The Extent and Impact of Variation in ADME Genes in Sub-Saharan African Populations.

Investigating variation in genes involved in the absorption, distribution, metabolism, and excretion (ADME) of drugs are key to characterizing pharmacogenomic (PGx) relationships. ADME gene variation is relatively well characterized in European and Asian populations, but data from African populations are under-studied-which has implications for drug safety and effective use in Africa. We identified significant ADME gene variation in African populations using data from 458 high-coverage whole genome sequences, 412 of which are novel, and from previously available African sequences from the 1,000 Genomes Project.

Revisiting early-stage COVID-19 strategy options.

: Early-stage interventions in a potential pandemic are important to understand as they can make the difference between runaway exponential growth that is hard to turn back and stopping the spread before it gets that far. COVID19 is an interesting case study because there have been very different outcomes in different localities. These variations are best studied after the fact if precision is the goal; while a pandemic is still unfolding less precise analysis is of value in attempting to guide localities to learn lessons of those that preceded them.

The FOXP2 forkhead domain binds to a variety of DNA sequences with different rates and affinities.

FOXP2 is a member of the P subfamily of FOX transcription factors, the DNA-binding domain of which is the winged helix forkhead domain (FHD). In this work we show that the FOXP2 FHD is able to bind to various DNA sequences, including a novel sequence identified in this work, with different affinities and rates as detected using surface plasmon resonance. Combining the experimental work with molecular docking, we show that high-affinity sequences remain bound to the protein for longer, form a greater number of interactions with the protein and induce a greater structural change in the protein than low-affinity sequences.

Transcription factor motif quality assessment requires systematic comparative analysis.

Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models.

Differential motif enrichment analysis of paired ChIP-seq experiments.

Background: Motif enrichment analysis of transcription factor ChIP-seq data can help identify transcription factors that cooperate or compete. Previously, little attention has been given to comparative motif enrichment analysis of pairs of ChIP-seq experiments, where the binding of the same transcription factor is assayed under different conditions. Such comparative analysis could potentially identify the distinct regulatory partners/competitors of the assayed transcription factor under different conditions or at different stages of development.

How to establish a bioinformatics postgraduate degree programme--a case study from South Africa.

The Research Unit in Bioinformatics at Rhodes University (RUBi), South Africa, offers a Masters of Science in Bioinformatics. Growing demand for bioinformatics qualifications results in applications from across Africa. Courses aim to bridge gaps in the diverse backgrounds of students who range from biologists with no prior computing exposure to computer scientists with no biology background.

Inferring direct DNA binding from ChIP-seq.

Genome-wide binding data from transcription factor ChIP-seq experiments is the best source of information for inferring the relative DNA-binding affinity of these proteins in vivo. However, standard motif enrichment analysis and motif discovery approaches sometimes fail to correctly identify the binding motif for the ChIP-ed factor. To overcome this problem, we propose 'central motif enrichment analysis' (CMEA), which is based on the observation that the positional distribution of binding sites matching the direct-binding motif tends to be unimodal, well centered and maximal in the precise center of the ChIP-seq peak regions.

Identification of molecular compartments and genetic circuitry in the developing mammalian kidney.

Lengthy developmental programs generate cell diversity within an organotypic framework, enabling the later physiological actions of each organ system. Cell identity, cell diversity and cell function are determined by cell type-specific transcriptional programs; consequently, transcriptional regulatory factors are useful markers of emerging cellular complexity, and their expression patterns provide insights into the regulatory mechanisms at play. We performed a comprehensive genome-scale in situ expression screen of 921 transcriptional regulators in the developing mammalian urogenital system.

MEME-ChIP: motif analysis of large DNA datasets.

Motivation: Advances in high-throughput sequencing have resulted in rapid growth in large, high-quality datasets including those arising from transcription factor (TF) ChIP-seq experiments. While there are many existing tools for discovering TF binding site motifs in such datasets, most web-based tools cannot directly process such large datasets.

Results: The MEME-ChIP web service is designed to analyze ChIP-seq 'peak regions'--short genomic regions surrounding declared ChIP-seq 'peaks'.

The value of position-specific priors in motif discovery using MEME.

Background: Position-specific priors have been shown to be a flexible and elegant way to extend the power of Gibbs sampler-based motif discovery algorithms. Information of many types-including sequence conservation, nucleosome positioning, and negative examples-can be converted into a prior over the location of motif sites, which then guides the sequence motif discovery algorithm. This approach has been shown to confer many of the benefits of conservation-based and discriminative motif discovery approaches on Gibbs sampler-based motif discovery methods, but has not previously been studied with methods based on expectation maximization (EM).