Dataset Information

Large scale library generation for high throughput sequencing.

ABSTRACT:

Background

Large efforts have recently been made to automate the sample preparation protocols for massively parallel sequencing in order to match the increasing instrument throughput. Still, the size selection through agarose gel electrophoresis separation is a labor-intensive bottleneck of these protocols.

Methodology/principal findings

In this study a method for automatic library preparation and size selection on a liquid handling robot is presented. The method utilizes selective precipitation of certain sizes of DNA molecules on to paramagnetic beads for cleanup and selection after standard enzymatic reactions.

Conclusions/significance

The method is used to generate libraries for de novo and re-sequencing on the Illumina HiSeq 2000 instrument with a throughput of 12 samples per instrument in approximately 4 hours. The resulting output data show quality scores and pass filter rates comparable to manually prepared samples. The sample size distribution can be adjusted for each application, and are suitable for all high throughput DNA processing protocols seeking to control size intervals.

SUBMITTER: Borgstrom E

PROVIDER: S-EPMC3083417 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Publications

Large scale library generation for high throughput sequencing.

Borgström Erik E Lundin Sverker S Lundeberg Joakim J

PloS one 20110427 4

<h4>Background</h4>Large efforts have recently been made to automate the sample preparation protocols for massively parallel sequencing in order to match the increasing instrument throughput. Still, the size selection through agarose gel electrophoresis separation is a labor-intensive bottleneck of these protocols.<h4>Methodology/principal findings</h4>In this study a method for automatic library preparation and size selection on a liquid handling robot is presented. The method utilizes selectiv ...[more]

PMID: 21589638

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Project description:BACKGROUND: The development of second generation sequencing methods has enabled large scale DNA variation studies at moderate cost. For the high throughput discovery of single nucleotide polymorphisms (SNPs) in species lacking a sequenced reference genome, we set-up an analysis pipeline based on a short read de novo sequence assembler and a program designed to identify variation within short reads. To illustrate the potential of this technique, we present the results obtained with a randomly sheared, enzymatically generated, 2-3 kbp genome fraction of six pooled Meleagris gallopavo (turkey) individuals. RESULTS: A total of 100 million 36 bp reads were generated, representing approximately 5-6% (approximately 62 Mbp) of the turkey genome, with an estimated sequence depth of 58. Reads consisting of bases called with less than 1% error probability were selected and assembled into contigs. Subsequently, high throughput discovery of nucleotide variation was performed using sequences with more than 90% reliability by using the assembled contigs that were 50 bp or longer as the reference sequence. We identified more than 7,500 SNPs with a high probability of representing true nucleotide variation in turkeys. Increasing the reference genome by adding publicly available turkey BAC-end sequences increased the number of SNPs to over 11,000. A comparison with the sequenced chicken genome indicated that the assembled turkey contigs were distributed uniformly across the turkey genome. Genotyping of a representative sample of 340 SNPs resulted in a SNP conversion rate of 95%. The correlation of the minor allele count (MAC) and observed minor allele frequency (MAF) for the validated SNPs was 0.69. CONCLUSION: We provide an efficient and cost-effective approach for the identification of thousands of high quality SNPs in species currently lacking a sequenced genome and applied this to turkey. The methodology addresses a random fraction of the genome, resulting in an even distribution of SNPs across the targeted genome.

Dataset Information

Large scale library generation for high throughput sequencing.

Background

Methodology/principal findings

Conclusions/significance

Publications

Large scale library generation for high throughput sequencing.

Similar Datasets

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