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Diverse Research and Development Projects in Computer Science and Bioinformatics

This presentation by Satyanarayan Rao outlines a varied array of projects undertaken from undergraduate studies to work at SCFBio at IIT Delhi. Key projects include developing a GUI application for tree data structures in Java, implementing a linear programming problem solver using Matlab and web technologies, and creating an HMM-based speech recognizer for applications in VoIP. During a master's thesis, a toolkit for high-resolution image processing was created alongside optimizations in protein energy minimization. The work emphasizes significant advancements in computational methods and applications in several domains.

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Diverse Research and Development Projects in Computer Science and Bioinformatics

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  1. Work and research experiences Presented by Satyanarayan Rao

  2. Outline • Projects during Undergrad (fall 2005 – summer 2008) • Projects during Master (fall 2008 – summer 2010) • Projects at SCFBio IIT Delhi (Jan 2011 – July 2012)

  3. Projects during undergrad • Tree tech tutor ( Guide: Dr. P. K. Das) • Tutorial for tree data structure for beginners • Developed GUI application • Implemented different tree data structure, • Binary search tree • Red black balanced search tree • AVL tree • Technology used: Java

  4. Continue… • Methods implemented • Create trees • Add new nodes • Delete nodes • Iteratively show the process

  5. Continued… • Online simulator for linear programming problem solving (as a part of coursework) • maximize c’x • Subjected to Ax <= b • X >= 0 • Implemented simplex algorithm to solve linear programming problems, e.g., transportation problem • Technology used • Matlab tool to implement algorithm. • Php and html to design the front end.

  6. B. Tech. Project • Modelling of HMM based speech recognizer and its application • Motivation: • An attempt to develop speech recognizer which can be imported to various applications. • Concept: • HMMs are statistical models which output a sequence of symbols or quantities. In HMM based speech recognition, it is assumed that the sequence of observed speech vectors corresponding to each word is generated by a markov model. • Why not other method, like Neural network • It is good for individual phones or isolated words . Not good for a sentence

  7. HTKbook

  8. Continued…

  9. Steps involved • Data preparation • Generate monophone HMMs • Generate tied-state Triphones • Recognizer evaluation

  10. Application of recognizer • Automated speech recognizer • Application of Speech recognition in Asterisk • Asterisk is a software suit which enable the attached phone to make call over VoIP. • We bought a computer telephony interface and installed on a cpu on which the asterisk server was installed. • Hands free children learning application

  11. Projects during master’s thesis • Toolkit for grid enabled high resolution image processing • Motivation: • Processing high resolution images for example satellite images. • Established a 3 node cluster using Condor scheduler ( similar as PBS ) • Used matlab tool for image processing.

  12. The size of high resolution images are about 20-200MB • The goal was to implement an easy to use interface for the researchers to do image processing.

  13. Projects at SCFBio, IIT Delhi • Optimization of energy minimization code • Issues: memory usage was highly dependent on sequence length. Redundant storage of parameters • The exploration of conformational space of native proteins • What is the hypothesis here? • Does “preferential interactions” between amino acids drive protein folding? Mittal A, Jayaram B et. al. 2010

  14. The backbone conformation has been analyzed • Basically for each protein a 20x20 matrix of number of “neighbors” within a defined neighborhood distance.

  15. Characteristic of curve • Sigmoidal in nature and follow the equation: • n and k are the free parameters in above equation. • I implemented the curve fitting (Levenberg–Marquardt algorithm) program in C++. • The claim is that for any pair of residues the neighborhood behavior is almost same.

  16. Mittal et. al. 2010

  17. Development of scoring function • Story behind it • Structure generation. • Need for robust scoring function in order to select the native or native like structures from the ensemble. • Important factors • Energy • Accessible area • Euclidian distance • Secondary structure • We designed the scoring function which assign a cumulative score (CS) to given structure. Mishra A., Rao S. et. al. 2013

  18. Smaller values infer better structure. A1: fractional area of exposed nonpolar residues A2: fractional area of exposed nonpolar part of residues A3: weighted exposed area A4: total surface area PH, PS : Helix, Sheet Penalty M1: Euclidian distance

  19. Tools used: • R, bash shell scripting, perl.

  20. Thanks!

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