dataesr/scientific-paragraphs-categorization huggingface dataset:
We removed mono-exonic transcripts wrongly defined because of issues with the read aligner TopHat (in which a read can be aligned to multiple positions with the same mapping score). We also observed numerous mono-exonic transcripts of the same directionality grouped in clusters, and these were frequently found 3′ of multi-exonic transcripts. Since these arrays could result from the incomplete annotation of single longer genes or extended multiexonic transcripts, we merged those transcripts present within a 2-kbp genomic interval of a 3′ end (after having tested multiple size windows and assuming that, theoretically, the number of independent mono-exonic genes on the same strand and on the opposite strand 3′ to a gene should be the same). Finally, since mono-exonic genes can be small, their FPKM values can be relatively high, resulting in artefacts caused by the background noise in RNA-Seq datasets.
We therefore re-quantified mono-exonic genes based solely on read count, and removed lowexpressed ones based on cutoffs determined using normalised random intergenic regions. By performing these corrections on the raw Cufflinks output, the number of multi-exonic transcripts was increased from 65,334 to 67,112 and the number of mono-exonic transcripts was decreased from 48,232 to 15,827. Of note, the majority of removed transcripts were shorter than 1 kbp, while the additional transcripts recovered were predominantly longer than 5 kbp (Fig. 1a ; Figure  S1c  in Additional file 1). The output of our Cufflinks assembly and curation is presented as an annotation track (. gtf file) in Additional file 5.
Transcriptome-related bioinformatic analyses The reference transcriptome used in this study was generated using Cuffmerge (Cufflinks v. 2. 1. 1) by merging Ensembl, UCSC and RefSeq gene models downloaded from UCSC Table Browser as available on 1 October 2014. Genes were defined as in Cuffcompare within Cufflinks v. 2. 1. 1 output. Oocyte gene coordinates were defined as the most upstream start and the most downstream end coordinates from all transcripts per gene. Transcripts were categorised into reference and novel by Cuffcompare, with categories =, c, j, and o marking the transcripts of reference genes and categories i, u, and x novel transcripts. CGIs and igDMRs were defined as published  [7, 9, 49, 50]  and lifted over using the UCSC liftover tool into the GRCm38 assembly, removing CGIs on Y chromosome.
CGIs were classified as TSS-associated if they overlap the most upstream TSS of a gene ±100 bp, intragenic if they overlap the gene but are not at the TSS, and intergenic without gene overlap. Coordinates for TEs (L1 and L2 LINEs, S2 and S4 SINEs, ERV1s, ERVKs, ERVLs, MaLRs) for the mouse GRCm38 genome build were generated using RepeatMasker. TSSs were classified as CGI-associated if a first base pair of a gene or transcript ±100-bp overlapped a CGI and as TEassociated if a first base pair of a gene or transcript overlapped a TE on the same strand. Expression of assembled transcripts in published oocyte, embryonic, and differentiated cell datasets (Table  S2  in Additional file 2) was quantified using Cufflinks v. 2. 1. 1 (option -G). Expression of genes was determined as a sum of FPKM values of all transcripts per gene.
Expression levels in individual embryonic datasets (single cells) were merged per stage taking the total read count in each dataset into consideration. Expression of upstream and reference TSSs at each stage or cell type was estimated in Seqmonk for exons containing upstream or reference TSSs as read count quantification corrected for length and then manually corrected for read count in individual or merged datasets to obtain RPKM values. To perform hierarchical clustering, only genes with a FPKM value of at least 0. 1 in at least one dataset were selected. Log transformed values were mean-centred and clustered based on Pearson's correlation using the hclust function in R v. 3. 0. 2. All statistical analyses (chi-squared tests) were performed in R v. 3. 0. 2.
Genome-wide DNA methylation analysis To define hyper-and hypomethylated domains (HyperD, HypoD), probes were designed over CpGs with data  [35] , merging 50 consecutive CpGs with step size of ten CpGs. Methylation percentage level was then quantified taking into account only CpGs covered by at least five reads and a minimum of three positions to count a probe. Exported data were then processed using custom Perl scripts (available on request) as shown in Figure  S4  in Additional file 1. Overlapping windows with methylation level >75 % and <25 % were merged into HyperDs and HypoDs, respectively, splitting overlapped regions between HyperDs and HypoDs into halves. Then, neighbouring domains of the same status were merged if a gap between them was <2 kbp, or if there was a small domain (<1 kbp) of the opposite status between them.
Small domains (<2 kpb) were then removed and, again, neighbouring domains of the same status were merged if a gap between them was <2 kbp. For correlation with the transcriptome, CGIs, TSSs, and 1-kbp regions (three CpGs with at least three reads) with ≥50 % methylation in NGOs or DNMT3Aor DNMT3L-deficient oocytes were excluded from the domains using a custom Perl script. TSSs excluded from the domain designation were defined as 2-kbp regions downstream of a gene's most upstream TSS. If a domain was divided into more parts, the information about the parental domain was preserved for adequate correlation with transcription and other features. Oocyte contigs were defined as genomic regions with three or more reads on the same strand in at least one of the oocyte datasets. Enrichment in ERVK elements and in intermediate levels of methylation (25-50 %) in NGOs or DNMT3A-and DNMT3L-deficient oocytes was quantified by the comparison of numbers of nontranscribed (<25 % overlap by transcripts) HyperDs and HypoDs with >50 % overlap with these features, requiring p value <0.
0001 in chi-squared test. A FPKM threshold of 0. 5 for gene bodies remaining unmethylated was defined by quantification of the proportion of unmethylated gene bodies from all gene bodies with increasing FPKM values (0-0. 1, 0. 1-0. 2, 0. 2-0. 3, etc. ). Below a FPKM of 0. 5, more genes were unmethylated than methylated. CpG density and GC content were quantified using a custom Perl script from GRCm38 genome assembly. All methylation levels were quantified in Seqmonk, using the following parameters: three CpGs with a minimum of three reads depth to count a probe for gene bodies; ten CpGs with a minimum of five reads depth for CGIs; a minimum of five reads depth for individual CpGs. Statistical analyses were performed in R v. 3. 0. 2.
Acknowledgements We thank all members of the  Kelsey laboratory  and  Wendy Dean  for helpful discussions,  Kristina Tabbada  for assistance with Illumina sequencing, and  Solenn Patalano  for help with RNA-Seq methodology. This work was supported by  MRC Programme  grant  MR/K011332/1 ,  MRC  Project grant  G0800013 ,  FP6   SAVEBETA , and a  Babraham Institute  and  University of Cambridge  graduate student scholarship.
Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug combination responses retain this experimental search space, as model definitions typically rely on extensive drug perturbation data. We developed a dynamical model representing a cell fate decision network in the AGS gastric cancer cell line, relying on background knowledge extracted from literature and databases. We defined a set of logical equations recapitulating AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances.
Logical modeling of gastric adenocarcinoma cell fate decisions Construction of a regulatory graph encompassing key signaling pathways. AGS cells harbor mutations in numerous genes encoding key signaling components known to be deregulated in gastric adenocarcinoma, for instance components of MAPK, PI3K, Wnt/β-catenin and NF-κB pathways  [24, 25]. Based on knowledge gathered from databases and scientific publications, we have integrated information about the MAPK pathways (JNK, p38 MAPK and ERK), the PI3K/AKT/mTOR pathways, the Wnt/β-catenin pathway, and the NF-κB pathway, as well as crosstalk between these pathways (see  Fig 2, Materials and Methods, and S1 Text). The resulting network comprises 75 signaling and regulatory components (proteins, protein complexes and genes) and 149 directed interactions.
Two readout nodes (outputs), named Prosurvival and Antisurvival, are included to represent cell fate phenotypes. The regulatory graph with annotations is available in SBML format (see S1 Dataset and S1 Table ). Construction of a logical model. The regulatory network was converted into a logical model, where the local activity state of each component (node) was represented by a Boolean variable (taking the values 0 or 1). A few nodes were associated with multileveled variables: the two output nodes, Prosurvival and Antisurvival, each taking four values (0, 1, 2, 3), and their immediate upstream nodes, Caspase 3/7 and CCND1, each taking three values (0, 1, 2). These multilevel variable nodes are only used for nodes governing the outputs of the model, and enabled us to model graded growth promoting/inhibitory effects (see Materials and Methods and S1 Text).
In addition, the value of the readout nodes Prosurvival was at its maximum, and Antisurvival at its minimum, representing strong proliferation (Prosurvival = 3, and Antisurvival = 0). This model stable state is thus consistent with published knowledge about molecular states in actively growing AGS cells. This model also complied with results from published perturbation experiments of AGS cells (see S1 Text and S6 Table ). The resulting logical model, encoded with the software GINsim v2. 9, is shown in Fig 2. The corresponding GINsim file is provided as S2 Dataset.
The computation of potential complex attractors is challenging because of the combinatorial explosion of states for large logical models. To cope with this problem, we used a model reduction method to obtain a compressed model preserving the selected drug targets, and compacted the state transition graphs in a hierarchical manner (see Materials and Methods and  [14] ). The reduced logical model (see  Fig 3 and S3 Dataset)  was obtained by iteratively removing components not targeted by drugs, and was sufficiently small to allow exhaustive asynchronous simulations and thorough characterization of both stable states and complex attractors, thereby enabling the analysis of all single and pairs of inhibitions. To ease interpretation, we defined the overall response growth, by subtracting the value of Antisurvival from the value of Prosurvival readout nodes (each multi-valued with state ranging from 0 to 3), with a value range from -3 to +3.
With such a success rate, it would have been sufficient to test only a quarter of the 21 possible drug combinations investigated and still not miss any synergistic pair. Our results are encouraging in light of the success rate reported from the recent DREAM challenge  [7] , where the best-performing method of synergy prediction would have allowed halving the size of screening experiments. However, there are important differences between our study design and that of the DREAM challenge: DREAM analyzed transcriptome changes following broad-acting chemotherapeutic drug treatments, while we investigated the action of inhibitors with specific targets, relying only on information from the unperturbed system. Contrary to network-based strategies, which commonly use correlation analysis of largescale datasets from different disease phenotypes  [45, 46] , or large-scale cell culture drug perturbation data to train models for drug response predictions  [6, 7, 9, 47, 48] , our modeling-based strategy exploits mechanistic molecular pathway knowledge, available in databases, along with baseline data from the unperturbed cancer cells of the chosen experimental system.
The configuration of component activities can then be inferred from baseline biomarkers measured in cancerous cells. Together, these model design principles enable us to generate a dynamic model tailored to specific cancer cells, yet not dependent on explicit extracellular input from specific growth promoting agents (e. g. growth hormones) and without the need for initial large-scale inhibitor perturbation data that would be difficult and costly to obtain. Our definition of drug synergy in experimental validation is based on Loewe additivity  [28] , and synergies are quantified with the combinatorial index  [29]. Several mathematical frameworks have been proposed to determine drug synergy  [53]. We chose the Loewe method as it is extensively used, and it correctly handles the sham zero-interaction experiment where a drug is 'combined with itself'.
Construction of the signaling network We used pathway information from databases as a source of signaling components, and included interactions for MAPK pathway (JNK, p38, ERK), the PI3K/AKT/mTOR pathway, the Wnt/β-catenin pathway and the NF-κB pathway. Each protein was annotated with its official gene symbol, Uniprot protein identifier, and mutational status in our AGS gastric adenocarcinoma experimental system. All interactions were substantiated by bibliographical references documenting experimental evidence. The model was composed of 75 signaling components and 149 directed interactions.
4. TCF: the impact of negative regulation by NLK was reduced so that TCF was active in the unperturbed case, in line with published data  [55, 66]. A listing of all model components and logical rules is provided in S5 Table, along with a model file to be opened with the software GINSIM v2. 9 or newer (model in S2 Dataset). Logical simulations. Using GINsim  [67] , we can compute the dynamical behavior of our AGS model for any initial state. The state of each model component is then iteratively updated, according to the logical formulae. The resulting dynamics is represented in terms of a state transition graph (STG). The nodes of the STG denote the states of the system, i. e. discrete vectors encompassing the activity values of all components (Boolean variables, except for the four multi-valued components), while the arcs connect successive states, denoting "state transitions".
Such distortion of the dynamics was assessed by checking the behavior of the original model or by using alternative reductions. For all reduced instances of the model, we found either a single stable state or a single complex attractor. Hierarchical state transition graphs. The analysis of state transition graphs becomes intractable as their size increases. To ease its interpretation and its manipulation, an STG can be compressed into a hierarchical transition graph (HTG), which preserves its main structural properties: as the STG is constructed, its nodes are gathered into groups of states sharing the same set of successors  [14]. The resulting HTG displays all reachable attractors, and their basins of attraction. Simulation of model perturbations. Using the logical formalism and GINsim, it is relatively straightforward to encode model modifications that account for different kinds of genetic or pharmacological perturbations: Simulation of drugs blocking the activity of a component, or knock-out of genes encoding specific components, is performed by fixing the activity value of the drug target component to the Boolean value 0.
All inhibitors were tested in combinations of 0. 5x GI50 concentrations, and two two-fold dilutions (0. 25x and 0. 125x GI50). Single inhibitors were also tested in the same experiment at GI50 concentrations and two two-fold dilutions (0. 5x and 0. 25x GI50). Whenever the combination of two drugs at 0. 5x GI50 was more effective than either of the single drugs at 1xGI50, a synergistic combination was declared. The combination index (CI) was calculated for each combinatorial experiment according to Chou and Talalay  [29] , using the CompuSyn software  [68]. CI was calculated from growth measured at 48 hours after adding inhibitors, because at this point the growth inhibitory effect had reached a stable level (see S1 Text). The combinatorial index is a mathematical description of synergy, based on a median-effect plot made from specific dose-response data  [68].
Numerical formulation for bubble dynamics problem To solve this problem numerically, we consider a projection method originally developed in  21  and also used in  8, 16. The method has been implemented in the numerical code PARIS, which is an acronym for PArallel Robust Interface Simulator, and it is freely available under the GPL license agreement. The projection method solves the system of equations in (1) by first calculating a provisional velocity field u *. This field is obtained by integrating all momentum contributions with the exception of the pressure P: u * -u n Δt = -u n • ∇ h u n , (5) where ∇ h is the discrete gradient operator. The superscript n refers to the n th time step of length Δt. The velocity at the next time n + 1 is then obtained by adding the pressure contribution to the provisional velocity u * u n+1 -u * Δt = - 1 ρ * ∇ h P * , (6) where ρ * is the interpolated density across the staggered grid.
The resulting col6a1 mutants (col6a1 ama605003 hereafter and in ZFIN and ZIRC databases) represent the first model to study collagen VI-related disorders based on a stable zebrafish line and will give the opportunity to study the disease's mechanisms and progression from embryo to ageing fish. Moreover, the col6a1 ama605003 line will facilitate the search for a treatment for these disorders since zebrafish are particularly amenable to pharmacological screens.
Confirmation of mutations by sequencing To confirm mutations in the col6a1 gene in mosaic F0 embryos or adult fish positive by T7 endonuclease I assay, PCRs on lysates encompassing the targeted site were cloned in the TA-pGEM-T Easy Vector (Promega) according to the manufacturer's protocol and transformed in One Shot TOP10 Chemically Competent bacteria (Life Technologies). Upon overnight bluewhite antibiotic selection, PCRs were performed on white colonies using the GoTaq Flexi DNA polymerase (Promega) in standard 10 μl reactions. PCR products were purified using a Nucleo-Fast 96 PCR Plate and sent for sequencing by the reverse primer. Sequences were aligned to the reference sequence using the CLC Main Workbench program (Qiagen). Only insertions/ deletions ! 2 bp were considered as mutations due to TALEN activity in order to eliminate possible errors generated in the amplification process.
Fish were tracked by the VirtualDub software (http://www. virtualdub. org) using a tracking plug-in developed by Yves Lhuillier. The total distance swum and the geographic coordinates of each fish in each frame were automatically determined. The distance swum between two consecutive frames was calculated from the geographical coordinates by triangulation. Instantaneous velocities were calculated for each second as the sum of the distances obtained every 25 frames and the maximum value of instantaneous speed was determined for each fish.
This was probably due to a greater fragility of mutant myofibers towards the contraction-induced fixation artifact  [48]. Generally, the muscular tissue of col6a1 ama605003 mutants appeared more fragile and the myofibers were not tethered to each other as well as in WT. The Richardson's staining appeared also weaker in the altered cells than in the healthy ones (Fig 5B  and 5C ). The presence of apoptosis was assessed by TUNEL on cryostat sections of trunk skeletal muscles at 3 wpf and 4 mpf on WT as well as on HT and HM col6a1 ama605003. No obvious difference in TUNEL was found between WT and either col6a1 ama605003 mutant (data not shown). Finally, at 5 mpf, hematoxylin-eosin-safran or Masson's trichrome stainings of transversal paraffin sections of white muscles were performed to visualize cell nuclei and collagen content respectively.
Acknowledgments We would like to thank  Emmanuel Dijoux ,  Céline Haye ,  Laurent Legendre  and  Benjamin Maurice  for excellent animal care at the AMAGEN fish facility, and  Sosthène Barbachou ,  Myriam Cousseau ,  Noémie de Crozé ,  Joanne Edouard ,  Emilie Guillon ,  Naïma El Khallouki ,  Emilie Menant  and  Yvette Monfils , for assistance and discussions. We are thankful to  Valérie Allamand ,  Matthew Guille ,  Françoise Hermel  and  Sylvie Retaux  for critical reading of the manuscript. We thank  Yves Lhuillier  for assistance with Virtualdub software (http://www. virtualdub. org). This work has benefited from the  EM facilities and expertise of the Imagif Cell Biology Unit of the Gif campus  (www. imagif. cnrs. fr).
frm. org). The electron microscopy facilities of the  Imagif Cell Biology Unit of the Gif campus  (www. imagif. cnrs. fr) is supported by the  Conseil Général de l'Essonne  (http://www. essonne. fr). The authors acknowledge the  France-BioImaging infrastructure  (http://francebioimaging. org) supported by the  French National Research Agency  ( ANR-10-INSB-04-01 ; http://www. agence-nationale-recherche. fr). This work has benefited from the support of  TEFOR-Investissement d'avenir  (# ANR-II-INBS-0014 ; http:// tefor. net/main/pages/main/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funder provided support in the form of salaries for ZR, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
WGD may represent a major evolutionary event in the study of adaptive mechanisms to carnivorism in rainbow trout. Indeed, after whole genome duplication, several scenarios can occur: the duplicated genes can be lost or fixed and maintained with three distinct outcomes, i. e. neofunctionalisation, subfunctionalisation, and conservation of function  (14). Whole genome duplication via gene duplication is thus a source of genetic novelty and can lead to adaptive innovation. In the study of the gluconeogenic pathway, Seiliez et al. (2011,  (49) ) were the first to grasp this feature of rainbow trout by identifying a second gene encoding a glucose-6-phosphatase (called g6pase-2) in EST databases (Expressed Sequence Tag, SIGENAE, http://www. sigenae. org) and showing that both genes displayed differential expression patterns in trout fed a high carbohydrate diet.
While the original gluconeogenic pathway in rainbow trout has been well documented by relating measurements of enzyme activity or gene expression gathered in EST databases, this metabolic pathway has never been considered to our knowledge from a genomic point of view, probably due to the unavailability of the genome sequence. Analysis of the recently sequenced rainbow trout genome (3) can now shed new light on processes which contribute to the hyperglycaemia and poor utilisation of dietary carbohydrates in this species. The aims of this study, focusing on the liver (the main gluconeogenic organ) in rainbow trout, were to characterise i) the evolutionary history of specific hepaticexpressed gluconeogenic genes in teleosts, i. e. cytosolic phosphoenol-pyruvate carboxykinase 1 and mitochondrial phosphoenol-pyruvate carboxykinase 2 (PCK1 and PCK2), fructose-1,6-bisphosphatase 1 (FBP1;  (13, 55) ) and glucose-6phosphatase 1 (G6PC;  (25) ) and ii) their expression profile under different nutritional conditions (i.