EasyQC: Tool with Interactive User Interface for Efficient Next-Generation Sequencing Data Quality Control

Abstract

The advent of next-generation sequencing (NGS) technologies has revolutionized the world of genomic research. Millions of sequences are generated in a short period of time and they provide intriguing insights to the researcher. Many NGS platforms have evolved over a period of time and their efficiency has been ever increasing. Still, primarily because of the chemistry, glitch in the sequencing machine and human handling errors, some artifacts tend to exist in the final sequence data set. These sequence errors have a profound impact on the downstream analyses and may provide misleading information. Hence, filtering of these erroneous reads has become inevitable and myriad of tools are available for this purpose. However, many of them are accessible as a command line interface that requires the user to enter each command manually. Here, we report EasyQC, a tool for NGS data quality control (QC) with a graphical user interface providing options to carry out trimming of NGS reads based on quality, length, homopolymer, and ambiguous bases. EasyQC also possesses features such as format converter, paired end merger, adapter trimmer, and a graph generator that generates quality distribution, length distribution, GC content, and base composition graphs. Comparison of raw and processed sequence data sets using EasyQC suggested significant increase in overall quality of the sequences. Testing of EasyQC using NGS data sets on a standalone desktop proved to be relatively faster. EasyQC is developed using PERL modules and can be executed in Windows and Linux platforms. With the various QC features, easy interface for end users, and cross-platform compatibility, EasyQC would be a valuable addition to the already existing tools facilitating better downstream analyses.

Genome-wide association studies

“…To increase sample size, GWAS is typically carried out in the context of a consortium such as the Psychiatric Genomics Consortium, the Genetic Investigation of Anthropometric Traits (GIANT) consortium or the Global Lipids Genetics Consortium where data from multiple cohorts are analysed together using tools such as METAL 39 , N-GWAMA or MA-GWAMA 40 and quality control pipelines such as those implemented in RICOPILI 23 or EasyQC 41 . For a detailed description of the quality control procedures specific to GWAMA, we refer readers to ref.…”

A Genome‐Wide Association Study Meta‐Analysis of Alpha Angle Suggests Cam‐Type Morphology May Be a Specific Feature of Hip Osteoarthritis in Older Adults

“…AA measurements were standardized to create a Z score (SD = 1; mean = 0). Subsequently, a GWAS meta-analysis of AAs was conducted between a GWAS performed in each study (38,173 (23), and single-nucleotide polymorphisms (SNPs) with a minor allele frequency <0.01 and imputation score <0.4 were removed. Meta-analysis between the studies was performed using fixed-effects inverse variance weighting using the Metal software package (24).…”

Hip shape shows a causal effect on hip fracture but not hip osteoarthritis: findings from a GWAS meta-analysis and causal analyses

“…In SC we used fastGWA in GCTA v1.93.2 beta, a mixed linear model (MLM) approach to control for population stratification and relatedness. A centralized quality control of cohorts’ summary statistics was implemented in EasyQC (Supplementary Methods) (18). Both cohorts used the hg19 build.…”