Computational tradeoffs in multiplex PCR assay design for SNP genotyping

BACKGROUND: Multiplex PCR is a key technology for detecting infectious microorganisms, whole-genome sequencing, forensic analysis, and for enabling flexible yet low-cost genotyping. However, the design of a multiplex PCR assays requires the consideration of multiple competing objectives and physical constraints, and extensive computational analysis must be performed in order to identify the possible formation of primer-dimers that can negatively impact product yield. RESULTS: This paper examines the computational design limits of multiplex PCR in the context of SNP genotyping and examines tradeoffs associated with several key design factors including multiplexing level (the number of primer pairs per tube), coverage (the % of SNP whose associated primers are actually assigned to one of several available tube), and tube-size uniformity. We also examine how design performance depends on the total number of available SNPs from which to choose, and primer stringency criterial. We show that finding high-multiplexing/high-coverage designs is subject to a computational phase transition, becoming dramatically more difficult when the probability of primer pair interaction exceeds a critical threshold. The precise location of this critical transition point depends on the number of available SNPs and the level of multiplexing required. We also demonstrate how coverage performance is impacted by the number of available snps, primer selection criteria, and target multiplexing levels. CONCLUSION: The presence of a phase transition suggests limits to scaling Multiplex PCR performance for high-throughput genomics applications. Achieving broad SNP coverage rapidly transitions from being very easy to very hard as the target multiplexing level (# of primer pairs per tube) increases. The onset of a phase transition can be "delayed" by having a larger pool of SNPs, or loosening primer selection constraints so as to increase the number of candidate primer pairs per SNP, though the latter may produce other adverse effects. The resulting design performance tradeoffs define a benchmark that can serve as the basis for comparing competing multiplex PCR design optimization algorithms and can also provide general rules-of-thumb to experimentalists seeking to understand the performance limits of standard multiplex PCR

National Plant Genome Initiative

This report is an update on progress of federal plant genome research. The focus in this report is on plants that are economically important to agribusiness

La scienza come ignoranza degli esperti ed il governo del numero

Reti per mettere in contatto la diversità e potenziare le singolarità oppure per produrre effetti di “campo medio”? Dinamiche della conoscenza in rete, nuove ed adattive, dai limiti mobili, oppure appiattimento normato su standard identici? Nella società, la “governance by numbers” impedisce il “governo” che dovrebbe risultare dall’interpretazione e dal dibattito nell’agorà. In scienza, il senso dei limiti e l’interpretazione dei dati contribuiscono alla novità del sapere. Le scelte vanno fatte ora, prima che Big Data non interpretati, dimostrabilmente ricchi di “correlazioni spurie”, l’immensa audience in rete di tweets senza senso e di articoli scientifici solo “main stream”, uccidano politica e scienza

THE ANALYSIS OF ANCIENT DNA: FROM MITOCHONDRIA TO PATHOGENS

Ancient DNA (aDNA) is arguably one of the most difficult science fields to work in due to the constant battle against contamination and degradation; however, it is also one of the most rewarding. aDNA researchers have consistently garnered interest the world over with their findings and sparking the curiosity of many who wish to know more about who we are as Homo sapiens. Mitochondrial DNA (mtDNA) and pathogen DNA were used in this dissertation to understand more about where populations came from, how they moved, and what their environment was like through the identification of their maternally inherited mtDNA and pathogens. This is a synthesis of my work and collaboration with other researchers both in lab and at the computer to add more data to the story of humankind

Combinatorial biological complexity: a study of amino acid side chains and alternative splicing

Both, laymen and experts have always been intrigued by nature’s vast complexity and variety. Often, these phenomena arise from combination of parts, as for example, cell types of the human body, or the diverse proteins of a cell. In this thesis I investigate three instances of combinatorial complexity: combinations of aliphatic amino acid side chains, alternative mRNA splicing in fungi, and mutually exclusively spliced exons in human and mouse. In the first part the number of aliphatic amino acid side chains is studied. Structural combinations yield a vast theoretical number, yet we find that only a fraction of them is realized in nature. Reasons especially with respect to restrictions by the genetic code are discussed. Moreover, strategies for the need for increased diversity are examined. In the second part, the extent of alternative splicing (AS) in fungi is investigated. A genome-wide, comparative multi-species study is conducted. I find that AS is common in fungi, but with lower frequency compared to plants and animals. AS is more common in more complex fungi, and is over-represented in pathogens. It is hypothesized that AS contributes to multi-cellular complexity in fungi. In the third part, mutually exclusive exons (MXEs) of mouse and human are detected and characterized. Rather unexpected patterns arose: the majority of MXEs originate from non-adjacent exons and frequently appear in clusters. Known regulatory mechanisms of MXE splicing are unsuitable for these MXEs, and thus, new mechanisms have to be sought. Summarizing it is hypothesized that complexity from combinations constitutes a universal principle in biology. However, there seems to be a need to restrict the combinatorial potential. This is highlighted by the interdependence of MXEs and the low number of realized amino acids in the genetic code. Combinatorial complexity and its restriction are discussed with respect to other biological systems to further substantiate the hypotheses

Whole-genome sequence analysis for pathogen detection and diagnostics

This dissertation focuses on computational methods for improving the accuracy of commonly used nucleic acid tests for pathogen detection and diagnostics. Three specific biomolecular techniques are addressed: polymerase chain reaction, microarray comparative genomic hybridization, and whole-genome sequencing. These methods are potentially the future of diagnostics, but each requires sophisticated computational design or analysis to operate effectively. This dissertation presents novel computational methods that unlock the potential of these diagnostics by efficiently analyzing whole-genome DNA sequences. Improvements in the accuracy and resolution of each of these diagnostic tests promises more effective diagnosis of illness and rapid detection of pathogens in the environment. For designing real-time detection assays, an efficient data structure and search algorithm are presented to identify the most distinguishing sequences of a pathogen that are absent from all other sequenced genomes. Results are presented that show these "signature" sequences can be used to detect pathogens in complex samples and differentiate them from their non-pathogenic, phylogenetic near neighbors. For microarray, novel pan-genomic design and analysis methods are presented for the characterization of unknown microbial isolates. To demonstrate the effectiveness of these methods, pan-genomic arrays are applied to the study of multiple strains of the foodborne pathogen, Listeria monocytogenes, revealing new insights into the diversity and evolution of the species. Finally, multiple methods are presented for the validation of whole-genome sequence assemblies, which are capable of identifying assembly errors in even finished genomes. These validated assemblies provide the ultimate nucleic acid diagnostic, revealing the entire sequence of a genome

A regulatory network comprising let-7 miRNA and SMUG1 is associated with good prognosis in ER+ breast tumours

Single-strand selective uracil–DNA glycosylase 1 (SMUG1) initiates base excision repair (BER) of uracil and oxidized pyrimidines. SMUG1 status has been associated with cancer risk and therapeutic response in breast carcinomas and other cancer types. However, SMUG1 is a multifunctional protein involved, not only, in BER but also in RNA quality control, and its function in cancer cells is unclear. Here we identify several novel SMUG1 interaction partners that functions in many biological processes relevant for cancer development and treatment response. Based on this, we hypothesized that the dominating function of SMUG1 in cancer might be ascribed to functions other than BER. We define a bad prognosis signature for SMUG1 by mapping out the SMUG1 interaction network and found that high expression of genes in the bad prognosis network correlated with lower survival probability in ER(+) breast cancer. Interestingly, we identified hsa-let-7b-5p microRNA as an upstream regulator of the SMUG1 interactome. Expression of SMUG1 and hsa-let-7b-5p were negatively correlated in breast cancer and we found an inhibitory auto-regulatory loop between SMUG1 and hsa-let-7b-5p in the MCF7 breast cancer cells. We conclude that SMUG1 functions in a gene regulatory network that influence the survival and treatment response in several cancers