dataesr/scientific-paragraphs-categorization huggingface dataset:
senegalensis and 34,949 reference transcripts in S. solea shared 17,562 IDs for RefSeq and 17,031 IDs for ENSEMBL, which clearly reflects the high level of orthology between these two sole species. True orthologs between both sole species were obtained after performing a reciprocal Blast using the reference transcriptomes. In this analysis, two transcripts were considered as Blast-based, true orthologs when a highly restrictive reciprocal Blast (>97% identity) always resulted with their sequences giving the best score and E values  [38]. A total of 26,291 reference transcripts of S. senegalensis were highly homologous to reference transcripts of S. solea, and 21,238 reference transcripts of S. solea were highly homologous to reference transcripts of S. senegalensis. Of these homologous sequences, only 11,953 could be considered as true, Blast-based orthologs (data not shown).
These include 210 unannotated transcripts with an average length of 900 nt in S. senegalensis and 1,199 nt in S. solea. More interestingly, 137 of them had a testcode ≥0. 94, indicating that they likely code for a specialized protein (Additional file 5, "newtranscripts" tab). To confirm this hypothesis, they were compared to other fish proteins (Gadus morhua, Oryzias latipes, Oreochromis niloticus, Tetraodon nigroviridis, Gasterosteus aculeatus) and only 35 (25. 5%) failed to find any orthology (Additional file 6). Moreover, 75 transcripts (54. 7%) showed a clear ortholog only in the flatfish C. semilaevis, suggesting that these they might represent flatfish-specific transcripts. In conclusion, 11,953 transcripts were identified as true, Blast-based orthologs between both sole species, from which 75 are likely to be flatfish-specific transcripts and 35 are putative new sole transcripts.
Transcriptome comparison across teleosts revealed a set of lineage-specific genes The 11,743 sole Blast-based orthologs with annotation (excluding the 210 unannotated transcripts, see above) were investigated based on their RefSeq and/or ENSEMBL ortholog for zebrafish. As shown in Figure  5 , most Blast-based orthologs (93. 8%) had a zebrafish ortholog. However, the most interesting finding is the small subset of sole orthologs lacking zebrafish similarity (Figure  5 ; 701 in S. senegalensis and 492 in S. solea, with 351 transcripts present in both species; Figure  5 ). Some of these transcripts without zebrafish ortholog were related to the immune system such as hepcidin antimicrobial peptides and some interleukins (e. g. IL11b, IL17A/ F-1, IL8, IL22, IL7). Hepcidins appear as a highly diversified family in acanthopterygians (HAMP2-like group) that favoured the radiation of teleosts in marine and brackish environments  [39, 40].
semilaevis (two sequences), S. solea (two sequences) and S. senegalensis (four sequences). Moreover, EST sequence analysis from Atlantic halibut (Hippoglossus hippoglossus) suggested that this flatfish also possesses crybb1, crybb2 and several crybb1-like sequences (X. Cousin, personal communication). Taken together these results suggest that flatfish have indeed lost Figure  5  Venn's diagrams reflecting coincidences by Solea species among sole, Blast-based orthologs and transcripts with RefSeq/ ENSEMBL ortholog for zebrafish. Diagrams are comparing the 11,743 Blast-based orthologs with the unique zebrafish RefSeq identifiers in SoleaDB for S. senegalensis (39,851) and S. solea (34,949) and with the unique zebrafish ENSEMBL identifiers in SoleaDB for S. senegalensis (39,270) and S. solea  (34.
389). Within the Venn's diagrams, the numbers refer to the amount of transcripts in SoleaDB for S. senegalensis (Sse) and S. solea (Sso), the number of transcript in SoleaDB with a zebrafish RefSeq identifier (R) of with a zebrafish ENSEMBL identifier (E). and retained specifically some crybb genes likely as a consequence of independent events indicative of divergent evolution and do not support a decay of the visual system as previously hypothesized in flatfish based on the set of crystallin-encoding genes  [26].
Sole transcriptomes as a source of molecular markers A transcriptome represents an important source of molecular markers, mainly SSRs and SNPs. A total of 37 SSR markers in Senegalese sole derived from ESTs in public databases were applied to evaluate SSR evolution in flatfish species  [13]. Sole transcriptomes described here will surely add more molecular markers suitable for a wide range of genetic applications. SSRs were determined in the whole sole transcriptomes (S. senegalensis v4 and S. solea v1), the reference transcriptomes (S. senegalensis v4. 1 and S. solea v1. 1) and the set of sole Blastbased orthologs (Table  3  and Additional file 8). It is noteworthy that in whole transcriptomes, dinucleotide repeats were the most abundant SSRs followed by trinucleotide repeats and tetra-nucleotide repeats.
It is worth mentioning the low representation of GATA repeats (<0. 2% total repeat motifs) confirmed by FISH analysis (Additional file 9). Comparison of SSRs Blast-based orthologs in soles (Table  3  and Additional file 8) identified 6,596 sole-conserved SSRs in S. senegalensis and 6,772 in S. solea, out of which 1,273 and 4,803 SSRs in S. senegalensis and S. solea, respectively, can be considered species-specific as they were only found in the orthologs of only one species. This analysis provides new and very useful SSR markers for development of multiplex assays, genetic mapping and deciphering genome evolution in flatfish as well as species identification in processed fish. A total of 337,315 SNPs were identified in S. senegalensis and 381,404 in S. solea transcriptomes. A significant proportion of SNPs occurred in transcripts containing an ORF (109,235 [32.
Conclusions De novo transcriptomes of S. solea and S. senegalensis covering their main developmental stages and organs were described based on a combined assembly approach that can be applied to other transcriptomic studies. The huge volume of reads processed in each species (>1,800 millions, the highest number of reads reported to date for any organism) produced a high number of transcripts that were mined to obtain a representative reference transcriptome   Fold-changes (FC) and p-values obtained for target genes by microarray and qPCR are indicated. Moreover, the transcript code in the SoleaDB for S. senegalensis v3 transcriptome is also shown. For qPCR, data were normalized to those of gapdh2 and referred to the calibrator group (36 ppt 3 DPH). for each species. The organization and deposit of all this information at SoleaDB offers the scientific community a new powerful resource for the management of genomic information in soles.
Transcriptome comparisons and orthology analyses showed that both species are highly homologous and even contain transcripts with the same sequence. Moreover, comparisons across teleost transcriptomes allowed for the identification of some subsets of transcripts considered as new, species-specific and flatfishspecific transcripts. Transcriptome analysis followed by a phylogenetic analysis confirmed the retention of crystallins crybb2 and crybb3 confirming species-specific events during flatfish evolution. In conclusion, this study not only provided functional information about soles, but also provides new tools to the scientific community in the form of a database, SSR and SNP markers, and a new microarray with 43,403 features in Senegalese sole.
Here MIRA3 (based on overlap-layoutconsensus algorithm  [52] ) and Euler-SR (based on a strict de Bruijn graph analyzed by an Eulerian path  [53] ) were used. For S. senegalensis, Roche/454 long-reads were pre-assembled using MIRA3 with 454 settings. The same long reads were also assembled using Euler-SR with the default parameters and a k-mer = 29 (maximum length allowed). To remove artifactual sequences, contigs (consensus sequences) obtained using Euler-SR were mapped with the original reads using Bowtie2  [54]  allowing 2 mismatches to confirm the goodness of the final consensus. Unmapped contigs were considered a sign of misassembling and were submitted to Full-LengtherNext (see below) analysis to recover putative coding sequences. Illumina short-reads were pre-assembled using Oases (based on de Bruijn graphs  [55] ) with two k-mers: a small k-mer to recover lowly-expressed transcripts and a big k-mer for recovering highly-expressed transcripts since the use of multiple k-mers is reported to improve the quality and good performance of de novo assembling.
k-mers from 19 to 69 were scanned in both species in seek of those that produce the lesser number of artifacts and the highest number of annotated transcripts (results not shown). As a result, 23 and 47 were used for S. senegalensis, and 25 and 69 for S. solea. Processed single reads and paired reads were assembled independently, providing distinct contig sets using a static coverage cutoff of 3. For computing efficiency and accuracy, the redundant and nested contigs were clustered using CD-HIT  [56]  at 99% identity, recovering only the longest contigs. After that, an in-house script was used to discard misassembled contigs based on the presence of exact, internal, direct or inverse repetitions. Pre-assemblies were finally reconciled using CAP3 with default parameters to provide the maximal set of transcripts for each transcriptome.
Transcriptome annotation Transcripts were annotated using Sma3s  [38]  with default parameters and the vertebrate division of Uni-ProtKB to provide gene description, GO terms, EC keys, KEGG maps and InterPro codes for every sequence. AutoFact  [57]  was used as a second gene description approach based on gene and EST databases. Orthology to zebrafish was determined using blastx with sole transcripts and the information available in ENSEMBL v72 and RefSeq at the time of writing (1/27/14), filtering for E <10 -10 and a minimal identity of 30%. Transcripts were also analyzed with Full-LengtherNext (http://www. scbi. uma. es/fulllengthernext [58] ) available at the Plataforma Andaluza de Bioinformática (University of Málaga, Spain) to provide a third gene description, as well as additional information about transcripts containing full-length ORFs, identification of ncRNAs, and transcripts with a putative start and stop codons and a predicted amino-acid sequence.
Moreover, this software was used to remove or split chimeric transcripts providing a quick preview of the transcriptome and extracting the minimum set of transcripts that can be considered a reference transcriptome. Finally, putative SSRs were detected using Mreps (http://bioinfo. lifl. fr/mreps/ [59] ) with default parameters counting repeats whose period was at least 2 and size at least 12 and a coverage of up to 1000 reads. The putative collection of SNPs was obtained mapping the original reads using Bowtie2 to the corresponding transcriptome and then analyzing the resulting SAM files with SAMtools  [60]  as described in http://samtools. sourceforge. net/mpileup. shtml. To map SSRs on the genome, we performed FISH analyses. Chromosome preparations, probe amplifications and FISH hybridization conditions were as previously reported  [18, 61, 62].
Database architecture SoleaDB was built using Ruby On Rails 2. 0 (http://rubyon rails. org/) that allows the use of a model-view-controller pattern to maintain strict separation between the web interface (views) code, database tables (models), and all methods that handle interactions between views and database (controllers), as well as testing and production environments for each development phase of the database. The database tables were implemented in MySQL. Bulk imports, updates, and database managements were automated by means of Ruby scripts. An automated pipeline that combines all tools described above is executed on every SoleaDB update. SoleaDB can be browsed, retrieved and downloaded at http://www. juntadeandalucia. es/agri culturaypesca/ifapa/soleadb_ifapa/.
Microarray design The microarray probes were designed following the workflow in Figure  7. The 252,416 transcripts of S. senegalensis v3 were analysed based on the Full-LengtherNext status provided for this transcriptome (available in SoleaDB). Transcripts qualified as N-terminal, internal or C-terminal were clustered by sequence to obtain the longest, nonredundant transcript that expands the maximum possible to the 3'-end that enable the design of specific probes in the fast evolving 3'-UTR region  [19]. Complete transcripts follow a similar reduction step but the criteria for representative transcript selection is based only on their length. Then, both collections were combined and clustered to provide the set of longest, non-redundant, annotated transcripts. Since the Agilent eArray (https://earray.
chem. agilent. com/earray/) panel was limited to 45,220 60-mer probes and required the inclusion of 1,417 Agilent controls and 400 features corresponding to replicate probes of putative housekeeping genes (40 × 10), 13,881 additional probes were selected from the set of 21,099 "Coding" transcripts predicted with Full-lengtherNext and OrfPredictor  [63]  after removal of redundant sequences and sorting by testcode index. Since the resulting number clearly overloaded the microarray capacity (51,218 transcripts), these transcripts were sorted according to their testcode index. Then, all the selected transcripts were divided into 8 categories according to Blast2go, Sma3 and AUTOFACT annotations. Those probes that did not satisfied the quality criteria for cross-hybridization (BC3 and BC4) were then discarded and replaced by new transcripts until the total number of probes required for the design of a 4 × 44 format microarray was reached.
Microarray hybridization and qPCR validation RNA labeling, hybridizations, scanning and data processing were performed according as previously described using the Agilent One-Color Microarray-Based Gene Expression analysis (Low Input Quick Amp Labeling kit) along with Agilent One-color RNA SpikeIn kit  [64]. Four pools of larvae incubated at 10 and 36 ppt were analyzed in two 4 × 44 chips. qPCR procedure for microarray validation was performed as previously described  [10, [65] [66] [67]. Three pools of larvae incubated at 10 and 36 ppt at day 3 were analyzed. Real-time analysis was carried out on a CFX96™ Real-Time System (Bio-Rad) using Senegalese sole specific primers (Additional file 10). Real-time reactions were performed in duplicate containing cDNA generated from 10 ng of original RNA template, 300 nM each of specific forward and reverse primers, and 5 μl of iQ™ SYBR Green Supermix (Bio-Rad) in a 10 μl final volume.
Acknowledgements This study has been funded by project  AQUAGENET  ( SOE2/P1/E287 ) program  INTERREG IVB SUDOE  and  European Regional Development Fund (FEDER /ERDF)  and  P10-CVI-6075  from  Junta de Andalucía. PA is supported by a PhD fellowship of  IFAPA (Consejería de Agricultura y Pesca de la Junta de Andalucía ) and funded by the  Operational Program of European Social Fund 2007-2013 of Andalucía , within the priority axis 3 (Expand and improve investment in human capital) in 80%. MR was supported by the  AQUAGENET  project. The authors also thankfully acknowledge the computer resources and the technical support provided by the  Plataforma Andaluza de Bioinformática of the University of Málaga.
Availability of supporting data All 454 and Illumina data have been deposited in the Sequence Read Archive (SRA) database with bioproject numbers PRJNA255461 (http://www. ncbi. nlm. nih. gov/ bioproject/255461), PRJNA241068 (http://www. ncbi. nlm. nih. gov/bioproject/241068) and PRJNA261151 (http:// www. ncbi. nlm. nih. gov/bioproject/261151) for S. senegalensis and PRJNA261810 for S. solea (http://www. ncbi. nlm. nih. gov/bioproject/261810). Microarray hybridization data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE57173 (http://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc=GSE57173). Additional information about the SSR codes for selected Illumina data used for transcriptome assembly in S. senegalensis is also included in the Additional file 1.
In this framework we are interested in strong scaling, i. e. , how fast a given network model of fixed size can be simulated. To explore what is currently achievable using standard offthe-shelf hardware and publicly available software, we compare the results and execution times of three typical network simulations-with and without plasticity. In particular we use the multi-purpose simulation frameworks NEST, Neuron and Brian and compare them with our own simulator Auryn. Auryn has specifically been optimized to study plasticity in large-timescale simulations (up to days of simulated time). To minimize run times in such simulations Auryn uses forward Euler integration and relies on single precision arithmetic. In this work we first analyze the trade-off between simulation precision and simulation speed.
Unless mentioned otherwise we used a 0. 8 ms synaptic delay between all synapses. The delay was chosen as a compromise between a small value, to stay as close as possible to the original  (Vogels and Abbott, 2005) , and a large value, to reduce inter-process communication for the cases where parallel execution was possible. Simulations were run for 60 s of simulated time. To avoid the spontaneous deactivation of the selfsustained activity state  (Vogels and Abbott, 2005) , all neurons received excitatory current input of 200 pA  (Vogels et al. , 2011). All benchmarks were run 5 times for statistics. For each simulation only the run time of the actual simulation was measured. The time to set up the weight matrices or to write data to disk was ignored. To simulate this network we used Neuron (version 7.
, 2005). Neuron models in Auryn are integrated continuously, while weight updates for many standard synaptic plasticity rules are implemented in an efficient event-based way. Quantities that require time continuous integration are typically neuronal state variables describing synaptic conductance and membrane voltage. In many neuronal networks large sets of identical or similar neurons need to be integrated. The required computation can be vectorized efficiently. The advantages of vectorization are the reduction of function calls, the efficient use of layered cache architectures deployed in modern CPUs, and giving the compiler the opportunity to use hardware for single instruction multiple data (SIMD) such as SSE or AVX. Vectorization is therefore widely used in existing simulators such as Brian  (Brette and Goodman, 2011)  as well as in our code.
Since many SIMD instructions can process twice the amount of single precision floating point operations per instruction, Auryn uses single precision arithmetic to increase performance. The logical extension to vectorization is parallelization. To run parallel code Auryn uses the message passing interface (MPI) as a general and versatile back-end to allow parallel simulations on a single machine with multiple cores or distributed over multiple physical machines in a cluster. Frequently used functionality in Auryn is implemented in highly specialized classes. One example is the generation of Poisson spike trains. In our simulations, we often use input from a homogeneous population of Poisson neurons which spike at low and identical firing rates. Hence, Auryn comes with an optimized solution for this particular scenario.
To check whether or not the differences between simulators using different integration schemes could be made negligible, we ran additional simulations for three different Euler time steps in Auryn ( t = [0. 4, 0. 1, 0. 01] ms; reference simulation: NEST with t = 0. 1 ms). We then compared the firing rates of the excitatory neurons in the simulation with Auryn to the ones observed in the reference simulation and find that they are highly correlated regardless of the time step used (Figures  3B, D, F ; Supplementary Figure  S1 ). Moreover, the observed distributions of the interspike-intervals (ISI) and the CV ISI are qualitatively similar in all cases (Figures  3C, E, G ; Supplementary Figure  S1 ). Detailed comparison (K-S test) shows that the observed samples of the rate distributions do not differ significantly from the reference data (Figures  3B, D, F ).
, 1998). Throughout the remaining manuscript we set the time step to 0. 1 ms, which is chosen as a compromise between speed and accuracy. In conjunction with the particular network and the hardware used, this allowed us to simulate the Vogels-Abbott benchmark faster than real-time when using NEST * or Auryn, while the simulation in Brian † was approximately three times slower than real-time. These data show that when precision is not a primary concern, real-time simulations of small neural networks are well in reach using standard hardware and simulation tools. While real-time simulations are important in situations were the network is to interact with the real word-as for instance in robotics applications or in certain in-vitro experiments, for plasticity studies it is often desirable to speed-up simulations as much as possible.
It has been described previously in plastic spiking network simulations, that a substantial part of communication time is spent at the implicit barrier in the AllGather directive, which is commonly used to communicate spikes between nodes, and not the actual communication latency  (Ananthanarayanan et al. , 2009). In particular that means that processes which finish their computation earlier have to wait for slower processes at every sync instance. We were wondering to what degree this effect was present and measurable in our simulations. To address this question we inserted additional code into the Auryn simulations of the 25 k cell network to record the wall clock time of every process after each integration time step. Figure  7A  shows the difference in wall time between two processes in the same simulation in which plasticity was initially disabled.
The observed time differences are small, which suggests that the processes roughly have the same execution time per duty cycle (the simulation performs an AllGather every 0. 8 ms of simulated time). This, however, changes once plastic updates are enabled (Figure  7A ). The number of spikes occurring in each process per duty cycle varies stochastically which in turn influences the execution time of the code responsible for plasticity updates. Since the number of spikes in different processes can vary independently, this gives rise to large fluctuations of the difference between the wall times of both processes. To estimate the impact of this effect on run times in our simulation we extracted the wall timings before each AllGather and determined their respective temporal offsets. We then computed the maximum temporal offset before each sync which correlates with the waiting time until the last process arrives at the barrier.
COMPUTATIONAL COST OF ALTERNATIVE STDP IMPLEMENTATIONS While the fastest run times with Auryn were achieved with an STDP implementation which purposefully breaks strong scaling, NEST uses a different approach to STDP. In particular, in its standard implementation synaptic weights are updated only at the arrival of a presynaptic spike. All previous weight updates caused by postsynaptic spikes are then executed in a batch for which presynaptic traces are computed retrospectively on the fly (Methods). In this approach scaling is preserved because no copies of presynaptic traces have to be kept locally, but the computation of synaptic updates becomes more expensive. To study which approach leads to faster run times we compared the performance of plastic network simulations in Auryn and NEST. To do so without having to implement new plasticity rules in NEST, we limited our study to code that already existed.