iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data.
Journal
NAR genomics and bioinformatics
ISSN: 2631-9268
Titre abrégé: NAR Genom Bioinform
Pays: England
ID NLM: 101756213
Informations de publication
Date de publication:
Sep 2022
Sep 2022
Historique:
received:
17
12
2021
revised:
17
06
2022
accepted:
04
07
2022
entrez:
28
7
2022
pubmed:
29
7
2022
medline:
29
7
2022
Statut:
epublish
Résumé
Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics. Our iCOMIC toolkit pipeline featuring many independent workflows is embedded in the popular Snakemake workflow management system. It can analyze whole-genome and transcriptome data and is characterized by a user-friendly GUI that offers several advantages, including minimal execution steps and eliminating the need for complex command-line arguments. Notably, we have integrated algorithms developed in-house to predict pathogenicity among cancer-causing mutations and differentiate between tumor suppressor genes and oncogenes from somatic mutation data. We benchmarked our tool against Genome In A Bottle benchmark dataset (NA12878) and got the highest F1 score of 0.971 and 0.988 for indels and SNPs, respectively, using the BWA MEM-GATK HC DNA-Seq pipeline. Similarly, we achieved a correlation coefficient of
Identifiants
pubmed: 35899080
doi: 10.1093/nargab/lqac053
pii: lqac053
pmc: PMC9310080
doi:
Types de publication
Journal Article
Langues
eng
Pagination
lqac053Informations de copyright
© The Author(s) 2022. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.
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