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iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data
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 s...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9310080/ https://www.ncbi.nlm.nih.gov/pubmed/35899080 http://dx.doi.org/10.1093/nargab/lqac053 |
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author | Anilkumar Sithara, Anjana Maripuri, Devi Priyanka Moorthy, Keerthika Amirtha Ganesh, Sai Sruthi Philip, Philge Banerjee, Shayantan Sudhakar, Malvika Raman, Karthik |
author_facet | Anilkumar Sithara, Anjana Maripuri, Devi Priyanka Moorthy, Keerthika Amirtha Ganesh, Sai Sruthi Philip, Philge Banerjee, Shayantan Sudhakar, Malvika Raman, Karthik |
author_sort | Anilkumar Sithara, Anjana |
collection | PubMed |
description | 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 r = 0.85 using the HISAT2-StringTie-ballgown and STAR-StringTie-ballgown RNA-Seq pipelines on the human monocyte dataset (SRP082682). Overall, our tool enables easy analyses of omics datasets, significantly ameliorating complex data analysis pipelines. |
format | Online Article Text |
id | pubmed-9310080 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-93100802022-07-26 iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data Anilkumar Sithara, Anjana Maripuri, Devi Priyanka Moorthy, Keerthika Amirtha Ganesh, Sai Sruthi Philip, Philge Banerjee, Shayantan Sudhakar, Malvika Raman, Karthik NAR Genom Bioinform Standard Article 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 r = 0.85 using the HISAT2-StringTie-ballgown and STAR-StringTie-ballgown RNA-Seq pipelines on the human monocyte dataset (SRP082682). Overall, our tool enables easy analyses of omics datasets, significantly ameliorating complex data analysis pipelines. Oxford University Press 2022-07-25 /pmc/articles/PMC9310080/ /pubmed/35899080 http://dx.doi.org/10.1093/nargab/lqac053 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Standard Article Anilkumar Sithara, Anjana Maripuri, Devi Priyanka Moorthy, Keerthika Amirtha Ganesh, Sai Sruthi Philip, Philge Banerjee, Shayantan Sudhakar, Malvika Raman, Karthik iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data |
title | iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data |
title_full | iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data |
title_fullStr | iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data |
title_full_unstemmed | iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data |
title_short | iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data |
title_sort | icomic: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data |
topic | Standard Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9310080/ https://www.ncbi.nlm.nih.gov/pubmed/35899080 http://dx.doi.org/10.1093/nargab/lqac053 |
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