dataesr/scientific-paragraphs-categorization huggingface dataset:
One hypothesis to explain the variability in copy numbers between different natural strains links the expression of TEs to the amount of piRNAs  (27). Kelleher and Barbash tested this model in two strains of D. melanogaster. In the present study, using three strains of D. simulans, we found a significant positive correlation between TE read counts and piRNA read counts for each strain (Pearson correlation tests on log transformed read counts: Chicharo: r = 0. 857, P-value < 2. 10 -16 , Makindu, r = 0. 866, P-value < 2. 10 -16 and Mayotte: r = 0. 860, P-value < 2. 10 -16 , Figure  4C ). This finding illustrates a general trend for which an increase in TE transcripts is associated with an increase in piRNA production. This result is expected because secondary piRNAs are implicated in the regulation due to the ping-pong amplification loop.
Thus, we searched for ping-pong signatures in the most highly expressed elements. In Supplementary Fig-  ure S3 , we show that the signature is strong for most of the TEs that have the highest amount of total piRNAs. Moreover, this analysis also reveals TE families that have no associated piRNAs but have reads in the RNA-seq data (197 (sub)families in Chicharo, 186 in Makindu and 222 in Mayotte). This result could indicate that these TEs are absent from piRNA clusters in these specific strains.
TE expression is negatively correlated with piRNA pathway gene activity The analysis of our dataset provides a demonstration of the huge natural variability in TE expression. Indeed, we find significant variation in the levels of TE transcripts between strains and this is correlated with the corresponding piRNA production levels. In a previous study, showed that GIPPs also displayed high transcription and sequence variability  (31). Therefore, we sought to confirm the GIPP variability in the present dataset and explore its relationship with TE expression variability. We focused on subsets of genes involved in the piRNA pathway and used other genes involved in the siRNA and immune pathways for comparison (see Supplementary Table S8 for the complete lists of genes). We find that the piRNA pathway genes are more frequently differentially expressed than other random sets of genes (piRNA pathway 19/19 versus total dataset 7416/16 169, P-value = 0, see 'Materials and Methods' section).
Therefore, the analysis of the present dataset confirms the existence of high intraspecific variability for GIPPs. Subsequently, we tested whether the variability in TE expression was related to GIPP activity estimated by the amount of transcripts. Based on the sum of the read counts for each category of sequences, we find a strong negative correlation between the activity of GIPPs and the global TE expression (Pearson correlation test, r = -0. 93, P-value = 0. 022, Figure  5 ). No significant correlations are found be-.
Advantages of TETOOLS In this manuscript, we present a new analysis pipeline dedicated to the analysis of TE expression for both messenger and small RNAs. Contrary to previous approaches, this method places emphasis on the TE copies rather than on consensus sequences. This approach allows us to consider more reads and thus to reduce the loss of information because we take into account reads mapping at several positions on the genome and the individual copy variability. Moreover, this pipeline uses raw counts as proposed by Anders and Huber  (16) , which is a less biased approach than other normalization methods used for RNA-seq data. The pipeline also allows the use of various types of mapper and expression analysis software. In the current version we use bowtie/bowtie2 and DESeq2, but the use of alternative programs is also possible.
piRNA production is positively correlated with TE expression Previous works on TE dynamics attempted to relate piRNA production to TE copy numbers (26,28,66) but found no significant correlation. A previous analysis of wild-type strains of D. simulans showed that TE copy numbers were not correlated with GIPP expression  (31). Song et al. (28)  found the same result for D. melanogaster inbred lines. Taking advantage of the present dataset, we tested whether piRNA production was related to TE expression instead of TE copy numbers. Indeed, only active (expressed) TE copies are the targets of piRNA inhibition. We find a significant positive correlation between piRNA production and TE expression. The most highly expressed TE families display the highest quantity of piRNAs and vice versa. This result is consistent with the work of Kelleher and Barbash  (27) , which was performed on two strains of D.
FUNDING Agence Nationale de la Recherche  [Exhyb  ANR-14-CE19-0016-01  to C. V. ];  Fondation pour la Recherche Médicale  [ DEP20131128536  to C. V. ];  CNRS ;  Institut Universitaire de France  (to C. V. ). Funding for open access charge:  Agence National de la Recherche.
Equimolar amounts of each library were mixed before NaOH denaturation. The Illumina Truseq PE cluster kit v3 (Catalog ID PE-401-3001) was used to generate clusters on the grafted Illumina Flowcell and the hybridized libraries were sequenced on six lines of a Flowcell on the Hiseq2000 with 100 cycles of a paired-end sequencing module using the Truseq SBS kit v3 (Catalog ID FC-401-3001). Quality control, mapping of RNA sequencing reads and counting mapped reads Initial control quality was assessed by the FastQC software version 0. 11. 3  [52]. Raw reads were than trimmed for low quality bases using the Trimmomatic tool version 0. 32  [53]  applying minimum Phred quality score >10 averaged across the sliding window of five bases. Furthermore, all reads with the length below 40 bp were removed.
The trimmed reads were mapped to the Gallus gallus reference genome (Gallus_gallus-4. 0, release 80  [54] ) using a spliced aligner TopHat2 version 2. 014  [55]. The Gallus gallus gene annotation used for mapping was retrieved from Ensembl database version 80 (www. ensembl. org). The mapping quality was assessed using a set of Python scripts within the RSeQC toolkit  [56]. The quality control assessment included inspection of the read coverage over the full gene body in order to assess if reads coverage was uniform and if there was any 5' or 3' bias as well as how the mapped reads were distributed over genome features. Gene count estimation was performed using the HTSeq-count tool in 'union' mode. The HTSeq-count is a Python script within the HTSeq framework, version 0. 7. 1, which is an open source toolkit that allows the input of raw counts from aligned reads to be annotated with gene names based on genomic features  [57].
Statistical analysis of the differential gene expression The read counts obtained were used to estimate gene expression and identify differentially expressed (DE) genes. This was achieved using Bioconductor package edgeR version 3. 10. 0 [58]  and limma version 3. 24. 5  [59]  following a previously described protocol  [24]. Before performing statistical analysis, genes with low levels of expression were filtered out using a threshold of least one read per million in n of the samples, where n is the size of the smallest group of replicates, which in this case was eight. In order to account for technical and biological effects reads counts were normalized using the "calc-NormFactors" function implemented in the edgeR package. This function normalizes the data by finding a set of scaling factors for the library sizes that minimizes the log-fold changes between the samples.
Acknowledgements EH benefited from a joint grant from the  European Commission and Cobb -Vantress Inc. within the framework of the  Erasmus-Mundus joint doctorate "EGS-ABG ". RBK, TSD, HJM, and BB were supported by the project " Development of genetic selection technology for polyvalent resistance to infectious diseases" (POLY-REID ) (grant number  10-093534 ) granted by the  Danish Council for Strategic Research , the  Danish Poultry Council, The Hatchery Hellevad, and Cobb -Vantress Inc. This work was also supported by a grant from the  European Union Seventh Framework Network of Animal Disease Infectiology Research Facilities  (NADIR; reference number  FP7-228394 ).
Availability of supporting data Raw and analyzed RNA-Seq data for this project have been deposited in GEO under accession code GSE73423 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE 73423). Other supporting data are included as Additional files 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10.
Background: In a structure-based virtual screening, the choice of the docking program is essential for the success of a hit identification. Benchmarks are meant to help in guiding this choice, especially when undertaken on a large variety of protein targets. Here, the performance of four popular virtual screening programs, Gold, Glide, Surflex and FlexX, is compared using the Directory of Useful Decoys-Enhanced database (DUD-E), which includes 102 targets with an average of 224 ligands per target and 50 decoys per ligand, generated to avoid biases in the benchmarking. Then, a relationship between these program performances and the properties of the targets or the small molecules was investigated.
Results: The comparison was based on two metrics, with three different parameters each. The BEDROC scores with α = 80. 5, indicated that, on the overall database, Glide succeeded (score > 0. 5) for 30 targets, Gold for 27, FlexX for 14 and Surflex for 11. The performance did not depend on the hydrophobicity nor the openness of the protein cavities, neither on the families to which the proteins belong. However, despite the care in the construction of the DUD-E database, the small differences that remain between the actives and the decoys likely explain the successes of Gold, Surflex and FlexX. Moreover, the similarity between the actives of a target and its crystal structure ligand seems to be at the basis of the good performance of Glide. When all targets with significant biases are removed from the benchmarking, a subset of 47 targets remains, for which Glide succeeded for only 5 targets, Gold for 4 and FlexX and Surflex for 2.
To achieve the latter benchmarks, a database with several active and inactive compounds is required. The DUD database  [7]  (Directory of Useful Decoys) had been specifically designed for this purpose in 2006. It included 40 targets (of which 30 enzymes) and a total of about 3000 active compounds and 36 fold more decoys. This database was used for several benchmarks of VS programs  [8] [9] [10] [11] [12] [13] , either to compare the programs performance or to improve the scoring functions and the docking procedures. However, despite the seriousness of its construction, DUD was criticized because of some biases, namely the difference of the mean formal charge between actives and decoys  [14] , the similarity between the actives  [15]  and the small number of different targets  [16]. This criticism brought Mysinger and al.
[17]  to release in 2012 a new version of the DUD database, DUD-E, "E" standing for "enhanced". In DUD-E, these problems were addressed first by increasing the number of proteins from 40 to 102 and diversifying them (only 61 proteins were grouped in 6 families: GPCR, Kinases, Nuclear Receptors, Proteases, Cytochrome P450 and Ion Channels). Second, the average number of ligands per target was increased from 98 in the old version to 224 in the new one and their diversity was based on their Bemis-Murcko atomic framework clustering. Finally, the number of decoys was increased from about 36 in DUD to 50 decoys per ligand in DUD-E, corresponding to about 2 % actives for each target, and their properties improved to have the same average net charge as the actives. With the total number of compounds exceeding 1.
4 million  (22, 886  actives and 1,411,214 decoys), DUD-E is one of the largest databases publicly available that offers the possibility to assess VS programs efficiency in discriminating ligands from inactive compounds. The benchmarks of programs were criticized  [15]  because their results depend on the calculation setup, the metrics used to evaluate the performance, the choice of the targets and the active/decoys selections. In this article, we use the DUD-E database to perform a benchmark of four of the most popular docking programs  [18] , Gold  [19] , Glide  [20] , Surflex  [21]  and FlexX  [22]. Our key objective is not only to compare their performance but mainly to determine what would influence it for each of them. The question addressed here is about the relationship between the performance of these programs and the protein target properties and/or the small molecules properties.
Docking enrichment The docking enrichment (DE) is an intuitive parameter, easy to apprehend. It represents the percentage of true positives found (h) among all the active molecules (n) for a given percentile of the top-ranked compounds (x %) of a chemical library: DEs for the 0. 5, 2, and 8 % top-ranked compounds were calculated (Fig. 1 , upper panels). We chose to focus on these percentiles because 2 % represents the proportion of active compounds in the DUD-E database, and 0. 5 and 8 % correspond to 4 times smaller and higher proportions, respectively. The value of 0. 5 % allows us to evaluate the performance of the programs in the early recognition VS, which is important when only few compounds can be tested experimentally, whereas 8 % is interesting for the cases where relatively high-throughput experiments are available.
Subsets of protein families We started by determining the protein families of the DUD-E database. These families were only partially based on the target subsets established originally in DUD-E  [17] , because we noticed that, in this database, the proposed families might contain targets with too low structural homologies. Therefore, keeping the DUD-E classification in mind, we determined the protein families according to three criteria: the sequence identity, the protein function and the protein fold. The sequences of all 102 proteins of the DUD-E were aligned using the Clustal Omega method  [25]. All pairs of proteins with more than 20 % sequence identity were analyzed. If these pairs shared, in addition, the same protein function and a similar protein fold, they were grouped in the same family.
The protein binding site properties To find out which properties of the protease family gave rise to such good scores, especially with Gold, the characteristics of the binding site for all the protein targets of the DUD-E database were investigated. Two properties would characterize well a protein binding site, its exposure to the solvent and its hydrophobicity. The exposure property was calculated using the SiteMap algorithm  [27]  from the Schrödinger suite, where it is described to "provide a measure of how open the site is to solvent". Regarding the hydrophobicity of the binding site, it did not seem to us appropriate to consider the nature of the residues (hydrophobic/hydrophilic), because even a hydrophilic residue may establish hydrophobic contacts with a small molecule and vice versa.
For α = 321. 9, the small correlation between the hydrophobicity of the binding site and the performance of FlexX is maintained (correlation coefficient of -0. 32 and p-value <10 -2 ) but not that of Gold, whereas for α = 20. 0, both program correlations, although questionable, are comforted with coefficients equal to -0. 36 for Gold and -0. 32 for FlexX and p-values <10 -3. In addition, we considered the size of the binding site. This size, as calculated by SiteMap, is well-correlated to the exposure of the site (r = -0. 4 and p-values <10 -4 ). Therefore, it does not constitute an independent variable. We instead took into account the total number of atoms in the binding site that are at less than 4 Å from the protein surface. The interest of this property is that it represents the total number of possible contacts that the protein may establish with the ligand that binds to its site.
Relationship between the programs performance and the small molecules characteristics The small molecules properties Since no clear correlation was observed between the protein binding site and the performance of the programs, one may ask if such correlations exist between the performance and the chemical libraries properties. To monitor this aspect we calculated 365 physicochemical properties for each small molecule using the Canvas program  [30]  from the Schrödinger suite. Based on these calculations, for each of the 102 chemical libraries, corresponding to the 102 targets of the DUD-E database, we considered separately two molecule subsets, the true actives and the decoys, and computed the averages of the properties for each of these subsets. Only the most relevant and non-redundant (despite some correlations between them) of these average properties were kept for the analysis: the molecular weight (MW), the octanol/water partition coefficient (AlogP), the electrotopological state (E-state), the polar surface area (PSA), the number of hydrogen bond acceptors (HBA), the number of hydrogen bond donors (HBD), the ring count (RC), the number of rotatable bonds (RB) and the embranchment count (EC).
The electrotopological state describes the intrinsic electronic state of the atom, taking into account the electronic influence of all other atoms in the molecule  [31]. The embranchment count was designed to account for the number of all atoms that establish three or more bonds, thus creating ramifications (see "Methods" section). Note that some of the nine properties considered here were taken into account by the authors of the DUD-E database for the construction of the decoys (MW, miLogP, RB, HBA and HBD)  [17]. For the 102 targets, the average properties of true actives and decoys were compared (Fig. 6 ). A more or less important systematic drift was observed: for the high values of the property, the actives reach generally higher average values than the decoys. We will see below that this bias may play an important role in the good performance of some programs.
Correlation between the small molecules properties and the programs performance The correlations between the program BEDROC scores and the nine properties of the true actives or the decoys were calculated. The results are summarized in Fig. 5 , middle panel for α = 80. 5, and Additional file 1: Figure  S4  for α = 321. 9, and α = 20. 0. They are qualitatively similar for the three α values, therefore the analysis is focused on the results of α = 80. 5. Glide performance is not correlated with any of those properties, whereas the three other programs performances, Gold, Surflex and FlexX, are significantly correlated to two or three properties among the following: PSA, HBD and EC. When the BEDROC scores of a program are correlated with PSA or HBD of the actives, they are also more or less Fig.
17 and Surflex's for Sim2D cryst-act > 0. 24), this was not the case of Gold and FlexX, where all targets with high Sim-2D cryst-act did not obtain high scores. What is common between Gold and FlexX is that their positioning of the ligand is rather free, using a genetic algorithm for Gold and based on ligand fragments for FlexX, while it is made in a certain framework for Glide and Surflex, i. e. , within a grid for Glide and using a protomol that fits the cavity in Surflex. Therefore, the relationship between the program performance and the similarities with the crystal structure ligand may be imputable to the positioning procedure of the program.
Conclusion From this study we may retain that, whatever the program, Gold, Glide, Surflex and FlexX, the VS performance is rather medium, since BEDROC scores >0. 5 were obtained for less than half the proteins of the DUD-E data set. There are programs that seemed to perform better than others, like Gold and Glide. However, it appears that the good performance of a program in discriminating between active molecules and decoys is mainly due to biases, although small, either in the construction of the chemical libraries or in the similarities between the actives and the crystal structure ligand. Therefore, the meaning of program performance classification has to be questioned. However, based on this VS benchmark, we propose some properties of the programs that could be of interest. Glide, and to a lesser extent Surflex, appear to be the most efficient programs to retrieve ligands close to the crystal structure, due to their positioning algorithms.
Target families In order to cluster the targets into families, we used the PDBAA database available on the NCBI web server, http://www. ncbi. nlm. nih. gov/books/NBK62345/. This database contains the protein sequence of all the PDB entries. The sequences of the 102 targets of the DUD-E were extracted from this database and aligned using the program Clustal Omega  [25]  available on the web server of the EBI (European Bioinformatics Institute, http:// www. ebi. ac. uk/Tools/msa/clustalo/). The target pairs with more than 20 % sequence identity were visualized and homologous structures with similar functions were gathered in families.
2D similarities To calculate Sim2D act-act and Sim2D cryst-act , first the 2D fingerprints of the molecules were calculated using the canvasFPGen  [30]  module, then the similarities were estimated using the Tanimoto method  [32] , all from the Schrödinger suite. For canvasFPGen, the default settings were conserved, i. e. , the linear fingerprint type with a 32 bits precision. Considering the Tanimoto method, the similarities range between 0 (no similarity) and 1 (same molecule). The similarities were then averaged, for each target, over the entire set of actives.
Conversely, the proposed annotations are not indispensable for the description of a functional application. Annotations can be added to an application description only in late stages of development, when optimization of the application memory footprint is needed. -Independence from the host language: The host language is the language used to specify the internal behavior of actors. The proposed optimization technique is not based on an automated analysis of the host code of actors. Instead, a script-based description of the mergeable buffers is provided by the application developer. This abstract description can be suitable for several implementations of a given actor in different host languages. -Lightweight and intuitive information: The scripts specifying the memory reuse opportunities for an actor are never longer than ten lines of code, and can be tested independently from the internal computations performed by this actor.
Actors Annotations with Memory Scripts The first input used to abstract the internal behavior of actors is called a memory script. The objective of memory scripts is to allow the application developer to to specify which input buffer can be merged with which output for a given actor, and the relative position of the resulting merged buffers. To this purpose, each actor of the SDF graph can optionally be associated with a memory script. Memory scripts are interpreted at compile time for each actor of the single-rate graph. For each actor, the script inputs are: a list of the input buffers, a list of the output buffers, and a list of parameters influencing the behavior of the actor. The script execution produces a list of matches between the input and output buffers of the actor. Each match associates a sub-range of bytes from an input buffer with a sub-range of bytes from an output buffer.
Applying a match consists of merging the memory allocated to the two sub-ranges in a unique address range of the memory. Figure  6  presents the memory script associated with the user-defined Split actor and illustrates the matches resulting from its execution. Memory scripts are written with a derivative of the Java language called BeanShell  [Niemeyer 2014 ]. The h, w, and n parameters correspond to the height and width of the sliced image and the number of slices produced by the actor respectively. Figure  6b  illustrates the matches obtained for the case of h = 9, w = 8, and n = 3. The output buffer of the Split actor is divided into three sub-ranges of equal size (h/nb slice +2) * w. Each output sub-range is matched to the corresponding sub-range of the input buffer, thus creating overlaps between the matched input sub-ranges.