Applications of next-generation sequencing technologies and computational tools in molecular evolution and aquatic animals conservation studies : a short review

Aquatic ecosystems that form major biodiversity hotspots are critically threatened due to environmental and anthropogenic stressors. We believe that, in this genomic era, computational methods can be applied to promote aquatic biodiversity conservation by addressing questions related to the evolutionary history of aquatic organisms at the molecular level. However, huge amounts of genomics data generated can only be discerned through the use of bioinformatics. Here, we examine the applications of next-generation sequencing technologies and bioinformatics tools to study the molecular evolution of aquatic animals and discuss the current challenges and future perspectives of using bioinformatics toward aquatic animal conservation efforts

Cloud Computing for Next-Generation Sequencing Data Analysis

High-throughput next-generation sequencing (NGS) technologies have evolved rapidly and are reshaping the scope of genomics research. The substantial decrease in the cost of NGS techniques in the past decade has led to its rapid adoption in biological research and drug development. Genomics studies of large populations are producing a huge amount of data, giving rise to computational issues around the storage, transfer, and analysis of the data. Fortunately, cloud computing has recently emerged as a viable option to quickly and easily acquire the computational resources for large-scale NGS data analyses. Some cloud-based applications and resources have been developed specifically to address the computational challenges of working with very large volumes of data generated by NGS technology. In this chapter, we will review some cloud-based systems and solutions for NGS data analysis, discuss the practical hurdles and limitations in cloud computing, including data transfer and security, and share the lessons we learned from the implementation of Rainbow, a cloud-based tool for large-scale genome sequencing data analysis

Big Data Proteogenomics and High Performance Computing: Challenges and Opportunities

Proteogenomics is an emerging field of systems biology research at the intersection of proteomics and genomics. Two high-throughput technologies, Mass Spectrometry (MS) for proteomics and Next Generation Sequencing (NGS) machines for genomics are required to conduct proteogenomics studies. Independently both MS and NGS technologies are inflicted with data deluge which creates problems of storage, transfer, analysis and visualization. Integrating these big data sets (NGS+MS) for proteogenomics studies compounds all of the associated computational problems. Existing sequential algorithms for these proteogenomics datasets analysis are inadequate for big data and high performance computing (HPC) solutions are almost non-existent. The purpose of this paper is to introduce the big data problem of proteogenomics and the associated challenges in analyzing, storing and transferring these data sets. Further, opportunities for high performance computing research community are identified and possible future directions are discussed

Big Data Proteogenomics and High Performance Computing: Challenges and Opportunities

Proteogenomics is an emerging field of systems biology research at the intersection of proteomics and genomics. Two high-throughput technologies, Mass Spectrometry (MS) for proteomics and Next Generation Sequencing (NGS) machines for genomics are required to conduct proteogenomics studies. Independently both MS and NGS technologies are inflicted with data deluge which creates problems of storage, transfer, analysis and visualization. Integrating these big data sets (NGS+MS) for proteogenomics studies compounds all of the associated computational problems. Existing sequential algorithms for these proteogenomics datasets analysis are inadequate for big data and high performance computing (HPC) solutions are almost non-existent. The purpose of this paper is to introduce the big data problem of proteogenomics and the associated challenges in analyzing, storing and transferring these data sets. Further, opportunities for high performance computing research community are identified and possible future directions are discussed

Deep Symbolic Learning Architecture for Variant Calling in NSG

[EN]In the era of genomics, efficient and accurate analysis of genomic sequences is essential. Next-generation sequencing (NGS) technology has revolutionised the field of genomics by providing a massive volume of data on an unprecedented scale. One of the critical steps in the analysis of this data is variant calling, where genetic variations are identified from DNA sequences. In this context, we have explored the use of Deep Symbolic Learning (DSL) as an innovative computational approach that combines deep learning with symbolic representations. In this article, we discuss the principles of DSL and its applicability in genomics. We examine the advantages and challenges of its use in the context of variant calling and highlight the importance of meticulous validation. To ensure the quality of the results, it is essential to adopt appropriate validation techniques and specific software tools. We provide a detailed overview of these techniques and tools, with the aim of establishing clear standards for the implementation and validation of DSL algorithms in genomic pipelines. This research highlights the potential of the DSL to improve the accuracy of variant discovery, offering promising prospects for the genomics of the future

EvoPipes.net: Bioinformatic Tools for Ecological and Evolutionary Genomics

Recent increases in the production of genomic data are yielding new opportunities and challenges for biologists. Among the chief problems posed by next-generation sequencing are assembly and analyses of these large data sets. Here we present an online server, http://EvoPipes.net, that provides access to a wide range of tools for bioinformatic analyses of genomic data oriented for ecological and evolutionary biologists. The EvoPipes.net server includes a basic tool kit for analyses of genomic data including a next-generation sequence cleaning pipeline (SnoWhite), scaffolded assembly software (SCARF), a reciprocal best-blast hit ortholog pipeline (RBH Orthologs), a pipeline for reference protein-based translation and identification of reading frame in transcriptome and genomic DNA (TransPipe), a pipeline to identify gene families and summarize the history of gene duplications (DupPipe), and a tool for developing SSRs or microsatellites from a transcriptome or genomic coding sequence collection (findSSR). EvoPipes.net also provides links to other software developed for evolutionary and ecological genomics, including chromEvol and NU-IN, as well as a forum for discussions of issues relating to genomic analyses and interpretation of results. Overall, these applications provide a basic bioinformatic tool kit that will enable ecologists and evolutionary biologists with relatively little experience and computational resources to take advantage of the opportunities provided by next-generation sequencing in their systems

Evaluation of experimental design and computational parameter choices affecting analyses of ChIP-seq and RNA-seq data in undomesticated poplar trees.

BackgroundOne of the great advantages of next generation sequencing is the ability to generate large genomic datasets for virtually all species, including non-model organisms. It should be possible, in turn, to apply advanced computational approaches to these datasets to develop models of biological processes. In a practical sense, working with non-model organisms presents unique challenges. In this paper we discuss some of these challenges for ChIP-seq and RNA-seq experiments using the undomesticated tree species of the genus Populus.ResultsWe describe specific challenges associated with experimental design in Populus, including selection of optimal genotypes for different technical approaches and development of antibodies against Populus transcription factors. Execution of the experimental design included the generation and analysis of Chromatin immunoprecipitation-sequencing (ChIP-seq) data for RNA polymerase II and transcription factors involved in wood formation. We discuss criteria for analyzing the resulting datasets, determination of appropriate control sequencing libraries, evaluation of sequencing coverage needs, and optimization of parameters. We also describe the evaluation of ChIP-seq data from Populus, and discuss the comparison between ChIP-seq and RNA-seq data and biological interpretations of these comparisons.ConclusionsThese and other "lessons learned" highlight the challenges but also the potential insights to be gained from extending next generation sequencing-supported network analyses to undomesticated non-model species