MAGA: A Supervised Method to Detect Motifs From Annotated Groups in Alignments.
Ontology highlight
ABSTRACT:
SUBMITTER: Mier P
PROVIDER: S-EPMC7218316 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
MAGA: A Supervised Method to Detect Motifs From Annotated Groups in Alignments.
Mier Pablo P Andrade-Navarro Miguel A MA
Evolutionary bioinformatics online 20200429
Multiple sequence alignments are usually phylogenetically driven. They are studied in the framework of evolution. But sometimes, it is interesting to study residue conservation at positions unconstrained by evolutionary rules. We present a supervised method to access a layer of information difficult to appreciate visually when many protein sequences are aligned. This new tool (MAGA; http://cbdm-01.zdv.uni-mainz.de/~munoz/maga/) locates positions in multiple sequence alignments differentially con ...[more]
Similar Datasets
Project description:BackgroundThe structure of many eukaryotic cell regulatory proteins is highly modular. They are assembled from globular domains, segments of natively disordered polypeptides and short linear motifs. The latter are involved in protein interactions and formation of regulatory complexes. The function of such proteins, which may be difficult to define, is the aggregate of the subfunctions of the modules. It is therefore desirable to efficiently predict linear motifs with some degree of accuracy, yet sequence database searches return results that are not significant.ResultsWe have developed a method for scoring the conservation of linear motif instances. It requires only primary sequence-derived information (e.g. multiple alignment and sequence tree) and takes into account the degenerate nature of linear motif patterns. On our benchmarking, the method accurately scores 86% of the known positive instances, while distinguishing them from random matches in 78% of the cases. The conservation score is implemented as a real time application designed to be integrated into other tools. It is currently accessible via a Web Service or through a graphical interface.ConclusionThe conservation score improves the prediction of linear motifs, by discarding those matches that are unlikely to be functional because they have not been conserved during the evolution of the protein sequences. It is especially useful for instances in non-structured regions of the proteins, where a domain masking filtering strategy is not applicable.
| S-EPMC2396637 | biostudies-literature
Project description:RNA performs a diverse array of important functions across all cellular life. These functions include important roles in translation, building translational machinery and maturing messenger RNA. More recent discoveries include the miRNAs and bacterial sRNAs that regulate gene expression, the thermosensors, riboswitches and other cis-regulatory elements that help prokaryotes sense their environment and eukaryotic piRNAs that suppress transposition. However, there can be a long period between the initial discovery of a RNA and determining its function. We present a bioinformatic approach to characterize RNA motifs, which are critical components of many RNA structure-function relationships. These motifs can, in some instances, provide researchers with functional hypotheses for uncharacterized RNAs. Moreover, we introduce a new profile-based database of RNA motifs--RMfam--and illustrate some applications for investigating the evolution and functional characterization of RNA. All the data and scripts associated with this work are available from: https://github.com/ppgardne/RMfam.
| S-EPMC4333381 | biostudies-literature
Project description:We present three clustered protein sequence databases, Uniclust90, Uniclust50, Uniclust30 and three databases of multiple sequence alignments (MSAs), Uniboost10, Uniboost20 and Uniboost30, as a resource for protein sequence analysis, function prediction and sequence searches. The Uniclust databases cluster UniProtKB sequences at the level of 90%, 50% and 30% pairwise sequence identity. Uniclust90 and Uniclust50 clusters showed better consistency of functional annotation than those of UniRef90 and UniRef50, owing to an optimised clustering pipeline that runs with our MMseqs2 software for fast and sensitive protein sequence searching and clustering. Uniclust sequences are annotated with matches to Pfam, SCOP domains, and proteins in the PDB, using our HHblits homology detection tool. Due to its high sensitivity, Uniclust contains 17% more Pfam domain annotations than UniProt. Uniboost MSAs of three diversities are built by enriching the Uniclust30 MSAs with local sequence matches from MMseqs2 profile searches through Uniclust30. All databases can be downloaded from the Uniclust server at uniclust.mmseqs.com. Users can search clusters by keywords and explore their MSAs, taxonomic representation, and annotations. Uniclust is updated every two months with the new UniProt release.
| S-EPMC5614098 | biostudies-literature
Project description:The occurrences of two recurrent motifs in ribosomal RNA sequences, the Kink-turn and the C-loop, are examined in crystal structures and systematically compared with sequence alignments of rRNAs from the three kingdoms of life in order to identify the range of the structural and sequence variations. Isostericity Matrices are used to analyze structurally the sequence variations of the characteristic non-Watson-Crick base pairs for each motif. We show that Isostericity Matrices for non-Watson-Crick base pairs provide important tools for deriving the sequence signatures of recurrent motifs, for scoring and refining sequence alignments, and for determining whether motifs are conserved throughout evolution. The systematic use of Isostericity Matrices identifies the positions of the insertion or deletion of one or more nucleotides relative to the structurally characterized examples of motifs and, most importantly, specifies whether these changes result in new motifs. Thus, comparative analysis coupled with Isostericity Matrices allows one to produce and refine structural sequence alignments. The analysis, based on both sequence and structure, permits therefore the evaluation of the conservation of motifs across phylogeny and the derivation of rules of equivalence between structural motifs. The conservations observed in Isostericity Matrices form a predictive basis for identifying motifs in sequences.
| S-EPMC1087784 | biostudies-literature
Project description:We present EvalMSA, a software tool for evaluating and detecting outliers in multiple sequence alignments (MSAs). This tool allows the identification of divergent sequences in MSAs by scoring the contribution of each row in the alignment to its quality using a sum-of-pair-based method and additional analyses. Our main goal is to provide users with objective data in order to take informed decisions about the relevance and/or pertinence of including/retaining a particular sequence in an MSA. EvalMSA is written in standard Perl and also uses some routines from the statistical language R. Therefore, it is necessary to install the R-base package in order to get full functionality. Binary packages are freely available from http://sourceforge.net/projects/evalmsa/for Linux and Windows.
| S-EPMC5127606 | biostudies-literature
Project description:OBJECTIVE(S):Klebsiella infections are caused mainly by K. pneumoniae and K. oxytoca. In the last two decades, a new type of invasive Klebsiella pneumoniae which contains mucoviscosity-associated gene (magA) has emerged. The aim of this study was to investigate the prevalence of magA gene and to detect antimicrobial susceptibility patterns of Klebsiella spp. isolated from clinical samples. MATERIALS AND METHODS:Klebsiella isolates were collected from patients admitted to referral hospitals of Hamadan, Iran, during a 12-month period from 2007 to 2008. The samples were analyzed by conventional microbiological methods and polymerase chain reaction (PCR). The hypermucoviscosity (HV) phenotype of Klebsiella isolates was characterized by formation of viscous strings >5 mm as a positive test. The susceptibility of isolates to routine antibiotics was assessed by agar disk diffusion method. RESULTS:Out of 105 Klebsiella isolates, 96.2% was identified as K. pneumoniae and 3.8% as K. oxytoca by PCR. magA gene was detected in 4 (3.8%) isolates of K. pneumoniae. The isolates of K. oxytoca contained no magA gene. From 4 isolates with positive magA gene, two of them were HV+ and two were HV- phenotype. Overall, sixty-four isolates (60.95%) of K. pneumoniae showed an HV positive phenotype and all isolates of K. oxytoca were HV- phenotype. The most effective antibiotics against the isolates were tobramycin (79.05%), ceftazidime (79.05%), ceftizoxime (78.09%), ciprofloxacin (76.19%), ceftriaxone (76.24%) and amikacin (74.29%). CONCLUSION:The results suggest that there is also magA associated serotype of the K. pneumoniae in this region. In addition, the presence of HV+ phenotype may not be associated with magA.
| S-EPMC3843861 | biostudies-literature
Project description:BackgroundTopic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature.ResultsWe have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naïve Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naïve Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics.ConclusionOur results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings.
| S-EPMC1472693 | biostudies-literature
Project description:Backgrounds & Aims: Transcriptomic classification of intrahepatic cholangiocarcinoma (iCCA) has been recently improved from two classes to five classes, associated with pathological features, targetable genetic alterations and survival. Despite its prognostic and therapeutic value, the classification is not routinely performed due to technical limits such as insufficient material or the cost of molecular analyses. Our aim was to predict iCCA transcriptomic classes on whole-slide digital histological images (WSI) using a self-supervised learning (SSL) model. Methods: Gene expression was investigated using RNA sequencing to class samples in the five transcriptomic classes. We then used a SSL method called GigaSSL. We first trained our model on a discovery set of 769 slides (biopsy and surgical samples, 246 patients) in a 5-fold cross-validation scheme. We then validated the model in a TCGA (n= 29) and a French external validation set (n=32). Results: Our model showed good performance for predicting each transcriptomic class in the discovery set (Area under the curve, AUC: 0.55-0.81), particularly for the Hepatic stem-like class (most frequent group, 37% of cases), with AUC 0.81. The model generalised well for the prediction of four on five of the transcriptomic classes into the two validation sets with AUCs ranging from 0.76 to 0.80 in the TCGA set and from 0.62 to 0.92 in the French external set. Conclusion: We have developed and validated a SSL based model able to predict iCCA transcriptomic classes on routine histological slides (biopsy and surgical samples), which could have an impact on the management of patients.
2023-11-01 | GSE244807 | GEO
Project description:BackgroundProteins that evolve from a common ancestor can change functionality over time, and it is important to be able identify residues that cause this change. In this paper we show how a supervised multivariate statistical method, Between Group Analysis (BGA), can be used to identify these residues from families of proteins with different substrate specifities using multiple sequence alignments.ResultsWe demonstrate the usefulness of this method on three different test cases. Two of these test cases, the Lactate/Malate dehydrogenase family and Nucleotidyl Cyclases, consist of two functional groups. The other family, Serine Proteases consists of three groups. BGA was used to analyse and visualise these three families using two different encoding schemes for the amino acids.ConclusionThis overall combination of methods in this paper is powerful and flexible while being computationally very fast and simple. BGA is especially useful because it can be used to analyse any number of functional classes. In the examples we used in this paper, we have only used 2 or 3 classes for demonstration purposes but any number can be used and visualised.
| S-EPMC1878507 | biostudies-literature
Project description:ObjectiveTo evaluate the impact of an exercise program organized into supervised walking groups in subjects with type 2 diabetes.Research design and methodsFifty-nine diabetic subjects were randomized to a control group receiving standard lifestyle recommendations or an intervention group assigned to three supervised walking sessions per week and counseling. Changes in metabolic features, weight, 6-min walk test, prescription of antidiabetic medications, and overall physical activity were assessed.ResultsFunctional capacity and overall physical activity were higher in the intervention group, whereas metabolic changes were not different between groups after 4 months. However, in subjects who attended at least 50% of scheduled walking sessions, changes in A1C and fasting glucose were greater than in control subjects. Discontinuation or reduction of antidiabetic drugs occurred in 33% of these patients versus 5% of control subjects (P<0.05).ConclusionsSupervised walking may be beneficial in diabetic subjects, but metabolic improvement requires adequate compliance.
| S-EPMC2963489 | biostudies-literature