Bioinformatics, M.S. Online

Online

Bioinformatics

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The last decade has seen unprecedented changes in biotech, biomedicine, biomanufacturing, and bioengineering. Most of it is fueled by new genomics and other omics technologies that generate massive amount of data, but also do so at a higher and higher resolution going down to single molecules and single cells. The resulting data need to be interpreted carefully, because a single mutation in a base (e.g., “​SNP​”​) could be the cause of a disease. ​The resulting data is massive, as biotech​’​s Moore​’​s law grows exponentially (doubling every five months in comparison to computers​’ ​doubling every eighteen months).

 

Why NYU Tandon Online for Bioinformatics?

 

Questions?

We are here to help! Call us at 646.997.3623, U.S. Toll-Free at 877.503.7659, or email us at tandon.online@nyu.edu.

How to Apply

Online Application

Tuition and Financial Aid

We at Tandon are educating and nurturing tomorrow​’​s biotech rock-stars, who can address infectious diseases (e.g., Zika or Ebola), genetic diseases (e.g., Cancer, Alzheimer or Autism), public health (Personalized Health Care Program, Diabetes or Obesity), agriculture (e.g., GMO, Genetically Modified Organisms) and green technology (e.g., Energy or GHG(Green House Gas)-sequestration.

Th​is NY State approved​​ program meets industry's demand for professionals with solid foundations in genomics, proteomics,​ and transcriptomics;​ Algorithms, statistics and biotechnology; programming (Python, Perl and R)​;​ ​data science, AI and ML; ​sequence and pathway analysis, as well as a host of genome informatics tools and algorithms such as BLAST, BioPython, BioPerl, Bioconductor, and UCSC genome browser.

Tandon also provides a bridge program to prepare students with insufficient background in core computer science before admission.

Students who earn a Bioinformatics Advanced Certificate may apply those credits towards the Bioinformatics Master's Degree. Note that only 9 credits from the Advanced Certificate can be used towards the Bioinformatics Master Degree program.

Our 30 credit program offers you a refined skill set including but not limited to functional annotation, statistical analysis, algorithmic development and genomics and proteomics.

Brooklyn Bridge

A Bridge to NYU Tandon

The Bridge to NYU Tandon Program is a prerequisite course recommended to those interested in applying for the Bioinformatics Master's Degree who are lacking a background in science or engineering.

Admission Requirements

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 a chemical and biological foundation in Bioinformatics course. If you are from a chemical or biological science background, you are required to take Introduction to Programming and Problem Solving and Data Structures and Algorithms.


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
    The GRE is required for full-time applicants to this program and is not required for part-time applicants. It cannot be substituted with the GMAT.
  • English Language Proficiency Testing

For more details on the above list, please review the Master’s and Advanced Certificate Application Checklist section.


Current Curriculum

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

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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).

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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.


Laboratory Science Concentration (Required Courses):

Course BI-GY7543 is a capstone.

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):

Course BI-GY7733 is a capstone.

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.