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438 results on '"Mahajan A"'

1. IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets.

Author

Mahajan, Priyanka, Fiehn, Oliver, and Barupal, Dinesh

Subjects
Female, Humans, Gene Ontology, Metabolomics, Proteins, Databases, Factual, Computational Biology
Abstract

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2393 metabolic GO terms and associated 3144 genes, 1,492 EC annotations, and 2621 metabolites. IDSL.GOA analysis of a case study of older versus young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR

Published
2024

3. Electronic data capture in resource-limited settings using the lightweight clinical data acquisition and recording system

Author

Vielhauer, Jakob, Mahajan, Ujjwal Mukund, Adorjan, Kristina, Benesch, Christopher, Oehrle, Bettina, Beyer, Georg, Sirtl, Simon, Johlke, Anna-Lena, Allgeier, Julian, Pernpruner, Anna, Erber, Johanna, Shamsrizi, Parichehr, Schulz, Christian, Albashiti, Fady, Hinske, Ludwig Christian, Mayerle, Julia, and Stubbe, Hans Christian

Published
2024
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36. In silico, in vitro and in vivo characterization of host-associated Latilactobacillus curvatus strains for potential probiotic applications in farmed Atlantic salmon (Salmo salar)

Author

Cathers, Hannah S., Mane, Shrinivasrao P., Tawari, Nilesh R., Balakuntla, Jayanth, Plata, Germán, Krishnamurthy, Madan, MacDonald, Alicia, Wolter, Marilyn, Baxter, Niel, Briones, Julian, Nagireddy, Akshitha, Millman, Gregory, Martin, Roberto E., Kumar (Mahajan), Arvind, and Gangaiah, Dharanesh

Published
2022
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39. A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries

Author

Xiong, Yuguang, Soumillon, Magali, Wu, Jie, Hansen, Jens, Hu, Bin, van Hasselt, Johan GC, Jayaraman, Gomathi, Lim, Ryan, Bouhaddou, Mehdi, Ornelas, Loren, Bochicchio, Jim, Lenaeus, Lindsay, Stocksdale, Jennifer, Shim, Jaehee, Gomez, Emilda, Sareen, Dhruv, Svendsen, Clive, Thompson, Leslie M, Mahajan, Milind, Iyengar, Ravi, Sobie, Eric A, Azeloglu, Evren U, and Birtwistle, Marc R

Subjects
Human Genome, Genetics, Generic health relevance, Case-Control Studies, Cells, Cultured, Gene Expression Profiling, Gene Expression Regulation, Gene Library, High-Throughput Nucleotide Sequencing, Humans, Induced Pluripotent Stem Cells, Models, Statistical, Muscular Atrophy, Spinal, Myocytes, Cardiac, RNA, Messenger, Sequence Analysis, RNA
Abstract

Creating a cDNA library for deep mRNA sequencing (mRNAseq) is generally done by random priming, creating multiple sequencing fragments along each transcript. A 3'-end-focused library approach cannot detect differential splicing, but has potentially higher throughput at a lower cost, along with the ability to improve quantification by using transcript molecule counting with unique molecular identifiers (UMI) that correct PCR bias. Here, we compare an implementation of such a 3'-digital gene expression (3'-DGE) approach with "conventional" random primed mRNAseq. Given our particular datasets on cultured human cardiomyocyte cell lines, we find that, while conventional mRNAseq detects ~15% more genes and needs ~500,000 fewer reads per sample for equivalent statistical power, the resulting differentially expressed genes, biological conclusions, and gene signatures are highly concordant between two techniques. We also find good quantitative agreement at the level of individual genes between two techniques for both read counts and fold changes between given conditions. We conclude that, for high-throughput applications, the potential cost savings associated with 3'-DGE approach are likely a reasonable tradeoff for modest reduction in sensitivity and inability to observe alternative splicing, and should enable many larger scale studies focusing on not only differential expression analysis, but also quantitative transcriptome profiling.

Published
2017

40. Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays

Author

Gidde, Prashant Sadashiv, Prasad, Shyam Sunder, Singh, Ajay Pratap, Bhatheja, Nitin, Prakash, Satyartha, Singh, Prateek, Saboo, Aakash, Takhar, Rohit, Gupta, Salil, Saurav, Sumeet, M. V., Raghunandanan, Singh, Amritpal, Sardana, Viren, Mahajan, Harsh, Kalyanpur, Arjun, Mandal, Atanendu Shekhar, Mahajan, Vidur, Agrawal, Anurag, Agrawal, Anjali, Venugopal, Vasantha Kumar, Singh, Sanjay, and Dash, Debasis

Published
2021
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41. A high-resolution time-depth view of dimethylsulphide cycling in the surface sea.

Author

Royer, S-J, Galí, M, Mahajan, AS, Ross, ON, Pérez, GL, Saltzman, ES, and Simó, R

Subjects
Biochemistry and Cell Biology, Other Physical Sciences
Abstract

Emission of the trace gas dimethylsulphide (DMS) from the ocean influences the chemical and optical properties of the atmosphere, and the olfactory landscape for foraging marine birds, turtles and mammals. DMS concentration has been seen to vary across seasons and latitudes with plankton taxonomy and activity, and following the seascape of ocean's physics. However, whether and how does it vary at the time scales of meteorology and day-night cycles is largely unknown. Here we used high-resolution measurements over time and depth within coherent water patches in the open sea to show that DMS concentration responded rapidly but resiliently to mesoscale meteorological perturbation. Further, it varied over diel cycles in conjunction with rhythmic photobiological indicators in phytoplankton. Combining data and modelling, we show that sunlight switches and tunes the balance between net biological production and abiotic losses. This is an outstanding example of how biological diel rhythms affect biogeochemical processes.

Published
2016

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