The employment of doubled-haploid (DH) technology in maize has vastly accelerated the efficiency of developing inbred lines. The selection of superior lines has to rely on genotypes with genomic selection (GS) model, rather than phenotypes due to the high expense of field phenotyping. In this work, we implemented 'genome optimization via virtual simulation (GOVS)' using the genotype and phenotype data of 1404 maize lines and their F1 progeny. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' or 'advantageous alleles' in a genetic pool. Such a virtually optimized genome, although can never be developed in reality, may help plot the optimal route to direct breeding decisions. GOVS assists in the selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. The assumption is that the more fragments of optimal genotypes a line contributes to the assembly, the higher the likelihood of the line favored in the F1 phenotype, e.g. grain yield. Compared to traditional GS method, GOVS-assisted selection may avoid using an arbitrary threshold for the predicted F1 yield to assist selection. Additionally, the selected lines contributed complementary sets of advantageous alleles to the virtual genome. This feature facilitates plotting the optimal route for DH production, whereby the fewest lines and F1 combinations are needed to pyramid a maximum number of advantageous alleles in the new DH lines. In summary, incorporation of DH production, GS and genome optimization will ultimately improve genomically designed breeding in maize. Short abstract: Doubled-haploid (DH) technology has been widely applied in maize breeding industry, as it greatly shortens the period of developing homozygous inbred lines via bypassing several rounds of self-crossing. The current challenge is how to efficiently screen the large volume of inbred lines based on genotypes. We present the toolbox of genome optimization via virtual simulation (GOVS), which complements the traditional genomic selection model. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' in a breeding population, and then assists in selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. Availability of GOVS (https://govs-pack.github.io/) to the public may ultimately facilitate genomically designed breeding in maize.

Motivation: DNA methylation is important for gene silencing and imprinting in both plants and animals. Recent advances in bisulfite sequencing allow detection of single nucleotide variations (SNVs) achieving high sensitivity, but accurately identifying heterozygous SNVs from partially C-to-T converted sequences remains challenging. Results: We designed two methods, BayesWC and BinomWC, that substantially improved the precision of heterozygous SNV calls from ∼80% to 99% while retaining comparable recalls. With these SNV calls, we provided functions for allele-specific DNA methylation (ASM) analysis and visualizing the methylation status on reads. Applying ASM analysis to a previous dataset, we found that an average of 1.5% of investigated regions showed allelic methylation, which were significantly enriched in transposon elements and likely to be shared by the same cell-type. A dynamic fragment strategy was utilized for DMR analysis in low-coverage data and was able to find differentially methylated regions (DMRs) related to key genes involved in tumorigenesis using a public cancer dataset. Finally, we integrated 40 applications into the software package CGmapTools to analyze DNA methylomes. This package uses CGmap as the format interface, and designs binary formats to reduce the file size and support fast data retrieval, and can be applied for context-wise, gene-wise, bin-wise, region-wise and sample-wise analyses and visualizations. Availability and implementation: The CGmapTools software is freely available at https://cgmaptools.github.io/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: The Brassicaceae family includes the most important plant model Arabidopsis thaliana and many cruciferous vegetable crops. A number of close relatives of Arabidopsis and economically important Brassica species are being sequenced with whole-genome shotgun sequencing technologies. However, de novo assembly of full chromosomes is difficult, since many non-model Brassicaceae species are lacking genetic and/or physical maps. As a unique feature for Brassicaceae, the genome of each member is composed of 24 conserved chromosomal blocks, and the arrangement of the 24 blocks can be obtained from karyotype analysis via comparative chromosome painting experiments. Taking this advantage, we developed a bioinformatic tool named KGBassembler to automatically finalize assembly of full chromosomes from contigs and/or scaffolds based on available karyotypes of Brassicaceae species. AVAILABILITY: KGBassembler was implemented in C++ with a graphical user interface. It is freely available to academic users at http://www.cmbb.arizona.edu/KGBassembler/. CONTACT: [email protected].

The unprecedented complexity of the transcriptomic data obtained in recent years creates opportunities for new genomic studies aimed at interpolating regulatory code of gene expression and tracing genome evolution. We report here the identification and characterization of a set of 851 intergenic loci that represent transcribed gene fragments (TGFs) ectopically duplicated from 1030 non-transposable element (non-TE) donor genes in the rice genome. We analyzed the genomic context of the TGFs and donor genes. We show that the TGFs have adopted transcriptional orientation and pattern independent of the donor genes. We further show that TGFs have undergone relaxed purifying selection, consistent with their being pseudogenized. We found that the donor genes, which are biased toward certain molecular functions, exhibit an accelerated evolution rate comparing to the genome average. Our results demonstrated a large number of actively TGFs in the rice genome and shed light on the origin, mode of action and function of the TGFs.

Abstract Summary: NMPP package is a bundle of user-customized tools based on established algorithms and methods to process self-designed NimbleGen microarray data. It features a command-line-based integrative processing procedure that comprises five major functional components, namely the raw microarray data parsing and integrating module, the array spatial effect smoothing and visualization module, the probe-level multi-array normalization module, the gene expression intensity summarization module and the gene expression status inference module. Availability: Contact: [email protected] Supplementary information: The supplementary materials, figures, testing dataset, software instructions and a typical workflow of using NMPP package are accessible through the above homepage.