BigBWA: approaching the Burrows–Wheeler aligner to Big Data technologies

Abuín, José Manuel; Pichel, Juan C.; Pena, Tomás F.; Amigo, Jorge

doi:10.1093/bioinformatics/btv506

Cited by 125 publications

(88 citation statements)

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“…Many sequence aligners which use big data technologies like Apache Hadoop and Spark were implemented in last few years. CloudBurst [22], CloudAligner [23], Halvade [27], SEAL [33], BigBWA [25] and SparkBWA [26] are mostly used sequence aligners which use big data technologies.…”

Section: Related Workmentioning

confidence: 99%

“…Big data technologies like Hadoop [19] and Spark [20] based on the MapReduce paradigm use distributed computing to handle massive volumes of data very effectively and efficiently. Early initiatives towards the trend of using MapReduce [21] based platforms like Hadoop and Spark for sequence alignment have already been taken, such as CloudBurst [22], CloudAligner [23], BlastReduce [24], BigBWA [25] and SparkBWA [26] to name a few. The results are very effective and promising.…”

mentioning

confidence: 99%

“…Sequence alignment tools like BigBWA [25], Halvade [27] and SparkBWA [26] are very accurate but they suffer from high time/space complexity for index generation.…”

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confidence: 99%

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StreamAligner: a streaming based sequence aligner on Apache Spark

Rathee

Kashyap

2018

J Big Data

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

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StreamAligner: a streaming based sequence aligner on Apache Spark

Rathee

Kashyap

2018

J Big Data

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“…al. [1] presented a big data approach to solving BWA scalability problems. They introduce a tool named BigBWA that enables them to run BWA on several machines although it does not provide a clear strategy for dividing the data or setting the number of instances.…”

Section: Related Workmentioning

confidence: 99%

A performance comparison of data and memory allocation strategies for sequence aligners on NUMA architectures

Lenis

Senar

2017

Cluster Comput

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Over the last several years, many sequence alignment tools have appeared and become popular for the fast evolution of next generation sequencing technologies. Obviously, researchers that use such tools are interested in getting maximum performance when they execute them in modern infrastructures. Today's NUMA (Non-uniform memory access) architectures present major challenges in getting such applications to achieve good scalability as more processors/cores are used. The memory system in NUMA systems shows a high complexity and may be the main cause for the loss of an application's performance. The existence of several memory banks in NUMA systems implies a logical increase in latency associated with the accesses of a given processor to a remote bank. This phenomenon is usually attenuated by the application of strategies that tend to increase the locality of memory accesses. However, NUMA systems may also suffer from contention problems that can occur when concurrent accesses are concentrated on a reduced number of banks. Sequence alignment tools use large data structures to contain reference genomes to which all reads are aligned. Therefore, these tools are very sensitive to performance problems related to the memory system. The main goal of this study is to explore the trade-offs between data locality and data dispersion in NUMA systems. We have performed experiments with several popular sequence alignment tools on two This work has been supported by MINECO-Spain under contract TIN2014-53234-C2-1-R. widely available NUMA systems to assess the performance of different memory allocation policies and data partitioning strategies. We find that there is not one method that is best in all cases. However, we conclude that memory interleaving is the memory allocation strategy that provides the best performance when a large number of processors and memory banks are used. In the case of data partitioning, the best results are usually obtained when the number of partitions used is greater, sometimes combined with an interleave policy.

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“…Big data technology, also called mass data mining technology, is characterized by large capacity, multiple types and high timeliness [1]. After 2012, the word big data was mentioned more and more.…”

Section: Introductionmentioning

confidence: 99%

Application of a Big Data Platform in the Course of Java Language Programming

Lin

2016

Int. J. Emerg. Technol. Learn.

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Abstract-This paper designed a big data teaching platform which adopts Hadoop technology in big data technology as its core. It provides a method to search teaching video data and integrates security technology and intelligent recognition technology. The teaching functions of this platform include teaching audio-visual presentation, intelligent data recognition, data mining sharing, data storage sharing and so on. The platform has strong advancement. To test the actual effect of this teaching platform in teaching Java language Programming, teaching contrast method was adopted. The class of computer specialty was chosen for teaching application analysis. The results show that the platform has good teaching applicability and significantly improves students' examination scores, learning interest, autonomous learning ability and innovation ability, so it owns very good application prospect.

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BigBWA: approaching the Burrows–Wheeler aligner to Big Data technologies

Abstract: BigBWA is available at the project GitHub repository: https://github.com/citiususc/BigBWA.

Cited by 125 publications

References 5 publications

StreamAligner: a streaming based sequence aligner on Apache Spark

StreamAligner: a streaming based sequence aligner on Apache Spark

A performance comparison of data and memory allocation strategies for sequence aligners on NUMA architectures

Application of a Big Data Platform in the Course of Java Language Programming

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