We believe in a borderless world of scientific knowledge. Our research aims to excel in the technical, biological and applied aspects of bioinformatics. Despite expanding horizontally in several areas, we hate being superficial and repetitive without innovation. Thus, we aim to build a team of researchers with genuine passion in science, trying to unravel solutions with insight, rigour and utmost honesty and sincerity.
1. Core Bioinformatics (Basic Research)
Development of sequence-based methods to understand and predict Biomolecular interactions in general and protein-DNA interactions in particular. We approach the problem from an integrated data-driven perspective taking cues from physics and informatics. Machine learning methods in general and neural networks in particular are our choice of technique and we often hop on to developing totally new training algorithm or neural network architecture while solving our problems.
2. Applied and Collaborative Research
Our recent interests in applied research are related to topics in systems biology. This includes providing predictive and molecular basis of gene expression and its regulation by transcription factors, miRNA and proteins. We have been working on novel methods of cross-experiment data processing, coherent themes in gene sets for enrichment analysis and tissue-specificity of biological pathways. Our current collaborative projects involve strain-specificity analysis of microbial genomes, condition-specific functional annotations and de novo genome assembly.
3. Tools and web servers
We have developed more than a dozen bioinformatics tools related to my research topics. Most popular among them are sequence-based method to predict binding sites, solvent accessibility and most recently DNA conformational dynamics.
Our bioinformatics research has focussed on understanding protein-DNA
(and to a smaller extent other biomolecular) interactions
with an ultimate objective of predicting them from sequence.
To this end, we have studied large scale data sets from protein-DNA complexes, ChIP-Seq and DNAse-seq experiments, developed machine
learning tools to predict DNA-binding residues and proteins, dissected thermodynamics and evolutionary patterns of protein-DNA interactions.
These results have been announced as a number of research papers, which have received due attention by scientific community evident from citation record.
Our more recent interests are related to systems biology track of bioinformatics research.
Primary aim of this research is to provide a molecular basis of gene expression and thereby provide predictive and testable hypothesis about biological outcomes such as disease, cell-fate and adverse effects of vaccination.
We have been working with experimental biologists by helping them in a variety of bioinformatics topics. Some of the topics covered include the discovery of novel targets of STAT3, identification of lineage-specific markers in stem-cells, whole exome analysis of epimutation signatures in UPD14 data and miRNA regulatory networks in influenza and cytosolic DNA.
Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism, Shandar Ahmad*, Philip Prathipati, Lokesh P Tripathi, Yi-An Chen, Ajay Arya, Yoichi Murakami and Kenji Mizuguchi, Nucleic Acids Research (In Press; 2018) read here
Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences, Munazah Andrabi, Andrew Paul Hutchins, Diego Miranda-Saavedra, Hidetoshi Kono, Ruth Nussinov, Kenji Mizuguchi and Shandar Ahmad*, Scientific Reports (2017) 7,4071 read here
Analysis and Prediction of DNA-binding proteins and their binding residues based on Composition, Sequence and Structural Information, Shandar Ahmad*, M. Michael Gromiha and Akinori Sarai, Bioinformatics 20 (2004), 477-486.
ReadOut: Structure-based Calculation of Direct and Indirect Readout Energies and Specificities for Protein-DNA Recognition, Shandar Ahmad, Hidetoshi Kono, Marcos J. Araœzo-Bravo, Akinori Sarai*, Nucleic Acids Res. 34 (2006), W124-W127.
DNA-binding proteins: structural, thermodynamic and clustering patterns of conserved residues in DNA-binding proteins, Shandar Ahmad, Ozlem Keskin, Akinori Sarai* and Ruth Nussinov, Nucleic Acids Res. 36 (2008), 5922-5932.
Distinct transcriptional regulatory modules underlie STAT3's cell type-independent and cell type-specific functions. Andrew Paul Hutchins, Diego Diez, Yoshiko Takahashi, Shandar Ahmad, Ralf Jauch, Michel Tremblay, Diego Miranda-Saavedra*, Nucleic Acids Research (2013) 41 (4), 2155-2170
Conformational changes in DNA-binding proteins: relationships with pre-complex features and contributions to specificity and stability, Munazah Andrabi, Kenji Mizuguchi and Shandar Ahmad*, Proteins: Structure, function and bioinformatics (2014) 82(5), 841–857 More ...
Partner-aware prediction of interacting residues in protein-protein complexes from sequence data, Shandar Ahmad* and Kenji Mizuguchi, PLoS One 6(12) e29104. (2011): doi:10.1371/journal.pone.0029104
Integrated Pathway Clusters with Coherent Biological Themes for Target Prioritisation, YA Chen, LP Tripathi, BH Dessailly, J Nyström-Persson, Shandar Ahmad, Kenji Mizuguchi, PloS one (2014) 9 (6), e99030