The Parallelism Motifs of Genomic Data Analysis

Genomic data sets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share this data with the research community, but some of these genomic data analysis problems require large scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high end parallel systems today and place different requirements on programming support, software libraries, and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high performance genomics analysis, including alignment, profiling, clustering, and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or motifs that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing

MORPH: Probabilistic Alignment Combined with Hidden Markov Models of cis-Regulatory Modules

The discovery and analysis of cis-regulatory modules (CRMs) in metazoan genomes is crucial for understanding the transcriptional control of development and many other biological processes. Cross-species sequence comparison holds much promise for improving computational prediction of CRMs, for elucidating their binding site composition, and for understanding how they evolve. Current methods for analyzing orthologous CRMs from multiple species rely upon sequence alignments produced by off-the-shelf alignment algorithms, which do not exploit the presence of binding sites in the sequences. We present here a unified probabilistic framework, called MORPH, that integrates the alignment task with binding site predictions, allowing more robust CRM analysis in two species. The framework sums over all possible alignments of two sequences, thus accounting for alignment ambiguities in a natural way. We perform extensive tests on orthologous CRMs from two moderately diverged species Drosophila melanogaster and D. mojavensis, to demonstrate the advantages of the new approach. We show that it can overcome certain computational artifacts of traditional alignment tools and provide a different, likely more accurate, picture of cis-regulatory evolution than that obtained from existing methods. The burgeoning field of cis-regulatory evolution, which is amply supported by the availability of many related genomes, is currently thwarted by the lack of accurate alignments of regulatory regions. Our work will fill in this void and enable more reliable analysis of CRM evolution

POLYA: A TOOL FOR ADJUDICATING COMPETING ANNOTATIONS OF BIOLOGICAL SEQUENCES

Annotation of a biological sequence is usually performed by aligning that sequence to a database of known sequence elements. When that database contains elements that are highly similar to each other, the proper annotation may be ambiguous, because several entries in the database produce high-scoring alignments. Typical annotation methods work by assigning a label based on the candidate annotation with the highest alignment score; this can overstate annotation certainty, mislabel boundaries, and fails to identify large scale rearrangements or insertions within the annotated sequence. Here, I present a new software tool, PolyA, that adjudicates between competing alignment-based annotations by computing estimates of annotation confidence, identifying a trace with maximal confidence, and recursively splicing/stitching inserted elements. PolyA communicates annotation certainty, identifies large scale rearrangements, and detects boundaries between neighboring elements

MUCHA: multiple chemical alignment algorithm to identify building block substructures of orphan secondary metabolites

[Background]In contrast to the increasing number of the successful genome projects, there still remain many orphan metabolites for which their synthesis processes are unknown. Metabolites, including these orphan metabolites, can be classified into groups that share the same core substructures, originated from the same biosynthetic pathways. It is known that many metabolites are synthesized by adding up building blocks to existing metabolites. Therefore, it is proposed that, for any given group of metabolites, finding the core substructure and the branched substructures can help predict their biosynthetic pathway. There already have been many reports on the multiple graph alignment techniques to find the conserved chemical substructures in relatively small molecules. However, they are optimized for ligand binding and are not suitable for metabolomic studies. [Results]We developed an efficient multiple graph alignment method named as MUCHA (Multiple Chemical Alignment), specialized for finding metabolic building blocks. This method showed the strength in finding metabolic building blocks with preserving the relative positions among the substructures, which is not achieved by simply applying the frequent graph mining techniques. Compared with the combined pairwise alignments, this proposed MUCHA method generally reduced computational costs with improving the quality of the alignment. [Conclusions]MUCHA successfully find building blocks of secondary metabolites, and has a potential to complement to other existing methods to reconstruct metabolic networks using reaction patterns

Against All Probabilities: A modeling paradigm for streaming applications that goes against common notions

Hardware and software design requires the right portion of skills and mental faculties. The design of a good system is an exercise in rational thinking, engineering, and art. The design process is further complicated when we aspire to build systems that exploit parallelism or are targeted to be deployed on architecturally diverse computing devices, FPGAs or GPUs to name just a few. The need to develop systems that can take advantage of computing devices beyond general purpose CPUs is real. There are several application domains and research efforts that will simply not be able to adequately perform or yield answers in a reasonable amount of time otherwise. Developing a mathematical model of a system is a key stepping stone to a high performance system, but often is absent from the design process due to the complexity of the model development. In this paper we offer an easy, yet solid approach to the development of such mathematical models. This adds a little bit more weight to engineering side of the design process in the form of a quantifiable method that enables designers to reason about their systems, identify bottlenecks, and gain vital information for performance improvements