István Albert, Bioinformatics, Penn State

Online Graduate Certificate in Applied Bioinformatics

I currently serve as the Program Director for the Online Graduate Certificate in Applied Bioinformatics offered by the Penn State World Campus.

This page collects common answers to questions submitted by applicants and current students. If you have a question not answered here or need more information on the program contact bioinformatics@psu.edu.

Email is the preferred mode of communication.

Please note that the academic staff are not knowledgeable about technical problems that may occur while using World Campus Resources. The speediest responses regarding technical problems, course registration and other administrative issues may be obtained by contacting pennstateonline@psu.edu or other resources on the PSU World Campus website.

Latest News (August 2016)

Courses will be offered next in the Fall of 2016 Enroll by August 21th, 2016 to participate in:

  • BMMB 554 Foundations in Life Sciences
  • STAT 555 Statistical Analysis of Genomic Data

What are the official sources of information?

How does online education work?

Penn State World Campus maintains a helpful overview here How It Works (FAQs). In a nutshell World Campus courses are designed with your busy schedule in mind, providing the flexibility you need to study at the times most convenient to you. The majority of the courses are structured for asynchronous learning to provide maximum flexibility. All course activities, assignments, and exams, however, must be completed by their respective due dates.

Can PSU resident students obtain the certificate?

Yes. Resident students that have completed the required courses (BMMB 551, BMMB 852, BMMB 554 and STAT 555) either online or in residential instruction will qualify for the Certificate. These students may obtain the certificate by applying to the program then requesting the certificate.

How long does it take to get a certificate?

Currently the shortest time frame to take all four courses is one year. We will do our best effort to schedule at least one offering of each of the four courses within a calendar year.

What is the best time to start?

Admissions are ongoing throughout the year. Course enrollment takes place three times a year according to the Penn State Academic Calendar . The Fall and Spring semesters are 15 weeks long, the Summer semester is 12 weeks long.

Are all courses offered at all times?

No. Courses are offered based on the academic schedule. We offer each course at least once over the period of one Acdemic Calendar year (three semesters): Fall, Spring, Summer. Depending on enrollment and instructor availability we may offer some courses more than once per Academic Calendar but that is determined at least one year in advance. We recommend that students enroll for courses as soon as feasible.

We try not to oversubscribe courses but due to the online nature and the different paths that students take through the program it is possible for a course to become full in a given semester.

What are the important dates for 2016?

Four courses will be offered in 2016. Two in Spring 2016 and two during the Fall 2016. Students may enroll in one or more courses depending on their own assessment. Please contact us for advice.

Is there a list of courses by dates?

  • BMMB 551 Genomics: Spring 2015, Spring 2016, Spring 2017
  • BMMB 852 Applied Bioinformatics: Summer 2015, Spring 2016, Summer 2017
  • BMMB 554 Foundations in Life Sciences: Fall 2015, Fall 2016, Fall 2017
  • STAT 555 Statistical Analysis in Life Sciences: Fall 2015, Fall 2016, Fall 2017

What is the cost of the program?

Prices are set by the Penn State World Campus as an institution and not by the department or the instructors. See the Graduate Certificate in Applied Bioinformatics page. The total cost is the number of credits Ă— cost for one credit.

Does the program offer financial aid?

The program itself does not directly offer financial aid, but since the course is offered by an accredited institution students enrolled in the course may qualify for other financial aid programs.

When/how are acceptance decisions made?

The application process in ongoing and we review the applications about once a month. If you need more information sooner contact admissions bioinformatics@psu.edu

How to apply to the program?

Visit the Graduate Certificate in Applied Bioinformatics page

What happens after being accepted into the program?

Upon being accepted into the BIONC program, students will receive an Access Account Activation email from the World Campus Helpdesk. This will provide them with instructions on how to establish their Penn State email account. Very soon after, the World Campus sends students a program Welcome Letter that provides them with instructions on how to register and pay for their first course.

Students will have the BIONC code added to their record, but will appear in the University systems as a grad non-degree student. They will see the program on their transcript upon completion.

How to schedule courses?

After receiving the communications about the acceptance into the program and following those steps, students can schedule their first course. Students can schedule courses using the instructions on the World Campus site.

How to troubleshoot problems with the World Campus Services?

The academic staff are not knowledgeable about technical problems that may occur while using World Campus Resources. The speediest responses may be obtained via pennstateonline@psu.edu

Is there a short overview of each course that is part of the certificate?

The official source of information can be seen on the course list as well a the course schedule.

Course: BMMB 551, Genomics

Throughout this course we will introduce and/or review the fundamentals of genomics. You will alos learn about the approaches used to asses function of genomic DNA. As you acquire this information, you will be asked to apply this information for cutting edge exploration of functional regions of genomes. Additionally, this course will serve as a forum to explore new approaches to self-motivation and group learning.

Phase 1: Genome Sequences and Resources

  • Fundamentals of genomics
  • Sequencing technologies
  • Aligning biological sequences
  • Genome and transcript assembly
  • Resources for comparative genomics: Browsers, Galaxy

Phase 2: Finding biological functions encoded in a genome

  • Finding protein-coding genes within genomes
  • Finding transcribed regions
  • Finding evolutionary signatures of function
  • Finding non-coding functional sequences: gene regulation
  • Finding function by phenotype

Course: BMMB 852, Applied Bioinformatics

The purpose of this course is to introduce students to the various applications of high-throughput sequencing including: chip-Seq, RNA-Seq, SNP calling, metagenomics, de-novo assembly and others. The course material will concentrate on presenting complete data analysis scenarios for each of these domains of applications and will introduce students to a wide variety of existing tools and techniques. We expect that by the end of the course work students will:

  • understand common bioinformatics data formats and standards
  • become familiar with the practice of analyzing short-read sequencing data from various instruments
  • develop a computationally oriented thinking that is necessary to take on large-scale data analysis projects
  • understand data analysis principles of methodologies such as:
    • short read and long read alignments
    • interval query and manipulation
    • SNP calling and genomic variation detection o genome assembly with open source tools
    • metagenomics analyses
    • Chip-Seq analysis and peak calling
    • filter, extract and combine data with scripting languages
    • automate tasks with shell scripts to create reusable data pipelines
    • plot and visualize results with R and other packages
Access to a Mac or Linux computer is necessary to perform the homework. We can provide access to Linux computational resources. Only Mac OSX (Tiger/Leopard) and Linux operating systems are supported.

Course: BMMB 554, Foundations in Data Driven Life Sciences

This course is designed as a preparation routine for graduate students in Life Sciences. It has several focus areas including evolution of life sciences as well as in-depth overview of sequencing technologies and their applications. A key feature of this course is a set of lectures intended to draw students’ attention to critical importance of speaking and writing skills for successful careers in highly competitive biomedical field.

Module 1: History

  • History of Genetics
  • History of Molecular Biology
  • History of Genomics
  • The Human Genome Project

Module 2: NGS in-depth:

  • Chemistry, Molecular Biology and Applications
  • Illumina: Chemistry and Molecular Biology
  • Illumina: Realities of the data
  • Sequencing by ligation
  • Non-optical sequencing
  • Single Molecule Sequencing
  • Nanopores
  • ddPCR, and a bit on optical mapping

Module 3: Biology with NGS:

  • Re-sequencing: Basic Ideas
  • Re-sequencing: GWAS
  • Transcriptomics: Chemistry, Molecular Biology, Algorithmics, and Applications
  • Ribosomal Footprinting and transcoding
  • RNA structure analysis
  • Analysis of spacial conformation of the genome
  • Nucleic Acid/Protein interactions: Chemistry and Molecular Biology
  • Metagenomics

Module 4: Key Skills for Survival

  • Oral Presentation
  • Resources
  • Writing Grants and Papers

Course: STAT 555, Statistical Analysis of Genomics Data

The course is dedicated to statistical and computational methods for the design and analysis of bioinformatics experiments. The course has no pre-requisites, but some computational skills and/or familiarity with basic concepts in statistics, bioinformatics and/or cell biology will help.

Topics (with approximate number of lectures):

  • Introduction to R and RStudio (2)
  • Introduction to cell biology. (2)
  • Introduction to measurement technologies: microarrays, sequencing, SNPs and ChIP. (2)
  • Basic statistics (2)
  • Gene Expression Microarrays: experimental designs, preprocessing and normalization, differential expression. (4)
  • RNA-seq: experimental designs, preprocessing and normalization, differential expression, splice variants (5)
  • SNPs (2)
  • ChIPs (2)
  • Replication and pooling (1)
  • Gene Set enrichment analysis (2)
  • Clustering samples and genes (3)
  • Classifying samples using statistical machine learning (3)
  • Dimension reduction (2)
  • Combining data from multiple platforms (3)
  • Selected topics such as gene networks, time course experiments
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