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System to assess genome sequencing needs for viral protein diagnostics and therapeutics.

ABSTRACT: Computational analyses of genome sequences may elucidate protein signatures unique to a target pathogen. We constructed a Protein Signature Pipeline to guide the selection of short peptide sequences to serve as targets for detection and therapeutics. In silico identification of good target peptides that are conserved among strains and unique compared to other species generates a list of peptides. These peptides may be developed in the laboratory as targets of antibody, peptide, and ligand binding for detection assays and therapeutics or as targets for vaccine development. In this paper, we assess how the amount of sequence data affects our ability to identify conserved, unique protein signature candidates. To determine the amount of sequence data required to select good protein signature candidates, we have built a computationally intensive system called the Sequencing Analysis Pipeline (SAP). The SAP performs thousands of Monte Carlo simulations, each calling the Protein Signature Pipeline, to assess how the amount of sequence data for a target organism affects the ability to predict peptide signature candidates. Viral species differ substantially in the number of genomes required to predict protein signature targets. Patterns do not appear based on genome structure. There are more protein than DNA signatures due to greater intraspecific conservation at the protein than at the nucleotide level. We conclude that it is necessary to use the SAP as a dynamic system to assess the need for continued sequencing for each species individually and to update predictions with each additional genome that is sequenced.

SUBMITTER: Gardner SN

PROVIDER: S-EPMC1081307 | biostudies-literature | 2005 Apr

REPOSITORIES: biostudies-literature

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System to assess genome sequencing needs for viral protein diagnostics and therapeutics.

Gardner Shea N SN Kuczmarski Thomas A TA Zhou Carol E CE Lam Marisa W MW Slezak Tom R TR

Journal of clinical microbiology 20050401 4

Computational analyses of genome sequences may elucidate protein signatures unique to a target pathogen. We constructed a Protein Signature Pipeline to guide the selection of short peptide sequences to serve as targets for detection and therapeutics. In silico identification of good target peptides that are conserved among strains and unique compared to other species generates a list of peptides. These peptides may be developed in the laboratory as targets of antibody, peptide, and ligand bindin ...[more]

PMID: 15815002

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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.

Project description:Massively parallel DNA sequencing (MPS) has the potential to revolutionize diagnostics, in particular for monogenic disorders. Inborn errors of metabolism (IEM) constitute a large group of monogenic disorders with highly variable clinical presentation, often with acute, nonspecific initial symptoms. In many cases irreversible damage can be reduced by initiation of specific treatment, provided that a correct molecular diagnosis can be rapidly obtained. MPS thus has the potential to significantly improve both diagnostics and outcome for affected patients in this highly specialized area of medicine.We have developed a conceptually novel approach for acute MPS, by analysing pulsed whole genome sequence data in real time, using automated analysis combined with data reduction and parallelization. We applied this novel methodology to an in-house developed customized work flow enabling clinical-grade analysis of all IEM with a known genetic basis, represented by a database containing 474 disease genes which is continuously updated. As proof-of-concept, two patients were retrospectively analysed in whom diagnostics had previously been performed by conventional methods. The correct disease-causing mutations were identified and presented to the clinical team after 15 and 18 hours from start of sequencing, respectively. With this information available, correct treatment would have been possible significantly sooner, likely improving outcome.We have adapted MPS to fit into the dynamic, multidisciplinary work-flow of acute metabolic medicine. As the extent of irreversible damage in patients with IEM often correlates with timing and accuracy of management in early, critical disease stages, our novel methodology is predicted to improve patient outcome. All procedures have been designed such that they can be implemented in any technical setting and to any genetic disease area. The strategy conforms to international guidelines for clinical MPS, as only validated disease genes are investigated and as clinical specialists take responsibility for translation of results. As follow-up in patients without any known IEM, filters can be lifted and the full genome investigated, after genetic counselling and informed consent.

Dataset Information

System to assess genome sequencing needs for viral protein diagnostics and therapeutics.

Publications

System to assess genome sequencing needs for viral protein diagnostics and therapeutics.

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