Evidence map›Paper›PMID 35899080›Full record

ArticleNAR genomics and bioinformatics2022

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

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

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Article in NAR genomics and bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Anjana Anilkumar SitharaDepartment of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600036, India.
Devi Priyanka MaripuriDepartment of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600036, India.
Keerthika MoorthyDepartment of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600036, India.
Sai Sruthi Amirtha GaneshDepartment of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600036, India.
Philge PhilipCentre for Integrative Biology and Systems mEdicine, IIT Madras, India.
Shayantan BanerjeeDepartment of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600036, India.ORCID https://orcid.org/0000-0003-4698-9435
Malvika SudhakarDepartment of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600036, India.
Karthik RamanDepartment of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600036, India.ORCID https://orcid.org/0000-0002-9311-7093

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Identifiers

PMID35899080
PMCPMC9310080

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.