Automated Organization ProfileIndian Institute of Chemical Biology
Indian Institute of Chemical Biology
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 7.7 (sum of 9 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
288.fastq: Nanopore reads 288_cleaned_R1 and 288_cleaned_R2.fastq: Illumina paired end reads
Authors
- Maulik, Aditi
288.fastq: Nanopore reads 288_cleaned_R1 and 288_cleaned_R2.fastq: Illumina paired end reads
Authors
- Maulik, Aditi
highQuality-reads.fastq: Nanopore reads 268I_R1.fastq and 268_R2.fastq: Illumina paired end reads
Authors
- Maulik, Aditi
highQuality-reads.fastq: Nanopore reads 268I_R1.fastq and 268_R2.fastq: Illumina paired end reads
Authors
- Maulik, Aditi
Supplementary Material 1
Authors
- Sarkar, Siddik ;
- Saha, Sarbar Ali ;
- Swarnakar, Abhishek ;
- Chakrabarty, Arnab ;
- Dey, Avipsa ;
- Sarkar, Poulomi ;
- Banerjee, Sarthak ;
- Mitra, Pralay
Supplementary Material 2
Authors
- Talukdar, Daizee ;
- Bandopadhyay, Purbita ;
- Ray, Yogiraj ;
- Paul, Shekhar Ranjan ;
- Sarif, Jafar ;
- D’Rozario, Ranit ;
- Lahiri, Abhishake ;
- Das, Santanu ;
- Bhowmick, Debaleena ;
- Chatterjee, Shilpak ;
- Das, Bhabatosh ;
- Ganguly, Dipyaman
Supplementary Material 2
Authors
- Talukdar, Daizee ;
- Bandopadhyay, Purbita ;
- Ray, Yogiraj ;
- Paul, Shekhar Ranjan ;
- Sarif, Jafar ;
- D’Rozario, Ranit ;
- Lahiri, Abhishake ;
- Das, Santanu ;
- Bhowmick, Debaleena ;
- Chatterjee, Shilpak ;
- Das, Bhabatosh ;
- Ganguly, Dipyaman
Supplementary Material 1
Authors
- Sarkar, Siddik ;
- Saha, Sarbar Ali ;
- Swarnakar, Abhishek ;
- Chakrabarty, Arnab ;
- Dey, Avipsa ;
- Sarkar, Poulomi ;
- Banerjee, Sarthak ;
- Mitra, Pralay
Proteins involved in interactions throughout the course of evolution tend to co-evolve and compensatory changes may occur in interacting proteins to maintain or refine such interactions. However, certain residue pair alterations may prove to be detrimental for functional interactions. Hence, determining co-evolutionary pairings that could be structurally or functionally relevant for maintaining the conservation of an inter-protein interaction is important. Inter-protein co-evolution analysis in several complexes utilizing multiple existing methodologies suggested that co-evolutionary pairings can occur in spatially proximal and distant regions in inter-protein interactions. Subsequently, the Co-Var (Correlated Variation) method based on mutual information and Bhattacharyya coefficient was developed, validated, and found to perform relatively better than CAPS and EV-complex. Interestingly, while applying the Co-Var measure and EV-complex program on a set of protein-protein interaction complexes, co-evolutionary pairings were obtained in interface and non-interface regions in protein complexes. The Co-Var approach involves determining high degree co-evolutionary pairings that include multiple co-evolutionary connections between particular co-evolved residue positions in one protein with multiple residue positions in the binding partner. Detailed analyses of high degree co-evolutionary pairings in protein-protein complexes involved in cancer metastasis suggested that most of the residue positions forming such co-evolutionary connections mainly occurred within functional domains of constituent proteins and substitution mutations were also common among these positions. The physiological relevance of these predictions suggests that Co-Var can predict residues that could be crucial for preserving functional protein-protein interactions. Finally, Co-Var web server (http://www.hpppi.iicb.res.in/ishi/covar/index.html) that implements this methodology identifies co-evolutionary pairings in intra and inter-protein interactions.
Authors
- Mukherjee, Ishita ;
- Chakrabarti, Saikat