Biology subjects

Kumar, M. S.

Publications and source records attributed to Kumar, M. S..

4 recordsLinked to original sources

bioRxiv2022

De novo design of anti-variant COVID vaccine with T-cell memory

Recent studies have shown the efficacy of hybrid SARS-COV-2 vaccines using wild-type nucleocapsid (N) and Spike (S) protein. We upgraded this strategy one step further using clinically proven spike protein by considering the delta and post-delta omicron variant of concern (VOC) mutations and nucleocapsid peptides conferring T-cell immunity. Nucleocapsid peptides are considered much better immunological replacement of nucleocapsid proteins. Hence, peptide linking strategy is applied which is more economic for cellular biosynthesis than whole proteins. An envelope peptide with potent T-cell immune response is also selected. All these peptides are clustered in this hybrid spikes designed cytoplasmic region separated by non-immunogenic helical linkers. The resulting domain is more folded in the construct devoid of transmembrane domain after AlphaFold analysis. Alongside, we also propose the idea of introduction of any T-cell peptide like other Human Corona Viruses (HuCoV) in these linker regions whenever required. In addition to SARS-COV-2, the same approach can be applied for any emergency or even long-term unsolved outbreaks of Influenza, Dengue and West Nile Virus etc. In this era of novelty as presented by subunit and nucleic acid vaccines, multiepitope strategies like this can help to combat multiple diseases successfully in real time to give hope for better future.

bioinformatics↗

bioRxiv2021

Differential richness inference for 16S rRNA marker gene surveys

Individual and environmental health outcomes are frequently linked to changes in the diversity of associated microbial communities. This makes deriving health indicators based on microbiome diversity measures essential. While microbiome data generated using high throughput 16S rRNA marker gene surveys are appealing for this purpose, 16S surveys also generate a plethora of spurious microbial taxa. When this artificial inflation in the observed number of taxa (i.e., richness, a diversity measure) is ignored, we find that changes in the abundance of detected taxa confound current methods for inferring differences in richness. Here we argue that the evidence of our own experiments, theory guided exploratory data analyses and existing literature, support the conclusion that most sub-genus discoveries are spurious artifacts of clustering 16S sequencing reads. We proceed based on this finding to model a 16S surveys systematic patterns of sub-genus taxa generation as a function of genus abundance to derive a robust control for false taxa accumulation. Such controls unlock classical regression approaches for highly flexible differential richness inference at various levels of the surveyed microbial assemblage: from sample groups to specific taxa collections. The proposed methodology for differential richness inference is available through an R package, Prokounter. Package availability: https://github.com/mskb01/prokounter

bioinformatics↗

bioRxiv2018

Assessing 16S marker gene survey data analysis methods using mixtures of human stool sample DNA extracts.

BackgroundAnalysis of 16S rRNA marker-gene surveys, used to characterize prokaryotic microbial communities, may be performed by numerous bioinformatic pipelines and downstream analysis methods. However, there is limited guidance on how to decide between methods, appropriate data sets and statistics for assessing these methods are needed. We developed a mixture dataset with real data complexity and an expected value for assessing 16S rRNA bioinformatic pipelines and downstream analysis methods. We generate an assessment dataset using a two-sample titration mixture design. The sequencing data were processed using multiple bioinformatic pipelines, i) DADA2 a sequence inference method, ii) Mothur a de novo clustering method, and iii) QIIME with open-reference clustering. The mixture dataset was used to qualitatively and quantitatively assess count tables generated using the pipelines.\n\nResultsThe qualitative assessment was used to evalute features only present in unmixed samples and titrations. The abundance of Mothur and QIIME features specific to unmixed samples and titrations were explained by sampling alone. However, for DADA2 over a third of the unmixed sample and titration specific feature abundance could not be explained by sampling alone. The quantitative assessment evaluated pipeline performance by comparing observed to expected relative and differential abundance values. Overall the observed relative abundance and differential abundance values were consistent with the expected values. Though outlier features were observed across all pipelines.\n\nConclusionsUsing a novel mixture dataset and assessment methods we quantitatively and qualitatively evaluated count tables generated using three bioinformatic pipelines. The dataset and methods developed for this study will serve as a valuable community resource for assessing 16S rRNA marker-gene survey bioinformatic methods.

bioinformatics↗

bioRxiv2017

Analysis And Correction Of Compositional Bias In Sparse Sequencing Count Data

Count data derived from high-throughput DNA sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances. We demonstrate that existing techniques for estimating compositional bias fail with sparse metagenomic 16S count data and propose an empirical Bayes normalization approach to overcome this problem. In addition, we clarify the assumptions underlying frequently used scaling normalization methods in light of compositional bias, including scaling methods that were not designed directly to address it.

genomics↗