Biology subjects

Cibulskis, K.

Publications and source records attributed to Cibulskis, K..

4 recordsLinked to original sources

bioRxiv2025

Accelerating scRNA-seq analysis: automated cell type annotation using representation learning and vector search

Cell type annotation in single-cell RNA sequencing (scRNA-seq) experiments is the fundamental step of assigning cell types to individual cells or clusters of cells based on their gene expression profiles. This process is crucial for developing biological insights from scRNA-seq experiments. We present a service that automates cell type annotation for 10x Genomics single-cell gene expression samples, enabling researchers to rapidly and accurately categorize cells within a sample. This service operates on the basis of reverse search: it compares each cells gene expression profile against the Chan Zuckerberg CELL by GENE (CZ CELLxGENE) Census, a comprehensive repository of published scRNA-seq datasets enriched with community-annotated cell types, and yields cell type annotations through summarizing the labels associated with similar cells. The annotation algorithm employed in this service avoids reliance on predefined marker genes or tissue-specific references, providing both fine-grained and coarse annotations. These initial annotations can be further refined by investigators to suit their specific research needs.

bioinformatics↗

bioRxiv2019

Calling Somatic SNVs and Indels with Mutect2

Mutect2 is a somatic variant caller that uses local assembly and realignment to detect SNVs and indels. Assembly implies whole haplotypes and read pairs, rather than single bases, as the atomic units of biological variation and sequencing evidence, improving variant calling. Beyond local assembly and alignment, Mutect2 is based on several probabilistic models for genotyping and filtering that work well with and without a matched normal sample and for all sequencing depths.

bioinformatics↗

bioRxiv2019

Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes critical for an organisms function will be depleted for such variants in natural populations, while non-essential genes will tolerate their accumulation. However, predicted loss-of-function (pLoF) variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here, we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence pLoF variants in this cohort after filtering for sequencing and annotation artifacts. Using an improved human mutation rate model, we classify human protein-coding genes along a spectrum representing tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve gene discovery power for both common and rare diseases.

genomics↗

bioRxiv2017

FireCloud, a scalable cloud-based platform for collaborative genome analysis: Strategies for reducing and controlling costs

FireCloud, one of three NCI Cloud Pilots, is a collaborative genome analysis platform built on a cloud computing infrastructure. FireCloud aims to solve the many challenges presented by the increasingly large data sets and computing requirements employed in cancer research. However, cost uncertainty associated with cloud computings pay-as-you-go model is proving to be a barrier to adoption of cloud computing. In this paper we present guidelines for optimizing workflows to minimize cost and reduce latency. Our guidelines include: (i) dynamic disk sizing to efficiently utilize virtual disks; (ii) tuned provisioning of virtual machines (VMs) using a performance monitoring tool; (iii) taking advantage of steep price discounts of preemptible VMs; and (iv) utilizing the optimal parallelization of a tasks workload.

bioinformatics↗