A multiple layer model to compare RNA secondary structures

International audienceWe formally introduce a new data structure, called MiGaL for ``Multiple Graph Layers'', that is composed of various graphs linked together by relations of abstraction/refinement. The new structure is useful for representing information that can be described at different levels of abstraction, each level corresponding to a graph. We then propose an algorithm for comparing two MiGaLs. The algorithm performs a step-by-step comparison starting with the most ``abstract'' level. The result of the comparison at a given step is communicated to the next step using a special colouring scheme. MiGaLs represent a very natural model for comparing RNA secondary structures that may be seen at different levels of detail, going from the sequence of nucleotides, single or paired with another to participate in a helix, to the network of multiple loops that is believed to represent the most conserved part of RNAs having similar function. We therefore show how to use MiGaLs to very efficiently compare two RNAs of any size at different levels of detail

BRASERO: A Resource for Benchmarking RNA Secondary Structure Comparison Algorithms

The pairwise comparison of RNA secondary structures is a fundamental problem, with direct application in mining databases for annotating putative noncoding RNA candidates in newly sequenced genomes. An increasing number of software tools are available for comparing RNA secondary structures, based on different models (such as ordered trees or forests, arc annotated sequences, and multilevel trees) and computational principles (edit distance, alignment). We describe here the website BRASERO that offers tools for evaluating such software tools on real and synthetic datasets

The Gapped-Factor Tree

International audienceWe present a data structure to index a specific kind of factors, that is of substrings, called gapped-factors. A gapped-factor is a factor containing a gap that is ignored during the indexation. The data structure presented is based on the suffix tree and indexes all the gapped-factors of a text with a fixed size of gap, and only those. The construction of this data structure is done online in linear time and space. Such a data structure may play an important role in various pattern matching and motif inference problems, for instance in text filtration

The pandemic toll and post-acute sequelae of SARS-CoV-2 in healthcare workers at a Swiss University Hospital.

Healthcare workers have potentially been among the most exposed to SARS-CoV-2 infection as well as the deleterious toll of the pandemic. This study has the objective to differentiate the pandemic toll from post-acute sequelae of SARS-CoV-2 infection in healthcare workers compared to the general population. The study was conducted between April and July 2021 at the Geneva University Hospitals, Switzerland. Eligible participants were all tested staff, and outpatient individuals tested for SARS-CoV-2 at the same hospital. The primary outcome was the prevalence of symptoms in healthcare workers compared to the general population, with measures of COVID-related symptoms and functional impairment, using prevalence estimates and multivariable logistic regression models. Healthcare workers (n=3,083) suffered mostly from fatigue (25.5%), headache (10.0%), difficulty concentrating (7.9%), exhaustion/burnout (7.1%), insomnia (6.2%), myalgia (6.7%) and arthralgia (6.3%). Regardless of SARS-CoV-2 infection, all symptoms were significantly higher in healthcare workers than the general population (n=3,556). SARS-CoV-2 infection in healthcare workers was associated with loss or change in smell, loss or change in taste, palpitations, dyspnea, difficulty concentrating, fatigue, and headache. Functional impairment was more significant in healthcare workers compared to the general population (aOR 2.28; 1.76-2.96), with a positive association with SARS-CoV-2 infection (aOR 3.81; 2.59-5.60). Symptoms and functional impairment in healthcare workers were increased compared to the general population, and potentially related to the pandemic toll as well as post-acute sequelae of SARS-CoV-2 infection. These findings are of concern, considering the essential role of healthcare workers in caring for all patients including and beyond COVID-19

Structural Stability of Human Protein Tyrosine Phosphatase ρ Catalytic Domain: Effect of Point Mutations

Protein tyrosine phosphatase ρ (PTPρ) belongs to the classical receptor type IIB family of protein tyrosine phosphatase, the most frequently mutated tyrosine phosphatase in human cancer. There are evidences to suggest that PTPρ may act as a tumor suppressor gene and dysregulation of Tyr phosphorylation can be observed in diverse diseases, such as diabetes, immune deficiencies and cancer. PTPρ variants in the catalytic domain have been identified in cancer tissues. These natural variants are nonsynonymous single nucleotide polymorphisms, variations of a single nucleotide occurring in the coding region and leading to amino acid substitutions. In this study we investigated the effect of amino acid substitution on the structural stability and on the activity of the membrane-proximal catalytic domain of PTPρ. We expressed and purified as soluble recombinant proteins some of the mutants of the membrane-proximal catalytic domain of PTPρ identified in colorectal cancer and in the single nucleotide polymorphisms database. The mutants show a decreased thermal and thermodynamic stability and decreased activation energy relative to phosphatase activity, when compared to wild- type. All the variants show three-state equilibrium unfolding transitions similar to that of the wild- type, with the accumulation of a folding intermediate populated at ∼4.0 M urea

Comparaison de structures secondaires d'ARN

RNAs are one of the fundamental elements of a cell. Generally, RNAs are defined as oriented sequences of nucleotides (denoted by A,C,G and U). Inside a cell, RNAs do not have a linear shape but fold in space. The molecular function of an RNA strongly depends on this tri-dimensional folding. Hence, the comparison of the tri-dimensional structure of two RNAs is essential to determine whether the RNAs share the same function. The structure of an RNA is generally divided into three parts. The first is the primary structure which corresponds to the sequence of nucleotides. The secondary structure is composed of the list of links between nucleotides that represent helices. Finally, the tertiary structure corresponds to the exact tri-dimensional folding of the RNA. Although the tertiary structure is the most accurate definition of the spatial structure adopted by an RNA, it is well-known that two RNAs sharing the same function will also have closely related secondary structures. A few other structural elements can be distinguished in an RNA secondary structure. These are the helices, multiloops, hairpin loops, internal loops and bulges. Up until now, essentially three data structures have been proposed to represent an RNA secondary structure : arc-annotated sequences, 2-intervals and rooted oriented trees. Arc-annotated sequences are sequences with arcs between nucleotides of the sequence that form a pair in the structure. 2-intervals generalise arc-annotated sequences and correspond to two disjoint subsets. An RNA secondary structure is then de?ned as a family of 2-intervals. Finally, rooted ordered trees can represent an RNA secondary structure at various levels, from the nucleotides up to the network of multiloops. One of the drawbacks of all these approaches is that they model the secondary structure of an RNA from a specific point of view (nucleotides, helices etc.). We decided to introduce a new model called RNA-MiGaL, made of four trees related among them. Each of these trees represents the structure of an RNA at a particular level of detail : the upper level models the network of multiloops that is considered as the skeleton of the secondary structure, while the lower level represents nucleotides. We use the tree edit distance to compare two RNA-MiGaLs. However, due to some limitations of the classical edit distance to compare trees representing RNA secondary structures, we introduced two new edit operations named "node fusion" and "edge fusion", thus providing a new edit distance. Using this distance, we developed an algorithm to compare two RNA-MiGaLs. The algorithm has been implemented in a package which allows RNA secondary structures to be compared in various ways.L'ARN, acide ribonucléique, est un des composants fondamentaux de la cellule. La majorité des ARN sont constitués d'une séquence orientée de nucléotides notés A,C,G et U. Une telle séquence se replie dans l'espace en formant des liaisons entre les nucléotides deux à deux. La fonction des ARN au sein de la cellule est liée à la conformation spatiale qu'ils adoptent. Ainsi, il est essentiel de pouvoir comparer deux ARN au niveau de leur conformation, par exemple pour déterminer si deux ARN ont la même fonction. On distingue trois niveaux dans la structure d'un ARN. La structure primaire correspond à la séquence de nucléotides, la structure secondaire est constistuée de la liste des liaisons formées entre les nucléotides tandis que la structure tertiaire consiste en la description exacte de la forme tridimensionnelle de la molécule (coordonnées de chaque nucléotide). Bien que la structure tertiaire soit celle qui décrit le mieux la forme spatiale de l'ARN, il est admis que deux ARN ayant une fonction moléculaire similaire ont une structure secondaire proche. Au niveau de la structure secondaire, une fois les liaisons nucléotidiques formées, on peut distinguer des éléments de structure secondaire telles que les hélices, les boucles multiples, les boucles terminales, les boucles internes et les renflements. Essentiellement deux formalismes ont été à ce jour proposés pour modéliser la structure secondaire des ARN. Les séquences annotées par des arcs permettent de représenter la séquence de l'ARN, les arcs codant alors pour les liaisons entre les lettres (nucléotides de la séquence). Les 2-intervalles, généralisation des séquences annotées, sont formés par deux intervalles disjoints. La structure secondaire peut alors être vue comme une famille de 2-intervalles. Enfin, les arbres racinés ordonnés offrent de nombreuses possibilités pour coder la structure secondaire, du niveau nucléotidique au niveau du réseau des boucles multiples. L'un des inconvénients de ces approches est qu'elles modélisent la structure secondaire de l'ARN selon un point de vue particulier (nucléotides, hélices etc). Nous proposons une nouvelle modélisation, appelée RNA-MiGaL, constituée de quatre arbres liés entre eux représentant la structure à différents niveaux de précision. Ainsi, le plus haut niveau code pour le réseau de boucles multiples considéré comme le squelette de la molécule. Le dernier niveau quant à lui détaille les nucléotides. Pour comparer de telles structures nous utilisons la notion de distance d'édition entre deux arbres. Cependant, au vu de certains limitations de celle-ci pour comparer des arbres représentant la structure secondaire à un haut niveau d'abstraction, nous avons introduit une nouvelle distance d'édition qui prend en compte deux nouvelles opérations d'édition: la fusion de noeud et la fusion d'arc. A l'aide de cette nouvelle distance, nous fournissons un algorithme permettant de comparer deux RNA-MiGaLs. Celui-ci est implémenté au sein d'un programme permettant la comparaison de deux structures secondaires d'ARN

Novel tree edit operations for RNA secondary structure

We describe an algorithm for comparing two RNA secondary structures coded in the form of trees that introduces two novel operations, called node fusion and edge fusion, besides the tree edit operations of deletion, insertion and relabelling classically used in the literature. This allows us to address some serious limitations of the more traditional tree edit operations when the trees represent RNAs and what is searched for is a common structural core of two RNAs. Although the algorithm complexity has an exponential term, this term depends only on the number of successive fusions that may be applied to a same node, not on the total number of fusions. The algorithm remains therefore e#cient in practice and is used for illustrative purposes on ribosomal as well as on other types of RNAs