Generation Sequencing Data Analysis Research Articles

Next Generation Sequencing Data Analysis in Primary Immunodeficiency Disorders - Future Directions.

Primary immunodeficiency diseases (PIDs) comprise a group of highly heterogeneous immune system diseases and around 300 forms of PID have been described to date. Next Generation Sequencing (NGS) has recently become an increasingly used approach for gene identification and molecular diagnosis of human diseases. Herein we summarize the practical considerations for the interpretation of NGS data and the techniques for searching disease-related PID genes, and suggest future directions for research in this field.

3′ and 5′ microRNA-end post-biogenesis modifications in plant transcriptomes: Evidences from small RNA next generation sequencing data analysis

The processing of miRNA from its precursors is a precisely regulated process and after biogenesis, the miRNAs are amenable to different kinds of modifications by the addition or deletion of nucleotides at the terminal ends. However, the mechanism and functions of such modifications are not well studied in plants. In this study, we have specifically analysed the terminal end non-templated miRNA modifications, using NGS data of rice, tomato and Arabidopsis small RNA transcriptomes from different tissues and physiological conditions. Our analysis reveals template independent terminal end modifications in the mature as well as passenger strands of the miRNA duplex. Interestingly, it is also observed that miRNA sequences terminating with a cytosine (C) at the 3′ end undergo a higher percentage of 5′ end modifications. The terminal end modifications did not correlate with the miRNA abundances and are independent of tissue types, physiological conditions and plant species. Our analysis indicates that the addition of nucleotides at miRNA ends is not influenced by the absence of RNA dependent RNA polymerase 6. Moreover the terminal end modified miRNAs are also observed amongst AGO1 bound small RNAs and have potential to alter target, indicating its important functional role in repression of gene expression.

The serine/threonine kinase 33 is present and expressed in palaeognath birds but has become a unitary pseudogene in neognaths about 100 million years ago.

BackgroundSerine/threonine kinase 33 (STK33) has been shown to be conserved across all major vertebrate classes including reptiles, mammals, amphibians and fish, suggesting its importance within vertebrates. It has been shown to phosphorylate vimentin and might play a role in spermatogenesis and organ ontogenesis. In this study we analyzed the genomic locus and expression of stk33 in the class Aves, using a combination of large scale next generation sequencing data analysis and traditional PCR.ResultsWithin the subclass Palaeognathae we analyzed the white-throated tinamou (Tinamus guttatus), the African ostrich (Struthio camelus) and the emu (Dromaius novaehollandiae). For the African ostrich we were able to generate a 62,778 bp long genomic contig and an mRNA sequence that encodes a protein showing highly significant similarity to STK33 proteins from other vertebrates. The emu has been shown to encode and transcribe a functional STK33 as well. For the white-throated tinamou we were able to identify 13 exons by sequence comparison encoding a protein similar to STK33 as well.In contrast, in all 28 neognath birds analyzed, we could not find evidence for the existence of a functional copy of stk33 or its expression. In the genomes of these 28 bird species, we found only remnants of the stk33 locus carrying several large genomic deletions, leading to the loss of multiple exons. The remaining exons have acquired various indels and premature stop codons.ConclusionsWe were able to elucidate and describe the genomic structure and the transcription of a functional stk33 gene within the subclass Palaeognathae, but we could only find degenerate remnants of stk33 in all neognath birds analyzed. This led us to the conclusion that stk33 became a unitary pseudogene in the evolutionary history of the class Aves at the paleognath-neognath branch point during the late cretaceous period about 100 million years ago. We hypothesize that the pseudogenization of stk33 might have become fixed in neognaths due to either genetic redundancy or a non-orthologous gene displacement and present potential candidate genes for such an incident.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1769-9) contains supplementary material, which is available to authorized users.

Leveraging the Power of High Performance Computing for Next Generation Sequencing Data Analysis: Tricks and Twists from a High Throughput Exome Workflow

Next generation sequencing (NGS) has been a great success and is now a standard method of research in the life sciences. With this technology, dozens of whole genomes or hundreds of exomes can be sequenced in rather short time, producing huge amounts of data. Complex bioinformatics analyses are required to turn these data into scientific findings. In order to run these analyses fast, automated workflows implemented on high performance computers are state of the art. While providing sufficient compute power and storage to meet the NGS data challenge, high performance computing (HPC) systems require special care when utilized for high throughput processing. This is especially true if the HPC system is shared by different users. Here, stability, robustness and maintainability are as important for automated workflows as speed and throughput. To achieve all of these aims, dedicated solutions have to be developed. In this paper, we present the tricks and twists that we utilized in the implementation of our exome data processing workflow. It may serve as a guideline for other high throughput data analysis projects using a similar infrastructure. The code implementing our solutions is provided in the supporting information files.

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

High-throughput sequencing technologies have been widely used in medical and biological research, especially in cancer biology. With the huge amounts of sequencing data being generated, data analysis has become the bottle-neck of the research procedure. We have designed and implemented NGSPERL, a semi-automated module-based framework, for high-throughput sequencing data analysis. Three major analysis pipelines with multiple tasks have been developed for RNA sequencing, exome sequencing, and small RNA sequencing data. Each task was developed as module. The module uses the output from the previous task as the input parameter to generate the corresponding portable batch system (PBS) script. The PBS scripts can be either submitted to cluster or run directly based on user choice. Multiple tasks can also be combined together as a single task to simplify the data analysis. Such a flexible framework will significantly automate and simplify the process of large scale sequencing data analysis.

Masking as an effective quality control method for next-generation sequencing data analysis.

BackgroundNext generation sequencing produces base calls with low quality scores that can affect the accuracy of identifying simple nucleotide variation calls, including single nucleotide polymorphisms and small insertions and deletions. Here we compare the effectiveness of two data preprocessing methods, masking and trimming, and the accuracy of simple nucleotide variation calls on whole-genome sequence data from Caenorhabditis elegans. Masking substitutes low quality base calls with ‘N’s (undetermined bases), whereas trimming removes low quality bases that results in a shorter read lengths.ResultsWe demonstrate that masking is more effective than trimming in reducing the false-positive rate in single nucleotide polymorphism (SNP) calling. However, both of the preprocessing methods did not affect the false-negative rate in SNP calling with statistical significance compared to the data analysis without preprocessing. False-positive rate and false-negative rate for small insertions and deletions did not show differences between masking and trimming.ConclusionsWe recommend masking over trimming as a more effective preprocessing method for next generation sequencing data analysis since masking reduces the false-positive rate in SNP calling without sacrificing the false-negative rate although trimming is more commonly used currently in the field. The perl script for masking is available at http://code.google.com/p/subn/. The sequencing data used in the study were deposited in the Sequence Read Archive (SRX450968 and SRX451773).

Abstract 1512: Detection of somatic alterations in plasma from lung cancer patients

Abstract Tumor-specific (somatic) mutations in plasma can serve as biomarkers for tumor detection, monitor tumor response to specific therapies, detect residual disease after surgery, and long-term follow-up. The intrinsic low abundance of circulating cell-free tumor DNA (cfDNA) makes the detection and quantification of such mutations in plasma a challenging task. This small scale study aims to establish a comprehensive strategy to be used in the detection of clinical relevant somatic alterations in plasma of lung cancer patients. Plasma samples obtained at different stages of disease progression and/or treatment were collected from a group of 11 lung cancer patients and used to isolate cfDNA. Genetic alterations in the EGFR gene (p.E746_A750del and p.L858R) identified at diagnosis in tumor biopsies were used as surrogate markers to optimize and validate the next generation sequencing strategy and data analysis workflow. The Ion AmpliSeq Colon and Lung cancer panel was used to analyze hotspot and targeted regions of 22 genes implicated in colon and lung cancers, including the EGFR gene mutations mentioned. The amplified products were used to prepare libraries and were sequenced using the Ion PGM system. Quantitative real time PCR and digital PCR assays were used to confirm selected results. Tumor derived genetic alterations could be identified in as little as 10ng of cfDNA. EGFR alterations identified in cfDNA mirrored the alterations identified in all tumor biopsies. EGFR alterations with allelic frequencies as low as 3% could be detected in cfDNA. Additionally, in case of samples collected after therapy, a clear decrease in the cfDNA allelic frequency of the EGFR mutation, that made the patient eligible for therapy, was observed. Furthermore, the screening of a larger panel of genes allowed the identification in two cases of additional gene mutations (e.g. MET and KRAS) that may have impact in the clinical management of patients. In this study, we demonstrate the capacity to identify clinically relevant somatic EGFR mutations in plasma. The possibility to assess information from a larger panel of genes makes this strategy attractive for further optimization of treatment options. The strategy is now being extended to a larger cohort of patients to push forward the concept of liquid biopsy in the clinical management of cancer patients. Citation Format: Jose L. Costa, Ana Justino, Ana Barroso, Barbara Parente, Jose C. Machado. Detection of somatic alterations in plasma from lung cancer patients. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1512. doi:10.1158/1538-7445.AM2014-1512

Implementation of Cloud based next generation sequencing data analysis in a clinical laboratory.

BackgroundThe introduction of next generation sequencing (NGS) has revolutionized molecular diagnostics, though several challenges remain limiting the widespread adoption of NGS testing into clinical practice. One such difficulty includes the development of a robust bioinformatics pipeline that can handle the volume of data generated by high-throughput sequencing in a cost-effective manner. Analysis of sequencing data typically requires a substantial level of computing power that is often cost-prohibitive to most clinical diagnostics laboratories.FindingsTo address this challenge, our institution has developed a Galaxy-based data analysis pipeline which relies on a web-based, cloud-computing infrastructure to process NGS data and identify genetic variants. It provides additional flexibility, needed to control storage costs, resulting in a pipeline that is cost-effective on a per-sample basis. It does not require the usage of EBS disk to run a sample.ConclusionsWe demonstrate the validation and feasibility of implementing this bioinformatics pipeline in a molecular diagnostics laboratory. Four samples were analyzed in duplicate pairs and showed 100% concordance in mutations identified. This pipeline is currently being used in the clinic and all identified pathogenic variants confirmed using Sanger sequencing further validating the software.

Integration and analysis of multi-type high-throughput data for biomolecular knowledge discovery

Erik Bongcam-Rudloff received his doctorate in medical sciences from Uppsala University, Sweden. He is now the Director of the SLU Global Bioinformatics Centre (SGBC) at the Swedish University of Agricultural Sciences. Erik was the chairman of EMBnet (2003-2010), a science-based group of worldwide collaborating bioinformatics nodes. He also coordinated the "Test Cases" work package 4 in the FP6 EMBRACE project (2005-2010). The goal of WP4 was to collect test cases from the scientific community to identify real research problems and to provide solutions for content and tool integration. Bongcam-Rudloff is today Chair of SeqAhead, a Biomedical European COST Action: "Next Generation Sequencing Data Analysis Network" and Coordinator of ALLBIO, a FP7 project: "Broadening the Bioinformatics Infrastructure to unicellular, animal, and plant science". He is also the founder of eBioinformatics.org the creators of eBiotools, eBioKit, eBioX and eBioKit. His main research deals with development of bioinformatics solutions for the Life Sciences community. In this talk he will discuss the new techniques that are now driving the generation of knowledge (especially in biomedicine and molecular life sciences) to new dimensions eg. NGS. He will also discuss the new opportunities in human and non-human research that these techniques create but also the new challenges in the design of ontologies for data and methods, and choosing common interoperability standards.

UPPNEX - A solution for Next Generation Sequencing data management and analysis

UPPNEX - A solution for Next Generation Sequencing data management and analysis

Slovak EMBnet node: AGM2011 report

Since 1999, Institute of Molecular Biology, Slovak Academy of Sciences, hosts the Slovak National EMBnet node. Activities of the node are focused mainly on providing the access to the basic bioinformatics databases and tools and on the training and education in the field of bioinformatics. The node has no direct funding (apart from EMBnet membership fee). The node actively participates on managing and production of the EMBnet.journal as well as on design and development of embnet.org web site. The node is involved also in several international project, namely - Next Generation Sequencing Data Analysis Network (COST) and ELIXIR - European Life Sciences Infrastructure For Biological Information (ESFRI).

Challenges and Strategies for Next Generation Sequencing (NGS) Data Analysis

Next Generation Sequencing (NGS) technologies enable the resequencing of entire - genomes or the sampling of entire transcriptomes more effi ciently and economically and with greater depth than ever before. Rather than sequencing individual genomes, now it is possible to sequence hundreds or even thousands of related genomes to sample genetic diversity within and between germplasm pools. Identifying and tracking genetic variation is now so effi cient and precise that thousands of variants can be tracked within large populations. While NGS technologies have signifi cant implications on crop breeding, optimization and availability of appropriate methodologies and tools to analyze, visualize and intrepret NGS data are still in their infancy. With an objective to help crop genetics and breeding community to utilize NGS data for crop improvement, Generation Challenge Programme, Mexico and ICRISAT, India organized an international workshop entitled “Next Generation Sequencing (NGS) Data Analysis” during Jul 21-23, 2009. More than 30 scientists from nine countries (USA, UK, France, Korea, Japan, Australia, Mexico, Philippines and India) participated in the workshop. The workshop had four technical sessions and two brainstorming sessions in which participants shared their experience/views on data generation and analysis by using NGS technologies for accelerating plant genome research to understand genome dynamics as well as to facilitate crop breeding. This article presents a report on this international workshop. All presentations made in this workshop have been made available online (http:// www.icrisat.org/bt-publicdomain-ngs.htm).