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Processed HIV prognostic dataset for control experiments.

ABSTRACT: This paper provides a control dataset of processed prognostic indicators for analysing drug resistance in patients on antiretroviral therapy (ART). The dataset was locally sourced from health facilities in Akwa Ibom State of Nigeria, West Africa and contains 14 attributes with 1506 unique records filtered from 3168 individual treatment change episodes (TCEs). These attributes include sex, before and follow-up CD4 counts (BCD4, FCD4), before and follow-up viral load (BRNA, FRNA), drug type/combination (DTYPE), before and follow-up body weight (Bwt, Fwt), patient response to ART (PR), and classification targets (C1-C5). Five (5) output membership grades of a fuzzy inference system ranging from very high interaction to no interaction were constructed to model the influence of adverse drug reaction (ADR) and subsequently derive the PR attribute (a non-fuzzy variable). The PR attribute membership clusters derived from a universe of discourse table were then used to label the classification targets as follows: C1=no interaction, C2=very low interaction, C3=low interaction, C4=high interaction, and C5=very high interaction. The classification targets are useful for building classification models and for detecting patients with ADR. This data can be exploited for the development of expert systems, for useful decision support to treatment failure classification [1] and effectual drug regimen prescription.

SUBMITTER: Ekpenyong ME

PROVIDER: S-EPMC8142042 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe organization of eukaryotic DNA into chromatin has a strong influence on the accessibility and regulation of genetic information. The locations and occupancies of a principle component of chromatin, nucleosomes, are typically assayed through use of enzymatic digestion with micrococcal nuclease (MNase). MNase is an endo-exo nuclease that preferentially digests naked DNA and the DNA in linkers between nucleosomes, thus enriching for nucleosome-associated DNA. To determine nucleosome organization genome-wide, DNA remaining from MNase digestion is sequenced using high-throughput sequencing technologies (MNase-seq). Unfortunately, the results of MNase-seq can vary dramatically due to technical differences and this confounds comparisons between MNase-seq experiments, such as examining condition-dependent chromatin organizations.ResultsIn this study we use MNase digestion simulations to demonstrate how MNase-seq signals can vary for different nucleosome configuration when experiments are performed with different extents of MNase digestion. Signal variation in these simulations reveals an important DNA sampling bias that results from a neighborhood effect of MNase digestion techniques. The presence of this neighborhood effect ultimately confounds comparisons between different MNase-seq experiments. To address this issue we present a standardized chromatin preparation which controls for technical variance between MNase-based chromatin preparations and enables the collection of similarly sampled (matched) chromatin populations. Standardized preparation of chromatin includes a normalization step for DNA input into MNase digestions and close matching of the extent of digestion between each chromatin preparation using gel densitometry analysis. The protocol also includes directions for successful pairing with multiplex sequencing reactions.ConclusionsWe validated our method by comparing the experiment-to-experiment variation between biological replicates of chromatin preparations from S. cerevisiae. Results from our matched preparation consistently produced MNase-seq datasets that were more closely correlated than other unstandardized approaches. Additionally, we validated the ability of our approach at enabling accurate downstream comparisons of chromatin structures, by comparing the specificity of detecting Tup1-dependent chromatin remodeling events in comparisons between matched and un-matched wild-type and tup1? MNase-seq datasets. Our matched MNase-seq datasets demonstrated a significant reduction in non-specific (technical) differences between experiments and were able to maximize the detection of biologically-relevant (Tup1-dependent) changes in chromatin structure.

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Processed HIV prognostic dataset for control experiments.

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