Dataset Information

Selection, periodicity and potential function for Highly Iterative Palindrome-1 (HIP1) in cyanobacterial genomes.

ABSTRACT: Highly Iterated Palindrome 1 (HIP1, GCGATCGC) is hyper-abundant in most cyanobacterial genomes. In some cyanobacteria, average HIP1 abundance exceeds one motif per gene. Such high abundance suggests a significant role in cyanobacterial biology. However, 20 years of study have not revealed whether HIP1 has a function, much less what that function might be. We show that HIP1 is 15- to 300-fold over-represented in genomes analyzed. More importantly, HIP1 sites are conserved both within and between open reading frames, suggesting that their overabundance is maintained by selection rather than by continual replenishment by neutral processes, such as biased DNA repair. This evidence for selection suggests a functional role for HIP1. No evidence was found to support a functional role as a peptide or RNA motif or a role in the regulation of gene expression. Rather, we demonstrate that the distribution of HIP1 along cyanobacterial chromosomes is significantly periodic, with periods ranging from 10 to 90 kb, consistent in scale with periodicities reported for co-regulated, co-expressed and evolutionarily correlated genes. The periodicity we observe is also comparable in scale to chromosomal interaction domains previously described in other bacteria. In this context, our findings imply HIP1 functions associated with chromosome and nucleoid structure.

SUBMITTER: Xu M

PROVIDER: S-EPMC5861425 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Publications

Selection, periodicity and potential function for Highly Iterative Palindrome-1 (HIP1) in cyanobacterial genomes.

Xu Minli M Lawrence Jeffrey G JG Durand Dannie D

Nucleic acids research 20180301 5

Highly Iterated Palindrome 1 (HIP1, GCGATCGC) is hyper-abundant in most cyanobacterial genomes. In some cyanobacteria, average HIP1 abundance exceeds one motif per gene. Such high abundance suggests a significant role in cyanobacterial biology. However, 20 years of study have not revealed whether HIP1 has a function, much less what that function might be. We show that HIP1 is 15- to 300-fold over-represented in genomes analyzed. More importantly, HIP1 sites are conserved both within and between ...[more]

PMID: 29432573

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Project description:BACKGROUND: We introduce Iterative Feature Removal (IFR) as an unbiased approach for selecting features with diagnostic capacity from large data sets. The algorithm is based on recently developed tools in machine learning that are driven by sparse feature selection goals. When applied to genomic data, our method is designed to identify genes that can provide deeper insight into complex interactions while remaining directly connected to diagnostic utility. We contrast this approach with the search for a minimal best set of discriminative genes, which can provide only an incomplete picture of the biological complexity. RESULTS: Microarray data sets typically contain far more features (genes) than samples. For this type of data, we demonstrate that there are many equivalently-predictive subsets of genes. We iteratively train a classifier using features identified via a sparse support vector machine. At each iteration, we remove all the features that were previously selected. We found that we could iterate many times before a sustained drop in accuracy occurs, with each iteration removing approximately 30 genes from consideration. The classification accuracy on test data remains essentially flat even as hundreds of top-genes are removed.Our method identifies sets of genes that are highly predictive, even when comprised of genes that individually are not. Through automated and manual analysis of the selected genes, we demonstrate that the selected features expose relevant pathways that other approaches would have missed. CONCLUSIONS: Our results challenge the paradigm of using feature selection techniques to design parsimonious classifiers from microarray and similar high-dimensional, small-sample-size data sets. The fact that there are many subsets of genes that work equally well to classify the data provides a strong counter-result to the notion that there is a small number of "top genes" that should be used to build classifiers. In our results, the best classifiers were formed using genes with limited univariate power, thus illustrating that deeper mining of features using multivariate techniques is important.

Dataset Information

Selection, periodicity and potential function for Highly Iterative Palindrome-1 (HIP1) in cyanobacterial genomes.

Publications

Selection, periodicity and potential function for Highly Iterative Palindrome-1 (HIP1) in cyanobacterial genomes.

Similar Datasets

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