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Topic in bioinformatics

Topic in bioinformatics


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I am looking for a presentation topic in bioinformatics. I haven't occupied with this field yet, but I find it really interesting.

It would be nice if the topic would include an algorithm. What source do you suggest to me?


A fairly easy to get to grips with topic, but that digs down to the meat of bioinformatics algorithms, is sequence alignment. Read around Needleman-Wunsch and Smith-Waterman alignment algorithms.

Wikipedia will be a perfectly adequate starting place :) - steal all the references at the bottom of the page!

I don't know how strong you maths ability is, but you'll find quite a few equations while you dig around in the bases of bioinformatics algorithms!


It depends on what you define as introduction. For a start you could always do what Joe Healy suggested and read around to see if it is a field that suits you.

If you want a textbook I found An Introduction to Bioinformatics by Jones and Pevzner to be indeed a great introduction to bioinformatics. It treats you like an absolute beginner and I think you will be able to follow no matter your programming or math abilities.

Now, if you want to get your hands dirty I can't recommend Project Rosalind enough. It introduces you to a variety of (mainly) programming problems while simultaneously teaching you the corresponding biologic concepts.

Phillip Compeau (co-founder of Rosalind) along with Pavel Pevzner have released a textbook of their own: Bioinformatics Algorithms: An Active-Learning Approach, which I admit I haven't read, but it is generally well recieved.

But before all that, I suggest to take a small introduction to Python (if you haven't already) and get familiar with the basic libraries (mainly BioPython). Perl is also a language you would like to consider, but far less beginner-friendly.


The fields of bioinformatics and computational biology at UCSF aim to investigate questions about biological composition, structure, function, and evolution of molecules, cells, tissues, and organisms using mathematics, informatics, statistics, and computer science.

Because these approaches allow large-scale and quantitative analyses of biological phenomena and data obtained from many disciplines, they can ask questions and achieve unique insights not imaginable before the genomic era.

Both bioinformatics and computational biology are frequently integrated in faculty laboratories, often with experimental studies as well, with bioinformatics emphasizing informatics and statistics, while computational biology emphasizes development of theoretical methods, mathematical modeling, and computational simulation techniques to answer these questions.

Examples of bioinformatics studies include analysis and integration of -omics data, prediction of protein function from sequence and structural information, and cheminformatics comparisons of protein ligands to identify off-target effects of drugs. Examples in computational biology include simulation of protein motion and folding and how proteins interact with each other.

Faculty members working in these areas include:


Research Stories

Chronic wounds

Prof. Heck's legacy

"This second annual CBCB research symposium highlights the rapid growth of the center, and its broadening impact across the University, the state and beyond," said Karl Steiner, senior associate professor for research development and professor of electrical and computer engineering.

Steiner added, "Bioinformatics provides the key tools to make new and exciting discoveries at the nexus of biology and information technology, and we are thrilled about the progress CBCB has made over the past three years.&rdquo

Cathy H. Wu, center director and the Edward G. Jefferson Chair of Bioinformatics and Computational Biology at UD, said this year's symposium was designed to showcase the newly formed Bioinformatics Network of Delaware (BiND) and to provide a forum to catalyze the cross-fertilization of ideas and foster multidisciplinary research collaborations.

BiND includes UD and partner institutions Christiana Care Health System, Delaware State University, Delaware Technical and Community College, Nemours/A.I. duPont Hospital for Children and Wesley College. It was established to integrate the bioinformatics and biostatistics resources available through the partner institutions to strengthen the Delaware INBRE (IDeA Networks of Biomedical Research Excellence) infrastructure and efforts to promote collaborative research and training.

The daylong symposium featured a keynote address by Mark Gerstein, the Albert L. Williams Professor of Biomedical Informatics at Yale University.

Gerstein spoke on the topic "Molecular Networks: The Next-Generation Annotation for Personal Genomes." His talk highlighted the development of algorithms used to tease apart the role of pseudogenes and transcription factors within a biological network.

Gerstein's research is a key example of applying bioinformatics to complex networks involving human processes.

The symposium was organized in conjunction with the Bioinformatics ShortCourse: RNA-Seq Data Analysis, which was hosted by CBCB.

This introductory certificate course prepared participants to analyze high-throughput transcriptomic sequencing (RNA-Seq) data with open source software packages. Lectures and hands-on exercises emphasized practical application, walking participants through the process of designing RNA-Seq experiments and analyzing/visualizing resulting data sets.

Attendees at the workshop and symposium included representatives from Nemours, Christiana Care Health System, DuPont, Delaware State University, Delaware Technical and Community College, U.S. Army Research Laboratory, Wesley College and UD.

The symposium also featured presentations and posters from all BiND institutions. Yogasudha Veturi, a master's student in the Department of Plant and Soil Sciences at UD, was selected as the winner of the student poster award and gave an award presentation about her poster.


2. Transcriptomics

Transcriptomics is the study of an organism’s transcriptome. The transcriptome is referred to as the sum of an organism’s RNA transcripts. The DNA information in the genome gets converted to RNA through a process called transcription. A segment of DNA that gets transcribed into an RNA molecule is called a transcription unit which encodes genes.

A few research problems in transcriptomics include,

  1. Transcriptome assembly
  2. Transcriptome mapping
  3. Applications of transcriptomics in autoimmune diseases
  4. Differential expression of miRNAs

  • The Genomics Lab includes an Illumina MiSeq where undergraduate students sequence and annotate whole-genomes of a variety of organisms.
  • Recent bioinformatics graduates are employed at the Cleveland Clinic, Newport Labs, and the Dana-Farber Cancer Institute.
  • Bioinformatics students gain career exposure and hands-on experience through a required co-op experience.
  • With a 100% outcomes rate, bioinformatics graduates jump into a number of exciting careers immediately after graduation. They utilize their analytical and computational skills to solve real-world problems.

Bioinformatics is the intersection of biology and computer science. In this major, you’ll analyze big data collected by the healthcare industry to discover, diagnose, and treat a wide range of medical conditions. A rapidly growing field that requires professionals to possess problem-solving skills, you’ll gain hands-on learning through distinct undergraduate research opportunities. Graduates pursue graduate degrees and go on to successful careers in bioinformatics software development, biomedical research, biotechnology, comparative genomics, genomics, molecular imaging, pharmaceutical research and development, proteomics, and vaccine development.

Bioinformaticists use computers to analyze, organize, and visualize biological data in ways that increase the understanding of this data and lead to new discoveries. In laboratory exercises and assignments, you’ll learn to sequence DNA and use computer programs to analyze DNA sequences and predict molecular models.

The bioinformatics degree was developed by faculty in the departments of biological sciences, chemistry, computer science, mathematics and statistics, and information technology, with the guidance from leaders in the bioinformatics and biotechnology industries. The major meets the needs of prospective employers in this challenging and rapidly changing and growing field.

Bioinformatics is a field that has been developing over the last thirty years. It is a discipline that represents a marriage between biotechnology and computer technologies and has evolved through the convergence of advances in each of these fields. Today bioinformatics is a field that encompasses all aspects of the application of computer technologies to biological data. Computers are used to organize, link, analyze and visualize complex sets of biological data.

With the advent of high-throughput technologies such as Next Generation Sequencing and proteomics, bioinformatics has become essential to the biological sciences in general. In the past, laboratories were able to manage and analyze their experimental data in spreadsheets. Many research labs now require the expertise of dedicated bioinformatics core centers or their own in-house bioinformaticists.

Graduates of our programs have entered such laboratories, both in industry and academia, as bioinformaticists. Some have also gone on to leverage their biotechnology experiences as wet lab experimentalists themselves. The diversity of skills our students cultivate has given them access to a wide range of career choices.

Nature of Work

Bioinformatics jobs come with several different areas of focus, which are less strictly hierarchical than bioscience discovery research jobs. The analyst/programmer job provides more focused computational analysis support. Analyst/programmers design and develop software, databases, and interfaces used to analyze and manipulate genomic databases. They collaborate with production to develop high-throughput data processing and analysis capability and to design and implement data queries, novel algorithms, and/or visualization techniques. Analyst/programmers also maintain large-scale DNA databases, prepare data for other scientists, monitor new data from integrating sequence-based/ functional knowledge about genes to help scientists analyze and interpret gene-expression data. They also analyze DNA information and identify opportunities for innovative solutions to analyze and manage biological data. In addition, they often assist in developing software and custom scripts to automate data retrieval, manipulation, and analysis application of statistics and visualization tools. (Source: Vault Career Guide to Biotech The Jobs in Lab Research)

Training/Qualifications

Within the bioinformatics field employers tend to look for the following skills/strengths: fundamental training/knowledge in molecular biology, biochemistry and biotechnology, particularly, genomics, relational database administration, and programming skills/e.g. using SQL, PERL, C, C++, etc. on a UNIX operating system, strong analytical abilities using relevant mathematical/statistical tools, a strong interest in utilizing computational skills to leverage the data outcomes of those working in the laboratory, meticulous, independent, patient to do the same task repetitively and multitask.

National Labs Career Fair

Hosted by RIT’s Office of Career Services and Cooperative Education, the National Labs Career Fair is an annual event that brings representatives to campus from the United States’ federally funded research and development labs. These national labs focus on scientific discovery, clean energy development, national security, technology advancements, and more. Students are invited to attend the career fair to network with lab professionals, learn about opportunities, and interview for co-ops, internships, research positions, and full-time employment.

Combined Accelerated Pathways

This program has an accelerated bachelor’s/master’s available, one of RIT's Combined Accelerated Pathways, which enables you to earn two degrees in as little as five years.

Accelerated 4+1 MBA

An accelerated 4+1 MBA option is available to students enrolled in any of RIT’s undergraduate programs. RIT’s Combined Accelerated Pathways can help you prepare for your future faster by enabling you to earn both a bachelor’s and an MBA in as little as five years of study.

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Watch video series on student perspectives, academics, admissions, and more.

60+ future-focused workshops for rising high school seniors to plan for college.


Bioinformatics Project Ideas/Topics Collection For Engineering Students

Here is a list of project ideas based on Bioinformatics. Students belonging to third year or final year can use these projects as mini-projects as well as mega-projects. This list has been complied after searching for project ideas across the net.

  1. Predicting Cellular Localization. Eukaryotic cells contain several sub-compartments, the Cellular Localization problem consists of predicting which compartment a protein is most likely to be found, on the basis of sequence information alone. The project may consist of a review of the literature and/or a novel analysis (I have access to a data-set that has never been used in a predictive context).
  2. Regulatory-motifs. Review of the literature on algorithms to automatically determine regulatory motifs (short sequence signals) in DNA sequence data. I have a Java library that can be used to implement a prototype application see suffix tree below.

Specifically, is there a way to extend the dynamic programming
structure prediction algorithms to obtain a polynomial time algorithm
for the above question?

2. Combinatorial Design of Universal DNA Tag Systems.
Short DNA tags are used to label molecules in a
chemical library or to anchor DNA molecules to DNA arrays. A
combinatorial approach to design of DNA tags was proposed by Ben-Dor
et al. (RECOMB, 2000), based on the of
strand duplexes. However, the measure of melting temperature used in
the paper is less than ideal more accurate measures, based on nearest
neighbour calculations, are preferred.

3. A resource bounded theory of DNA self-assembly.
Molecular self-assembly naturally gives rise to
numerous complex forms. Self-assembly of DNA molecules with
programmable interactions can be used to construct structures at a
nano-metre scale.

Rothemund and Winfree propose a formal model for studying
self-assembled objects from the point of view of computational
complexity, and study the complexity of self-assembly of a
square in this model.

4. Phylogenetic classification of organisms based on sequence tags.
Given a (potentially partial) phylogenetic tree, develop strategies
and algorithms for placing sequences in that tree based on knowledge
of a short subsequence (tag) only.

This project is potentially of immediate and significant practical relevance
in the context of a collaboration we have with Bill Mohn and Michael Murphy
from the UBC Dept of Microbiology and Immunology.

5. RNA Secondary Structure Prediction.
Develop and test a (heuristic) algorithm that can predict RNA secondary structures
with pseudoknots. Since the problem is combinatorially hard, we expect that
stochastic local seach techniques might be particularly effective for solving
it algorithmically.

6. Multiple digestion restriction site mapping.
Implement a few known techniques for solving this problem,
try to improve them with modern heuristic techniques, and
do a comparative analysis of their performance on real genomic data.


Bioinformatics - Lab Report Example

Blast P Down syndrome is a condition that occurs because of a genetic defect. A normal person has 46 chromosomes while a person with Down syndrome has 47 chromosomes. This extra chromosome affects the brain and body development. Down syndrome is very common and is the cause of many birth defects in humans (Selikowitz 6). Down syndrome can be diagnosed at birth: the doctor can look at the appearance of the baby or listen to the baby’s chest using a stethoscope. A blood test can also be done to confirm the diagnosis by checking the extra chromosome.

I chose a protein from a human being accession number NP_001380 (Down syndrome cell adhesion molecule isoform CHD2-42 precursor). After conducting a blast p search, I chose the following proteins EFR29682.1 a hypothetical protein from Anopheles darlingi it had an E value of 10. This protein is not in any way related to Down syndrome. This is a protein from mosquito saliva and matches by chance. This E value is high its significance is low in relation to Down syndrome (Pevsner 7).Protein 2 is EGW12244.

1 known as Down syndrome critical region protein 3–like from Chinese hamster (Cricetulus griseus). This protein has an E value of 1 and is related to Down syndrome since the E value is very low. The hamster genome is similar to the human genome (Pevsner 15). Protein 3 was XP_002122877 with an E value of 0.4 it is a protein predicted to be similar to Down syndrome critical region protein 3 (Down syndrome critical region protein A). It is a protein from Ciona intestinalis, an invertebrate that is closest to humans and shares 80 % of the genome.


Open thesis topics

Advances in sequencing technologies have enabled the study of microbial communities, their composition and their genetic potential. In a culture-independent approach known as metagenomics, DNA is obtained directly from the environment and the resulting sequence data is analyzed in order to derive taxonomic community profiles as well as metabolic potential. In a similar manner, translated mRNA of bacterial communities is sequenced (environmental ``RNA-Seq'' or metatranscriptomics) and processed, thereby allowing to identify transcribed genes and perform differential gene expression analysis.

For the management, storage and analysis of such datasets, we have developed and operate the MGX platform, a client-server framework enabling scientists to conveniently process sequence data obtained from microbial communities. MGX offers a wide range of different analysis workflows for taxonomic and functional profiling of metagenome datasets based on unassembled sequence data. Recently, MGX was also extended with a pipeline based on the Common Workflow Language (CWL) supporting metagenome assembly, gene prediction, quantification and taxonomic binning.
Within the scope of de.NBI, the German network for bioinformatics infrastructure, MGX is provided as one the flagship services by the Bielefeld-Giessen Center for microbial bioinformatics.

Thesis aims

  • Design and implementation of a bioinformatics pipeline for the de novo assembly, quantification, and binning of metatranscriptome datasets based on CWL,
  • performance evaluation of the pipeline with regard to choice of transcriptome
    assembler software (e.g.Trinity, rnaSPAdes) , and
  • exemplary analysis of a sample metatranscriptome dataset.

Prerequisites

  • In-depth knowledge of the Linux command line
  • Prior experience with workflow systems, preferably Common Workflow Language (CWL)
  • Ability to work independently
  • Methodical way of working

Also, additional thesis topics are offered by the group for Algorithmic Bioinfomatics of Prof. Stefan Janssen a comprehensive list of open theses is available.


36 semester credit hours minimum

Mathematics Faculty

Professors: Larry P. Ammann @ammann , Zalman I. Balanov @zxb105020 , Swati Biswas @sxb125731 , Pankaj K. Choudhary @pkc022000 , Mieczyslaw K. Dabkowski @mkd034000 , Vladimir Dragovic @vxd123630 , Sam Efromovich @sxe062000 , Yulia Gel @yxg142030 , M. Ali Hooshyar @ali , Wieslaw Krawcewicz @wzk091000 , Susan E. Minkoff @sem120030 , L. Felipe Pereira @lfp140030 , Dmitry Rachinskiy @dxr124030 , Viswanath Ramakrishna @vish , Janos Turi @turi , John Zweck @jwz120030

Associate Professors: Yan Cao @yxc069200 , Min Chen @mxc136030

Assistant Professors: Maxim Arnold @mxa149530 , Carlos Arreche @cxa171230 , Bhargab Chattopadhyay @bxc126030 , Sy Han (Steven) Chiou @sxc172931 , Qingwen Hu @qxh102020 , Frank Konietschke @fxk141230 , Yifei Lou @yxl145331 , Oleg Makarenkov @oxm130230 , Tomoki Ohsawa @txo140730 , Sunyoung Shin @sxs177233 , Anh Tran @att140830 , Nathan Williams @nxw170830

Professors Emeritus: Patrick Odell @pxo062000 , John W. Van Ness @ness

Clinical Professors: Natalia Humphreys @nah103020 , Wenyi (Roy) Lu @wxl153330

Clinical Associate Professor: Mohammad Akbar @mma110020

Associate Professor of Instruction: My Linh Nguyen @mln018200

Senior Lecturers: Mohammad Ahsan @mka120030 , Kelly Aman @kxa143530 , Malgorzata Dabkowska @mxd066000 , Rabin Dahal @rxd153030 , Anatoly Eydelzon @axe031000 , Manjula Foley @mxf091000 , Bentley T. Garrett @btg032000 , Yuly Koshevnik @yxk055000 , David L. Lewis @dlewis , Changsong Li @cxl109120 , Brady McCary @bcm052000 , Derege Mussa @dxm146130 , Paul Stanford @phs031000 , Julie Sutton @jxs158030 , Tristan Whalen @tgw100020

UT Dallas Affiliated Faculty: Hervé Abdi @herve , Titu Andreescu @txa051000 , Alain Bensoussan @axb046100 , Stefano Leonardi @sxl139330 , Faruck Morcos @afg150230 , Zhenyu Xuan @zxx091000 , Hyuntae Yoo @hxy103120 , Michael Qiwei Zhang @mqz091000

Mathematics Faculty With Research Interests in Bioinformatics and Computational Biology: Swati Biswas, Yan Cao, and Min Chen

Biology Faculty

Professors: Rockford K. Draper @draper , Juan E. González @jgonzal , Lawrence J. Reitzer @reitzer , Stephen Spiro @sxs067400 , Li Zhang @lxz075000 , Michael Qiwei Zhang @mqz091000

Associate Professors: John G. Burr @burr , Jeff L. DeJong @dejong , Heng Du @hxd131030 , Tae Hoon Kim @txk142630 , Kelli Palmer @klp120030 , Duane D. Winkler @ddw130330 , Zhenyu Xuan @zxx091000

Assistant Professors: Zachary Campbell @zxc153030 , Nicole De Nisco @njd160330 , Nikki Delk @nad140230 , Jyoti Misra @jrm190003 , Faruck Morcos @afg150230

Professors Emeritus: Hans Bremer @hxb068000 , Lee A. Bulla @bulla , Donald M. Gray @dongray

Associate Professors Emeritius: Gail A. M. Breen @breen , Dennis L. Miller @dmiller

Clinical Professor: David Murchison @dfm100020

Research Assistant Professors: Lan Guo @lxg132130 , Li Liu @lliu

Assistant Professors of Instruction: Caitlin Braitsch @cmb170830 , Ida Klang @ixk190014 , Eva Sadat @exs190014 , Zhuoru Wu @zxw190014

Senior Lecturers: Mehmet Candas @candas , Wen-Ju Lin @wenju , Meenakshi Maitra @mxm172731 , Robert C. Marsh @rmarsh , Iti Mehta @ixm121430 , Jing Pan @jxp134330 , Elizabeth Pickett @eaw016100 , Ruben D. Ramirez @rdr092000 , Scott A. Rippel @rippel , Ilya Sapozhnikov @isapoz , Subha Sarcar @sns064000 , Uma Srikanth @ukrish , Michelle Wilson @mxw084000 , Wen-Ho Yu @why061000

UT Dallas Affiliated Faculty: Leonidas Bleris @lxb092000 , Sheena D'Arcy @sxd156730 , Stephen D. Levene @sdlevene , Jonathan E. Ploski @jep101000 , Lucien (Tres) Thompson @tres

Biological Sciences Faculty With Research Interests in Bioinformatics and Computational Biology: Faruck Morcos, Zhenyu Xuan, Hyuntae Yoo, and Michael Q. Zhang

Program Objective

The Master of Science program in Bioinformatics and Computational Biology is an interdisciplinary program offered jointly by the Departments of Mathematical Sciences and Biological Sciences, with the former serving as the administrative unit. By combining coursework from the disciplines of Biology, Computer Science, Mathematics, and Statistics, it caters to the growing demand of a new breed of scientists who have expertise in all these disciplines. In addition to coursework, the program also provides opportunities to gain practical experience by getting involved in research with faculty members.

A successful applicant to the program is expected to have a Bachelor's degree in Biology, Mathematics, Statistics, or in another science/engineering discipline, and must have completed Differential and Integral Calculus courses. Additional coursework in one or more of the disciplines of Biology, Computer Science, Mathematics, and Statistics is desirable but is not required.

Degree Requirements

The University's general degree requirements are discussed on the Graduate Policies and Procedures page.

The MS program in Bioinformatics and Computational Biology requires completion of at least 36 semester credit hours. The program offers a choice between two tracks. Track 1 is designed for students with a general background in science/engineering, whereas Track 2 is designed for students with a strong background in biology. To build further expertise, both tracks offer a choice of three elective groups, namely, Computer Science oriented, Statistics oriented, and Biology oriented elective groups. Both also offer opportunities for research. Students are expected to choose a track and an elective group based on their backgrounds and interests in consultation with the Graduate Advisor for the program.

Track 1 (MS)

I. Core: 15 semester credit hours

BMEN 6374 Genes, Proteins and Cell Biology for Engineers

BIOL 6V00 Topics in Biological Sciences (Computational Molecular Evolution)

MATH 5303 Advanced Calculus and Linear Algebra

STAT 5351 Probability and Statistics I (for Elective Group 2)

or STAT 5353 Probability and Statistics for Data Science and Bioinformatics (for Elective Groups 1 and 3)

II. Elective Groups (Choose one elective group)

Elective Group 1 (Computer Science Oriented): 15 semester credit hours

CS 5343 Algorithm Analysis and Data Structures 1

MATH 6312 Combinatorics and Graph Theory

or BIOL 5376 Applied Bioinformatics

CS 6307 Introduction to Big Data Management and Analytics for non CS-Majors

CS 6314 Web Programming Languages

Elective Group 2 (Statistics Oriented): 18 semester credit hours

STAT 5352 Probability and Statistics II

STAT 6337 Advanced Statistical Methods I

STAT 6338 Advanced Statistical Methods II

STAT 6340 Statistical and Machine Learning

or BIOL 5376 Applied Bioinformatics

Elective Group 3 (Biology oriented): 15 semester credit hours

or BIOL 5376 Applied Bioinformatics

MATH 6345 Mathematical Methods in Medicine and Biology

or BMEN 6389 Computational Biology

or MATH 6343 Computational Biology

III. Research or Elective(s) or a Combination Thereof

  • Elective Group 1: 6 semester credit hours
  • Elective Group 2: 3 semester credit hours
  • Elective Group 3: 6 semester credit hours

Track 2 (MS)

I. Core: 14 semester credit hours

STAT 5351 Probability and Statistics I (for Elective Group 2)

or STAT 5353 Probability and Statistics for Data Science and Bioinformatics (for Elective Groups 1 and 3)

MATH 5303 Advanced Calculus and Linear Algebra

II. Elective Groups (Choose one elective group)

Elective Group 1 (Computer Science oriented): 18 semester credit hours

CS 5343 Algorithm Analysis and Data Structures 1

MATH 6312 Combinatorics and Graph Theory

or BIOL 5376 Applied Bioinformatics

CS 6307 Introduction to Big Data Management and Analytics for non CS-Majors

CS 6314 Web Programming Languages

Elective Group 2 (Statistics oriented): 18 semester credit hours

STAT 5352 Probability and Statistics II

STAT 6337 Advanced Statistical Methods I

STAT 6338 Advanced Statistical Methods II

STAT 6340 Statistical and Machine Learning

or BIOL 5376 Applied Bioinformatics

Elective Group 3 (Biology oriented): At least 18 semester credit hours

or BIOL 5376 Applied Bioinformatics

MATH 6345 Mathematical Methods in Medicine and Biology

or BMEN 6389 Computational Biology

or MATH 6343 Computational Biology

BIOL 6V00 Topics in Biological Sciences (Computational Molecular Evolution)

BIOL 6V00 Topics in Biological Sciences (Introduction to Programming for Biological Sciences)

III. Research or Elective(s) or a Combination Thereof

All Elective Groups: 4 semester credit hours

Other Requirements

  • For a PhD bound student in the Department of Biological Sciences, BIOL 5440 Cell Biology and BIOL 5460 Quantitative Biology (or an equivalent) are required. This requirement can be fulfilled by taking these courses as 'electives' in the Bioinformatics and Computational Biology program.
  • Electives must be approved by the Graduate Advisor of the program.
  • Substitutions for required courses may be made if approved by the Graduate Advisor of the program and the Head of the Mathematical Sciences Department.
  • A student may choose to write an MS thesis under the supervision of a faculty member. The thesis project can count for 3 to 6 semester credit hours of electives towards the required 36 hours, in accordance with University policies. The thesis must be approved by the Head of the Mathematical Sciences Department. Once the thesis project is completed, the student must successfully defend it before his/her thesis committee.

1. Students who have not taken the CS 5333 Discrete Structures prerequisite for CS 5343 Algorithm Analysis and Data Structures should consult with their Graduate Advisor from the Mathematical Sciences Department to determine eligibility.


List of Biotechnology Seminar Topics 2021

  1. Plant Biotechnology for crop improvement
  2. Enzyme technology in the beverage industry
  3. Recent advances in biotechnology as a biochemist
  4. Stem cell technology to cure eye diseases
  5. Gene silencing in human embryonic stem cells by RNA interference
  6. Recombinant DNA Techniques
  7. Microplate Spectrophotometer
  8. Computational immunology
  9. Biopower generator
  10. Biocatalyst biosensors
  11. Cancer treatment using nanotechnology
  12. Biosensors
  13. Genome mapping
  14. Gene silencing and DNA methylation processes
  15. Cell cycle modeling using discrete-event systems
  16. Signaling pathways in Stem Cell differentiation
  17. Concentrated Fed-Batch Technology
  18. Bridging Polymer Science to Biotechnology Application
  19. Normal stem cells and cancer stem cells: similar and different
  20. Disease Detection Using Bio robotics
  21. Bacteria Rhodopsin Memory
  22. Treating Cardiac Disease With Catheter-Based Tissue Heating
  23. Palm Vein Technology
  24. Antisense technology for crop improvement.
  25. Antibody inks replace ELISA in biomedical research
  26. Biofertilizers
  27. Gene Therapy
  28. Replication of DNA
  29. DNA Chips
  30. Cell Banking
  31. Bio Fermentation
  32. Bone marrow transplantation
  33. Transformation
  34. Mutation
  35. Plant growth hormones
  36. Multi functional Nucleolus
  37. Role of Stem cells in Health sciences and medicine
  38. Human genome project
  39. Bioprocess Economics and Plant Design
  40. Bioprocess Dynamics and Control
  41. Bio-fluid Mechanics and Heat Transfer
  42. Environmental Biotechnology
  43. Pharmaceutical Biotechnology
  44. Bioinformatics
  45. Instrumental Methods in Biotechnology
  46. Biotechnology and Applications of Biotechnology
  47. Industrial Biotechnology
  48. Bio-reaction Engineering
  49. Bio-fluid Mechanics and Heat Transfer
  50. Plant or Animal Biotechnology
  51. Microbiology: biotech seminar topics
  52. Computer Application in Bio processes
  53. Recombinent DNA technology
  54. Chloroplast Transformation for enhanced expression of Foreign genes
  55. Challenge of Biotechnology
  56. Tuberculosis One in All the Threatening Diseases
  57. Biotechnology innovation in biological control of plant diseases
  58. Current scenario of transgenic crops in India
  59. Health Care Biotech Industry
  60. Applied genomic research in rice genetic improvement
  61. Biotechnology in Defence Sector
  62. Defending Against Biological attack :Importance of Biotechnology in Preparedness
  63. Biotechnology in India – Current Scene
  64. Biotechnology in the 21st century
  65. Genetic Engineering for Maize Improvement in China
  66. Gene Transfer Techniques
  67. Biotransformation of Drugs
  68. Retinitis pigmentosa
  69. Removal of metals from water
  70. Animal bioreactors
  71. Post-transcriptional gene silencing in plants
  72. Microtechnology: the role of fungi in biotechnology
  73. Plant disease resistance and genetic engineering
  74. Impacts of Green Biotechnology
  75. Antibody inks replace ELISA in biomedical research
  76. Bioreactor Remote Access
  77. Small Molecule API Manufacturing
  78. Advanced Biodecontamination
  79. High Performance Liquid Chromatography
  80. Enhancement of biogas production
  81. Challenges to the central dogma
  82. Gel permeation chromatography
  83. Banding of chromosomes
  84. Chemiluminiscence biosensors
  85. Primary tissue explanation technique
  86. Mushrooms role as bio-remediation agent
  87. Artificial rbc using nanotechnology
  88. Molecular markers in cereal breeding
  89. Venom proteins drug design: scorpion
  90. Gene transfer techniques
  91. Nano Technology in medicine
  92. Extraction of DNA from onions
  93. Recombinant DNA technology
  94. Evolution of human being
  95. Dynamic aspects of Protein structure
  96. Xeno Biotic transformation
  97. Bio sequence extractors
  98. Mushroom Culture: biotechnology topics for assignment
  99. The excitement of Biotechnology in the New Economy

I hope you all like the above-given list of the Latest Biotechnology Seminar Topics 2021. In this way, students can choose the right seminar topics as per their area of interest in the biotechnology field.

Also, If you need some more seminar topics for biotechnology for presentation. Please comments below, so that I can provide some more biotechnology topics for you.


Watch the video: Finding a topic for your bioinformatics research project short (June 2022).


Comments:

  1. Cathmor

    I congratulate, it seems magnificent idea to me is

  2. Ancenned

    It's just awesome :)

  3. Sudi

    I believe that you are wrong. Let's discuss this.

  4. Paegastun

    Indeed, and as I have never understood



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