Biology subjects

Kartashov, A.

Publications and source records attributed to Kartashov, A..

3 recordsLinked to original sources

bioRxiv2024

Accelerating Single-Cell Sequencing Data Analysis with SciDAP: A User-Friendly Approach

Single-cell (sc) RNA, ATAC and Multiome sequencing became powerful tools for uncovering biological and disease mechanisms. Unfortunately, manual analysis of sc data presents multiple challenges due to large data volumes and complexity of configuration parameters. This complexity, as well as not being able to reproduce a computational environment, affects the reproducibility of analysis results. The Scientific Data Analysis Platform (https://SciDAP.com) allows biologists without computational expertise to analyze sequencing-based data using portable and reproducible pipelines written in Common Workflow Language (CWL). Our suite of computational pipelines addresses the most common needs in scRNA-Seq, scATAC-Seq and scMultiome data analysis. When executed on SciDAP, it offers a user-friendly alternative to manual data processing, eliminating the need for coding expertise. In this protocol, we describe the use of SciDAP to analyze scMultiome data. Similar approaches can be used for analysis of scRNA-Seq, scATAC-Seq and scVDJ-Seq datasets.

bioinformatics↗

bioRxiv2019

Co-Stimulation-Induced AP-1 Activity is Required for Chromatin Opening During T Cell Activation

Activation of T cells is dependent on organized and timely opening and closing of chromatin. Herein, we identify AP-1 as the transcription factor that directs most of this remodeling. Chromatin accessibility profiling showed quick opening of closed chromatin in naive T cells within 5 hours of activation. These newly open regions were strongly enriched for the AP-1 motif, and indeed, ChIP-seq demonstrated AP-1 binding at more than 70% of them. Broad inhibition of AP-1 activity prevented chromatin opening at AP-1 sites and reduced expression of nearby genes. Similarly, induction of anergy in the absence of co-stimulation during activation, was associated with reduced induction of AP-1 and a failure of proper chromatin remodeling. The translational relevance of these findings was highlighted by the substantial overlap of AP-1-dependent elements with risk loci for multiple immune diseases, including multiple sclerosis, inflammatory bowel disease and allergic disease. Our findings define AP-1 as the key link between T cell activation and chromatin remodeling.

immunology↗

bioRxiv2018

CWL-Airflow: a lightweight pipeline manager supporting Common Workflow Language

BackgroundMassive growth in the amount of research data and computational analysis has led to increased utilization of pipeline managers in biomedical computational research. However, each of more than 100 such managers uses its own way to describe pipelines, leading to difficulty porting workflows to different environments and therefore poor reproducibility of computational studies. For this reason, the Common Workflow Language (CWL) was recently introduced as a specification for platform-independent workflow description, and work began to transition existing pipelines and workflow managers to CWL.\n\nFindingsHere, we present CWL-Airflow, an extension for the Apache Airflow pipeline manager supporting CWL. CWL-Airflow utilizes CWL v1.0 specification and can be used to run workflows on standalone MacOS/Linux servers, on clusters, or on a variety of cloud platforms. A sample CWL pipeline for processing of ChIP-Seq data is provided.\n\nConclusionsCWL-Airflow will provide users with the features of a fully-fledged pipeline manager and an ability to execute CWL workflows anywhere Airflow can run--from a laptop to cluster or cloud environment.\n\nAvailabilityCWL-Airflow is available under Apache license v.2 and can be downloaded from https://barski-lab.github.io/cwl-airflow, http://doi.org/10.5281/zenodo.2669582, RRID: SCR_017196.

bioinformatics↗