marine environment, it will not serve as a universal database of eukaryotic diversity that can be easily applied to other environments. While the taxonomic diversity included in the project is amazing (Figure  2 ), it is also immediately clear that many major groups of eukaryotes are not covered by MMETSP transcriptomes. In some cases, this is because these lineages are not abundant in the oceans (e.
g. , many excavates), but in others it is simply because members of the lineage are difficult to cultivate and are generally poorly represented in molecular data (e. g. , most rhizarians), even if they are abundant and important in the ocean. For other major environments (e. g. , freshwater, soil) similar databases could be developed in a focused manner, but all such efforts rely on a detailed knowledge of what lives in that environment, which is not always adequate. To remedy these gaps in our knowledge, we advocate a taxonomy-based approach similar to the Genomic Encyclopedia of Bacteria and Archaea (www. jgi. doe. gov/programs/ GEBA/)  [2, 4]. This undertaking will require a focus on developing the necessary tools for gaining access to the transcriptomes and genomes of uncultivated organisms and would represent a major advance for all aspects of the study of microbial eukaryotes.
This graph is only intended to give a snapshot of one marine sample; relative distributions vary based on distance from shore and depth, and several studies provide more detailed reviews of available SSU rRNA gene sequence surveys, see e. g. , [21, 32]. (B) Taxonomic diversity of eukaryotes with complete genome sequences, as summarized in the Genomes Online Database (GOLD: http://genomesonline. org). Note that multicellular organisms are included (unlike in A or C); animals, land plants, and multicellular rhodophytes are included in the opisthokont, viridiplantae, and rhodophyte categories, respectively. (C) Taxon breakdown of the MMETSP sequencing project, collapsed at the strain level (for some strains, cells were grown under multiple conditions and these have been counted only once). (D) Comparison of currently available complete genomes and MMETSP transcriptomes by Class for two diverse and well-studied groups of algae, prasinophytes [14]  and dinoflagellates [15, 16].
Acknowledgments MMETSP sequence data is available at NCBI under BioProject  PRJNA231566. We are deeply grateful to the many technicians, students, post-doctoral scientists, and other collaborators and colleagues who contributed to growing cultures and preparing RNA. The number of people involved in this project at all levels was too great to allow all to be included in the author list, but in recognition of their tremen-cillia Gourvil,  Florence Le Gall  (RCC);  Marcus V. X. Senra  ( Federal University of Rio de Janeiro );  Federico Buonanno ,  Claudio Ortenzi  ( University of Macerata );  Susanna Theroux  (JGI);  Sophie Sanchez-Ferandin (UPMC) ;  Sheree Yau  (CNRS);  Philipp Assmy ,  Sara Beszteri ,  Fabian Kilpert ,  Christine Klaas ,  Jan Meyer  (AWI);  Gurjeet Kohli  ( UTS) ;  Sarah D'Adamo ,  Robert Jinkerson ,  Huiya Gu  (CSM).
Ethical Conduct of the Study The study was conducted in accordance with the protocol, ICH Good Clinical Practices standards, Declaration of Helsinki and applicable regulatory requirements as well as any European and Swedish applicable laws and regulations relating. The trial is registered with ClinicalTrials. gov, NCT01188512. The Swedish Medical Product Agency (MPA) and the regional ethical review board in Stockholm approved the study and the amendments. Since this was a first-in-man study of a genetically modified microorganism it was a requirement from the MPA, that only males should be enrolled in the study. The volunteers signed the informed consent form after receiving written and oral information. Information was given before and during visit 1, so the subject had sufficient time to consider participation, which started on visit 2.
Study Subjects Healthy adult male volunteers aged 19-31 years, born in Sweden between 1979 and 1991, when no pertussis vaccination was performed in Sweden, were included step-wise. A volunteer was eligible for inclusion in the study if he had not experienced clinical pertussis during the preceding 10 years and had serum anti-PT IgG #20 IU/ml. Persons in frequent contacts with children less than one year of age were not included. Other inclusion and exclusion criteria are listed in Method S1 and all scheduled visits are shown in Table  S1. The first visit of the first study subject was on August 10 th 2010 and the last visit of the last subject was on June 21 st 2011. Baseline data of the volunteers are shown in Table  1.
In addition, from the day of vaccination to day 14 after vaccination the subjects recorded AE in a diary, which was returned on day 28 to the investigator. Severe AE (SAE) were reported at all visits, and the subjects were instructed to immediately contact the investigator in case of any SAE. The relation of the adverse events to the vaccine were determined jointly and unanimously by the principal investigator, who was also the clinical investigator (author NA), and the author responsible for safety issues (BT). All serious adverse events were discussed with the sponsor and the Independent Data Monitoring Committee (IDMC). Group 1 was followed for at least 7 weeks before group 2 was vaccinated. Group 2 was followed for at least 9 weeks before group 3 was vaccinated. Interim safety meetings were held with IDMC and the sponsor before administering the next dose.
Results There were no differences in baseline data between the volunteers assigned to vaccine or placebo except for the volunteers in the low dose group, who had lower systolic blood pressure than other groups, but still within normal limits (Table  1 ). In total 107 subjects were recruited of which 48 were vaccinated. All vaccinated volunteers were evaluable for safety and immunogenicity during the first month after vaccination, and only one was lost to follow up after 5-6 months (Figure  1 ).
Adverse Events There was one SAE. The first subject vaccinated openly with the low dose died 7 weeks after vaccination. The SAE was reported to the sponsor and the sponsor notified the Swedish MPA, the European Medical Agency Eudravigilance database and the Ethics Committee. After careful assessment, the death was judged by the IDMC as not vaccine-related. Out of consideration for the deceased and his family it was decided not to give more information about the death. Immediate AE within 6 hours of vaccination were all trivial and short-lasting with no differences between the placebo and different dosage groups. Local and systemic solicited AE, which were noted in the diary and confirmed with interviews at subsequent visits, were all mild to moderate. The most common were rhinorrhoea, sneezing and cough.
There were no differences between placebo and any of the treatment groups (Tables 2, 3, S2 and S3) or between colonized and non-colonized subjects (Table  S4 ). Ten volunteers experienced cough during the first week after vaccination and five during the second week. The cough episodes were evenly distributed among the placebo and treatment groups, short-lasting and without similarity with whooping cough. There were no abnormalities in vital signs or blood haematology in any volunteer during the study (data not shown). All volunteers were asked unsolicited questions about AE at all visits, including the last 5-6 months after the vaccination. There was no SAE and only mild to moderate episodes of upper respiratory tract infections and short-lasting fever with no differences between groups.
Acknowledgments The sponsor  Inserm  and the  Strategic Planning and Clinical Trial Monitoring Committee (COSSEC)  of  Inserm  represented by  Sebastien Boy  and  Anne Puech  and the  European Clinical Research Infrastructure Network (ECRIN)  for their support;  Dominique Raze ,  Linda Kostic ,  Eva Hansson Pihlainen ,  Carina Bengtsson ,  Siri Wahlquist  for skilful technical assistance at the laboratory;  Ria Bral  and  Geert Deschamps  from  Innogenetics ,  Martine Vandermarliere  and  Marijke Verhaeghe  from  Innogenetics  and  Q-Biologicals  for their input and work during the development and cGMP production; The study team at the  Karolinska Trial Alliance (KTA)  for vaccination, sample collection, and excellent surveillance of the volunteers at the clinic. The members of the  Scientific Advisory Board  and the Independent Data Monitoring Committee,  Patrick Olin ,  Ragnar Norrby ,  Roland Dobbelaer ,  Odile Launay ,  Marta Granstro  ¨m and  Micha Roumianzeff  for invaluable advice during the study.
Whole genome sequencing Genomic DNA was isolated from Δhns-stpA and evolved strains using SIGMA GenElute TM Bacterial Genomic DNA Kit (Cat. No. NA2120) using the manufacturer's protocol. Sequencing library was prepared for Illumina sequencing and the quality of the libraries checked using Agilent Bioanalyzer at the genomics facility, Centre for Cellular and Molecular Platforms (C-CAMP), Bangalore. DNA isolated from HS100 and HS250 populations was sequenced for 50 cycles from one end, and that from the single colonies sequenced for 100 cycles from both ends. The sequencing was performed on the Illumina HiSeq1000 platform at C-CAMP. Raw data have been deposited with NCBI-SRA under the accession number SRP043310.
ncbs. res. in/hns evol. SNPs identified using this analysis were validated by Sanger sequencing using specific primers. Read coverage across the genome was calculated for nonoverlapping windows of 200 nt each using custom scripts and normalized by the mode of the distribution across these bins as described earlier  (6). Coverage is defined by the number of sequencing reads that map to a region on the chromosome, and is represented on the log, base 2 scale.
Transcriptome data analysis 50-mer single-end sequence reads were mapped to the E. coli K12 MG1655 genome using BWA  (12). Gene annotations were obtained from the Ecocyc database  (17). The number of reads falling within each gene was calculated based on the base position to which first nucleotide of the read was mapped. A matrix containing the read count for each gene across the sequenced samples was fed into the Bioconductor (http://www. bioconductor. org) package EdgeR  (18)  for analysis of differential expression. The genes that are differentially expressed by at least 2-fold with a P-value of 0. 00001 were considered for further analysis. Differential expression in the ori 2 strain was calculated differently. Here, the mode of the distribution of per-gene read counts (6) across only the non-amplified portion of the genome was used as a normalization factor.
Analysis of publicly-available microarray data Publicly-available gene expression (transcriptome) data for E. coli were downloaded from the M3D database (http: //m3d. mssm. edu; (20) ). This comprises Robust Multi-array Average (RMA)-normalized gene expression measurements from Affymetrix microarrays across ∼300 conditions. Pearson correlation coefficients (PCC) between pairs of gene expression vectors were calculated using a combination of PERL and R scripts. High positive PCC between a pair of genes indicates that their expression levels increase or decrease similarly across the conditions included in the data set. A high negative PCC, on the other hand, indicates opposing gene expression patterns for the two genes (referred to as anti-correlation): an increase in the expression level of one gene is met with a corresponding decrease in that of the other.
Validation of RNA sequencing data using reverse transcriptase-PCR Selected RNA sequencing results--from comparisons involving rpoS mutants--were validated using quantitative reverse transcriptase-PCR (RT-PCR). mRNA was isolated from the cells collected at the same time points as for RNA sequencing using TRIZOL, followed by DNase treatment. mRNA after DNase treatment was precipitated using ammonium chloride as described in the MICROBExpress RNA purification kit and used for the RT-PCR. RT-PCR reactions were carried out using the reagents provided with the Takara one step RT-PCR kit using manufacturer's instructions. 16S RNA gene, rrsA, was used as an internal control. The outcome of these experiments is presented in Supplementary Table  S2  (resulting in a Spearman correlation coefficient > 0.
Further, a significant proportion of genes that were downregulated in rpoS mut are known targets of 38 (∼30%; P < 10 -10 ; Fisher's exact test) identified by microarray experiments from the Hengge lab  (28)  and/or present in the RegulonDB database (29) (∼400 genes in this collection of 38 targets). These suggest that the transcriptional effects of rpoS mut are indeed a result of 38 inactivation. Remarkably, a significant proportion of genes downregulated in rpoS mut when compared to Δhns-stpA had previously been upregulated in Δhns-stpA relative to the wild type (∼35%; P ∼ 10 -7 ; Fisher's exact test; Figure  4a ). Similarly, many genes upregulated in the former had been downregulated in the latter comparison (∼50%; P < 10 -10 ; Fisher's exact test; Figure  4a ). We emphasize that upregulation of gene expression resulting from the inactivation of a -factor is likely to be an indirect effect.
This representation is derived from the NuST server (http://www. lgm. upmc. fr/nust/). insC1 and insC5, which mark the boundaries of the duplicated domain in ori 2 , are marked by the green dashed lines. (c) Similar to (b), but shows the subset of gene up-or downregulated in the comparison, but reversed at least in part by one of the two suppressors. different physiological outcomes during distinct stages of growth  (32). Inactivation of H-NS increases 38 levels by stabilizing it through a cascade of regulatory interactions  (33). This would predict that many 38 targets should be upregulated when H-NS-mediated gene regulation is impaired, as demonstrated for up to ∼20 genes by the Hengge group  (31). However, in our data only a small fraction of genes known to be 38 targets (∼15% of 38 targets in our database) are affected by Δhns-stpA in comparison to the wild type.
In fact, this number appears to be slightly less than expected by random chance (P < 0. 001; Fisher's exact test). Thus, the list of previously known targets of 38 is substantially exclusive of genes upregulated in Δhns-stpA and the effect of rpoS mut on these targets of H-NS (and StpA). 38 is known to activate transcription of several H-NSrepressed genes. This has been shown for selected promoters including those for the stationary phase-related nucleoidassociated protein Dps  (34). At present, ∼20 H-NS targets documented in the RegulonDB database are known to be regulated by 38. Despite this, our data show that the overlap between the targets of H-NS and 38 is much bigger. What could be the possible sources of this difference? Most 38 targets known to date were identified by perturbing 38 activity in a wild-type background.
ACKNOWLEDGEMENTS We acknowledge  C-CAMP  for providing next-generation sequencing services. Thanks are due to  Avantika Lal  for compiling the list of genes regulated by RpoS. We thank  Aalap Mogre  for cross-verifying mutation lists and  Ranabir Das  and  Savita Chib  for comments on the manuscript. We also thank the anonymous referees for their comments. FUNDING  Department of Science and Technology, National Centre for Biological Sciences  [ SB/SO/BB-0073/2012  to A. S. N. S. ];  CEFIPRA-IFCPAR [5103-3  to A. S. N. S. , M. C. L. ];  Ramanujan Fellowship  [ SR/S2/RJN-49/2010INSPIRE  to A. S. N. S. ; IF110025 to R. S. ],  Department of Science and Technology (Government of India)  [to A. S. N. S. ];  INSPIRE Fellowship, Department of Science and Technology (Government of India)  [to R. S.
Results Population genome-wide data set. We generated genome-wide data from a collection of ethnologically well-defined populations of RHG and AGR, specifically chosen to study admixture processes (Supplementary Table  1 ). These populations include Western RHG (Baka of Gabon, Baka of Cameroon, Bongo of South and East Gabon), Eastern RHG (Batwa of Uganda) and AGR neighbouring populations (Nzime, Nzebi and Bakiga; Fig. 1a ). We genotyped 1,048,713 SNPs in 327 individuals using the Illumina HumanOmni1 SNP array. In total, 930,134 autosomal and X-linked SNPs passed quality control filters, and 32 individuals were discarded owing to low call rates, Mendelian inconsistencies among trios and cryptic relatedness (Methods; Supplementary Fig. 1 ). Ultimately, 295 individuals were retained for subsequent analyses, including 216 unrelated individuals, 21 complete trios and 8 duos, totalling 266 unrelated samples.
1 ). We phased RHG and AGR separately using SHAPEIT  47  , and estimated the effective recombination rate r (with r ¼ 4N e r for autosomes and r ¼ 3N e r for the X chromosome  48, 49  ) in each population, using LDhat  50. As expected, r estimates were highly correlated between the two populations (log-transformed rates per kb: r ¼ 0. 89, t-test Po0. 0001, Supplementary Fig. 13 ). N e was then estimated by comparing the total r map length of RHG and AGR genomes with the pedigree-based deCODE recombination map  51. For autosomes, N e estimates of Western RHG (N e ¼ 13,442, (12,118-15,333)) were lower than those of AGR (N e ¼ 19,537, (17,038-22,185)), yielding an RHG-to-AGR N e ratio of 0. 69 ((0. 64-0. 74)), Fig. 4a ; Supplementary Fig. 14 ). These results clearly attest to a systematic difference in effective population size between RHG and AGR communities.
Sample exclusion. Of the 327 genotyped samples, 9 individuals were excluded because of a call rate o95%. Relatedness among our samples was evaluated by estimating the relatedness coefficient of all possible pairs of samples, using the pairwise correlation coefficient implemented in smartrel, corrected for the top eigenvalues obtained using the EIGENSTRAT program  31. We consistently obtained a coefficient of B0. 5 for most of parent-offspring pairs (average: 0. 48, s. d. : 0. 007). However, five parent-offspring pairs presented unexpectedly low coefficients (o0. 10). Using PLINK  61  , we obtained a rate of Mendelian inconsistencies 410% for the five corresponding trios, while this rate was o0. 05% in all the other complete trios. In all subsequent analyses, the five trios were considered as duos or as unrelated samples.
We also observed cryptic relatedness among our samples: 23 pairs of samples exceeded a correlation coefficient of 0. 1 ; dbGaP study accession: phs000449. v2. p1). We restricted the three data sets to the SNPs that were genotyped in all, yielding a total of 363,088 polymorphic SNPs in 516 individuals. No relatedness or population differentiation was detected between the Baka RHG of Cameroon of this study and those retrieved from a previous study  26  ; the two populations were thus considered as a single population in all subsequent analyses. Runs of homozygosity. We searched for ROH within the genome of the selected 516 African individuals. To minimize the bias introduced by SNP ascertainment, we restricted this analysis to 165,702 SNPs whose population frequency was higher than 5% in every population.