LAUSR

The accurate discovery of DNA and RNA regulatory motifs and their combinations is still a topic of active research, focusing to date mainly on the analysis of ChIP-Seq data (Kingsley et al., 2019; Kong et al., 2020), on gene co-expression analysis (Rouault et al., 2014; Teague et al., 2021) and on the general investigation of the properties of binding motifs (Zeitlinger 2020). Many bioinformatics methods have been (Zambelli et al., 2013) and are still being developed (Bentsen et al., 2022; Hammelman et al., 2022) to improve prediction accuracy and fully address the advantages of novel experimental and computational techniques, such as that based on deep learning (Auslander et al., 2021). However, when it comes to the practical use of bioinformatic predictors, a researcher is often puzzled, first by selecting an appropriate bioinformatic program and then by a huge list of predictions that such programs usually produce. Once several programs are used to increase the chances of one at least finding a real functional motif, the list of predictions becomes too long for experimental verification (Deyneko et al., 2016), even though independently found similar motifs are more likely to be correct and can be given higher priority (Machanick and Kibet, 2017). The main problem that complicates the choice of a favorable approach for a specific task is the insufficient number of comparative tests of the published methods, partly due to the difficulty of defining a universal motif assessment approach (Kibet and Machanick, 2016). The inadequate testing of many newly suggested algorithms has already been discussed (Smith et al., 2013) and can be summarized as 1) an insufficient and subjective selection of methods for comparison; 2) use of non-common metrics; and 3) use of non-standard datasets. Nevertheless, many studies that present novel methods for motif detection repeatedly appear without adequate comparative evaluation. The main issues include comparison against no or only a single method, despite several comparable methods existing (Alvarez-Gonzalez and Erill, 2021; Hammelman et al., 2022), the use of only one dataset, usually with unknown true positives (Levitsky et al., 2022), and the use of uncommon statistical metrics (Zhang et al., 2019). The last can be exemplified with a criterion of the correct prediction—if, within the top ten, there is a motif similar (not identical!) to the original, the motif is counted as positively recovered. In real applications, when the target motif is unknown, the reliability of such predictions is far from being experimentally testable. In contrast, there are many methods with well-performed comparisons, including novel deep learning methods (Bentsen et al., 2022; Iqbal et al., 2022). This work is addressed not only to researchers, who may use the presented principles to better reveal the power of the software presented, but also to peer reviewers and journal editorial boards, who may use it as a starting point for their own requirements for software articles. Obviously, comprehensive comparative testing of new methods will not only reveal the best OPEN ACCESS