A Survey of Software and Hardware Approaches to Performing Read Alignment in Next Generation Sequencing

Abstract

Computational genomics is an emerging field that is enabling us to reveal the origins of life and the genetic basis of diseases such as cancer. Next Generation Sequencing (NGS) technologies have unleashed a wealth of genomic information by producing immense amounts of raw data. Before any functional analysis can be applied to this data, read alignment is applied to find the genomic coordinates of the produced sequences. Alignment algorithms have evolved rapidly with the advancement in sequencing technology, striving to achieve biological accuracy at the expense of increasing space and time complexities. Hardware approaches have been proposed to accelerate the computational bottlenecks created by the alignment process. Although several hardware approaches have achieved remarkable speedups, most have overlooked important biological features, which have hampered their widespread adoption by the genomics community. In this paper, we provide a brief biological introduction to genomics and NGS. We discuss the most popular next generation read alignment tools and algorithms. Furthermore, we provide a comprehensive survey of the hardware implementations used to accelerate these algorithms.

Hardware acceleration of genomics data analysis: challenges and opportunities

“…The variant calling bioinformatics pipeline is contained within the data pre-processing and data analysis stages of a much larger bioinformatics-based research study as illustrated in the upper panel. Data file formats at each step are presented in the lower panel ( Al Kawam et al , 2017 ; Lightbody et al , 2019 ). *Platform-specific raw sequence output either .BAM or .FASTQ or .HDF5 ( NCBI, 2019 ).…”

Metagenomic Classification using Centrifuge

“…BWT was a text compression algorithm which was later used for genomic classification applications [3]. Along with FM-index this algorithm has found applications in genomic industry.…”

“…3) The transformation result required is the last column of the strings obtained [3]. Illustrating BWT with an example considering the string "BANANA$" ($ is the string terminator): Table 2: Sorting Therefore, BWT(BANANA$)=ANNB$AA BWT is particularly useful as it creates an array whose rows are formed by cyclic shifting of the target string, that may be a DNA sequence.…”

New evaluation methods of read mapping by 17 aligners on simulated and empirical NGS data: an updated comparison of DNA- and RNA-Seq data from Illumina and Ion Torrent technologies

“…Therefore, bioinformatics researchers started to think about new ways to efficiently manage and analyze such enormous amount of data. The first crucial step in the analysis of next-generation sequencing (NGS) data, posterior to quality control and filtering steps, is alignment (mapping) of generated sequencing reads to the respective reference [6]. However, this step is biased by many errors due to the following reasons [7]: (1) a reference genome is generally long (* billions) and presents complex regions such as repetitive elements (repetitive regions are usually masked because there is no consensus about how to deal with them, yet), (2) reads are short in length (typically, 50-150 bp), causing issues with efficiency and accuracy, aligning more likely in multiple locations rather than to unique positions in the reference genome, (3) the subject genome could inherently be different from the reference genome due to acquired alterations over time.…”