dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, tRNAs with unusual structure, such as those lacking an arm, were searched using specific patterns designed with the PatSearch program  (Pesole et al. 2000). All the above-predicted tRNA sequences were manually checked through multiple sequence alignment to orthologous tRNAs of other ascidians and deuterostome representatives. Thus, the final tRNA boundaries were defined based on sequence similarity and on the presence of a conserved cloverleaf secondary structure. The boundaries of the two rRNA genes were inferred as abutted to the flanking genes. As an exception, in A. aspersa we hypothesized the presence of small noncoding (NC) regions upstream rrnL and downstream both rrnL and rrnS. Indeed, these sequences lack similarity to other ascidian rRNA genes. The new mtDNA sequences were deposited in the EMBL-EBI European Nucleotide Archive under accession numbers HF54855-HF548561.
The web-based CREx  (Bernt et al. 2007 ) was used to calculate all pairwise breakpoint distances. Gene blocks conserved between different ascidian mt genomes were detected using the GeneSyn program  (Pavesi et al. 2004). Direct repeats longer than 10 bp were identified using the REPFIND program  (Betley et al. 2002)  and web-server (http:// cagt. bu. edu/page/REPFIND_submit, last accessed June 14, 2013). Transfer RNA genes of all 19 available tunicate species (including the two sequences for C. lepadiformis) were manually aligned based on their secondary structure. To identify cases of tRNA gene recruitment, two different alignments were analyzed: one including the entire tRNA sequence and the other excluding the tRNA loop regions, as these often produce unreliable alignments. Following  Lavrov and Lang (2005) , neighbor-joining phylogenetic trees were then reconstructed based on uncorrected pairwise distances (p distances) using PAUP* 4.
Phylogenetic Reconstructions The sequences of the 13 mitochondrial protein-coding genes for the 20 available tunicate species, including the six new species obtained in this study, were recovered from the whole mitogenomic sequences. Following the taxonomic sampling of  Singh et al. (2009) , the sequences of 17 nontunicate deuterostomes were added to the data set to reconstruct phylogenetic relationships. The tree was rooted using a protostome (i. e. , the mollusk Haliotis rubra) as an outgroup. Given the wide taxonomic scale of our sampling and the high evolutionary rate of the ascidian mt genomes, phylogenetic inference at the nucleotide level would be inadequate because of saturation and erosion of the evolutionary signal due to multiple substitutions (see saturation analysis in supplementary file S1, Supplementary Material online).
Therefore, we performed analyses at the amino-acid level to attenuate the saturation problem. The sequences of each independent gene were aligned and translated using MACSE version mtDNA Assembly without Barcodes GBE Genome Biol. Evol. 5(6):1185-1199. doi:10. 1093/gbe/evt081 Advance Access publication May 23, 2013 0. 9_beta1  (Ranwez et al. 2011) , which allows the use of different genetic codes while respecting the open reading frames. Subsequently, ambiguous regions of the protein sequence alignments were filtered using TrimAl v1. 4rev7 (Capella-Gutierrez et al. 2009) under the parameters set in the automated1 option. This yielded a total of 3,038 unequivocally aligned amino acid sites, which were used as input for the Bayesian phylogenetic inference. Sequence alignments are available in the Dryad repository: doi:10.
5061/dryad. ph920. Bayesian phylogenetic analyses were performed with Phylobayes 3. 3b  (Lartillot et al. 2009 ) under the CAT + GTR + À model. The site-heterogeneous CAT mixture model  (Lartillot and Philippe 2004)  accounts for site-specific amino acid replacement preferences, making it well suited for phylogenomic studies. Four Markov chains Monte Carlo (MCMC) were run and sampled every 10 cycles. Convergence of the chains was monitored through the evolution of the likelihood and model parameters across generations using GNUPLOT (http://www. gnuplot. info/, last accessed June 14, 2013) and confirmed with the bpcomp utility included in Phylobayes. Specifically, each chain was stopped after sampling more than 9,000 trees, that is, when the maximum difference in posterior probability for a given node, as estimated by the 4 independent MCMCs, reached less than 0.
Acknowledgments The authors thank  Naomi Paz  for editing the text,  Riccardo Brunetti  for confirming the identity of Botrylloides aff. leachii,  Adriana Giumbo  for her comments on genome annotation, and  Liron Goren  for taking the Botrylloides picture. Revital Ben-David-Zaslow  provided invaluable help with the collections of the  Zoological Museum at Tel Aviv University. They thank  Moran Yassour  for assembly and Trinity use advice. They also thank the  Israeli Ministry of Science Culture & Sport  for supporting the National Collections of Natural History at  Tel Aviv University. This work was supported by the  Israel Science Foundation  ( 663/10 ) to D. H. and the  Ministero dell'Istruzione, dell'Universitae della Ricerca, Italy  ( PRIN-2009 ) to C. G. This publication is the contribution no.
Several metabolic databases with rich information are available: one of the most comprehensive is MetaCyc and its associated BioCyc collection of pathway/genome databases (5); similarly, KEGG is a database resource that integrates genomics, chemical and systemic functional information (6); BRENDA is another database that contains one of the most complete collections of enzyme functional data (7). Gaps or incomplete knowledge, however, are still present in many cases, especially when looking for novel ways to synthesize compounds. In this regard, computational approaches can provide new alternatives by predicting putative heterologous pathways producing the target compound. In order to successfully handle this challenging task, the design process needs to be rationalized by following the principles of synthetic biology: modeling of the biological system of interest, modular design through standardization, goal-oriented optimization, and experimental validation.
The method for retrosynthetic design of heterologous pathways consists of the following steps: 1. First, the problem is defined by choosing the chassis organism and the target compound. 2. Second, an in silico reconstructed model of the organism containing at least the stoichiometric reactions involved in its metabolism is defined from biological databases and literature. 3. Third, the metabolic space is constructed from all known metabolic reactions and expanded to putative promiscuous reactions. 4. Fourth, heterologous pathways producing the target compound from endogenous metabolites in the chassis are enumerated using a retrosynthetic algorithm. 5. Fifth, gene sequences encoding heterologous enzymes are chosen in order to maximize gene expression and enzyme performance in the chassis organism.
Choosing the chassis An early decision that necessarily influences the rest of the retrosynthetic design process is the choice of the chassis organism where the desired compound will be produced. For example, in order to increase the production of a compound naturally produced in plants, its biosynthetic pathway, if known, is imported into an industrial chassis organism. Factors that need to be considered when choosing the chassis include the following: 1. The extent and level of curation of the organism's metabolic pathways in databases. 2. The availability of a genome-wide reconstructed in silico model that has been experimentally verified (16), and that is ready to be used in constraint-based modeling to quantitatively estimate steady-state fluxes (see Note 2). 3. The availability of information about toxicity effects of heterologous metabolite intermediates in the organism.
Selecting an in silico model for the chassis In silico organisms models are currently available for many industrial strains, including strains evolved for efficient production in Escherichia coli, Saccharomyces cerevisiae or Bacillus Subtilis (19-21). Most of them have been deposited in open databases such as BIGG (8) or BioModels (9) and numerous tools exist for their analysis and simulation (3). Example of chassis selection for production of resveratrol in E. coli. Resveratrol (3,5,4'trihydroxy-trans-stilbene) is a plant phenolic compound with important associated health benefits like prevention of cardiovascular diseases, cancer and promotion of longevity in several animal systems (22). Resveratrol, however, is only found in a limited number of plant species, including grape (Vitis sp. ) and peanut (Aracis hypogaea).
The combinatorial complexity associated with such representations is a major issue that we have recently addressed by proposing a tradeoff solution based on molecular signatures (15). The main advantage of the molecular signature method relies on the control of the complexity of the pathway search through the selection of the level of specificity in the reaction representation. For any reaction found through reaction rules, there are two essential questions to consider: a) Is it a putative reaction or has it been reported previously in the databases? b) Is there any known enzyme sequence annotated for such biochemical transformation? In addition, as much information as possible about the selected reaction needs to be collected, including if it has been observed for some enzyme as a promiscuous or side reaction, or what are their kinetics constants, cell localization, or phylogenetic diversity.
Note 6). Under this set up, each elementary mode will correspond to a pathway that produces heterologous compounds. Because the number of pathways needs to be kept minimal, all pathways of interest producing a target compound should be contained in the elementary modes. In addition, elementary modes containing loops should not be considered as pathway candidates. Several software packages are available that compute the elementary modes of a given metabolic network. A popular implementation is Metatool (28). Pathway enumeration through the topological approach. In the topological approach, each reaction is represented by a node of a hypergraph that is connected through hyperedges to the substrates (see Note 7). The strategy used in the hypergraph approach for pathway enumeration is the application of a recursive backward algorithm that traverses the network starting from the target in order to search for all possible pathways connecting the target to the source (see Note 8).
The main advantage of this approach is computational efficiency. Another remarkable feature of the topological approach is that it allows for supplements and bootstraps molecules identification. Supplements are compounds that provide new biosynthetic pathways if added to the medium because they act as precursors of the target compounds. Bootstraps are compounds that are needed to be present in the medium at least in small amounts in order to allow the reactions in the pathway to start producing the target compound. A software tool for enumerating pathways using the topological approach is Metahype (10). Example of pathway enumeration of resveratrol producing pathways in E. coli. Starting from precursors in E. coli, five alternative viable pathways producing resveratrol are identified by the retrosynthetic approach (shown in Fig.
Homology search of heterologous genes: A blast search of the National Center of Biotechnology Information (NCBI) nucleotide data bank can identify sequences predicted to encode the enzyme having the desired activity. Phylogenetic trees can be built to identify groups of the different enzymes identified (31). Minimizing the phylogenetic distance between the chassis organism and the organism where the gene is endogenous can also help in order to choose the homologue enzyme. 3. Scoring gene compatibility: An adequate strategy for gene sequence selection is to associate a score to each sequence, so that only sequences with top score are further considered. In a simple approach, the score can be built as a weighted sum of the considered factors, such as GC content or phylogenetic distance. Because of the multiplicity of factors that can influence enzyme expression, it might be difficult to blindly assign weighting priorities to each factor.
Estimating yield and drains The next step in pathway design corresponds to metabolic analysis of the enumerated pathways in order to get an estimation of growth and yield of the target product associated with each metabolic intervention. This step is typically performed for the steady state through flux balance analysis (FBA) (3). To that end, an in silico model of the metabolic network of the chassis organism, normally given in SBML representation, needs to be obtained from the literature or from databases such as BIGG. Several software packages for FBA like the COBRA toolbox (11) are available. The following steps should be followed to perform flux balance analysis of the heterologous pathways: 1. Consistency check between the in silico SBML model and the metabolic database: Because they contain only reactions reconstructed from high-throughput omics data, genome-wide reconstructed in silico models do not contain such level of detail in the pathways as the one in metabolic databases like MetaCyc or KEGG.
Therefore, a minimum level of consistency needs to be guaranteed between the metabolic network model from databases described in Section 3. 3 and the in silico SBML model for FBA, since it might happen otherwise that the heterologous pathway obtained from pathway enumeration in Section 3. 4 is fully or partially disconnected from the chassis in the in silico model. Therefore, it is necessary to verify that the endogenous precursors in the pathway are present in the in silico model, either because they are being produced by some enzymatic reaction or by adding the ones missing to the medium. 2. Inserting the heterologous pathway and their corresponding transport and exchange processes: Next, the heterologous pathway has to be imported into the model by adding as many reactions as enzymatic steps are present in the pathway.
Toxicity effects of heterologous pathways In metabolic engineering, the importance of compound toxicity has been pointed it out by several authors (35,36). Indeed, for pathway performance the less toxic molecule is usually desired, while conversely the highly toxic molecule is wanted when producing therapeutics such as antimicrobials. In both cases, detailed information on compound toxicity is important in the design of metabolic pathways and toxicity is a parameter that should be included in the computer-aided pathway design framework (15). In order to establish a reference database of toxicity data in the chassis organism, a library of MIC (minimal inhibitory concentration) or IC 50 (half maximal inhibitory concentration) experimental values should be built (14). These experimental values can then be used to develop a quantitative structure-activity relationship (QSAR) (37) model for toxicity of chemicals towards the chassis organism.
3. A fresh pure culture of E. coli strain (such as E. coli ATCC 25922 usually chosen for toxicity assay) is used for the inoculum. Bacteria are grown in liquid medium at 37°C, and bacterial growth determined at the stationary phase after incubation for a defined period (for example 18 hours). Toxicity assay can be performed in 96-well microtiter plates. The chemicals are screened by serial dilution to assess their toxicity towards E. coli. Controls are important to add for each experiment. A positive control (as a triplicate) consists to bacterial culture without any chemical, and a negative control (as a triplicate) to monitor the absence of contamination consists to the media only. 4. Bacterial cell growth is monitored by measuring the turbidity (at 600 nm) at the stationary phase and then data analyses are carried out to determine MIC or IC 50.
K(S(r) ) is the cost associated with sequence S of the enzyme catalyzing the reaction r in the pathway r, and T(p) is the toxicity (-log 10 (IC 50 )) associated with the metabolite p product of reaction r, as defined in Section 3. 7. 2. The cost for the sequence K(S(r)) has to take into account the fact of whether reaction is found annotated in databases for the given sequence or it is found based on a prediction as described in Section 3. 3. In the case of a putative reaction, a penalty is added to the cost as follows: ! K (S(r)) = " pred (r) + #(S) (3) where ! "(S) is the compatibility for sequence S as defined in Section 3. 5, and ! " pred (r) is defined in order to assign an additional cost to those enzyme sequences S where the corresponding reaction r is either catalyzed as a promiscuous or side reaction or its assignment is only putative.
Therefore: ! " pred (r) = " penalty promiscuous/predicted 0 annotated # $ % (4) with penalty constant G penalty arbitrarily set to a value that is a upper bound for the score of sequence compatibility: ! "(S) # $ penalty. 3. The choice of values for parameters (l flux , l path , l tox , G penalty ) depends on each experimental set up as well as on the preferences set up by expert designers. A first approach is to set the values so that the cost function assigns less cost to that pathways that contain only enzymes annotated in databases (no putative enzymes). In (15), parameters were fitted in this way to (0. 025,1. 0,0. 398,5. 0).
in silico model of the chassis organism: from repositories of in silico model organisms like the databases BIGG (8) or BioModels (9). Construction of the metabolic space: metabolic databases such as MetaCyc (5) or KEGG (6) and enzymatic activity databases such as Brenda (7). Pathway enumeration: software MetaHype (10). Gene selection: genomics databases (e. g. UniProt, NCBI Entrez) focused on protein (enzyme) families. 6. Flux balance analysis: metabolic analysis software (e. g. COBRA (11), OptFlux (12), COPASI (13)). 7. Metabolite toxicity data for the chassis, either experimental or predicted (e. g. EcoliTox (14)). 8. Definition of a final cost function (e. g. RetroPath (15)). Experimental implementation: a) Molecular biology reagents for PCR and cloning. b) Bacterial strains for cloning and expression.
In order to predict toxicity values of intermediates, a quantitative structure-activity relationship (QSAR) model of toxicity has to be developed from the experimental dataset by using a statistical software package like the pls library in R (38). Descriptors for the compounds in the dataset might be chosen in the same fashion as the ones selected for performing the clustering of chemicals. For regularization purposes, IC 50 values should be transformed into log(IC 50 ). Two basic statistical methods can then be applied in order to build the QSAR model: a) Principal component analysis (PCA) in order to reduce the dimension of the signature vectors, by keeping only components whose variance is above some given cutoff ratio of the total variance in the set. b) Model fitting by the partial least squares (PLS) regression method (38).
3. 4. 5. 9. 3459 in silico model of the chassis organism: from repositories of in silico model organisms like the databases BIGG (8) or BioModels (9). Construction of the metabolic space: metabolic databases such as MetaCyc (5) or KEGG (6) and enzymatic activity databases such as Brenda (7). Pathway enumeration: software MetaHype (10). Gene selection: genomics databases (e. g. UniProt, NCBI Entrez) focused on protein (enzyme) families. 6. Flux balance analysis: metabolic analysis software (e. g. COBRA (11), OptFlux (12), COPASI (13)). 7. Metabolite toxicity data for the chassis, either experimental or predicted (e. g. EcoliTox (14)). 8. Definition of a final cost function (e. g. RetroPath (15)). Experimental implementation: a) Molecular biology reagents for PCR and cloning. b) Bacterial strains for cloning and expression.
5. 4 , 54 In order to predict toxicity values of intermediates, a quantitative structure-activity relationship (QSAR) model of toxicity has to be developed from the experimental dataset by using a statistical software package like the pls library in R (38). Descriptors for the compounds in the dataset might be chosen in the same fashion as the ones selected for performing the clustering of chemicals. For regularization purposes, IC 50 values should be transformed into log(IC 50 ). Two basic statistical methods can then be applied in order to build the QSAR model: a) Principal component analysis (PCA) in order to reduce the dimension of the signature vectors, by keeping only components whose variance is above some given cutoff ratio of the total variance in the set. b) Model fitting by the partial least squares (PLS) regression method (38).
This circumpolar dataset of the comatulid (Echinodermata: Crinoidea) Promachocrinus kerguelensis  (Carpenter, 1888)  from the Southern Ocean, documents biodiversity associated with the specimens sequenced in Hemery et al. (2012). The aim of Hemery et al. (2012)  paper was to use phylogeographic and phylogenetic tools to assess the genetic diversity, demographic history and evolutionary relationships of this very common and abundant comatulid, in the context of the glacial history of the Antarctic and Sub-Antarctic shelves  (Thatje et al. 2005 (Thatje et al. , 2008)). Over one thousand three hundred specimens (1307) used in this study were collected during seventeen cruises from 1996 to 2010, in eight regions of the Southern Ocean: Kerguelen Plateau, Davis Sea, Dumont d'Urville Sea, Ross Sea, Amundsen Sea, West Antarctic Peninsula, East Weddell Sea and Scotia Arc including the tip of the Antarctic Peninsula and the Bransfield Strait.
Project details Project title: Comprehensive sampling reveals circumpolarity and sympatry in seven mitochondrial lineages of the Southern Ocean crinoid species Promachocrinus kerguelensis  (Echinodermata)  Personnel: Lenaïg G. Hemery Funding: French ANR ANTFLOCKS (n° 07-BLAN-0213-01); MNHN Paris intern grants (DMPA's BQR, ATMs "Biominéralisation"; "Biodiversité actuelle et fossile; crises, stress, restaurations et panchronisme: le message systématique"; "Taxonomie moléculaire: DNA Barcode et gestion durable des collections"). Study area descriptions/descriptor: The 1307 specimens in this dataset were collected from the Southern Ocean, south of the Sub-Antarctic Front (SAF): Kerguelen Plateau (Kerguelen and Heard islands), Davis Sea, Dumont d'Urville Sea, Ross Sea, Amundsen Sea, West Antarctic Peninsula, East Weddell Sea and Scotia Arc (from the tip of the Antarctic Peninsula and the Bransfield Strait to the South Georgia island).
The bathymetric range extended from 65 to 1162 meters deep. Design description: This dataset was gathered to conduct a circumpolar phylogeographic study of the crinoid species Promachocrinus kerguelensis  (Hemery et al. 2012 ) and designed to spatially improve the sampling of  Wilson et al. (2007) , which was limited to the Atlantic sector of the Southern Ocean. The aim of  Hemery et al. (2012)  was to test the circumpolarity of the genetic lineages of  Wilson et al. (2007) , and to test whether these lineages represented an under-sampling artifact of a large and genetically diverse metapopulation or whether they were truly representative of the Southern Ocean. The authors used a sampling strategy designed to cover the broadest possible genetic variation and to explore the evolutionary relationships among the seven lineages, in order to be able to conduct population analyses  (Meyer and Paulay 2005).
The Cytochrome c Oxydase subunit I (COI) was successfully sequenced for 1307 of these specimens. Both collection data and produced sequences were digitized in appropriate databases, used or ready to be used for publishing purpose (Figure  1 ). Data published through GBIF: http://ipt. biodiversity. aq/resource. do?r=proke as an Excel spreadsheet of the dataset, available through the Darwin Core Archive format at http://ipt. biodiversity. aq/archive. do?r=proke.