Dataset Information

FastProNGS: fast preprocessing of next-generation sequencing reads.

ABSTRACT:

Background

Next-generation sequencing technology is developing rapidly and the vast amount of data that is generated needs to be preprocessed for downstream analyses. However, until now, software that can efficiently make all the quality assessments and filtration of raw data is still lacking.

Results

We developed FastProNGS to integrate the quality control process with automatic adapter removal. Parallel processing was implemented to speed up the process by allocating multiple threads. Compared with similar up-to-date preprocessing tools, FastProNGS is by far the fastest. Read information before and after filtration can be output in plain-text, JSON, or HTML formats with user-friendly visualization.

Conclusions

FastProNGS is a rapid, standardized, and user-friendly tool for preprocessing next-generation sequencing data within minutes. It is an all-in-one software that is convenient for bulk data analysis. It is also very flexible and can implement different functions using different user-set parameter combinations.

SUBMITTER: Liu X

PROVIDER: S-EPMC6580563 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

FastProNGS: fast preprocessing of next-generation sequencing reads.

Liu Xiaoshuang X Yan Zhenhe Z Wu Chao C Yang Yang Y Li Xiaomin X Zhang Guangxin G

BMC bioinformatics 20190617 1

<h4>Background</h4>Next-generation sequencing technology is developing rapidly and the vast amount of data that is generated needs to be preprocessed for downstream analyses. However, until now, software that can efficiently make all the quality assessments and filtration of raw data is still lacking.<h4>Results</h4>We developed FastProNGS to integrate the quality control process with automatic adapter removal. Parallel processing was implemented to speed up the process by allocating multiple th ...[more]

PMID: 31208325

Similar Datasets

Project description:BackgroundAdapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequencing and transcriptome RNA/cDNA sequencing, where fragments shorter than a read are sometimes obtained because of the limitations of NGS protocols. For the newly emerged Nextera long mate-pair (LMP) protocol, junction adapters are located in the middle of all properly constructed fragments; hence, adapter trimming is essential to gain the correct paired reads. However, our investigations have shown that few adapter trimming tools meet both efficiency and accuracy requirements simultaneously. The performances of these tools can be even worse for paired-end and/or mate-pair sequencing.ResultsTo improve the efficiency of adapter trimming, we devised a novel algorithm, the bit-masked k-difference matching algorithm, which has O(kn) expected time with O(m) space, where k is the maximum number of differences allowed, n is the read length, and m is the adapter length. This algorithm makes it possible to fully enumerate all candidates that meet a specified threshold, e.g. error ratio, within a short period of time. To improve the accuracy of this algorithm, we designed a simple and easy-to-explain statistical scoring scheme to evaluate candidates in the pattern matching step. We also devised scoring schemes to fully exploit the paired-end/mate-pair information when it is applicable. All these features have been implemented in an industry-standard tool named Skewer (https://sourceforge.net/projects/skewer). Experiments on simulated data, real data of small RNA sequencing, paired-end RNA sequencing, and Nextera LMP sequencing showed that Skewer outperforms all other similar tools that have the same utility. Further, Skewer is considerably faster than other tools that have comparative accuracies; namely, one times faster for single-end sequencing, more than 12 times faster for paired-end sequencing, and 49% faster for LMP sequencing.ConclusionsSkewer achieved as yet unmatched accuracies for adapter trimming with low time bound.

Dataset Information

FastProNGS: fast preprocessing of next-generation sequencing reads.

Background

Results

Conclusions

Publications

FastProNGS: fast preprocessing of next-generation sequencing reads.

Similar Datasets

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