Structural variations (SVs) are deletions, duplications and rearrangements of medium to large segments (>100 base pairs (bp)) of the genome. Such genomic mutations are often described as being the primary cause of many diseases, including cancer. Breakpoint detection using next-generation sequencing (NGS) platforms still remains an open problem since computational methods to detect SVs face the big challenge of accurately predicting the precise location of breakpoints, which are typically spanned by a very small number of reads among millions that are generated during the sequencing process. In this work, we propose a method called TIDE to identify reads from paired-end sequencing data containing inter-chromosomal translocation or insertion breakpoints, which are specific types of SVs involving different chromosomes. To achieve this, we use discordant read pairs to narrow the search space and split prospective breakpoint-spanning reads into windows that are subsequently represented by a sequence of k-mers indexes, which we call fingerprints. We then apply a distance-preserving embedding algorithm to solve the approximate nearest neighbor problem of pairing the most similar fingerprints originated from the sample and reference genome. Experimental results show the efficacy of the method to find reads containing breakpoints characterizing the PAX8-PPARγ rearrangement found in thyroid cancer samples. We also compare our results with the ones provided by two recently published algorithms for detecting structural variation in clinical data.

SUMMARY: Pyrosequencing of 16S rDNA is widely used to study microbial communities, and a rich set of software tools support this analysis. Pyrosequencing of protein-coding genes, which can help elucidate functional differences among microbial communities, significantly lags behind 16S rDNA in availability of sequence analysis software. In both settings, frequent homopolymer read errors inflate the estimation of microbial diversity, and de-noising is required to reduce that bias. Here we describe FunFrame, an R-based data-analysis pipeline that uses recently described algorithms to de-noise functional gene pyrosequences and performs ecological analysis on de-noised sequence data. The novelty of this pipeline is that it provides users a unified set of tools, adapted from disparate sources and designed for different applications, that can be used to examine a particular protein coding gene of interest. We evaluated FunFrame on functional genes from four PCR-amplified clones with sequence depths ranging from 9084 to 14 494 sequences. FunFrame produced from one to nine Operational Taxanomic Units for each clone, resulting in an error rate ranging from 0 to 0.18%. Importantly, FunFrame reduced spurious diversity while retaining more sequences than a commonly used de-noising method that discards sequences with frameshift errors. AVAILABILITY: Software, documentation and a complete set of sample data files are available at http://faculty.www.umb.edu/jennifer.bowen/software/FunFrame.zip.