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Featured researches published by Anish Kejariwal.


Nucleic Acids Research | 2004

The PANTHER database of protein families, subfamilies, functions and pathways

Huaiyu Mi; Betty Lazareva-Ulitsky; Rozina Loo; Anish Kejariwal; Jody Vandergriff; Steven Rabkin; Nan Guo; Anushya Muruganujan; Olivier Doremieux; Michael J. Campbell; Hiroaki Kitano; Paul D. Thomas

PANTHER is a large collection of protein families that have been subdivided into functionally related subfamilies, using human expertise. These subfamilies model the divergence of specific functions within protein families, allowing more accurate association with function (ontology terms and pathways), as well as inference of amino acids important for functional specificity. Hidden Markov models (HMMs) are built for each family and subfamily for classifying additional protein sequences. The latest version, 5.0, contains 6683 protein families, divided into 31 705 subfamilies, covering ∼90% of mammalian protein-coding genes. PANTHER 5.0 includes a number of significant improvements over previous versions, most notably (i) representation of pathways (primarily signaling pathways) and association with subfamilies and individual protein sequences; (ii) an improved methodology for defining the PANTHER families and subfamilies, and for building the HMMs; (iii) resources for scoring sequences against PANTHER HMMs both over the web and locally; and (iv) a number of new web resources to facilitate analysis of large gene lists, including data generated from high-throughput expression experiments. Efforts are underway to add PANTHER to the InterPro suite of databases, and to make PANTHER consistent with the PIRSF database. PANTHER is now publicly available without restriction at http://panther.appliedbiosystems.com.


Nucleic Acids Research | 2007

New developments in the InterPro database

Nicola Mulder; Rolf Apweiler; Teresa K. Attwood; Amos Marc Bairoch; Alex Bateman; David Binns; Peer Bork; Virginie Buillard; Lorenzo Cerutti; Richard R. Copley; Emmanuel Courcelle; Ujjwal Das; Louise Daugherty; Mark Dibley; Robert D. Finn; Wolfgang Fleischmann; Julian Gough; Daniel H. Haft; Nicolas Hulo; Sarah Hunter; Daniel Kahn; Alexander Kanapin; Anish Kejariwal; Alberto Labarga; Petra S. Langendijk-Genevaux; David M. Lonsdale; Rodrigo Lopez; Ivica Letunic; John Maslen; Craig McAnulla

InterPro is an integrated resource for protein families, domains and functional sites, which integrates the following protein signature databases: PROSITE, PRINTS, ProDom, Pfam, SMART, TIGRFAMs, PIRSF, SUPERFAMILY, Gene3D and PANTHER. The latter two new member databases have been integrated since the last publication in this journal. There have been several new developments in InterPro, including an additional reading field, new database links, extensions to the web interface and additional match XML files. InterPro has always provided matches to UniProtKB proteins on the website and in the match XML file on the FTP site. Additional matches to proteins in UniParc (UniProt archive) are now available for download in the new match XML files only. The latest InterPro release (13.0) contains more than 13 000 entries, covering over 78% of all proteins in UniProtKB. The database is available for text- and sequence-based searches via a webserver (), and for download by anonymous FTP (). The InterProScan search tool is now also available via a web service at .


Nucleic Acids Research | 2003

PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification

Paul D. Thomas; Anish Kejariwal; Michael J. Campbell; Huaiyu Mi; Karen Diemer; Nan Guo; Istvan Ladunga; Betty Ulitsky-Lazareva; Anushya Muruganujan; Steven Rabkin; Jody Vandergriff; Olivier Doremieux

The PANTHER database was designed for high-throughput analysis of protein sequences. One of the key features is a simplified ontology of protein function, which allows browsing of the database by biological functions. Biologist curators have associated the ontology terms with groups of protein sequences rather than individual sequences. Statistical models (Hidden Markov Models, or HMMs) are built from each of these groups. The advantage of this approach is that new sequences can be automatically classified as they become available. To ensure accurate functional classification, HMMs are constructed not only for families, but also for functionally distinct subfamilies. Multiple sequence alignments and phylogenetic trees, including curator-assigned information, are available for each family. The current version of the PANTHER database includes training sequences from all organisms in the GenBank non-redundant protein database, and the HMMs have been used to classify gene products across the entire genomes of human, and Drosophila melanogaster. The ontology terms and protein families and subfamilies, as well as Drosophila gene c;assifications, can be browsed and searched for free. Due to outstanding contractual obligations, access to human gene classifications and to protein family trees and multiple sequence alignments will temporarily require a nominal registration fee. PANTHER is publicly available on the web at http://panther.celera.com.


Nucleic Acids Research | 2006

Applications for protein sequence–function evolution data: mRNA/protein expression analysis and coding SNP scoring tools

Paul D. Thomas; Anish Kejariwal; Nan Guo; Huaiyu Mi; Michael J. Campbell; Anushya Muruganujan; Betty Lazareva-Ulitsky

The vast amount of protein sequence data now available, together with accumulating experimental knowledge of protein function, enables modeling of protein sequence and function evolution. The PANTHER database was designed to model evolutionary sequence–function relationships on a large scale. There are a number of applications for these data, and we have implemented web services that address three of them. The first is a protein classification service. Proteins can be classified, using only their amino acid sequences, to evolutionary groups at both the family and subfamily levels. Specific subfamilies, and often families, are further classified when possible according to their functions, including molecular function and the biological processes and pathways they participate in. The second application, then, is an expression data analysis service, where functional classification information can help find biological patterns in the data obtained from genome-wide experiments. The third application is a coding single-nucleotide polymorphism scoring service. In this case, information about evolutionarily related proteins is used to assess the likelihood of a deleterious effect on protein function arising from a single substitution at a specific amino acid position in the protein. All three web services are available at .


Nucleic Acids Research | 2007

PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways

Huaiyu Mi; Nan Guo; Anish Kejariwal; Paul D. Thomas

PANTHER is a freely available, comprehensive software system for relating protein sequence evolution to the evolution of specific protein functions and biological roles. Since 2005, there have been three main improvements to PANTHER. First, the sequences used to create evolutionary trees are carefully selected to provide coverage of phylogenetic as well as functional information. Second, PANTHER is now a member of the InterPro Consortium, and the PANTHER hidden markov Models (HMMs) are distributed as part of InterProScan. Third, we have dramatically expanded the number of pathways associated with subfamilies in PANTHER. Pathways provide a detailed, structured representation of protein function in the context of biological reaction networks. PANTHER pathways were generated using the emerging Systems Biology Markup Language (SBML) standard using pathway network editing software called CellDesigner. The pathway collection currently contains ∼1500 reactions in 130 pathways, curated by expert biologists with authorship attribution. The curation environment is designed to be easy to use, and the number of pathways is growing steadily. Because the reaction participants are linked to subfamilies and corresponding HMMs, reactions can be inferred across numerous different organisms. The HMMs can be downloaded by FTP, and tools for analyzing data in the context of pathways and function ontologies are available at .


Genome Research | 2003

PANTHER: A Library of Protein Families and Subfamilies Indexed by Function

Paul D. Thomas; Michael J. Campbell; Anish Kejariwal; Huaiyu Mi; Brian Karlak; Robin Daverman; Karen Diemer; Anushya Muruganujan; Apurva Narechania


Science | 2003

Inferring Nonneutral Evolution from Human-Chimp-Mouse Orthologous Gene Trios

Andrew G. Clark; Stephen Glanowski; Rasmus Nielsen; Paul D. Thomas; Anish Kejariwal; Melissa A. Todd; David M. Tanenbaum; Daniel Civello; Fu Lu; Brian Murphy; Steve Ferriera; Gary Wang; Xianqgun Zheng; Thomas J. White; John J. Sninsky; Mark D. Adams; Michele Cargill


Proceedings of the National Academy of Sciences of the United States of America | 2004

Coding single-nucleotide polymorphisms associated with complex vs. Mendelian disease: Evolutionary evidence for differences in molecular effects

Paul D. Thomas; Anish Kejariwal


PLOS Genetics | 2005

Accurate Prediction of the Functional Significance of Single Nucleotide Polymorphisms and Mutations in the ABCA1 Gene

Liam R. Brunham; Roshni R. Singaraja; Terry D. Pape; Anish Kejariwal; Paul D. Thomas; Michael R. Hayden


Cold Spring Harbor Symposia on Quantitative Biology | 2003

Positive Selection in the Human Genome Inferred from Human–Chimp–Mouse Orthologous Gene Alignments

Andrew G. Clark; Stephen Glanowski; Rasmus Nielsen; Paul D. Thomas; Anish Kejariwal; M.J. Todd; David M. Tanenbaum; Daniel Civello; Fu Lu; Brian Murphy; Steve Ferriera; Gary Wang; Xiaole Zheng; Thomas J. White; John J. Sninsky; Mark D. Adams; M. Cargill

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Paul D. Thomas

University of Southern California

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Anushya Muruganujan

University of Southern California

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Huaiyu Mi

University of Southern California

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Istvan Ladunga

University of Nebraska–Lincoln

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