Sequence Alignment Applications Research Articles

BSAlign: A Library for Nucleotide Sequence Alignment.

Increasing the accuracy of the nucleotide sequence alignment is an essential issue in genomics research. Although classic dynamic programming (DP) algorithms (e.g., Smith-Waterman and Needleman-Wunsch) guarantee to produce the optimal result, their time complexity hinders the application of large-scale sequence alignment. Many optimization efforts that aim to accelerate the alignment process generally come from three perspectives: redesigning data structures [e.g., diagonal or striped Single Instruction Multiple Data (SIMD) implementations], increasing the number of parallelisms in SIMD operations (e.g., difference recurrence relation), or reducing search space (e.g., banded DP). However, no methods combine all these three aspects to build an ultra-fast algorithm. In this study, we developed a Banded Striped Aligner (BSAlign) library that delivers accurate alignment results at an ultra-fast speed by knitting a series of novel methods together to take advantage of all of the aforementioned three perspectives with highlights such as active F-loop in striped vectorization and striped move in banded DP. We applied our new acceleration design on both regular and edit distance pairwise alignment. BSAlign achieved 2-fold speed-up than other SIMD-based implementations for regular pairwise alignment, and 1.5-fold to 4-fold speed-up in edit distance-based implementations for long reads. BSAlign is implemented in C programing language and is available at https://github.com/ruanjue/bsalign.

A comprehensive estimation of country-level basic reproduction numbers R0 for COVID-19: Regime regression can automatically estimate the end of the exponential phase in epidemic data.

In a compartmental epidemic model, the initial exponential phase reflects a fixed interaction between an infectious agent and a susceptible population in steady state, so it determines the basic reproduction number R0 on its own. After the exponential phase, dynamic complexities like societal responses muddy the practical interpretation of many estimated parameters. The computer program ARRP, already available from sequence alignment applications, automatically estimated the end of the exponential phase in COVID-19 and extracted the exponential growth rate r for 160 countries. By positing a gamma-distributed generation time, the exponential growth method then yielded R0 estimates for COVID-19 in 160 countries. The use of ARRP ensured that the R0 estimates were largely freed from any dependency outside the exponential phase. The Prem matrices quantify rates of effective contact for infectious disease. Without using any age-stratified COVID-19 data, but under strong assumptions about the homogeneity of susceptibility, infectiousness, etc., across different age-groups, the Prem contact matrices also yielded theoretical R0 estimates for COVID-19 in 152 countries, generally in quantitative conflict with the R0 estimates derived from the exponential growth method. An exploratory analysis manipulating only the Prem contact matrices reduced the conflict, suggesting that age-groups under 20 years did not promote the initial exponential growth of COVID-19 as much as other age-groups. The analysis therefore supports tentatively and tardily, but independently of age-stratified COVID-19 data, the low priority given to vaccinating younger age groups. It also supports the judicious reopening of schools. The exploratory analysis also supports the possibility of suspecting differences in epidemic spread among different age-groups, even before substantial amounts of age-stratified data become available.

Enabling fast and energy-efficient FM-index exact matching using processing-near-memory

Memory bandwidth and latency constitutes a major performance bottleneck for many data-intensive applications. While high-locality access patterns take advantage of the deep cache hierarchies available in modern processors, unpredictable low-locality patterns cause a significant part of the execution time to be wasted waiting for data. An example of those memory bound applications is the exact matching algorithm based on FM-index, used in some well-known sequence alignment applications. Processing-Near-Memory (PNM) has been proposed as a strategy to overcome the memory wall problem, by placing computation close to data, speeding up memory bound workloads by reducing data movements. This paper presents a performance and energy evaluation of two classes of processor architectures when executing the FM-index exact matching algorithm, as a reference algorithm for exact sequence alignment. One architecture class is processor-centric, based on complex cores and DDR3/4 SDRAM memory technology. The other architecture class is memory-centric, based on simple cores and ultra-high-bandwidth hybrid memory cube (HMC) 3D-stacked memory technologies. The results show that the PNM solution improves performance between 1.26 $$\times$$ and 3.7 $$\times$$ and the energy consumption per operation is reduced between 21 $$\times$$ and 40 $$\times$$ . In addition, a synthetic benchmark RANDOM was developed that mimics the memory access pattern of the FM-index exact matching algorithm, but with a user configurable operational intensity. This benchmark allows us to extend the evaluation to the class of algorithms with similar memory behaviour but running over a range of operational intensity values.

Efficient Multiple Sequences Alignment Algorithm Generation via Components Assembly Under PAR Framework.

As a key algorithm in bioinformatics, sequence alignment algorithm is widely used in sequence similarity analysis and genome sequence database search. Existing research focuses mainly on the specific steps of the algorithm or is for specific problems, lack of high-level abstract domain algorithm framework. Multiple sequence alignment algorithms are more complex, redundant, and difficult to understand, and it is not easy for users to select the appropriate algorithm; some computing errors may occur. Based on our constructed pairwise sequence alignment algorithm component library and the convenient software platform PAR, a few expansion domain components are developed for multiple sequence alignment application domain, and specific multiple sequence alignment algorithm can be designed, and its corresponding program, i.e., C++/Java/Python program, can be generated efficiently and thus enables the improvement of the development efficiency of complex algorithms, as well as accuracy of sequence alignment calculation. A star alignment algorithm is designed and generated to demonstrate the development process.

Cherry senescence – a response to application of ABA and partial sequence alignment of α-mannosidase gene

Cherry senescence – a response to application of ABA and partial sequence alignment of α-mannosidase gene

SparkBLAST: scalable BLAST processing using in-memory operations

BackgroundThe demand for processing ever increasing amounts of genomic data has raised new challenges for the implementation of highly scalable and efficient computational systems. In this paper we propose SparkBLAST, a parallelization of a sequence alignment application (BLAST) that employs cloud computing for the provisioning of computational resources and Apache Spark as the coordination framework. As a proof of concept, some radionuclide-resistant bacterial genomes were selected for similarity analysis.ResultsExperiments in Google and Microsoft Azure clouds demonstrated that SparkBLAST outperforms an equivalent system implemented on Hadoop in terms of speedup and execution times.ConclusionsThe superior performance of SparkBLAST is mainly due to the in-memory operations available through the Spark framework, consequently reducing the number of local I/O operations required for distributed BLAST processing.

FPGASW: Accelerating Large-Scale Smith-Waterman Sequence Alignment Application with Backtracking on FPGA Linear Systolic Array.

The Smith-Waterman (SW) algorithm based on dynamic programming is a well-known classical method for high precision sequence matching and has become the gold standard to evaluate sequence alignment software. In this paper, we propose fine-grained parallelized SW algorithms using affine gap penalty and implement a parallel computing structures to accelerating the SW with backtracking on FPGA platform. We analysis the dynamic parallel computing features of anti-diagonal elements and storage expansion problem resulting from backtracking stage, and propose a series of optimization strategies to eliminate data dependency, reduce storage requirements, and overlap memory access latency. Our implementation is capable of supporting multi-type, large-scale biological sequence alignment applications. We obtain a speedup between 3.6 and 25.2 over the typical SW algorithm running on a general-purpose computer configured with an Intel Core i5 3.2 GHz CPU. Moreover, our work is superior to other FPGA implementations in both array size and clock frequency, and the experiment results show that it can get a performance closed to that of the latest GPU implementation, but the power consumption is only about 26% of that of the GPU platforms.

KCMBT: a k-mer Counter based on Multiple Burst Trees.

A massive number of bioinformatics applications require counting of k-length substrings in genetically important long strings. A k-mer counter generates the frequencies of each k-length substring in genome sequences. Genome assembly, repeat detection, multiple sequence alignment, error detection and many other related applications use a k-mer counter as a building block. Very fast and efficient algorithms are necessary to count k-mers in large data sets to be useful in such applications. We propose a novel trie-based algorithm for this k-mer counting problem. We compare our devised algorithm k-mer Counter based on Multiple Burst Trees (KCMBT) with available all well-known algorithms. Our experimental results show that KCMBT is around 30% faster than the previous best-performing algorithm KMC2 for human genome dataset. As another example, our algorithm is around six times faster than Jellyfish2. Overall, KCMBT is 20-30% faster than KMC2 on five benchmark data sets when both the algorithms were run using multiple threads. KCMBT is freely available on GitHub: (https://github.com/abdullah009/kcmbt_mt). rajasek@engr.uconn.edu Supplementary data are available at Bioinformatics online.

A Novel Distance Metric for Aligning Multiple Sequences Using DNA Hybridization Process

This paper elucidates a new approach for aligning mult iple sequences using DNA operations. A new distance measure using DNA hybridization melt ing temperature that gives appro ximate solutions for the mu ltip le sequence alignment (MSA ) problem is proposed. This paper provides proof for the proposed distance measure using the distance function properties. With this distance metric, a d istance measure is constructed that generates a guide tree for the align ment. Prov iding an accurate solution in less computational t ime is considered to be a challenging task for the MSA problem. Developing an algorith m for the MSA problem is essentially a trade-off between finding an accurate solution and that can be completed in less computational time. In order to reduce the time co mplexity, the Bio- inspired technique called the DNA co mputing is applied in calculat ing the distance between the sequences. The main application of this mu ltiple sequence alignment (MSA) is to identify the sub-sequences for the functional study of the whole genome sequences. The detailed theoretical study of this approach is explained in this paper.

CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU.

BackgroundHMMER software suite is widely used for analysis of homologous protein and nucleotide sequences with high sensitivity. The latest version of hmmsearch in HMMER 3.x, utilizes heuristic-pipeline which consists of MSV/SSV (Multiple/Single ungapped Segment Viterbi) stage, P7Viterbi stage and the Forward scoring stage to accelerate homology detection. Since the latest version is highly optimized for performance on modern multi-core CPUs with SSE capabilities, only a few acceleration attempts report speedup. However, the most compute intensive tasks within the pipeline (viz., MSV/SSV and P7Viterbi stages) still stand to benefit from the computational capabilities of massively parallel processors.ResultsA Multi-Tiered Parallel Framework (CUDAMPF) implemented on CUDA-enabled GPUs presented here, offers a finer-grained parallelism for MSV/SSV and Viterbi algorithms. We couple SIMT (Single Instruction Multiple Threads) mechanism with SIMD (Single Instructions Multiple Data) video instructions with warp-synchronism to achieve high-throughput processing and eliminate thread idling. We also propose a hardware-aware optimal allocation scheme of scarce resources like on-chip memory and caches in order to boost performance and scalability of CUDAMPF. In addition, runtime compilation via NVRTC available with CUDA 7.0 is incorporated into the presented framework that not only helps unroll innermost loop to yield upto 2 to 3-fold speedup than static compilation but also enables dynamic loading and switching of kernels depending on the query model size, in order to achieve optimal performance.ConclusionsCUDAMPF is designed as a hardware-aware parallel framework for accelerating computational hotspots within the hmmsearch pipeline as well as other sequence alignment applications. It achieves significant speedup by exploiting hierarchical parallelism on single GPU and takes full advantage of limited resources based on their own performance features. In addition to exceeding performance of other acceleration attempts, comprehensive evaluations against high-end CPUs (Intel i5, i7 and Xeon) shows that CUDAMPF yields upto 440 GCUPS for SSV, 277 GCUPS for MSV and 14.3 GCUPS for P7Viterbi all with 100 % accuracy, which translates to a maximum speedup of 37.5, 23.1 and 11.6-fold for MSV, SSV and P7Viterbi respectively. The source code is available at https://github.com/Super-Hippo/CUDAMPF.

MASA

Biological sequence alignment is a very popular application in Bioinformatics, used routinely worldwide. Many implementations of biological sequence alignment algorithms have been proposed for multicores, GPUs, FPGAs and CellBEs. These implementations are platform-specific; porting them to other systems requires considerable programming effort. This article proposes and evaluates MASA, a flexible and customizable software architecture that enables the execution of biological sequence alignment applications with three variants (local, global, and semiglobal) in multiple hardware/software platforms with block pruning, which is able to reduce significantly the amount of data processed. To attain our flexibility goals, we also propose a generic version of block pruning and developed multiple parallelization strategies as building blocks, including a new asynchronous dataflow-based parallelization, which may be combined to implement efficient aligners in different platforms. We provide four MASA aligner implementations for multicores (OmpSs and OpenMP), GPU (CUDA), and Intel Phi (OpenMP), showing that MASA is very flexible. The evaluation of our generic block pruning strategy shows that it significantly outperforms the previously proposed block pruning, being able to prune up to 66.5% of the cells when using the new dataflow-based parallelization strategy.

A run-time reconfigurable system for adaptive high performance efficient computing

Field programmable hardware gives electronic systems the ability to be reconfigured at run time. This allows electronic systems to be more efficiently customized on demand and on-the-fly depending on user requirements and environmental changes. This paper presents a run-time reconfigurable system that allows computing tasks to adjust their sizes in response to current available resources, optimizing the overall performance by maximally exploiting all the resources on the chip. In particular, we present a novel run-time task assembler, which assembles tasks with desired parameters on-the-fly, together with an efficacious run-time task placer to rapidly configure tasks at optimum locations. The system is demonstrated with a dynamic programming-based pairwise sequence alignment application. Real hardware implementation result shows that our run-time reconfigurable system optimizes resource usage on the fly by ~ 3x, while matching the performance of carefully hand-crafted static solutions.

Performance Evaluation for Field Programmable Gate Array-based Bioinformatics Sequence Alignment

Sequence alignment is a computationally expensive activity in bioinformatics that can benefit significantly from Field Programmable Gate Array-based high-performance computing systems. This paper evaluates the performance of such systems for bioinformatics sequence alignment applications considering various parameters like data bus width (W), the number of processing elements, and the maximum operating frequency.

Performance versus Power Analysis for Bioinformatics Sequence Alignment

Due to the utilization of abundant hardware resources, power consumption is becoming an important constraint forbioinformatics sequence alignment applications. In this paper, the dynamic power consumption for such applicationsand its impact on performance is evaluated. Additionally, resource utilization and performance results are provided forimplementation with a number of different FPGA platforms. The results obtained using Xilinx ISE tools and Matlabdemonstrate that the performance per unit Watt increases rapidly when increasing the number of ProcessingElements (PEs). Increasing the number of PEs beyond a certain number slows down the performance per unit Wattsignificantly. This behavior is used for approximating the number of PEs that gives an optimized performance per unitWatt.

A Storage-Efficient Suffix Tree Construction Algorithm for Human Genome Sequences

The suffix tree is one of most widely adopted indexes in the application of genome sequence alignment. Although it supports very fast alignment, it has a couple of shortcomings, such as a very long construction time and a very large volume size. Loh et al. [7] proposed a suffix tree construction algorithm with dramatically improved performance; however, the size still remains as a challenging problem. We propose an algorithm by extending the one by Loh et al. to reduce the suffix tree size. As a result of our experiments, our algorithm constructed a suffix tree of approximately 60% of the size within almost the same time period.

Cloud Technologies for Bioinformatics Applications

Executing large number of independent jobs or jobs comprising of large number of tasks that perform minimal intertask communication is a common requirement in many domains. Various technologies ranging from classic job schedulers to the latest cloud technologies such as MapReduce can be used to execute these "many-tasks” in parallel. In this paper, we present our experience in applying two cloud technologies Apache Hadoop and Microsoft DryadLINQ to two bioinformatics applications with the above characteristics. The applications are a pairwise Alu sequence alignment application and an Expressed Sequence Tag (EST) sequence assembly program. First, we compare the performance of these cloud technologies using the above applications and also compare them with traditional MPI implementation in one application. Next, we analyze the effect of inhomogeneous data on the scheduling mechanisms of the cloud technologies. Finally, we present a comparison of performance of the cloud technologies under virtual and nonvirtual hardware platforms.

No so HoT - heads or tails is not able to reliably compare multiple sequence alignments.

Most phylogenetic-tree building applications use multiple sequence alignments as a starting point. A recent meta-level methodology, called Heads or Tails, aims to reveal the quality of multiple sequence alignments by comparing alignments taken in the forward direction with the alignments of the same sequences when the sequences are reversed. Through an examination of a special case for multiple sequence alignment - pair-wise alignments, where an optimal algorithm exists - and the use of a modified global-alignment application, it is shown that the forward and reverse alignments, even when they are the same, do not capture all the possible variations in the alignments and when the forward and reverse alignments differ there may be other alignments that remain unaccounted for. The implication is that comparing just the forward and (biologically irrelevant) reverse alignments is not sufficient to capture the variability in multiple sequence alignments, and the Heads or Tails methodology is therefore not suitable as a method for investigating multiple sequence alignment accuracy. Part of the reason is the inability of individual multiple sequence alignment applications to adequately sample the space of possible alignments. A further implication is that the Hall [Hall, B.G., 2008. Mol. Biol. Evol. 25, 1576-1580] methodology may create optimal synthetic multiple sequence alignments that extant aligners will be unable to completely recover ab initio due to alternative alignments being possible at particular sites. In general, it is shown that more divergent sequences will give rise to an increased number of alternative alignments, so sequence sets with a higher degree of similarity are preferable to sets with lower similarity as the starting point for phylogenetic tree building. © The Willi Hennig Society 2009.

CBESW: Sequence Alignment on the Playstation 3

BackgroundThe exponential growth of available biological data has caused bioinformatics to be rapidly moving towards a data-intensive, computational science. As a result, the computational power needed by bioinformatics applications is growing exponentially as well. The recent emergence of accelerator technologies has made it possible to achieve an excellent improvement in execution time for many bioinformatics applications, compared to current general-purpose platforms. In this paper, we demonstrate how the PlayStation® 3, powered by the Cell Broadband Engine, can be used as a computational platform to accelerate the Smith-Waterman algorithm.ResultsFor large datasets, our implementation on the PlayStation® 3 provides a significant improvement in running time compared to other implementations such as SSEARCH, Striped Smith-Waterman and CUDA. Our implementation achieves a peak performance of up to 3,646 MCUPS.ConclusionThe results from our experiments demonstrate that the PlayStation® 3 console can be used as an efficient low cost computational platform for high performance sequence alignment applications.

Distributed Sequence Alignment Applications for the Public Computing Architecture

The public computer architecture shows promise as a platform for solving fundamental problems in bioinformatics such as global gene sequence alignment and data mining with tools such as the basic local alignment search tool (BLAST). Our implementation of these two problems on the Berkeley open infrastructure for network computing (BOINC) platform demonstrates a runtime reduction factor of 1.15 for sequence alignment and 16.76 for BLAST. While the runtime reduction factor of the global gene sequence alignment application is modest, this value is based on a theoretical sequential runtime extrapolated from the calculation of a smaller problem. Because this runtime is extrapolated from running the calculation in memory, the theoretical sequential runtime would require 37.3 GB of memory on a single system. With this in mind, the BOINC implementation not only offers the reduced runtime, but also the aggregation of the available memory of all participant nodes. If an actual sequential run of the problem were compared, a more drastic reduction in the runtime would be seen due to an additional secondary storage I/O overhead for a practical system. Despite the limitations of the public computer architecture, most notably in communication overhead, it represents a practical platform for grid- and cluster-scale bioinformatics computations today and shows great potential for future implementations.

Methods and application of genomic sequence alignment and alignment between genomic sequence and transcripts

Methods and application of genomic sequence alignment and alignment between genomic sequence and transcripts