Biology subjects

Dwivedi, S. K.

Publications and source records attributed to Dwivedi, S. K..

5 recordsLinked to original sources

bioRxiv2023

Data-driven representations using deep network-coherent methylation autoencoders to identify robust disease and risk factor signatures

Data analysis in systems medicine often employs knowledge-driven approaches, which leverage prior biological insights to guide and inform the study of large omic sets. However, the current state of knowledge in biology is still partial and biased. For example, cancer-associated genes are overrepresented in protein interaction networks. As a result, these approaches may fail to capture novel or unexpected phenomena. In this study, we present a data-driven workflow for the functional analysis of large DNA methylation data using deep autoencoders with biologically relevant latent embeddings (network-coherent autoencoders, NCAEs). We observed an increasing gene co-localization gradient, consistent with the human interactome, within the learned representation of a deep methylation autoencoder. We showcased the capacity of this coherent compressed space to discover signatures for classification associated with aging, smoking, and disease. We believe this approach can improve the understanding of complex epigenetic processes and help develop more effective diagnostic and therapeutic strategies.

bioinformatics↗

bioRxiv2020

Survey, Identification and Evaluation of biodiversity of Seabuckthorn (Hippophae salicifolia) in hills of Uttarakhand

Extensive studies were carried out during 2017-2019 to evaluate the Hippophae salicifolia (Seabuckthorn) biodiversity in Uttarakhand for their physico-chemical/biochemical parameters of ripe fruits like acidity (1.78-3.01 %), pH (2.15-2.87), Vitamin C (271-494mg/100g), Total soluble solids (5.1-9.8) etc. Variation was also observed in fruit color (light yellow, yellow, light yellow orange, orange and orange red), fruit size (length 6.29-8.77mm, width 4.80-7.59mm), Juice (44.23-76.18%), Pomace (8.01-28.30%) and seed (6.8-21.1%). On the basis of above parameters, promising morphotypes were identified and conserved at field gene bank at DIBER field station, Auli (Joshimath) 3142m AMSL.

plant biology↗

bioRxiv2020

Potential malaria vector Anopheles minimus (species A) still persisting in North East India

BackgroundVector borne infectious diseases affect two third of the worlds human population and cause mortality in millions each year. Malaria remains one of the major killers in the Indian sub-continent and transmitted uninterruptedly by many efficient vectors and their sibling species. In North East India (NE), Anopheles minimus has been recognized as an important vector which shares majority of malaria cases. This study primarily focuses on to recognize the presence and distribution of sibling species of An. minimus in certain endemic area of NE India. MethodsAnopheles species were collected and identified using available morphological keys. The genomic DNA was extracted from the mosquito specimen and used to perform species specific PCR (ss PCR) for molecular identification of major malaria vector An. minimus sibling species ResultMorphological identification suggested the presence of An. minimus sl in low density in the study area. The specimen of An. minimus subjected to ss PCR confirmed the prevalence of only one sibling species namely, An. minimus A in Sialmari and Chandubi. ConclusionThough in low density, but malaria vector An. minimus is still present in certain endemic areas of NE India. The ss PCR assay employed presently suggested that An. minimus sibling species A is prevailing in the region. Presently used ss PCR assay was simpler, faster, cheaper and more readily interpreted than earlier assays. This information could be useful in understanding of current prevalence and distribution of An. minimus sibling species complex in NE region of India.

ecology↗

bioRxiv2020

Detection of 'relict' western lineage of citrus tristeza virus virulent genotype in declining Arunachal Wakro orange

Eastern Himalayan foothills are known to have optimal agro-climatic conditions for production of quality citrus fruits including oranges. Among the citrus growing regions of eastern Himalayas, Wakro in the far eastern state of Arunachal Pradesh is known for its superior quality oranges, popular as the Wakro orange or the Arunachal orange, which has been included in Geographical Indication Registry by the Government of India. However, during last few years, Arunachal orange orchards have been experiencing severe infestation of aphid associated with rapid decline disease, causing catastrophe to the farmers as well as state economy. Therefore, in 2015, an intensive survey of severely affected orange orchards was carried out to investigate the aetiological factors of citrus decline. RNA samples extracted from leaf and aphid specimens collected from Wakro orchards were subjected to CTV detection through 3-UTR specific RT-PCR. Subsequently, ORF1 and CP genetic regions were amplified and clonal-sequencing was performed. Although, BLAST search showed close homology of the present sequences with other virulent genotype VT sequences, detailed phylogenetic analysis demonstrated affinity and clustering of present sequences with VT sequences belonging to the western lineage. This finding is considerably distinct from CTV sequences reported from citrus growing orchards in India and other neighbouring countries. Additionally, low diversity of CP gene sequences, recombination patterns and presence of sequence segments identical to the present ones in other CTV genotypes was also revealed. Collectively, these observations indicate pristine and primeval nature of present CTV sequences, corroborating well with the proposed origin of CTV in this part of the globe. We here report our finding of western lineage of CTV virulent genotype VT, which is distinct from CTV molecular epidemiology in other parts of India and discuss the implications of these findings.

microbiology↗

bioRxiv2019

Deriving Disease Modules from the Compressed Transcriptional Space Embedded in a Deep Auto-encoder

Disease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, commonly used to define such modules are incomplete and biased toward some well-studied disease genes. Here we ask whether disease-relevant modules of genes can be discovered without assuming the prior knowledge of a biological network. To this end we train a deep auto-encoder on a large transcriptional data-set. Our hypothesis is that such modules could be discovered in the deep representations within the auto-encoder when trained to capture the variance in the input-output map of the transcriptional profiles. Using a three-layer deep auto-encoder we find a statistically significant enrichment of GWAS relevant genes in the third layer, and to a successively lesser degree in the second and first layers respectively. In contrast, we found an opposite gradient where a modular protein-protein interaction signal was strongest in the first layer but then vanishing smoothly deeper in the network. We conclude that a data-driven discovery approach, without assuming a particular biological network, is sufficient to discover groups of disease-related genes.

systems biology↗