HiGene: A high-performance platform for genomic data analysis

Deng, Liqun; Huang, Guowei; Zhuang, Yuzheng; Wei, Jiansheng; Yan, Youliang

doi:10.1109/bibm.2016.7822584

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…It is released using dual licensing, as an open-source project, but with the option of a premium license with support. For more information, we would like to point the reader to our extensive documentation online, previous publications [1,2,7], or published use cases [8][9][10][11][12][13][14][15] in addition to this paper.…”

Section: Discussionmentioning

confidence: 99%

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Multithreaded variant calling in elPrep 5

et al. 2021

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We present elPrep 5, which updates the elPrep framework for processing sequencing alignment/map files with variant calling. elPrep 5 can now execute the full pipeline described by the GATK Best Practices for variant calling, which consists of PCR and optical duplicate marking, sorting by coordinate order, base quality score recalibration, and variant calling using the haplotype caller algorithm. elPrep 5 produces identical BAM and VCF output as GATK4 while significantly reducing the runtime by parallelizing and merging the execution of the pipeline steps. Our benchmarks show that elPrep 5 speeds up the runtime of the variant calling pipeline by a factor 8-16x on both whole-exome and whole-genome data while using the same hardware resources as GATK4. This makes elPrep 5 a suitable drop-in replacement for GATK4 when faster execution times are needed.

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Section: Discussionmentioning

confidence: 99%

“…This creates additional complexity from the implementation side, leading us to develop multiple new algorithms [1,2,7]. From a user's perspective, however, it makes elPrep a drop-in replacement for other tools, resulting in its adoption by different bioinformatics projects [8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Multithreaded variant calling in elPrep 5

et al. 2021

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“…The FWER increases with the increase of marker candidates [2,3]. It is investigated that there is a thoughtful issue of computation slant in genomic data, i.e., the size of the input file is the same while processing time of variant calling is still significantly different [4]. Single nucleotide polymorphism (SNP) is a variant of a single nucleotide which exists at a particular locus in the genome, where respective variant exists up-to noticeable degree in a population of a residence [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Fast and Scalable Workflow for SNPs Detection in Genome Sequences Using Hadoop Map-Reduce

Tahir

Sardaraz

2020

Genes

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Next generation sequencing (NGS) technologies produce a huge amount of biological data, which poses various issues such as requirements of high processing time and large memory. This research focuses on the detection of single nucleotide polymorphism (SNP) in genome sequences. Currently, SNPs detection algorithms face several issues, e.g., computational overhead cost, accuracy, and memory requirements. In this research, we propose a fast and scalable workflow that integrates Bowtie aligner with Hadoop based Heap SNP caller to improve the SNPs detection in genome sequences. The proposed workflow is validated through benchmark datasets obtained from publicly available web-portals, e.g., NCBI and DDBJ DRA. Extensive experiments have been performed and the results obtained are compared with Bowtie and BWA aligner in the alignment phase, while compared with GATK, FaSD, SparkGA, Halvade, and Heap in SNP calling phase. Experimental results analysis shows that the proposed workflow outperforms existing frameworks e.g., GATK, FaSD, Heap integrated with BWA and Bowtie aligners, SparkGA, and Halvade. The proposed framework achieved 22.46% more efficient F-score and 99.80% consistent accuracy on average. More, comparatively 0.21% mean higher accuracy is achieved. Moreover, SNP mining has also been performed to identify specific regions in genome sequences. All the frameworks are implemented with the default configuration of memory management. The observations show that all workflows have approximately same memory requirement. In the future, it is intended to graphically show the mined SNPs for user-friendly interaction, analyze and optimize the memory requirements as well.

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“…The sequence alignment/map format (SAM/BAM) [1] is the de facto standard in the bioinformatics community for storing mapped sequencing data. There exists a large body of work on tools for processing SAM/BAM files for analysis [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]. The SAMtools [1], Picard [2], and GATK [3] software packages developed by the Broad and Sanger institutes are considered to be reference implementations for many operations on SAM/BAM files, examples of which include sorting reads, marking PCR and optical duplicates, recalibrating base quality scores, indel realignment, and various filtering options, which typically precede variant calling.…”

Section: Introductionmentioning

confidence: 99%

A comparison of three programming languages for a full-fledged next-generation sequencing tool

Costanza¹,

Herzeel²,

Verachtert³

2019

Preprint

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Background elPrep is an established multi-threaded framework for preparing SAM and BAM files in sequencing pipelines. To achieve good performance, its software architecture makes only a single pass through a SAM/BAM file for multiple preparation steps, and keeps sequencing data as much as possible in main memory. Similar to other SAM/BAM tools, management of heap memory is a complex task in elPrep, and it became a serious productivity bottleneck in its original implementation language during recent further development of elPrep. We therefore investigated three alternative programming languages: Go and Java using a concurrent, parallel garbage collector on the one hand, and C++17 using reference counting on the other hand for handling large amounts of heap objects. We reimplemented elPrep in all three languages and benchmarked their runtime performance and memory use.Results The Go implementation performs best, yielding the best balance between runtime performance and memory use. While the Java benchmarks report a somewhat faster runtime than the Go benchmarks, the memory use of the Java runs is significantly higher. The C++17 benchmarks run signif- * Equal contributor

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HiGene: A high-performance platform for genomic data analysis

Cited by 9 publications

References 15 publications

Multithreaded variant calling in elPrep 5

Multithreaded variant calling in elPrep 5

A Fast and Scalable Workflow for SNPs Detection in Genome Sequences Using Hadoop Map-Reduce

A comparison of three programming languages for a full-fledged next-generation sequencing tool

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