Ribosome biosynthesis is not necessary for initiation of DNA replication.

Synthesis of mature 28-S ribosomal RNA and 60-S ribosomal subunits is inhibited in baby hamster kidney (BHK) cell line ts 422E at non-permissive temperature (39 degrees C). This leads to a 66% decrease of total ribosomes per cell, a marked imbalance between the large and small ribosomal subunits in the cytoplasm and a decrease of cells per dish after prolonged culture at 30 degrees C. However, inhibition of ribosome synthesis does not affect progression of cells through the G1 period of the cell division cycle, the length of the pre-replicative period, and the rate of entry of cells into S phase. In contrast to culture at non-permissive temperature, culture of BHK ts 422E cells in the presence of 0.04 micrograms/ml actinomycin D at 33 degrees C inhibits markedly the entry into S period. It is concluded that low doses of actinomycin D exert their inhibitory effect on cell growth by preventing maturation and transport of mRNA rather than by interfering with ribosome synthesis. Microfluorometric analysis revealed only slight differences in the distribution of BHK ts 422E cells in G1, S and G2 phases of the cycle either when cultured at 33 degrees C or at 39 degrees C. When too few ribosomes per cell are produced in BHK ts 422E cells at 39 degrees C, cells do not seem to be arrested reversibly at a specific point of the cell cycle but rather to die at random.     

ArrayIDer: automated structural re-annotation pipeline for DNA microarrays

Background  

Systems biology modeling from microarray data requires the most contemporary structural and functional array annotation. However, microarray annotations, especially for non-commercial, non-traditional biomedical model organisms, are often dated. In addition, most microarray analysis tools do not readily accept EST clone names, which are abundantly represented on arrays. Manual re-annotation of microarrays is impracticable and so we developed a computational re-annotation tool (ArrayIDer) to retrieve the most recent accession mapping files from public databases based on EST clone names or accessions and rapidly generate database accessions for entire microarrays.     

Accelerating the reconstruction of genome-scale metabolic networks

Background  

The genomic information of a species allows for the genome-scale reconstruction of its metabolic capacity. Such a metabolic reconstruction gives support to metabolic engineering, but also to integrative bioinformatics and visualization. Sequence-based automatic reconstructions require extensive manual curation, which can be very time-consuming. Therefore, we present a method to accelerate the time-consuming process of network reconstruction for a query species. The method exploits the availability of well-curated metabolic networks and uses high-resolution predictions of gene equivalency between species, allowing the transfer of gene-reaction associations from curated networks.     

A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes

Background

Knowing the phase of marker genotype data can be useful in genome-wide association studies, because it makes it possible to use analysis frameworks that account for identity by descent or parent of origin of alleles and it can lead to a large increase in data quantities via genotype or sequence imputation. Long-range phasing and haplotype library imputation constitute a fast and accurate method to impute phase for SNP data.

Methods

A long-range phasing and haplotype library imputation algorithm was developed. It combines information from surrogate parents and long haplotypes to resolve phase in a manner that is not dependent on the family structure of a dataset or on the presence of pedigree information.

Results

The algorithm performed well in both simulated and real livestock and human datasets in terms of both phasing accuracy and computation efficiency. The percentage of alleles that could be phased in both simulated and real datasets of varying size generally exceeded 98% while the percentage of alleles incorrectly phased in simulated data was generally less than 0.5%. The accuracy of phasing was affected by dataset size, with lower accuracy for dataset sizes less than 1000, but was not affected by effective population size, family data structure, presence or absence of pedigree information, and SNP density. The method was computationally fast. In comparison to a commonly used statistical method (fastPHASE), the current method made about 8% less phasing mistakes and ran about 26 times faster for a small dataset. For larger datasets, the differences in computational time are expected to be even greater. A computer program implementing these methods has been made available.

Conclusions

The algorithm and software developed in this study make feasible the routine phasing of high-density SNP chips in large datasets.