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1. Single‐parent expression drives dynamic gene expression complementation in maize hybrids

   https://doi.org/10.1111/tpj.15042  

   Li, Zhi; Zhou, Peng; Della Coletta, Rafael; Zhang, Tifu; Brohammer, Alex B.; H. O’Connor, Christine; Vaillancourt, Brieanne; Lipzen, Anna; Daum, Chris; Barry, Kerrie; et al (November 2020, The Plant Journal)

   SUMMARY Single‐parent expression (SPE) is defined as gene expression in only one of the two parents. SPE can arise from differential expression between parental alleles, termed non‐presence/absence (non‐PAV) SPE, or from the physical absence of a gene in one parent, termed PAV SPE. We used transcriptome data of diverseZea mays(maize) inbreds and hybrids, including 401 samples from five different tissues, to test for differences between these types of SPE genes. Although commonly observed, SPE is highly genotype and tissue specific. A positive correlation was observed between the genetic distance of the two inbred parents and the number of SPE genes identified. Regulatory analysis showed that PAV SPE and non‐PAV SPE genes are mainly regulated byciseffects, with a small fraction undertransregulation. Polymorphic transposable element insertions in promoter sequences contributed to the high level ofcisregulation for PAV SPE and non‐PAV SPE genes. PAV SPE genes were more frequently expressed in hybrids than non‐PAV SPE genes. The expression of parentally silent alleles in hybrids of non‐PAV SPE genes was relatively rare but occurred in most hybrids. Non‐PAV SPE genes with expression of the silent allele in hybrids are more likely to exhibit above high parent expression level than hybrids that do not express the silent allele, leading to non‐additive expression. This study provides a comprehensive understanding of the nature of non‐PAV SPE and PAV SPE genes and their roles in gene expression complementation in maize hybrids. 

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2. Exploring Regular Expression Evolution

   https://doi.org/10.1109/SANER.2019.8667972  

   Wang, Peipei; Bai, Gina R.; Stolee, Kathryn T. (February 2019, IEEE 26th International Conference on Software Analysis, Evolution and Reengineering)

   Although there are tools to help developers understand the matching behaviors between a regular expression and a string, regular-expression related faults are still common. Learning developers’ behavior through the change history of regular expressions can identify common edit patterns, which can inform the creation of mutation and repair operators to assist with testing and fixing regular expressions. In this work, we explore how regular expressions evolve over time, focusing on the characteristics of regular expression edits, the syntactic and semantic difference of the edits, and the feature changes of edits. Our exploration uses two datasets. First, we look at GitHub projects that have a regular expression in their current version and look back through the commit logs to collect the regular expressions’ edit history. Second, we collect regular expressions composed by study participants during problem- solving tasks. Our results show that 1) 95% of the regular expressions from GitHub are not edited, 2) most edited regular expressions have a syntactic distance of 4-6 characters from their predecessors, 3) over 50% of the edits in GitHub tend to expand the scope of regular expression, and 4) the number of features used indicates the regular expression language usage increases over time. This work has implications for supporting regular expression repair and mutation to ensure test suite quality. 

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3. Cell-free gene expression

   https://doi.org/10.1038/s43586-021-00046-x  

   Garenne, David; Haines, Matthew C.; Romantseva, Eugenia F.; Freemont, Paul; Strychalski, Elizabeth A.; Noireaux, Vincent (December 2021, Nature Reviews Methods Primers)

   null (Ed.)
4. Exploring regular expression comprehension

   https://doi.org/10.1109/ASE.2017.8115653  

   Chapman, Carl; Wang, Peipei; Stolee, Kathryn T. (October 2017, Automated Software Engineering (ASE))
5. Transcriptome‐wide expression landscape and starch synthesis pathway co‐expression network in sorghum

   https://doi.org/10.1002/tpg2.20448  

   Hu, Zhenbin; Chen, Junhao; Olatoye, Marcus O; Zhang, Hengyou; Lin, Zhenguo (June 2024, The Plant Genome)

   Abstract The gene expression landscape across different tissues and developmental stages reflects their biological functions and evolutionary patterns. Integrative and comprehensive analyses of all transcriptomic data in an organism are instrumental to obtaining a comprehensive picture of gene expression landscape. Such studies are still very limited in sorghum, which limits the discovery of the genetic basis underlying complex agricultural traits in sorghum. We characterized the genome‐wide expression landscape for sorghum using 873 RNA‐sequencing (RNA‐seq) datasets representing 19 tissues. Our integrative analysis of these RNA‐seq data provides the most comprehensive transcriptomic atlas for sorghum, which will be valuable for the sorghum research community for functional characterizations of sorghum genes. Based on the transcriptome atlas, we identified 595 housekeeping genes (HKGs) and 2080 tissue‐specific expression genes (TEGs) for the 19 tissues. We identified different gene features between HKGs and TEGs, and we found that HKGs have experienced stronger selective constraints than TEGs. Furthermore, we built a transcriptome‐wide co‐expression network (TW‐CEN) comprising 35 modules with each module enriched in specific Gene Ontology terms. High‐connectivity genes in TW‐CEN tend to express at high levels while undergoing intensive selective pressure. We also built global and seed‐preferential co‐expression networks of starch synthesis pathways, which indicated that photosynthesis and microtubule‐based movement play important roles in starch synthesis. The global transcriptome atlas of sorghum generated by this study provides an important functional genomics resource for trait discovery and insight into starch synthesis regulation in sorghum. 

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