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Abstract Background Availability of plant genome sequences has led to significant advances. However, with few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues that could significantly impact any downstream analysis of plant genomes. In tomato for example, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively. Results To understand what the causes may be for such uneven coverage, we first established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have higher simple sequence repeat and tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available tomato long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially misassembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a predictive model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. Conclusions Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to plant genome misassembly when using short reads and the generality of these causes and factors should be tested further in other species.

Abstract Background Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task. Results We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae. Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators (STE12-TEC1 and RAP1-HAP1-MSN4). Conclusion Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

Background: Green algae belong to a group of photosynthetic organisms that occupy diverse habitats, are closely related to land plants, and have been studied as sources of food and biofuel. Although multiple green algal genomes are available, a global comparative study of algal gene families has not been carried out. To investigate how gene families and gene expression have evolved, particularly in the context of stress response that have been shown to correlate with gene family expansion in multiple eukaryotes, we characterized the expansion patterns of gene families in nine green algal species, and examined evolution of stress response among gene duplicates in Chlamydomonas reinhardtii. Results: Substantial variation in domain family sizes exists among green algal species. Lineage-specific expansion of families occurred throughout the green algal lineage but inferred gene losses occurred more often than gene gains, suggesting a continuous reduction of algal gene repertoire. Retained duplicates tend to be involved in stress response, similar to land plant species. However, stress responsive genes tend to be pseudogenized as well. When comparing ancestral and extant gene stress response state, we found that response gains occur in 13% of duplicate gene branches, much higher than 6% in Arabidopsis thaliana. Conclusion: The frequent gains of stress response among green algal duplicates potentially reflect a high rate of innovation, resulting in a species-specific gene repertoire that contributed to adaptive response to stress. This could be further explored towards deciphering the mechanism of stress response, and identifying suitable green algal species for oil production. [ABSTRACT FROM AUTHOR]