"Large scale multi-species palindrome study using distributed inmemory computing"

Devin Petersohn, Matt Spencer, Alex Fratila, and Chi-Ren Shyu

Published in the 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Abstract

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.

"Mining large-scale repetitive sequences in a MapReduce setting"

Hongfei Cao, Michael Phinney, Devin Petersohn, Benjamin Merideth, and Chi-Ren Shyu

Published in the International Journal of Data Mining and Bioinformatics

Abstract

Recent research suggests DNA repeats play critical roles in cellular regulatory functions and disease development. The challenge associated with identifying repeats across a collection of genomes arises from the amount of data stored within DNA, and intermediate data generated by alignment- and hash-based approaches are substantial. We present a MapReduce-based method for repeat identification and propose efficient storage and search techniques. Our approach distributes the computation and storage across a cluster of commodity computers, lending a cost-effective, flexible, robust, and scalable solution to the challenge of identifying various types of repetitive sequences across a collection of genomes. In this study, we benchmark our method using a collection of six genomes, totalling approximately 14.2 billion base pairs. We demonstrate a tenfold speedup over previous state-of-the-art approaches and linear scalability. In addition, we conduct a deeper scalability analysis by processing a collection of 39 genomes, approximately 104 billion base pairs.

"MRSMRS: Mining repetitive sequences in a MapReduce setting"

Hongfei Cao, Michael Phinney, Devin Petersohn, Benjamin Merideth, and Chi-Ren Shyu

Published in the 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)