Understanding the packaging of DNA into chromatin has become a crucial aspect in the study of gene regulatory mechanisms. Heterochromatin establishment and maintenance dynamics have emerged as some of the main features involved in genome stability, cellular development, and diseases. The most extensively studied heterochromatin protein is HP1a. This protein has two main domains, namely the chromoshadow and the chromodomain, separated by a hinge region. Over the years, several works have taken on the task of identifying HP1a partners using different strategies. In this review, we focus on describing these interactions and the possible complexes and subcomplexes associated with this critical protein. Characterization of these complexes will help us to clearly understand the implications of the interactions of HP1a in heterochromatin maintenance, heterochromatin dynamics, and heterochromatin’s direct relationship to gene regulation and chromatin organization.

Identifying groups that share common features among datasets through clustering analysis is a typical problem in many fields of science, particularly in post-omics and systems biology research. In respect of this, quantifying how a measure can cluster or organize intrinsic groups is important since currently there is no statistical evaluation of how ordered is, or how much noise is embedded in the resulting clustered vector. Many of the literature focuses on how well the clustering algorithm orders the data, with several measures regarding external and internal statistical measures; but none measure has been developed to statistically quantify the noise in an arranged vector posterior a clustering algorithm, i.e., how much of the clustering is due to randomness. Here, we present a quantitative methodology, based on autocorrelation, to assess this problem.

Identifying groups that share common features among datasets through clustering analysis is a typical problem in many fields of science, particularly in post-omics and systems biology research. In respect of this, quantifying how a measure can cluster or organize intrinsic groups is important since currently there is no statistical evaluation of how ordered is, or how much noise is embedded in the resulting clustered vector. Much of the literature focuses on how well the clustering algorithm orders the data, with several measures regarding external and internal statistical validation; but no score has been developed to quantify statistically the noise in an arranged vector posterior to a clustering algorithm, i.e., how much of the clustering is due to randomness. Here, we present a quantitative methodology, based on autocorrelation, in order to assess this problem.

No abstract

Identifying groups that share common features among datasets through clustering analysis is a typical problem in many fields of science, particularly in post-omics and systems biology research. In respect of this, quantifying how a measure can cluster or organize intrinsic groups is important since currently there is no statistical evaluation of how ordered is, or how much noise is embedded in the resulting clustered vector. Many of the literature focuses on how well the clustering algorithm orders the data, with several measures regarding external and internal statistical measures; but none measure has been developed to statistically quantify the noise in an arranged vector posterior a clustering algorithm, i.e., how much of the clustering is due to randomness. Here, we present a quantitative methodology, based on autocorrelation, to assess this problem.