dataesr/scientific-paragraphs-categorization huggingface dataset:
SAM (Significance Analysis of Microarrays) was used  [51]  to detect genes with a significant change in transcript level. Data sets for the two treatment periods (24 h and 48 h) were analysed by one-class analysis, in which transcripts were evaluated that showed the same direction of regulation for both time points (sustained regulation direction). Here, genes with a q-value #0. 01 were considered as significantly differentially transcribed between the inhibitor-treated and the control worm populations comprising protein-coding genes and putative antisense-oriented oligonucleotide probes (labelled as ''to be used in analysis = YES'' in the updated annotation of the array  [49, 50] ). Spotfire was used for hierarchical clustering  [52]. For first functional analyses of differentially transcribed protein-coding genes, Gene Ontology (GO) enrichment analysis was performed  [53]  using the software tool Ontologizer  [54].
In silico analyses In addition to those already mentioned, the following public domain tools were used: SchistoDB (http://www. schistodb. net; [63] ), BLASTx (http://www. ncbi. nlm. nih. gov/BLAST), the Welcome Trust Sanger Institute S. mansoni OmniBlast (http://www. sanger. ac. uk/cgi-bin/blast/submitblast/s_mansoni/omni), BLAST (http:// blast. ncbi. nlm. nih. gov/), and Gene Cards, which is a database of human genes providing concise genome-related information on all known and predicted human genes, to authenticate IPA-identified gene acronyms (http://www. genecards. org).
Two out of the four SmAbl2 interactions are shared with the human Abl2 interactions (Figure  2 ). For SmAbl1 the situation was similar. Key residues involved in direct interactions with Imatinib in the human Abl proteins were found to be conserved for all four protein sequences. Four out of the six directed interactions were also detected for the SmAbl1 homology model. In contrast to human Abl 1, one of these residues (D568) did not interact directly with Imatinib; however, it did via an Hbond network involving a water molecule. Since we docked Imatinib to homology models, the recovery of individual directed interactions should not be overstated. Although we docked Imatinib to homology models, the data clearly indicated that Imatinib is able to bind both SmAbl1 and SmAbl2.
01 mM and completely suppressed GVBD at 0. 1 mM. At the latter concentration still 90% GVBD was observed for SmAbl1-TK, whose activity was completely suppressed using 1 mM Imatinib (Figure  3 ). These results showed that Imatinib effectively inhibits both Abl kinases of S. mansoni, which is supported by the modeling data presented above.
Therefore, the 50 mM concentration and both time points were chosen for comparative transcriptomics. Based on the results of the GVBD assays it was anticipated that 50 mM Imatinib would not induce potential offtarget effects through co-influencing Src kinases such as SmTK3 since its inhibition will not occur using this inhibitor concentration  [39;  this study]. With this set-up, the expectation was to find genes differing in their transcript levels under inhibitor influence. It was hypothesized that transcript levels of genes strongly regulated by signaling pathways including SmAbl1/2-kinases would show a continuous tendency of regulation during the treatment period of 48 h representing sustained transcriptional changes. Following microarray hybridization and data evaluation a oneclass statistical analysis was performed that revealed sustained transcriptional changes of 1429 significantly differentially transcribed genes which were up-regulated following Imatinib-treatment.
784, p,0. 001;  [58] ). Extended analyses indicated that Imatinib treatment can lead to a sustained effect on specific genes. By qRT-PCR analyses of RNA of couples treated with Imatinib for 24 h or 48 h, the amounts of gene transcripts increased or decreased over time as demonstrated exemplary by the analyses of three genes. Compared to 24 h treatment higher transcript levels were determined for the ribosomal S6 kinase after 48 h (Supplementary data S9), whereas transcript levels declined for hemoglobinase and GCP from 24 h to 48 h (Supplementary data S10, S11).
Results of a previous study suggested GCP to be part of TGFb signalling pathways  [82]. Furthermore, the effect of a specific TbRI kinase-inhibitor (TRIKI) was investigated in schistosomes. In vitro-culture experiments with couples provided first evidence for a role of the TGFb pathway during the regulation of mitotic activity and egg production  [26]. Subsequently, it was shown by microarray analysis using the same technical platform that genes contributing to these processes, such as egg shell-forming genes, were slightly up-regulated upon TRIKI treatment  [47]. Analysing the microarray data following Imatinib treatment in the present study we observed that a number of specific genes were differentially regulated that had shown up in the previous analysis as well. Since there is evidence from the literature that Abl-kinases can be part of TGFb signalling pathways  [87, 88]  we investigated whether this may apply for schistosomes as well and compared both data sets in a merging Comparative qRT-PCR analyses of genes identified by microarray analyses to be down-regulated in schistosome couples 48 h following Imatinib (50 mM) treatment.
05 to identify a comprehensive set of differentially transcribed genes found in common in the two conditions. This approach identified 6754 differentially transcribed protein-coding genes in total, of which 1800 were common in both data sets. The merging analyses finally indicated that out of 2339 genes found in this study to be downregulated upon Imatinib treatment, 900 matched those differentially (up-and down-) regulated upon TRIKI treatment. Out of these 480 were up-regulated and 420 down-regulated by TRIKI. Furthermore, out of 2616 genes found in this study to be upregulated upon Imatinib treatment, 900 corresponded to those differentially regulated by TRIKI. Out of these 822 were upregulated and 78 down-regulated by TRIKI. By definition, no gene was found within the intersection of Imatinib up-and downregulated genes (Figure  6 ; Supplementary data S13, S14).
These experimental data were well supported by the modeling and docking data generated confirming that Imatinib is able to bind to both schistosome Abl kinases. With regard to results of the in vitro study showing increasing physiological and morphological effects between 24 h and 48 h treatment using 50 mM Imatinib  [38] , we performed transcriptional profiling for these time-points to get a broader view of molecular processes potentially affected by Imatinib. Microarray and subsequent bioinformatics analyses revealed a broad spectrum of genes being differentially regulated following inhibitor treatment. The transcription of genes involved in male-female interaction, gut physiology, muscle activities, and egg production were among those highlighted by the analyses. For qRT-PCR verification a number of genes were selected with regard to GO and IPA results but also to the phenotypes obtained in the preceding in vitro study, which comprised reduced pairing stability (i), reduced sizes of the gonads of both genders combined with disturbed spermatocyte/oocyte differentiation (ii), a degradation of the gastrodermis (iii), and tremor-like movements pointing to altered locomotion activity (iv)  [38].
Similar findings were made in in vitro studies with adult S. mansoni showing that genes encoding multiple drug transporter as well as calcium regulation, stress and apoptosis-related proteins were up-regulated  [101]. In contrast to these findings we observed a down-regulation of genes coding for lipoproteins, motor and/or muscle proteins, drug efflux proteins, calmodulin and other calcium binding proteins in S. mansoni couples following Imatinib treatment. With respect to apoptosis-related genes the picture is puzzling since there much variation within all PZQ and Imatinib data sets and little correspondence among these different data. This justifies no precise conclusion on the participation of defined apoptosis-related signaling processes in the primary effects on schistosomes caused by these drugs.
Our previous in vitro studies exhibited strong effects of Imatinib on schistosome morphology, physiology and survival in vitro suggesting that this compound may be one of the candidates for the design of alternative strategies to fight schistosomiasis  [38, 42, 43]. This was confirmed by an independent approach recently, which reproduced similar phenotypes in vitro, although a first in vivo experiment failed  [106]. Nonetheless, the data obtained in this study support the conclusion that Imatinib exerts broad negative effects on worm physiology substantiating the hypothesized role of PKs as potential targets  [25, 28, 36, 42, 43]. In this respect it was encouraging to note that according to our microarray analysis also multidrug resistance (MDR) genes (Smp_089200, Smp_151290) were among the significantly down-regulated genes following Imatinib treatment.
Background: Microbial communities of traditional cheeses are complex and insufficiently characterized. The origin, safety and functional role in cheese making of these microbial communities are still not well understood. Metagenomic analysis of these communities by high throughput shotgun sequencing is a promising approach to characterize their genomic and functional profiles. Such analyses, however, critically depend on the availability of appropriate reference genome databases against which the sequencing reads can be aligned. Results: We built a reference genome catalog suitable for short read metagenomic analysis using a low-cost sequencing strategy. We selected 142 bacteria isolated from dairy products belonging to 137 different species and 67 genera, and succeeded to reconstruct the draft genome of 117 of them at a standard or high quality level, including isolates from the genera Kluyvera, Luteococcus and Marinilactibacillus, still missing from public database.
Background Cheeses harbour a diverse microbial community, composed of a resident "house flora", that interacts with strains deliberately inoculated as starter or adjunct cultures  [1] [2] [3] [4] [5]. The cheese microorganisms mainly consist of Firmicutes (lactic acid bacteria, staphylococci), Actinobacteria (coryneform bacteria), Proteobacteria, Bacteroidetes, yeasts and moulds. Their concentration in the final product sometimes exceeds 10 10 cells per gram and it is generally accepted that most of them are cultivable in laboratory growth media  [6] [7] [8] [9]. An inventory of microorganisms with a history of use in food fermentations was established recently  [10]. It contains 195 bacterial species  (30 genera)  and 71 yeast and mould species  (35 genera). Among these bacterial species, only 80 (41%) comprise at least one isolate for which a genome sequence isolated from food is available, with almost half of them within Lactobacillus species (NCBI database, May 2014).
The characterization of metagenomic datasets relies on the use of reference databases that contain sequences of known origin and phenotype. Many of these studies are carried out by pyrosequencing of single target genes, such as 16S rDNA sequences, that provide information restricted to the phylogenetic composition of the samples  [25] [26] [27] [28] [29] [30] [31]. On the other hand, shotgun sequencing of whole community DNA provides additional information about the functions performed by the microbial community  [32, 33]. The length of the reads generated by current high throughput sequencing technologies is too short to allow accurate comparative analyses against distantly related genomes, thus requiring the availability of reference genomes closely related to organisms from the environment being studied.
Currently, the international genome databases are biased towards model organisms and pathogens, and, according to Huson et al. [34] , up to 90% of the sequences of a metagenomic dataset may remain unidentified due to the lack of adequate reference sequences. The sequencing of several hundred genomes is no longer a technical issue. However, this process still remains costly, mainly due to the cost of the construction of individual libraries for each genome being sequenced. In the present study, we selected and sequenced 142 bacterial strains of dairy origin that belong to 137 different species and 67 genera. In order to exemplify the relevance of these new genomes to the understanding of food microbiota, we used the newly created catalog to analyse the microbiota of three cheese surfaces sequenced through whole metagenomic sequencing.
Creation of a dairy reference genome catalog After bibliographic investigation for bacterial species occurring in dairy products, we collected 142 dairy bacteria of various origins. The origin of the isolates and their taxonomy are shown in Additional file 1: Table  S1. The collection comprised 36% Gram negative bacteria, 35% low GC Gram positive bacteria and 29% high GC Gram positive bacteria. Among the 67 corresponding genera, four are genera for which no genome sequence was available in NCBI databases (May 2014 release): Kluyvera, Luteococcus, Marinilactibacillus, and Mycetocola. The distribution of the strains according to the type of dairy product and their geographic origin is shown in Figure  1. We designed a low-cost library sequencing strategy, by pooling the bacterial genomes together in a controlled way prior to sequencing in order to reduce library construction costs.
We assessed the quality of the clustering procedure by mapping the Illumina contigs to the closest NCBI genome sequences (using BLASTN  [37] , identity threshold > =90%). Furthermore, one organism in our collection -Arthrobacter arilaitensis Re117had already been sequenced and was added to one sequencing pool in order to validate the clustering procedure. We evaluated the correctness of contig clusters by computing two measures: the dominant genusthe percentage of the contigs that could be mapped to related genomes belonging to the same genus as the organism represented by the cluster; and the reference coveragepercentage of the total contig size of the pool that could be mapped to a genome from the dominant genus. We performed this analysis for the 53 draft genomes for which we could identify at least five genomes belonging to the same genus in the NCBI database.
In order to further assess the quality of the draft genomes (including those without near-neighbors in public databases), we relied on the six quality submission criteria established by the Human Microbiome Project (HMP)  [38] , plus two additional criteria that identify potential miss-assignment events during the clustering step: phylogenetic marker redundancy and tetranucleotide homogeneity. For the HMP draft genome quality criteria, 5 criteria correspond to contig and scaffold assembly length and coverage (see Methods section). The last HMP quality criteria checks the presence of 99 bacterial essential genes  [39] , which gives an indication of the proportion of the genome that has been assembled (see Method section for the threshold used and supplementary information for the additional criteria).
Sixteen additional assemblies presented incomplete sets of the HMP essential genes criterion but passed the two chimeric test criteria. For the 25 remaining draft genomes, 17 were too incomplete (<1 Mb in contig cumulative size) and 8 did not pass one of the two chimeric criteria. The genome of Arthrobacter arilaitensis Re117  [20] , which was used in pool 1 as control for the procedure, passed all the HMP and chimeric criteria, and a comparison with the previously sequenced genome (Genbank project PRJNA53509) showed an average identity of 99. 98%, a completion level of 95. 63% and the absence of improperly assigned contigs. The two plasmids present in this bacterium were also partially present in our draft. The missing sequences corresponded mainly to transposase regions which were not assembled possibly due to the assembly and clustering procedure which often has difficulties reconstructing repeated variable regions.
In total 117 of the draft genome sequences (101 which passed all quality controls and an Additional 16 that had no evidence of chimeric contigs) were considered suitable for submission to public databases (see Additional file 1: Table  S1 ). The 25 draft genomes with poor quality or possible contamination were not submitted to public databases, but were used in our project with caution for phylogenetic analyses. From the 195 bacterial species or subspecies listed by Bourdichon et al. [10]  to occur in food products, only 80 had at least one food isolate for which a genome sequence was available in the NCBI database (NCBI May 2014 release, see Additional file 5: Table  S4 ). The present study provides genome sequences for 78 additional dairy isolates, which effectively doubles the number of available genome sequences of relevance to the study of fermented dairy products.
In order to better characterize the diversity of the bacterial strains studied here, we reconstructed their phylogenetic relationships. For that purpose, the genes corresponding to the 40 phylogenetic protein markers proposed by Mende et al. [40]  were extracted from the 117 high quality draft genomes in order to build a phylogenetic tree (Figure  2 ). The tree shows that the selected bacterial isolates cover a large biodiversity. Four other trees were constructed (see Additional file 6: Figure  S1 , Additional file 7: Figure  S2 , Additional file 8: Figure  S3  and Additional file 9: Figure  S4 ) by inclusion of 328 genomic sequences from foodrelated bacteria or closely-related species (Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria) extracted from the NCBI database (see Additional file 10: Table  S5 , genome references for phylogeny sheet).
1% (cheese L) to 57. 1% (cheese E) (Figure  3 ). Interestingly, the reads that mapped only to the dairy genomes sequenced in the present study accounted for a large proportion of the good quality reads (from 16. 7% for cheese L to 23. 7% for cheese G). In cheese G, 11. 2% of the reads mapped to the Bos taurus genome (compared to 0. 5% and 0. 1% for cheeses L and E, respectively). A deeper investigation of the cheese E good quality unmapped reads indicated that they may correspond to (i) strain specific genes belonging to the pan-genome (including prophages and mobile elements) and/or missing regions of the draft genomes, (ii) microorganisms for which genomes are still absent from the databases, (iii) distant genomic regions containing indels or more than 3 mismatches, which cannot be mapped with Bowtie.
The good quality reads coming from 3 samples of cheese surface were aligned to 5873 genomes coming from NCBI and 117 genomes coming from our project. The repartition of the good quality reads that map only on the NCBI genomes (blue), on the genome sequenced in our project (green), on both NCBI and our genome (light green) and on Bos taurus genome (orange) is presented in pie charts. The unmapped good reads are presented in dark and light grey, respectively those lacking a reference and those potentially unmappable for technical reason. For each cheese, the data presented correspond to the eight reference genomes with the highest numbers of mapped reads. quality reads, followed by Psychrobacter immobilis PG1 and Vibrio litoralis B4, with 8. 5 and 5. 2% of the reads, respectively. Furthermore, between 95.
This fact prompts us to suggest that the use of these drafts for comparative genomics should be restricted to high quality draft genomes and to genes present in long contigs or scaffolds (i. e. bigger than 1 kb). Lastly, the bioinformatics analysis pipeline is more complex to set up than simple assembly procedure in single genome sequencing. Despite these limitations, 117 of the 142 sequenced genomes resulted in good or high quality draft genomes suitable for submission to public database. Some of the remaining 25 genomes may still be useful as references for metagenomic analyses. The microbial composition of the surfaces of three cheeses was investigated by high throughput metagenomic profiling. As all the DNA present in the cheese samples is sequenced, the high throughput sequencing may detect any type of DNA (bacteria, archaea, eukaryotes and viruses), provided that adequate references are used.
Conclusion The genomes sequenced in the present study considerably increased the numbers of mapped reads, although a significant proportion of the metagenomic reads remained unassigned (~20% once taken into account unmapped reads inherent to SOLiD technology). These data indicate that even if more than 6000 genomes are currently available in public databases (including the genomes we generated here), additional microorganisms found in traditional cheeses are still missing from this reference collection. Further studies are necessary to complete this reference in order to provide a complete view of the cheese ecosystem. To our surprise, collecting the present reference strain set constituted a laborious work, since strains corresponding to non-starter species are frequently not conserved once described.
Co-variance clustering To assign the contigs to their original genomes, we used a clustering method based on the co-variance principle, derived from method described by Nielsen et al. [35]  and Le Chatelier et al. [59]  (see the Additional file 2, supplementary information). For this purpose, six new DNA pools were created by mixing genomic DNA so that each DNA sample has a different combination in the pools, and therefore each genome has a unique presence signature (see Additional file 3: Table  S2 , composition pool sheet). The six new DNA pools were sequenced using SOLiD technology 4, with around 90 million single reads in each pool of 50 nucleotide length. The SOLiD reads were aligned to the five Illumina contigs using the Bowtie aligner  [60]  (see Additional file 3: Table  S2 , SOLiD mapping data sheet).
This resulted in the creation of five independent contig coverage matrix, used for the clustering process. This alignment allows calculating a coverage vector for each contig, corresponding to the presence and absence of the contig in the 6 combinatorial pools. The contig coverage vectors are compared to the strain presence and absence in the 6 combinatorial pools, using the Pearson correlation coefficient. The contigs are assigned to the strain with which they share the highest Pearson correlation, as long as the correlation value is equal to or higher than 0. 95. The Illumina and SOLiD samples are accessible under the project ID PRJEB6314 at the SRA database.
Genes and contigs taxonomical annotation All contigs were taxonomically annotated by sequence similarity using BLASTN to a database containing 1411 reference genomes (extracted from NCBI database, June 2012 version). Sequence similarity > =90% on at least 100 nucleotide was used for genus level annotation. This taxonomical assignment was used to calculate the dominant genus assignment and the reference coverage of the clustering, by considering the 53 strains with at least 5 different species present in the NCBI database. This restriction was made in order to reduce the likelihood of incorrect assignments by the BLASTN procedure (see Additional file 4: Table  S3 , cluster BLASTN assignation sheet). The unassigned contigs were considered as part of the dominant genus assignment, to differentiate them from the contigs assigned to a different genus which represent a potential mis-assignment from the clustering step.