Biology subjects

Lazaro-Guevara, J. M.

Publications and source records attributed to Lazaro-Guevara, J. M..

3 recordsLinked to original sources

bioRxiv2022

The genomics of linkage drag in sunflower

Crop wild relatives represent valuable sources of alleles for crop improvement, including adaptation to climate change and emerging diseases. However, introgressions from wild relatives might have deleterious effects on desirable traits, including yield, due to linkage drag. Here we comprehensively analyzed the genomic and phenotypic impacts of wild introgressions into cultivated sunflower to estimate the impacts of linkage drag. First, we generated new reference sequences for seven cultivated and one wild sunflower genotype, as well as improved assemblies for two additional cultivars. Next, relying on previously generated sequences from wild donor species, we identified introgressions in the cultivated reference sequences, as well as the sequence and structural variants they contain. We then used a ridge regression model to test the effects of the introgressions on phenotypic traits in the cultivated sunflower association mapping population. We found that introgression has introduced substantial sequence and structural variation into the cultivated sunflower gene pool, including > 3,000 new genes. While introgressions reduced genetic load at protein-coding sequences and positively affected traits associated with abiotic stress resistance, they mostly had negative impacts on yield and quality traits. Introgressions found at high frequency in the cultivated gene pool had larger effects than low frequency introgressions, suggesting that the former likely were targeted by artificial selection. Also, introgressions from more distantly related species were more likely to be maladaptive than those from the wild progenitor of cultivated sunflower. Thus, pre-breeding efforts should focus, as far as possible, on closely related and fully compatible wild relatives.

genomics↗

bioRxiv2019

A simple computer vision algorithm as a clinical aid for the pathologist

Histological analysis for cancer detection or stratification is performed by observing and examining a small portion of a biopsied tissue under a microscope. Nevertheless, to assign clinical meaning to the findings, the analysis and interpretation of an experienced Pathologist is always necessary. Using high-resolution images, these experts visually examine the sample looking for specific characteristics on the cell shapes and tissue distributions, so they could decide whether tissue regions are cancerous, and establish the malignancy level of it. However, with the increasing demand for work for those pathologists and the importance of accuracy on diagnostics, multiple attempts to simplify their work have been performed. Current Imaging technologies allow novel horizons in the automatized selection of some of the characteristics that indicate malignancy in a biopsy. In this work, we propose a simple computer vision algorithm that can be implemented as a screening method for focusing in histological areas with higher risk of malignancy saving time to the pathologist and helping to perform a more standardized work, an easy observation with the potential to become in an aid to daily clinical work.

bioinformatics↗

bioRxiv2019

A New approximate matching compression algorithm for DNA sequences

1.Undeveloped countries like Guatemala, where access to high-speed internet connections is limited, downloading and sharing Biological information of thousands of Mega Bits is a huge problem for the beginning and development of Bioinformatics. Based on that information is an urgent necessity to find a better way to share this biological data. There is when the compression algorithms become relevant. With all this information in mind, born the idea of creating a new algorithm using redundancy and approximate selection. MethodsUsing the probability given by the transition matrix of the three-word tuple and relative frequencies. Calculating the relative and total frequencies given by the permutation formula (nr) and compressing 6 bits of information into 1 implementing the ASCII table code (0...255 characters, 28), using clusters of 102 DNA bases compacted into 17 string sequences. For decompressing, the inverse process must be done, except that the triplets must be selected randomly (or use a matrix dictionary, 4102). ConclusionThe compression algorithm has a better compression ratio than LZW and Huffmans algorithm. However, the time needed for decompressing makes this algorithm incompatible for massive data. The functionality as MD5sum need more research but is a promising helpful tool for DNA checking.

bioinformatics↗