Normalized and standardized estimated sample size: two novel techniques for convergence assessment and burn-in estimation for multi-parameter Markov chain Monte Carlo traces
a) Pipeline for GenFamClust – The pipeline of the synteny aware method I and my collaborators have proposed for inferring homology.
b) Accuracy of GenFamClust (GFC) – When compared with other existing homology inferring software on a benchmark homology dataset, GFC is certainly the BEST!
MCMC Tree analysis – Post-processing the results of Bayesian phylogeny.
Snapshots of 3 panes of VMCMC – When a standard run of Bayesian phylogeny (containing around a million samples) needs to be processed within a few seconds, whose convergence needs to be assured before any analysis, whose mixing is an enigma for all and whose results need to be summed up as a single tree out of a million trees, VMCMC comes to your rescue. Simple, clean and concise analysis, isn’t it?
Multiple Sequence Alignment – Issues, errors and accuracy.
Two panes showing the Divvier methodology and its results in comparison with various filtering methods – When there are a lot of errors in an MSA from any MSA method, whereby some of the sequence regions can have extremely unlikely correct alignment, no MSA method will give you the correct solution. The only way out is filtering these regions, and that is where Divvier is the most optimal solution out there!
Domain Phylogeny and Evolution – Evolutionary history of Kindlin and S18
a) Evolutionary relatedness of FDCPs – A phylogenetic tree showing the reconciled evolutionary history of Ferm Domain containing proteins (FDCPs) of which Kindlin is a member, a beauty in colors and display, aint it?
b) Evolutionary history of S18 gene family – The phylogenetic gene tree grown inside a species tree showing the evolutionary history along with possible duplication and loss events for S18 family of genes in various taxonomic groups.
Molecular Biology – Role of certain motifs in B-cell receptor activation
Mapping of motif onto a phylogeny and constructing the neighborhood graph of interacting proteins – Some times a large data set might result in lots of clustered, ugly graphs but when handled correctly by an expert, they reveal beautiful images. But “beauty lies in the eye of the beholder”.
Research Collaborators
I have had research collaborations with researchers from a wide variety of backgrounds and nations. Some are still in contact with me and some I have lost contact of. Here is a list of most important collaborators, and their most recent profiles! They are listed in no specific order!
