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publications
Interactions between species introduce spurious associations in microbiome studies
Published in PLoS Computational Biology, 2017
Interactions between populations of microbes in microbiomes influence one anothers population distributions, thus standard statistical analysis between two microbiome datasets may find more statistically significant, but non-interpretable, taxa of difference. This method, termed Direct Association Analysis, uses maximum entropy principles to correct for microbial interaction noise and identify potential causative taxa by removing taxa that are influencing one another, rather than driving the differential.
Recommended citation: Menon,R.;Ramanan,V.;Korolev,K. (2017) "Interactions between species introduce spurious associations in microbiome studies." PLoS Computational Biology. DOI:10.1371/journal.pcbi.1005939. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005939#sec007
Clustered 8-Oxo-Guanine Mutations and Oncogenic Gene Fusions in Microsatellite-Unstable Colorectal Cancer
Published in JCO Precision Oncology, 2022
MSI-H colorectal cancer cases are often characterized by oncogenic fusions in driver genes. Junction sequences of these fusions (50 base pair combinations surrounding the breakpoint) revealed a particular type of mutation, known as 8-oxo-guanine mutations, that are linked to environmental causes and potentially to the microbial metabolite, butyrate, which can modulate oxidative damage.
Recommended citation: Madison,R.;Hu,X.;Ramanan,V.;et al. (2022). "Clustered 8-Oxo-Guanine Mutations and Oncogenic Gene Fusions in Microsatellite-Unstable Colorectal Cancer." JCO Precision Ongoloy. DOI: 10.1200/PO.21.00477. http://vivekramanan.github.io/files/VR-ASCO-Paper.pdf
Genbank as a source to monitor and analyze Host-Microbiome Data
Published in Bioinformatics, 2022
This paper uses data mining, natural language processing, and phylogenetics techniques to explore the current status of Genbank microbiome data. As an important source of reference genomes, Genbank is a valuable resource to demonstrate our current understanding and data collection on the microbiome thus far.
Recommended citation: Ramanan,V.;Mechery,S.;Sarkar,I.N. (2022). "Genbank as a source to monitor and analyze Host-Microbiome Data." Bioinformatics. DOI: 10.1093/bioinformatics/btac487 https://academic.oup.com/bioinformatics/article/38/17/4172/6633928?login=true
Augmenting bacterial similarity measures using a graph-based genome representation
Published in ASM Journals: mSystems, 2024
Genome relationships amongst bacteria are commonly performed using 16S rRNA comparisons or whole genome comparisons. A motivation of interest in this common protocol was how to consider orthologous sections of the genomes that may have moved around in dynamic circular bacterial genomes.This method is an exploration of taking functional regions of genomes and using them to calculate a distance metric, using a linear genome representation and a geometric distance measure instead of a nucleotide based measure.
Recommended citation: Ramanan V, Sarkar IN. 2024. Augmenting bacterial similarity measures using a graph-based genome representation. mSystems 9:e00497-24. https://doi.org/10.1128/msystems.00497-24 https://journals.asm.org/doi/full/10.1128/msystems.00497-24
talks
A Novel Approach for monitoring Host-Microbiome Data in Genbank
Published:
Genbank holds valuable data that is often used as references for microbiome data analysis and identification. Analyzing Genbank metadata allows us to monitor the current state of data collection in Genbank and reveals a clear pathogenic bias of microbial organisms in humans, as well as the value of eukaryotic microorganisms shared amongst microbiomes across the animal kingdom.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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