Guillem Hernández Gu ...
Bachelors degree in bioinformatics
The Bachelor’s Degree in Bioinformatics includes a mandatory Bachelor's Degree Final Project of a scientific-professional nature to be carried out during the last two terms of students’ final year.
The Bachelor's Degree Final Project can either be a comprehensive project in the field of technologies specific to bioinformatics that brings together the competences acquired over the course of the degree or a project that explores an innovative idea (e.g. a computer program, scientific model for a biomedical question or a biological phenomenon).
Students will be overseen by a supervisor throughout the process of preparing their final project, which they will present to a board of examiners made up of lecturers on the degree course at the end of the final academic year.
Guillem Hernández Gu ...
Author: Guillem Hernández Guillamet
Project supervisor: Mireia Olivella i Beatriz Lòpez
Emotion prediction using physiological signals. Method pipeline that transform time-series to qualitative representations, reducing the complexity, to mine from a structural and feature-driven point of view using adopted techniques from bioinformatics and text mining tools. Method gathers diverse signals to perform multiple classification procedure simultaneously for a posterior consensus analysis, aiming the best prediction.
Ignasi Andreu Godall
Author: Ignasi Andreu Godall
Project supervisor: Gabriel Valiente
Finding an optimal solution to a multiple sequence alignment problem instance for more than two sequences is a hard optimization problem that is prohibitively computationally expensive, even for a few sequences of moderate length. Consequently, research on multiple sequence alignment has mainly focused on heuristic methods. However, recent advances in solvers for integer linear programming have made it possible to find exact, optimal solutions to multiple sequence alignment problem instances for several sequences of moderate length.
Laura Aviñó Esteban
Author: Laura Aviñó Esteban
Project supervisor: Daniel Zerbino
Despite knowing where genes and the genomic regions regulate them, called enhancers; we still do not know which one interacts with which gene. In this work, I have generated models based on machine learning techniques using different subsets data, including Hi-C, epigenomics and eQTLs, to predict such conections. I have also created a method to impute interactions using dimensionality reduction techniques in the expression of RNA. All in all, I have shown that integrating different data sources in the same method increases its performance.
Rubén Molina Fernand ...
Author: Rubén Molina Fernandez
Project supervisor: Baldomero Oliva
We modelled the potential interaction between AB-Amyloid and the Insulin Receptor, under the hypothesis that a feedback loop could be in the system, generating Alzheimer when the Insulin Receptor dimerization does not take place, like in Diabetes disease.
Author: Jordi Busoms
Project supervisor: Xavier Jalencas i Jordi Mestres
Unexpected binding of drugs to proteins beyond the intended protein target is one of the main causes of adverse drug reactions. Computational approaches to profiling compounds over thousands of proteins rely mostly on fast ligand-based methodologies. Structure-based methods are still computationally demanding. The main objective of this project is to investigate more efficient structure-based approaches to anticipating the likely binding of small molecules to thousand of proteins without the need to process them all individually. We have obtained a fully working algorithm that is able to get groups of similar binding sites from three-dimensional protein structures and generate representative signatures of the entire group of proteins.
José Miguel Ramírez
Author: José Miguel Ramírez
Project supervisor: Eva Novoa
RNA modifications have recently emerged as key regulators in many biological processes. However, current methods to genome-wide map RNA modifications using next-generation sequencing (NGS) are only available for 5 % of the known modifications, among other drawbacks. A new method is the direct RNA sequencing platform developed by Oxford Nanopore Technologies, which is able to sequence native RNA molecules. Here, we systematically compare state-of-the-art base-calling and mapping algorithms, as well as their ability to detect and distinguish RNA modifications using systematic base-calling ‘errors’. We find that Guppy 3.0.3 produces the highest accuracy and qualities, being also able to detect RNA modifications with the highest accuracy, and that GraphMap performs better than minimap2. However, distinguishing between different types of RNA modifications that modify the same ribonucleotide, such as 5-methylcytosine and 5-hydroxymethylcytosine, still remains a challenge.