Harvey Mudd College has developed a sixth-generation circuit board with an FPGA and microcontroller for an embedded systems laboratory course. Students begin by assembling and testing their circuit board, then complete a series of laboratory projects with the chips. The course concludes with an open-ended team-based design project.

Though the genetic etiology of autism is complex, our understanding can be improved by identifying genes and gene-gene interactions that contribute to the development of specific autism subtypes. Identifying such gene groupings will allow individuals to be diagnosed and treated according to their precise characteristics. To this end, we developed a method to associate gene combinations with groups with shared autism traits, targeting genetic elements that distinguish patient populations with opposing phenotypes. Our computational method prioritizes genetic variants for genome-wide association, then utilizes Frequent Pattern Mining to highlight potential interactions between variants. We introduce a novel genotype assessment metric, the Unique Inherited Combination support, which accounts for inheritance patterns observed in the nuclear family while estimating the impact of genetic variation on phenotype manifestation at the individual level. High-contrast variant combinations are tested for significant subgroup associations. We apply this method by contrasting autism subgroups defined by severe or mild manifestations of a phenotype. Significant associations connected 286 genes to the subgroups, including 193 novel autism candidates. 71 pairs of genes have joint associations with subgroups, presenting opportunities to investigate interacting functions. This study analyzed 12 autism subgroups, but our informatics method can explore other meaningful divisions of autism patients, and can further be applied to reveal precise genetic associations within other phenotypically heterogeneous disorders, such as Alzheimer's disease.

Inverted Repeats in DNA sequences have long been known to have both major beneficial and detrimental effects in regards to how DNA is transcribed and duplicated. Palindromic sequences are frequently translated into proteins and may also facilitate DNA repair in some instances. However, they are also associated with significantly increased risk of mutation. Current methods are either slow or limited in the ways they can process imperfections due to tradeoffs between computational complexity and completeness in results. Our method allows for the efficient extraction of imperfect inverted repeats, featuring the ability to define the level of imperfection by the proportion of mismatching bases. By using GPU acceleration, we achieve order of magnitude speedups compared to the current leading method for imperfect inverted repeat extraction while allowing for more flexible results. We conducted a study on protein-coding exons contained entirely within inverted repeats. We found that these exons were significantly more likely to be included in multiple gene transcripts and were less likely to be spliced out.

Quasi-palindromes can be harmful or helpful, but most of this functionality is attributed to the formation of cruciforms. Unfortunately, the general properties a sequence must have to facilitate cruciform formation are poorly understood, as most research has focused on case studies of the unusual secondary structures of a few specific sequences. In this work, we investigate the general sequence properties leading to the formation of cruciforms by exploiting the fact that cruciforms lead to an increased mutation rate. We use evolutionary divergences in protein-coding sequences to calculate the mutation rates in quasi-palindromes with different degrees of self-complementarity. We perform this calculation to test different quasi-palindrome properties, then compare the observed patterns with the expected results. The most promising trials indicate that quasi-palindromes must be 10-30bp long with a self-complementarity at least 94% to form cruciforms, and that nucleotides near the center of the sequence are protected from mutation.

DNA palindromes are known to have many beneficial and detrimental functions in cell biology. Most of these functions are shared between perfect and imperfect palindromic sequences, but imperfect palindromes can be of particular interest due to their additional ability to dramatically increase the local mutation rate. Almost all available tools capable of extracting genetic palindromes were designed to accommodate inverted repeats, which are palindromic sequences with centralized non-matching nucleotides. We developed computational methods that focus specifically on perfect and imperfect palindromes, allowing us to take advantage of palindrome-specific properties which led to an 8× to 40× increase in speed and an approximately 10× decrease in memory requirements compared to the leading inverted repeats detection method.

Palindromic DNA has many interesting and functional properties, including the ability to form non-canonical DNA structures such as hairpins, cruciforms, and slipped strand structures. Palindromes also serve important roles in binding sites and enzyme activity, and have a strong effect on mutation rates. Palindromes are abundant in most genomes, often occurring within coding sequences, though in many instances it is still not clear how their presence affects genomic functions. The identification and study of palindromic DNA is essential to the progression of our understanding of the genome. To address this need, we present a novel method using an in-memory computing environment for identifying, extracting, and indexing palindromes in a searchable database for all mammals in Ensembl release 80. We discuss the preliminary results of a multi-species study on palindromic DNA, focusing on the size, frequency, and distribution of palindromes. Utilizing a Big Data ecosystem enables us to generate the largest palindrome database to date, comprising 42 genomes. Our study offers new insight into the dynamics of palindromes and facilitates future investigation.

Processing of data in many data analysis applications can be represented as an acyclic, coarse grain data flow, from data sources to the client. This paper is concerned with scheduling of multiple data analysis operations, each of which is represented as a pipelined chain of processing on data. We define the scheduling problem for effectively placing components onto Grid resources, and propose two scheduling algorithms. Experimental results are presented using a visualization application.