論文 - 福永　津嵩

REPrise: de novo interspersed repeat detection using inexact seeding.

Atsushi Takeda, Daisuke Nonaka, Yuta Imazu, Tsukasa Fukunaga, Michiaki Hamada

Mobile DNA 16 （ 1 ） 16 - 16 2025年04月

　概要を見る

BACKGROUND: Interspersed repeats occupy a large part of many eukaryotic genomes, and thus their accurate annotation is essential for various genome analyses. Database-free de novo repeat detection approaches are powerful for annotating genomes that lack well-curated repeat databases. However, existing tools do not yet have sufficient repeat detection performance. RESULTS: In this study, we developed REPrise, a de novo interspersed repeat detection software program based on a seed-and-extension method. Although the algorithm of REPrise is similar to that of RepeatScout, which is currently the de facto standard tool, we incorporated three unique techniques into REPrise: inexact seeding, affine gap scoring and loose masking. Analyses of rice and simulation genome datasets showed that REPrise outperformed RepeatScout in terms of sensitivity, especially when the repeat sequences contained many mutations. Furthermore, when applied to the complete human genome dataset T2T-CHM13, REPrise demonstrated the potential to detect novel repeat sequence families. CONCLUSION: REPrise can detect interspersed repeats with high sensitivity even in long genomes. Our software enhances repeat annotation in diverse genomic studies, contributing to a deeper understanding of genomic structures.

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Phylogenetic Profiling Analysis of the Phycobilisome Revealed a Novel State-Transition Regulator Gene in Synechocystis sp. PCC 6803.

Tsukasa Fukunaga, Takako Ogawa, Wataru Iwasaki, Kintake Sonoike

Plant & cell physiology 2024年07月

筆頭著者, 査読有り

　概要を見る

Phycobilisomes play a crucial role in the light-harvesting mechanisms of cyanobacteria, red algae, and glaucophytes, but the molecular mechanism of their regulation is largely unknown. In the cyanobacterium, Synechocystis sp. PCC 6803, we identified a gene, slr0244, as a phycobilisome-related gene using phylogenetic profiling analysis, a method to predict gene function based on comparative genomics. To investigate the physiological function of the slr0244 gene, we characterize the slr0244 mutants spectroscopically. The disruption of the slr0244 gene impaired state transition, a process by which the distribution of light energy absorbed by the phycobilisomes between two photosystems was regulated in response to the changes in light conditions. The Slr0244 protein seems to act somewhere at or downstream of the sensing step of the redox state of the plastoquinone pool in the process of state transition. These findings, together with the past report of the interaction of this gene product with thioredoxin or glutaredoxin, suggest that the slr0244 gene is a novel state-transition regulator that integrates the redox signal of plastoquinone pools with that of photosystem I-reducing side. The protein has two USP (universal stress protein) motifs in tandem. The second motif has two conserved cysteine residues found in USPs of other cyanobacteria and land plants. These redox-type USPs with conserved cysteines may function as redox regulators in various photosynthetic organisms. Our study also showed the efficacy of the phylogenetic profiling analysis in predicting the function of cyanobacterial genes that have not been annotated so far.

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DeepRaccess: high-speed RNA accessibility prediction using deep learning

Kaisei Hara, Natsuki Iwano, Tsukasa Fukunaga, Michiaki Hamada

Frontiers in Bioinformatics （Frontiers Media SA）  3 2023年10月

責任著者, 査読有り

　概要を見る

RNA accessibility is a useful RNA secondary structural feature for predicting RNA-RNA interactions and translation efficiency in prokaryotes. However, conventional accessibility calculation tools, such as Raccess, are computationally expensive and require considerable computational time to perform transcriptome-scale analysis. In this study, we developed DeepRaccess, which predicts RNA accessibility based on deep learning methods. DeepRaccess was trained to take artificial RNA sequences as input and to predict the accessibility of these sequences as calculated by Raccess. Simulation and empirical dataset analyses showed that the accessibility predicted by DeepRaccess was highly correlated with the accessibility calculated by Raccess. In addition, we confirmed that DeepRaccess could predict protein abundance in E.coli with moderate accuracy from the sequences around the start codon. We also demonstrated that DeepRaccess achieved tens to hundreds of times software speed-up in a GPU environment. The source codes and the trained models of DeepRaccess are freely available at https://github.com/hmdlab/DeepRaccess.

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Neat1 lncRNA organizes the inflammatory gene expressions in the dorsal root ganglion in neuropathic pain caused by nerve injury

Motoyo Maruyama, Atsushi Sakai, Tsukasa Fukunaga, Yoshitaka Miyagawa, Takashi Okada, Michiaki Hamada, Hidenori Suzuki

Frontiers in Immunology （Frontiers Media SA）  14 2023年08月

査読有り

　概要を見る

Primary sensory neurons regulate inflammatory processes in innervated regions through neuro-immune communication. However, how their immune-modulating functions are regulated in concert remains largely unknown. Here, we show that Neat1 long non-coding RNA (lncRNA) organizes the proinflammatory gene expressions in the dorsal root ganglion (DRG) in chronic intractable neuropathic pain in rats. Neat1 was abundantly expressed in the DRG and was upregulated after peripheral nerve injury. Neat1 overexpression in primary sensory neurons caused mechanical and thermal hypersensitivity, whereas its knockdown alleviated neuropathic pain. Bioinformatics analysis of comprehensive transcriptome changes indicated the inflammatory response was the most relevant function of genes upregulated through Neat1. Consistent with this, upregulation of proinflammatory genes in the DRG following nerve injury was suppressed by Neat1 knockdown. Expression changes of these proinflammatory genes were regulated through Neat1-mRNA interaction-dependent and -independent mechanisms. Notably, Neat1 increased proinflammatory genes by stabilizing its interacting mRNAs in neuropathic pain. Finally, Neat1 in primary sensory neurons contributed to spinal inflammatory processes that mediated peripheral neuropathic pain. These findings demonstrate that Neat1 lncRNA is a key regulator of neuro-immune communication in neuropathic pain.

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Web Services for RNA-RNA Interaction Prediction

Tsukasa Fukunaga, Junichi Iwakiri, Michiaki Hamada

Methods in molecular biology (Clifton, N.J.) 2586   175 - 195 2023年

筆頭著者

　概要を見る

Non-coding RNAs have various biological functions such as translational regulation, and RNA-RNA interactions play essential roles in the mechanisms of action of these RNAs. Therefore, RNA-RNA interaction prediction is an important problem in bioinformatics, and many tools have been developed for the computational prediction of RNA-RNA interactions. In addition to the development of novel algorithms with high accuracy, the development and maintenance of web services is essential for enhancing usability by experimental biologists. In this review, we survey web services for RNA-RNA interaction predictions and introduce how to use primary web services. We present various prediction tools, including general interaction prediction tools, prediction tools for specific RNA classes, and RNA-RNA interaction-based RNA design tools. Additionally, we discuss the future perspectives of the development of RNA-RNA interaction prediction tools and the sustainability of web services.

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Fast RNA-RNA Interaction Prediction Methods for Interaction Analysis of Transcriptome-Scale Large Datasets

Tsukasa Fukunaga, Michiaki Hamada

Methods in molecular biology (Clifton, N.J.) 2586   163 - 173 2023年

筆頭著者

　概要を見る

The computational prediction of RNA-RNA interactions has long been studied in RNA informatics. Most of the existing approaches focused on the interaction prediction of short RNAs in small datasets. However, in recent years, two fast prediction methods, RIsearch2 and RIblast, have been developed to predict transcriptome-scale interactions or long RNA interactions. The key idea of the software acceleration of these tools was the integration of a seed-and-extend method, which is used in fast sequence alignment tools, into RNA-RNA interaction prediction. As a result, the two software programs were ten to a thousand times faster than the existing tools; because of this acceleration, detection of genome-wide microRNA target sites or interaction partners of function-unknown long noncoding RNAs has become possible. In this review, we describe the basic concept of the algorithm, its applications, and the future perspectives of the fast RNA-RNA interaction prediction tools.

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Bioinformatics approaches for unveiling virus-host interactions.

Hitoshi Iuchi, Junna Kawasaki, Kento Kubo, Tsukasa Fukunaga, Koki Hokao, Gentaro Yokoyama, Akiko Ichinose, Kanta Suga, Michiaki Hamada

Computational and structural biotechnology journal 21   1774 - 1784 2023年

査読有り

　概要を見る

The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus-host interactions through host range prediction and protein-protein interaction prediction. Although many algorithms have been developed to predict virus-host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus-host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus-host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.

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LinAliFold and CentroidLinAliFold: fast RNA consensus secondary structure prediction for aligned sequences using beam search methods

Tsukasa Fukunaga, Michiaki Hamada

Bioinformatics Advances （Oxford University Press (OUP)）  2 （ 1 ）  2022年10月

筆頭著者, 責任著者, 査読有り

　概要を見る

Abstract

Motivation

RNA consensus secondary structure prediction from aligned sequences is a powerful approach for improving the secondary structure prediction accuracy. However, because the computational complexities of conventional prediction tools scale with the cube of the alignment lengths, their application to long RNA sequences, such as viral RNAs or long non-coding RNAs, requires significant computational time.

Results

In this study, we developed LinAliFold and CentroidLinAliFold, fast RNA consensus secondary structure prediction tools based on minimum free energy and maximum expected accuracy principles, respectively. We achieved software acceleration using beam search methods that were successfully used for fast secondary structure prediction from a single RNA sequence. Benchmark analyses showed that LinAliFold and CentroidLinAliFold were much faster than the existing methods while preserving the prediction accuracy. As an empirical application, we predicted the consensus secondary structure of coronaviruses with approximately 30 000 nt in 5 and 79 min by LinAliFold and CentroidLinAliFold, respectively. We confirmed that the predicted consensus secondary structure of coronaviruses was consistent with the experimental results.

Availability and implementation

The source codes of LinAliFold and CentroidLinAliFold are freely available at https://github.com/fukunagatsu/LinAliFold-CentroidLinAliFold.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

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Mirage 2.0: fast and memory-efficient reconstruction of gene-content evolution considering heterogeneous evolutionary patterns among gene families.

Tsukasa Fukunaga, Wataru Iwasaki

Bioinformatics (Oxford, England) 2022年06月

筆頭著者, 責任著者, 査読有り

　概要を見る

SUMMARY: We present Mirage 2.0, which accurately estimates gene-content evolutionary history by considering heterogeneous evolutionary patterns among gene families. Notably, we introduce a deterministic pattern mixture (DPM) model, which makes Mirage substantially faster and more memory-efficient to be applicable to large datasets with thousands of genomes. AVAILABILITY: The source code is freely available at https://github.com/fukunagatsu/Mirage. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Inverse Potts model improves accuracy of phylogenetic profiling.

Tsukasa Fukunaga, Wataru Iwasaki

Bioinformatics (Oxford, England) 2022年01月

筆頭著者, 責任著者, 査読有り

　概要を見る

MOTIVATION: Phylogenetic profiling is a powerful computational method for revealing the functions of function-unknown genes. Although conventional similarity metrics in phylogenetic profiling achieved high prediction accuracy, they have two estimation biases: an evolutionary bias and a spurious correlation bias. While previous studies reduced the evolutionary bias by considering a phylogenetic tree, few studies have analyzed the spurious correlation bias. RESULTS: To reduce the spurious correlation bias, we developed metrics based on the inverse Potts model (IPM) for phylogenetic profiling. We also developed a metric based on both the IPM and a phylogenetic tree. In an empirical dataset analysis, we demonstrated that these IPM-based metrics improved the prediction performance of phylogenetic profiling. In addition, we found that the integration of several metrics, including the IPM-based metrics, had superior performance to a single metric. AVAILABILITY: The source code is freely available at https://github.com/fukunagatsu/Ipm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Bioinformatics Approaches for Determining the Functional Impact of Repetitive Elements on Non-coding RNAs.

Chao Zeng, Atsushi Takeda, Kotaro Sekine, Naoki Osato, Tsukasa Fukunaga, Michiaki Hamada

Methods in molecular biology (Clifton, N.J.) 2509   315 - 340 2022年

　概要を見る

With a large number of annotated non-coding RNAs (ncRNAs), repetitive sequences are found to constitute functional components (termed as repetitive elements) in ncRNAs that perform specific biological functions. Bioinformatics analysis is a powerful tool for improving our understanding of the role of repetitive elements in ncRNAs. This chapter summarizes recent findings that reveal the role of repetitive elements in ncRNAs. Furthermore, relevant bioinformatics approaches are systematically reviewed, which promises to provide valuable resources for studying the functional impact of repetitive elements on ncRNAs.

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Mirage: Estimation of ancestral gene-copy numbers by considering different evolutionary patterns among gene families

Tsukasa Fukunaga, Wataru Iwasaki

Bioinformatics Advances （Oxford University Press (OUP)）  2021年07月

筆頭著者, 責任著者, 査読有り

　概要を見る

<title>Abstract</title>
<sec>
<title>Motivation</title>
Reconstruction of gene copy number evolution is an essential approach for understanding how complex biological systems have been organized. Although various models have been proposed for gene copy number evolution, existing evolutionary models have not appropriately addressed the fact that different gene families can have very different gene gain/loss rates.


</sec>
<sec>
<title>Results</title>
In this study, we developed Mirage (MIxtuRe model for Ancestral Genome Estimation), which allows different gene families to have flexible gene gain/loss rates. Mirage can use three models for formulating heterogeneous evolution among gene families: the discretized Γ model, PDF model, and PM model. Simulation analysis showed that Mirage can accurately estimate heterogeneous gene gain/loss rates and reconstruct gene content evolutionary history. Application to empirical datasets demonstrated that the PM model fits genome data from various taxonomic groups better than the other heterogeneous models. Using Mirage, we revealed that metabolic function-related gene families displayed frequent gene gains and losses in all taxa investigated.


</sec>
<sec>
<title>Availability</title>
The source code of Mirage is freely available at https://github.com/fukunagatsu/Mirage.


</sec>
<sec>
<title>Supplementary information</title>
Supplementary data are available at Bioinformatics Advances online.


</sec>

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Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model.

Shion Hosoda, Tsukasa Fukunaga, Michiaki Hamada

Bioinformatics (Oxford, England) 37 （ Suppl_1 ） i16-i24 2021年07月

査読有り

　概要を見る

MOTIVATION: Accumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka-Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions. RESULTS: In this study, we developed unsupervised learning-based microbial interaction inference method using Bayesian estimation (Umibato), a method for estimating time-varying microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota. AVAILABILITY AND IMPLEMENTATION: The C++ and python source codes of the Umibato software are available at https://github.com/shion-h/Umibato. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Novel metric for hyperbolic phylogenetic tree embeddings.

Hirotaka Matsumoto, Takahiro Mimori, Tsukasa Fukunaga

Biology methods & protocols 6 （ 1 ） bpab006 2021年

査読有り

　概要を見る

Advances in experimental technologies, such as DNA sequencing, have opened up new avenues for the applications of phylogenetic methods to various fields beyond their traditional application in evolutionary investigations, extending to the fields of development, differentiation, cancer genomics, and immunogenomics. Thus, the importance of phylogenetic methods is increasingly being recognized, and the development of a novel phylogenetic approach can contribute to several areas of research. Recently, the use of hyperbolic geometry has attracted attention in artificial intelligence research. Hyperbolic space can better represent a hierarchical structure compared to Euclidean space, and can therefore be useful for describing and analyzing a phylogenetic tree. In this study, we developed a novel metric that considers the characteristics of a phylogenetic tree for representation in hyperbolic space. We compared the performance of the proposed hyperbolic embeddings, general hyperbolic embeddings, and Euclidean embeddings, and confirmed that our method could be used to more precisely reconstruct evolutionary distance. We also demonstrate that our approach is useful for predicting the nearest-neighbor node in a partial phylogenetic tree with missing nodes. Furthermore, we proposed a novel approach based on our metric to integrate multiple trees for analyzing tree nodes or imputing missing distances. This study highlights the utility of adopting a geometric approach for further advancing the applications of phylogenetic methods.

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Representation learning applications in biological sequence analysis.

Hitoshi Iuchi, Taro Matsutani, Keisuke Yamada, Natsuki Iwano, Shunsuke Sumi, Shion Hosoda, Shitao Zhao, Tsukasa Fukunaga, Michiaki Hamada

Computational and structural biotechnology journal 19   3198 - 3208 2021年

査読有り

　概要を見る

Although remarkable advances have been reported in high-throughput sequencing, the ability to aptly analyze a substantial amount of rapidly generated biological (DNA/RNA/protein) sequencing data remains a critical hurdle. To tackle this issue, the application of natural language processing (NLP) to biological sequence analysis has received increased attention. In this method, biological sequences are regarded as sentences while the single nucleic acids/amino acids or k-mers in these sequences represent the words. Embedding is an essential step in NLP, which performs the conversion of these words into vectors. Specifically, representation learning is an approach used for this transformation process, which can be applied to biological sequences. Vectorized biological sequences can then be applied for function and structure estimation, or as input for other probabilistic models. Considering the importance and growing trend for the application of representation learning to biological research, in the present study, we have reviewed the existing knowledge in representation learning for biological sequence analysis.

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MotiMul: A significant discriminative sequence motif discovery algorithm with multiple testing correction

Koichi Mori, Haruka Ozaki, Tsukasa Fukunaga

Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020    186 - 193 2020年12月

最終著者, 責任著者, 査読有り

　概要を見る

Sequence motifs play essential roles in intermolecular interactions such as DNA-protein interactions. The discovery of novel sequence motifs is therefore crucial for revealing gene functions. Various bioinformatics tools have been developed for finding sequence motifs, but until now there has been no software based on statistical hypothesis testing with statistically sound multiple testing correction. Existing software therefore could not control for the type-l error rates. This is because, in the sequence motif discovery problem, conventional multiple testing correction methods produce very low statistical power due to overly-strict correction. We developed MotiMul, which comprehensively finds significant sequence motifs using statistically sound multiple testing correction. Our key idea is the application of Tarone's correction, which improves the statistical power of the hypothesis test by ignoring hypotheses that never become statistically significant. For the efficient enumeration of the significant sequence motifs, we integrated a variant of the PrefixSpan algorithm with Tarone's correction. Simulation and empirical dataset analysis showed that MotiMul is a powerful method for finding biologically meaningful sequence motifs. The source code of MotiMul is freely available at https://github.com/ko-ichimo-ri/MotiMul.

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Revealing the microbial assemblage structure in the human gut microbiome using latent Dirichlet allocation

Shion Hosoda, Suguru Nishijima, Tsukasa Fukunaga, Masahira Hattori, Michiaki Hamada

Microbiome 8 （ 1 ） 95 - 95 2020年06月

査読有り

　概要を見る

Background: The human gut microbiome has been suggested to affect human health and thus has received considerable attention. To clarify the structure of the human gut microbiome, clustering methods are frequently applied to human gut taxonomic profiles. Enterotypes, i.e., clusters of individuals with similar microbiome composition, are well-studied and characterized. However, only a few detailed studies on assemblages, i.e., clusters of co-occurring bacterial taxa, have been conducted. Particularly, the relationship between the enterotype and assemblage is not well-understood. Results: In this study, we detected gut microbiome assemblages using a latent Dirichlet allocation (LDA) method. We applied LDA to a large-scale human gut metagenome dataset and found that a 4-assemblage LDA model could represent relationships between enterotypes and assemblages with high interpretability. This model indicated that each individual tends to have several assemblages, three of which corresponded to the three classically recognized enterotypes. Conversely, the fourth assemblage corresponded to no enterotypes and emerged in all enterotypes. Interestingly, the dominant genera of this assemblage (Clostridium, Eubacterium, Faecalibacterium, Roseburia, Coprococcus, and Butyrivibrio) included butyrate-producing species such as Faecalibacterium prausnitzii. Indeed, the fourth assemblage significantly positively correlated with three butyrate-producing functions. Conclusions: We conducted an assemblage analysis on a large-scale human gut metagenome dataset using LDA. The present study revealed that there is an enterotype-independent assemblage. [MediaObject not available: see fulltext.]

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Logicome profiler: Exhaustive detection of statistically significant logic relationships from comparative omics data

Tsukasa Fukunaga, Wataru Iwasaki

PLoS ONE 15 （ 5 ） e0232106 2020年05月

筆頭著者, 責任著者, 査読有り

　概要を見る

Logic relationship analysis is a data mining method that comprehensively detects item triplets that satisfy logic relationships from a binary matrix dataset, such as an ortholog table in comparative genomics. Thanks to recent technological advancements, many binary matrix datasets are now being produced in genomics, transcriptomics, epigenomics, metagenomics, and many other fields for comparative purposes. However, regardless of presumed interpretability and importance of logic relationships, existing data mining methods are not based on the framework of statistical hypothesis testing. That means, the type-1 and type-2 error rates are neither controlled nor estimated. Here, we developed Logicome Profiler, which exhaustively detects statistically significant triplet logic relationships from a binary matrix dataset (Logicome means ome of logics). To test all item triplets in a dataset while avoiding false positives, Logicome Profiler adjusts a significance level by the Bonferroni or Benjamini-Yekutieli method for the multiple testing correction. Its application to an ocean metagenomic dataset showed that Logicome Profiler can effectively detect statistically significant triplet logic relationships among environmental microbes and genes, which include those among urea transporter, urease, and photosynthesis-related genes. Beyond omics data analysis, Logicome Profiler is applicable to various binary matrix datasets in general for finding significant triplet logic relationships. The source code is available at https://github.com/fukunagatsu/LogicomeProfiler.

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Targeting the TR4 nuclear receptor-mediated lncTASR/AXL signaling with tretinoin increases the sunitinib sensitivity to better suppress the RCC progression

Hangchuan Shi, Yin Sun, Miao He, Xiong Yang, Michiaki Hamada, Tsukasa Fukunaga, Xiaoping Zhang, Chawnshang Chang

Oncogene 39 （ 3 ） 530 - 545 2020年01月

査読有り,  ISSN  0950-9232

　概要を見る

Renal cell carcinoma (RCC) is one of the most lethal urological tumors. Using sunitinib to improve the survival has become the first-line therapy for metastatic RCC patients. However, the occurrence of sunitinib resistance in the clinical application has curtailed its efficacy. Here we found TR4 nuclear receptor might alter the sunitinib resistance to RCC via altering the TR4/lncTASR/AXL signaling. Mechanism dissection revealed that TR4 could modulate lncTASR (ENST00000600671.1) expression via transcriptional regulation, which might then increase AXL protein expression via enhancing the stability of AXL mRNA to increase the sunitinib resistance in RCC. Human clinical surveys also linked the expression of TR4, lncTASR, and AXL to the RCC survival, and results from multiple RCC cell lines revealed that targeting this newly identified TR4-mediated signaling with small molecules, including tretinoin, metformin, or TR4-shRNAs, all led to increase the sunitinib sensitivity to better suppress the RCC progression, and our preclinical study using the in vivo mouse model further proved tretinoin had a better synergistic effect to increase sunitinib sensitivity to suppress RCC progression. Future successful clinical trials may help in the development of a novel therapy to better suppress the RCC progression.

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Discovering novel mutation signatures by latent Dirichlet allocation with variational Bayes inference

Taro Matsutani, Yuki Ueno, Tsukasa Fukunaga, Michiaki Hamada

Bioinformatics 35 （ 22 ） 4543 - 4552 2019年11月

査読有り,  ISSN  1367-4803

　概要を見る

A cancer genome includes many mutations derived from various mutagens and mutational processes, leading to specific mutation patterns. It is known that each mutational process leads to characteristic mutations, and when a mutational process has preferences for mutations, this situation is called a 'mutation signature.' Identification of mutation signatures is an important task for elucidation of carcinogenic mechanisms. In previous studies, analyses with statistical approaches (e.g. non-negative matrix factorization and latent Dirichlet allocation) revealed a number of mutation signatures. Nonetheless, strictly speaking, these existing approaches employ an ad hoc method or incorrect approximation to estimate the number of mutation signatures, and the whole picture of mutation signatures is unclear. Results: In this study, we present a novel method for estimating the number of mutation signatures- latent Dirichlet allocation with variational Bayes inference (VB-LDA)-where variational lower bounds are utilized for finding a plausible number of mutation patterns. In addition, we performed cluster analyses for estimated mutation signatures to extract novel mutation signatures that appear in multiple primary lesions. In a simulation with artificial data, we confirmed that our method estimated the correct number of mutation signatures. Furthermore, applying our method in combination with clustering procedures for real mutation data revealed many interesting mutation signatures that have not been previously reported.

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