Structural equation modeling (SEM) is a multivariate statistical technique for estimating complex relationships between observed and latent variables. Although numerous SEM packages currently exist, they each have limitations and more importantly they are not free or open-source. The only package that is both free and open-source is lavaan. However, because this package is written in R, it is often difficult to integrate it with other programming language functionalities. This paper provides an overview of a new Python package called semopy that was specifically developed to overcome these limitations. Using illustrative examples we introduce the new package and then compare its performance in accuracy and execution time to lavaan.

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.

SUMMARY: Gene expression is regulated by transcription factors (TFs), which recognize specific DNA sequence motifs. Several hundred putative human TFs, identified mainly by an apparent DNA-binding domain, lack known binding motifs ¹ , and even for well-characterized TFs, it remains controversial to what degree motifs accurately reflect binding sites in living cells ^2,3 . Here, we describe a systematic effort (“Codebook”) to determine the sequence specificity of 332 putative and poorly characterized human TFs. Over 4,000 independent experiments, encompassing multiple in vitro and in vivo assays, produced motifs for just over half (177, or 53%), of which most are unique to a single protein, thereby extending the vocabulary of sequence recognition encoded by human TFs by ∼100 distinct motifs. Moreover, binding motifs identified in vitro are strongly enriched within cellular binding sites. Collectively, the data reveal tens of thousands of previously unknown, conserved, and direct TF binding sites across the human genome. These sites are concentrated in promoter regions, and are predictive of gene expression, illustrating that this new data atlas provides an important step forward in decoding the human genome.

Gene expression is regulated by transcription factors (TFs), which recognize specific DNA sequence motifs. Several hundred putative human TFs, identified mainly by an apparent DNA-binding domain, lack known binding motifs ¹ . Furthermore, even for well-characterized TFs, it remains controversial the degree to which motifs accurately reflect binding sites in living cells ² . Here we describe a systematic effort (‘Codebook’) to determine the sequence specificity of 332 putative and poorly characterized human TFs. More than 4,000 independent experiments, encompassing multiple in vitro and in vivo assays, produced motifs for just over half (177; 53%) of the TFs, of which most are associated with only a single protein. These results extend the vocabulary of sequence recognition encoded by human TFs by around 130 distinct motifs. Moreover, binding motifs identified in vitro are strongly enriched in cellular binding sites. Collectively, the data reveal tens of thousands of previously unknown, conserved and direct TF-binding sites across the human genome. These sites are concentrated in promoter regions and are predictive of gene expression. In summary, this new codebook provides an important step forward in decoding the human genome.

Motivation: The increasing volume of data from high-throughput experiments including parallel reporter assays facilitates the development of complex deep learning approaches for 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 the Python code under the MIT license to reproduce the results presented in the study and a Jupyter Notebook tutorial. Supplementary Information: Online-only supplementary data are available at Bioinformatics online. Contact: dmitrypenzar1996@gmail.com , ivan.kulakovskiy@gmail.com

High-throughput sequencing facilitates large-scale studies of gene regulation and allows tracing the associations of individual genomic variants with changes in gene expression. Compared to classic association studies, allelic imbalance at heterozygous variants captures the functional effects of the regulatory genome variation with smaller sample sizes and higher sensitivity. Yet, the identification of allele-specific events from allelic read counts remains non-trivial due to multiple sources of technical and biological variability, which induce data-dependent biases and overdispersion. Here we present MIXALIME, a novel computational framework for calling allele-specific events in diverse omics data with a repertoire of statistical models accounting for read mapping bias and copy-number variation. We benchmark MIXALIME against existing tools and demonstrate its practical usage by constructing an atlas of allele-specific chromatin accessibility, UDACHA, from thousands of available datasets obtained from diverse cell types. Availability: https://github.com/autosome-ru/MixALime , https://udacha.autosome.org