Biology subjects

Quinteros, B. I.

Publications and source records attributed to Quinteros, B. I..

2 recordsLinked to original sources

bioRxiv2023

TidyGEO: Preparing analysis-ready datasets from Gene Expression Omnibus

TidyGEO is a Web-based tool for downloading, tidying, and reformatting data series from Gene Expression Omnibus (GEO). As a freely accessible repository with data from over 4 million biological samples across more than 4,000 organisms, GEO provides diverse opportunities for secondary research. Transcriptomic data are most common in GEO, but other measurement types are also prevalent, including DNA methylation levels, genotypes, and chromatin-accessibility profiles. GEOs diversity and expansiveness present opportunities and challenges. Although scientists may find assay data relevant to a given research question, most analyses require sample annotations, such as a samples treatment group, disease subtype, or age. In GEO, such annotations are stored alongside assay data in delimited, text-based files. However, the structure and semantics of the annotations vary widely from one series to another, and many annotations are not useful for analysis purposes. Thus, every GEO series must be tidied before it can be analyzed. Manual approaches may be used, but these are error prone and take time away from other research tasks. Custom computer scripts can be written, but many scientists lack the computational expertise to create such scripts. To address these challenges, we created TidyGEO, which supports essential data-cleaning tasks for sample-level annotations, such as selecting informative columns, renaming columns, splitting or merging columns, standardizing data values, and filtering samples. Additionally, users can integrate annotations with assay data, restructure assay data, and generate code that enables others to reproduce these steps. The source code for TidyGEO is at https://github.com/srp33/TidyGEO.

bioinformatics↗

bioRxiv2021

The DNA methylation landscape of five pediatric-tumor types

Fewer DNA mutations have been identified in pediatric tumors than adult tumors, suggesting that alternative tumorigenic mechanisms, including aberrant DNA methylation, may play a prominent role in pediatric tumors. Methylation is an epigenetic process of regulating gene expression in which methyl groups are attached to DNA molecules, often in promoter regions. In Wilms tumors and acute myeloid leukemias, increased levels of epigenetic silencing have been associated with worse patient outcomes. However, to date, researchers have studied methylation primarily in adult tumors and for specific genes but not on a pan-pediatric cancer scale. We addressed these gaps first by aggregating methylation data from 309 noncancerous samples and establishing baseline expectations for each gene. Even though these samples represent diverse tissue types and population ancestral groups, methylation levels were highly consistent for most genes. Second, we compared tumor methylation levels against these baseline values for five pediatric cancer types--Wilms tumors, clear cell sarcomas of the kidney, rhabdoid tumors, neuroblastomas, and osteosarcomas. Hypermethylation was more common than hypomethylation--as many as 11.8% of genes were hypermethylated in a given tumor, compared to a maximum of 4.8% for hypomethylated genes. For each cancer type, genes with the highest variance exhibited consistently divergent methylation patterns for distinct patient subsets. We evaluated whether genomic and epigenomic abnormalities contribute to pediatric tumorigenesis in a mutually exclusive manner but did not find evidence of this phenomenon. Furthermore, even though oncogenes are commonly upregulated in tumors, and tumor-suppressor genes are commonly downregulated in tumors, we did not find statistical evidence that methylation drives such patterns on a broad scale in pediatric tumors.

genomics↗