ArticlePloS one2019
elPrep 4: A multithreaded framework for sequence analysis.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- SIGANEO: Similarity network with GAN enhancement for immunogenic neoepitope prediction.Computational and structural biotechnology journal · 2023Article
- Assessing and assuring interoperability of a genomics file format.Bioinformatics (Oxford, England) · 2022Article
- Multithreaded variant calling in elPrep 5.PloS one · 2021Article
- Optimizing performance of GATK workflows using Apache Arrow In-Memory data framework.BMC genomics · 2020Article
- A comparison of three programming languages for a full-fledged next-generation sequencing tool.BMC bioinformatics · 2019Article
- Comparing Ease of Programming in C++, Go, and Java for Implementing a Next-Generation Sequencing Tool.Evolutionary bioinformatics online · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present elPrep 4, a reimplementation from scratch of the elPrep framework for processing sequence alignment map files in the Go programming language. elPrep 4 includes multiple new features allowing us to process all of the preparation steps defined by the GATK Best Practice pipelines for variant calling. This includes new and improved functionality for sorting, (optical) duplicate marking, base quality score recalibration, BED and VCF parsing, and various filtering options. The implementations of these options in elPrep 4 faithfully reproduce the outcomes of their counterparts in GATK 4, SAMtools, and Picard, even though the underlying algorithms are redesigned to take advantage of elPrep's parallel execution framework to vastly improve the runtime and resource use compared to these tools. Our benchmarks show that elPrep executes the preparation steps of the GATK Best Practices up to 13x faster on WES data, and up to 7.4x faster for WGS data compared to running the same pipeline with GATK 4, while utilizing fewer compute resources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.