Dataset Information

PVT: an efficient computational procedure to speed up next-generation sequence analysis.

ABSTRACT: BACKGROUND: High-throughput Next-Generation Sequencing (NGS) techniques are advancing genomics and molecular biology research. This technology generates substantially large data which puts up a major challenge to the scientists for an efficient, cost and time effective solution to analyse such data. Further, for the different types of NGS data, there are certain common challenging steps involved in analysing those data. Spliced alignment is one such fundamental step in NGS data analysis which is extremely computational intensive as well as time consuming. There exists serious problem even with the most widely used spliced alignment tools. TopHat is one such widely used spliced alignment tools which although supports multithreading, does not efficiently utilize computational resources in terms of CPU utilization and memory. Here we have introduced PVT (Pipelined Version of TopHat) where we take up a modular approach by breaking TopHat's serial execution into a pipeline of multiple stages, thereby increasing the degree of parallelization and computational resource utilization. Thus we address the discrepancies in TopHat so as to analyze large NGS data efficiently. RESULTS: We analysed the SRA dataset (SRX026839 and SRX026838) consisting of single end reads and SRA data SRR1027730 consisting of paired-end reads. We used TopHat v2.0.8 to analyse these datasets and noted the CPU usage, memory footprint and execution time during spliced alignment. With this basic information, we designed PVT, a pipelined version of TopHat that removes the redundant computational steps during 'spliced alignment' and breaks the job into a pipeline of multiple stages (each comprising of different step(s)) to improve its resource utilization, thus reducing the execution time. CONCLUSIONS: PVT provides an improvement over TopHat for spliced alignment of NGS data analysis. PVT thus resulted in the reduction of the execution time to ~23% for the single end read dataset. Further, PVT designed for paired end reads showed an improved performance of ~41% over TopHat (for the chosen data) with respect to execution time. Moreover we propose PVT-Cloud which implements PVT pipeline in cloud computing system.

SUBMITTER: Maji RK

PROVIDER: S-EPMC4063226 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

PVT: an efficient computational procedure to speed up next-generation sequence analysis.

Maji Ranjan Kumar RK Sarkar Arijita A Khatua Sunirmal S Dasgupta Subhasis S Ghosh Zhumur Z

BMC bioinformatics 20140604

<h4>Background</h4>High-throughput Next-Generation Sequencing (NGS) techniques are advancing genomics and molecular biology research. This technology generates substantially large data which puts up a major challenge to the scientists for an efficient, cost and time effective solution to analyse such data. Further, for the different types of NGS data, there are certain common challenging steps involved in analysing those data. Spliced alignment is one such fundamental step in NGS data analysis w ...[more]

PMID: 24894600

Similar Datasets

Project description:BACKGROUND: Mitochondrial disorders can originate from mutations in one of many nuclear genes controlling the organelle function or in the mitochondrial genome (mitochondrial DNA (mtDNA)). The large numbers of potential culprit genes, together with the little guidance offered by most clinical phenotypes as to which gene may be causative, are a great challenge for the molecular diagnosis of these disorders. METHODS: We developed a novel targeted resequencing assay for mitochondrial disorders relying on microarray-based hybrid capture coupled to next-generation sequencing. Specifically, we subjected the entire mtDNA genome and the exons and intron-exon boundary regions of 362 known or candidate causative nuclear genes to targeted capture and resequencing. We here provide proof-of-concept data by testing one HapMap DNA sample and two positive control samples. RESULTS: Over 94% of the targeted regions were captured and sequenced with appropriate coverage and quality, allowing reliable variant calling. Pathogenic mutations blindly tested in patients' samples were 100% concordant with previous Sanger sequencing results: a known mutation in Pyruvate dehydrogenase alpha 1 subunit (PDHA1), a novel splicing and a known coding mutation in Hydroxyacyl-CoA dehydrogenase alpha subunit (HADHA) were correctly identified. Of the additional variants recognized, 90 to 94% were present in dbSNP while 6 to 10% represented new alterations. The novel nonsynonymous variants were all in heterozygote state and mostly predicted to be benign. The depth of sequencing coverage of mtDNA was extremely high, suggesting that it may be feasible to detect pathogenic mtDNA mutations confounded by low level heteroplasmy. Only one sequencing lane of an eight lane flow cell was utilized for each sample, indicating that a cost-effective clinical test can be achieved. CONCLUSIONS: Our study indicates that the use of next generation sequencing technology holds great promise as a tool for screening mitochondrial disorders. The availability of a comprehensive molecular diagnostic tool will increase the capacity for early and rapid identification of mitochondrial disorders. In addition, the proposed approach has the potential to identify new mutations in candidate genes, expanding and redefining the spectrum of causative genes responsible for mitochondrial disorders.

Project description:Viruses cause significant yield and quality losses in a wide variety of cultivated crops. Hence, the detection and identification of viruses is a crucial facet of successful crop production and of great significance in terms of world food security. Whilst the adoption of molecular techniques such as RT-PCR has increased the speed and accuracy of viral diagnostics, such techniques only allow the detection of known viruses, i.e., each test is specific to one or a small number of related viruses. Therefore, unknown viruses can be missed and testing can be slow and expensive if molecular tests are unavailable. Methods for simultaneous detection of multiple viruses have been developed, and (NGS) is now a principal focus of this area, as it enables unbiased and hypothesis-free testing of plant samples. The development of NGS protocols capable of detecting multiple known and emergent viruses present in infected material is proving to be a major advance for crops, nuclear stocks or imported plants and germplasm, in which disease symptoms are absent, unspecific or only triggered by multiple viruses. Researchers want to answer the question "how many different viruses are present in this crop plant?" without knowing what they are looking for: RNA-sequencing (RNA-seq) of plant material allows this question to be addressed. As well as needing efficient nucleic acid extraction and enrichment protocols, virus detection using RNA-seq requires fast and robust bioinformatics methods to enable host sequence removal and virus classification. In this review recent studies that use RNA-seq for virus detection in a variety of crop plants are discussed with specific emphasis on the computational methods implemented. The main features of a number of specific bioinformatics workflows developed for virus detection from NGS data are also outlined and possible reasons why these have not yet been widely adopted are discussed. The review concludes by discussing the future directions of this field, including the use of bioinformatics tools for virus detection deployed in analytical environments using cloud computing.

Dataset Information

PVT: an efficient computational procedure to speed up next-generation sequence analysis.

Publications

PVT: an efficient computational procedure to speed up next-generation sequence analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets