Biology subjects

Belyaeva, A.

Publications and source records attributed to Belyaeva, A..

3 recordsLinked to original sources

bioRxiv2022

Best: A Tool for Characterizing Sequencing Errors

SummaryPlatform-dependent sequencing errors must be understood to develop accurate sequencing technologies. We propose a new tool, best (Bam Error Stats Tool), for efficiently quantifying and summarizing error types in sequenced reads. best ingests reads aligned to a high-quality reference assembly and produces per-read metrics, summary statistics, and stratified metrics across genomic intervals. We show that best is 16 times faster than a prior method. In addition to being useful to support development that improves the accuracy of sequencing platforms, best can also be applied to evaluate and improve other experimental factors such as library preparation and error correction methods. Availability and implementationbest is an open-source command-line utility available on Github (github.com/google/best) under an MIT license. Contactdanielecook@google.com

bioinformatics↗

bioRxiv2020

DCI: Learning Causal Differences between Gene Regulatory Networks

SummaryDesigning interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale expression datasets from different conditions, cell types, disease states and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we propose an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e., edges that appeared, disappeared or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to bulk and single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions. Availability and implementationAll algorithms are freely available as a Python package at http://uhlerlab.github.io/causaldag/dci Contactcuhler@mit.edu

bioinformatics↗

bioRxiv2019

Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders

The development of single-cell methods for capturing different data modalities including imaging and sequencing have revolutionized our ability to identify heterogeneous cell states. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct sub-populations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery.

bioinformatics↗