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Sequence analysis of the genome of the unicellular cyanobacterium Synechocystis sp. strain PCC6803. II. Sequence determination of the entire genome and assignment of potential protein-coding regions.

TLDR
The sequence determination of the entire genome of the Synechocystis sp.
Abstract
The sequence determination of the entire genome of the Synechocystis sp. strain PCC6803 was completed. The total length of the genome finally confirmed was 3,573,470 bp, including the previously reported sequence of 1,003,450 bp from map position 64% to 92% of the genome. The entire sequence was assembled from the sequences of the physical map-based contigs of cosmid clones and of lambda clones and long PCR products which were used for gap-filling. The accuracy of the sequence was guaranteed by analysis of both strands of DNA through the entire genome. The authenticity of the assembled sequence was supported by restriction analysis of long PCR products, which were directly amplified from the genomic DNA using the assembled sequence data. To predict the potential protein-coding regions, analysis of open reading frames (ORFs), analysis by the GeneMark program and similarity search to databases were performed. As a result, a total of 3,168 potential protein genes were assigned on the genome, in which 145 (4.6%) were identical to reported genes and 1,257 (39.6%) and 340 (10.8%) showed similarity to reported and hypothetical genes, respectively. The remaining 1,426 (45.0%) had no apparent similarity to any genes in databases. Among the potential protein genes assigned, 128 were related to the genes participating in photosynthetic reactions. The sum of the sequences coding for potential protein genes occupies 87% of the genome length. By adding rRNA and tRNA genes, therefore, the genome has a very compact arrangement of protein- and RNA-coding regions. A notable feature on the gene organization of the genome was that 99 ORFs, which showed similarity to transposase genes and could be classified into 6 groups, were found spread all over the genome, and at least 26 of them appeared to remain intact. The result implies that rearrangement of the genome occurred frequently during and after establishment of this species.

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Citations
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Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

TL;DR: A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original.
Journal ArticleDOI

The Complete Genome Sequence of Escherichia coli K-12

TL;DR: The 4,639,221-base pair sequence of Escherichia coli K-12 is presented and reveals ubiquitous as well as narrowly distributed gene families; many families of similar genes within E. coli are also evident.
Journal ArticleDOI

The complete genome sequence of the Gram-positive bacterium Bacillus subtilis

F. Kunst, +154 more
- 20 Nov 1997 - 
TL;DR: Bacillus subtilis is the best-characterized member of the Gram-positive bacteria, indicating that bacteriophage infection has played an important evolutionary role in horizontal gene transfer, in particular in the propagation of bacterial pathogenesis.
Journal ArticleDOI

A genomic perspective on protein families

TL;DR: Comparison of proteins encoded in seven complete genomes from five major phylogenetic lineages and elucidation of consistent patterns of sequence similarities allowed the delineation of 720 clusters of orthologous groups (COGs), which comprise a framework for functional and evolutionary genome analysis.
References
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Journal ArticleDOI

Whole-genome random sequencing and assembly of Haemophilus influenzae Rd.

TL;DR: An approach for genome analysis based on sequencing and assembly of unselected pieces of DNA from the whole chromosome has been applied to obtain the complete nucleotide sequence of the genome from the bacterium Haemophilus influenzae Rd.
Journal ArticleDOI

GENMARK: Parallel gene recognition for both DNA strands

TL;DR: The initial idea of the method combines the specific Markov models of coding and non-coding region together with Bayes' decision making function and allows easy generalization for employing of higher order Markov chain models.
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