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Biology subjects

Adebiyi, E.

Publications and source records attributed to Adebiyi, E..

3 recordsLinked to original sources

Transcriptome analysis reveals a novel DNA element that may interact with chromatin-associated proteins in Plasmodium berghei during erythrocytic development

BackgroundThe life cycle of Plasmodium parasites is intricate and multistage, alternating between dynamic environments. Temporal regulation of transcription by stage-specific transcription factor binding at particular regulatory regions within gene promoters facilitates its progression. As a result, each new developmental stage is endowed with its unique gene sets, whose just-in-time expression enables the parasite to completely adapt to the necessary circumstances. Our understanding of these transcriptome-level regulatory processes is limited, and more so, a thorough examination of the entire life cycle in the experimentally tractable rodent model organism P. berghei is lacking. ResultsWe performed a genome-wide analysis of RNA-Seq data from different developmental stages of P. berghei. Integrated data from the human malaria parasites P. falciparum and P. vivax demonstrated that Plasmodium parasites have a unique transcriptional signature. We identified the sets of genes differentially expressed at each stage, clustered them based on similarities of their expression profiles, and predicted the regulatory motifs governing their expression. We interpreted the motifs using known binding sites for established eukaryotic transcription factors, including those of the ApiAP2s, and identified eight potentially novel motifs. Additionally, we expanded the annotation of another motif--AGGTAA--found in genes exclusive to erythrocytic development and identified members of the PfMORC and GCN5 complexes among its possible interacting proteins. ConclusionThis study provides new insights into gene usage and its regulation during P. berghei development.

bioinformatics↗

A deep population reference panel of tandem repeat variation

Tandem repeats (TRs) represent one of the largest sources of genetic variation in humans and are implicated in a range of phenotypes. Here we present a deep characterization of TR variation based on high coverage whole genome sequencing from 3,550 diverse individuals from the 1000 Genomes Project and H3Africa cohorts. We develop a method, EnsembleTR, to integrate genotypes from four separate methods resulting in high-quality genotypes at more than 1.7 million TR loci. Our catalog reveals novel sequence features influencing TR heterozygosity, identifies population-specific trinucleotide expansions, and finds hundreds of novel eQTL signals. Finally, we generate a phased haplotype panel which can be used to impute most TRs from nearby single nucleotide polymorphisms (SNPs) with high accuracy. Overall, the TR genotypes and reference haplotype panel generated here will serve as valuable resources for future genome-wide and population-wide studies of TRs and their role in human phenotypes.

genomics↗

Identifying essential genes across eukaryotes by machine learning

Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60,381 genes, using 41,635 features derived from sequence, gene functions and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed a high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and validated predictions experimentally yielding similar performance. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies.

bioinformatics↗