Additionally, previous efforts lack a historical perspective on algorithm development. Since the time those efforts were published, many new alignment algorithms have been developed. Previous efforts that provide overviews of various algorithms and techniques used by read aligners are presented elsewhere, including studies that present benchmarks of existing tools. Over the past several decades, a plethora of tools were developed to align reads onto reference genomes across various domains of biology. The continued increase in the throughput of modern sequencing technologies creates additional demand for efficient algorithms for read alignment. These sequencing errors and read lengths, which are typically short, make the read alignment problem computationally challenging. These differences can be caused by either real genetic variants in the sequenced genome or errors generated by the sequencing platform. Read alignment enables observation of the differences between the read and the reference genome. Instead, todayвЂ™s efficient bioinformatics algorithms enable fast and accurate read alignment and can be thousands of orders of magnitude faster when compared to the naive brute force approach (Supplementary Note 1). Read alignment can be performed in a brute force manner but is impractical for modern sequencing platforms capable of producing hundreds of millions of reads. For example, most trans-eQTL signals were shown to be solely caused by alignment errors. Accuracy of alignment has a strong effect on many downstream analyses. This process, read alignment (also known as read mapping), typically requires the reference genome which is used to determine the potential location of each read. The main challenge in genome analysis today is to reconstruct the complete genome of an individual. Sequenced reads lack information about the order and origin (i.e., which part, homolog, and strand of the subject genome) of reads. For example, errors in protein-coding regions can bias the accuracy of protein predictions. The increased error rate of todayвЂ™s emerging long-read technologies may negatively impact biological interpretations. The magnitude of error rates in data produced by state-of-the-art sequencing platforms varies from ~вЂ‰10 в€’3 for short reads to ~вЂ‰15 Г— 10 в€’2 for the relatively new long and ultra-long reads. The trade-off for decreased cost and increased throughput offered by modern sequencing technologies is a larger margin of noise in sequencing data. Contemporary sequencing technologies are capable of generating tens of millions to billions of reads per sample, with read lengths ranging from a few hundred to a few million base pairs. The output of high-throughput sequencing consists of sets of relatively short genomic sequences, usually referred to as reads. Modern high-throughput sequencing techniques are capable of producing millions of nucleotide sequences of an individualвЂ™s DNA and providing multifold coverage of whole genomes or particular genomic regions. Recent advances in high-throughput sequencing technologies have enabled cost-effective and time-efficient probing of the DNA sequences of living organisms through a process known as DNA sequencing.

The Human Genome Project cost approximately $3 billion over 13вЂ‰years to sequence the genome of an individual human. In April 2003, the high-throughput sequencing era started with the Human Genome Project, which led to the successful sequencing of a nearly complete human genome and establishment of a reference genome that is still in use. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

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