Engineering topics

Oehmen, Christopher S

Publications and source records attributed to Oehmen, Christopher S.

7 records

DOE OSTI2013

ScalaBLAST 2.0: Rapid and robust BLAST calculations on multiprocessor systems

BLAST remains one of the most widely used tools in computational biology. The rate at which new sequence data is available continues to grow exponentially, driving the emergence of new fields of biological research. At the same time multicore systems and conventional clusters are more accessible. ScalaBLAST has been designed to run on conventional multiprocessor systems with an eye to extreme parallelism, enabling parallel BLAST calculations using over 16,000 processing cores with a portable, robust, fault-resilient design. ScalaBLAST 2.0 source code can be freely downloaded from http://omics.pnl.gov/software/ScalaBLAST.php.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2012

An Efficient Machine Learning Approach To Low-Complexity Filtering In Biological Sequences

Biological sequences contain low-complexity regions (LCRs) which produce superfluous matches in homology searches, and lead to slow execution of database search algorithms such as BLAST. These regions are efficiently identified by low-complexity filtering algorithms such as SDUST and SEG, which are included in the BLAST tool-suite. These algorithms target differing notions of complexity, so an algorithm which combines their sensitivities is pursued. A variety of features are derived from these algorithms, as well as a new filtering algorithm based on Lempel-Ziv complexity. Artificial sequences with known LCRs are used to train and evaluate an SVM classifier, which significantly outperforms the standalone filtering algorithms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2011

A Model of Cyclic Transcriptomic Behavior in Cyanobacterium Cyanothece sp. ATCC 51142

Systems biology attempts to reconcile large amounts of disparate data with existing knowledge to provide models of functioning biological systems. Useful and predictive models aim to summarize complex and dynamic processes and represent the relationships between these processes. The cyanobacterial Cyanothece species Strain sp. ATCC 51142 is an excellent candidate for such systems studies because: (i) it displays tight functional regulation as it must separate the opposing processes of oxygen-generating photosynthesis and oxygen-sensitive nitrogen fixation temporally in the same cell, ; (ii) it has robust cyclic patterns at the genetic, protein and metabolomic levels, ; and (iii) and it has potential applications for bioenergy and carbon sequestration, and thus a predictive model of its function is of practical use. We have represented the transcriptomic data from Cyanothece 51142 under diurnal light/dark cycles as a high-level functional abstraction and describe development of a predictive in silico model of diurnal and circadian behavior in terms of regulatory and metabolic processes in Cyanothece 51142. Our model provides a way to integrate disparate data types into a framework that can be used to explain behavior, generate high-quality predictions for validation, and to suggest future experiments. We show that incorporating network topology into the model improves performance in terms of our ability to explain the behavior of the system under new conditions. The model presented robustly describes transcriptomic behavior of Cyanothece 51142 under different cyclic and non-cyclic growth conditions robustly, and represents a significant advance in the understanding of gene regulation in this important organism.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2011

Computational prediction of type III and IV secreted effectors in Gram-negative bacteria

In this review, we provide an overview of the methods employed by four recent papers that described novel methods for computational prediction of secreted effectors from type III and IV secretion systems in Gram-negative bacteria. The results of the studies in terms of performance at accurately predicting secreted effectors and similarities found between secretion signals that may reflect biologically relevant features for recognition. We discuss the web-based tools for secreted effector prediction described in these studies and announce the availability of our tool, the SIEVEserver (http://www.biopilot.org). Finally, we assess the accuracy of the three type III effector prediction methods on a small set of proteins not known prior to the development of these tools that we have recently discovered and validated using both experimental and computational approaches. Our comparison shows that all methods use similar approaches and, in general arrive at similar conclusions. We discuss the possibility of an order-dependent motif in the secretion signal, which was a point of disagreement in the studies. Our results show that there may be classes of effectors in which the signal has a loosely defined motif, and others in which secretion is dependent only on compositional biases. Computational prediction of secreted effectors from protein sequences represents an important step toward better understanding the interaction between pathogens and hosts.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2010

An Adaptive Middleware Framework for Scientific Computing at Extreme Scales

Large computing systems including clusters, clouds, and grids, provide high-performance capabilities that can be utilized for many applications. But as the ubiquity of these systems increases and the scope of analysis being done on them grows, there is a growing need for applications that 1) do not require users to learn the details of high performance systems, and 2) are flexible and adaptive in their usage of these systems to accommodate the best time-to-solution for end users. We introduce a new adaptive interface design and a prototype implementation within the framework of an established middleware framework, MeDICi, for high performance computing systems and describe the applicability of this adaptive design to a real-life scientific workflow. This adaptive framework provides an access model for implementing a processing pipeline using high performance systems that are not local to the data source, making it possible for the compute capabilities at one site to be applied to analysis on data being generated at another site in an automated process. This adaptive design improves overall time-to-solution by moving the data analysis task to the most appropriate resource dynamically, reacting to failures and load fluctuations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2010

An Organic Model for Detecting Cyber Events

Cyber entities in many ways mimic the behavior of organic systems. Individuals or groups compete for limited resources using a variety of strategies and effective strategies are re-used and refined in later ‘generations’. Traditionally this drift has made detection of malicious entities very difficult because 1) recognition systems are often built on exact matching to a pattern that can only be ‘learned’ after a malicious entity reveals itself and 2) the enormous volume and variation in benign entities is an overwhelming source of previously unseen entities that often confound detectors. To turn the tables of complexity on the would-be attackers, we have developed a method for mapping the sequence of behaviors in which cyber entities engage to strings of text and analyze these strings using modified bioinformatics algorithms. Bioinformatics algorithms optimize the alignment between text strings even in the presence of mismatches, insertions or deletions and do not require an a priori definition of the patterns one is seeking. Nor does it require any type of exact matching. This allows the data itself to suggest meaningful patterns that are conserved between cyber entities. We demonstrate this method on data generated from network traffic. The impact of this approach is that it can rapidly calculate similarity measures of previously unseen cyber entities in terms of well-characterized entities. These measures may also be used to organize large collections of data into families, making it possible to identify motifs indicative of each family.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2010

Applications in Data-Intensive Computing

This book chapter, to be published in Advances in Computers, Volume 78, in 2010 describes applications of data intensive computing (DIC). This is an invited chapter resulting from a previous publication on DIC. This work summarizes efforts coming out of the PNNL's Data Intensive Computing Initiative. Advances in technology have empowered individuals with the ability to generate digital content with mouse clicks and voice commands. Digital pictures, emails, text messages, home videos, audio, and webpages are common examples of digital content that are generated on a regular basis. Data intensive computing facilitates human understanding of complex problems. Data-intensive applications provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements through the development of new classes of software, algorithms, and hardware.

99 GENERAL AND MISCELLANEOUS↗