MOTIVATION: The bisulfite sequencing technology has been widely used to study the DNA methylation profile in many species. However, most of the current visualization tools for bisulfite sequencing data only provide high-level views (i.e. overall methylation densities) while miss the methylation dynamics at nucleotide level. Meanwhile, they also focus on CpG sites while omit other information (such as genotypes on SNP sites) which could be helpful for interpreting the methylation pattern of the data. A bioinformatics tool that visualizes the methylation statuses at nucleotide level and preserves the most essential information of the sequencing data is thus valuable and needed. RESULTS: We have developed BSviewer, a lightweight nucleotide-level visualization tool for bisulfite sequencing data. Using an imprinting gene as an example, we show that BSviewer could be specifically helpful for interpreting the data with allele-specific DNA methylation pattern. AVAILABILITY AND IMPLEMENTATION: BSviewer is implemented in Perl and runs on most GNU/Linux platforms. Source code and testing dataset are freely available at http://sunlab.cpy.cuhk.edu.hk/BSviewer/. CONTACT: [email protected].

One of the key challenges facing genomics today is how to efficiently analyze the massive amounts of data produced by next-generation sequencing platforms. With general-purpose computing systems struggling to address this challenge, specialized processors such as the Field-Programmable Gate Array (FPGA) are receiving growing interest. The means by which to leverage this technology for accelerating genomic data analysis is however largely unexplored. In this paper, we present a runtime reconfigurable architecture for accelerating short read alignment using FPGAs. This architecture exploits the reconfigurability of FPGAs to allow the development of fast yet flexible alignment designs. We apply this architecture to develop an alignment design which supports exact and approximate alignment with up to two mismatches. Our design is based on the FM-index, with optimizations to improve the alignment performance. In particular, the n-step FM-index, index oversampling, a seed-and-compare stage, and bi-directional backtracking are included. Our design is implemented and evaluated on a 1U Maxeler MPC-X2000 dataflow node with eight Altera Stratix-V FPGAs. Measurements show that our design is 28 times faster than Bowtie2 running with 16 threads on dual Intel Xeon E5-2640 CPUs, and nine times faster than Soap3-dp running on an NVIDIA Tesla C2070 GPU.

This paper proposes a novel reconfigurable architecture for accelerating DNA sequence alignment. This architecture is applied to bisulfite sequence alignment, a stage in recently developed bioinformatics pipelines for cancer and non-invasive prenatal diagnosis. Alignment is currently the bottleneck in such pipelines, accounting for over 50% of the total analysis time. Our design, Ramethy (Reconfigurable Acceleration of METHYlation data analysis), performs alignment of short reads with up to two mismatches. Ramethy is based on the FM-index, which we optimise to reduce the number of search steps and improve approximate matching performance. We implement Ramethy on a 1U Maxeler MPC-X1000 data flow node consisting of 8 Altera Stratix-V FPGAs. Measured results show a 14.9 times speedup compared to soap2 running with 16 threads on dual Intel Xeon E5-2650 CPUs, and 3.8 times speedup compared to soap3-dp running on an NVIDIA GTX 580 GPU. Upper-bound performance estimates for the MPC-X1000 indicate a maximum speedup of 88.4 times and 22.6 times compared to soap2 and soap3-dp respectively. In addition to runtime, Ramethy consumes over an order of magnitude lower energy while having accuracy identical to soap2 and soap3-dp, making it a strong candidate for integration into bioinformatics pipelines.

Recent improvements in the throughput of nextgeneration DNA sequencing machines poses a great computational challenge in analysing the massive quantities of data produced. This paper proposes a novel approach, based on reconfigurable computing technology, for accelerating short read mapping, where the positions of millions of short reads are located relative to a known reference sequence. Our approach consists of two key components: an exact string matcher for the bulk of the alignment process, and an approximate string matcher for the remaining cases. We characterise interesting regions of the design space, including homogeneous, heterogeneous and run-time reconfigurable designs and provide back of envelope estimations of the corresponding performance. We show that a particular implementation of this architecture targeting a single FPGA can be up to 293 times faster than BWA on an Intel X5650 CPU, and 134 times faster than SOAP3 on an NVIDIA GTX 580 GPU.

Recent trends in the cost and demand of next generation DNA sequencing (NGS) has revealed a great computational challenge in analysing the massive quantities of sequenced data produced. Given that the projected increase in sequenced data far outstrips Moore's Law, the current technologies used to handle the data are likely to become insufficient. This paper explores the use of reconfigurable hardware in accelerating short read alignment. In this application, the positions of millions of short DNA sequences (called reads) are located in a known reference genome. This work proposes a new general approach for accelerating suffix-trie based short read alignment methods using reconfigurable hardware. In the proposed approach, specialised filters are designed to align short reads to a reference genome with a specific edit distance. The filters are arranged in a pipeline according to increasing edit distance, where short reads unable to be aligned by a given filter are forwarded to the next filter in the pipeline for further processing. Run-time reconfiguration is used to fully populate an accelerator device with each filter in the pipeline in turn. In our implementation a single FPGA is populated with specialised filters based on a novel bidirectional backtracking version of the FM-index, and it is found that in this particular implementation the alignment time can be up to 14.7 and 18.1 times faster than SOAP2 and BWA run on dual Intel X5650 CPUs.

MOTIVATION: The fractional fetal DNA concentration is one of the critical parameters for non-invasive prenatal diagnosis based on the analysis of DNA in maternal plasma. Massively parallel sequencing (MPS) of DNA in maternal plasma has been demonstrated to be a powerful tool for the non-invasive prenatal diagnosis of fetal chromosomal aneuploidies. With the rapid advance of MPS technologies, the sequencing cost per base is dramatically reducing, especially when using targeted MPS. Even though several approaches have been developed for deducing the fractional fetal DNA concentration, none of them can be used to deduce the fractional fetal DNA concentration directly from the sequencing data without prior genotype information. RESULT: In this study, we implement a statistical mixture model, named FetalQuant, which utilizes the maximum likelihood to estimate the fractional fetal DNA concentration directly from targeted MPS of DNA in maternal plasma. This method allows the improved deduction of the fractional fetal DNA concentration, obviating the need of genotype information without loss of accuracy. Furthermore, by using Bayes' rule, this method can distinguish the informative single-nucleotide polymorphism loci where the mother is homozygous and the fetus is heterozygous. We believe that FetalQuant can help expand the spectrum of diagnostic applications using MPS on DNA in maternal plasma. AVAILABILITY: Software and simulation data are available at http://sourceforge.net/projects/fetalquant/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.