rmvPFBAM: Removing Primers from BAM Files Based on Amplicon-Based Next-Generation Sequencing and Cloud Computing When Analyzing Personal Genome Data

Personal genomic data constitute one important part of personal health data. However, due to the large amount of personal genomic data obtained by the next-generation sequencing technology, special tools are needed to analyze these data. In this article, we will explore a tool analyzing cloud-based large-scale genome sequencing data. Analyzing and identifying genomic variations from amplicon-based next-generation sequencing data are necessary for the clinical diagnosis and treatment of cancer patients. When processing the amplicon-based next-generation sequencing data, one essential step is removing primer sequences from the reads to avoid detecting false-positive mutations introduced by nonspecific primer binding and primer extension reactions. At present, the removing primer tools usually discard primer sequences from the FASTQ file instead of BAM file, but this method could cause some downstream analysis problems. Only one tool (BAMClipper) removes primer sequences from BAM files, but it only modified the CIGAR value of the BAM file, and false-positive mutations falling in the primer region could still be detected based on its processed BAM file. So, we developed one cutting primer tool (rmvPFBAM) removing primer sequences from the BAM file, and the mutations detected based on the processed BAM file by rmvPFBAM are highly credible. Besides that, rmvPFBAM runs faster than other tools, such as cutPrimers and BAMClipper.

Download Full-text

Compression of Next-Generation Sequencing Data and of DNA Digital Files

Algorithms ◽

10.3390/a13060151 ◽

2020 ◽

Vol 13 (6) ◽

pp. 151

Author(s):

Bruno Carpentieri

Keyword(s):

Next Generation Sequencing ◽

Dna Sequences ◽

Network Traffic ◽

Large Scale ◽

Genomic Data ◽

Biological Data ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Generation Sequencing

The increase in memory and in network traffic used and caused by new sequenced biological data has recently deeply grown. Genomic projects such as HapMap and 1000 Genomes have contributed to the very large rise of databases and network traffic related to genomic data and to the development of new efficient technologies. The large-scale sequencing of samples of DNA has brought new attention and produced new research, and thus the interest in the scientific community for genomic data has greatly increased. In a very short time, researchers have developed hardware tools, analysis software, algorithms, private databases, and infrastructures to support the research in genomics. In this paper, we analyze different approaches for compressing digital files generated by Next-Generation Sequencing tools containing nucleotide sequences, and we discuss and evaluate the compression performance of generic compression algorithms by confronting them with a specific system designed by Jones et al. specifically for genomic file compression: Quip. Moreover, we present a simple but effective technique for the compression of DNA sequences in which we only consider the relevant DNA data and experimentally evaluate its performances.

Download Full-text

Speeding Up Large-Scale Next Generation Sequencing Data Analysis with pBWA

Journal of Applied Bioinformatics & Computational Biology ◽

10.4172/2329-9533.1000101 ◽

2017 ◽

Vol 01 (01) ◽

Cited By ~ 4

Author(s):

Darren Peters ◽

Xuemei Luo ◽

Ke Qiu ◽

Ping Liang

Keyword(s):

Data Analysis ◽

Next Generation Sequencing ◽

Large Scale ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Generation Sequencing ◽

Sequencing Data Analysis

Download Full-text

WBFQC: A new approach for compressing next-generation sequencing data splitting into homogeneous streams

Journal of Bioinformatics and Computational Biology ◽

10.1142/s021972001850018x ◽

2018 ◽

Vol 16 (05) ◽

pp. 1850018 ◽

Cited By ~ 1

Author(s):

Sanjeev Kumar ◽

Suneeta Agarwal ◽

Ranvijay

Keyword(s):

Next Generation Sequencing ◽

Genomic Data ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Compression Technique ◽

Compression Algorithms ◽

Ngs Data ◽

And Storage ◽

Generation Sequencing

Genomic data nowadays is playing a vital role in number of fields such as personalized medicine, forensic, drug discovery, sequence alignment and agriculture, etc. With the advancements and reduction in the cost of next-generation sequencing (NGS) technology, these data are growing exponentially. NGS data are being generated more rapidly than they could be significantly analyzed. Thus, there is much scope for developing novel data compression algorithms to facilitate data analysis along with data transfer and storage directly. An innovative compression technique is proposed here to address the problem of transmission and storage of large NGS data. This paper presents a lossless non-reference-based FastQ file compression approach, segregating the data into three different streams and then applying appropriate and efficient compression algorithms on each. Experiments show that the proposed approach (WBFQC) outperforms other state-of-the-art approaches for compressing NGS data in terms of compression ratio (CR), and compression and decompression time. It also has random access capability over compressed genomic data. An open source FastQ compression tool is also provided here ( http://www.algorithm-skg.com/wbfqc/home.html ).

Download Full-text

NGSPERL: a semi-automated framework for large scale next generation sequencing data analysis

International Journal of Computational Biology and Drug Design ◽

10.1504/ijcbdd.2015.072082 ◽

2015 ◽

Vol 8 (3) ◽

pp. 203

Author(s):

Quanhu Sheng ◽

Shilin Zhao ◽

Mingsheng Guo ◽

Yu Shyr

Keyword(s):

Data Analysis ◽

Next Generation Sequencing ◽

Large Scale ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Generation Sequencing ◽

Sequencing Data Analysis

Download Full-text

GenoPheno: cataloging large-scale phenotypic and next-generation sequencing data within human datasets

Briefings in Bioinformatics ◽

10.1093/bib/bbaa033 ◽

2020 ◽

Author(s):

Alba Gutiérrez-Sacristán ◽

Carlos De Niz ◽

Cartik Kothari ◽

Sek Won Kong ◽

Kenneth D Mandl ◽

...

Keyword(s):

Next Generation Sequencing ◽

Web Application ◽

Large Scale ◽

Human Subjects ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Phenotypic Data ◽

Data Repositories ◽

Generation Sequencing

Abstract Precision medicine promises to revolutionize treatment, shifting therapeutic approaches from the classical one-size-fits-all to those more tailored to the patient’s individual genomic profile, lifestyle and environmental exposures. Yet, to advance precision medicine’s main objective—ensuring the optimum diagnosis, treatment and prognosis for each individual—investigators need access to large-scale clinical and genomic data repositories. Despite the vast proliferation of these datasets, locating and obtaining access to many remains a challenge. We sought to provide an overview of available patient-level datasets that contain both genotypic data, obtained by next-generation sequencing, and phenotypic data—and to create a dynamic, online catalog for consultation, contribution and revision by the research community. Datasets included in this review conform to six specific inclusion parameters that are: (i) contain data from more than 500 human subjects; (ii) contain both genotypic and phenotypic data from the same subjects; (iii) include whole genome sequencing or whole exome sequencing data; (iv) include at least 100 recorded phenotypic variables per subject; (v) accessible through a website or collaboration with investigators and (vi) make access information available in English. Using these criteria, we identified 30 datasets, reviewed them and provided results in the release version of a catalog, which is publicly available through a dynamic Web application and on GitHub. Users can review as well as contribute new datasets for inclusion (Web: https://avillachlab.shinyapps.io/genophenocatalog/; GitHub: https://github.com/hms-dbmi/GenoPheno-CatalogShiny).

Download Full-text