Abhishek Tiwari
Welcome to webpage of Abhishek Tiwari
The current focus of my research is the application of computational methods and tools to solve biological problems of fundamental significance. More details can be found in Research section. Recently I completed my BS Bioinformatics from VIT, Vellore, India and currently working as Jr. Research Associate with Informatics Group at GVK Biosciences. I am also a member of Prof Gautam R. Desiraju Research Group at School of Chemistry at University of Hyderabad. My research network is known as iCODONS.
Latest Articles by Abhishek Tiwari
Hot Computational Papers
PLoS Computational Biology Volume 2 | Issue 8 | AUGUST 2006
- An Integrative Method for Accurate Comparative Genome Mapping
Synopsis
Comparative genomics is an important discipline with applications in evolutionary, genetic, and ge nome rearrangement studies. When comparing genomes, one is usually interested in investigating the relation between the genomic segments to establish their evolutionary origin: are the segments orthologous, and hence inherited from their most recent common ancestor? Are they paralogs, and hence duplicated from an ancestral segment? Did the segments undergo reordering? Were the segments deleted or inserted and—if so—how (insertion sequence, prophage, horizontal gene transfer)?
In this paper, Swidan et al. present MAGIC, a new approach for comparative genome mapping. The main novelty of this approach is the biologically intuitive clustering step, which aims towards both calculating reorder-free segments and identifying orthologous segments. The authors demonstrate MAGIC's robustness, relative to both its initial input and to its parameters' values. MAGIC's scalability is demonstrated by running it on distantly related organisms and on large genomes. In addition, Swidan et al. provide a detailed analysis of the differences between MAGIC and other comparative mapping methods.
Applying MAGIC to several prokaryotic pairs enabled the authors to address the aforementioned questions and to quantitatively study the different evolutionary forces shaping the prokaryotic genome as well as to investigate their breakpoint distribution.
PLoS Computational Biology Volume 2 | Issue 8 | AUGUST 2006
- The Ion Channel Inverse Problem: Neuroinformatics Meets Biophysics
Synopsis
Ion channels are the building blocks of the information processing capability of neurons: any realistic computational model of a neuron must include reliable and effective ion channel components. With the growing availability of computational resources, numerical inverse approaches are increasingly used across a range of disciplines. In this review paper authors suggest same type of inverse methodology for the study of ion channels. Inverse Problem approach address the question “what system gave rise to these observations?” usually by starting with a parameterized model that is expected to correspond to the real system for some point in its parameter space. A computational model of the recording process is built so that it can take any set of parameters and generate the data that they would have given rise to in exactly the same format as the experimental data. The model can then be compared to the real system in the space—that of the real data—where the most information is present. The forward calculation is then repeated over and over for different parameter sets guided by an optimization process to find the model or models that best represent the data.
