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Haloferax volcanii is a well-established model species for haloarchaea. Small scale RNomics and bioinformatics predictions were used to identify small non-coding RNAs (sRNAs), and deletion mutants revealed that sRNAs have important regulatory functions. A recent dRNA-Seq study was used to characterize the primary transcriptome. Unexpectedly, it was revealed that, under optimal conditions, H. volcanii contains more non-coding sRNAs than protein-encoding mRNAs. However, the dRNA-Seq approach did not contain any length information. Therefore, a mixed RNA-Seq approach was used to determine transcript length and to identify additional transcripts, which are not present under optimal conditions. In total, 50 million paired end reads of 150 nt length were obtained. 1861 protein-coding RNAs (cdRNAs) were detected, which encoded 3092 proteins. This nearly doubled the coverage of cdRNAs, compared to the previous dRNA-Seq study. About 2/3 of the cdRNAs were monocistronic, and 1/3 covered more than one gene. In addition, 1635 non-coding sRNAs were identified. The highest fraction of non-coding RNAs were cis antisense RNAs (asRNAs). Analysis of the length distribution revealed that sRNAs have a median length of about 150 nt. Based on the RNA-Seq and dRNA-Seq results, genes were chosen to exemplify characteristics of the H. volcanii transcriptome by Northern blot analyses, e.g. 1) the transcript patterns of gene clusters can be straightforward, but also very complex, 2) many transcripts differ in expression level under the four analyzed conditions, 3) some genes are transcribed into RNA isoforms of different length, which can be differentially regulated, 4) transcripts with very long 5’-UTRs and with very long 3’-UTRs exist, and 5) about 30% of all cdRNAs have overlapping 3’-ends, which indicates, together with the asRNAs, that H. volcanii makes ample use of sense-antisense interactions. Taken together, this RNA-Seq study, together with a previous dRNA-Seq study, enabled an unprecedented view on the H. volcanii transcriptome.
uORF-tools—workflow for the determination of translation-regulatory upstream open reading frames
(2019)
Ribosome profiling (ribo-seq) provides a means to analyze active translation by determining ribosome occupancy in a transcriptome-wide manner. The vast majority of ribosome protected fragments (RPFs) resides within the protein-coding sequence of mRNAs. However, commonly reads are also found within the transcript leader sequence (TLS) (aka 5’ untranslated region) preceding the main open reading frame (ORF), indicating the translation of regulatory upstream ORFs (uORFs). Here, we present a workflow for the identification of translation-regulatory uORFs. Specifically, uORF-Tools uses Ribo-TISH to identify uORFs within a given dataset and generates a uORF annotation file. In addition, a comprehensive human uORF annotation file, based on 35 ribo-seq files, is provided, which can serve as an alternative input file for the workflow. To assess the translation-regulatory activity of the uORFs, stimulus-induced changes in the ratio of the RPFs residing in the main ORFs relative to those found in the associated uORFs are determined. The resulting output file allows for the easy identification of candidate uORFs, which have translation-inhibitory effects on their associated main ORFs. uORF-Tools is available as a free and open Snakemake workflow at https://github.com/Biochemistry1-FFM/uORF-Tools. It is easily installed and all necessary tools are provided in a version-controlled manner, which also ensures lasting usability. uORF-Tools is designed for intuitive use and requires only limited computing times and resources.