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2017 ; 12
(9
): e0184119
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Discovery of numerous novel small genes in the intergenic regions of the
Escherichia coli O157:H7 Sakai genome
#MMPMID28902868
Hücker SM
; Ardern Z
; Goldberg T
; Schafferhans A
; Bernhofer M
; Vestergaard G
; Nelson CW
; Schloter M
; Rost B
; Scherer S
; Neuhaus K
PLoS One
2017[]; 12
(9
): e0184119
PMID28902868
show ga
In the past, short protein-coding genes were often disregarded by genome
annotation pipelines. Transcriptome sequencing (RNAseq) signals outside of
annotated genes have usually been interpreted to indicate either ncRNA or
pervasive transcription. Therefore, in addition to the transcriptome, the
translatome (RIBOseq) of the enteric pathogen Escherichia coli O157:H7 strain
Sakai was determined at two optimal growth conditions and a severe stress
condition combining low temperature and high osmotic pressure. All intergenic
open reading frames potentially encoding a protein of ? 30 amino acids were
investigated with regard to coverage by transcription and translation signals and
their translatability expressed by the ribosomal coverage value. This led to
discovery of 465 unique, putative novel genes not yet annotated in this E. coli
strain, which are evenly distributed over both DNA strands of the genome. For 255
of the novel genes, annotated homologs in other bacteria were found, and a
machine-learning algorithm, trained on small protein-coding E. coli genes,
predicted that 89% of these translated open reading frames represent bona fide
genes. The remaining 210 putative novel genes without annotated homologs were
compared to the 255 novel genes with homologs and to 250 short annotated genes of
this E. coli strain. All three groups turned out to be similar with respect to
their translatability distribution, fractions of differentially regulated genes,
secondary structure composition, and the distribution of evolutionary constraint,
suggesting that both novel groups represent legitimate genes. However, the
machine-learning algorithm only recognized a small fraction of the 210 genes
without annotated homologs. It is possible that these genes represent a novel
group of genes, which have unusual features dissimilar to the genes of the
machine-learning algorithm training set.
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