Bioinformatics, M.S.

In order to be eligible to apply for any of our master’s programs, you must meet the following criteria:

You must hold a bachelor's degree from an accredited institution, which includes a minimum of four years of full-time study. Bachelor of Engineering degrees (based on 180+ ECTS credits) may also be considered. Attention will be given to the programs accredited by ABET and programs accredited/approved by other various regional accrediting associations.

This program requires a graduate status and certain prerequisite courses depending on your background. If you have a background in computer science or a similar program, you are required to take the Biology and Biotechnology course. If you are from a chemical or biological science background, you are required to take Problem Solving for Bioinformatics as well as Algorithms and Data Structures for Bioinformatics.

The following is a list of all action items required to apply.

• Application
• Application Fee
• Personal Statement
• Resume
• Official Transcripts
• Letters of Recommendation
• GRE or GMAT Score
  We recognize that due to COVID-19 restrictions some applicants may experience difficulty in taking the GRE test and, in response, we are committed to being flexible. For the Spring 2021 and Fall 2021 terms, the GRE is not required for all NYU Tandon Online MS programs. For more information, see the GRE/GMAT Requirements page.
• English Language Proficiency Testing

NYU Tandon Bridge

The 100% online NYU Tandon Bridge course prepares students without a Bioinformatics degree or other substantial related experience to apply for select NYU Tandon Master’s Degree programs. In the course, students will learn computer science fundamentals and programming with C++. Students’ performance in the Bridge will count toward their Master’s degree application decisions. The Bridge is a non-credit certificate course, and those who complete the Bridge with a final grade of C or above will earn a Certificate of Completion, and those who earn a B+ or above will receive a Certificate of Completion with Distinction. Note: regardless of performance, successful completion of the Bridge course does not guarantee admission to any academic program.

The NYU Tandon Bridge course is taught by faculty members of the Computer Science department at the NYU Tandon School of Engineering, aided by NYU Tandon Graduate student teaching assistants. Students will participate in interactive online modules, live webinars, assignments, and tests.

NYU Tandon Bridge

3 Credits Algorithms and Data Structures for Bioinformatics BI-GY7453

The online course is aimed at introducing the foundational ideas from computer science in designing and implementing bioinformatics algorithms. The goal of the underlying algorithms and data structures is to accurately abstract and model the biological problems and to devise provably correct procedures with efficient computational complexity bounds. The algorithms will be described in pseudo-codes in order to simplify the correctness and complexity analysis, but with sufficient details to enable the students implement them in any suitable software pipelines and hardware architectures.
Prerequisites: MA-UY 2314

3 Credits Problem Solving for Bioinformatics BI-GY7663

This course will introduce students to programming in Bioinformatics. The focus will be on object oriented techniques of scripting. Cancer data will be used as examples throughout the course.

3 Credits Biology and Biotechnology for Bioinformatics BI-GY7683

The online course is aimed at introducing the key ideas from biology and biochemistry and how they are used in modern biotechnology. The goal of this course is to develop students? critical thinking and analytical reasoning skills in the specific context of biotechnology and its modern applications. This course will explore a plethora of technologies used in the fields of genetic engineering, forensics, agriculture, bioremediation and medicine in order to give the students a basic but fundamental experimental skill set which can be applied in conjunction with computational skills to solve biological problems in a scalable manner. Students enroll into this course should have knowledge of basic Sciences (Biology, Physics and Chemistry).

3 Credits Statistics and Mathematics for Bioinformatics BI-GY7723

The online course is aimed at introducing the fundamental concepts from mathematics, probability and statistics, as relevant to bioinformatics and computational biology. Students enroll into this course should have knowledge of Calculus and Discrete Mathematics.

3 Credits Applied Biostatistics for Bioinformatics BI-GY7673

This online course will introduce the basics of statistics and its applications in various fields of biology. It will lean towards practical applications, allowing for an intuitive understanding of concepts and some rigor in the application of statistics. It will use R for all the programming exercises. The course will not be requiring a lot of programming, and the requisite skills will be introduced in the lectures. The problems, exercises and assignments will be drawn from real-life problems in research papers and books. The student should be able the initiate and solve problems in the field at the end of the course. Students enroll into this course should have knowledge of basics of programming, probability and statistics.

3 Credits Machine Learning and Data Science for Bioinformatics BI-GY7743

This online course is aimed at developing practical machine learning and econometric (time series) skills with applications to biological data. The course will use examples from bioinformatics application areas throughout and will emphasize translational aspects.

Capstone Project

At least three credits of Capstone course (BI-GY7753 BIOINFORMATICS GUIDED STUDIES) are required to fulfill the M.S.

Bioinformatics requirement for graduation.

Will Industry internships count for the capstone? If so, what is the process for formalizing that as NYU credits.

Industry internships can count as capstone credits, but the student has to register for capstone BI-GY7753. If it meets the programs capstone criteria, significant programming component(Python, R, and/Shell scripting). The agreement is formalized between us and the manager/PI. We avoid all agreements that deal with proprietary data.

Students must find the internships themselves (although we will help).

What are the guidelines for the project?

Projects often deal with NGS analysis. Basically, the student demonstrates their skills in a live environment.

Is it with academic labs?

Most of our relationships are with academic labs. We don't get involved  in legal issues concerning IP.

When should they sign up and how do they find a lab?

Completion of:

• Problem Solving
• NGS
• Applied biostatistics

There are several labs with multiple projects that we work with. We can place students in any of them.

Laboratory Science Concentration (Required Courses):

3 Credits Proteomics for Bioinformatics BI-GY7543

The online proteomics course contributes an application focused specialty class to the bioinformatics curriculum. It will be a tour-de-force of modern proteomics methods and analysis in the context of practical research and clinical applications. The course will teach fundamentals, applications, experiments and predictions in parallel. Thus, each week will include a mix of interactive approaches from background learning, to understanding experimental methodology pro and con, to software usage and sophisticated bioinformatics approaches to prediction. Limitations and complementary of prediction methods will be emphasized. It is desirable (but not required) for students to complete a Biochemistry course before taking this course.
Prerequisites: Bioinformatics I.

3 Credits Next Generation Sequence Analysis for Bioinformatics BI-GY7653

The online course is aimed at developing practical bioinformatics skills of next generation sequencing analysis. Students will be introduced to current best practices and in high-throughput sequence data analysis and they will have the opportunity to analyze real data in a high-performance Unix-based computing environment. Special attention will be given to understand the advantages, limitations, and assumptions of most widely bioinformatics methods and the challenges involved in the analysis of large scale datasets. Some of the topics that will be covered include, current sequencing platforms, data formats (FASTA, SAM, BAM, VCF), sequence alignment, sequence assembly, variant calling, RNA-seq analysis, and their biological applications. Students enroll into this course should have knowledge of Basic of programming, unix tools, and shell scripting.

Translational Science Concentration (Required Courses):

3 Credits Translational Genomics and Computational Biology BI-GY7733

This online course will introduce will expose the students to different aspects of the data analysis and modeling activities that are expected of a Bioinformatician or a Computational Biologist. This course will offer a wide spectrum of examples of applications roughly divided in two broad parts: (a) data analysis in a "translational" settings and (b) more "computational" approaches to Biology pertaining the simulation of biological systems. This course will explore a different set of online resources that contain complex data models of data (e.g., cancer data from TCGA and ICGC); the data thus collected will be used to expose novel model reconstruction tools. Other online resources and related exchange formats will be explored in order to show how simulation of biological systems models (and the related problem of their parameter tuning) in its different forms has become more and more usable and an important tool for biomedicine. Students enroll into this course should have knowledge of basics of programming, undergraduate calculus, probability and statistics, introductory cell biology.
Pre-requisites: BI-GY 7673

3 Credits Population Genetics and Evolutionary Biology for Bioinformatics BI-GY7693

The online course is aimed at introducing the key ideas from population genetics and how they are used to understand the interaction of basic evolutionary processes (e.g., including mutation, natural selection, genetic drift, inbreeding, recombination and gene flow) that determine the genetic composition and evolutionary trajectories of natural populations. The goal of this course is to develop students? critical thinking and analytical reasoning skills in the specific context of many mechanisms shaping genetic variations and within and between populations. This course will equip the students with mathematical and experimental skills to address public health issues.

3 Credits Transcriptomics BI-GY7633

Screening of differential expression of genes using microarray technology builds the opportunities for personalized medicine converging soon to medical informatics and to our health care system. The course will start with a discussion of gene expression biology, presenting microarray platforms, design of experiments, and Affymetrix file structures and data storage. R programming is introduced for the preprocessing Affymetrix data for Image analysis, quality control and array normalization, log transformation and putting the data together. Bioconductor software will be dealt with data importing, filtering, annotation and analysis. Machine learning concepts and tools for statistical genomics will be addressed along with distance concept, cluster analysis, heat map and class discovery. Case studies link the methodology to biomolecular pathways, gene ontology, genome browsing and drug signatures.

3 Credits Special Topics in “informatics in Chemical and Biological Sciences” BI-GY7573

This course covers special topics on various advanced or specialized topics in chemo- or bioinformatics that are presented at intervals.