Bioinformatics Analysis of Genotyping: Introduction, Analysis Methods, and Contents

Bioinformatics Analysis of Genotyping: Introduction, Analysis Methods, and Contents

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Introduction to Genotyping

Genotyping-By-Sequencing (GBS) is an effective, cost-effective technique for obtaining genome-wide variability data. In a nutshell, a GBS procedure begins with DNA digestion using one or more well-known restriction enzymes, with the goal of reducing the intricacy of the genome to be sequenced. Fragments of appropriate length (less than 800 bp) are then ligated to adapters, amplified, and sequenced on an Illumina high-throughput platform. Various samples can be sequenced in one lane while barcodes are added. Demultiplexed reads can then be evaluated de novo or aligned to a reference genome if one is accessible. Variations can be recognized in the latter situation using analysis pipelines related to those used to analyze whole-genome resequencing data. The primary factor of this procedure is that it sequences a small but well-distributed and repeatable part of the entire genome, allowing thousands of genomic variants to be identified and genotyped across the genome and across. GBS is becoming the preferred technique for a variety of applications in plant genomics and breeding, including population dynamics assessment, high-density genetic map construction, genetic mapping of complicated characteristics through Genome-Wide Association Studies (GWAS), and genomic selection breeding value estimation.

Applications of Genotyping Analysis

Genotyping is the procedure of examining an individual's DNA sequence using biological experiments and evaluating the target sequence to another individual's sequence or a reference sequence to assess distinctions in genetic makeup (genotype).

Individual distinctions in genetic makeup, such as single nucleotide polymorphisms (SNPs) and large structural shifts in DNA, can be defined using genotyping. The study of genes and gene variations linked to disease relies heavily on genotyping analysis. On a molecular level, next-generation sequencing (NGS) and microarrays allow for a better comprehension of disease etiology. With numerous genomic targets that could play a role in disease, assessment must be flexible and precise. Data analysis tools for SNP genotyping and copy number variation (CNV) can evaluate findings for millions of markers and probes and detect sample outliers, giving an overview into the functional consequences of genetic diversity SNP genotyping is used in a variety of scientific fields, including customized medicine, plant and animal biotechnology, and genetic engineering.

The following are some examples of known genotyping analysis usages:

  • Genome-wide affiliation studies on a large scale.
  • Disease susceptibility loci and susceptibility genes study.
  • Population genetics study and animal and plant variety breeding
  • Pharmacogenomics.
  • Diagnostics.

Bioinformatics Analysis of Genotyping

The bioinformatics pipeline needed to evaluate the reads and find polymorphic sites within the sequenced population is an important part of any GBS protocol. GBS data was analyzed using widely utilized bundles such as bwa or bowtie2 for short read alignment and Samtools or GATK for variant identification and genotyping Custom bundles, such as Stacks or the Tassel GBS pipeline, have lately been established to analyze the kinds of reads generated by GBS innovations. The major benefit of these methodologies over previous methods is that they can be used even if a reference genome is not available. Tassel, in specific, takes the opportunity of the essence of GBS reads to calculate genomic variants in a very efficient manner.

Bioinformatics Analysis Content

Bioinformatics Analysis Content Genotyping bioinformatic assessment involves data quality control, variance assessment, map to the reference genome, SNP identification and annotation, and CNV identification and annotation, and InDel identification (for whole-genome sequencing data or whole-exome sequencing data).

About CD Genomics Bioinformatics Analysis

The bioinformatics analysis department of CD Genomics provides novel solutions for data-driven innovation aimed at discovering the hidden potential in biological data, tapping new insights related to life science research, and predicting new prospects.


  1. Lecompte L, Peterlongo P, Lavenier D, Lemaitre C. SVJedi: Genotyping structural variations with long reads. Bioinformatics. 2020, 36(17).
  2. Zhang J, Long Y, Wang L, et al. Consensus genetic linkage map construction and QTL mapping for plant height-related traits in linseed flax (Linum usitatissimum L.). BMC plant biology. 2018, 18(1).
  3. Perea C, De La Hoz JF, Cruz DF, et al. Bioinformatic analysis of genotype by sequencing (GBS) data with NGSEP. BMC genomics. 2016, 17(5).
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