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Recent Lab-News

02/06/2022

Database: RicePathDLNet Deep Learning inspire Data-Visualization

20/May/2022

Many Congrats to Ravi, Abhishek Khatri for acceptance of a paper entitled " Deep learning uncovers distinct behaviour of rice network to pathogens response" in iScience (IF =5.48)!!!!!

23/Nov/2021

Many congratulations to Meetal Sharma for acceptance of our paper in Free Radical Biology and Medicine (Elsevier) (IF=7.376) !!!!

12/Jul/2021

Many congratulations to Meetal Sharma for her PhD work selected as a oral presentation organized by SIB Swiss Institute of Bioinformatics, Basel Computational Biology Conference 2021 during 13 -15 September, 2021

01/Jan/2021

Dr. Vishal Acharya elected as "Associate" of National Academy of Agriculture Sciences (NAAS) under Social Sciences (Bioinformatics)-2021

About

Situated among pristine environment in the lap of Dhauladhar ranges, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT) is the only laboratory of the Council of Scientific and Industrial Research in the State of Himachal Pradesh (H.P.), India. Our Institute has a focused research mandate on bioresources for catalyzing bioeconomy in a sustainable manner. So much diverse is the Flora, fauna and microbes spread across the virgin Himalayas, there will be need to preserve their genebank as well as conservation.

Infrastructure

Functional Genomics & Complex System Lab (FG&CSL) was established @CSIR-IHBT to conduct large-scale bioinformatics analysis of available Himalayan Bio-resources. CSIR-IHBT has excellent infrastructure facilities in the form of third generation sequencing platforms “PAC-BIO” and “NOVA-SEQ Illumina” that generated large scale transcriptomes and genomes with sometimes more than 10 TB of datasets. As such, complex data can be handled by high-end computer workstations and high-throughput software to produce meaningful information.

FG&CSL have such kind of well-equipped high-end computing server having more than 8 cluster nodes with > ~2 TB RAM and ~200 TB HDD. With recent advances in computational techniques, Artificial Intelligence (AI)/deep learning can utilized for discrimination of large-scale datasets for future empirical validation.