Guidelines on the performance evaluation of motif recognition methods in bioinformatics

Deyneko, Igor V.

doi:10.3389/fgene.2023.1135320

Cited by 2 publications

(4 citation statements)

References 26 publications

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“…Testing of performance is a critical step, describing the general advantages of a method and also specific cases where a method can be of the most use. The following principles were suggested in [37]: testing should use the same datasets and metrics as previous methods so the results are directly comparable; the selection of comparative methods should be objective and the number of methods substantial; and finally, self-made datasets and user-defined performance metrics are used only in addition to the above to show a method's superiority in specific applications. Accordingly, performance testing was performed using the established datasets [5] containing known CRMs as targets and a dataset of tissue-specific promoters [38] with unknown CRMs.…”

Section: Resultsmentioning

confidence: 99%

“…The stringency of the search can be easily adjusted using a minimal and maximal presence of CRMs, which has a clear biological interpretation of transcriptional co-regulation. Testing according to the guidelines for objective comparison [37], including the recognition of known CRMs and comparison with other programs using established datasets, showed that BestCRM consistently outperforms alternative methods. In a specific study focusing on tissue-specific promoters, the program successfully pinpointed a candidate regulatory module unique to breast-and prostate-active promoters, offering a promising avenue for further investigation.…”

Section: Discussionmentioning

confidence: 99%

“…The method was tested according to the principles suggested in [37]. Accordingly, the performance testing was performed using the established datasets [5] and a dataset of tissue-specific promoters [38].…”

Section: Datasets Used For Performance Testingmentioning

confidence: 99%

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BestCRM: An Exhaustive Search for Optimal Cis-Regulatory Modules in Promoters Accelerated by the Multidimensional Hash Function

Deyneko

2024

IJMS

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The concept of cis-regulatory modules located in gene promoters represents today’s vision of the organization of gene transcriptional regulation. Such modules are a combination of two or more single, short DNA motifs. The bioinformatic identification of such modules belongs to so-called NP-hard problems with extreme computational complexity, and therefore, simplifications, assumptions, and heuristics are usually deployed to tackle the problem. In practice, this requires, first, many parameters to be set before the search, and second, it leads to the identification of locally optimal results. Here, a novel method is presented, aimed at identifying the cis-regulatory elements in gene promoters based on an exhaustive search of all the feasible modules’ configurations. All required parameters are automatically estimated using positive and negative datasets. To be computationally efficient, the search is accelerated using a multidimensional hash function, allowing the search to complete in a few hours on a regular laptop (for example, a CPU Intel i7, 3.2 GH, 32 Gb RAM). Tests on an established benchmark and real data show better performance of BestCRM compared to the available methods according to several metrics like specificity, sensitivity, AUC, etc. A great practical advantage of the method is its minimum number of input parameters—apart from positive and negative promoters, only a desired level of module presence in promoters is required.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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BestCRM: An Exhaustive Search for Optimal Cis-Regulatory Modules in Promoters Accelerated by the Multidimensional Hash Function

Deyneko

2024

IJMS

Self Cite

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“…The promoter region of DHHC gene family members harbors various cis-regulatory elements, including those responsive to light, circadian clock, growth and development, and stress-related elements, indicating their involvement in gene regulation. The presented analysis is based on bioinformatic predictions using PantCare [36], which has some level of false discoveries [53], which serves as a base for searching more complex cis-regulatory modules [54], so the functionality of the presented regulatory elements should be further verified experimentally.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Genome-Wide Investigation and Transcriptional Regulation of the DHHC Gene Family in Cotton Seed and Fiber Development

Silaiyiman,

Zheng,

Wang

et al. 2024

Agronomy

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Protein palmitoylation, the most common and the only reversible post-translational lipid modification following protein translation, plays a pivotal role in the biochemical and physiological processes of both animals and plants. DHHC proteins, enriched with DHHC (Asp-His-His-Cys) domains, serve as catalyst for protein palmitoylation. However, research on DHHC in cotton remains scarce. This study conducted a systematic characterization and bioinformatics analysis on G. arboreum, G. raimondii, G. hirsutum, and G. barbadense, detecting 38, 37, 74, and 74 DHHC genes, respectively. Phylogenetic analysis categorized the DHHC gene family into six subgroups, consistent with previous evolutionary studies in Arabidopsis and rice. A further examination of protein structure revealed a correlation between genetic relatedness, structural similarity, and functional identity. Cis-element analysis identified elements predominantly associated with light response, stress, growth and development, and plant hormones. The integration of cotton seed development transcriptome, tissue expression pattern analysis, and population transcriptome data collectively suggests that Ghir_A05G027650 and Ghir_D05G027670 are promising candidate genes influencing seed development in upland cotton. Conversely, Gbar_A04G010750 and Gbar_A12G020520 emerge as potential candidates affecting both seed and fiber development in sea island cotton. These findings lay down a theoretical foundation for delving into the functional diversity of DHHC genes in cotton, thereby paving the way for the development of new breeding strategies and the optimization of cotton seed and fiber production, ultimately contributing to improved crop yield and quality.

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Guidelines on the performance evaluation of motif recognition methods in bioinformatics

Cited by 2 publications

References 26 publications

BestCRM: An Exhaustive Search for Optimal Cis-Regulatory Modules in Promoters Accelerated by the Multidimensional Hash Function

BestCRM: An Exhaustive Search for Optimal Cis-Regulatory Modules in Promoters Accelerated by the Multidimensional Hash Function

Comprehensive Genome-Wide Investigation and Transcriptional Regulation of the DHHC Gene Family in Cotton Seed and Fiber Development

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