Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/.

Helicobacter pylori, strain 26695, has a circular genome of 1,667,867 base pairs and 1,590 predicted coding sequences. Sequence analysis indicates that H. pylori has well-developed systems for motility, for scavenging iron, and for DNA restriction and modification. Many putative adhesins, lipoproteins and other outer membrane proteins were identified, underscoring the potential complexity of host-pathogen interaction. Based on the large number of sequence-related genes encoding outer membrane proteins and the presence of homopolymeric tracts and dinucleotide repeats in coding sequences, H. pylori, like several other mucosal pathogens, probably uses recombination and slipped-strand mispairing within repeats as mechanisms for antigenic variation and adaptive evolution. Consistent with its restricted niche, H. pylori has a few regulatory networks, and a limited metabolic repertoire and biosynthetic capacity. Its survival in acid conditions depends, in part, on its ability to establish a positive inside-membrane potential in low pH.

ABSTRACTcagA, a gene that codes for an immunodominant antigen, is present only in Helicobacter pylori strains that are associated with severe forms of gastroduodenal disease (type I strains). We found that the genetic locus that contains cagA (cag) is part of a 40-kb DNA insertion that likely was acquired horizontally and integrated into the chromosomal glutamate racemase gene. This pathogenicity island is f lanked by direct repeats of 31 bp. In some strains, cag is split into a right segment (cagI) and a left segment (cagII) by a novel insertion sequence (IS605). In a minority of H. pylori strains, cagI and cagII are separated by an intervening chromosomal sequence. Nucleotide sequencing of the 23,508 base pairs that form the cagI region and the extreme 3 end of the cagII region reveals the presence of 19 ORFs that code for proteins predicted to be mostly membrane associated with one gene (cagE), which is similar to the toxin-secretion gene of Bordetella pertussis, ptlC, and the transport systems required for plasmid transfer, including the virB4 gene of Agrobacterium tumefaciens. Transposon inactivation of several of the cagI genes abolishes induction of IL-8 expression in gastric epithelial cell lines. Thus, we believe the cag region may encode a novel H. pylori secretion system for the export of virulence determinants.

The complete 1.66-megabase pair genome sequence of an autotrophic archaeon, Methanococcus jannaschii, and its 58- and 16-kilobase pair extrachromosomal elements have been determined by whole-genome random sequencing. A total of 1738 predicted protein-coding genes were identified; however, only a minority of these (38 percent) could be assigned a putative cellular role with high confidence. Although the majority of genes related to energy production, cell division, and metabolism in M. jannaschii are most similar to those found in Bacteria, most of the genes involved in transcription, translation, and replication in M. jannaschii are more similar to those found in Eukaryotes.

Improving the accuracy of prediction of gene starts is one of a few remaining open problems in computer prediction of prokaryotic genes. Its difficulty is caused by the absence of relatively strong sequence patterns identifying true translation initiation sites. In the current paper we show that the accuracy of gene start prediction can be improved by combining models of protein-coding and non-coding regions and models of regulatory sites near gene start within an iterative Hidden Markov model based algorithm. The new gene prediction method, called GeneMarkS, utilizes a non-supervised training procedure and can be used for a newly sequenced prokaryotic genome with no prior knowledge of any protein or rRNA genes. The GeneMarkS implementation uses an improved version of the gene finding program GeneMark.hmm, heuristic Markov models of coding and non-coding regions and the Gibbs sampling multiple alignment program. GeneMarkS predicted precisely 83.2% of the translation starts of GenBank annotated Bacillus subtilis genes and 94.4% of translation starts in an experimentally validated set of Escherichia coli genes. We have also observed that GeneMarkS detects prokaryotic genes, in terms of identifying open reading frames containing real genes, with an accuracy matching the level of the best currently used gene detection methods. Accurate translation start prediction, in addition to the refinement of protein sequence N-terminal data, provides the benefit of precise positioning of the sequence region situated upstream to a gene start. Therefore, sequence motifs related to transcription and translation regulatory sites can be revealed and analyzed with higher precision. These motifs were shown to possess a significant variability, the functional and evolutionary connections of which are discussed.