baerhunter: An R package for the discovery and analysis of expressed non-coding regions in bacterial RNA-seq data

Abstract

11Summary 12 Standard bioinformatics pipelines for the analysis of bacterial transcriptomic 13 data commonly ignore non-coding but functional elements e.g. small RNAs, long 14 antisense RNAs or untranslated regions (UTRs) of mRNA transcripts. The root of 15 this problem is the use of incomplete genome annotation files. Here, we present 16 baerhunter, a method implemented in R, that automates the discovery of 17 expressed non-coding RNAs and UTRs from RNA-seq reads mapped to a 18 reference genome. The core algorithm is part of a pipeline that facilitates 19 downstream analysis of both coding and non-coding features. The method is 20 simple, easy to extend and customize and, in limited tests with simulated and 21 real data, compares favourably against the currently most popular alternative. 22 Availability 23The baerhunter R package is available from: 24 https://github.com/irilenia/baerhunter 25 Contact 26 i.nobeli@bbk.ac.uk 27

Challenges in defining the functional, non‐coding, expressed genome of members of the Mycobacterium tuberculosis complex

“…Additionally, fine‐tuning cut‐off parameters to distinguish signal from noise is ultimately still up to the user. Somewhat surprisingly, the added complexity of such methods does not always translate into more accurate results: in limited comparisons between methods that use additional information and our own simpler, signal‐only based method, we found that our naïve approach performs comparatively well, most likely because more sophisticated methods often require more tuning of their parameters to take advantage of their added complexity (Ozuna et al., 2019). As the responsibility of parameter tuning is left up to the user, it is obvious that methods with fewer parameters, such as Rockhopper, baerhunter or APERO, may be less error‐prone and, ultimately, more appealing, especially to non‐computational users looking for quick and easy to implement solutions.…”

“…Progress in the field, and an easy comparison between approaches, has been hindered by the fact that few of the labs publishing computational predictions have made their code readily available. In response to this challenge, several groups have created publicly available prediction programs or workflows such as Rockhopper (McClure et al., 2013), DETR’PROK (Toffano‐Nioche et al., 2013), ANNOgesic (Yu et al., 2018), APERO (Leonard et al., 2019), and baerhunter (Ozuna et al., 2019). Users of all of these transcriptomics‐based methods are required to set thresholds for separating background noise (whatever its origin) from signal in the data.…”

“…To eliminate guesswork by the user to adjust for noise versus signal, the program normalizes for read counts using the upper quartile of non‐zero gene expression values and generates a transcriptional map of the predicted non‐coding elements. Baerhunter (Ozuna et al., 2019) and APERO (Leonard et al., 2019) are lighter tools to install, both written in R and requiring only the most commonly used BAM format alignment files and relevant reference annotations. Like Rockhopper, the output of baerhunter is a transcriptional map (in .gff format) and can consolidate annotations from multiple samples.…”

Using a Whole Genome Co-expression Network to Inform the Functional Characterisation of Predicted Genomic Elements fromMycobacterium tuberculosisTranscriptomic Data

“…Each dataset was run through the R-package, baerhunter (Ozuna et al, 2019), using the ‘ feature_file_editor’ function optimised to the most appropriate parameters for the sequencing depth (https://github.com/jenjane118/mtb_modules). ‘ Count_features’ and ‘ tpm_norm_flagging’ functions were used for transcript quantification and to identify low expression hits (less than or equal to 10 transcripts per million) in each dataset, which were subsequently eliminated.…”

The Prognostic Role of Glutathione Peroxidase 1 and Immune Infiltrates in Glioma Investigated Using Public Datasets

“…We used the Spearman method to determine the correlation between GPX1 and immune gene mark. Statistical analysis was performed with SPSS software 26.0 (SPSS Inc.), R software v3.6.3 ( http://www.r-project.org/ ) [ 27 ], and Prism 8 (GraphPad Software, Inc). Data were considered significant at P <0.05.…”