dataesr/scientific-paragraphs-categorization huggingface dataset:
Interactions between Functional Groups of Genes Analysis of interactions between functional groups of differentially expressed genes was conducted using the applications ClueGO and CluePedia in Cytoscape software, calculating not only the interactions between polyphenol-affected pathways, but also the role and involvement of modulated genes within the network. Similar to pathways enrichment analyses, disease-related pathways were excluded from the graphical presentation (Figure  3 ). Analysis of interactions between functional groups of genes confirms the predominant effects of polyphenols on pathways involved in cell signaling related to inflammation, endothelial dysfunction, and lipid metabolism. In addition, this analysis demonstrates that TNF and NFKBIA are shared among at least three of the identified clusters of pathways, depicted in different colors in Figure  3.
Transcription Factors We also aimed to elucidate which transcription factors could have their activity modulated by polyphenols and be involved in the regulation of the expression of genes extracted from the literature. Using the bioinformatic tool Enrichr to access the database TRRUST Transcription Factors 2019, we identified N = 37 transcription factors that are significantly associated with our set of mRNAs (adjusted p-value < 0. 05). The top 5 transcription factors retrieved with this analysis include: RELA and NFKB1 with more than 15 hits each, STAT1 with 9 hits, SPI1 and CEBPD (Figure  5A ). Interconnections between predicted transcription factors and polyphenol modulated genes extracted from the literature are depicted in Figure  5B.
miRNA Targets Whilst analyzing the eligible papers for data extraction, we identified N = 5 miRNAs which expression was modulated by polyphenols (Table  1 ). To elucidate their biological functions, we first aimed to retrieve their target mRNAs. To this aim, for each miRNA, their targets were mapped in 3 databases (TargetMiner, TargetScanVert, and miRDB), which were accessed through miRBase. For each miRNA, the targets that were in common for all 3 databases were considered for further analyses, such as pathways enrichment analyses and integration analyses. Using this approach, we obtained: hsa-miR-21-5p:74 targets; hsa-miR-181b-5p:49 targets; hsa-miR-663a:81 targets; hsa-miR-30c-2-3p:290 targets; hsa-miR-34a-5p:278 targets. Interactions between miRNAs and their predicted target mRNAs are presented in Figure  6.
mRNAs, miRNAs and Transcription Factors Integration Analysis To visualize interactions between polyphenol modulated genes (N = 58) and miRNAs (N = 5), as well as the predicted transcription factors (N = 37) that regulate the expression of polyphenol modulated genes, we used the bioinformatic tool OmicsNet. PPIs of proteins that are coded by polyphenol modulated genes are retrieved using the STRING database, and are presented in the middle of the 3D-layer presentation (Figure  10 ). Transcription factors, retrieved using the TRRUST database, are presented above the PPIs, in green color. Modulated miRNAs by polyphenols are presented in blue color. Figure  10  clearly depicts the presence of numerous but specific interactions between these molecules, as determinants of the positive effects of polyphenols on cardiometabolic health in humans.
Moreover, several pathways related to neuronal function have been identified, such as axon guidance or glutamatergic synapse. It has been shown that genes that are expressed in neuronal cells could also be expressed in other type of cells. For example, PPP2R3A, expressed also in blood cells, was found to promote activation of NF-kappa B via coupling to Gα12/13, the small GTPase protein RhoA and RhoA-activated kinase, mechanism which increases expression of adhesion molecules and inflammatory mediators  [93]. Other genes, such as GLUL, SLC1A2, HOMER1, or GRIN2D were observed to be expressed in immune cells (from GEO database (https://www. ncbi. nlm. nih. gov/geoprofiles, accessed on 5 November 2020)). Expression of some of these genes has been observed to be affected in patients with neurological disorders.
SnappNet is available as a package of the well-known beast 2 software. Recently, the MCMC BiMarkers method, implemented in PhyloNet, also extended Snapp to networks. Both methods take biallelic markers as input, rely on the same model of evolution and sample networks in a Bayesian framework, though using different methods for computing priors. However, SnappNet relies on algorithms that are exponentially more time-efficient on non-trivial networks. Using simulations, we compare performances of SnappNet and MCMC BiMarkers. We show that both methods enjoy similar abilities to recover simple networks, but SnappNet is more accurate than MCMC BiMarkers on more complex network scenarios. Also, on complex networks, SnappNet is found to be extremely faster than MCMC BiMarkers in terms of time required for the likelihood computation.
Following this principle, Yu et al. devised a maximum likelihood method  [48] , then a Bayesian sampling technique  [51]. However, using locus trees as a proxy for molecular sequences loses some information contained in the alignments  [16]  and is subject to tree reconstruction errors. For this reasons, recent work considers jointly estimating the locus trees and the underlying network. This brings the extra advantage that better locus trees are likely to be obtained  [56] , but running time may become prohibitive already for inferences on few species. Wen et al. in the PhyloNet software  [52]  and  Zhang et al. with the SpeciesNetwork method  [53]  both proposed Bayesian methods following this principle. Though a number of trees for a same locus are considered during such inference processes, they are still considered one at a time, which may lead to a precision loss (and a time loss) compared to an inference process that would consider all possible trees for a given locus at once.
When data consists of a set of biallelic markers (e. g. , SNPs), the ground-breaking work of Bryant et al. [19]  allows to compute likelihoods while integrating over all gene trees, under the MSC model (i. e. , when representing the history as a tree). This work was recently extended to the MSNC context by Zhu et al  [54]. In this paper, we present a novel way to compute the probability of biallelic markers, given a network. This likelihood computation is at the heart of a Bayesian network inference method we called SnappNet, as it extends the Snapp method  [19]  to networks. SnappNet is available at https://github. com/rabier/MySnappNet and distributed as a package of the well-known Beast 2 software  [57, 58]. This package partly relies on code from Snapp  [19]  to handle sequence evolution and on code from SpeciesNetwork  [53]  to modify the network during the MCMC as well as to compute network priors.
This topology may seem odd but it is intended as the backbone of a more complex and realistic network with subtrees hanging from the different internal branches of the ladder, in which case the complexity issue remains. In summary, SnappNet relies on 16 MCMC operators, described in SnappNet's manual (https://github. com/rabier/MySnappNet). We refer to the original publications introducing these operators for more details  [19, 53]. August 26, 2021 13/42 Simulation study.
Simulated data We implemented a simulator called SimSnappNet, an extension to networks of the SimSnapp software  [19]. SimSnappNet handles the MSNC model whereas SimSnapp relies on the MSC model. SimSnappNet is available at https://github. com/rabier/SimSnappNet. In all simulations, we considered a given phylogenetic network, and a gene tree was simulated inside the network, according to the MSNC model. Next, a Markov process was generated along the branches of the gene tree, in order to simulate the evolution of a marker. Note that markers at different sites rely on different gene trees. In all cases, we set the u and v rates to 1. Moreover, we used the same θ = 0. 005 value, for all network branches. Our configuration differs slightly from the one of  [54]. These authors considered θ = 0.
Accuracy of SnappNet In order to evaluate SnappNet's ability to recover the true network topology, the posterior probability of the true topology was estimated by taking the proportion of sampled network topologies matching the true topology. Note that unlike previous works  [54] , we did not use a measure of topological dissimilarity, because most of the proposed measures can equal 0 even when the network topologies are different  [38, 71]. In order to verify whether a sampled network and the true network have the same topologies, we used the isomorphism tester program available at https://github. com/igel-kun/phylo_tools. We report the average (estimated) posterior probability of the true network topology over the different replicates. For some networks, we also investigated the ability of estimating continuous parameters, including network length (the sum of all branch lengths) and network height (the distance between the root and the leaves).
1) Ability to recover the network topology Tables  2  and 3   to recover the correct topology of network C. We considered one lineage in species O, A and D, and let the number of lineages in species B and C vary. We studied either a) 1 lineage, or b) 4 lineages, in these hybrid species. In order to limit the computational burden for SnappNet, the ESS criterion was decreased to 100 and the number of reticulations was also bounded by 2. In order to closely mimic what was done in  [54]  for networks A and B, we let MCMC BiMarkers run for 1,500,000 iterations instead of adopting an ESS criterion. Data were simulated with simBiMarker  [54]. Indeed, like SimSnapp, SimSnappNet generates only count data (the number of alleles per site and per species). In contrast, simBiMarker generates actual sequences, a prerequisite for running MCMC BiMarkers.
60% reported for m = 100, 000 and 4 lineages, is based only on 8 replicates. Surprisingly, in most cases studied, MCMC BiMarkers was unable to recover the true topology of network C. The different behaviors of MCMC BiMarkers and SnappNet may be due to the different network priors. Indeed, while the frequency of trees, 1-reticulation networks and 2-reticulations networks sampled by the two methods were globally similar (cf. Fig 14) , we remarked that MCMC BiMarkers seems to be unable, for these data sets, to sample networks with two reticulations on top of each other. Alternatively, we may be in the presence of failed or partial convergence of the MCMC process. Note the small ESS values for MCMC BiMarkers, especially when only one lineage was sampled in hybrid species B and C. However, we attempted increasing the number of iterations from 1.
Real data analysis Real data derived from recent studies on rice were used to illustrate the application of SnappNet. Diversity among Asian rice cultivars is structured around two major types which display worldwide distributions, namely Japonica and Indica, and two types localised around the Himalayas, namely circum Aus (cAus) and circum Basmati (cBasmati)  [73, 74]. Japonica and Indica each have several subgroups with geographical contrast (see  [73]  as the most detailed description). Domestication scenarios that have been put forwards since the availability of whole genome sequences propose one to three domestications corresponding either to an early pivotal process in Japonica  [72] , or to multiple parallel dynamics in Japonica, Indica and cAus  [12, 27]  they consider the contribution of domestication alleles by the Japonica origin as predominant or as one among others.
With sufficiently large datasets, SnappNet recovered the correct scenario in approximately 50% of samples whereas MCMC BiMarkers inferred this history in less than 5% of cases. Although these differences may be due to the different network priors used by the two methods, more work is needed to elucidate the reasons behind them. To conclude the discussion on priors, we also observed that, on simulated data, SnappNet's accuracy did not really deteriorate with incorrect priors on population sizes, although assuming a prior distribution skewed towards small population sizes has a tendency to favor hybridization over ILS as an explanation for non tree-like signals. Similar robustness properties were observed by  [54]  for MCMC BiMarkers. The second major difference between MCMC BiMarkers and SnappNet lies in the way they compute the likelihood of a network.
Since network space is significantly more complex than tree space, it is easy to predict that this topic will keep researchers busy for a long time. A good starting point to address convergence issues in SnappNet would be to integrate it to the new Beast 2 package coupled MCMC  [86] , which tackles local optima issues thanks to heated chains. Also note that in this work we limited our experiments to relatively simple networks, with few reticulations and few species (leaves). While the number of reticulations represents a strong limitation of all existing Bayesian approaches, the number of species is a much weaker limiting factor. Networks over more species can already be inferred by SnappNet and related approaches, but MCMC inference for such networks will then necessitate much more complex downstream analyses than the ones used here.
Protocol In the second experiment we assess how population size priors and network priors influence SnappNet's inferences, in particular the rate of convergence and sampling efficiency of the MCMC sampler. Recall that the network prior specifies a hybridization rate, whereas the prior on population sizes affects the probability of coalescence, and therefore that of ILS. Thus, these two priors have an important role in determining the relative probability of hybridization and ILS as causes of incongruent (non-tree-like) signals in the data. We simulated 10,000 SNPs for network A and network B under the multispecies network coalescent using SimSnappNet. For each of these two simulated SNP datasets, we ran 12 (for network A) or 24 (for network B) MCMC chains, for 500,000 iterations each. See Table G for details on the priors specified for each chain.
In this experiment we only use the exponential prior with mean 0. 1 on the network origin. Briefly, as in the experiment of Sec. 8. 1, we specified a network prior using the birth-hybridisation model of Zhang et al. [7]. Again, we fixed the birth rate to 20 for all MCMC chains and chose a hybridisation rate so that the mean number of reticulations is close to zero, one or two. Furthermore we specified either a 'correct' or 'incorrect' prior on population size ('correct' implies the mean of the prior distribution corresponds to the population size parameter used to simulate the SNP dataset). The 'correct' population size prior on each 95% HPD Interval  [-16134. 1, -16116. 4.
Results: We present NIQKI, a novel structure using well-designed fingerprints that lead to theoretical and practical query time improvements, outperforming state-of-the-art by orders of magnitude. Our contribution is threefold. First, we generalize the concept of Hyperminhash fingerprints in (h,m)-HMH fingerprints that can be tuned to present the lowest false positive rate given the expected sub-sampling applied. Second, we provide a structure able to index any kind of fingerprints based on inverted indexes that provide optimal queries, namely linear with the size of the output. Third, we implemented these approaches in a tool dubbed NIQKI that can index and calculate pairwise distances for over one million bacterial genomes from GenBank in a matter of days on a small cluster. We show that our approach can be orders of magnitude faster than state-of-the-art with comparable precision.
Introduction Historically, genomic databases such as GenBank are growing exponentially  1. Lower sequencing costs and required investment, broader access to sequencing technologies, and breakthrough in genome assembly practices will surely fuel the explosion of available genomes in a near future. While those data are still widely unprocessed the ability to explore and delve into such rich archives presents countless applications  (1, 2)  Allowing to query such database at a reasonable cost is a growing research subject  (3) (4) (5) (6). To avoid relying on computation-intensive steps, a good way to approximate the similarity of two sequences is to represent them as a set of k-mer (sub-word of length k) and compare their k-mer contents. Namely, the fraction of shared k-mer, that corresponds to the Jaccard index was found to be a good proxy for Average Nucleotide Identity  (7)  between genomes.
It can either improve the memory footprint of sketches or improve their accuracy by increasing the number of fingerprints used for the same amount of memory. Those techniques were successfully applied in bioinformatics to index and compare genomes databases. Mash  (7)  implemented S minimal values and Bindash  (11)  implement S partitions , b-bit Minhash with optimal densification. Such work showed that genomes and whole sequencing datasets could be precisely compared by using sketches orders of magnitude smaller than their amount of distinct k-mers leading to tremendous performances improvement when working at a large scale. An alternative to Minhash fingerprint is Hyperloglog  (12)  fingerprints. Instead of storing the whole hash, Hyperloglog fingerprints store its log value (e. g. the position of the leading 1).
The interest of such fingerprints is twofold, they are very small, a 64bits hash needs a 6bits Hyperloglog fingerprint (since 2 6 = 64) and as hard to saturate as their associated hashes. In practice, 6 bits seem enough to cover most real-world cases as 64bit hashes seem unlikely to be saturated. The downside is that their collision rate is very high. This fact can be leveraged by using a large number of fingerprints. Dashing  (13)  showed that large S partitions sketches of Hyperloglog fingerprints can lead to very precise estimations of Jaccard index between genomes. Interestingly another approach tried to benefit from the trailing zeroes by combining the b-bit Minhash and Hyperloglog ideas. Hyperminhash (  14 ) build a fingerprint from a given hash using 6 bits to encode the first run of zero (which is equivalent to a Hyperloglog fingerprint) and combine them with the lowest 10 bits (which is equivalent to a b bit Minhash fingerprint) to obtain a 16bits fingerprints.
The interest of this sketch is to be very hard to saturate, due to the Hyperloglog fingerprint, and to limit the number of collisions with the additional Minhash bits. Such fingerprint can be seen as an efficient lossy compression of the hash value that represents more than 16 bits from the hash (if the run length of zero is longer than 6). In bioinformatics, such fingerprint indexes have numerous applications such as very fast clustering or phylogeny construction from large genome collection, finding potentially related genomes from assembled contigs or sequence of interest, quantification of a given gene, strain, or species in databases etc. Their main feature is to be able to find possible matches for a query sequence inside a database, acting as filters to focus only on relevant entries.
They do so by efficiently estimating similarities between query and indexes sequences. The main bottleneck of existing methods is that a query requires the queried sketch to be compared to all sketches present in the database. In that sense, the query is O(N. S) with N the number of indexed entries. With the exponential growth of available genomic resources, it simply cannot be considered scalable. To cope with this algorithmic problem we design a structure able to perform queries in Θ(#hits) where #hits is the number of fingerprint hits between the queried sketch and the indexed sketches. Since the expected output of a query is a list of genomes identifier associated with a number of hits (or an approximation of the Jaccard index) we can consider our query time as close to optimal. However such structure presents an overhead exponential with the fingerprint size.
Methods Index structure. To generalize the concept of indexing a sequence of fingerprints as Hyperminhash, b-bit Minhash, or Hyperloglog, we introduce the notion of sketches. In all the following, we combine bit-vectors of size n and integers of {0,. , 2 n -1}. Let S, W and N be three integers. A sketch p i of size S is a sequence of S fingerprints where a fingerprint is an element between 0 and 2 W -1, i. e. p i = f i,1. f i,S where ∀j ∈ {1,. , S}, f i,j ∈ {0,. , 2 W -1}. We present a new data structure to index a set B = {p 1 ,. , p N } of N sketches where we want to optimize the time complexity of the query find_hits(B, p , m) which corresponds to find all the sketches p i = f i,1. f i,S of B such that |{j ∈ {1,. , S} | f i,j = f j }| ≥ m with p = f 1. f S a sketch and m an integer. Instead of index sketch by sketch, we choose to split the set of sketches B by column et for each column j between 1 and S, we denote by B j the sequence of fingerprints f 1,j.
2 W ) as we allocate S. 2 W vectors to represent all possible values of each partition. Once the index created, inserting the nth sketch p n = f 1. f S is inserting n in the vector f i + i. 2 w. The inserted elements cost Θ(N. log(N )) space and a each partition can be queried optimally in Θ(#hits). If this approach presents a costly overhead, its interest is that query a sketch performs at worst S "costly" random access. We argue that in practice, using relatively small well-designed fingerprints, our scheme can perform very fast queries with a reasonable memory footprint. Optimal Hyperminhash fingerprint. Since our proposed scheme presents an overhead exponential with W , we need to rely on very space-efficient fingerprints and make the most of each bit. When building a fingerprint database, the users tune the sketches' size according to the expected genome sizes they index along with the needed precision according to their application.
Implementation details. Our proof of concept is dubbed NIQKI (stand for Next Index to Query k-mer Intersection) is opensource and available on Github https://github. com/Malfoy/NIQKI. In this section, we detail some practical aspects of this implementation. Our NIQKI index is technically able to index any kind of fingerprint. In practice, from a given fingerprint size (12 by default), we use the best possible (h,m)HMH fingerprint when the user specifies an expected genome size or a regular Hyperminhash   fingerprint if the expert user precise the size of the Hyperloglog fingerprint. If no input is given, we use a default Hyperloglog fingerprint of four. NIQKI is written in C++ and parallelized with straightforward OpenMP instructions. Each thread inserts or queries a set of sequences independently.
Results Fingerprint impact. As we previously evaluated the impact of the size of the Hyperloglog fingerprint, we now aim to evaluate the impact of the fingerprint size itself. The fingerprint size can be seen as the main parameter of our index along with the amount of fingerprint used because our index has a θ(S. 2 W ) memory overhead. More importantly, the fingerprint size will impact the amount of false-positive hits where two distinct hashes in compared sketches get the same fingerprint. Such falsepositive hits over-estimate the similarity and may negatively impact downstream results by reporting irrelevant matches. To access the number of false positives found in practice, we created a synthetic dataset of one thousand randomly generated genomes of 10 million bases that share no k-mers.