The Long March: A Sample Preparation Technique that Enhances Contig Length and Coverage by High-Throughput Short-Read Sequencing
Author(s) -
Katherine Sorber,
Charles Y. Chiu,
Dale R. Webster,
Michelle Dimon,
J. Graham Ruby,
Armin Hekele,
Joseph L. DeRisi
Publication year - 2008
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0003495
Subject(s) - contig , adapter (computing) , sequence assembly , amplicon , computational biology , restriction enzyme , biology , dna sequencing , deep sequencing , metagenomics , reference genome , computer science , genetics , transcriptome , genome , dna , polymerase chain reaction , gene , operating system , gene expression
High-throughput short-read technologies have revolutionized DNA sequencing by drastically reducing the cost per base of sequencing information. Despite producing gigabases of sequence per run, these technologies still present obstacles in resequencing and de novo assembly applications due to biased or insufficient target sequence coverage. We present here a simple sample preparation method termed the “long march” that increases both contig lengths and target sequence coverage using high-throughput short-read technologies. By incorporating a Type IIS restriction enzyme recognition motif into the sequencing primer adapter, successive rounds of restriction enzyme cleavage and adapter ligation produce a set of nested sub-libraries from the initial amplicon library. Sequence reads from these sub-libraries are offset from each other with enough overlap to aid assembly and contig extension. We demonstrate the utility of the long march in resequencing of the Plasmodium falciparum transcriptome, where the number of genomic bases covered was increased by 39%, as well as in metagenomic analysis of a serum sample from a patient with hepatitis B virus (HBV)-related acute liver failure, where the number of HBV bases covered was increased by 42%. We also offer a theoretical optimization of the long march for de novo sequence assembly.
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