Biology subjects

Ochoa, J. M.

Publications and source records attributed to Ochoa, J. M..

3 recordsLinked to original sources

bioRxiv2021

Machine learning techniques to derive bioclimatic classifications for Colombia

Bioclimatic classifications seek to divide a study region into geographic areas with similar bioclimatic characteristics. In this study we proposed two bioclimatic classifications for Colombia using machine learning techniques. We firstly characterized the precipitation space of Colombia using principal component analysis. Based on Lang classification, we then projected all background sites in the precipitation space with their corresponding categories. We sequentially fit logistic regression models to reclassify all background sites in the precipitation space with six redefined Lang categories. New categories were the used to define a new modified Lang and Caldas-Lang classifications.

ecology↗

bioRxiv2021

A supervised learning algorithm to evaluate occurrence records in virtual species

Digital and open access of occurrence data have encouraged the development of tools to improve biodiversity conservation and management. In this study, we proposed a methodology to evaluate point-occurrence records based on expert knowledge. We firstly generated virtual data to test our methodology without confounding factors by simulating geographical distributions, virtual sampling, and expert checking of occurrence records. We used a set of non-linear bioclimatic variables and principal component analysis (PCA) to define a duality function between niche and biotope spaces. Subsequently, a supervised-learning model was fit to classify records between true and doubtful presence based on the virtual expert checking. We then tested our methodology using three virtual species and 10-fold cross validation. Also, we evaluated the prediction performance of the supervise model compared with the virtual observer using a virtual external database of occurrence data.

ecology↗

bioRxiv2021

MCPdb: The Bacterial Microcompartment Database

Bacterial microcompartments are organelle-like structures composed entirely of proteins. They have evolved to carry out several distinct and specialized metabolic functions in a wide variety of bacteria. Their outer shell is constructed from thousands of tessellating protein subunits, encapsulating enzymes that carry out the internal metabolic reactions. The shell proteins are varied, with single, tandem and permuted versions of the PF00936 protein family domain comprising the primary structural component of their polyhedral architecture, which is reminiscent of a viral capsid. While considerable amounts of structural and biophysical data have been generated in the last 15 years, current resources present challenges for understanding the functional and structural properties of microcompartments (MCPs) and their diversity. In order to make the remarkable structural features of bacterial microcompartments accessible to a broad community of scientists and non-specialists, we developed MCPdb: The Bacterial Microcompartment Database (https://mcpdb.mbi.ucla.edu/). MCPdb is a comprehensive resource that categorizes and organizes known microcompartment protein structures and their larger assemblies. To emphazise the critical roles symmetric assembly and architecture play in microcompartment function, each structure in the MCPdb is validated and annotated with respect to: (1) its predicted natural assembly state (2) tertiary structure and topology and (3) the metabolic compartment type from which it derives. The current database includes 134 structures and is available to the public with the anticipation that it will serve as a growing resource for scientists interested in understanding protein-based metabolic organelles in bacteria.

bioinformatics↗