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On page 1 showing 1 ~ 6 papers out of 6 papers

Revisiting operons: an analysis of the landscape of transcriptional units in E. coli.

  • Xizeng Mao‎ et al.
  • BMC bioinformatics‎
  • 2015‎

Bacterial operons are considerably more complex than what were thought. At least their components are dynamically rather than statically defined as previously assumed. Here we present a computational study of the landscape of the transcriptional units (TUs) of E. coli K12, revealed by the available genomic and transcriptomic data, providing new understanding about the complexity of TUs as a whole encoded in the genome of E. coli K12.


A greedy alignment-free distance estimator for phylogenetic inference.

  • Sharma V Thankachan‎ et al.
  • BMC bioinformatics‎
  • 2017‎

Alignment-free sequence comparison approaches have been garnering increasing interest in various data- and compute-intensive applications such as phylogenetic inference for large-scale sequences. While k-mer based methods are predominantly used in real applications, the average common substring (ACS) approach is emerging as one of the prominent alignment-free approaches. This ACS approach has been further generalized by some recent work, either greedily or exactly, by allowing a bounded number of mismatches in the common substrings.


Learning to predict expression efficacy of vectors in recombinant protein production.

  • Wen-Ching Chan‎ et al.
  • BMC bioinformatics‎
  • 2010‎

Recombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is to fuse a target protein into different vectors in Escherichia coli (E. coli). However, the production efficacy of different vectors varies for different target proteins. Trial-and-error is still the common practice to find out the efficacy of a vector for a given target protein. Previous studies are limited in that they assumed that proteins would be over-expressed and focused only on the solubility of expressed proteins. In fact, many pairings of vectors and proteins result in no expression.


Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology.

  • Kang Ning‎ et al.
  • BMC bioinformatics‎
  • 2010‎

In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN).


HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms.

  • Maria Persico‎ et al.
  • BMC bioinformatics‎
  • 2005‎

The application of high throughput approaches to the identification of protein interactions has offered for the first time a glimpse of the global interactome of some model organisms. Until now, however, such genome-wide approaches have not been applied to the human proteome.


Cgaln: fast and space-efficient whole-genome alignment.

  • Ryuichiro Nakato‎ et al.
  • BMC bioinformatics‎
  • 2010‎

Whole-genome sequence alignment is an essential process for extracting valuable information about the functions, evolution, and peculiarities of genomes under investigation. As available genomic sequence data accumulate rapidly, there is great demand for tools that can compare whole-genome sequences within practical amounts of time and space. However, most existing genomic alignment tools can treat sequences that are only a few Mb long at once, and no state-of-the-art alignment program can align large sequences such as mammalian genomes directly on a conventional standalone computer.


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