Computational and Systems Biology

eLife assessment

Understanding how genomic regulatory elements interact to control spatiotemporal gene expression is essential to explaining cell type diversification, function, and delineating genetic variation and disease. In this important study, the authors provide solid evidence showing that, in general, enhancers influence gene expression in an additive way. The findings contribute to ongoing discussions about the selectivity and combination of regulatory elements. Improved clarity regarding the statistical analysis, computational methods, and definitions used would strengthen the conclusions.

eLife assessment

This study presents useful insights into core genome mutations that could have contributed to the emergence of the Staphylococcus aureus lineage USA300, a frequent cause of community-acquired infections. The solid approach used is innovative in combining genome-wide association studies and RNA-expression analyses, both applied to extensive publicly available datasets. This strategy reduces the rate of false positives attributed to high genome-wide linkage disequilibrium. It is noted that this method cannot be used for most phenotype-genotype studies, especially those requiring essential population structure correction, and it can therefore not be readily replicated in different datasets.

eLife assessment

This manuscript describes an important web resource for kinases connected to cytokines. The compelling information will be used by researchers across a number of fields including analysts, modelers, wet lab experimentalists and clinician-researchers, who are looking to improve our understanding of pathologies and means to correct them through modulating the immune response.

eLife assessment

This important study addresses the problem of detecting weak similarity between protein sequences, a procedure commonly used to infer homology or assign putative functions to uncharacterized proteins. The authors present a convincing approach that combines recently developed protein language models with well-established methods. The benchmarks provided show that the proposed tool is fast and accurate for remote homology detection, making this paper of general interest to all researchers working in the fields of protein evolution and genome annotation.

eLife assessment

This paper presents H3-OPT, a deep learning method that effectively combines existing techniques for the prediction of antibody structure. This work is important because the method can aid in the design of antibodies, which are key tools in many research and industrial applications. The experiments for validation are convincing, but some further statistical evaluation would be helpful for the readers.

eLife assessment

Tissue phenotyping is central to nearly all areas of biology. In this study, the authors use an advanced form of micro-CT (X-ray histotomography) in zebrafish to phenotype blood cells in the intact animal. These approaches build upon prior work from this group and others showing this is a scalable imaging method that could readily be applied to other cell types, and provide an excellent complement to histological analysis of tissues. This is important work, as it demonstrates that the method can provide an approach that is orthogonal to conventional histology. The strength of the presented data is compelling, with description of both the hardware and software needed to implement the protocol, which will make it accessible to other researchers in the field.

eLife assessment

This study provides valuable insight into the role of miR-199a/b-5p in cartilage formation. The evidence supporting the significance of the identified miRNA and its target mRNA transcripts is convincing, however further experiments and a broader contextual analysis are warranted to draw a more robust conclusion. This paper will likely primarily benefit scientists focused on diseases related to this biological process, such as osteoarthritis. Furthermore, researchers interested in miRNAs as a broader subject may find the computational model development methodology useful.

eLife assessment

This study presents valuable findings that examine both how Down syndrome (DS)-related physiological, behavioral, and phenotypic traits track across time, as well as how chronic treatment with green tea extracts 25 enriched in epigallocatechin-3-gallate (GTE-EGCG), administered in drinking water spanning prenatal through 5 months of age, impacts these measures in wild-type and Ts65Dn mice. The strength of the evidence is solid, due to high variability across measures, perhaps in part attributable to a failure to include sex as a factor for measures known to be sexually dimorphic. This study is of interest to scientists interested in Down Syndrome and its treatment, as well as scientists who study disorders that impact multiple organ systems.

eLife assessment

This important study introduces a novel AI method for the analysis of published data, with practical implications for early cancer diagnosis. The results are supported by compelling evidence.

eLife assessment

The presented study introduces a valuable non-AI computational method for segmenting noisy grayscale images, particularly highlighting its applicability in identifying immunostained potassium ion channel clusters. While the method's avoidance of AI training appeals to those lacking computational know-how and shows improved accuracy over basic threshold-based techniques, there are valid concerns regarding its performance in comparison to advanced methodologies. The evidence supporting the method's efficacy is solid but incomplete, necessitating comparisons to more advanced techniques and the provision of user-friendly computational tools for a comprehensive evaluation.

eLife assessment

This valuable study advances our understanding of spatial-temporal cell dynamics both in vivo and in vitro. The authors provide solid evidence for their innovative deep learning-based apoptosis detection system, ADeS, which utilizes the principle of activity recognition. This work will be of broad interest to cell biologists and neuroscientists.

eLife assessment

The study presented in this manuscript makes important contributions to our understanding of cell fate decisions and the role of noise in gene regulatory networks. Through computational and theoretical analysis, the authors provide solid support for distinguishing distinct driving forces behind fate decisions based on noise profiles and reprogramming trajectories. While acknowledging the potential limitations of small gene regulatory networks in capturing the richness of whole-transcriptome sequencing datasets, this study offers a creative approach for formulating hypotheses about gene regulation during stem cell differentiation using single-cell sequencing data.

eLife assessment

The authors developed a valuable mathematical model that describes the spatiotemporal dynamics of cells in the intestinal crypt. The proposed model makes an important contribution to the field, allowing a better understanding of the formation and response dynamics of the intestinal crypt through the effective evaluation of health, disease, and treatment conditions. The authors provided solid evidence of the validity of their model and their conclusions, but some minor claims are not properly justified in the current manuscript. This paper is meant for computational biologists and cancer researchers working on oncotherapies for the intestinal epithelium.

eLife assessment

The study presents a valuable tool for searching molecular dynamics simulation data, making such data sets accessible for open science. The authors provide convincing evidence that it is possible to identify useful molecular dynamics simulation data sets and their analysis can produce valuable information.

eLife assessment

This fundamental study provides a unique tool for assessing the range of phosphorylation in insulin reactions due to genetic variation and dietary influence through the utilization of genetically distinct mouse strains. The discoveries of this study hold substantial importance, as they shed light on the interplay between genetic attributes and environmental conditions in shaping the insulin-signaling network within skeletal muscle, a crucial regulator of metabolism. The supporting evidence presented is compelling, and the work is anticipated to captivate a wide audience within the metabolism discipline due to its extensive appeal and by providing inspiration for further hypothesis-driven research.

eLife assessment

The authors have made important contributions to our understanding of the pathogenesis of erectile dysfunction (ED) in diabetic patients. They have identified the gene Lbh, expressed in pericytes of the penis and decreased in diabetic animals. Overexpression of Lbh appears to counteract ED in these animals. The authors also confirm Lbh as a potential marker in cavernous tissues in both humans and mice. While solid evidence supports Lbh's functional role as a marker gene, further research is needed to elucidate the specific mechanisms by which it exerts its effects. This work is of interest to those working in the fields of ED and angiogenesis.

eLife assessment

This important study deepens our understanding of macrophage phenotypes in pathological contexts and identifies a new macrophage state associated with tissue fibrosis, as well as putative drivers of this cellular state. The authors provide convincing evidence and performed a well-thought-out and thoroughly described computational analysis of single-cell RNA-sequencing data. This work will be of broad interest to the fields of tissue inflammation, fibrosis, macrophage biology, and immunology.

eLife assessment

This valuable study presents a screening pipeline (SPICE) for detecting DNA motif spacing preferences between TF partners. SPICE predicts previously known composite elements, but experiments to elucidate the nature of the predicted novel interaction between JUN and IKZF1 are incomplete. These experiments would benefit from more rigorous approaches using other databases to explore additional relevant data. The work will be of broad interest to those involved in dissecting the regulatory logic of mammalian enhancers and promoters.

eLife assessment

This valuable study reports novel active learning batch selection methods that have been applied to optimization tasks related to ADMET and affinity properties relevant within the drug discovery field. While the evidence is solid, the paper could have benefited from a clearer and deeper description of methods as well as interpretation of the obtained models, and a wider comparison to existing methods. The article will be of general interest to scientist working in the field of drug discovery and, in general, to researchers within the fields of machine learning and data analysis.

eLife assessment

This manuscript describes a model to estimate what fraction of DNA from specific human tissues becomes cell-free DNA in plasma. This fundamental study, supported by convincing evidence, will be of great interest to the community, as the amount of DNA from a certain tissue (for example, a tumor) that becomes available for detection in the blood has significant implications for disease detection.