Regulatory activity is the default DNA state in eukaryotes.

Genomes encode for genes and non-coding DNA, both capable of transcriptional activity. However, unlike canonical genes, many transcripts from non-coding DNA have limited evidence of conservation or function. Here, to determine how much biological noise is expected from non-genic sequences, we quantify the regulatory activity of evolutionarily naive DNA using RNA-seq in yeast and computational predictions in humans. In yeast, more than 99% of naive DNA bases were transcribed. Unlike the evolved transcriptome, naive transcripts frequently overlapped with opposite sense transcripts, suggesting selection favored coherent gene structures in the yeast genome. In humans, regulation-associated chromatin activity is predicted to be common in naive dinucleotide-content-matched randomized DNA. Here, naive and evolved DNA have similar co-occurrence and cell-type specificity of chromatin marks, challenging these as indicators of selection. However, in both yeast and humans, extreme high activities were rare in naive DNA, suggesting they result from selection. Overall, basal regulatory activity seems to be the default, which selection can hone to evolve a function or, if detrimental, repress.

Evaluation and optimization of sequence-based gene regulatory deep learning models.

Neural networks have emerged as immensely powerful tools in predicting functional genomic regions, notably evidenced by recent successes in deciphering gene regulatory logic. However, a systematic evaluation of how model architectures and training strategies impact genomics model performance is lacking. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast, to best capture the relationship between regulatory DNA and gene expression. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. While some benchmarks produced similar results across the top-performing models, others differed substantially. All top-performing models used neural networks, but diverged in architectures and novel training strategies, tailored to genomics sequence data. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide any given model into logically equivalent building blocks. We tested all possible combinations for the top three models and observed performance improvements for each. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets. Overall, we demonstrate that high-quality gold-standard genomics datasets can drive significant progress in model development.

LegNet: a best-in-class deep learning model for short DNA regulatory regions.

MOTIVATION: The increasing volume of data from high-throughput experiments including parallel reporter assays facilitates the development of complex deep-learning approaches for modeling DNA regulatory grammar. RESULTS: Here, we introduce LegNet, an EfficientNetV2-inspired convolutional network for modeling short gene regulatory regions. By approaching the sequence-to-expression regression problem as a soft classification task, LegNet secured first place for the autosome.org team in the DREAM 2022 challenge of predicting gene expression from gigantic parallel reporter assays. Using published data, here, we demonstrate that LegNet outperforms existing models and accurately predicts gene expression per se as well as the effects of single-nucleotide variants. Furthermore, we show how LegNet can be used in a diffusion network manner for the rational design of promoter sequences yielding the desired expression level. AVAILABILITY AND IMPLEMENTATION: https://github.com/autosome-ru/LegNet. The GitHub repository includes Jupyter Notebook tutorials and Python scripts under the MIT license to reproduce the results presented in the study.

GIL: a python package for designing custom indexing primers.

SUMMARY: Generate Indexes for Libraries (GIL) is a software tool for generating primers to be used in the production of multiplexed sequencing libraries. GIL can be customized in numerous ways to meet user specifications, including length, sequencing modality, color balancing, and compatibility with existing primers, and produces ordering and demultiplexing-ready outputs. AVAILABILITY AND IMPLEMENTATION: GIL is written in Python and is freely available on GitHub under the MIT license: https://github.com/de-Boer-Lab/GIL and can be accessed as a web-application implemented in Streamlit at https://dbl-gil.streamlitapp.com.

Acetylation Phenotype Variation in Patients with Allergic Contact Dermatitis.

BACKGROUND: Studies have been done on acetylation phenotype in different diseases but not with allergic contact dermatitis (ACD). PATIENTS AND METHODS: Thirty-five ACD patients and 67 healthy volunteers were enrolled in the study. After an overnight fast, each participant received a single oral dose of 100 mg of dapsone. Three hours later, a blood sample was taken from each participant and plasma was separated to determine dapsone and its metabolite (monoacetyldapsone) using high-performance liquid chromatography. RESULTS: Nearly 60% of the patients (21 out of 35 ACD patients) were slow acetylators compared with 72% (48 out of 67 controls), and a statistically significant difference was found. No association between the acetylator status and personal/family history of allergy, patch test positivity, and even the sites of ACD was detected. In the ACD patients with slow acetylators, the lesions of dermatitis presented mainly on the face, neck, and upper limbs, while those with rapid acetylator have predominant lesions in the chest, oral region, and axillae. CONCLUSION: A rapid acetylator status might predispose to ACD without influencing other features of this disease.

Soil solarization: a safe, affective and practicable technique for the control of soil born fungi and nematodes.

A technique i.e., Soil Solarization and Amendments (neem, chicken farmyard manure, farmyard manure and biokhad viz synthetic bio fertilizer), towards the natural cropping system has been evaluated for its effectiveness and practicability at the National Agricultural Research Center Islamabad Pakistan. Soil solarization and amendments were analyzed as a control measure against soil born fungi and nematodes. Eight weeks of solarization resulted in about 11 degrees C increase in the soil temperature. This increase in soil temperature caused a reduction of about 70 to 80% in the fungal population and about 99% in nematode population at various depths. Neem and Biokhad amendments were proved synergistic for solarization and also improved the properties of soil in the benefit of crop plants. Fusarium sp., Macrophomina phyaseolina and Verticillium sp. of fungi and Tylenchus sp., Haplolaimus sp., Xiphenema sp. and almost all of the parasitic nematodes were significantly (p<0.01) controlled. It was found that even after 40 days the solarized plots contain significantly less number of fungi and nematodes as compared to the nonsolarized plots, which confirmed the durability of this process.