Biology subjects

Sese, J.

Publications and source records attributed to Sese, J..

10 recordsLinked to original sources

bioRxiv2026

Complex-valued representations of time-series gene expression profiles for network analysis

Time-series RNA sequencing provides a powerful framework for studying dynamic gene regulation, yet conventional analyses usually represent gene expression profiles as real-valued vectors in Euclidean space and quantify similarity using correlation or distance. Inspired by quantum information theory, we present a framework for encoding time-series gene expression profiles as complex-valued vectors comprising amplitude and phase components in Hilbert space. We designed multiple encoding models to represent gene expression in the amplitude of complex-valued vectors, encode temporal differences in the phase, and extend the phase representation to incorporate the direction of local expression changes. Gene-gene similarity was then quantified using fidelity, which measures the overlap between two encoded vectors. Evaluation using time-series RNA-seq datasets across diverse species and biological contexts showed that different encoding models produced distinct fidelity distributions that were related to, but distinct from, conventional correlation measures. We then constructed gene-gene networks using pairwise fidelity values and detected communities containing genes with similar temporal profiles. Although fidelity distributions differed across encoding models, the resulting communities captured major temporal expression programs, and functional annotations based on gene ontology and Kyoto encyclopedia of genes and genomes pathway analyses provided exploratory biological context. The detected communities were comparable to those obtained using conventional methods, including weighted correlation network analysis and fuzzy c-means clustering. Furthermore, as a proof-of-concept, we performed SWAP-test circuit simulations to mimic fidelity computation on a quantum computer; under noise-aware conditions, these simulations produced less accurate fidelity estimates with higher computational cost than classical computation. As a proof-of-concept, this study provides a complementary view of temporal transcriptome organization, rather than a uniformly superior alternative to conventional methods.

bioinformatics↗

bioRxiv2025

A moderated statistical test for detecting shifts in homeolog expression ratios in allopolyploids

Allopolyploids arise through hybridization between related species, carrying multiple sets of chromosomes from distinct progenitor, referred to as subgenomes. Within allopolyploids, duplicated genes across subgenomes, called homeologs, are thought to enhance environmental robustness by shifting their expression ratios depending on environmental and developmental changes. However, existing methods for detecting such ratio shifts, including HomeoRoq and Fishers exact test, are limited to allopolyploids with two subgenome sets inherited from two progenitors, and thus cannot handle more complex cases (e.g., allohexaploid wheat). Here, we present the HOmeolog Bias Identification Test (HOBIT), a statistical method for detecting shifts in homeolog expression ratios across different conditions using RNA-Seq count data. HOBIT performs a likelihood ratio test for each homeolog, comparing a full model allowing homeolog expression ratios to vary across conditions with a reduced model assuming constant ratios. Simulation benchmarks for allotetraploids and allohexaploids demonstrated that HOBIT outperforms existing methods in both area under the receiver operating characteristic curve and F1 score. Application to real RNA-Seq datasets from allotetraploid Cardamine flexuosa, allotriploid Cardamine insueta, and Triticum aestivum (wheat) produced biologically consistent results reflecting experimental settings. HOBIT provides a promising framework for uncovering homeolog regulation and adaptive responses in allopolyploids, without restrictions of ploidy complexities.

bioinformatics↗

bioRxiv2022

PlantServation: time-series phenotyping using machine learning revealed seasonal pigment fluctuation in diploid and polyploid Arabidopsis

Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers, but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software to analyze field images, where the plant shape, color, and background vary over months. We estimated the anthocyanin contents of small individuals of four Arabidopsis species using color information and verified the results experimentally. We obtained >4 million plant images over three field seasons to study anthocyanin fluctuations. We found significant effects of past radiation, coldness, and precipitation on the anthocyanin content in the field. The synthetic allopolyploid A. kamchatica recapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit and combine the traits of progenitors. PlantServation pipeline facilitates the study of plant responses to complex environments termed "in natura."

plant biology↗

bioRxiv2020

ARPEGGIO: Automated Reproducible Polyploid EpiGenetic GuIdance workflOw

Whole genome duplication (WGD) events are common in the evolutionary history of many living organisms. For decades, researchers have been trying to understand the genetic and epigenetic impact of WGD and its underlying molecular mechanisms. Particular attention was given to allopolyploid study systems, species resulting from an hybridization event accompanied by WGD. Investigating the mechanisms behind the survival of a newly formed allopolyploid highlighted the key role of DNA methylation. With the improvement of high-throughput methods, such as whole genome bisulfite sequencing (WGBS), an opportunity opened to further understand the role of DNA methylation at a larger scale and higher resolution. However, only a few studies have applied WGBS to allopolyploids, which might be due to lack of genomic resources combined with a burdensome data analysis process. To overcome these problems, we developed the Automated Reproducible Polyploid EpiGenetic GuIdance workflOw (ARPEGGIO): the first workflow for the analysis of epigenetic data in polyploids. This workflow analyzes WGBS data from allopolyploid species via the genome assemblies of the allopolyploids parent species. ARPEGGIO utilizes an updated read classification algorithm (EAGLE-RC), to tackle the challenge of sequence similarity amongst parental genomes. ARPEGGIO offers automation, but more importantly, a complete set of analyses including spot checks starting from raw WGBS data: quality checks, trimming, alignment, methylation extraction, statistical analyses and downstream analyses. A full run of ARPEGGIO outputs a list of genes showing differential methylation. ARPEGGIOs design focuses on ease of use and reproducibility. ARPEGGIO was made simple to set up, run and interpret, and its implementation includes both package management and containerization. Here we discuss all the steps, challenges and implementation strategies; example datasets are provided to show how to use ARPEGGIO. In addition, we also test EAGLE-RC with publicly available datasets given a ground truth, and we show that EAGLE-RC decreases the error rate by 3 to 4 times compared to standard approaches. The goal of ARPEGGIO is to promote, support and improve polyploid research with a reproducible and automated set of analyses in a convenient implementation.

bioinformatics↗

bioRxiv2020

A recently formed triploid Cardamine insueta inherits leaf vivipary and submergence tolerance traits of parents

Contemporary speciation provides a unique opportunity to directly observe the traits and environmental responses of a new species. Cardamine insueta is an allotriploid species that appeared within the past 150 years in a Swiss village, Urnerboden. In contrast to its two progenitor species, C. amara and C. rivularis that live in wet and open habitats, respectively, C. insueta is found in-between their habitats with temporal water level fluctuation. This triploid species propagates clonally and serves as a triploid bridge to form higher ploidy species. Although niche separation is observed in field studies, the mechanisms underlying the environmental robustness of C. insueta are not clear. To characterize responses to a fluctuating environment, we performed a time-course analysis of homeolog gene expression in C. insueta in response to submergence treatment. For this purpose, the two parental (C. amara and C. rivularis) genome sequences were assembled with a reference-guided approach, and homeolog-specific gene expression was quantified by using HomeoRoq software. We found that C. insueta and C. rivularis initiated vegetative propagation by forming ectopic meristems on leaves, while C. amara did not. We examined homeolog-specific gene expression of three species at nine time points during the treatment. The genome-wide expression ratio of homeolog pairs was 2:1 over the time-course, consistent with the ploidy number. By searching the genes with high coefficient of variation of expression over time-course transcriptome data, we found many known key transcriptional factors related to meristem development and formation upregulated in both C. rivularis and rivularis-homeolog of C. insueta, but not in C. amara. Moreover, some amara-homeologs of these genes were also upregulated in the triploid, suggesting trans-regulation. In turn, Gene Ontology analysis suggested that the expression pattern of submergence tolerant genes in the triploid was inherited from C. amara. These results suggest that the triploid C. insueta combined advantageous patterns of parental transcriptomes to contribute to its establishment in a new niche along a water-usage gradient.

plant biology↗

bioRxiv2019

Fine-scale ecological and transcriptomic data reveal niche differentiation of an allopolyploid from diploid parents in Cardamine

Polyploidization, or whole genome duplication, is one of the major mechanisms of plant speciation. Allopolyploids (species that harbor polyploid genomes originating from hybridization of different diploid species) have been hypothesized to occupy a niche with intermediate, broader, or fluctuating environmental conditions compared with parental diploids. It remains unclear whether empirical data support this hypothesis and whether specialization of expression patterns of the homeologs (paralogous gene copies resulting from allopolyploidization) relates to habitat environments. Here, we studied the ecology and transcriptomics of a wild allopolyploid Cardamine flexuosa and its diploid parents C. hirsuta and C. amara at a fine geographical scale in their native area in Switzerland. We found that the diploid parents favored opposite extremes in terms of soil moisture, soil carbon-to-nitrogen ratios, and light availability. The habitat of the allopolyploid C. flexuosa was broader compared with those of its parental species and overlapped with those of the parents, but not at its extremes. In C. flexuosa, the genes related to water availability were overrepresented among those at both the expression level and the expression ratio of homeolog pairs, which varied among habitat environments. These findings provide empirical evidence for niche differentiation between an allopolyploid and its diploid parents at a fine scale, where both ecological and transcriptomic data indicated water availability to be the key environmental factor for niche differentiation.Significance statement Polyploidization, or whole genome duplication, is common in plants and may contribute to their ecological diversification. However, little is known about the niche differentiation of wild allopolyploids relative to their diploid parents and the gene expression patterns that may underlie such ecological divergence. We detected niche differentiation between the allopolyploid Cardamine flexuosa and its diploid parents C. amara and C. hirsuta along water availability gradient at a fine scale. The ecological differentiation was mirrored by the dynamic control of water availability-related gene expression patterns according to habitat environments. Thus, both ecological and transcriptomic data revealed niche differentiation between an allopolyploid species and its diploid parents.Competing Interest StatementThe authors have declared no competing interest.View Full Text

plant biology↗

bioRxiv2018

Homeolog expression quantification methods for allopolyploids

Genome duplication with hybridization, or allopolyploidization, occurs in animals, fungi, and plants, and is especially common in crop plants. There is increasing interest in the study of allopolyploids due to advances in polyploid genome assembly, however the high level of sequence similarity in duplicated gene copies (homeologs) pose many challenges. Here we compared standard RNA-seq expression quantification approaches used currently for diploid species against subgenome-classification approaches which maps reads to each subgenome separately. We examined mapping error using our previous and new RNA-seq data in which a subgenome is experimentally added (synthetic allotetraploid Arabidopsis kamchatica) or reduced (allohexaploid wheat Triticum aestivum versus extracted allotetraploid) as ground truth. The error rates in the two species were very similar. The standard approaches showed higher error rates (> 10% using pseudo-alignment with Kallisto) while subgenome-classification approaches showed much lower error rates (< 1% using EAGLE-RC, < 2% using HomeoRoq). Although downstream analysis may partly mitigate mapping errors, the difference in methods was substantial in hexaploid wheat, where Kallisto appeared to have systematic differences relative to other methods. Only approximately half of the differentially expressed homeologs detected using Kallisto overlapped with those by any other method. In general, disagreement in low expression genes was responsible for most of the discordance between methods, which is consistent with known biases in Kallisto. We also observed that there exist uncertainties in genome sequences and annotation which can affect each method differently. Overall, subgenome-classification approaches tend to perform better than standard approaches with EAGLE-RC having the highest precision.

bioinformatics↗

bioRxiv2018

reactIDR: Evaluation of the statistical reproducibility of high-throughput structural analyses for a robust RNA reactivity classification

MotivationRecently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures called high-throughput RNA structural (HTS) analy- sis, and dozens of different protocols were used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on experimental data generation methodology, which results in many difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods.\n\nResultsHere, we introduced a statistical framework, reactIDR, which is applicable to the experimental data obtained using multiple HTS methodologies, and it classifies the nucleotides into three structural categories, stem, loop, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model (HMM) to discriminate accurately between the true and spurious signals obtained in the replicated HTS experiments. In reactIDR, IDR and HMM parameters are efficiently optimized by using an expectation-maximization algorithm. Furthermore, if known reference structures are given, a supervised learning can be applicable in a semi-supervised manner. The results of our analyses for real HTS data showed that reactIDR achieved the highest accuracy in the classification problem of stem/loop structures of rRNA using both individual and integrated HTS datasets as well as the best correspondence with the three-dimensional structure. Because reactIDR is the first method to compare HTS datasets obtained from multiple sources in a single unified model, it has a great potential to increase the accuracy of RNA secondary structure prediction at transcriptome-wide level with further experiments performed.\n\nAvailabilityreactIDR is implemented in Python. Source code is publicly available at https://github.com/carushi/reactIDRhttps://github.com/carushi/reactIDR.\n\nContactkawaguchi-rs@aist.go.jp\n\nSupplementary informationSupplementary data are available at online.

bioinformatics↗

bioRxiv2018

Integrative analysis of transcription factor occupancy at enhancers and disease risk loci in noncoding genomic regions

Noncoding regions of the human genome possess enhancer activity and harbor risk loci for heritable diseases. Whereas the binding profiles of multiple transcription factors (TFs) have been investigated, integrative analysis with the large body of public data available so as to provide an overview of the function of such noncoding regions has remained a challenge. Here we have fully integrated public ChIP-seq and DNase-seq data (n ~ 70,000), including those for 743 human transcription factors (TFs) with 97 million binding sites, and have devised a data- mining platform --designated ChIP-Atlas--to identify significant TF-genome, TF-gene, and TF-TF interactions. Using this platform, we found that TFs enriched at macrophage or T-cell enhancers also accumulated around risk loci for autoimmune diseases, whereas those enriched at hepatocyte or macrophage enhancers were preferentially detected at loci associated with HDL-cholesterol levels. Of note, we identified \"hotspots\" around such risk loci that accumulated multiple TFs and are therefore candidates for causal variants. Integrative analysis of public chromatin-profiling data is thus able to identify TFs and tissues associated with heritable disorders.

genomics↗

bioRxiv2018

Patterns of polymorphism, selection and linkage disequilibrium in the subgenomes of the allopolyploid Arabidopsis kamchatica

Although genome duplication is widespread in wild and crop plants, little is known about genome-wide selection due to the complexity of polyploid genomes. In allopolyploid species, the patterns of purifying selection and adaptive substitutions would be affected by masking owing to duplicated genes or homeologs as well as by effective population size. We resequenced 25 distribution-wide accessions of the allotetraploid Arabidopsis kamchatica, which has a relatively small genome size (450 Mb) derived from the diploid species A. halleri and A. lyrata. The level of nucleotide polymorphism and linkage disequilibrium decay were comparable to A. thaliana, indicating the feasibility of association studies. A reduction in purifying selection compared with parental species was observed. Interestingly, the proportion of adaptive substitutions () was significantly positive in contrast to the majority of plant species. A recurrent pattern observed in both frequency and divergence-based neutrality tests is that the genome-wide distributions of both subgenomes were similar, but the correlation between homeologous pairs was low. This may increase the opportunity of different evolutionary trajectories such as in the HMA4 gene involved in heavy metal hyperaccumulation.

evolutionary biology↗