DIMACS REU 2013

Name:	Scott Norton
Email:	scott.s.norton@hotmail.com
Office:	CoRE 434
Home Institution:	University of Connecticut - Map - Website
Project:	Statistical Methods for Finding New miRNA Motifs
Curriculum Vitae:	Link

---

About My Project

miRNA (microRNA) are a class of short (~22-nucleotide) RNA molecules found in the cytoplasm of eukaryotic cells. Their function in the cell is to inhibit gene expression after transcription into messenger RNA (mRNA) but before translation into protein. Within the miRNA is a short (6-8 nucleotide, sometimes split) segment which binds directly with the mRNA at the downstream regulatory region or 3' untranslated region (3' UTR). This study will help us identify miRNA motifs associated with different cellular processes such as metabolism, sleep/hibernation, and replication. When problems occur in miRNA actions, certain vital processes are either silenced or over-expressed. If these problems interfere with DNA maintenance or apoptosis (programmed cell death), they present an elevated risk for the development of cancer.

Our goal is to develop a new statistical method for correlating RNA microarray expression data with specific miRNA motifs.

---

Research Log

Week 1: Jun 3-7

In progress: coding a module for Python that performs a multiple linear regression from a vector-to-scalar mapping. Linear regressions will be the foundation for the motif predicting model. First version is a naive regression on the sum of terms against the residuals in each iteration.

Pesentation slides, June 7

Week 2: Jun 10-14

The multiple linear regression module has passed the test for small integer and random input - the output is sensible and reasonably close to an ideal fit. This week, I will be familiarizing myself with the primary software package, GEMMA, which correlates SNP (single nucleotide polymorphism) genotype and phenotype data. It is possible that SNPs will come into play when we analyze data from knockout mice - that is, mice where we negate, at conception, the functionality of a gene by inducing a single nucleotide mutation.

Week 3: Jun 17-21

The source data come from two sources: the 3' UTR sequences originate from UCSC and are labeled with RefSeq accession numbers, and the microarray data were obtained experimentally and are labeled with Ensembl accession numbers. The first task for this week is to pair the two data sets and filter out any missing entries from the lookup table. After that, I need to filter out any data points for whom the 3' UTR nucleotide sequence is a substring of another 3' UTR sequence. This ensures that the data do not have duplicates. From there, the kinship matrix must be computed. This matrix will be generated by correlating the k-mer frequencies for each sequence, where \(k\leq4\).

Week 4: Jun 24-28

After forming the kinship matrix, the genotypes will be computed based on the presence or absence of each possible 6-mer in each 3' UTR sequence. Two types of genotype data will be computed: a simple motif count, and a binary yes/no table. These, in addition to the expression (phenotype) data, will be input into GEMMA to determine which motifs are most correlated with decreased transcription output. We will also compute two additional kinds of kinship matrices: the dimer count kinship matrix which is computed using the same algorithm as last week with \(k=2\), and the shared motifs kinship matrix which takes the dot product of two rows of the binary matrix normalized to fall in \([0, 1]\).

Friday 6.28: Happy Tau Day, and Happy Birthday to me!

Week 5: Jul 1-5

The p-values from the Wald linear mixed model will be used for each motif to test the hypothesis that the presence or absense of the motif is positively correlated with the expression data. Using an \(\alpha\) of 0.05 and a Bonferroni-corrected \(\alpha\) of \(1.22 \cdot 10^{-5}\), the motifs such that (\p<\alpha\) will be compared to the set of known motifs. This is the test of validity for our method: if there is a significant overlap between the established results and ours, we say our method is suitable; otherwise, our method is flawed.

---

References & Links

Here are the papers I have read for my project:

1. Lee RC, Feinbaum RL, Ambros V (December 1993). "The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14". Cell 75 (5): 843-54.
2. Ben-Moshe, N. B., Avraham, R., Kedmi, M., Zeisel, A., Yitzhaky, A., Yarden, Y., & Domany, E. (2012, August 29). Context Specific MicroRNA Analysis (CoSMic): Identification of Functional MicroRNAs and their mRNA targets - Supplementary Data. Retrieved from Nucleic Acids Research: Oxford Journals
3. Bussemaker, H. J., Li, H., & Siggia, E. D. (2001). Regulatory element detection using correlation with expression. Nature, 167-171.
4. Searle, Shayle R., Casella, George, McCulloch, Charles E. Variance Components. Cornell University, Ithaca, New York. John Wiley & Sons, Inc., 1992. New York, NY et. al.

Here is my mentor's website, and the REU website:

• The Chen Group
• The REU Website