HiC-TE: a computational pipeline for Hi-C data analysis shows a possible role of repeat family interactions in the genome 3D organization

The role of repetitive DNA in the 3D organization of the interphase nucleus in plant cells is a subject of intensive study. High-throughput chromosome conformation capture (Hi-C) is a sequencing-based method detecting the proximity of DNA segments in nuclei. We combined Hi-C data, plant reference genome data and tools for the characterization of genomic repeats to build a Nextflow pipeline identifying and quantifying the contacts of specific repeats revealing the preferential homotypic interactions of ribosomal DNA, DNA transposons and some LTR retrotransposon families. We provide a novel way to analyze the organization of repetitive elements in the 3D nucleus.

Development of SSR Databases Available for Both NGS and Capillary Electrophoresis in Apple, Pear and Tea

Developing new varieties in fruit and tea breeding programs is very costly and labor-intensive. Thus, establishing a variety discrimination system is important for protecting breeders’ rights and producers’ profits. Simple sequence repeat (SSR) databases that can be utilized for both next-generation sequencing (SSR-GBS) and polymerase chain reaction–capillary electrophoresis (PCR-CE) would be very useful in variety discrimination. In the present study, SSRs with tri-, tetra- and pentanucleotide repeats were examined in apple, pear and tea. Out of 37 SSRs that showed clear results in PCR-CE, 27 were suitable for SSR-GBS. Among the remaining markers, there was allele dropout for some markers that caused differences between the results of PCR-CE and SSR-GBS. For the selected 27 markers, the alleles detected by SSR-GBS were comparable to those detected by PCR-CE. Furthermore, we developed a computational pipeline for automated genotyping using SSR-GBS by setting a value “α” for each marker, a criterion whether a genotype is homozygous or heterozygous based on allele frequency. The set of 27 markers contains 10, 8 and 9 SSRs for apple, pear and tea, respectively, that are useful for both PCR-CE and SSR-GBS and suitable for automation. The databases help researchers discriminate varieties in various ways depending on sample size, markers and methods.

De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens

Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline which collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo which enable us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations, and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies which were highly varied across patients demonstrating the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals.

A computational pipeline for functional gene discovery

Many computational pipelines exist for the detection of differentially expressed genes. However, computational pipelines for functional gene detection rarely exist. We developed a new computational pipeline for functional gene identification from transcriptome profiling data. Key features of the pipeline include batch effect correction, clustering optimization by gap statistics, gene ontology analysis of clustered genes, and literature analysis for functional gene discovery. By leveraging this pipeline on RNA-seq datasets from two mouse retinal development studies, we identified 7 candidate genes involved in the formation of the photoreceptor outer segment. The expression of top three candidate genes (Pde8b, Laptm4b, and Nr1h4) in the outer segment of the developing mouse retina were experimentally validated by immunohistochemical analysis. This computational pipeline can accurately predict novel functional gene for a specific biological process, e.g., development of the outer segment and synapses of the photoreceptor cells in the mouse retina. This pipeline can also be useful to discover functional genes for other biological processes and in other organs and tissues.

Going through phages: A Computational approach to Revealing the role of prophage in Staphylococcus aureus

Prophages have important roles in virulence, antibiotic resistance and genome evolution in Staphylococcus aureus. Rapid growth in the number of sequenced S. aureus genomes allows for an investigation of prophage sequences in S. aureus at an unprecedented scale. We developed a computational pipeline to detect and analyze prophage sequences in nearly 10,011 S. aureus genomes, discovering thousands of putative prophage sequences with genes encoding virulence factors and antibiotic resistance.

A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices

Cardiac magnetic resonance (CMR) imaging is a valuable modality in the diagnosis and characterization of cardiovascular diseases, since it can identify abnormalities in structure and function of the myocardium non-invasively and without the need for ionizing radiation. However, in clinical practice, it is commonly acquired as a collection of separated and independent 2D image planes, which limits its accuracy in 3D analysis. This paper presents a completely automated pipeline for generating patient-specific 3D biventricular heart models from cine magnetic resonance (MR) slices. Our pipeline automatically selects the relevant cine MR images, segments them using a deep learning-based method to extract the heart contours, and aligns the contours in 3D space correcting possible misalignments due to breathing or subject motion first using the intensity and contours information from the cine data and next with the help of a statistical shape model. Finally, the sparse 3D representation of the contours is used to generate a smooth 3D biventricular mesh. The computational pipeline is applied and evaluated in a CMR dataset of 20 healthy subjects. Our results show an average reduction of misalignment artefacts from 1.82 ± 1.60 mm to 0.72 ± 0.73 mm over 20 subjects, in terms of distance from the final reconstructed mesh. The high-resolution 3D biventricular meshes obtained with our computational pipeline are used for simulations of electrical activation patterns, showing agreement with non-invasive electrocardiographic imaging. The automatic methodologies presented here for patient-specific MR imaging-based 3D biventricular representations contribute to the efficient realization of precision medicine, enabling the enhanced interpretability of clinical data, the digital twin vision through patient-specific image-based modelling and simulation, and augmented reality applications. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

Studying mutation rate evolution in primates—the effects of computational pipelines and parameter choices

This commentary investigates the important role of computational pipeline and parameter choices in performing mutation rate estimation, using the recent article published in this journal by Bergeron et al. entitled “The germline mutational process in rhesus macaque and its implications for phylogenetic dating” as an illustrative example.

Beyond the reach of homology: successive computational filters find yeast pheromone genes

The mating of fungi depends on pheromones that mediate communication between two mating types. Most species use short peptides as pheromones, which are either unmodified (e.g., α-factor in Saccharomyces cerevisiae) or C-terminally farnesylated (e.g., a-factor in S. cerevisiae). Peptide pheromones have been found by genetics or biochemistry in small number of fungi, but their short sequences and modest conservation make it impossible to detect homologous sequences in most species. To overcome this problem, we used a four-step computational pipeline to identify candidate a-factor genes in sequenced genomes of the Saccharomycotina, the fungal clade that contains most of the yeasts: we require that candidate genes have a C-terminal prenylation motif, are fewer than 100 amino acids long, contain a proteolytic processing motif upstream of the potential mature pheromone sequence, and that closely related species contain highly conserved homologs of the potential mature pheromone sequence. Additional manual curation exploits the observation that many species carry more than one a-factor gene, encoding identical or nearly identical pheromones. From 332 fungal genomes, we identified strong candidate pheromone genes in 238 genomes, covering 13 clades that are separated from each other by at least 100 million years, the time required for evolution to remove detectable sequence homology. For one small clade, the Yarrowia, we demonstrated that our algorithm found the a-factor genes: deleting all four related genes in the a-mating type of Yarrowia lipolytica prevents mating.

Toward scalable in vitro models: a novel experimental and computational pipeline for the identification of cellular metabolic parameters

Oxygen utilization by cells has a crucial role in the design of advanced in vitro models. The aim of this study is to develop an experimental and computational pipeline for identifying oxygen metabolism parameters. We applied the approach to HepG2 cell monolayer cultures, demonstrating that such parameters depend on cell density.