A comparative benchmark of classic DNA motif discovery tools on synthetic data

Castellana, Stefano; Biagini, Tommaso; Parca, Luca; Petrizzelli, Francesco; Bianco, Salvatore Daniele; Vescovi, Angelo L.; Carella, Massimo; Mazza, Tommaso

doi:10.1093/bib/bbab303

Cited by 5 publications

(2 citation statements)

References 52 publications

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“…So far, many studies have promoted the concept of the synthetic sequences as efficient for evaluation of the performance of motif finding in ChIP-seq data. This concept implied the generation of synthetic sequences by Markov chains of various orders (33-36 Tompa et al, 2005;Boeva et al, 2016;Jayaram et al, 2016;Castellana et al, 2021), or these sequences were taken as a complete dictionary of k-mers, i.e. equal frequencies of nucleotides were presumed (6 Kulakovskiy and Makeev 2013).…”

Section: Introductionmentioning

confidence: 99%

Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data

Raditsa,

Tsukanov,

Bogomolov

et al. 2024

Preprint

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Efficient de novo motif discovery from the results of wide-genome mapping of transcription factor binding sites (ChIP-seq) is dependent on the choice of background nucleotide sequences. The foreground sequences (peaks) represent not only specific motifs of target transcription factors, but also the motifs overrepresented throughout the genome, such as simple sequence repeats. We performed a massive comparison of the synthetic and genomic approaches to generate background sequences for de novo motif discovery. The synthetic approach shuffled nucleotides in peaks, while in the genomic approach randomly selected sequences from the reference genome or only from gene promoters according to the fraction of A/T nucleotides in each sequence. We compiled the benchmark collections of ChIP-seq datasets for mammalian and Arabidopsis, and performed de novo motif discovery. We showed that the genomic approach has both more robust detection of the known motifs of target transcription factors and more stringent exclusion of the simple sequence repeats as possible non-specific motifs. The advantage of the genomic approach over the synthetic one was greater in plants compared to mammals. We developed the AntiNoise web service (https://denovosea.icgbio.ru/antinoise/) which implements a genomic approach to extract genomic background sequences for twelve eukaryotic genomes.

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Section: Introductionmentioning

confidence: 99%

Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data

Raditsa,

Tsukanov,

Bogomolov

et al. 2024

Preprint

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“…Inspired by the work of Ofer et al [ 24 ], which considered biological sequences, such as DNA sequences, as human language and used advanced NLP tools to tackle biological tasks, we aimed to model Chinese EHRs as DNA-like sequences and mine linguistic patterns with advanced bioinformatics tools. In a recent review, Castellana et al [ 25 ] surveyed 16 classic DNA motif discovery tools and evaluated their ability to discover sequence motifs nested in 29 simulated sequence data sets. The MEME (Multiple Expectation Maximums for Motif Elicitation) motif discovery tool performed best among the 16 classic DNA motif discovery tools.…”

Section: Introductionmentioning

confidence: 99%

Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation

Li¹,

Zhang²,

Chen³

et al. 2022

J Med Internet Res

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Background Phenotype information in electronic health records (EHRs) is mainly recorded in unstructured free text, which cannot be directly used for clinical research. EHR-based deep-phenotyping methods can structure phenotype information in EHRs with high fidelity, making it the focus of medical informatics. However, developing a deep-phenotyping method for non-English EHRs (ie, Chinese EHRs) is challenging. Although numerous EHR resources exist in China, fine-grained annotation data that are suitable for developing deep-phenotyping methods are limited. It is challenging to develop a deep-phenotyping method for Chinese EHRs in such a low-resource scenario. Objective In this study, we aimed to develop a deep-phenotyping method with good generalization ability for Chinese EHRs based on limited fine-grained annotation data. Methods The core of the methodology was to identify linguistic patterns of phenotype descriptions in Chinese EHRs with a sequence motif discovery tool and perform deep phenotyping of Chinese EHRs by recognizing linguistic patterns in free text. Specifically, 1000 Chinese EHRs were manually annotated based on a fine-grained information model, PhenoSSU (Semantic Structured Unit of Phenotypes). The annotation data set was randomly divided into a training set (n=700, 70%) and a testing set (n=300, 30%). The process for mining linguistic patterns was divided into three steps. First, free text in the training set was encoded as single-letter sequences (P: phenotype, A: attribute). Second, a biological sequence analysis tool—MEME (Multiple Expectation Maximums for Motif Elicitation)—was used to identify motifs in the single-letter sequences. Finally, the identified motifs were reduced to a series of regular expressions representing linguistic patterns of PhenoSSU instances in Chinese EHRs. Based on the discovered linguistic patterns, we developed a deep-phenotyping method for Chinese EHRs, including a deep learning–based method for named entity recognition and a pattern recognition–based method for attribute prediction. Results In total, 51 sequence motifs with statistical significance were mined from 700 Chinese EHRs in the training set and were combined into six regular expressions. It was found that these six regular expressions could be learned from a mean of 134 (SD 9.7) annotated EHRs in the training set. The deep-phenotyping algorithm for Chinese EHRs could recognize PhenoSSU instances with an overall accuracy of 0.844 on the test set. For the subtask of entity recognition, the algorithm achieved an F1 score of 0.898 with the Bidirectional Encoder Representations from Transformers–bidirectional long short-term memory and conditional random field model; for the subtask of attribute prediction, the algorithm achieved a weighted accuracy of 0.940 with the linguistic pattern–based method. Conclusions We developed a simple but effective strategy to perform deep phenotyping of Chinese EHRs with limited fine-grained annotation data. Our work will promote the second use of Chinese EHRs and give inspiration to other non–English-speaking countries.

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A survey on algorithms to characterize transcription factor binding sites

Tognon

Giugno

Pinello

2023

Briefings in Bioinformatics

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Transcription factors (TFs) are key regulatory proteins that control the transcriptional rate of cells by binding short DNA sequences called transcription factor binding sites (TFBS) or motifs. Identifying and characterizing TFBS is fundamental to understanding the regulatory mechanisms governing the transcriptional state of cells. During the last decades, several experimental methods have been developed to recover DNA sequences containing TFBS. In parallel, computational methods have been proposed to discover and identify TFBS motifs based on these DNA sequences. This is one of the most widely investigated problems in bioinformatics and is referred to as the motif discovery problem. In this manuscript, we review classical and novel experimental and computational methods developed to discover and characterize TFBS motifs in DNA sequences, highlighting their advantages and drawbacks. We also discuss open challenges and future perspectives that could fill the remaining gaps in the field.

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A comparative benchmark of classic DNA motif discovery tools on synthetic data

Cited by 5 publications

References 52 publications

Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data

Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data

Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation

A survey on algorithms to characterize transcription factor binding sites

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