Xiaoqian Zhu

SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data

We have developed a new method, SOAPfuse, to identify fusion transcripts from paired-end RNA-Seq data. SOAPfuse applies an improved partial exhaustion algorithm to construct a library of fusion junction sequences, which can be used to efficiently identify fusion events, and employs a series of filters to nominate high-confidence fusion transcripts. Compared with other released tools, SOAPfuse achieves higher detection efficiency and consumed less computing resources. We applied SOAPfuse to RNA-Seq data from two bladder cancer cell lines, and confirmed 15 fusion transcripts, including several novel events common to both cell lines. SOAPfuse is available at http://soap.genomics.org.cn/soapfuse.html.

SOAP3-dp: Fast, Accurate and Sensitive GPU-Based Short Read Aligner

To tackle the exponentially increasing throughput of Next-Generation Sequencing (NGS), most of the existing short-read aligners can be configured to favor speed in trade of accuracy and sensitivity. SOAP3-dp, through leveraging the computational power of both CPU and GPU with optimized algorithms, delivers high speed and sensitivity simultaneously. Compared with widely adopted aligners including BWA, Bowtie2, SeqAlto, CUSHAW2, GEM and GPU-based aligners BarraCUDA and CUSHAW, SOAP3-dp was found to be two to tens of times faster, while maintaining the highest sensitivity and lowest false discovery rate (FDR) on Illumina reads with different lengths. Transcending its predecessor SOAP3, which does not allow gapped alignment, SOAP3-dp by default tolerates alignment similarity as low as 60%. Real data evaluation using human genome demonstrates SOAP3-dps power to enable more authentic variants and longer Indels to be discovered. Fosmid sequencing shows a 9.1% FDR on newly discovered deletions. SOAP3-dp natively supports BAM file format and provides the same scoring scheme as BWA, which enables it to be integrated into existing analysis pipelines. SOAP3-dp has been deployed on Amazon-EC2, NIH-Biowulf and Tianhe-1A.

Genome-wide adaptive complexes to underground stresses in blind mole rats Spalax

The blind mole rat (BMR), Spalax galili, is an excellent model for studying mammalian adaptation to life underground and medical applications. The BMR spends its entire life underground, protecting itself from predators and climatic fluctuations while challenging it with multiple stressors such as darkness, hypoxia, hypercapnia, energetics and high pathonecity. Here we sequence and analyse the BMR genome and transcriptome, highlighting the possible genomic adaptive responses to the underground stressors. Our results show high rates of RNA/DNA editing, reduced chromosome rearrangements, an over-representation of short interspersed elements (SINEs) probably linked to hypoxia tolerance, degeneration of vision and progression of photoperiodic perception, tolerance to hypercapnia and hypoxia and resistance to cancer. The remarkable traits of the BMR, together with its genomic and transcriptomic information, enhance our understanding of adaptation to extreme environments and will enable the utilization of BMR models for biomedical research in the fight against cancer, stroke and cardiovascular diseases.

Interface structure and work function of W-Cu interfaces

First principles calculation reveals that W-Cu interfaces have high interface strength when the number of overlayers is less than 2, and that (111)Cu/(110)W and (110)Cu/(110)W interfaces with one overlayer are both energetically favorable with big negative interface energies. Calculation also shows that negative interface energy serves as the driving force for interdiffusion and alloying of immiscible W and Cu, and that interface orientation fundamentally induces different behaviors of work functions of W-Cu interfaces. The calculated results agree well with experimental observations, and clarify two experimental controversies regarding interface stability and work function of W-Cu interfaces in the literature.

mBWA: A Massively Parallel Sequence Reads Aligner

Mapping sequenced reads to a reference genome, also known as sequence reads alignment, is central for sequence analysis. Emerging sequencing technologies such as next generation sequencing (NGS) lead to an explosion of sequencing data, which is far beyond the process capabilities of existing alignment tools. Consequently, sequence alignment becomes the bottleneck of sequence analysis. Intensive computing power is required to address this challenge. A key feature of sequence alignment is that different reads are independent. Considering this property, we proposed a multi-level parallelization strategy to speed up BWA, a widely used sequence alignment tool and developed our massively parallel sequence aligner: mBWA. mBWA contains two levels of parallelization: firstly, parallelization of data input/output (IO) and reads alignment by a three-stage parallel pipeline; secondly, parallelization enabled by Intel Many Integrated Core (MIC) coprocessor technology. In this paper, we demonstrate that mBWA outperforms BWA by a combination of those techniques. To the best of our knowledge, mBWA is the first sequence alignment tool to run on Intel MIC and it can achieve more than 5-fold speedup over the original BWA while maintaining the alignment precision.

Corrigendum: Genome-wide adaptive complexes to underground stresses in blind mole rats Spalax

Corrigendum: Genome-wide adaptive complexes to underground stresses in blind mole rats Spalax

Erratum: Corrigendum: Genome-wide adaptive complexes to underground stresses in blind mole rats Spalax

Corrigendum: Genome-wide adaptive complexes to underground stresses in blind mole rats Spalax

B-MIC: An Ultrafast Three-Level Parallel Sequence Aligner Using MIC

Sequence alignment is the central process for sequence analysis, where mapping raw sequencing data to reference genome. The large amount of data generated by NGS is far beyond the process capabilities of existing alignment tools. Consequently, sequence alignment becomes the bottleneck of sequence analysis. Intensive computing power is required to address this challenge. Intel recently announced the MIC coprocessor, which can provide massive computing power. The Tianhe-2 is the world’s fastest supercomputer now equipped with three MIC coprocessors each compute node. A key feature of sequence alignment is that different reads are independent. Considering this property, we proposed a MIC-oriented three-level parallelization strategy to speed up BWA, a widely used sequence alignment tool, and developed our ultrafast parallel sequence aligner: B-MIC. B-MIC contains three levels of parallelization: firstly, parallelization of data IO and reads alignment by a three-stage parallel pipeline; secondly, parallelization enabled by MIC coprocessor technology; thirdly, inter-node parallelization implemented by MPI. In this paper, we demonstrate that B-MIC outperforms BWA by a combination of those techniques using Inspur NF5280M server and the Tianhe-2 supercomputer. To the best of our knowledge, B-MIC is the first sequence alignment tool to run on Intel MIC and it can achieve more than fivefold speedup over the original BWA while maintaining the alignment precision.

BWTCP: A Parallel Method for Constructing BWT in Large Collection of Genomic Reads

Short-read alignment and assembly are fundamental procedures for analyses of DNA sequencing data. Many state-of-the-art short-read aligners employ Burrows-Wheeler transform (BWT) as an in-memory index for the reference genome. BWT has also found its use in genome assembly, for indexing the reads. In a typical data set, the volume of reads can be as large as several hundred Gigabases. Consequently, fast construction of the BWT index for reads is essential for an efficient sequence processing. In this paper, we present a parallel method called BWTCP for BWT construction at a large scale. BWTCP is characterized by its ability to harness heterogeneous computing power including multi-core CPU, multiple CPUs, and accelerators like GPU or Intel Xeon Phi. BWTCP is also featured by its novel pruning strategy. Using BWTCP, we managed to construct the BWT for 1 billion 100bp reads within 30 m using 16 compute nodes (2 CPUs per node) on Tianhe-2 Supercomputer. It significantly outperforms the baseline tool BCR, which would need 13 h to finish all processing for the same dataset. BWTCP is freely available at https://github.com/hwang91/BWTCP.