Citations

Abstract Background Illumina paired-end sequencing has been increasingly popular for 16S rRNA gene-based microbiota profiling. It provides higher phylogenetic resolution than single-end reads due to a longer read length. However, the reverse read (R2) often has significant low base quality, and a large proportion of R2s will be discarded after quality control, resulting in a mixture of paired-end and single-end reads. A typical 16S analysis pipeline usually processes either paired-end or single-end reads but not a mixture. Thus, the quantification accuracy and statistical power will be reduced due to the loss of a large amount of reads. As a result, rare taxa may not be detectable with the paired-end approach, or low taxonomic resolution will result in a single-end approach. Results To have both the higher phylogenetic resolution provided by paired-end reads and the higher sequence coverage by single-end reads, we propose a novel OTU-picking pipeline, hybrid-denovo, that can process a hybrid of single-end and paired-end reads. Using high-quality paired-end reads as a gold standard, we show that hybrid-denovo achieved the highest correlation with the gold standard and performed better than the approaches based on paired-end or single-end reads in terms of quantifying the microbial diversity and taxonomic abundances. By applying our method to a rheumatoid arthritis (RA) data set, we demonstrated that hybrid-denovo captured more microbial diversity and identified more RA-associated taxa than a paired-end or single-end approach. Conclusions Hybrid-denovo utilizes both paired-end and single-end 16S sequencing reads and is recommended for 16S rRNA gene targeted paired-end sequencing data.

Abstract Objectives and Rationale: Structural variants (SV) including large copy number aberrations (CNV), translocations, inversions, and large insertions and deletions (INDEL) play a critical role in tumorigenesis and progression. In fact, we now know that tumors can be categorized according to the size of mutations harbored. In several cancers, including ovarian and breast cancer, the large structural mutations, rather than single site mutations, play a dominate role in tumor etiology. Therefore, it's critical to implement reliable algorithm for the detection of structural variants in DNA sequencing data. Mate-pair sequencing is a protocol specifically implemented for detection of the whole-genome level structural variants. It requires less sequencing depth therefor is cost effective, and enables the detection of CNVs, translocations, and inversions simultaneously. However, so far there has been no reliable bioinformatics pipeline for the analyses of the mate-pair sequencing data. Methods: Our novel algorithm, the SnowShoes-SV, is an exhaustive search algorithm designed specifically for mate-pair DNA sequencing data analyses. It calls the SVs based on disconcordant read pairs. The false SVs are filtered according to the following criteria: (i) the number of the supporting read pairs; (ii) the lack of reads from control data that implicate SV at the same region; (iii) the mappability and uniqueness of the region based on data from the ENCODE project; (iv) consistencies of the mapping orientations of the supporting read pairs; (v) the similar sizes between sequencing library and the two end read clusters. Results: Using a set of samples previously genotyped by aCGH, the SnowShoes-SV successfully detected all known CNVs and other SVs. It also identified copy number neutral translocations and inversions previously not identified by aCGH. In addition, the algorithm nominated novel SVs which are to be validated by PCR. Conclusions: SnowShoes-SV is a highly sensitive and specific algorithm for SV detection from the mate-pair DNA sequencing data. Citation Format: Yan W Asmann, Chen Wang, Brian M Necela, Xianfeng Chen, Jean-Pierre A Kocher, Matthew J Maurer, Thomas M Habermann, Susan L Slager, Andrew L Feldman, Anne J Novak, James R Cerhan, Edith A Perez, E Aubrey Thompson. An exhaustive algorithm for detecting copy number aberrations and large structural variants in whole-genome mate-pair sequencing data [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-07-03.