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iCOMIC：一个由图形界面驱动的用于分析癌症组学数据的生物信息学流程。

iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data.

作者信息

Anilkumar Sithara Anjana, Maripuri Devi Priyanka, Moorthy Keerthika, Amirtha Ganesh Sai Sruthi, Philip Philge, Banerjee Shayantan, Sudhakar Malvika, Raman Karthik

机构信息

Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600036, India.

Centre for Integrative Biology and Systems mEdicine, IIT Madras, India.

出版信息

NAR Genom Bioinform. 2022 Jul 25;4(3):lqac053. doi: 10.1093/nargab/lqac053. eCollection 2022 Sep.

DOI:10.1093/nargab/lqac053
PMID:35899080
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9310080/
Abstract

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  = 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.

摘要

尽管现代测序技术产生的组学数据大幅增加，但其分析可能很棘手，通常需要生物信息学方面的大量专业知识。为了解决这一问题，我们开发了一种用户友好的流程来分析（癌症）基因组数据，该流程将原始测序数据（FASTQ格式）作为输入，并输出有见地的统计信息。我们具有许多独立工作流程的iCOMIC工具包流程嵌入在流行的Snakemake工作流管理系统中。它可以分析全基因组和转录组数据，其特点是具有用户友好的图形用户界面（GUI），具有多个优点，包括最少的执行步骤以及无需复杂的命令行参数。值得注意的是，我们整合了内部开发的算法，以预测致癌突变中的致病性，并从体细胞突变数据中区分肿瘤抑制基因和癌基因。我们使用BWA MEM-GATK HC DNA-Seq流程，将我们的工具与“瓶中的基因组”基准数据集（NA12878）进行基准测试，对于插入缺失和单核苷酸多态性（SNP），分别获得了最高F1分数0.971和0.988。同样，在人类单核细胞数据集（SRP082682）上，我们使用HISAT2-StringTie-ballgown和STAR-StringTie-ballgown RNA-Seq流程实现了相关系数r = 0.85。总体而言，我们的工具能够轻松分析组学数据集，显著改善复杂的数据分析流程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c7/9310080/8d4340b90c20/lqac053fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c7/9310080/3fd7b38dd8d9/lqac053fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c7/9310080/c58ee3377d2f/lqac053fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c7/9310080/e27c2a471f62/lqac053fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c7/9310080/433a01a069f9/lqac053fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c7/9310080/192acc2b98da/lqac053fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c7/9310080/8d4340b90c20/lqac053fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c7/9310080/3fd7b38dd8d9/lqac053fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c7/9310080/c58ee3377d2f/lqac053fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c7/9310080/e27c2a471f62/lqac053fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c7/9310080/433a01a069f9/lqac053fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c7/9310080/192acc2b98da/lqac053fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c7/9310080/8d4340b90c20/lqac053fig6.jpg

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