Search bioRxiv⌕ Search

bioRxiv · 10.1101/2022.04.19.488737

SiCoDEA: a simple, fast and complete app for analyzing the effect of individual drugs and their combinations

Abstract

The administration of combinations of drugs is a method widely used in the treatment of different pathologies as it can lead to an increase in the therapeutic effect and a reduction in the dose compared to the administration of the single drugs. For these reasons, it is of interest to study combinations of drugs and in particular to determine whether a specific combination has a synergistic, antagonistic or additive effect, i.e greater, less than or equal to the effect expected by the sum of the individual drugs. For this purpose, various mathematical models have been developed, which use different methods to evaluate the synergy of a combination of drugs. Most of these methods are based on the Loewe Additivity Principle (Loewe et al., 1953), which has its key step in choosing the model used for predicting the effect of individual drugs. Creating a model for this purpose and calculating its parameters, however, requires a certain level of mathematical and programming knowledge or the use of commercial software. For this purpose, therefore, we have developed an open access and easy to use app that allows to explore different models and to choose the most fitting for the specific experimental data: SiCoDEA (Single and Combined Drug Effect Analysis, https://sicodea.shinyapps.io/shiny/). The data used to test SiCoDEA comes from cell line samples treated with different drug combinations and analyzed through metabolic or viability assays. SiCoDEA is developed through a Shiny interactive and easy-to-use interface (R based). There are five models taken into consideration for the analysis of single drugs and the calculation of combination index. The first is one of the most used, that of the median-effect; while the others are different forms of the log-logistic equation, with two, three and four parameters. The purpose of SiCoDEA is, on the one hand, to provide an easy-to-use tool for analyzing drug combination data and, on the other hand, also to have a view of the various steps and to offer different results based on the model chosen. An important prerequisite in analyzing drug combinations is in fact the dose-response curve calculated for individual drugs. For this purpose, SiCoDEA allows you to view the plots of the individual drugs both to evaluate the distribution of the calculated points and therefore identify any outliers, and to view the curve of the different models taken into consideration and evaluate which one best fits the data. A table showing all the R2 values for the five different models is created with the curve plot. In addition to the type of model, it is also possible to choose between two different normalization methods, one based on the maximum or minimum value and the other on the value calculated at drug concentrations equal to zero. For the chosen options, a plot is then created that shows the trend of the combination index for the different drug combinations and consequently whether it is synergy, antagonism or additivity. Finally, it is possible to export the results in single png files or in a summary report in pdf. SiCoDEA is an open-source app among the most complete and offers more functions even than the famous CompuSyn (Chou et al., 2010), as it allows you to analyze drug curves with different models, rather than just one, and it also allows the analysis of single drug curves.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Spinozzi, G., Tini, V., Ferrari, A., Gionfriddo, I., Ranieri, R., Milano, F., Pierangeli, S., Donnini, S., Silvestri, S., Falini, B., Martelli, M. P.. 2022-04-19. SiCoDEA: a simple, fast and complete app for analyzing the effect of individual drugs and their combinations. https://doi.org/10.1101/2022.04.19.488737

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

CellChat Hotspot: A Targeted Approach to Cell Cell Communication Analysis With Spatial transcriptomics Validation.

Abstract. In recent years, single cell RNA sequencing (scRNA seq) has enabled the investigation of intercellular communication through Cell Cell Communication (CCC) tools such as CellChat. Despite their potential, these approaches remain challenging to interpret, particularly when global analyses generate complex interaction networks that may include biologically irrelevant signals. To address this limitation, we developed a framework termed CellChat Hotspot, designed to improve the biological interpretability of inferred communication networks. This approach integrates gene set enrichment analysis with a context driven filtering strategy, selecting both genes and cells based on their functional relevance prior to communication inference. Applied to stage II colon cancer data, CellChat Hotspot enabled the identification of condition-specific interaction patterns associated with recurrence. Non recurrent patients showed enrichment of immune related processes, whereas recurrent cases were characterized by pathways associated with invasion and metastasis. Notably, a loss of interaction in the HSPG pathway between CALB1+ and stromal cells was observed in recurrent patients, suggesting a potential disruption of cell matrix adhesion mechanisms. To verify the reliability of this finding, we applied the framework to spatial transcriptomics data, which confirmed the presence of the HSPG2 DAG1 interaction in non-recurrent tumors and its near absence in recurrent ones. Overall, this framework provides a biologically guided strategy for studying intercellular communication in complex systems such as the tumor microenvironment.

bioinformatics↗

SIFT: Syncmer-Informed Selection of Haplotypes for T2T Imputation

Genome patching improves the completeness of fragmented assemblies by filling unresolved gaps with sequences from reference genomes. Pangenome panels provide diverse candidate haplotypes for this task, but identifying suitable references efficiently becomes increasingly challenging as these panels grow. We present SIFT, a compressed indexing method for selecting reference haplotypes for genome patching with ImpuT2T. SIFT combines closed-syncmer parsing, phrase-level indexing, and pseudo-maximal exact match (pseudo-MEM) queries. Closed syncmers provide a context-independent parsing rule with bounded distances between consecutive selected positions, enabling consistent parsing of reference and query sequences. We demonstrate that two closed syncmers determine the distance at which they can occur consecutively in any string, so that equal substrings of two parses represent identical sequences and no gap sequence need be stored. SIFT indexes the reference parse and its reverse using run-length compressed suffix arrays and adapts the Boyer-Moore-Li search procedure to identify pseudo-MEMs between the reference parse and query parse. It then aggregates matching evidence to identify candidate reference haplotypes for downstream patching. This framework connects compressed sequence indexing with haplotype selection and provides a basis for querying large reference panels at the phrase level. SIFT is publicly available under the GNU license at https://github.com/EddieFerro/SIFT.

bioinformatics↗

Assessing the Capacity of Genomic Language Models to Generate Informative Embeddings of Viral Genome Fragments

Genomic language models (gLMs) trained on large volumes of nucleotide sequences are becoming an alternative to classical methods in metagenomic analysis, particularly for studying genomic "dark matter". In this work, we investigate the ability of four gLMs (DNABERT-2, DNABERT-S, ViraLM, and Evo2-7B) to generate taxonomically informative vector representations of short viral genome fragments. We constructed a balanced reference dataset of viruses based on the ICTV Virus Metadata Resource and compared the representations generated by gLMs with conventional k-mer profiles and MinHash sketches using clustering and neighborhood-based metrics (adjusted Rand index (ARI), precision@k, and CROSS precision@k). On the reference dataset, three of the four gLMs outperformed the 4-mer profile in terms of ARI and precision@5 under the same evaluation protocol. The best performance was generally achieved by intermediate model layers. Conventional k-mer profiles consistently outperformed MinHash sketches. We further evaluated the methods on two viral orders (egoviruses and mriyaviruses) absent from the reference dataset, with model layers and k-mer lengths selected exclusively on the reference dataset. DNABERT-S showed the most consistently strong performance across these two orders, ranking first on mriyaviruses and second on egoviruses, while k-mer profiles with Jensen-Shannon distance remained competitive. We also observed the consistency of representations between forward fragments and their reverse complements. Overall, the best-performing gLMs surpassed conventional baselines, although their advantage depended on the dataset and evaluation metric. The consistently strong performance of DNABERT-S suggests a potential benefit of contrastive fine-tuning and indicates that larger models are not necessarily more effective for generating taxonomically informative representations.

bioinformatics↗