Mining for Class-Specific Motifs in Protein Sequence Classification

Background: In protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class. Results: We present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as Wordspy. We have validated our enriched set of class-specific motifs against the functionally important motifs obtained from the NLSdb, Prosite and ELM databases. We demonstrate that this method is very generic; thus can be widely applied to detect class-specific motifs in many protein sequence classification tasks. Conclusion: The proposed scoring function and methodology is able to identify class-specific motifs using discriminative n-grams derived from the protein sequences. The implementation of amino acid substitution scores for similarity detection, and the dampening factor to normalize the unbalanced datasets have significant effect on the performance of the scoring function. Our multipronged validation tests demonstrate that this method can detect class-specific motifs from a wide variety of protein sequence classes with a potential application to detecting proteome-specific motifs of different organisms.

Genome sequence of Desulfosporosinus sp. OT, an acidophilic sulfate-reducing bacterium from copper mining waste in Norilsk, Northern Siberia

We have sequenced the genome of Desulfosporosinus sp. OT, a Gram-positive, acidophilic sulfate-reducing Firmicute isolated from copper tailing sediment in the Norilsk mining-smelting area in Northern Siberia, Russia. This represents the first sequenced genome of a Desulfosporosinus species. The genome has a size of 5.7 Mb and encodes 6,222 putative proteins.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.

Uranium mining industry views on ICRP statement on radon

In 2009, the International Commission on Radiological Protection issued a statement on radon which stated that the dose conversion factor for radon progeny would likely double, and the calculation of risk from radon should move to a dosimetric approach, rather than the longstanding epidemiological approach. Through the World Nuclear Association, whose members represent over 90% of the world's uranium production, industry has been examining this issue with a goal of offering expertise and knowledge to assist with the practical implementation of these evolutionary changes to evaluating the risk from radon progeny. Industry supports the continuing use of the most current epidemiological data as a basis for risk calculation, but believes that further examination of these results is needed to better understand the level of conservatism in the potential epidemiological-based risk models. With regard to adoption of the dosimetric approach, industry believes that further work is needed before this is a practical option. In particular, this work should include a clear demonstration of the validation of the dosimetric model which includes how smoking is handled, the establishment of a practical measurement protocol, and the collection of relevant data for modern workplaces. Industry is actively working to address the latter two items.

Alteration of host cell phenotype by Theileria annulata and Theileria parva: mining for manipulators in the parasite genomes

The apicomplexan parasites Theileria annulata and Theileria parva cause severe lymphoproliferative disorders in cattle. Disease pathogenesis is linked to the ability of the parasite to transform the infected host cell (leukocyte) and induce uncontrolled proliferation. It is known that transformation involves parasite dependent perturbation of leukocyte signal transduction pathways that regulate apoptosis, division and gene expression, and there is evidence for the translocation of Theileria DNA binding proteins to the host cell nucleus. However, the parasite factors responsible for the inhibition of host cell apoptosis, or induction of host cell proliferation are unknown. The recent derivation of the complete genome sequence for both T. annulata and T. parva has provided a wealth of information that can be searched to identify molecules with the potential to subvert host cell regulatory pathways. This review summarizes current knowledge of the mechanisms used by Theileria parasites to transform the host cell, and highlights recent work that has mined the Theileria genomes to identify candidate manipulators of host cell phenotype.