Adam Godzik

Dr. Adam Godzik

orcid

Research Focus & Metrics

Academic Career Length

41Years (1986-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 April 2018 in Cell
Verified
Journal Article
Open access
MBMatthew H. Bailey
Matthew H. Bailey
CTCollin Tokheim
Collin Tokheim
EPEduard Porta-Pardo
Eduard Porta-Pardo
SSSohini Sengupta
Sohini Sengupta
DBDenis Bertrand
Denis Bertrand
RKRachel Karchin
Rachel Karchin
Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.

1 June 2004 in Cell
Verified
Journal Article
Closed access
AAAndres Alonso
Andres Alonso
JSJoanna Sasin
Joanna Sasin
NBNunzio Bottini
Nunzio Bottini
IFIlan Friedberg
Ilan Friedberg
IFIddo Friedberg
Iddo Friedberg
TMTomas Mustelin
Tomas Mustelin

1 March 2001 in Bioinformatics
Verified
Journal Article
Closed access
WLWeizhong Li
Weizhong Li
LJLukasz Jaroszewski
Lukasz Jaroszewski
AGAdam Godzik
Adam Godzik
Summary: We present a fast and flexible program for clustering large protein databases at different sequence identity levels. It takes less than 2 h for the all-against-all sequence comparison and clustering of the non-redundant protein database of over 560000 sequences on a high-end PC. The output database, including only the representative sequences, can be used for more efficient and sensitive database searches. Availability: The program is available from http://bioinformatics.burnham-inst.org/cd-hi Contact: liwz@sdsc.edu or adam@burnham-inst.org * To whom correspondence should be addressed.

3 April 2009 in Science
Verified
Journal Article
Closed access
PDF file
DCDong-Hyung Cho
Dong-Hyung Cho
TNTomohiro Nakamura
Tomohiro Nakamura
JFJianguo Fang
Jianguo Fang
PCPiotr Cieplak
Piotr Cieplak
AGAdam Godzik
Adam Godzik
SLStuart A. Lipton
Stuart A. Lipton
Mitochondria continuously undergo two opposing processes, fission and fusion. The disruption of this dynamic equilibrium may herald cell injury or death and may contribute to developmental and neurodegenerative disorders. Nitric oxide functions as a signaling molecule, but in excess it mediates neuronal injury, in part via mitochondrial fission or fragmentation. However, the underlying mechanism for nitric oxide–induced pathological fission remains unclear. We found that nitric oxide produced in response to β-amyloid protein, thought to be a key mediator of Alzheimer's disease, triggered mitochondrial fission, synaptic loss, and neuronal damage, in part via S-nitrosylation of dynamin-related protein 1 (forming SNO-Drp1). Preventing nitrosylation of Drp1 by cysteine mutation abrogated these neurotoxic events. SNO-Drp1 is increased in brains of human Alzheimer's disease patients and may thus contribute to the pathogenesis of neurodegeneration.

1 March 2008 in Immunity
Verified
Journal Article
Closed access
JTJenny P.-Y. Ting
Jenny P.-Y. Ting
RLRuth Caroline Lovering
Ruth Caroline Lovering
EAEmad S. Alnemri
Emad S. Alnemri
JBJohn Bertin
John Bertin
JBJeremy M. Boss
Jeremy M. Boss
Peter A WardPeter A Ward
Peter A Ward

13 March 2007 in PLOS Biology
Verified
Journal Article
Open access
PDF file
SYShibu Yooseph
Shibu Yooseph
GSGranger Sutton
Granger Sutton
DRDouglas B Rusch
Douglas B Rusch
AHAaron L Halpern
Aaron L Halpern
SWShannon J Williamson
Shannon J Williamson
JVJ Craig Venter
J Craig Venter
Metagenomics projects based on shotgun sequencing of populations of micro-organisms yield insight into protein families. We used sequence similarity clustering to explore proteins with a comprehensive dataset consisting of sequences from available databases together with 6.12 million proteins predicted from an assembly of 7.7 million Global Ocean Sampling (GOS) sequences. The GOS dataset covers nearly all known prokaryotic protein families. A total of 3,995 medium- and large-sized clusters consisting of only GOS sequences are identified, out of which 1,700 have no detectable homology to known families. The GOS-only clusters contain a higher than expected proportion of sequences of viral origin, thus reflecting a poor sampling of viral diversity until now. Protein domain distributions in the GOS dataset and current protein databases show distinct biases. Several protein domains that were previously categorized as kingdom specific are shown to have GOS examples in other kingdoms. About 6,000 sequences (ORFans) from the literature that heretofore lacked similarity to known proteins have matches in the GOS data. The GOS dataset is also used to improve remote homology detection. Overall, besides nearly doubling the number of current proteins, the predicted GOS proteins also add a great deal of diversity to known protein families and shed light on their evolution. These observations are illustrated using several protein families, including phosphatases, proteases, ultraviolet-irradiation DNA damage repair enzymes, glutamine synthetase, and RuBisCO. The diversity added by GOS data has implications for choosing targets for experimental structure characterization as part of structural genomics efforts. Our analysis indicates that new families are being discovered at a rate that is linear or almost linear with the addition of new sequences, implying that we are still far from discovering all protein families in nature. The rapidly emerging field of metagenomics seeks to examine the genomic content of communities of organisms to understand their roles and interactions in an ecosystem. Given the wide-ranging roles microbes play in many ecosystems, metagenomics studies of microbial communities will reveal insights into protein families and their evolution. Because most microbes will not grow in the laboratory using current cultivation techniques, scientists have turned to cultivation-independent techniques to study microbial diversity. One such technique—shotgun sequencing—allows random sampling of DNA sequences to examine the genomic material present in a microbial community. We used shotgun sequencing to examine microbial communities in water samples collected by the Sorcerer II Global Ocean Sampling (GOS) expedition. Our analysis predicted more than six million proteins in the GOS data—nearly twice the number of proteins present in current databases. These predictions add tremendous diversity to known protein families and cover nearly all known prokaryotic protein families. Some of the predicted proteins had no similarity to any currently known proteins and therefore represent new families. A higher than expected fraction of these novel families is predicted to be of viral origin. We also found that several protein domains that were previously thought to be kingdom specific have GOS examples in other kingdoms. Our analysis opens the door for a multitude of follow-up protein family analyses and indicates that we are a long way from sampling all the protein families that exist in nature.