Unraveling genomic variation from next generation sequencing data

Abstract

Elucidating the content of a DNA sequence is critical to deeper understand and decode the genetic information for any biological system. As next generation sequencing (NGS) techniques have become cheaper and more advanced in throughput over time, great innovations and breakthrough conclusions have been generated in various biological areas. Few of these areas, which get shaped by the new technological advances, involve evolution of species, microbial mapping, population genetics, genome-wide association studies (GWAs), comparative genomics, variant analysis, gene expression, gene regulation, epigenetics and personalized medicine. While NGS techniques stand as key players in modern biological research, the analysis and the interpretation of the vast amount of data that gets produced is a not an easy or a trivial task and still remains a great challenge in the field of bioinformatics. Therefore, efficient tools to cope with information overload, tackle the high complexity and provide meaningful visualizations to make the knowledge extraction easier are essential. In this article, we briefly refer to the sequencing methodologies and the available equipment to serve these analyses and we describe the data formats of the files which get produced by them. We conclude with a thorough review of tools developed to efficiently store, analyze and visualize such data with emphasis in structural variation analysis and comparative genomics. We finally comment on their functionality, strengths and weaknesses and we discuss how future applications could further develop in this field.

Figure 1

DNA sequencing.DNA sequencing: 1st step: The DNA of interest is purified and extracted. 2nd step: Creation of multiple copies of DNA. 3nd step: DNA is shattered into smaller pieces. 4rd step: DNA fragment sequencing. 5th step: A computer maps the small pieces to an already known reference genome.

Figure 2

DNA assembly.DNA assembly: 1st step: The DNA is purified and extracted. 2nd step: DNA is fragmented into smaller pieces. 3rd step: DNA fragment sequencing. 4th step: A computer matches the overlapping parts of the fragments to get a continuous sequence. 5th step: The whole sequence is reassembled. No prior knowledge about the DNA sequence is necessary.

Figure 3

SNP example. A difference in a single nucleotide between two DNA fragments from different individuals. In this case we say that there are two alleles: C and T.

Figure 4

Structural Variations. This figure illustrates the basic structural variations. A) Inversion. B) Translocation within the same chromosome. C) Translocation across different chromosomes. D) Duplication. E) Deletion.

Figure 5

PEM signatures. Basic PEM signatures. A) Insertion. B) Deletion. C) Inversion. More PEM signatures are visually presented in [47].

Figure 6

Read depth. Read depth: A) Fragments of DNA (Reads) are mapped to the original reference genome. B) Plotting the frequency of each nucleotide that was mapped at the reference genome.

Figure 7

FASTQ file. 1st line always starts with the symbol ‘@’ followed by the sequence identifier. 2nd line contains the sequence. 3rd line starts with the symbol ‘+’ symbol which is optionally followed by the same sequence identifier and any description. It indicates the end of the sequence and the beginning of the quality score string. 4th line contains the quality score (QS) in ASCII format. The current example shows an Illumina representation.

Figure 8

BAM/SAM files. Example of an alignment to the reference sequence (pileup). A) Read r001/1 and r001/2 constitute a read pair; r003 is a chimeric read; r004 represents a split alignment. B) The corresponding SAM file and their tags for each field.

Figure 9

VCF file. This figure demonstrates an example of a CVF file. A) Different types of variations and polymorphisms that can be stored in CVF format. B) Example of a CVF format and its fields.