EE 372: Project Abstracts

EE 372: Project Abstracts

Next-generation Next-generation Basecallers

Andre Cornman and Logan Spear

Our project was on basecallers for ONT data. When ONT reads were first released, basecalling was done based off of stochastic models involving multiple bases being inside of the pore during each measurement. As discussing in class, for this model, the raw data is first turned into levels, which are supposed to correspond to the presence of a particular context in the pore, and then bases are called using Viterbi. Modern basecallers have made the switch to directly using the raw signal and from using HMM models to Neural Networks (RNN’s). Basecallers that employ RNN’s typically see a greater accuracy than HMM based basecallers, although, as our results and results of others suggest, the RNN basecallers are very sensitive to the species of DNA that they are trained on.

Improved Long-Read Assembly from Hi-C Information

Mark Nishimura

Hi-C-seq is a useful tool for determining the chromosome’s three-dimensional conformation in the nucleus. In this work, we attempt to use Hi-C information, in the form of a contact map, to achieve improved long-read assembly. By solving an LP relaxation of the Traveling Salesman Problem, HINGE + Hi-C provides better repeat resolution on simulated data.

Calling SNPs from Maternal Cell-Free RNA Sequencing to Inform Prenatal Care

Mira Moufarrej

Approximately 15 million neonates are born preterm every year worldwide. To date, we are still unaware of what exactly causes spontaneous preterm delivery and more importantly, how we might prevent it. To address this and to better target future research into the causes of and potential therapies for spontaneous preterm delivery, we must address two conflated questions of origin – maternal tissue and fetal fraction, which furthermore likely change dramatically over gestation. To answer part of this question and understand where cfRNA transcripts in the blood originated, here, I present progress I have made toward developing statistical methods that identify fetal single nucleotide polymorphisms from total cfRNA.

Parallelizing Medoid Calculations through Spark

Daniel Hsu and Brijesh Patel

We seek to find a representative cell from a single cell gene expression dataset from mice brain cells with high dimensions. The Med-dit algorithm uses the concept of multi-armed bandits to find medoids in almost linear time. We want to see the effect on runtime of this algorithm put in a Spark framework and in conjunction with Google Cloud. Spark is an open source MapReduce framework for distributed systems that is used in most modern systems for distributed computing. It provides a clean API for managing datasets and running parallelizable algorithms, which is what we are hoping to utilize for speedup in our algorithm.

Single Cell Co-Expression Subnetwork Detection

Laura Shen and Po-Hsuan Wei

The transcriptome reflects the genes being expressed at any given time since it can vary depending on external environmental conditions. Many genes do not function in isolation, but work in a coordinated fashion with other genes to create complex proteins and other organic molecules required for biological function. Recent work has been done to assess different detection methods to find these gene networks with single-cell data. This project aims to find possible gene networks with single-cell data using different subnetwork detection methods, with corresponding metrics to evaluate the performances.

Limitations of Single Cell RNA-Seq in Neuronal Tissues: The Effect of Dissociation on Gene Expression

Michelle Drews

With the increasing feasibility and versatility of high throughput sequencing technologies, single cell RNA sequencing has arisen as a very popular method in neuroscience for interrogating the cellular compositions of different brain regions. However, a huge potential limitation of single cell sequencing in tissues like the brain, is that these tissues do not exist naturally as single cells, but instead exist as a highly interconnected and anchored tissue. To make single cells, a fairly lengthy dissociation process must be undertaken, where a digestive enzyme breaks down the solid tissue into single cells. This then raises the question what effect does this dissociation process have on gene expression, and could it be altering downstream clustering analyses. To address this question, we have taken brains from five adult mice and immediately harvested RNA from the left half, while the right half was dissociated into single cells like one would do for single cell RNAseq, and RNA harvested from the single cells in bulk. These RNA were then sequenced by the Quake lab, and raw read counts mapped to the mus musculus RefSeq transcriptome were returned. Differential expression analysis in DESeq2 revealed that, even with Bonferroni correction at a threshold of 0.001, approximately 15% of the transcriptome was perturbed following dissociation. Included among the upregulated genes were inflammatory and damage response genes such as HSPa1a and KLF2, as well as neuronal immediate early genes such as Fos and Jun. Included among the downregulated genes were synaptic organizing proteins and synaptic transmission proteins such as CAMK2a and KCNAB2. When these genes were removed from single cell clustering analysis, different clusters were obtained, indicating that these genes may be driving single cell clustering – not due to inherent biological variation, but rather as an artifact of the preparation to single cell sequencing.

Single-Cell and Disease: Modeling the Effects of Disease-Induced Transcriptional Changes on Cell Clustering and Classification

Jacob Blum

A unifying theme of neurodegeneration is that particular populations of neurons are more susceptible to cell death and degeneration than others. The reasons given for this selective vulnerability center around the notion that some cell types are better equipped to deal with the cell biological damage associated with neuronal function. It stands to reason that, given the immense heterogeneity of cell types in the central nervous system, single-cell RNA sequencing (scRNAseq) may offer a glimpse into how different cells respond transcriptionally to cell damage. Understanding this heterogeneous response may offer key insights into why certain cells degenerate and not others–and provide the key to understanding and fixing neurodegenerative disease. The major barrier preventing scRNAseq from being used to study neurodegenerative disease is the following–if cells are undergoing a stress response, will it still be possible to reliably determine which cell type they belong to? To answer this question, I have modeled the effects of a generic proteotoxic cell stress on single-cell RNA sequencing data, and subsequently attempted to assign cell labels to these ‘perturbed’ cells using consensus clustering, and machine-learning support vector machine (SVM) classifier. Here I report that the presence of perturbed cells drastically alters the estimated number of cell clusters by consensus clustering. However, if the number of clusters is determined in the healthy condition and used as a parameter in the perturbed condition, cell label assignment is still exceptionally accurate with consensus clustering. Similarly, the SVM trained on healthy data is able to correctly classify perturbed cells.