Efficient algorithms for the discovery of gapped factors

Background

With an increasing number of plant genome sequences, it has become important to develop a robust computational method for detecting plant promoters. Although a wide variety of programs are currently available, prediction accuracy of these still requires further improvement. The limitations of these methods can be addressed by selecting appropriate features for distinguishing promoters and non-promoters.

Methods

In this study, we proposed two feature selection approaches based on hexamer sequences: the Frequency Distribution Analyzed Feature Selection Algorithm (FDAFSA) and the Random Triplet Pair Feature Selecting Genetic Algorithm (RTPFSGA). In FDAFSA, adjacent triplet-pairs (hexamer sequences) were selected based on the difference in the frequency of hexamers between promoters and non-promoters. In RTPFSGA, random triplet-pairs (RTPs) were selected by exploiting a genetic algorithm that distinguishes frequencies of non-adjacent triplet pairs between promoters and non-promoters. Then, a support vector machine (SVM), a nonlinear machine-learning algorithm, was used to classify promoters and non-promoters by combining these two feature selection approaches. We referred to this novel algorithm as PromoBot.

Results

Promoter sequences were collected from the PlantProm database. Non-promoter sequences were collected from plant mRNA, rRNA, and tRNA of PlantGDB and plant miRNA of miRBase. Then, in order to validate the proposed algorithm, we applied a 5-fold cross validation test. Training data sets were used to select features based on FDAFSA and RTPFSGA, and these features were used to train the SVM. We achieved 89% sensitivity and 86% specificity.

Conclusions

We compared our PromoBot algorithm to five other algorithms. It was found that the sensitivity and specificity of PromoBot performed well (or even better) with the algorithms tested. These results show that the two proposed feature selection methods based on hexamer frequencies and random triplet-pair could be successfully incorporated into a supervised machine learning method in promoter classification problem. As such, we expect that PromoBot can be used to help identify new plant promoters. Source codes and analysis results of this work could be provided upon request.     

Global nucleosome occupancy in yeast

Background  

Although eukaryotic genomes are generally thought to be entirely chromatin-associated, the activated PHO5 promoter in yeast is largely devoid of nucleosomes. We systematically evaluated nucleosome occupancy in yeast promoters by immunoprecipitating nucleosomal DNA and quantifying enrichment by microarrays.     

Different binding of RNA polymerase to individual promoters

Summary The amount of E. coli RNA polymerase which can be bound to individual promoters on pgal and dgal phage DNA in a stable heparin-resistant form was measured by assessing its capacity to transcribe, upon addition of the nucleoside triphosphates, the RNA sequences starting at these promoters. These RNA species were analysed by competition hybridization to separated single strands of , pgal and dgal phage DNA.Individual promoters bind, at saturation, different numbers of polymerase molecules. From the amount of polymerase necessary to saturate all promoters (Fig. 3), from the proportions of RNA synthesized at the individual promoters (Table 1) and from the amounts of -³²P-ATP or-GTP label incorporated into the different RNA species (Tables 2 and 3) we calculate polymerase storage capacities for the promoters as follows: gal: 6 molecules; l-strand specific: 3–5 molecules starting with ATP and 1 molecule starting with GTP; r-strand specific: 3–5 molecules starting with ATP (and perhaps one molecule starting with GTP); these estimates are lower limits and may be too small by a factor of up to three.The heparin resistant binding of six polymerase molecules to the gal promoter is dependent on CGA protein and cAMP, but ATP and GTP can allow one polymerase to bind to the same site or to a very close one.Several parameters of polymerase binding are different with the individual promoters tested.     

The vasa regulatory region mediates germline expression and maternal transmission of proteins in the malaria mosquito Anopheles gambiae: a versatile tool for genetic control strategies

Background  

Germline specific promoters are an essential component of potential vector control strategies which function by genetic drive, however suitable promoters are not currently available for the main human malaria vector Anopheles gambiae.     

MicroRNA-restricted transgene expression in the retina

Background

Gene transfer using adeno-associated viral (AAV) vectors has been successfully applied in the retina for the treatment of inherited retinal dystrophies. Recently, microRNAs have been exploited to fine-tune transgene expression improving therapeutic outcomes. Here we evaluated the ability of retinal-expressed microRNAs to restrict AAV-mediated transgene expression to specific retinal cell types that represent the main targets of common inherited blinding conditions.

Methodology/Principal Findings

To this end, we generated AAV2/5 vectors expressing EGFP and containing four tandem copies of miR-124 or miR-204 complementary sequences in the 3′UTR of the transgene expression cassette. These vectors were administered subretinally to adult C57BL/6 mice and Large White pigs. Our results demonstrate that miR-124 and miR-204 target sequences can efficiently restrict AAV2/5-mediated transgene expression to retinal pigment epithelium and photoreceptors, respectively, in mice and pigs. Interestingly, transgene restriction was observed at low vector doses relevant to therapy.

Conclusions

We conclude that microRNA-mediated regulation of transgene expression can be applied in the retina to either restrict to a specific cell type the robust expression obtained using ubiquitous promoters or to provide an additional layer of gene expression regulation when using cell-specific promoters.