Eye Splicer is a genome browser powered by Biodalliance JavaScript library that lets you explore alternative splicing events across different developmental stages in lens from mouse samples. Eye Splicer contains the most abundant alternative splicing events, skipped exons and retained introns, from our analysis on mouse RNA-Seq datasets covering developmental stages from E15 (embryo 15 days old) to P9 (post-natal 9 days old).
Author: Janga Lab
ExSurv: A web resource for prognostic analyses of exons across human cancers using clinical transcriptomes
ExSurv is a web resource for studying the survival contributions of exons across human cancers using RNA-seq data. ExSurv is the first web server which provides exon level survival significance by using the RNA-seq expression datasets and the clinical metadata for four cancer types from The Cancer Genome Atlas (TCGA) project. We pre-calculated the prognostic significance of more than 600000 annotated exons in Ensembl using survival package in R. We stored the TCGA clinical data, exon survival p-values and the expression of the significant exons for visualizing the survival curves in a MySQL database. We developed an integrated backend using PHP and R and used JavaScript in the frontend. The PHP/R backend is reponsible for querying the MySQL database upon user input, calling R to visualize the corresponding database results (using survival package) and returning these results to the frontend. In the frontend, the results are shown to the users in an organized way as a table where each row corresponds to an exon in the queried gene symbol or Ensembl gene ID. It is possible to export the survival plots in SVG (scalable vector graphics) format and the raw data used to generate the plot in TSV (tab-separated values) format.
Developing Units to Train High School Teachers in Bioinformatics, Genomics and Systems Biology
Below is a proposed outline of the units which will comprise of video and accompanying presentation slides along with lab activities.
- Unit 1: Introduction and overview of genomics and bioinformatics, including career paths and opportunities in the broader fields of biomedical informatics.
- Unit 2: Fundamentals of molecular biology that provide the necessary biology foundation for the below units (Units 4-7).
- Unit 3: Fundamentals of computational and systems biology that provide the foundation for the below units (Units 4-7)
- Unit 4: Biological sequence comparison
- Unit 5: Regulatory motif finding and their evolution
- Unit 6: Genome sequencing and personalized medicine
- Unit 7: Network visualization in systems biology
- Lab 1: A 1-week long lab that leverages bioinformatics tools and applications (instead of software development) to solve an interesting bioinformatics problem.
Since units 1-3 would be basic and essential for all the students interested in bioinformatics they can be taught to biology, mathematics or advancement placement in biology or computer science students before taking the laboratory component on which it depends. The other lectures can me made optional depending on the school or teacher teaching it, in the respective school. For instance, for advanced placement biology students a teacher might prefer to use units 1-3 together with units 6, 7 and lab or just limit to units 1-3 and lab if the schedule doesn’t permit extensive integration while APCS (Advanced Placement Computer Science) looking for CS experience might prefer to use slightly different material from this list (such as units 1-3 together with units 4-6 and the lab because they are likely to have more available slots for integration). The reason would be motif finding and sequence comparison are historically more demanding computational problems while other more genomics-oriented material and visualization tools could be easily taught to biology students. Although the teacher can finally choose to include as many or as few units from this list by tailoring the lab with the PI’s help.
The outreach efforts to area schools will build on the Janga Lab’s experiences working high school teachers and their students here in central Indiana. We are also aware and have learned from the experiences of others who have worked to introduce bioinformatics into the high school curriculum such as reported by Form and Lewitter (2011) and Gallagher et al., (2011). The modular learning experience we design will be compatible with the limited experience most high school students have had applying math and computer science to biological problems. Initially, our work with schools will focus on developing models that will help teachers to use bioinformatics to teach the central concepts of biology that figure prominently in the major curriculum frameworks. Our aim is to raise the level of understanding of the field of bioinformatics by initiially helping teachers to use bioinformatics as an affordable teaching tool that will help teachers better achieve existing curricular goals rather than adding new ones. Using bioinformatics as a teaching tool to enhance student understanding of the existing curriculum in a school creates the opportunity to use the modules we develop at various times during the school year. By varying the gene and protein that are explored and visualized, the modules we develop could be used at different points in the year-long curriculum to give students a deeper experience and understanding of the topic. Early enzyme catalysis labs, later plasmid transfer labs or topics involving proteins and human physiology, almost any unit in the general biology curriculum involves genes and proteins that could be explored using bioinformatics and protein visualization tools that would connect the topic in the unit to big ideas of diversity, adaptive fitness and evolution.
References
Form, D., & Lewitter, F. (2011). Ten simple rules for teaching bioinformatics at the high school level. PLoS Comput Biol, 7(10), e1002243-e1002243.
Gallagher, S. R., Coon, W., Donley, K., Scott, A., & Goldberg, D. S. (2011). A first attempt to bring computational biology into advanced high school biology classrooms. PLoS Comput Biol, 7(10), e1002244.
Programming for Science Informatics (B573)
Credit Hours: 3
Day/Time: Tuesdays, 6–8:40 pm
Location: IT 270, 535 West Michigan Street, Indianapolis, IN 46202 [map]
May have some guest lectures, not necessarily in the same room and time
First Class:
Website:
Instructor: Sarath Chandra Janga, Ph.D., Assistant Professor, Bioinformatics
Office Hours: Tuesdays and Thursdays, 11 am–12 pm or by Appointment
Office: WK 309, Walker Plaza Building, 719 Indiana Avenue, Indianapolis, IN 46202 [map]
Phone: (317) 278-4147 (Office)
Email: jangalab@iupui.edu
Website: http://www.iupui.edu/~jangalab/
Prerequisites:
Description
In this course, we will cover the basics of programming as they are relevant to understanding and analyzing biological datasets. This will be achieved by giving a biology background to motivate a computational need/task. At the end of the course, you should be able to describe solutions (preferably elegant) to address a wide range of basic biological and biomedical problems.
The course is aimed at giving a good foundation in UNIX based administration, PERL programming, MySQL database management, R statistical analysis and application development in omics settings using these programming languages/tools.
The instructor will give introductions to each of these programming languages and commonly used applications in bioinformatics/systems biology in the first 10 weeks. Then the students will be asked to present recent articles published in the last 4 years (each student has to present a paper), present a project work (as a group of 2 to 3 students on a particular theme/problem) and submission of the project report.
Translational Bioinformatics Applications (B656)
Credit Hours: 3
Day/Time:
Location: WK 321, Walker Plaza Building, 719 Indiana Avenue, Indianapolis, IN 46202 [map]
May have some guest lectures, not necessarily in the same room and time
First Class:
Website:
Instructor: Sarath Chandra Janga, Ph.D., Assistant Professor, Bioinformatics
Office Hours: Tuesdays and Thursdays, 11 am–12 pm or by Appointment
Office: WK 309, Walker Plaza Building, 719 Indiana Avenue, Indianapolis, IN 46202 [map]
Phone: (317) 278-4147 (Office)
Email: jangalab@iupui.edu
Website: http://www.iupui.edu/~jangalab/
Prerequisites:
Description
Translational medicine (TM) attempts to bring clinical and biomedical research practices and outcomes to patient care (“bench to bedside”). Informaticians have assisted clinicians and biomedical scientists in dealing with the large volumes of data derived from various high-throughput methodologies, developing disease models and drug design. This course will focus on the complexities of low, medium and high-throughput applications in translational medicine and train the students in solving translational medicine data management problems employing various informatics frameworks.
Computational Approaches for Analysing High-throughput Data in Biology (I590)
Credit Hours: 3
Day/Time: Mondays, 6–8:30 pm
Location: IT 271, 535 West Michigan Street, Indianapolis, IN 46202 [map]
May have some guest lectures, not necessarily in the same room and time
First Class:
Website:
Instructor: Sarath Chandra Janga, Ph.D., Assistant Professor, Bioinformatics
Office Hours: Tuesdays and Thursdays, 11 am–12 pm or by Appointment
Office: WK 309, Walker Plaza Building, 719 Indiana Avenue, Indianapolis, IN 46202 [map]
Phone: (317) 278-4147 (Office)
Email: jangalab@iupui.edu
Website: http://www.iupui.edu/~jangalab/
Prerequisites:
Description
In this course, we will cover the advanced concepts of genomics, molecular and systems biology and explore different (computational) approaches for analyzing high-throughput datasets resulting from these respective fields. This will be achieved by giving a biology background to motivate a computational need/task with most assignments involving a computational exercise to handle such datasets or to implement relevant algorithms. At the end of the course, you should be able to describe solutions (preferably elegant) to address a wide range of basic biological and biomedical problems.
The course is aimed at students who have some experience in programming (or are willing to learn it at a quick pace) and are willing to apply these skills in omics settings using a variety of programming languages/tools.
The instructor will give a detailed introduction to each of the areas below and introduce commonly used applications in bioinformatics/systems biology in the first 9-10 weeks. Then the students will be asked to present recent articles published in the last 2 years (each student has to present a paper or two) along with details of their project work (each student chooses a particular theme/problem related to the paper/s presented) and submit a project report on the research problem (project work) they addressed, towards the end of the semester.
Next Generation Genomic Data Analytics (B636)
Credit Hours: 3
Day/Time: Thursdays, 6–8:40 pm
Location: WK 321, Walker Plaza Building, 719 Indiana Avenue, Indianapolis, IN 46202 [map]
May have some guest lectures, not necessarily in the same room and time
First Class: August 27th, 2015
Website:
Instructor: Sarath Chandra Janga, Ph.D., Assistant Professor, Bioinformatics
Office Hours: Tuesdays and Thursdays, 11 am–12 pm or by Appointment
Office: WK 309, Walker Plaza Building, 719 Indiana Avenue, Indianapolis, IN 46202 [map]
Phone: (317) 278-4147 (Office)
Email: jangalab@iupui.edu
Website: http://www.iupui.edu/~jangalab/
Prerequisites: I573 or basic knowledge of programming, R, and Unix system management
Description
This course covers advanced concepts of genomic sequencing datasets from a number of sequencing platforms, including how the data motivates computational needs and tasks for analysis. The student learns how to devise approaches for analyzing massive clinical and biomedical sequencing datasets and for developing sound hypotheses and predictions from them.
Seten: Gene set enrichment on CLIP-seq RNA-binding protein binding signals datasets
Seten is a tool for systematic identification and comparison of processes, phenotypes and diseases associated with RNA-binding proteins from condition-specific CLIP-seq profiles. Seten is a web-based as well as a command line tool which can identify and compare processes, phenotypes and diseases associated with RBPs from condition-specific CLIP-seq profiles. Seten currently provides precomputed results for more than 150 CLIP-seq datasets and can be used to analyze other kinds of binding profile data such as ChIP-seq data.
OperomeDB: Operon prediction for bacterial genomes
In prokaryotic organisms, a substantial fraction of adjacent genes are related functionally and most of these are organized into operons. Operons are a group of co-directionally organized genes in prokaryotic genomes with the presence of a common promoter and terminator. Operons provide insight into the cellular functions and also help in determining distinct experimental designs. It is now clear that the structure of an operon is not static but rather changes with environmental conditions in bacterial species.
READDB: RNA-binding protein expression and disease dynamics database
RNA-binding Protein Expression and Disease Dynamics database is a non-redundant, curated database of human RNA-binding proteins (RBPs). We collected RBPs data from multiple experimental studies and reported their annotation, tissue expression, microRNA predictions, disease associations, protein-protein interactions and their sequence motifs.