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Christian Dillard

dblp:370/5806 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
genomics
0.712023
SCExecute: custom cell barcode-stratified analyses of scRNA-seq data · Bioinform. 2023
Bioinformatics and computational biology › single-cell analysis › single-cell RNA sequencing
single-cell RNA-seq analysis
0.712023
SCExecute: custom cell barcode-stratified analyses of scRNA-seq data · Bioinform. 2023
Bioinformatics and computational biology › sequence analysis › sequence variation analysis
single nucleotide variant detection
0.212023
SCExecute: custom cell barcode-stratified analyses of scRNA-seq data · Bioinform. 2023

Methods — techniques the papers use, named apart from their topics

variant calling · 0.7barcode stratification · 0.7
YearPublicationVenuePosition
2023 SCExecute: custom cell barcode-stratified analyses of scRNA-seq data
abstract
MOTIVATION: In single-cell RNA-sequencing (scRNA-seq) data, stratification of sequencing reads by cellular barcode is necessary to study cell-specific features. However, apart from gene expression, the analyses of cell-specific features are not sufficiently supported by available tools designed for high-throughput sequencing data. RESULTS: We introduce SCExecute, which executes a user-provided command on barcode-stratified, extracted on-the-fly, single-cell binary alignment map (scBAM) files. SCExecute extracts the alignments with each cell barcode from aligned, pooled single-cell sequencing data. Simple commands, monolithic programs, multi-command shell scripts or complex shell-based pipelines are then executed on each scBAM file. scBAM files can be restricted to specific barcodes and/or genomic regions of interest. We demonstrate SCExecute with two popular variant callers-GATK and Strelka2-executed in shell-scripts together with commands for BAM file manipulation and variant filtering, to detect single-cell-specific expressed single nucleotide variants from droplet scRNA-seq data (10X Genomics Chromium System).In conclusion, SCExecute facilitates custom cell-level analyses on barcoded scRNA-seq data using currently available tools and provides an effective solution for studying low (cellular) frequency transcriptome features. AVAILABILITY AND IMPLEMENTATION: SCExecute is implemented in Python3 using the Pysam package and distributed for Linux, MacOS and Python environments from https://horvathlab.github.io/NGS/SCExecute. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Nathan Edwards, Christian Dillard, N. M. Prashant, Liu Hongyu, Mia Yang, Evgenia Ulianova, Anelia Horvath
Bioinform.2