The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals. With our years of experience in manually curating genomes for the Joint Genome Institute, we developed a new gene prediction algorithm called Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm). With Prodigal, we focused specifically on the three goals of improved gene structure prediction, improved translation initiation site recognition, and reduced false positives. We compared the results of Prodigal to existing gene-finding methods to demonstrate that it met each of these objectives. We built a fast, lightweight, open source gene prediction program called Prodigal http://compbio.ornl.gov/prodigal/
 . Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.

/pdf/prodigal-prokaryotic-gene-recognition-and-translation-4my2za2vic.pdf

Prodigal: prokaryotic gene recognition and translation initiation site identification

The availability of complete genomic sequences and technologies that allow comprehensive analysis of global expression profiles of messenger RNA have greatly expanded our ability to monitor the internal state of a cell. Yet biological systems ultimately need to be explained in terms of the activity, regulation and modification of proteins--and the ubiquitous occurrence of post-transcriptional regulation makes mRNA an imperfect proxy for such information. To facilitate global protein analyses, we have created a Saccharomyces cerevisiae fusion library where each open reading frame is tagged with a high-affinity epitope and expressed from its natural chromosomal location. Through immunodetection of the common tag, we obtain a census of proteins expressed during log-phase growth and measurements of their absolute levels. We find that about 80% of the proteome is expressed during normal growth conditions, and, using additional sequence information, we systematically identify misannotated genes. The abundance of proteins ranges from fewer than 50 to more than 10(6) molecules per cell. Many of these molecules, including essential proteins and most transcription factors, are present at levels that are not readily detectable by other proteomic techniques nor predictable by mRNA levels or codon bias measurements.

/pdf/global-analysis-of-protein-expression-in-yeast-551l3z9q24.pdf

Global analysis of protein expression in yeast

KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

■ Abstract Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more pro- teins of known structure (templates). The prediction process consists of fold assign- ment, target-template alignment, model building, and model evaluation. The number of protein sequences that can be modeled and the accuracy of the predictions are in- creasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software. Further advances are nec- essary in recognizing weak sequence-structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences. The use of individual comparative models in biology is already rewarding and increasingly widespread. A major new challenge for comparative modeling is the integration of it with the torrents of data from genome sequencing projects as well as from functional and structural genomics. In particular, there is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes. Such large-scale modeling is likely to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network.

Comparative protein structure modeling of genes and genomes

Motivation: The Glimmer gene-finding software has been successfully used for finding genes in bacteria, archaea and viruses representing hundreds of species. We describe several major changes to the Glimmer system, including improved methods for identifying both coding regions and start codons. We also describe a new module of Glimmer that can distinguish host and endosymbiont DNA. This module was developed in response to the discovery that eukaryotic genome sequencing projects sometimes inadvertently capture the DNA of intracellular bacteria living in the host.

Results: The new methods dramatically reduce the rate of false-positive predictions, while maintaining Glimmer's 99% sensitivity rate at detecting genes in most species, and they find substantially more correct start sites, as measured by comparisons to known and well-curated genes. We show that our interpolated Markov model (IMM) DNA discriminator correctly separated 99% of the sequences in a recent genome project that produced a mixture of sequences from the bacterium Prochloron didemni and its sea squirt host, Lissoclinum patella.

Availability: Glimmer is OSI Certified Open Source and available at http://cbcb.umd.edu/software/glimmer

Contact: adelcher@umiacs.umd.edu

/pdf/identifying-bacterial-genes-and-endosymbiont-dna-with-yjmpazwe9q.pdf

Identifying bacterial genes and endosymbiont DNA with Glimmer

A protein is usually classified into one of the following five struc- tural classes: a!, j3, a! +j3, a!/j3, and ( (irregular). The structural class of aprotein is correlated with its amino acid composition. However, given the amino acid composition of aprotein, how may one predict its structural class? Various efforts have been made in addressing this problem. This review addresses the progress in this field, with the focus on the state of the art, which is featured by a novel prediction algorithm and a recently developed database. The novel algorithm is characterized by a covariance matrix that takes into account the coupling effect among different amino acid components of a protein. The new database was established based on the requirement that the classes should have (1) as many nonhomologous structures as possible, (2) good quality structure, and (3) typical or distinguishable features for each of the structural classes concerned. The very high success rate for both the training-set proteins and the testing-set proteins, which has been further validated by a simulated analysis and a jackknife analysis, indicates that it is possible to predict the structural class of a protein according to its amino acid composition if an ideal and complete database can be established. It also suggests that the overall fold of a protein is basically determined by its amino acid composition.

Prediction of protein structural classes.

Essential genes are genes that are indispensable to support cellular life. These genes constitute a minimal gene set required for a living cell. We have constructed a Database of Essential Genes (DEG), which contains all the essential genes that are currently available. The functions encoded by essential genes are considered a foundation of life and therefore are likely to be common to all cells. Users can BLAST the query sequences against DEG. If homologous genes are found, it is possible that the queried genes are also essential. Users can search for essential genes by their function or name. Users can also browse and extract all the records in DEG. Essential gene products comprise excellent targets for antibacterial drugs. Analysis of essential genes could help to answer the question of what are the basic functions necessary to support cellular life. DEG is freely accessible from the website http://tubic.tju.edu.cn/deg/.

DEG: a database of essential genes

Background: The h-index has already been used by major citation databases to evaluate the academic performance of individual scientists. Although effective and simple, the h-index suffers from some drawbacks that limit its use in accurately and fairly comparing the scientific output of different researchers. These drawbacks include information loss and low resolution: the former refers to the fact that in addition to h 2 citations for papers in the h-core, excess citations are completely ignored, whereas the latter means that it is common for a group of researchers to have an identical h-index. Methodology/Principal Findings: To solve these problems, I here propose the e-index, where e 2 represents the ignored excess citations, in addition to the h 2 citations for h-core papers. Citation information can be completely depicted by using the h-index together with the e-index, which are independent of each other. Some other h-type indices, such as a and R, are h-dependent, have information redundancy with h, and therefore, when used together with h, mask the real differences in excess citations of different researchers. Conclusions/Significance: Although simple, the e-index is a necessary h-index complement, especially for evaluating highly cited scientists or for precisely comparing the scientific output of a group of scientists having an identical h-index.

/pdf/the-e-index-complementing-the-h-index-for-excess-citations-1n3fenrc1s.pdf

The e-index, complementing the h-index for excess citations.

A novel method mapping the DNA or RNA sequence into a folding curve in three dimensional space, the Z curve, has been proposed based on the symmetry of the regular tetrahedrons. There exists a unique Z curve for a given DNA sequence, on the contrary, the DNA sequence can be uniquely determined by the given Z curve. The properties of the Z curves have been studied in great details. The symmetry, the periodicity, the local motif, and the global feather of the distribution of bases of the DNA sequences are reflected by the rich folding structures of the Z curves. The Z curves may be smoothed by the B-spline functions of different orders. Therefore, the Z curves may have any resolution by choosing the suitable spline functions. The higher the order of the B-spline function chosen, the lower the resolution of the Z curve. So, the Z curves are suitable for visualizing and analyzing the DNA sequences with any length. The study of the Z curves develops further a new area to visualizing and analyzing the ...

Z Curves, An Intutive Tool for Visualizing and Analyzing the DNA Sequences

Chromosomal replication is the central event in the bacterial cell cycle. Identification of replication origins (oriC s) is necessary for almost all newly sequenced bacterial genomes. Given the increasing pace of genome sequencing, the current available software for predicting oriC s, however, still leaves much to be desired. Therefore, the increasing availability of genome sequences calls for improved software to identify oriC s in newly sequenced and unannotated bacterial genomes. We have developed Ori-Finder, an online system for finding oriC s in bacterial genomes based on an integrated method comprising the analysis of base composition asymmetry using the Z-curve method, distribution of DnaA boxes, and the occurrence of genes frequently close to oriC s. The program can also deal with unannotated genome sequences by integrating the gene-finding program ZCURVE 1.02. Output of the predicted results is exported to an HTML report, which offers convenient views on the results in both graphical and tabular formats. A web-based system to predict replication origins of bacterial genomes has been presented here. Based on this system, oriC regions have been predicted for the bacterial genomes available in GenBank currently. It is hoped that Ori-Finder will become a useful tool for the identification and analysis of oriC s in both bacterial and archaeal genomes.

/pdf/ori-finder-a-web-based-system-for-finding-oric-s-in-3ms0tgpw3j.pdf

Chun-Ting Zhang

Papers

Prediction of protein structural classes.

DEG: a database of essential genes

The e-index, complementing the h-index for excess citations.

Z Curves, An Intutive Tool for Visualizing and Analyzing the DNA Sequences

Ori-Finder: A web-based system for finding oriC s in unannotated bacterial genomes