Gabriel Moreno-Hagelsieb

The focus of the Lab of Computational consequences is on the study, prediction, and evolution of genetic functional networks. The main data used is the sequence databases containing complete and draft genomes, as well as metagenomes (DNA sequences from random samples of a given environment). One of our main goals is the prediction and understanding of the functions of the vast amount of uncharacterized genes.

I commonly supervise PhD, MSc, undergrad Theses, and undergrad directed studies. Please note that the work at the Lab of Computational conSequences is computer-based. You should have command of a programming language, like PERL or PYTHON, and familiarity with a UNIX-based operative system and the command line. Otherwise you still have to demonstrate that you have loads of energy to learn these skills. Don’t come to talk to me without having read and tried hard to understand a few of our most recent articles. I welcome proposals, but they have to be well investigated. Remember that any research project requires you to have a lot of energy, not only to carry on the necessary steps towards answering research questions, but also to continue reading the pertinent literature.

• Hernandez-Salmeron JE, Irani T, Moreno-Hagelsieb G (2023) Fast genome-based delimitation of Enterobacterales species. PLoS One 18: e0291492
• Juan M Escorcia-Rodríguez, Mario Esposito, Julio A Freyre-González, Gabriel Moreno-Hagelsieb (2022) Non-synonymous to synonymous substitutions suggest that orthologs tend to keep their functions, while paralogs are a source of functional novelty. PeerJ 10, e13843
• Ward, N. & Moreno-Hagelsieb, G. Quickly finding orthologs as reciprocal best hits with BLAT, LAST, and UBLAST: how much do we miss? PLoS One 9, e101850 (2014).
• Babu, M. et al. Quantitative genome-wide genetic interaction screens reveal global epistatic relationships of protein complexes in Escherichia coli. PLoS Genet 10, e1004120 (2014).
• Moreno-Hagelsieb, G. & Jokic, P. The evolutionary dynamics of functional modules and the extraordinary plasticity of regulons: the Escherichia coli perspective. Nucleic Acids Research 40, 7104-7112 (2012).
• Stearns, J.C. et al. Bacterial biogeography of the human digestive tract. Scientific reports 1 (2011).
• Janga, S.C., Diaz-Mejia, J.J. & Moreno-Hagelsieb, G. Network-based function prediction and interactomics: The case for metabolic enzymes. Metabolic Engineering 13, 1-10 (2011).
• Hu, P. et al. Global Functional Atlas of Escherichia coli Encompassing Previously Uncharacterized Proteins. Plos Biology 7, 929-947 (2009).
• Moreno-Hagelsieb, G. & Latimer, K. Choosing BLAST options for better detection of orthologs as reciprocal best hits. Bioinformatics 24, 319-324 (2008).
• Gonzalez, V. et al. The partitioned Rhizobium etli genome: genetic and metabolic redundancy in seven interacting replicons. Proc Natl Acad Sci U S A 103, 3834-3839 (2006).
• Janga, S.C., Collado-Vides, J. & Moreno-Hagelsieb, G. Nebulon: a system for the inference of functional relationships of gene products from the rearrangement of predicted operons. Nucleic Acids Res 33, 2521-2530 (2005).
• Moreno-Hagelsieb, G. & Collado-Vides, J. A powerful non-homology method for the prediction of operons in prokaryotes. Bioinformatics 18 Suppl 1, S329-336 (2002).
• Salgado, H., Moreno-Hagelsieb, G., Smith, T.F. & Collado-Vides, J. Operons in Escherichia coli: genomic analyses and predictions. Proc Natl Acad Sci U S A 97, 6652-6657 (2000).

• BI326: Bioinformatics
• BI426: Genome Dynamics