Publications

2023

2022

1. Multi-hallmark long noncoding RNA maps reveal non-small cell lung cancer vulnerabilities

   Roberta Esposito, Taisia Polidori, Dominik F. Meise, and 26 more authors

   Cell Genomics, Sep 2022

2021

1. Enhancing CRISPR deletion via pharmacological delay of DNA-PKcs

   Núria Bosch-Guiteras, Tina Uroda, Hugo A. Guillen-Ramirez, and 7 more authors

   Genome Research, Mar 2021

   Abs

   CRISPR-Cas9 deletion (CRISPR-del) is the leading approach for eliminating DNA from mammalian cells and underpins a variety of genome-editing applications. Target DNA, defined by a pair of double-strand breaks (DSBs), is removed during nonhomologous end-joining (NHEJ). However, the low efficiency of CRISPR-del results in laborious experiments and false-negative results. By using an endogenous reporter system, we show that repression of the DNA-dependent protein kinase catalytic subunit (DNA-PKcs)—an early step in NHEJ—yields substantial increases in DNA deletion. This is observed across diverse cell lines, gene delivery methods, commercial inhibitors, and guide RNAs, including those that otherwise display negligible activity. We further show that DNA-PKcs inhibition can be used to boost the sensitivity of pooled functional screens and detect true-positive hits that would otherwise be overlooked. Thus, delaying the kinetics of NHEJ relative to DSB formation is a simple and effective means of enhancing CRISPR-deletion.

2020

1. Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach

   Longendri Aguilera-Mendoza, Yovani Marrero-Ponce, César R. García-Jacas, and 4 more authors

   Scientific Reports, Oct 2020

   Abs

   Abstract The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a large variety of biological databases published over the last years. However, the knowledge discovery process from these heterogeneous data sources is a nontrivial task, becoming the essence of our research endeavor. Therefore, we devise a unified data model based on molecular similarity networks for representing a chemical reference space of bioactive peptides, having an implicit knowledge that is currently not explicitly accessed in existing biological databases. Indeed, our main contribution is a novel workflow for the automatic construction of such similarity networks, enabling visual graph mining techniques to uncover new insights from the “ocean” of known bioactive peptides. The workflow presented here relies on the following sequential steps: (i) calculation of molecular descriptors by applying statistical and aggregation operators on amino acid property vectors; (ii) a two-stage unsupervised feature selection method to identify an optimized subset of descriptors using the concepts of entropy and mutual information; (iii) generation of sparse networks where nodes represent bioactive peptides, and edges between two nodes denote their pairwise similarity/distance relationships in the defined descriptor space; and (iv) exploratory analysis using visual inspection in combination with clustering and network science techniques. For practical purposes, the proposed workflow has been implemented in our visual analytics software tool ( http://mobiosd-hub.com/starpep/ ), to assist researchers in extracting useful information from an integrated collection of 45120 bioactive peptides, which is one of the largest and most diverse data in its field. Finally, we illustrate the applicability of the proposed workflow for discovering central nodes in molecular similarity networks that may represent a biologically relevant chemical space known to date.

2019

1. Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis

   Longendri Aguilera-Mendoza, Yovani Marrero-Ponce, Jesus A Beltran, and 3 more authors

   Bioinformatics, Nov 2019

   Abs

   Abstract Motivation Bioactive peptides have gained great attention in the academy and pharmaceutical industry since they play an important role in human health. However, the increasing number of bioactive peptide databases is causing the problem of data redundancy and duplicated efforts. Even worse is the fact that the available data is non-standardized and often dirty with data entry errors. Therefore, there is a need for a unified view that enables a more comprehensive analysis of the information on this topic residing at different sites. Results After collecting web pages from a large variety of bioactive peptide databases, we organized the web content into an integrated graph database (starPepDB) that holds a total of 71 310 nodes and 348 505 relationships. In this graph structure, there are 45 120 nodes representing peptides, and the rest of the nodes are connected to peptides for describing metadata. Additionally, to facilitate a better understanding of the integrated data, a software tool (starPep toolbox) has been developed for supporting visual network analysis in a user-friendly way; providing several functionalities such as peptide retrieval and filtering, network construction and visualization, interactive exploration and exporting data options. Availability and implementation Both starPepDB and starPep toolbox are freely available at http://mobiosd-hub.com/starpep/. Supplementary information Supplementary data are available at Bioinformatics online.

2. Particle size distribution from extinction and absorption data of metallic nanoparticles

   J. Gabriela Calvillo-Vázquez, Hugo A. Guillén-Ramírez, Melissa DiazDuarte-Rodríguez, and 2 more authors

   Applied Optics, Dec 2019

2018

1. Classification of riboswitch sequences using k-mer frequencies

   Hugo A. Guillén-Ramírez, and Israel M. Martínez-Pérez

   Biosystems, Dec 2018
2. A benchmark of heart sound classification systems based on sparse decompositions

   Roilhi Frajo Ibarra-Hernández, Nancy Bertin, Miguel Angel Alonso-Arévalo, and 1 more author

   In 14th International Symposium on Medical Information Processing and Analysis, Dec 2018

2017

1. Accurate classification of immunomodulatory RNA sequences

   Hugo A. Guillen-Ramirez, Jose Colbes, Carlos A. Brizuela, and 1 more author

   In 2017 International Joint Conference on Neural Networks (IJCNN), May 2017

2013

1. Improving an evolutionary multi-objective algorithm for the biclustering of gene expression data

   Carlos A. Brizuela, Jorge E. Luna-Taylor, Israel Martinez-Perez, and 3 more authors

   In 2013 IEEE Congress on Evolutionary Computation, Jun 2013