Recent Lab-News


Database: RicePathDLNet Deep Learning inspire Data-Visualization


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)!!!!!


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


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


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


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.


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.