Discovery and assembly of repeat family pseudomolecules from sparse genomic sequence data using the Assisted Automated Assembler of Repeat Families (AAARF) algorithm

<p>Abstract</p> <p>Background</p> <p>Higher eukaryotic genomes are typically large, complex and filled with both genes and multiple classes of repetitive DNA. The repetitive DNAs, primarily transposable elements, are a rapidly evolving genome component that can provide the raw material for novel selected functions and also indicate the mechanisms and history of genome evolution in any ancestral lineage. Despite their abundance, universality and significance, studies of genomic repeat content have been largely limited to analyses of the repeats in fully sequenced genomes.</p> <p>Results</p> <p>In order to facilitate a broader range of repeat analyses, the Assisted Automated Assembler of Repeat Families algorithm has been developed. This program, written in PERL and with numerous adjustable parameters, identifies sequence overlaps in small shotgun sequence datasets and walks them out to create long pseudomolecules representing the most abundant repeats in any genome. Testing of this program in maize indicated that it found and assembled all of the major repeats in one or more pseudomolecules, including coverage of the major Long Terminal Repeat retrotransposon families. Both Sanger sequence and 454 datasets were appropriate.</p> <p>Conclusion</p> <p>These results now indicate that hundreds of higher eukaryotic genomes can be efficiently characterized for the nature, abundance and evolution of their major repetitive DNA components.</p

MADS-Box Genes and Gibberellins Regulate Bolting in Lettuce (Lactuca sativa L.)

Bolting in lettuce is promoted by high temperature and bolting resistance is of great economic importance for lettuce production. But how bolting is regulated at the molecular level remains elusive. Here, a bolting resistant line S24 and a bolting sensitive line S39 were selected for morphological, physiological, transcriptomic and proteomic comparisons. A total of 12204 genes were differentially expressed in S39 vs S24. Line S39 was featured with larger leaves, higher levels of chlorophyll, soluble sugar, anthocyanin and auxin, consistent with its up-regulation of genes implicated in photosynthesis, oxidation-reduction and auxin actions. Proteomic analysis identified 30 differentially accumulated proteins in lines S39 and S24 upon heat treatment, and 19 out of the 30 genes showed differential expression in the RNA-Seq data. Exogenous gibberellins (GA) treatment promoted bolting in both S39 and S24, while 12 flowering promoting MADS-box genes were specifically induced in line S39, suggesting that although GA regulates bolting in lettuce, it may be the MADS-box genes, not GA, that plays a major role in differing the bolting resistance between these two lettuce lines

Integrative profiling of metabolome and transcriptome of skeletal muscle after acute exercise intervention in mice

This study aims to explore the molecular regulatory mechanisms of acute exercise in the skeletal muscle of mice. Male C57BL/6 mice were randomly assigned to the control group, and the exercise group, which were sacrificed immediately after an acute bout of exercise. The study was conducted to investigate the metabolic and transcriptional profiling in the quadriceps muscles of mice. The results demonstrated the identification of 34 differentially expressed metabolites (DEMs), with 28 upregulated and 6 downregulated, between the two groups. Metabolic pathway analysis revealed that these DEMs were primarily enriched in several, including the citrate cycle, propanoate metabolism, and lysine degradation pathways. In addition, the results showed a total of 245 differentially expressed genes (DEGs), with 155 genes upregulated and 90 genes downregulated. KEGG analysis indicated that these DEGs were mainly enriched in various pathways such as ubiquitin mediated proteolysis and FoxO signaling pathway. Furthermore, the analysis revealed significant enrichment of DEMs and DEGs in signaling pathways such as protein digestion and absorption, ferroptosis signaling pathway. In summary, the identified multiple metabolic pathways and signaling pathways were involved in the exercise-induced physiological regulation of skeletal muscle, such as the TCA cycle, oxidative phosphorylation, protein digestion and absorption, the FoxO signaling pathway, ubiquitin mediated proteolysis, ferroptosis signaling pathway, and the upregulation of KLF-15, FoxO1, MAFbx, and MuRF1 expression could play a critical role in enhancing skeletal muscle proteolysis

Go Static: Contextualized Logging Statement Generation

Logging practices have been extensively investigated to assist developers in writing appropriate logging statements for documenting software behaviors. Although numerous automatic logging approaches have been proposed, their performance remains unsatisfactory due to the constraint of the single-method input, without informative programming context outside the method. Specifically, we identify three inherent limitations with single-method context: limited static scope of logging statements, inconsistent logging styles, and missing type information of logging variables. To tackle these limitations, we propose SCLogger, the first contextualized logging statement generation approach with inter-method static contexts. First, SCLogger extracts inter-method contexts with static analysis to construct the contextualized prompt for language models to generate a tentative logging statement. The contextualized prompt consists of an extended static scope and sampled similar methods, ordered by the chain-of-thought (COT) strategy. Second, SCLogger refines the access of logging variables by formulating a new refinement prompt for language models, which incorporates detailed type information of variables in the tentative logging statement. The evaluation results show that SCLogger surpasses the state-of-the-art approach by 8.7% in logging position accuracy, 32.1% in level accuracy, 19.6% in variable precision, and 138.4% in text BLEU-4 score. Furthermore, SCLogger consistently boosts the performance of logging statement generation across a range of large language models, thereby showcasing the generalizability of this approach.Comment: This paper was accepted by The ACM International Conference on the Foundations of Software Engineering (FSE 2024

Knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid Approach

Due to the scale and complexity of cloud systems, a system failure would trigger an "alert storm", i.e., massive correlated alerts. Although these alerts can be traced back to a few root causes, the overwhelming number makes it infeasible for manual handling. Alert aggregation is thus critical to help engineers concentrate on the root cause and facilitate failure resolution. Existing methods typically utilize semantic similarity-based methods or statistical methods to aggregate alerts. However, semantic similarity-based methods overlook the causal rationale of alerts, while statistical methods can hardly handle infrequent alerts. To tackle these limitations, we introduce leveraging external knowledge, i.e., Standard Operation Procedure (SOP) of alerts as a supplement. We propose COLA, a novel hybrid approach based on correlation mining and LLM (Large Language Model) reasoning for online alert aggregation. The correlation mining module effectively captures the temporal and spatial relations between alerts, measuring their correlations in an efficient manner. Subsequently, only uncertain pairs with low confidence are forwarded to the LLM reasoning module for detailed analysis. This hybrid design harnesses both statistical evidence for frequent alerts and the reasoning capabilities of computationally intensive LLMs, ensuring the overall efficiency of COLA in handling large volumes of alerts in practical scenarios. We evaluate COLA on three datasets collected from the production environment of a large-scale cloud platform. The experimental results show COLA achieves F1-scores from 0.901 to 0.930, outperforming state-of-the-art methods and achieving comparable efficiency. We also share our experience in deploying COLA in our real-world cloud system, Cloud X.Comment: Accepted by Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice (ICSE SEIP 2024

A DNA 3′ Phosphatase Functions in Active DNA Demethylation in Arabidopsis

DNA methylation is an important epigenetic mark established by the combined actions of methylation and demethylation reactions. Plants use a base excision repair pathway for active DNA demethylation. After 5-methylcytosine removal, the Arabidopsis DNA glycosylase/lyase ROS1 incises the DNA backbone and part of the product has a single-nucleotide gap flanked by 3′- and 5′-phosphate termini. Here we show that the DNA phosphatase ZDP removes the blocking 3′ phosphate, allowing subsequent DNA polymerization and ligation steps needed to complete the repair reactions. ZDP and ROS1 interact in vitro and colocalize in vivo in nucleoplasmic foci. Extracts from zdp mutant plants are unable to complete DNA demethylation in vitro, and the mutations cause DNA hypermethylation and transcriptional silencing of a reporter gene. Genome-wide methylation analysis in zdp mutant plants identified hundreds of hypermethylated endogenous loci. Our results show that ZDP functions downstream of ROS1 in one branch of the active DNA demethylation pathway