Demo Data from EDBT 2026 / reviewers in the wild / expert
Seesandra V. Rajagopala
dblp:90/3330
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 3 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › biological database
protein interaction database |
0.2 | 2 | 2008 | MPI-LIT: a literature-curated dataset of microbial binary protein--protein interactions · Bioinform. 2008 MPIDB: the microbial protein interaction database · Bioinform. 2008 |
Bioinformatics and computational biology › biocuration
literature curation |
0.1 | 1 | 2008 | MPI-LIT: a literature-curated dataset of microbial binary protein--protein interactions · Bioinform. 2008 |
Bioinformatics and computational biology
protein-protein interaction prediction |
0.1 | 1 | 2008 | MPI-LIT: a literature-curated dataset of microbial binary protein--protein interactions · Bioinform. 2008 |
Methods — techniques the papers use, named apart from their topics
text mining · 0.1manual curation · 0.1interaction conservation · 0.1genomic context · 0.1co-purification · 0.1co-expression analysis · 0.13d domain contact · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Mining Host-Pathogen Protein Interactions to Characterize Burkholderia mallei Infectivity MechanismsabstractBurkholderia pathogenicity relies on protein virulence factors to control and promote bacterial internalization, survival, and replication within eukaryotic host cells. We recently used yeast two-hybrid (Y2H) screening to identify a small set of novel Burkholderia proteins that were shown to attenuate disease progression in an aerosol infection animal model using the virulent Burkholderia mallei ATCC 23344 strain. Here, we performed an extended analysis of primarily nine B. mallei virulence factors and their interactions with human proteins to map out how the bacteria can influence and alter host processes and pathways. Specifically, we employed topological analyses to assess the connectivity patterns of targeted host proteins, identify modules of pathogen-interacting host proteins linked to processes promoting infectivity, and evaluate the effect of crosstalk among the identified host protein modules. Overall, our analysis showed that the targeted host proteins generally had a large number of interacting partners and interacted with other host proteins that were also targeted by B. mallei proteins. We also introduced a novel Host-Pathogen Interaction Alignment (HPIA) algorithm and used it to explore similarities between host-pathogen interactions of B. mallei, Yersinia pestis, and Salmonella enterica. We inferred putative roles of B. mallei proteins based on the roles of their aligned Y. pestis and S. enterica partners and showed that up to 73% of the predicted roles matched existing annotations. A key insight into Burkholderia pathogenicity derived from these analyses of Y2H host-pathogen interactions is the identification of eukaryotic-specific targeted cellular mechanisms, including the ubiquitination degradation system and the use of the focal adhesion pathway as a fulcrum for transmitting mechanical forces and regulatory signals. This provides the mechanisms to modulate and adapt the host-cell environment for the successful establishment of host infections and intracellular spread. Vesna Memisevic, Nela Zavaljevski, Seesandra V. Rajagopala, Keehwan Kwon, Rembert Pieper, David DeShazer, Jaques Reifman, Anders Wallqvist |
PLoS Comput. Biol. | 3 |
| 2008 | MPIDB: the microbial protein interaction databaseabstractSUMMARY: The microbial protein interaction database (MPIDB) aims to collect and provide all known physical microbial interactions. Currently, 22,530 experimentally determined interactions among proteins of 191 bacterial species/strains can be browsed and downloaded. These microbial interactions have been manually curated from the literature or imported from other databases (IntAct, DIP, BIND, MINT) and are linked to 24,060 experimental evidences (PubMed ID, PSI-MI methods). In contrast to these databases, interactions in MPIDB are further supported by 8150 additional evidences based on interaction conservation, co-purification and 3D domain contacts (iPfam, 3did). AVAILABILITY: http://www.jcvi.org/mpidb/ Johannes Goll, Seesandra V. Rajagopala, Shen C. Shiau, Hank Wu, Brian T. Lamb, Peter Uetz |
Bioinform. | 2 |
| 2008 | MPI-LIT: a literature-curated dataset of microbial binary protein--protein interactionsabstractUNLABELLED: Prokaryotic protein-protein interactions are underrepresented in currently available databases. Here, we describe a 'gold standard' dataset (MPI-LIT) focusing on microbial binary protein-protein interactions and associated experimental evidence that we have manually curated from 813 abstracts and full texts that were selected from an initial set of 36 852 abstracts. The MPI-LIT dataset comprises 1237 experimental descriptions that describe a non-redundant set of 746 interactions of which 659 (88%) are not reported in public databases. To estimate the curation quality, we compared our dataset with a union of microbial interaction data from IntAct, DIP, BIND and MINT. Among common abstracts, we achieve a sensitivity of up to 66% for interactions and 75% for experimental methods. Compared with these other datasets, MPI-LIT has the lowest fraction of interaction experiments per abstract (0.9) and the highest coverage of strains (92) and scientific articles (813). We compared methods that evaluate functional interactions among proteins (such as genomic context or co-expression) which are implemented in the STRING database. Most of these methods discriminate well between functionally relevant protein interactions (MPI-LIT) and high-throughput data. AVAILABILITY: http://www.jcvi.org/mpidb/interaction.php?dbsource=MPI-LIT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Seesandra V. Rajagopala, Johannes Goll, N. D. Deve Gowda, Kumar C. Sunil, Bjoern Titz, Sharmila S. Mary, Naresh Raviswaran, Chetan S. Poojari, Srinivas Ramachandra, Svetlana Shtivelband, Stephen M. Blazie, Julia Hofmann, Peter Uetz |
Bioinform. | 1 |