F-Seq2: improving the feature density based peak caller with dynamic statistics

Computational tools for plant genomics and breeding

Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design.

ROCCO: a robust method for detection of open chromatin via convex optimization

MOTIVATION

Analysis of open chromatin regions across multiple samples from two or more distinct conditions can determine altered gene regulatory patterns associated with biological phenotypes and complex traits. The ATAC-seq assay allows for tractable genome-wide open chromatin profiling of large numbers of samples. Stable, broadly applicable genomic annotations of open chromatin regions are not available. Thus, most studies first identify open regions using peak calling methods for each sample independently. These are then heuristically combined to obtain a consensus peak set. Reconciling sample-specific peak results post hoc from larger cohorts is particularly challenging, and informative spatial features specific to open chromatin signals are not leveraged effectively.

RESULTS

We propose a novel method, ROCCO, that determines consensus open chromatin regions across multiple samples simultaneously. ROCCO employs robust summary statistics and solves a constrained optimization problem formulated to account for both enrichment and spatial dependence of open chromatin signal data. We show this formulation admits attractive theoretical and conceptual properties as well as superior empirical performance compared to current methodology.

AVAILABILITY AND IMPLEMENTATION

Source code, documentation, and usage demos for ROCCO are available on GitHub at: https://github.com/nolan-h-hamilton/ROCCO. ROCCO can also be installed as a stand-alone binary utility using pip/PyPI.

Optimized Tensor Decomposition and Principal Component Analysis Outperforming State-of-the-Art Methods When Analyzing Histone Modification Chromatin Immunoprecipitation Profiles

It is difficult to identify histone modification from datasets that contain high-throughput sequencing data. Although multiple methods have been developed to identify histone modification, most of these methods are not specific to histone modification but are general methods that aim to identify protein binding to the genome. In this study, tensor decomposition (TD) and principal component analysis (PCA)-based unsupervised feature extraction with optimized standard deviation were successfully applied to gene expression and DNA methylation. The proposed method was used to identify histone modification. Histone modification along the genome is binned within the region of length L. Considering principal components (PCs) or singular value vectors (SVVs) that PCA or TD attributes to samples, we can select PCs or SVVs attributed to regions. The selected PCs and SVVs further attribute p-values to regions, and adjusted p-values are used to select regions. The proposed method identified various histone modifications successfully and outperformed various state-of-the-art methods. This method is expected to serve as a de facto standard method to identify histone modification. For reproducibility and to ensure the systematic analysis of our study is applicable to datasets from different gene expression experiments, we have made our tools publicly available for download from gitHub.

Genome-wide chromatin accessibility landscape and dynamics of transcription factor networks during ovule and fiber development in cotton

BACKGROUND

The development of cotton fiber is regulated by the orchestrated binding of regulatory proteins to cis-regulatory elements associated with developmental genes. The cis-trans regulatory dynamics occurred throughout the course of cotton fiber development are elusive. Here we generated genome-wide high-resolution DNase I hypersensitive sites (DHSs) maps to understand the regulatory mechanisms of cotton ovule and fiber development.

RESULTS

We generated DNase I hypersensitive site (DHS) profiles from cotton ovules at 0 and 3 days post anthesis (DPA) and fibers at 8, 12, 15, and 18 DPA. We obtained a total of 1185 million reads and identified a total of 199,351 DHSs through ~ 30% unique mapping reads. It should be noted that more than half of DNase-seq reads mapped multiple genome locations and were not analyzed in order to achieve a high specificity of peak profile and to avoid bias from repetitive genomic regions. Distinct chromatin accessibilities were observed in the ovules (0 and 3 DPA) compared to the fiber elongation stages (8, 12, 15, and 18 DPA). Besides, the chromatin accessibility during ovules was particularly elevated in genomic regions enriched with transposable elements (TEs) and genes in TE-enriched regions were involved in ovule cell division. We analyzed cis-regulatory modules and revealed the influence of hormones on fiber development from the regulatory divergence of transcription factor (TF) motifs. Finally, we constructed a reliable regulatory network of TFs related to ovule and fiber development based on chromatin accessibility and gene co-expression network. From this network, we discovered a novel TF, WRKY46, which may shape fiber development by regulating the lignin content.

CONCLUSIONS

Our results not only reveal the contribution of TEs in fiber development, but also predict and validate the TFs related to fiber development, which will benefit the research of cotton fiber molecular breeding.