Ontology highlight
ABSTRACT:
SUBMITTER: Smith C
PROVIDER: S-EPMC9094748 | biostudies-literature | 2022 Mar
REPOSITORIES: biostudies-literature
eLife 20220328
Most bacterial ORFs are identified by automated prediction algorithms. However, these algorithms often fail to identify ORFs lacking canonical features such as a length of >50 codons or the presence of an upstream Shine-Dalgarno sequence. Here, we use ribosome profiling approaches to identify actively translated ORFs in <i>Mycobacterium tuberculosis</i>. Most of the ORFs we identify have not been previously described, indicating that the <i>M. tuberculosis</i> transcriptome is pervasively transl ...[more]