Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit.

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

Development of a genetic risk score for obesity predisposition evaluation.

Obesity is a major public health issue resulting from an interaction between genetic and environmental factors. Genetic risk scores (GRSs) are useful to summarize the effects of many genetic variants on obesity risk. In this study, we aimed to assess the association of previously well-studied genetic variants with obesity and develop a genetic risk score to anticipate the risk of obesity development in the Iranian population. Among 968 participants, 599 (61.88%) were obese, and 369 (38.12%) were considered control samples. After genotyping, an initial screening of 16 variants associated with body mass index (BMI) was performed utilizing a general linear model (p < 0.25), and seven genetic variants were selected. The association of these variants with obesity was examined using a multivariate logistic regression model (p < 0.05), and finally, five variants were found to be significantly associated with obesity. Two gene score models (weighted and unweighted), including these five loci, were constructed. To compare the discriminative power of the models, the area under the curve was calculated using tenfold internal cross-validation. Among the studied variants, ADRB3 rs4994, FTO rs9939609, ADRB2 rs1042714, IL6 rs1800795, and MTHFR rs1801133 polymorphisms were significantly associated with obesity in the Iranian population. Both of the constructed models were significantly associated with BMI (p < 0.05) and the area under the mean curve of the weighted GRS and unweighted GRS were 70.22% ± 0.05 and 70.19% ± 0.05, respectively. Both GRSs proved to predict obesity and could potentially be utilized as genetic tools to assess the obesity predisposition in the Iranian population. Also, among the studied variants, ADRB3 rs4994 and FTO rs9939609 polymorphisms have the highest impacts on the risk of obesity.

Genome-Wide Expression Analysis of Root Tips in Contrasting Rice Genotypes Revealed Novel Candidate Genes for Water Stress Adaptation.

Root system architecture (RSA) is an important agronomic trait with vital roles in plant productivity under water stress conditions. A deep and branched root system may help plants to avoid water stress by enabling them to acquire more water and nutrient resources. Nevertheless, our knowledge of the genetics and molecular control mechanisms of RSA is still relatively limited. In this study, we analyzed the transcriptome response of root tips to water stress in two well-known genotypes of rice: IR64, a high-yielding lowland genotype, which represents a drought-susceptible and shallow-rooting genotype; and Azucena, a traditional, upland, drought-tolerant and deep-rooting genotype. We collected samples from three zones (Z) of root tip: two consecutive 5 mm sections (Z1 and Z2) and the following next 10 mm section (Z3), which mainly includes meristematic and maturation regions. Our results showed that Z1 of Azucena was enriched for genes involved in cell cycle and division and root growth and development whereas in IR64 root, responses to oxidative stress were strongly enriched. While the expansion of the lateral root system was used as a strategy by both genotypes when facing water shortage, it was more pronounced in Azucena. Our results also suggested that by enhancing meristematic cell wall thickening for insulation purposes as a means of confronting stress, the sensitive IR64 genotype may have reduced its capacity for root elongation to extract water from deeper layers of the soil. Furthermore, several members of gene families such as NAC, AP2/ERF, AUX/IAA, EXPANSIN, WRKY, and MYB emerged as main players in RSA and drought adaptation. We also found that HSP and HSF gene families participated in oxidative stress inhibition in IR64 root tip. Meta-quantitative trait loci (QTL) analysis revealed that 288 differentially expressed genes were colocalized with RSA QTLs previously reported under drought and normal conditions. This finding warrants further research into their possible roles in drought adaptation. Overall, our analyses presented several major molecular differences between Azucena and IR64, which may partly explain their differential root growth responses to water stress. It appears that Azucena avoided water stress through enhancing growth and root exploration to access water, whereas IR64 might mainly rely on cell insulation to maintain water and antioxidant system to withstand stress. We identified a large number of novel RSA and drought associated candidate genes, which should encourage further exploration of their potential to enhance drought adaptation in rice.