Dataset Information

Positional clustering improves computational binding site detection and identifies novel cis-regulatory sites in mammalian GABAA receptor subunit genes.

ABSTRACT: Understanding transcription factor (TF) mediated control of gene expression remains a major challenge at the interface of computational and experimental biology. Computational techniques predicting TF-binding site specificity are frequently unreliable. On the other hand, comprehensive experimental validation is difficult and time consuming. We introduce a simple strategy that dramatically improves robustness and accuracy of computational binding site prediction. First, we evaluate the rate of recurrence of computational TFBS predictions by commonly used sampling procedures. We find that the vast majority of results are biologically meaningless. However clustering results based on nucleotide position improves predictive power. Additionally, we find that positional clustering increases robustness to long or imperfectly selected input sequences. Positional clustering can also be used as a mechanism to integrate results from multiple sampling approaches for improvements in accuracy over each one alone. Finally, we predict and validate regulatory sequences partially responsible for transcriptional control of the mammalian type A gamma-aminobutyric acid receptor (GABA(A)R) subunit genes. Positional clustering is useful for improving computational binding site predictions, with potential application to improving our understanding of mammalian gene expression. In particular, predicted regulatory mechanisms in the mammalian GABA(A)R subunit gene family may open new avenues of research towards understanding this pharmacologically important neurotransmitter receptor system.

SUBMITTER: Reddy TE

PROVIDER: S-EPMC1807961 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Positional clustering improves computational binding site detection and identifies novel cis-regulatory sites in mammalian GABAA receptor subunit genes.

Reddy Timothy E TE Shakhnovich Boris E BE Roberts Daniel S DS Russek Shelley J SJ DeLisi Charles C

Nucleic acids research 20070103 3

Understanding transcription factor (TF) mediated control of gene expression remains a major challenge at the interface of computational and experimental biology. Computational techniques predicting TF-binding site specificity are frequently unreliable. On the other hand, comprehensive experimental validation is difficult and time consuming. We introduce a simple strategy that dramatically improves robustness and accuracy of computational binding site prediction. First, we evaluate the rate of re ...[more]

PMID: 17204484

Similar Datasets

Project description:BackgroundMolecular phylogenetics studies the evolutionary relationships among the individuals of a population through their biological sequences. It may provide insights about the origin and the evolution of viral diseases, or highlight complex evolutionary trajectories. A key task is inferring phylogenetic trees from any type of sequencing data, including raw short reads. Yet, several tools require pre-processed input data e.g. from complex computational pipelines based on de novo assembly or from mappings against a reference genome. As sequencing technologies keep becoming cheaper, this puts increasing pressure on designing methods that perform analysis directly on their outputs. From this viewpoint, there is a growing interest in alignment-, assembly-, and reference-free methods that could work on several data including raw reads data.ResultsWe present phyBWT2, a newly improved version of phyBWT (Guerrini et al. in 22nd International Workshop on Algorithms in Bioinformatics (WABI) 242:23-12319, 2022). Both of them directly reconstruct phylogenetic trees bypassing both the alignment against a reference genome and de novo assembly. They exploit the combinatorial properties of the extended Burrows-Wheeler Transform (eBWT) and the corresponding eBWT positional clustering framework to detect relevant blocks of the longest shared substrings of varying length (unlike the k-mer-based approaches that need to fix the length k a priori). As a result, they provide novel alignment-, assembly-, and reference-free methods that build partition trees without relying on the pairwise comparison of sequences, thus avoiding to use a distance matrix to infer phylogeny. In addition, phyBWT2 outperforms phyBWT in terms of running time, as the former reconstructs phylogenetic trees step-by-step by considering multiple partitions, instead of just one partition at a time, as previously done by the latter.ConclusionsBased on the results of the experiments on sequencing data, we conclude that our method can produce trees of quality comparable to the benchmark phylogeny by handling datasets of different types (short reads, contigs, or entire genomes). Overall, the experiments confirm the effectiveness of phyBWT2 that improves the performance of its previous version phyBWT, while preserving the accuracy of the results.

Project description:Phasic GABAergic inhibition in hippocampus and neocortex falls into two kinetically distinct categories, GABA(A,fast) and GABA(A,slow). In hippocampal area CA1, GABA(A,fast) is generally believed to underlie gamma oscillations, whereas the contribution of GABA(A,slow) to hippocampal rhythms has been speculative. Hypothesizing that GABA(A) receptors containing the beta(3) subunit contribute to GABA(A,slow) inhibition and that slow inhibitory synapses control excitability as well as contribute to network rhythms, we investigated the consequences of this subunit's absence on synaptic inhibition and network function. In pyramidal neurons of GABA(A) receptor beta(3) subunit-deficient (beta(3)(-/-)) mice, spontaneous GABA(A,slow) inhibitory postsynaptic currents (IPSCs) were much less frequent, and evoked GABA(A,slow) currents were much smaller than in wild-type mice. Fittingly, long-lasting recurrent inhibition of population spikes was less powerful in the mutant, indicating that receptors containing beta(3) subunits contribute substantially to GABA(A,slow) currents in pyramidal neurons. By contrast, slow inhibitory control of GABA(A,fast)-producing interneurons was unaffected in beta(3)(-/-) mice. In vivo hippocampal network activity was markedly different in the two genotypes. In beta(3)(-/-) mice, epileptiform activity was observed, and theta oscillations were weaker, slower, less regular and less well coordinated across laminae compared with wild-type mice, whereas gamma oscillations were weaker and faster. The amplitude modulation of gamma oscillations at theta frequency ("nesting") was preserved but was less well coordinated with theta oscillations. With the caveat that seizure-induced changes in inhibitory circuits might have contributed to the changes observed in the mutant animals, our results point to a strong contribution of beta(3) subunits to slow GABAergic inhibition onto pyramidal neurons but not onto GABA(A,fast) -producing interneurons and support different roles for these slow inhibitory synapses in the generation and coordination of hippocampal network rhythms.

Dataset Information

Positional clustering improves computational binding site detection and identifies novel cis-regulatory sites in mammalian GABAA receptor subunit genes.

Publications

Positional clustering improves computational binding site detection and identifies novel cis-regulatory sites in mammalian GABAA receptor subunit genes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets