Strand-specific transcriptome profiling with directly labeled RNA on genomic tiling microarrays

<p>Abstract</p> <p>Background</p> <p>With lower manufacturing cost, high spot density, and flexible probe design, genomic tiling microarrays are ideal for comprehensive transcriptome studies. Typically, transcriptome profiling using microarrays involves reverse transcription, which converts RNA to cDNA. The cDNA is then labeled and hybridized to the probes on the arrays, thus the RNA signals are detected indirectly. Reverse transcription is known to generate artifactual cDNA, in particular the synthesis of second-strand cDNA, leading to false discovery of antisense RNA. To address this issue, we have developed an effective method using RNA that is directly labeled, thus by-passing the cDNA generation. This paper describes this method and its application to the mapping of transcriptome profiles.</p> <p>Results</p> <p>RNA extracted from laboratory cultures of <it>Porphyromonas gingivalis </it>was fluorescently labeled with an alkylation reagent and hybridized directly to probes on genomic tiling microarrays specifically designed for this periodontal pathogen. The generated transcriptome profile was strand-specific and produced signals close to background level in most antisense regions of the genome. In contrast, high levels of signal were detected in the antisense regions when the hybridization was done with cDNA. Five antisense areas were tested with independent strand-specific RT-PCR and none to negligible amplification was detected, indicating that the strong antisense cDNA signals were experimental artifacts.</p> <p>Conclusions</p> <p>An efficient method was developed for mapping transcriptome profiles specific to both coding strands of a bacterial genome. This method chemically labels and uses extracted RNA directly in microarray hybridization. The generated transcriptome profile was free of cDNA artifactual signals. In addition, this method requires fewer processing steps and is potentially more sensitive in detecting small amount of RNA compared to conventional end-labeling methods due to the incorporation of more fluorescent molecules per RNA fragment.</p

Psychological Behavior Analysis Using Advanced Signal Processing Techniques for fMRI Data

Psychological analysis related to voluntary reciprocal trust games were obtained using functional magnetic resonance imaging (fMRI) hyperscanning for 44 pairs of strangers throughout 36 trust games (TG) and 16 control games (CG). Hidden Markov models (HMMs) are proposed to train and classify the fMRI data acquired from these brain regions and extract the essential features of the initial decision of the first player to trust or not trust the second player. These results are evaluated using the different versions of the multifold cross-validation technique and compared to other speech data and other advanced signal processing techniques including linear classification, support vector machines (SVMs), and HMMs. With above 80% classification accuracy for HMM as compared to no more than 66% classification accuracy of a linear classifier and SVM, the corresponding experimental results demonstrate that the HMMs can be adopted as an outstanding paradigm to predict the psychological financial (trust/non-trust) activities reflected by the neural responses recorded using fMRI. Additionally, extracting the specific decision period and clustering the continuous time series proved to increase the classification accuracy by almost 20%

Parallelization of dynamic programming recurrences in computational biology

The rapid growth of biosequence databases over the last decade has led to a performance bottleneck in the applications analyzing them. In particular, over the last five years DNA sequencing capacity of next-generation sequencers has been doubling every six months as costs have plummeted. The data produced by these sequencers is overwhelming traditional compute systems. We believe that in the future compute performance, not sequencing, will become the bottleneck in advancing genome science. In this work, we investigate novel computing platforms to accelerate dynamic programming algorithms, which are popular in bioinformatics workloads. We study algorithm-specific hardware architectures that exploit fine-grained parallelism in dynamic programming kernels using field-programmable gate arrays: FPGAs). We advocate a high-level synthesis approach, using the recurrence equation abstraction to represent dynamic programming and polyhedral analysis to exploit parallelism. We suggest a novel technique within the polyhedral model to optimize for throughput by pipelining independent computations on an array. This design technique improves on the state of the art, which builds latency-optimal arrays. We also suggest a method to dynamically switch between a family of designs using FPGA reconfiguration to achieve a significant performance boost. We have used polyhedral methods to parallelize the Nussinov RNA folding algorithm to build a family of accelerators that can trade resources for parallelism and are between 15-130x faster than a modern dual core CPU implementation. A Zuker RNA folding accelerator we built on a single workstation with four Xilinx Virtex 4 FPGAs outperforms 198 3 GHz Intel Core 2 Duo processors. Furthermore, our design running on a single FPGA is an order of magnitude faster than competing implementations on similar-generation FPGAs and graphics processors. Our work is a step toward the goal of automated synthesis of hardware accelerators for dynamic programming algorithms

Computational Biology in Acute Myeloid Leukemia with CEBPA Abnormalities

__Abstract__ In the last decade, tiling-array and next-generation sequencing technologies allowed quantitative measurements of different cellular processes, such as mRNA expression, genomic changes including deletions or amplifications, DNA-methylation, chromatin modifications or Protein-DNA-binding interactions. Using these technologies, thousands of features can now be measured simultaneously in a patient cell sample. The use of for instance mRNA expression profiles or DNA-methylation profiles have already provided new insight into the molecular biology of patients with Acute Myeloid Leukemia (AML). AML is a blood cell malignancy, in which primitive myeloid cells have been transformed and accumulate in the bone marrow and blood. Different forms of AML exist with different molecular abnormalities that associate with distinct responses to therapy. Many subgroups with comparable mRNA expression or DNA-methylation patterns were identified. These studies also revealed the existence of novel previously undefined AML subtypes. Among those was a group of patients with a mutation in a gene called CEBPA. CEBPA is a gene that encodes the transcription factor CCAAT Enhancer Binding Protein Alpha (C/EBPα), which controls the expression of genes in myeloid progenitor cells. Mutated CEBPA encodes a dysfunctional C/EBPα-protein, which consequently results in aberrant control of “target genes”. In this thesis we focus particularly on the role of CEBPA. We studied the predictive and prognostic relevance of mutated CEBPA, and analyzed in a genome wide fashion the mRNA expression, DNA-methylation and the protein-DNA-binding levels corresponding to (mutated) CEBPA in AML. For the analysis of protein-DNA-binding, we developed a novel statistical methodology. With this statistical methodology we studied the fundamental role of (mutant) C/EBPα binding and the effect on gene expression levels. We also integrated gene expression with DNA-methylation profiles of hundreds of AML patients and revealed the existence of two previously unidentified AML subtypes

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

Motif discovery in sequential data

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (v. 2, leaves [435]-467).In this thesis, I discuss the application and development of methods for the automated discovery of motifs in sequential data. These data include DNA sequences, protein sequences, and real-valued sequential data such as protein structures and timeseries of arbitrary dimension. As more genomes are sequenced and annotated, the need for automated, computational methods for analyzing biological data is increasing rapidly. In broad terms, the goal of this thesis is to treat sequential data sets as unknown languages and to develop tools for interpreting an understanding these languages. The first chapter of this thesis is an introduction to the fundamentals of motif discovery, which establishes a common mode of thought and vocabulary for the subsequent chapters. One of the central themes of this work is the use of grammatical models, which are more commonly associated with the field of computational linguistics. In the second chapter, I use grammatical models to design novel antimicrobial peptides (AmPs). AmPs are small proteins used by the innate immune system to combat bacterial infection in multicellular eukaryotes. There is mounting evidence that these peptides are less susceptible to bacterial resistance than traditional antibiotics and may form the basis for a novel class of therapeutics.(cont.) In this thesis, I described the rational design of novel AmPs that show limited homology to naturally-occurring proteins but have strong bacteriostatic activity against several species of bacteria, including Staphylococcus aureus and Bacillus anthracis. These peptides were designed using a linguistic model of natural AmPs by treating the amino acid sequences of natural AmPs as a formal language and building a set of regular grammars to describe this language. is set of grammars was used to create novel, unnatural AmP sequences that conform to the formal syntax of natural antimicrobial peptides but populate a previously unexplored region of protein sequence space. The third chapter describes a novel, GEneric MOtif DIscovery Algorithm (Gemoda) for sequential data. Gemoda can be applied to any dataset with a sequential character, including both categorical and real-valued data. As I show, Gemoda deterministically discovers motifs that are maximal in composition and length. As well, the algorithm allows any choice of similarity metric for finding motifs. These motifs are representation-agnostic: they can be represented using regular expressions, position weight matrices, or any other model for sequential data.(cont.) I demonstrate a number of applications of the algorithm, including the discovery of motifs in amino acids and DNA sequences, and the discovery of conserved protein sub-structures. The final chapter is devoted to a series of smaller projects, employing tool methods indirectly related to motif discovery in sequential data. I describe the construction of a software tool, Biogrep that is designed to match large pattern sets against large biosequence databases in a parallel fashion. is makes biogrep well-suited to annotating sets of sequences using biologically significant patterns. In addition, I show that the BLOSUM series of amino acid substitution matrices, which are commonly used in motif discovery and sequence alignment problems, have changed drastically over time.The fidelity of amino acid sequence alignment and motif discovery tools depends strongly on the target frequencies implied by these underlying matrices. us, these results suggest that further optimization of these matrices is possible. The final chapter also contains two projects wherein I apply statistical motif discovery tools instead of grammatical tools.(cont.) In the first of these two, I develop three different physiochemical representations for a set of roughly 700 HIV-I protease substrates and use these representations for sequence classification and annotation. In the second of these two projects, I develop a simple statistical method for parsing out the phenotypic contribution of a single mutation from libraries of functional diversity that contain a multitude of mutations and varied phenotypes. I show that this new method successfully elucidates the effects of single nucleotide polymorphisms on the strength of a promoter placed upstream of a reporter gene. The central theme, present throughout this work, is the development and application of novel approaches to finding motifs in sequential data. The work on the design of AmPs is very applied and relies heavily on existing literature. In contrast, the work on Gemoda is the greatest contribution of this thesis and contains many new ideas.by Kyle L. Jensen.Ph.D