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     

Computational evaluation of TIS annotation for prokaryotic genomes

Background  

Accurate annotation of translation initiation sites (TISs) is essential for understanding the translation initiation mechanism. However, the reliability of TIS annotation in widely used databases such as RefSeq is uncertain due to the lack of experimental benchmarks.     

A new classification scheme for the family Araliaceae

The present paper deals with the following three aspects: 1. It attempts to discuss the problems on primitive forms of the family Araliaceae. The genus Tupidanthus Hook. f. & Thoms. was considered by H. Harms (1894) and H. L. Li (1942) as primitive, whilst another genus Plerandra A. Gray was regarded as primitive by R. H. Eyde & C. C. Tseng in 1971. Having made a detailed comparison of the taxonomical characters of these two genera, the present authors believe that both genera are not the most primitive in the Araliaceae. Their affinit yis not close enough and they possibly evolved in parallel lines from a common ancestor which is so far unknown yet. 2. By studying the systems of the past, the present authors believe that none of them is entirely satisfactory. Bentham (1867) recognized five ‘series’ (in fact, equivalent to ‘tribe’ with the ending-eae of names) based on the petaline arrangement in the bud, the numbers of stamen and the types of endospem. This is a plausible fundamental treatment for the Araliaceae, but choosing the endosperm as a criteria in dividing tribe is artifical. As we know today, both ruminate and uniform endosperm are usually presente in the same genus. Seemann’s system (1868) divided the Hederaceae (excl. Trib. Aralieae) into five tribes, in addition to the locules of ovary. The criteria are essentially the same as Bentham’s. The system of Hams (1894) divided the family into three tribes. Two tribes, Aralieae and Mackinlayeae, of Bentham are retained, but other groups were combined in the Trib. Schefflereae. However, Harms did not retain one of those three oldest legitimate names which had named by Bentham, that is contrary to the law of priority in the International Code of Botanical Nomenelature. Hutchinson (1967) adopted seven tribes for the family. The criteria essentially follow those of Bentham, but the inflorescence is overstressed. The inflorescence is an artifical taxonomical character in dividing tribes, because of some dioecious plants, such as Meryta sinclairii (Hook. f.) Seem., have two types of inflorescence in male and female plants. According to Hutchinson’s arrangement, the male and female plants would be put in separate tribes. 3. The present authors are of the opinion that in the study of a natural classification of plant groups emphasis should be laid not only on the characters of the reproductive organs, but on those of vegetative organs as well. The present revised system is based principally upon the characters of both flowers and leaves of the five tribes as follows: Trib. 1. Plerandreae Benth. emend. Hoo & Tseng Trib. 2. Tetraplasandreae Hoo & Tseng Trib. 3. Mackinlayeae Benth. Trib. 4. Aralieae Benth. Trib. 5. Panaceae Benth. emend. Hoo & Tseng     

A classification scheme for mobilization regions of bacterial plasmids

Transmissible plasmids can be classified according to their mobilization ability, as being conjugative (self-transmissible) or mobilizable (transmissible only in the presence of additional conjugative functions). Naturally occurring mobilizable plasmids carry the genetic information necessary for relaxosome formation and processing, but lack the functions required for mating pair formation. Mobilizable plasmids have a tremendous impact in horizontal gene transfer in nature, including the spread of antibiotic resistance. However, analysis of their promiscuity and diversity has attracted less attention than that of conjugative plasmids. This review will focus on the analysis of the diversity of mobilizable plasmids. For this purpose, we primarily compared the amino acid sequences of their relaxases and, when pertinent, we compared these enzymes with conjugative plasmid relaxases. In this way, we established phylogenetic relationships among the members of each superfamily. We conducted a database and literature analysis that led us to propose a classification system for small mobilizable plasmids in families and superfamilies according to their mobilization regions. This review outlines the genetic organization of each family of mobilization regions, as well as the most relevant properties and relationships among their constituent encoded proteins. In this respect, the present review constitutes a first approach to the characterization of the global gene pool of mobilization regions of small mobilizable plasmids.     

A new avenue for classification and prediction of olive cultivars using supervised and unsupervised algorithms

Various methods have been used to identify cultivares of olive trees; herein we used different bioinformatics algorithms to propose new tools to classify 10 cultivares of olive based on RAPD and ISSR genetic markers datasets generated from PCR reactions. Five RAPD markers (OPA0a21, OPD16a, OP01a1, OPD16a1 and OPA0a8) and five ISSR markers (UBC841a4, UBC868a7, UBC841a14, U12BC807a and UBC810a13) selected as the most important markers by all attribute weighting models. K-Medoids unsupervised clustering run on SVM dataset was fully able to cluster each olive cultivar to the right classes. All trees (176) induced by decision tree models generated meaningful trees and UBC841a4 attribute clearly distinguished between foreign and domestic olive cultivars with 100% accuracy. Predictive machine learning algorithms (SVM and Naïve Bayes) were also able to predict the right class of olive cultivares with 100% accuracy. For the first time, our results showed data mining techniques can be effectively used to distinguish between plant cultivares and proposed machine learning based systems in this study can predict new olive cultivars with the best possible accuracy.     

An unsupervised neural network model for the development of reflex co-ordination

In this paper, we present a model for the development of connections between muscle afferents and motoneurones in the human spinal cord. The model consists of a limb with six muscles, one motoneurone pool, one pooled (Ia-like) afferent for each muscle and a central programme generator. The weights of the connections between the afferents and the motoneurone pools are adapted during centrally induced movements of the limb. The connections between the afferents and the motoneurone pools adapt in a hebbian way, using only local information present at the synapses. This neural network is tested in two examples of a limb with two degrees of freedom and six muscles. Despite the simplifications, the model predicts the pattern of autogenic and heterogenic monosynaptic reflexes quite realistically.     

Phylogenetic classification of prokaryotic and eukaryotic Sir2-like proteins

Sirtuins (Sir2-like proteins) are present in prokaryotes and eukaryotes. Here, two new human sirtuins (SIRT6 and SIRT7) are found to be similar to a particular subset of insect, nematode, plant, and protozoan sirtuins. Molecular phylogenetic analysis of 60 sirtuin conserved core domain sequences from a diverse array of organisms (including archaeans, bacteria, yeasts, plants, protozoans, and metazoans) shows that eukaryotic Sir2-like proteins group into four main branches designated here as classes I-IV. Prokaryotic sirtuins include members of classes II and III. A fifth class of sirtuin is present in gram positive bacteria and Thermotoga maritima. Saccharomyces cerevisiae has five class I sirtuins. Caenorhabditis elegans and Drosophila melanogaster have sirtuin genes from classes I, II, and IV. The seven human sirtuin genes include all four classes: SIRT1, SIRT2, and SIRT3 are class I, SIRT4 is class II, SIRT5 is class III, and SIRT6 and SIRT7 are class IV.     

Neural networks and cluster analysis for unsupervised classification of cultivated species of Tilia (Malvaceae)

A case study of the classification of 19 species of the genus Tilia (Malvaceae) using morphological data is presented. The value of a self‐organizing map (SOM) neural network when applied to this problem is compared with that of a standard cluster analysis method. An interpretation of the results and a discussion of traditional classification systems are provided in order to evaluate the practical usefulness of the SOM methodology. The SOM technique is shown to provide an easily understandable two‐dimensional topological map which enables simple linear separation of taxonomic groups. Good separation of traditional infrageneric groups in Tilia is achieved and suggestions about changes to the existing classification are provided. It is concluded that the SOM and cluster analysis methods, whilst both producing results similar to existing classifications, are most effective at different hierarchical levels. © 2009 The Linnean Society of London, Botanical Journal of the Linnean Society, 2009, 159 , 300–314.     

A classification scheme for scoring subcutaneous fat depots of shorebirds

ABSTRACT.   Different scoring systems have been developed for assessing fat stores in birds. Most have been used with passerines, and there is no widely used standard for studies of shorebirds. I developed a new fat-scoring classification scheme for shorebirds that was tested in Poland with juvenile Dunlins ( Calidris alpina ) during autumn and Wood Sandpipers ( Tringa glareola ) during spring migration. My proposed fatness scale is based on a qualitative assessment of the amount of fat in the axillary region and, if necessary, the furcular region. The resulting fat scores do not predict the actual amount of fat carried by a bird, but can be used to assign birds to a particular state of fatness and allow comparison of birds with different scores. Analysis revealed that the relationship between fat scores and predicted fat mass was approximately linear for both Dunlins and Wood Sandpipers. Although the scale I propose was tested with 20 species of shorebirds, sample sizes for larger species were small and consisted exclusively of lean and moderately fat birds. As a result, my proposed fat-scoring system should be evaluated using larger and fatter shorebirds because it has only been validated for one small and one medium-sized species.     

Iridescence of a marine bacterium and classification of prokaryotic structural colors

Iridescence is a property of structural color that is occasionally encountered in higher eukaryotes but that has been poorly documented in the prokaryotic kingdom. In the present work, we describe a marine bacterium, identified as Cellulophaga lytica, isolated from the surface of an anemone, that exhibits bright green iridescent colonies under direct epi-illumination. This phenomenon has not previously been investigated in detail. In this study, color changes of C. lytica colonies were observed at various angles of direct illumination or observation. Its iridescent green appearance was dominant on various growth media. Red and violet colors were also discerned on colony edges. Remarkable C. lytica bacterial iridescence was revealed and characterized using high-resolution optical spectrometry. In addition to this, by culturing other bacterial strains to which various forms of faintly iridescent traits have previously been attributed, we identify four principal appearance characteristics of structural color in prokaryotes. A new general classification of bacterial iridescence is therefore proposed in this study. Furthermore, a specific separate class is described for iridescent C. lytica strains because they exhibit what is so far a unique intense glitter-like iridescence in reflection. C. lytica is the first prokaryote discovered to produce the same sort of intense iridescence under direct illumination as that associated with higher eukaryotes, like some insects and birds. Due to the nature of bacterial biology, cultivation, and ubiquity, this discovery may be of significant interest for both ecological and nanoscience endeavors.     

An unsupervised machine learning method for assessing quality of tandem mass spectra

Background

In a single proteomic project, tandem mass spectrometers can produce hundreds of millions of tandem mass spectra. However, majority of tandem mass spectra are of poor quality, it wastes time to search them for peptides. Therefore, the quality assessment (before database search) is very useful in the pipeline of protein identification via tandem mass spectra, especially on the reduction of searching time and the decrease of false identifications. Most existing methods for quality assessment are supervised machine learning methods based on a number of features which describe the quality of tandem mass spectra. These methods need the training datasets with knowing the quality of all spectra, which are usually unavailable for the new datasets.

Results

This study proposes an unsupervised machine learning method for quality assessment of tandem mass spectra without any training dataset. This proposed method estimates the conditional probabilities of spectra being high quality from the quality assessments based on individual features. The probabilities are estimated through a constraint optimization problem. An efficient algorithm is developed to solve the constraint optimization problem and is proved to be convergent. Experimental results on two datasets illustrate that if we search only tandem spectra with the high quality determined by the proposed method, we can save about 56 % and 62% of database searching time while losing only a small amount of high-quality spectra.

Conclusions

Results indicate that the proposed method has a good performance for the quality assessment of tandem mass spectra and the way we estimate the conditional probabilities is effective.

     

Survey for g-proteins in the prokaryotic genomes: prediction of functional roles based on classification

The members of the family of G-proteins are characterized by their ability to bind and hydrolyze guanosine triphosphate (GTP) to guanosine diphosphate (GDP). Despite a common biochemical function of GTP hydrolysis shared among the members of the family of G-proteins, they are associated with diverse biological roles. The current work describes the identification and detailed analysis of the putative G-proteins encoded in the completely sequenced prokaryotic genomes. Inferences on the biological roles of these G-proteins have been obtained by their classification into known functional subfamilies. We have identified 497 G-proteins in 42 genomes. Seven small GTP-binding protein homologues have been identified in prokaryotes with at least two of the diagnostic sequence motifs of G-proteins conserved. The translation factors have the largest representation (234 sequences) and are found to be ubiquitous, which is consistent with their critical role in protein synthesis. The GTP_OBG subfamily comprises of 79 sequences in our dataset. A total of 177 sequences belong to the subfamily of GTPase of unknown function and 154 of these could be associated with domains of known functions such as cell cycle regulation and t-RNA modification. The large GTP-binding proteins and the alpha-subunit of heterotrimeric G-proteins are not detected in the genomes of the prokaryotes surveyed.     

ARC: automated resource classifier for agglomerative functional classification of prokaryotic proteins using annotation texts

Functional classification of proteins is central to comparative genomics. The need for algorithms tuned to enable integrative interpretation of analytical data is felt globally. The availability of a general,automated software with built-in flexibility will significantly aid this activity. We have prepared ARC (Automated Resource Classifier), which is an open source software meeting the user requirements of flexibility. The default classification scheme based on keyword match is agglomerative and directs entries into any of the 7 basic non-overlapping functional classes: Cell wall, Cell membrane and Transporters (C), Cell division (D), Information (I), Translocation (L), Metabolism (M), Stress(R), Signal and communication (S) and 2 ancillary classes: Others (O) and Hypothetical (H).The keyword library of ARC was built serially by first drawing keywords from Bacillus subtilis and Escherichia coli K12. In subsequent steps,this library was further enriched by collecting terms from archaeal representative Archaeoglobus fulgidus, Gene Ontology, and Gene Symbols. ARC is 94.04% successful on 6,75,663 annotated proteins from 348 prokaryotes. Three examples are provided to illuminate the current perspectives on mycobacterial physiology and costs of proteins in 333 prokaryotes. ARC is available at http://arc.igib.res.in.     

ARC: Automated Resource Classifier for agglomerative functional classification of prokaryotic proteins using annotation texts

Functional classification of proteins is central to comparative genomics. The need for algorithms tuned to enable integrative interpretation of analytical data is felt globally. The availability of a general, automated software with built-in flexibility will significantly aid this activity. We have prepared ARC (Automated Resource Classifier), which is an open source software meeting the user requirements of flexibility. The default classification scheme based on keyword match is agglomerative and directs entries into any of the 7 basic non-overlapping functional classes: Cell wall, Cell membrane and Transporters (C), Cell division (

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), Information (I), Translocation (\(\mathcal{L}\)), Metabolism (

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), Stress (

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), Signal and communication(S) and 2 ancillary classes: Others (O) and Hypothetical (

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). The keyword library of ARC was built serially by first drawing keywords from Bacillus subtilis and Escherichia coli K12. In subsequent steps, this library was further enriched by collecting terms from archaeal representative Archaeoglobus fulgidus, Gene Ontology, and Gene Symbols. ARC is 94.04% successful on 6,75,663 annotated proteins from 348 prokaryotes. Three examples are provided to illuminate the current perspectives on mycobacterial physiology and costs of proteins in 333 prokaryotes. ARC is available at http://arc.igib.res.in .

     

An unsupervised conditional random fields approach for clustering gene expression time series

MOTIVATION: There is a growing interest in extracting statistical patterns from gene expression time-series data, in which a key challenge is the development of stable and accurate probabilistic models. Currently popular models, however, would be computationally prohibitive unless some independence assumptions are made to describe large-scale data. We propose an unsupervised conditional random fields (CRF) model to overcome this problem by progressively infusing information into the labelling process through a small variable voting pool. RESULTS: An unsupervised CRF model is proposed for efficient analysis of gene expression time series and is successfully applied to gene class discovery and class prediction. The proposed model treats each time series as a random field and assigns an optimal cluster label to each time series, so as to partition the time series into clusters without a priori knowledge about the number of clusters and the initial centroids. Another advantage of the proposed method is the relaxation of independence assumptions.