Gradient-based optimization of kernel-target alignment for sequence kernels applied to bacterial gene start detection

Biological data mining using kernel methods can be improved by a task-specific choice of the kernel function. Oligo kernels for genomic sequence analysis have proven to have a high discriminative power and to provide interpretable results. Oligo kernels that consider subsequences of different lengths can be combined and parameterized to increase their flexibility. For adapting these parameters efficiently, gradient-based optimization of the kernel-target alignment is proposed. The power of this new, general model selection procedure and the benefits of fitting kernels to problem classes are demonstrated by adapting oligo kernels for bacterial gene start detection     

Inhibition of Membrane Lipid Peroxidation by a Radical Scavenging Mechanism: a Novel Function for Hydroxyl-Containing Ionophores

In the present study we show that K⁺/H⁺ hydroxyl-containing ionophores lasalocid-A (LAS) and nigericin (NIG) in the nanomolar concentration range, inhibit Fe²⁺-citrate and 2,2'-azobis(2-amidinopropane) di-hydrochloride (ABAP)-induced lipid peroxidation in intact rat liver mitochondria and in egg phosphatidyl-choline (PC) liposomes containing negatively charged lipids—dicetyl phosphate (DCP) or cardiolipin (CL)—and KCl as the osmotic support. In addition, monensin (MON), a hydroxyl-containing ionophore with higher affinity for Na⁺ than for K⁺, promotes a similar effect when NaCl is the osmotic support. The protective effect of the ionophores is not observed when the osmolyte is sucrose. Lipid peroxidation was evidenced by mitochondrial swelling, antimycin A-insensitive O₂ consumption, formation of thiobarbituric acid-reactive substances (TBARS), conjugated dienes, and electron paramagnetic resonance (EPR) spectra of an incorporated lipid spin probe. A time-dependent decay of spin label EPR signal is observed as a consequence of lipid peroxidation induced by both inductor systems in liposomes. Nitroxide destruction is inhibited by buty-lated hydroxytoluene, a known antioxidant, and by the hydroxyl-containing ionophores. In contrast, vali-nomycin (VAL), which does not possess alcoholic groups, does not display this protective effect. Effective order parameters (S_eff), determined from the spectra of an incorporated spin label are larger in the presence of salt and display a small increase upon addition of the ionophores, as a result of the increase of counter ion concentration at the negatively charged bilayer surface. This condition leads to increased formation of the ion-ionophore complex, the membrane binding (uncharged) species. The membrane-incorporated complex is the active species in the lipid peroxidation inhibiting process. Studies in aqueous solution (in the absence of membranes) showed that NIG and LAS, but not VAL, decrease the Fe²⁺-citrate-induced production of radicals derived from piperazine-based buffers, demonstrating their property as radical scavengers. Both Fe²⁺-citrate and ABAP promote a much more pronounced decrease of LAS fluorescence in PC/CL liposomes than in dimyristoyl phosphatidyl-choline (DMPC, saturated phospholipid)-DCP liposomes, indicating that the ionophore also scavenges lipid peroxyl radicals. A slow decrease of fluorescence is observed in the latter system, for all lipid compositions in sucrose medium, and in the absence of membranes, indicating that the primary radicals stemming from both inductors also attack the ionophore. Altogether, the data lead to the conclusion that the membrane-incorporated cation complexes of NIG, LAS and MON inhibit lipid peroxidation by blocking initiation and propagation reactions in the lipid phase via a free radical scavenging mechanism, very likely due to the presence of alcoholic hydroxyl groups in all three molecules and to the attack of the aromatic moiety of LAS.     

Evolutionary optimization of sequence kernels for detection of bacterial gene starts

Oligo kernels for biological sequence classification have a high discriminative power. A new parameterization for the K-mer oligo kernel is presented, where all oligomers of length K are weighted individually. The task specific choice of these parameters increases the classification performance and reveals information about discriminative features. For adapting the multiple kernel parameters based on cross-validation the covariance matrix adaptation evolution strategy is proposed. It is applied to optimize the trimer oligo kernels for the detection of bacterial gene starts. The resulting kernels lead to higher classification rates, and the adapted parameters reveal the importance of particular triplets for classification, for example of those occurring in the Shine-Dalgarno Sequence.     

TICO: a tool for improving predictions of prokaryotic translation initiation sites

SUMMARY: We provide the tool 'TICO' (Translation Initiation site COrrection) for improving the results of conventional gene finders for prokaryotic genomes with regard to exact localization of the translation initiation site (TIS). At the current state TICO provides an interface for direct post processing of the predictions obtained from the widely used program GLIMMER. Our program is based on a clustering algorithm for completely unsupervised scoring of potential TIS locations. AVAILABILITY: Our tool can be freely accessed through a web interface at http://tico.gobics.de/ CONTACT: maike@gobics.de     

An unsupervised classification scheme for improving predictions of prokaryotic TIS

Background  

Although it is not difficult for state-of-the-art gene finders to identify coding regions in prokaryotic genomes, exact prediction of the corresponding translation initiation sites (TIS) is still a challenging problem. Recently a number of post-processing tools have been proposed for improving the annotation of prokaryotic TIS. However, inherent difficulties of these approaches arise from the considerable variation of TIS characteristics across different species. Therefore prior assumptions about the properties of prokaryotic gene starts may cause suboptimal predictions for newly sequenced genomes with TIS signals differing from those of well-investigated genomes.     

Improving Hidden Markov Models for classification of human immunodeficiency virus-1 subtypes through linear classifier learning

Profile Hidden Markov Models (pHMMs) are widely used to model nucleotide or protein sequence families. In many applications, a sequence family classified into several subfamilies is given and each subfamily is modeled separately by one pHMM. A major drawback of this approach is the difficulty of coping with subfamilies composed of very few sequences.Correct subtyping of human immunodeficiency virus-1 (HIV-1) sequences is one of the most crucial bioinformatic tasks affected by this problem of small subfamilies, i.e., HIV-1 subtypes with a small number of known sequences. To deal with small samples for particular subfamilies of HIV-1, we employ a machine learning approach. More precisely, we make use of an existing HMM architecture and its associated inference engine, while replacing the unsupervised estimation of emission probabilities by a supervised method. For that purpose, we use regularized linear discriminant learning together with a balancing scheme to account for the widely varying sample size. After training the multiclass linear discriminants, the corresponding weights are transformed to valid probabilities using a softmax function.We apply this modified algorithm to classify HIV-1 sequence data (in the form of partial-length HIV-1 sequences and semi-artificial recombinants) and show that the performance of pHMMs can be significantly improved by the proposed technique.     

MarVis: a tool for clustering and visualization of metabolic biomarkers

Background  

A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters.     

Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models

Background  

Horizontal gene transfer (HGT) is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs) or more specifically pathogenicity or symbiotic islands.     

Role of Fe(III) in Fe(II)citrate-mediated peroxidation of mitochondrial membrane lipids

In this report we study the effect of Fe(III) on lipid peroxidation induced by Fe(II)citrate in mitochondrial membranes, as assessed by the production of thiobarbituric acid-reactive substances and antimycin A-insensitive oxygen uptake. The presence of Fe(III) stimulates initiation of lipid peroxidation when low citrate:Fe(II) ratios are used ( 4:1). For a citrate:total iron ratio of 1:1 the maximal stimulation of lipid peroxidation by Fe(III) was observed when the Fe(II):Fe(III) ratio was in the range of 1:1 to 1:2. The lag phase that accompanies oxygen uptake was greatly diminished by increasing concentrations of Fe(III) when the citrate:total iron ratio was 1:1, but not when this ratio was higher. It is concluded that the increase of lipid peroxidation by Fe(III) is observed only when low citrate:Fe(II) ratios were used. Similar results were obtained using ATP as a ligand of iron. Monitoring the rate of spontaneous Fe(II) oxidation by measuring oxygen uptake in buffered medium, in the absence of mitochondria, Fe(III)-stimulated oxygen consumption was observed only when a low citrate:Fe(II) ratio was used. This result suggests that Fe(III) may facilitate the initiation and/or propagation of lipid peroxidation by increasing the rate of Fe(II)citrate-generated reactive oxygen species.