Using comparative RNASeq to identify small non-coding RNAs in bacterial clades
2020-03-10T03:58:35Z (GMT) by
Small non-coding RNAs are involved in regulation of a wide range of cell processes. There are a number of tools that exist that try to identify these RNAs using a range of methods, however challenges with predicting non-coding RNAs from the sequence alone and transcriptional noise making the use of RNASeq data unreliable has hindered annotation of functional elements (Jose et al. 2019). While these methods manage to predict RNAs it can be hard to determine whether results from RNASeq data are the result of a real RNA or noise and to deal with this problem we are using a comparative approach by taking RNASeq data from multiple genomes within a clade (Lindgreen et al. 2014). We have designed a pipeline that identifies peaks in intergenic regions of RNASeq data that may by functional RNAs and uses genome alignments to check if there are conserved regions of expression that would indicate the transcription that is observed is for functional RNAs. By using a comparative approach we aim to improve the signal to noise ratio in our results and better list of candidate small non-coding RNAs.
1. Jose, B. R., Gardner, P. P., & Barquist, L. (2019). Transcriptional noise and exaptation as sources for bacterial sRNAs. Biochemical Society Transactions, 47(2), 527-539.
2. Lindgreen, S., Umu, S. U., Lai, A. S. W., Eldai, H., Liu, W., McGimpsey, S., ... & Poole, A. M. (2014). Robust identification of noncoding RNA from transcriptomes requires phylogeneticallyinformed sampling. PLoS computational biology, 10(10), e1003907.
ABOUT THE AUTHOR(S)
Tom is a PhD student in the Department of Biochemistry at the Univrsity of Otago. His research focusses on the bioinformatic analysis of small non-coding RNAs in prokaryotes.