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A General Guide on NGS Data Analysis

A General Guide on NGS Data Analysis

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Next-generation sequencing (NGS) technologies have revolutionized the field of genomics and have become a cornerstone of modern biological research. However, the massive amounts of data generated by NGS can be challenging to process, analyze, and interpret. In this blog, we will provide a general guide to quality control and pre-processing of NGS data, which is essential for obtaining reliable and meaningful results.

Standard pipelines for NGS analysisStandard pipelines for NGS analysis (DC Koboldt, 2020)

Read Trimming

Read trimming is the process of removing low-quality reads and adapter sequences from the sequencing data. This step is critical to reduce sequencing errors and improve the quality of downstream analysis. There are several tools available for read trimming, including Trimmomatic, Cutadapt, and BBDuk. The choice of tool depends on the sequencing platform used and the specific quality control requirements of the study. In general, a minimum read length of 50-75 base pairs (bp) is recommended, and reads with a Phred quality score of less than 20 should be removed.

Quality Control

Before and after read trimming, it is essential to assess the quality of the sequencing data using tools such as FastQC, which provides information on the sequence quality, sequence length distribution, GC content, and adapter contamination. Based on the quality control results, additional trimming or filtering steps may be required to remove low-quality reads or adapter contamination.

Alignment

The next step is to align the trimmed reads to a reference genome or transcriptome. Several tools are available for alignment, including Bowtie, BWA, and HISAT2. The choice of tool depends on the sequencing platform used, the quality of the reads, and the reference genome or transcriptome used. It is important to select a tool that can handle the type of sequencing data generated, such as single-end or paired-end reads. The output of the alignment step is a Sequence Alignment/Map (SAM) file, which can be converted to a Binary Alignment/Map (BAM) file for downstream analysis.

Post-alignment Processing

The BAM file can be manipulated and processed using tools such as Samtools and Picard. The post-alignment processing step includes sorting, merging, and duplicate removal. Sorting the BAM file improves the efficiency of downstream analysis, while merging BAM files from multiple samples simplifies the analysis pipeline. Duplicate removal is critical to reduce the effects of PCR amplification bias and improve the accuracy of downstream analysis.

Quantification

Quantification of gene expression levels can be performed using tools such as HTSeq or FeatureCounts. These tools count the number of reads that map to each gene and provide a table of read counts for each sample. The output of the quantification step is a matrix of read counts for each gene in each sample, which can be used for downstream analysis.

Differential Expression Analysis

Differential expression analysis is the process of identifying genes that are differentially expressed between two or more conditions. This step is critical to understand the biological processes and pathways associated with the experimental conditions. Several tools are available for differential expression analysis, including DESeq2 and edgeR. These tools use statistical methods to identify genes that are significantly differentially expressed between conditions. These statistical juggernauts utilize cutting-edge and state-of-the-art algorithms and methodologies to meticulously analyze vast amounts of data and determine which genes are significantly and differentially expressed between the conditions under investigation.

Functional Analysis

Functional analysis is the process of identifying the biological processes and pathways associated with the differentially expressed genes. This step provides insights into the underlying biology of the experimental conditions and can guide the interpretation of the results. Several tools are available for functional analysis, including GOSeq and GSEA. These cutting-edge and state-of-the-art tools utilize complex and intricate algorithms and methodologies to scrutinize the genes that have been differentially expressed and provide a comprehensive and detailed analysis of the biological processes and pathways that are associated with them.

In conclusion, each step of NGS data analysis is essential for ensuring the accuracy and quality of the data and enabling the identification of differentially expressed genes and biological processes. By following the best practices outlined in this blog, researchers can ensure that they are obtaining high-quality data that can be used for downstream analysis and interpretation.

CD Genomics has developed a complete large-scale data analysis platform encompassing data processing, gene annotation and functional analysis, network analysis, and data visualization dedicated to advancing progress and discovery in the field of life sciences, drug development, and agriculture and the environment.

Reference

  1. Koboldt D C. Best practices for variant calling in clinical sequencing[J]. Genome Medicine, 2020, 12(1): 1-13.
* For Research Use Only. Not for use in diagnostic procedures.
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