dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, understanding RNA function beyond protein coding requires a detailed knowledge of RNA 3D structure and dynamics as well as thermodynamics, which strongly governs the folding of RNA and RNA-RNA interactions as well as other interactions within the environment. Experimental determination of 3D structures of RNA molecules at high resolution is largely owing to the work of X-ray crystallography, and NMR spectroscopy, but it is very laborious and requires substantial expertise and resources (review:  [4] ). Consequently, the majority of known RNAs (e. g. , represented by sequences in the RNAcentral database  [5] ) remain structurally uncharacterized. To help cull the number of expensive and time consuming experiments that are needed to determine RNA 3D structures, computational methods have been developed to predict these structures based solely on the information about RNA sequences, or by taking into account additional biochemical information  [6] [7] [8].
The result is a potential energy function -often referred to as a force field (ff), though a potential energy function is actually more like what it is. MM is probably most often encountered in the parlance of all atom simulations. Some common examples of AA force fields (AAff) are AMBER  [33, 34] , CHARMM  [35, 36] , GROMOS  [37] , MMFF  [38] , and OPLS  [39, 40]. Historically, some of these all atom molecular mechanics (AAMM) software packages have also been known by the name of their molecular mechanics force field; e. g. , AMBER, CHARMM and GROMACS. The potential energy function for CG method is also called a force field (CGff), and such ffs Fig. 1. Graphical description of the relationship between coarse-grained and allatom simulation methods. Each level/stage of building these models offers a range of approaches which can be combined in many ways.
A force field can be derived by analyzing known structures using KBP or TBP. The TBP involve employing a variety of equations to describe all the different types of physical interactions. The knowledge-based potentials tend to work the other way around, starting with the answer and relying on the accumulation of statistics to obtain the frequency of a particular interaction or configuration. Most AAMM methods tend to be TBP, though importing some experimental information is basically mixing in some KBP. For CGMM, the TBP involve simplifying the complexity of the residue (e. g. , by grouping related atoms together to form a bead and employing implicit solvent) while still retaining enough of the essential physics to generate similar results as the AAMM. On the other hand, KBP in CGMM do not require complex equations describing the interaction of different physical entities, but require gathering massive volumes of information from databases to accumulate sufficient statistics to estimate the probability of a particular configuration.
3 Å RMSD to the reference crystal structure. Our lab has also developed a RNA structure prediction refinement program QRNAS (Juliusz Stasiewicz and J. M. B. , unpublished) that uses a variant of the AMBER force field to fix various clashes in coarsegrained structures resulting from inaccurate bond lengths, angles, base planarity, hydrogen bonds, etc. Another way to reduce the computational cost of RNA folding simulations is to focus on sampling of structures using some type of empirical or knowledge-based function. FARNA/FARFAR  [70] [71] [72] [73]  is a MC method that generates structural fragments over a large span of conformational space, scores them using the Rosetta potential that includes both knowledge-based and physics-based terms, and then relies on clustering to identify representative structures from the set.
Challenges to building good AAMM models As noted in the introduction, there are some specially designed microprocessors that can increase the simulation to a time window in the order of 1 ms  [15]. These processors are highly specialized and have certain limitations of their own due to being first generation  [35]. The main drawback for AAMM methods is the extreme computational demand, voluminous data proliferation and weaknesses in the force field that compound these costs. Consequently, AA-MD methods are generally restricted to refining structures pre-folded with other methods or examining the ''near global minimum" dynamics of experimentally measured structures obtained from the Protein Data Bank (PDB). Nevertheless, it is noted that significant improvements in the stability of the all atom force fields have permitted AA simulations of structures like an 18 base pair nucleic acid duplex lasting up to 46 ls  [35].
Examples of different bead strategies used in RNA CG modeling are shown in Fig. 2. These will be discussed in the sections that follow. A critical step in the CG approach is deciding on the representation of the beads: whether the beads themselves represent physical objects, where the interactions use TBP, or whether the beads simply represent positions or orientations that are assigned statistical weights from a database of information; i. e. , KBP. The former we call TBP-like and the latter KBP-like. TBP-like methods attempt to reduce the cost of the simulation while maintaining the essential physics by grouping related atoms together into a single bead, whose behavior is governed by physical forces usually obtained from some particular averaging scheme. KBP-like methods take advantage of already available knowledge to reconstruct (from the known answer) the process by which a folded structures forms.
Furthermore, they introduced the two torsion angles in the notation of the virtual bonds: g (C4 0 -P-C4 0 -P) and h (P-C4 0 -P-C4 0 )  [118] , where the underlined region indications the bond axis where rotation occurs, Fig. 3  (bottom). Chen and coworkers observed that these dihedral angles largely fall into three general conformation clusters that could be approximated with a diamond (i. e. , tetrahedral) lattice  [119] , at least for short loops where a lattice model is a valid approximation of the degeneracy  [120]. Therefore, although in principle an eight-dimensional problem, it can be largely reduced Fig. 2. Examples of real coarse grained models using different numbers of beads and different definitions for the types of beads (PDB id: 2f87). (A) A one bead model using C3 0 as used in NAST  [24].
Even under harmonic restraints, many misfolded structures were observed  [124, 130]. In the model from Harvey's group, the stems were first built using one bead, followed by expanding the whole stem to 5 beads, followed by one bead per residue, where the phosphate (P) was used as the reference point  [123, 124, 130]  (Fig. 4 ). The one bead per residue model YAMMP  [124]  and the interface package YUP  [131]  are one of the earliest CG methods for RNA 3D structure modeling. The YAMMP method  [124]  featured a machine readable script file that was quite complex. The upgraded version of the program called YUP was developed to reduce the input/output complexity  [131]. Because of the pre-established harmonic restraints, these models also contain a fair degree of KBP-like modeling concepts; nevertheless, they sit more in the spirit of TBP-like CG perspectives in attempting to import experimental information.
Challenges to building good KBP-like CG models The main issues with statistical potentials are as follows: (1) completeness of the statistical information and (2) the choice of beads demarcating the structure and the nature of their moving parts. Obtaining complete statistical information is problematical because one has difficulty claiming that any set of statistics is sufficiently complete to guarantee that the number of configurations observed is a representative set. The Protein Data Bank (PDB) Fig. 6. Examples of knowledge based models with 1, 3, 5 and 6 beads per residue. (A) A one bead per base model of RNA based on the C3 0 position, used in NAST  [24, 196]. (B) A three bead model with P-C4 0 -N1/N9, used in Vfold  [144, 146]. (C) A five bead model with P-C4 0 -(N1, C2, C4: pyrimidine)/(N9, C2, C6: purine), used in SimRNA  [99].
(D) A six bead model with P-C4 0 -C1 0 (C2, C4, C6), used in RNAkb  [153, 197]. contains mostly the structures of molecules that have been crystallized or which have been determined by nuclear magnetic resonance, and only a handful of structures determined with other techniques. These molecules with determined structures often have been truncated or otherwise altered to help in the structure determination process. For most of the RNA molecules with experimentally determined sequences, especially those that cannot be crystallized or obtained in high quantity and concentration, structures are not available and are unlikely to be obtained in the near future. Hence, some information is clearly missing. Therefore, a formidable problem in justifying statistical potentials and their result is the fact that the very dataset they depend on is biased toward structures that can be determined using existing high-resolution methods.
An example of using two CG methods In Fig. 7 , we show an example of a flow diagram for using two CG applications: oxRNA (TBP, top) and SimRNA (KBP, bottom) and the approximate flow of carrying out a simulation. On the left, a sequence is entered, the programs translate that sequence into an input structure, the structure goes through a MC simulation, and a final structure is obtained after clustering. The input sequence is obtained from the native structure shown in Fig. 2  (PDB id 2f87). It can be seen that the results of these two methods are qualitatively quite similar for this short sequence and the structures closely resemble those shown in Fig. 2. For the TBP example, the oxRNA package  [50]  was designed to directly adhere to physical parameters. Therefore, temperature and salt concentration can be clearly specified; quantitative physical parameters can be used and obtained from this approach.
Indeed, the model is even built to evaluate the Kuhn length of RNA and persistence length of dsDNA  [158]. The set up specifications for the input file (''input") are more involved than typical programs. The program is designed to recapitulate the physical properties of RNA with less emphasis on obtaining the ''correct" structure (the PDB file reference). As a result, the transformation between the CG and AA representation is not as trivial or one-toone in character. There are other TBP like DMD that are more oriented to obtaining the native structure. For the KBP example, the SimRNA program  [99]  was designed with the intention of ease of input and transformation between the CG reduced representation and the AA structure. The native structure in AA representation is shown on the far right of Fig.
Further multiplexed REMD (MREMD) has been employed in solving protein folding problems using distributed computing  [168] , which is not so amenable to standard REMD/REMC. The program oxRNA  [50]  employs the virtual move MC (VMMC) algorithm: a cluster-move MC algorithm  [169]  and an approach where the landscape is explored in a hierarchical fashion  [154]. The Rosetta-based fragment assembly MC for RNA (FARNA) approach starts from an extended structure. From there, a random position is chosen in the chain and the torsion angles for three residues are replaced with values taken from a fragment randomly chosen from a database of experimentally determined RNA structures, whereupon the moves are accepted or rejected based upon the Metropolis algorithm  [161, 170]. Bayesian methods have also been employed in sampling  [171].
Summary We have presented a brief introduction to the topic of coarsegrained modeling covering both all atom approach and lower resolution strategies. We have also discussed how physics based approaches (building from fundamental science) compare with more recent knowledge based methods (using databases of information to solve problems). We have discussed in the course of this review both the strengths and weaknesses of these approaches. It is hoped that the reader will find this experience and advice useful in deciding on what approach is best used for the task of interest.
Acknowledgements We thank members of the  Bujnicki group , in particular  Michał Boniecki ,  Marcin Magnus , and  Grzegorz Łach , for stimulating discussions. We also thank the reviewers for their very helpful comments. J. M. B. and M. M. were supported by the  European Research Council (ERC, StG  Grant  RNA+P=123D  project number  261351 , to J. M. B. ). W. K. D. was supported by the  European Commission (E. C. , REGPOT  Grant FishMed contract number  316125 , to  Jacek Kuz  ´nicki at IIMCB). E. J. J. was supported by the  Polish National Science Centre (NCN , Grant  2012/04/A/NZ2/00455  to J. M. B. ). The development of bioinformatics servers in the  Bujnicki laboratory  was funded mainly by the  Polish Ministry of Science and Higher Education (MNiSW , Grant  POIG. 02. 03. 00-00-003/09  to J.
Results: In this paper, we propose a multiple alignment strategy that exploits functional collinearity shared by related strains of bacteriophages, and uses partial orders to capture mosaicism of sets of genomes. As classical alignments do, the computed alignments can be used to predict that genes have the same biological function, even in the absence of detectable similarity. The Alpha aligner implements these ideas in visual interactive displays, and is used to compute several examples of alignments of Staphylococcus aureus and Mycobacterium bacteriophages, involving up to 29 genomes. Using these datasets, we prove that Alpha alignments are at least as good as those computed by standard aligners. Comparison with the progressiveMauve aligner -which implements a partial order strategy, but whose alignments are linearized -shows a greatly improved interactive graphic display, while avoiding misalignments.
As an example of this last feature, Fig. 1  compares two segments of S. aureus bacteriophages 88 and 92 (see Table  1  for all accession numbers of bacteriophages discussed in the paper). The figure is composed of 5 columns, three of which corresponding to exact matches of length 259, 35 and 49, and the other two columns containing large segments occupying the same position but without any 'detectable' similarity, meaning neither the nucleotide nor the translated sequences produce any significant BLAST hit. The mosaic patterns exhibited by the comparison of two bacteriophages, as illustrated in Fig. 1 , have given rise to the modular theory of phage genome organization  [5] , which postulates that biological functions are grouped into modules whose order is mostly conserved along the genomic sequence.
Results and discussion In this section, we first present examples of multiple alignments and what can be deduced from them. We next assess the validity of the gapless alignments inferred by Alpha by submitting the aligned sequences to three standard aligners. Finally, we compare the results of Alpha and progressiveMauve  [14]  on whole genome alignments. Alpha is an interactive tool that allows the manipulation and the visualization of whole genome alignment graphs with hundreds of vertices, involving dozens of species: we can only hope to give a glimpse of the possibilities. Alpha's graph layouts are powered by the open source graph visualization software Graphviz  [18].
Bacteriophage genomes have been sequenced for decades, and since they can adopt different linear and circular configurations in their complex life cycle, there is no universal consensus as to where the sequence begins. Functional collinearity is biologically defined up to a circular permutation of the sequences, but bacteriophage Fig. 4  A local alignment of four S. aureus bacteriophages. In column 3, three phages are in a 587 bp gapless alignment, with 99 % identical columns. A major deletion in phage B236 spans columns 2, 3, and 4, and the corresponding arrow is dotted to reflect the fact that some basepairs are not shown, 6 bp in this case sequences in databases are linear. If a set of bacteriophage genomes was obtained from different projects, or different laboratories, it may be necessary to synchronize them using a simple procedure that looks for the largest similar sequence shared by all genomes, and sets the beginning there.
We call this process normalization, and Alpha checks its input to decide whether normalization is needed. Figure  5  shows a group of S. aureus bacteriophages whose sequences have been normalized: positions of bacteriophage phiNM1 are offset by more than 16 500 bp with respect to the other genomes, while maintaining collinearity with them. In this figure the second and fourth columns identify two pairs of large sequences. These pairs share similar loci within the alignment but they lack detectable similarity, and they are predicted to be variants of the same module. However, the second column splits sequences {A, D} from {B, C}, while the fourth splits sequences {A, B} from {C, D}, illustrating the very peculiar behavior of horizontal transfer in bacteriophages.
Assessing the validity of alpha gapless alignments In order to assess the gapless alignments produced by Alpha, we ran it on three datasets. Table  2  presents the principal characteristics of these datasets such as N , the total length of genomes, the value of m used in the computation, and M , the total length of matches. Dataset Staph6 contains 6 complete S. aureus bacteriophage genomes: 29, 53, 85, 88, 92 and 187. Dataset Myco29 contains 29 mycobacteriophage genomes from cluster A1 of the Actinobacteriophage Database (http:// phagesdb. org). Myco6 contains bacteriophage genomes {U2, Alvin, DD5, BillKnuckles, Perseus, Dreamboat}, a subset of Myco29 chosen for diversity within cluster A1. Some of the mycobacteriophage genomes were trimmed in order to automatically run the experiments with small values of m; transpositions at the end of the sequences cause Alpha to increase the value of m (see Table  1  for details).
Alpha computed 491 gapless alignments of 2 or more sequences on these datasets. All 491 alignments were re-aligned using Clustal Omega  [21] , T-Coffee  [22]  and Muscle  [23] , using the default values for DNA alignments, and we report the number of alignments that include gaps. For each dataset, Table  3  gives the number of vertices in the expanded and contracted alignment graphs, the number of vertices containing at least two sequences, the number of contracted vertices, and the number of gapped alignments obtained by the three aligners. All four aligners agree on 479 of the 491 = 78 + 154 + 259 gapless alignments proposed by Alpha. The remaining 12 were contested by one or more of the aligners, for a total of 18 gapped alignments. They cover 8 different regions of the genomes since, in four cases, aligners proposed gapped alignments for the same region in both the Myco6 and Myco29 datasets.
All sequences and alignments are available in the Additional file 1. For Region 5, the three aligners proposed three different gapped alignments, when aligning the 6 genomes of dataset Myco6, but they all switched to gapless alignments when aligning the same region in the 29 genomes of dataset Myco29, which contains Myco6. Using the principle that alignments with more sequences should be more accurate, those three gapped alignments were also ruled out. Of the remaining three gapped alignments, one was proposed by Muscle, while Alpha, Clustal Omega and T-Coffee proposed gapless alignments (Region 7). Given the overall poor performance of Muscle, we chose the rule of the majority. Finally, for Region 6, there was a tie between the four aligners, and there was no decision. As the results of Table  4  clearly indicate, Alpha gapless alignments were confirmed in all cases except for the tie in Region 6, where Alpha and Clustal Omega -the two best gapless aligners -predict a gapless alignment against T-Coffee and Muscle, who have the most confirmed misalignments.
6. Working with a large number of phages allows us to quickly identify subsets of phages that have distinct patterns, such as phages A, C and E (U2, Alvin and Solon) in this example more than a handful of genomes, such as the Myco29 dataset, this type of visualization quickly becomes impractical (see Additional file 2: Figure  S1  that shows the Mauve alignment of the region spanned in Fig. 7 ). Comparing partial order alignments stemming from two different aligners turned out to be a daunting task: an alignment proposed by one aligner can be broken into several alignments by the other, since alignments may involve  All blocks predicted by both aligners were considered as valid alignments. Furthermore, short blocks (less than 100 bp) that were predicted by only one aligner -mostly by Mauve -were considered as valid: they averaged 24 Fig.
8  Visual comparison. Alpha and Mauve alignments of the same region of the Staph4 dataset. Numbers in red were added to show the correspondence between Alpha alignments and Mauve color blocks. All Alpha alignments are found in the Mauve alignment, but Mauve apparently detects an alignment of the four genomes that is missed by Alpha: the block labeled 'x'. We show in the text that block x is a misalignment bp. Three of the remaining large blocks -two predicted only by Mauve, and one only by Alpha -were validated using protein alignments. Figure  9  shows, in the various green-blue-shaded rectangles, the repartition of valid alignments, measured in total bp length, between common and exclusive alignments for Mauve and Alpha. These results are further partitioned by discriminating whether a block is an alignment, or an extension of an alignment.
When one excludes Mauve misalignments -that is, considering the green and blue rectangles of Fig. 9  -both aligners generally agree on the topology of the alignment graphs. As could be predicted, due to Mauve's seed and extend strategy, it has wider alignments compared to Alpha, and this accounts for the 778 bp extensions that Alpha could not predict. On the other hand, the 8 bp extensions that Alpha alone predicted, in four different blocks, were in fact verified to be exact matches that Mauve somehow missed. The two aligners also failed to detect a few small alignments, with a little advantage to Alpha: Mauve detected 294 bp that Alpha did not, while Alpha detected 358 bp that Mauve did not. The high rate of Mauve misalignments on bacteriophage genomes is problematic. More than 20 % of Mauve column alignments on this dataset are either random, or belong to nucleotide alignments whose gaps contradicts the corresponding annotated proteins alignments.
We also showed that Alpha, used on bacteriophage genomes, produces biologically meaningful alignments, while avoiding the high rate of misalignments of complex heuristics such as progressiveMauve. Our model supposes that all genomes under consideration are functionally collinear. This is often the case, but not always. Our program can detect when this condition is not satisfied -indicating the presence of rearrangements -but does not perform the alignment in such a case. A short-term goal is to extend our mathematical model and aligner to deal with rearrangements. Some bacteriophage genomes present in the online databases are well annotated, other are less so. Another goal is to extend the aligner in order to perform automated transfer of annotations using the generated alignments. Finally, while we focussed this study on Siphoviridae, we plan to test Alpha on more general viruse families in which horizontal transfer is widespread, and for which the collinearity property may hold.