Pipelines for TCR-Seq data analysis Go to repository »
This repository contains several pipelines and ilustrative notebooks with analyses of T cell receptor (TCR) repertoires from blood, visceral adipose tissue, subcutaneous adipose tissue and liver tissue.
Cite us: "SARS-CoV-2-specific T cell receptors after disease and vaccination" / "Análisis de redes y especificidad de antígenos de repertorios de receptores de células T como respuesta a Covid-19". Master Thesis. Marisol Benítez-Cantos. Murcia, July 2021. Supervisors: C. Cano y J.M. Vivo. Máster en Bioinformática. Universidad de Murcia. Disponible aquí
Quantum Computing for Genome Assembly Go to repository »
Report on how to tackle the Genome Assembly problem using a Quantum Annealer
Cite us: “De novo genome assembly using quantum annealing”. Trabajo Fin de Grado. J.A. Álvarez-Ocete. Supervisión: C. Cano y A. Lasanta. 2021.
Report: Review of tools for TCR-Seq data analysis Go to repository »
Signals of past and present infections are encoded in the set of up to 10^10 different T cell receptors (TCRs) that bind to antigens to trigger an immune response. TCR sequencing (TCR-Seq) techniques yield a complex dataset of reads with a region that can not be aligned against a reference genome, since it is de novo generated with random nucleotide addition and / or deletion that ensures a high variability necessary for antigen recognition. In this scenario sequencing errors difficult the data preprocessing and subsequent interpretation. This is a review of tools for analyzing such sequences.
LociGenesis Go to repository »
Pipeline for the generation of bencharmk data for TCR repertoire analysis
TADdistRGo to TADdistR repository »
TADdistR is a tool for annotation of genetic signatures based on Topologically Associating Domains
Cite us: TaDdistR: a Tool for Annotation of Genetic Signatures Based on Topologically Associating Domains. Verbeni, Cano, Navarro, Benitez-Cantos, Gonzalez-Aguilar, Durán-Ogalla, Benavides, Pedrinaci, López de Hierro-Ruiz, Martínez-Tirado, Ruiz-Cabello, Martín-Ruiz, Blanco, Lizardi. International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO).
ProphTools General Prioritization Tools for Heterogeneous Biological Networks Go to ProphTools repository »
ProphTools is a general-purpose hetreogeneous network prioritization tool based on the successful ad-hoc prioritization methodologies in ProphNet and DrugNet. ProphTools is open-source and multiplatform and can be downloaded from its GitHub repository and easily installed via pip.
Additional datasets for lncRNA-disease prioritization: general, specific.
Cite us: Navarro, C., Martínez, V., Blanco, A., Cano, C. (2017). ProphTools: General Prioritization Tools for Heterogeneous Biological Networks. GigaScience.
CisMiner Regulatory module predictionGo to CisMiner site »
CisMiner allows to perform a blind search of CRMs without any prior information about target CRMs nor limitation in the number of motifs. CisMiner tackles the combinatorial complexity of genome-wide cis-regulatory module extraction using a natural representation of motif combinations as itemsets and applying the Top-Down Fuzzy Frequent-Pattern Tree algorithm to identify significant itemsets. Fuzzy technology allows CisMiner to better handle the imprecision and noise inherent to regulatory processes.
ProphNet Disease-gene prioritizationGo to ProphNet site »
ProphNet is a network disease-gene prioritization tool. Given a set of diseases of interest, ProphNet returns a list of prioritized diseases that are related to the disease.
DrugNet Computational drug repositioningGo to DrugNet site »
Computational drug repositioning can lead to a considerable reduction in cost and time in any drug development process. Recent approaches have addressed the network-based nature of biological information for performing complex prioritization tasks. In this work, we apply a new methodology based on heterogeneous network prioritization that can aid researchers in the drug repositioning process.
SC Intuit Intuitionistic sequence-motif scoring Download SCIntuit
SC_intuit is a scoring method for measuring sequence-motif affinity based on IFS theory. Unlike other methods that consider dependencies between positions, SC_intuit is designed to prevent overestimation of less conserved positions of TFBSs. For a given pair of bases, SC_intuit is computed not only as a function of their combined probability of occurrence, but also taking into account the individual importance of each single base at its corresponding position.
Cite us: Garcia-Alcalde, F., Blanco, A., Shepherd, A. J. (2010). An intuitionistic approach to scoring DNA sequences against transcription factor binding site motifs. BMC bioinformatics, 11(1), 551.