Bioinformatics Advanced Certificate | Online | NYU Tandon School of Engineering

Request Information


Emerging from unprecedented investigations into biological phenomena over the last decades, the in-demand field of bioinformatics organizes and translates vast streams of data from living organisms generated by the Human Genome Project and other more recent studies. If you are seeking a role as an expert in bioinformatics, you need a thorough appreciation of biology, chemistry, and computer science. This online graduate certificate prepares you to join a talented cadre of creative specialists in the fast-paced pharmaceutical and biotechnology industries.

Students who earn a Bioinformatics Advanced Certificate may apply those credits (up to nine) towards the Bioinformatics Master's Degree. 


Why NYU Tandon for Bioinformatics?



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

How to Apply

Online Application

Tuition and Financial Aid

This 12 credit, 100% online Advanced Certificate prepares you to join a talented cadre of creative specialists in the fast-paced pharmaceutical and biotechnology industries.


3 Credits Bioinformatics I: Sequence Analysis BI-GY7533
This course covers computer representations of nucleic acid and protein sequences; pairwise and multiple alignment methods; available databases of nucleic acid and protein sequences; database search methods; scoring functions for assessment of alignments; nucleic acid to protein sequence translation and codon usage; genomic organization and gene structure in prokaryotes and eukaryotes; introns and exons; prediction of open reading frames; alternative splicing; existing databases of mRNA, DNA protein and genomic information; and an overview of available programs and of Web resources.
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.
3 Credits Computational Tools Perl & Bioperl BI-GY7643
This course is designed to introduce students to the Perl programming language, its bioinformatics toolbox BioPerl and Unix commands for processing high throughput genomic and/or proteomic data.
Prerequisite: BI-GY 7573

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 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.