Complex microbial communities play an important role in numerous fields, from human health to bioremediation. One critical challenge in their data analysis is to separate true biological data from contamination of various sources. While contemporary experimental procedures include various negative controls, a comprehensive statistical approach for their analysis has not been developed. Such a framework would have a far-reaching impact on the field.
Complex microbiomes play an important role in numerous fields. One critical challenge in their data analysis is to separate true biological data from contamination. Contemporary experimental procedures include negative controls from various sources, but their analysis is complicated by “well-to-well” contamination: contamination that associates with the position of samples during experimental procedures. This causes bacteria sampled from a true biological source to appear in nearby control samples, and vice versa. An analytic approach that accounts for this source of contamination would have a far-reaching impact on the field.
Our lab is interested in aneuploidy, or the incorrect number of whole chromosomes and chromosome arms. A challenge in this area of research is that karyotypes require a large number of proliferating cells for analysis. To address this, our lab and collaborators developed new algorithms to identify aneuploidy alterations from DNA sequencing data. Here, the project goal is to implement these algorithms at Columbia, and subsequently to apply these analysis methods to samples generated in the lab and patient samples. Building on this, the DSI student may also develop new algorithms for use with single-cell sequencing data and RNA sequencing data. Experience in one or more of the following is a must: UNIX, R, and python. The DSI student will be mentored by Dr. Alison Taylor, and he/she will also work closely with all lab members.
Understanding the interaction between human-associated microbial communities and human health is expected to revolutionize healthcare. Recent work found that this interaction is, in part, shaped by genetic differences between otherwise identical species in the microbiome. Detecting this variation, however, is a significant challenge. This project aims to profile microbial genetic variation within and across multiple patients' microbiomes. This will allow us to better compare and interpret this variation in the context of human disease, gaining mechanistic insight into complex human-microbiome interactions.
This project will focus on the identification of genetic factors involved in various forms of hereditary diseases, including neurodevelopmental disorders, hearing loss, skeletal disorders and more. Some of these children endure years-long diagnostic odysseys of trial-and-error testing with inconclusive results and misdirected treatments. We are dedicated to track down their molecular causes by integrating various “-omics” technologies, including genomics, transcriptomics and epigenomics.
A highly collaborative project is available in Dr. Alison Taylor’s and Dr. Fatemeh Momen-Heravi’s lab. This project aims to identify molecular changes such as mutations and RNA signature of head and neck cancer in Black/African American and Hispanic minority populations with the goal of identifying novel therapies for cancer patients and reduce health disparities. The project entails analysis of DNA and RNA sequencing data.
This project works with a novel corpus of text-based school data to develop a multi-dimensional measure of the degree to which American colleges and universities offer a liberal arts education. We seek a data scientist for various tasks on a project that uses analysis of multiple text corpora to better understand the liberal arts. This is an ongoing three-year project with opportunities for future collaborations, academic publications, and developing and improving existing data science and machine learning skills. Tasks likely include: (1) Using Amazon Web Services to create and maintain cloud-based storage (SQL, S3 buckets) of the project’s expanding library of data. (2) Extracting information (named entities, times, places, books, and so on) from millions of plain-text syllabus records. (3) Merging multiple forms of data into a single dataset. (4) Scraping websites for relevant information (e.g., college course offerings, school rankings). Some pages may include dynamically created content that requires the use of a program such as Selenium.
