A probabilistic method for identifying start codons in bacterial genomes.

As the pace of genome sequencing has accelerated, the need for highly accurate gene prediction systems has grown. Computational systems for identifying genes in prokaryotic genomes have sensitivities of 98-99% or higher (Delcher et al., Nucleic Acids Res., 27, 4636-4641, 1999). These accuracy figures are calculated by comparing the locations of verified stop codons to the predictions. Determining the accuracy of start codon prediction is more problematic, however, due to the relatively small number of start sites that have been confirmed by independent, non-computational methods. Nonetheless, the accuracy of gene finders at predicting the exact gene boundaries at both the 5' and 3' ends of genes is of critical importance for microbial genome annotation, especially in light of the important signaling information that is sometimes found on the 5' end of a protein coding region. In this paper we propose a probabilistic method to improve the accuracy of gene identification systems at finding precise translation start sites. The new system, RBSfinder, is tested on a validated set of genes from Escherichia coli, for which it improves the accuracy of start site locations predicted by computational gene finding systems from the range 67-77% to 90% correct.     

Activation of the Murine Sarcoma Virus Genome After Infection with the Murine Leukemia Virus as Determined by Cell Agglutination

Non-virus-producing NIH/3T3 cells transformed by the murine sarcoma virus are agglutinated by conconavalin A to the same low level as normal NIH/3T3 cells. Infection with the murine leukemia virus greatly increases the agglutination of transformed cells but not that of normal cells. These data suggest that the morphological expression of cell transformation and the surface alterations associated with increased cell agglutination are controlled by the expressions of different sarcoma virus genes.     

Caffeine interaction with fluorescent calcium indicator dyes.

We report that caffeine, in millimolar concentrations, interacts strongly with four common calcium indicator dyes: mag-fura-2, magnesium green, fura-2, and fluo-3. Fluorescence intensities are either noticeably enhanced (mag-fura-2, fura-2) or diminished (magnesium green, fluo-3). The caffeine-induced changes in the fluorescence spectra are clearly distinct from those of metal ion binding at the indicator chelation sites. Binding affinities for calcium of either mag-fura-2 or magnesium green increased only slightly in the presence of caffeine. Caffeine also alters the fluorescence intensities of two other fluorescent dyes lacking a chelation site, fluorescein and sulforhodamine 101, implicating the fluorophore itself as the interaction site for caffeine. In the absence of caffeine, variation of solution hydrophobicity by means of water/dioxane mixtures yielded results similar to those for caffeine. These observations suggest that hydrophobic substances, in general, can alter dye fluorescence in a dye-specific manner. For the particular case of caffeine, and perhaps other commonly used pharmacological agents, the dye interactions can seriously distort fluorescence measurements of intracellular ion concentrations with metal indicator dyes.     

The complete genome sequence of Bacillus anthracis Ames "Ancestor"

The pathogenic bacterium Bacillus anthracis has become the subject of intense study as a result of its use in a bioterrorism attack in the United States in September and October 2001. Previous studies suggested that B. anthracis Ames Ancestor, the original Ames fully virulent plasmid-containing isolate, was the ideal reference. This study describes the complete genome sequence of that original isolate, derived from a sample kept in cold storage since 1981.     

Searching for SNPs with cloud computing

As DNA sequencing outpaces improvements in computer speed, there is a critical need to accelerate tasks like alignment and SNP calling. Crossbow is a cloud-computing software tool that combines the aligner Bowtie and the SNP caller SOAPsnp. Executing in parallel using Hadoop, Crossbow analyzes data comprising 38-fold coverage of the human genome in three hours using a 320-CPU cluster rented from a cloud computing service for about $85. Crossbow is available from .     

Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source .     

Clustering metagenomic sequences with interpolated Markov models

Background  

Sequencing of environmental DNA (often called metagenomics) has shown tremendous potential to uncover the vast number of unknown microbes that cannot be cultured and sequenced by traditional methods. Because the output from metagenomic sequencing is a large set of reads of unknown origin, clustering reads together that were sequenced from the same species is a crucial analysis step. Many effective approaches to this task rely on sequenced genomes in public databases, but these genomes are a highly biased sample that is not necessarily representative of environments interesting to many metagenomics projects.     

Genomic Features of a Bumble Bee Symbiont Reflect Its Host Environment

Here, we report the genome of one gammaproteobacterial member of the gut microbiota, for which we propose the name “Candidatus Schmidhempelia bombi,” that was inadvertently sequenced alongside the genome of its host, the bumble bee, Bombus impatiens. This symbiont is a member of the recently described bacterial order Orbales, which has been collected from the guts of diverse insect species; however, “Ca. Schmidhempelia” has been identified exclusively with bumble bees. Metabolic reconstruction reveals that “Ca. Schmidhempelia” lacks many genes for a functioning NADH dehydrogenase I, all genes for the high-oxygen cytochrome o, and most genes in the tricarboxylic acid (TCA) cycle. “Ca. Schmidhempelia” has retained NADH dehydrogenase II, the low-oxygen specific cytochrome bd, anaerobic nitrate respiration, mixed-acid fermentation pathways, and citrate fermentation, which may be important for survival in low-oxygen or anaerobic environments found in the bee hindgut. Additionally, a type 6 secretion system, a Flp pilus, and many antibiotic/multidrug transporters suggest complex interactions with its host and other gut commensals or pathogens. This genome has signatures of reduction (2.0 megabase pairs) and rearrangement, as previously observed for genomes of host-associated bacteria. A survey of wild and laboratory B. impatiens revealed that “Ca. Schmidhempelia” is present in 90% of individuals and, therefore, may provide benefits to its host.     

A reference-quality,fully annotated genome from a Puerto Rican individual

Until 2019, the human genome was available in only one fully annotated version, GRCh38, which was the result of 18 years of continuous improvement and revision. Despite dramatic improvements in sequencing technology, no other genome was available as an annotated reference until 2019, when the genome of an Ashkenazi individual, Ash1, was released. In this study, we describe the assembly and annotation of a second individual genome, from a Puerto Rican individual whose DNA was collected as part of the Human Pangenome project. The new genome, called PR1, is the first true reference genome created from an individual of African descent. Due to recent improvements in both sequencing and assembly technology, and particularly to the use of the recently completed CHM13 human genome as a guide to assembly, PR1 is more complete and more contiguous than either GRCh38 or Ash1. Annotation revealed 37,755 genes (of which 19,999 are protein coding), including 12 additional gene copies that are present in PR1 and missing from CHM13. Fifty-seven genes have fewer copies in PR1 than in CHM13, 9 map only partially, and 3 genes (all noncoding) from CHM13 are entirely missing from PR1.