Linear amplification for deep sequencing

Linear amplification for deep sequencing (LADS) is an amplification method that produces representative libraries for Illumina next-generation sequencing within 2 d. The method relies on attaching two different sequencing adapters to blunt-end repaired and A-tailed DNA fragments, wherein one of the adapters is extended with the sequence for the T7 RNA polymerase promoter. Ligated and size-selected DNA fragments are transcribed in vitro with high RNA yields. Subsequent cDNA synthesis is initiated from a primer complementary to the first adapter, ensuring that the library will only contain full-length fragments with two distinct adapters. Contrary to the severely biased representation of AT- or GC-rich fragments in standard PCR-amplified libraries, the sequence coverage in T7-amplified libraries is indistinguishable from that of nonamplified libraries. Moreover, in contrast to amplification-free methods, LADS can generate sequencing libraries from a few nanograms of DNA, which is essential for all applications in which the starting material is limited.     

GPMiner: an integrated system for mining combinatorial cis-regulatory elements in mammalian gene group

Background

Massively parallel sequencing technology is revolutionizing approaches to genomic and genetic research. Since its advent, the scale and efficiency of Next-Generation Sequencing (NGS) has rapidly improved. In spite of this success, sequencing genomes or genomic regions with extremely biased base composition is still a great challenge to the currently available NGS platforms. The genomes of some important pathogenic organisms like Plasmodium falciparum (high AT content) and Mycobacterium tuberculosis (high GC content) display extremes of base composition. The standard library preparation procedures that employ PCR amplification have been shown to cause uneven read coverage particularly across AT and GC rich regions, leading to problems in genome assembly and variation analyses. Alternative library-preparation approaches that omit PCR amplification require large quantities of starting material and hence are not suitable for small amounts of DNA/RNA such as those from clinical isolates. We have developed and optimized library-preparation procedures suitable for low quantity starting material and tolerant to extremely high AT content sequences.

Results

We have used our optimized conditions in parallel with standard methods to prepare Illumina sequencing libraries from a non-clinical and a clinical isolate (containing ~53% host contamination). By analyzing and comparing the quality of sequence data generated, we show that our optimized conditions that involve a PCR additive (TMAC), produces amplified libraries with improved coverage of extremely AT-rich regions and reduced bias toward GC neutral templates.

Conclusion

We have developed a robust and optimized Next-Generation Sequencing library amplification method suitable for extremely AT-rich genomes. The new amplification conditions significantly reduce bias and retain the complexity of either extremes of base composition. This development will greatly benefit sequencing clinical samples that often require amplification due to low mass of DNA starting material.     

Subtraction of cap-trapped full-length cDNA libraries to select rare transcripts

The normalization and subtraction of highly expressed cDNAs from relatively large tissues before cloning dramatically enhanced the gene discovery by sequencing for the mouse full-length cDNA encyclopedia, but these methods have not been suitable for limited RNA materials. To normalize and subtract full-length cDNA libraries derived from limited quantities of total RNA, here we report a method to subtract plasmid libraries excised from size-unbiased amplified lambda phage cDNA libraries that avoids heavily biasing steps such as PCR and plasmid library amplification. The proportion of full-length cDNAs and the gene discovery rate are high, and library diversity can be validated by in silico randomization.     

A computational method for estimating the PCR duplication rate in DNA and RNA-seq experiments

Background

PCR amplification is an important step in the preparation of DNA sequencing libraries prior to high-throughput sequencing. PCR amplification introduces redundant reads in the sequence data and estimating the PCR duplication rate is important to assess the frequency of such reads. Existing computational methods do not distinguish PCR duplicates from “natural” read duplicates that represent independent DNA fragments and therefore, over-estimate the PCR duplication rate for DNA-seq and RNA-seq experiments.

Results

In this paper, we present a computational method to estimate the average PCR duplication rate of high-throughput sequence datasets that accounts for natural read duplicates by leveraging heterozygous variants in an individual genome. Analysis of simulated data and exome sequence data from the 1000 Genomes project demonstrated that our method can accurately estimate the PCR duplication rate on paired-end as well as single-end read datasets which contain a high proportion of natural read duplicates. Further, analysis of exome datasets prepared using the Nextera library preparation method indicated that 45–50% of read duplicates correspond to natural read duplicates likely due to fragmentation bias. Finally, analysis of RNA-seq datasets from individuals in the 1000 Genomes project demonstrated that 70–95% of read duplicates observed in such datasets correspond to natural duplicates sampled from genes with high expression and identified outlier samples with a 2-fold greater PCR duplication rate than other samples.

Conclusions

The method described here is a useful tool for estimating the PCR duplication rate of high-throughput sequence datasets and for assessing the fraction of read duplicates that correspond to natural read duplicates. An implementation of the method is available at https://github.com/vibansal/PCRduplicates.