ORCID Profile
0000-0003-1295-8972
Current Organisation
Universidad de Santiago de Chile
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Publisher: IEEE
Date: 11-2011
DOI: 10.1109/SCCC.2011.3
Publisher: IEEE
Date: 10-2017
Publisher: Cold Spring Harbor Laboratory
Date: 09-03-2012
Abstract: Transcriptomic analyses have identified tens of thousands of intergenic, intronic, and cis -antisense long noncoding RNAs (lncRNAs) that are expressed from mammalian genomes. Despite progress in functional characterization, little is known about the post-transcriptional regulation of lncRNAs and their half-lives. Although many are easily detectable by a variety of techniques, it has been assumed that lncRNAs are generally unstable, but this has not been examined genome-wide. Utilizing a custom noncoding RNA array, we determined the half-lives of ∼800 lncRNAs and ∼12,000 mRNAs in the mouse Neuro-2a cell line. We find only a minority of lncRNAs are unstable. LncRNA half-lives vary over a wide range, comparable to, although on average less than, that of mRNAs, suggestive of complex metabolism and widespread functionality. Combining half-lives with comprehensive lncRNA annotations identified hundreds of unstable (half-life 2 h) intergenic, cis -antisense, and intronic lncRNAs, as well as lncRNAs showing extreme stability (half-life 16 h). Analysis of lncRNA features revealed that intergenic and cis -antisense RNAs are more stable than those derived from introns, as are spliced lncRNAs compared to unspliced (single exon) transcripts. Subcellular localization of lncRNAs indicated widespread trafficking to different cellular locations, with nuclear-localized lncRNAs more likely to be unstable. Surprisingly, one of the least stable lncRNAs is the well-characterized paraspeckle RNA Neat1 , suggesting Neat1 instability contributes to the dynamic nature of this subnuclear domain. We have created an online interactive resource ( stability.matticklab.com ) that allows easy navigation of lncRNA and mRNA stability profiles and provides a comprehensive annotation of ∼7200 mouse lncRNAs.
Publisher: Elsevier BV
Date: 03-2020
Publisher: MDPI AG
Date: 23-12-2019
DOI: 10.3390/MICROORGANISMS8010032
Abstract: Massive sequencing projects executed in Saccharomyces cerevisiae have revealed in detail its population structure. The recent “1002 yeast genomes project” has become the most complete catalogue of yeast genetic ersity and a powerful resource to analyse the evolutionary history of genes affecting specific phenotypes. In this work, we selected 22 nitrogen associated genes and analysed the sequence information from the 1011 strains of the “1002 yeast genomes project”. We constructed a total evidence (TE) phylogenetic tree using concatenated information, which showed a 27% topology similarity with the reference (REF) tree of the “1002 yeast genomes project”. We also generated in idual phylogenetic trees for each gene and compared their topologies, identifying genes with similar topologies (suggesting a shared evolutionary history). Furthermore, we pruned the constructed phylogenetic trees to compare the REF tree topology versus the TE tree and the in idual genes trees, considering each phylogenetic cluster/subcluster within the population, observing genes with cluster/subcluster topologies of high similarity to the REF tree. Finally, we used the pruned versions of the phylogenetic trees to compare four strains considered as representatives of S. cerevisiae clean lineages, observing for 15 genes that its cluster topologies match 100% the REF tree, supporting that these strains represent main lineages of yeast population. Altogether, our results showed the potential of tree topologies comparison for exploring the evolutionary history of a specific group of genes.
Publisher: IEEE
Date: 10-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2018
Publisher: IEEE
Date: 06-2017
Publisher: IEEE
Date: 10-2017
Publisher: Elsevier BV
Date: 03-2009
DOI: 10.1016/J.RADONC.2008.09.019
Abstract: We sought to categorize longitudinal radiation-induced rectal toxicity data obtained from men participating in a randomised controlled trial for locally advanced prostate cancer. Data from self-assessed questionnaires of rectal symptoms and clinician recorded remedial interventions were collected during the TROG 96.01 trial. In this trial, volunteers were randomised to radiation with or without neoadjuvant androgen deprivation. Characterization of longitudinal variations in symptom intensity was achieved using prevalence data. An integrated visualization and clustering approach based on memetic algorithms was used to define the compositions of symptom clusters occurring before, during and after radiation. The utility of the CTC grading system as a means of identifying specific injury profiles was evaluated using concordance analyses. Seven well-defined clusters of rectal symptoms were present prior to treatment, 25 were seen immediately following radiation and 7 at years 1, 2 and 3 following radiation. CTC grading did not concord with the degree of rectal 'distress' and 'problems' at all time points. Concordance was not improved by adding urgency to the CTC scale. The CTC scale has serious shortcomings. A powerful new technique for non-hierarchical clustering may contribute to the categorization of rectal toxicity data for genomic profiling studies and detailed patho-physiological studies.
Publisher: IEEE
Date: 06-2017
Publisher: IEEE
Date: 10-2016
Publisher: MDPI AG
Date: 11-02-2020
DOI: 10.3390/MOLECULES25040772
Abstract: Glucosinolates are secondary plant metabolites of Brassicaceae. They exert their effect after enzymatic hydrolysis to yield aglycones, which become nitriles and epithionitriles through the action of epithiospecifier (ESP) and nitrile-specifier proteins (NSP). The mechanism of action of broccoli ESP and NSP is poorly understood mainly because ESP and NSP structures have not been completely characterized and because aglycones are unstable, thus hindering experimental measurements. The aim of this work was to investigate the interaction of broccoli ESP and NSP with the aglycones derived from broccoli glucosinolates using molecular simulations. The three-dimensional structure of broccoli ESP was built based on its amino-acid sequence, and the NSP structure was constructed based on a consensus amino-acid sequence. The models obtained using Iterative Threading ASSEmbly Refinement (I-TASSER) were refined with the OPLS-AA/L all atom force field of GROMACS 5.0.7 and were validated by Veryfy3D and ERRAT. The structures were selected based on molecular dynamics simulations. Interactions between the proteins and aglycones were simulated with Autodock Vina at different pH. It was concluded that pH determines the stability of the complexes and that the aglycone derived from glucoraphanin has the highest affinity to both ESP and NSP. This agrees with the fact that glucoraphanin is the most abundant glucosinolate in broccoli florets.
Publisher: Elsevier BV
Date: 12-2015
DOI: 10.1016/J.COMPBIOLCHEM.2015.08.006
Abstract: Tertiary protein structure prediction is one of the most challenging problems in structural bioinformatics. Despite the advances in algorithm development and computational strategies, predicting the folded structure of a protein only from its amino acid sequence remains as an unsolved problem. We present a new computational approach to predict the native-like three-dimensional structure of proteins. Conformational preferences of amino acid residues and secondary structure information were obtained from protein templates stored in the Protein Data Bank and represented as an Angle Probability List. Two knowledge-based prediction methods based on Genetic Algorithms and Particle Swarm Optimization were developed using this information. The proposed method has been tested with twenty-six case studies selected to validate our approach with different classes of proteins and folding patterns. Stereochemical and structural analysis were performed for each predicted three-dimensional structure. Results achieved suggest that the Angle Probability List can improve the effectiveness of metaheuristics used to predicted the three-dimensional structure of protein molecules by reducing its conformational search space.
Publisher: Public Library of Science (PLoS)
Date: 12-2010
Publisher: IEEE
Date: 06-2013
Publisher: Springer US
Date: 2006
Publisher: Springer Science and Business Media LLC
Date: 07-08-2018
Publisher: ACM
Date: 11-07-2015
Publisher: IEEE
Date: 10-2016
Publisher: IEEE
Date: 05-2015
Publisher: IEEE
Date: 07-2018
Publisher: IEEE
Date: 07-2018
Publisher: Public Library of Science (PLoS)
Date: 29-08-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 07-2018
Publisher: Elsevier BV
Date: 03-2022
DOI: 10.1016/J.BIOSYSTEMS.2022.104606
Abstract: The analysis of evolutionary data allows uncovering information about the organisms and how they have adapted and evolved. This information could provide us with new insights about the specialisation of organisms (or part of them), how they adapt, how similar they are with other species, among others. Unfortunately, this evolutionary history can only be estimated, and for that, several computational methods exist. Among the methods, optimisation methods are one of the main approaches to deal with this problem, with multiobjective optimisation producing promising results. In this paper, we deal with multiobjective phylogenetic inference, using a multi-modal metaheuristic approach that exploits the decision space in the multiobjective formulation of the problem. In particular, we incorporate a new metric based on a topological tree distance. We compare the method with state of the art algorithms in terms of performance. Additionally, we perform a thorough analysis of a study case on a yeast Saccharomyces cerevisiae dataset. Results show that our proposal is able to improve the ersity of solutions while improving or keeping the quality of solutions in terms of hypervolume.
Publisher: Public Library of Science (PLoS)
Date: 18-01-2011
Publisher: Mary Ann Liebert Inc
Date: 03-2017
Abstract: The exponential growth in the number of experimentally determined three-dimensional protein structures provide a new and relevant knowledge about the conformation of amino acids in proteins. Only a few of probability densities of amino acids are publicly available for use in structure validation and prediction methods. NIAS (Neighbors Influence of Amino acids and Secondary structures) is a web-based tool used to extract information about conformational preferences of amino acid residues and secondary structures in experimental-determined protein templates. This information is useful, for ex le, to characterize folds and local motifs in proteins, molecular folding, and can help the solution of complex problems such as protein structure prediction, protein design, among others. The NIAS-Server and supplementary data are available at sbcb.inf.ufrgs.br/nias .
Publisher: Springer New York
Date: 29-11-2016
DOI: 10.1007/978-1-4939-6613-4_16
Abstract: In this chapter, we illustrate the use of an integrated mathematical method for joint clustering and visualization of large-scale datasets. In applying these clustering methodologies to biological datasets, we aim to identify differentially expressed genes according to cell type by building molecular signatures supported by statistical scores. In doing so, we also aim to find a global map of highly co-expressed clusters. Variations in these clusters may well indicate other pathological trends and changes.
Publisher: Springer Science and Business Media LLC
Date: 15-02-2016
DOI: 10.1007/S10916-016-0458-9
Abstract: The public health system has restricted economic resources. Because of that, it is necessary to know how the resources are being used and if they are properly distributed. Several works have applied classical approaches based in Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) for this purpose. However, if we have hospitals with different casemix, this is not the best approach. In order to avoid biases in the comparisons, other works have recommended the use of hospital production data corrected by the weights from Diagnosis Related Groups (DRGs), to adjust the casemix of hospitals. However, not all countries have this tool fully implemented, which limits the efficiency evaluation. This paper proposes a new approach for evaluating the efficiency of hospitals. It uses a graph-based clustering algorithm to find groups of hospitals that have similar production profiles. Then, DEA is used to evaluate the technical efficiency of each group. The proposed approach is tested using the production data from 2014 of 193 Chilean public hospitals. The results allowed to identify different performance profiles of each group, that differs from other studies that employs data from partially implemented DRGs. Our results are able to deliver a better description of the resource management of the different groups of hospitals. We have created a website with the results ( bioinformatic.diinf.usach.cl ublichealth ). Data can be requested to the authors.
Location: Chile
No related grants have been discovered for Mario Inostroza-Ponta.