Chad Nusbaum

143.9k total citations · 15 hit papers
79 papers, 28.6k citations indexed

About

Chad Nusbaum is a scholar working on Molecular Biology, Plant Science and Genetics. According to data from OpenAlex, Chad Nusbaum has authored 79 papers receiving a total of 28.6k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Molecular Biology, 22 papers in Plant Science and 15 papers in Genetics. Recurrent topics in Chad Nusbaum's work include Genomics and Phylogenetic Studies (28 papers), RNA and protein synthesis mechanisms (19 papers) and RNA modifications and cancer (10 papers). Chad Nusbaum is often cited by papers focused on Genomics and Phylogenetic Studies (28 papers), RNA and protein synthesis mechanisms (19 papers) and RNA modifications and cancer (10 papers). Chad Nusbaum collaborates with scholars based in United States, Israel and Canada. Chad Nusbaum's co-authors include B Bernstein, Wei Li, Tao Liu, X. Shirley Liu, Jérôme Eeckhoute, Myles Brown, David S. Johnson, Clifford A. Meyer, R Myers and Yong Zhang and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Chad Nusbaum

79 papers receiving 28.3k citations

Hit Papers

Model-based Analysis of ChIP-Seq (MACS) 2001 2026 2009 2017 2008 2008 2010 2009 2010 2.5k 5.0k 7.5k 10.0k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Chad Nusbaum United States 52 22.0k 5.3k 4.8k 4.5k 2.1k 79 28.6k
Vladimı́r Beneš Germany 65 18.9k 0.9× 3.2k 0.6× 4.1k 0.9× 4.6k 1.0× 2.6k 1.2× 268 30.7k
Ewan Birney United Kingdom 63 22.2k 1.0× 5.4k 1.0× 5.8k 1.2× 2.2k 0.5× 1.9k 0.9× 176 31.2k
Andrew Fire United States 76 28.6k 1.3× 5.3k 1.0× 4.3k 0.9× 4.6k 1.0× 2.3k 1.1× 177 36.6k
A Mortazavi United States 36 19.8k 0.9× 6.8k 1.3× 3.6k 0.7× 4.7k 1.0× 2.6k 1.2× 99 29.7k
Marco A. Marra Canada 83 18.9k 0.9× 5.8k 1.1× 5.0k 1.0× 5.2k 1.2× 2.1k 1.0× 361 30.0k
B Wold United States 59 27.6k 1.3× 6.9k 1.3× 5.6k 1.2× 5.3k 1.2× 3.0k 1.4× 114 38.6k
Brian A. Williams United States 24 16.7k 0.8× 6.2k 1.2× 3.2k 0.7× 4.1k 0.9× 2.0k 1.0× 34 25.4k
Aaron R. Quinlan United States 37 17.1k 0.8× 5.4k 1.0× 6.8k 1.4× 3.5k 0.8× 1.3k 0.6× 86 24.6k
Harold Pimentel United States 11 16.4k 0.7× 6.4k 1.2× 3.3k 0.7× 4.1k 0.9× 2.8k 1.3× 21 26.3k
Ira M. Hall United States 28 16.6k 0.8× 5.8k 1.1× 5.8k 1.2× 3.3k 0.7× 1.2k 0.6× 37 23.0k

Countries citing papers authored by Chad Nusbaum

Since Specialization
Citations

This map shows the geographic impact of Chad Nusbaum's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Chad Nusbaum with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chad Nusbaum more than expected).

Fields of papers citing papers by Chad Nusbaum

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chad Nusbaum. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Chad Nusbaum. The network helps show where Chad Nusbaum may publish in the future.

Co-authorship network of co-authors of Chad Nusbaum

This figure shows the co-authorship network connecting the top 25 collaborators of Chad Nusbaum. A scholar is included among the top collaborators of Chad Nusbaum based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chad Nusbaum. Chad Nusbaum is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hou, Lei, Xushen Xiong, Yongjin Park, et al.. (2023). Multitissue H3K27ac profiling of GTEx samples links epigenomic variation to disease. Nature Genetics. 55(10). 1665–1676. 11 indexed citations
3.
Wala, Jeremiah A., Pratiti Bandopadhayay, Noah F. Greenwald, et al.. (2018). SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Research. 28(4). 581–591. 181 indexed citations
4.
Russ, Carsten, B. Franz Lang, Zehua Chen, et al.. (2016). Genome Sequence ofSpizellomyces punctatus. Genome Announcements. 4(4). 15 indexed citations
5.
Russ, Carsten, Chad Nusbaum, Qiandong Zeng, et al.. (2015). Mitochondrial genome sequences reveal evolutionary relationships of the Phytophthora 1c clade species. Current Genetics. 61(4). 567–577. 22 indexed citations
6.
Ross, Michael, Carsten Russ, Maura Costello, et al.. (2013). Characterizing and measuring bias in sequence data. Genome biology. 14(5). R51–R51. 575 indexed citations breakdown →
7.
Taylor, D. Lee, Teresa N. Hollingsworth, Jack W. McFarland, et al.. (2013). A first comprehensive census of fungi in soil reveals both hyperdiversity and fine‐scale niche partitioning. Ecological Monographs. 84(1). 3–20. 248 indexed citations
8.
Haas, Brian J., Melissa Chin, Chad Nusbaum, Bruce W. Birren, & Jonathan Livny. (2012). How deep is deep enough for RNA-Seq profiling of bacterial transcriptomes?. BMC Genomics. 13(1). 734–734. 174 indexed citations
9.
Raffaele, Sylvain, Rhys A. Farrer, Liliana M. Cano, et al.. (2010). Genome Evolution Following Host Jumps in the Irish Potato Famine Pathogen Lineage. Science. 330(6010). 1540–1543. 303 indexed citations
10.
Gnerre, Sante, Iain MacCallum, Dariusz Przybylski, et al.. (2010). High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Proceedings of the National Academy of Sciences. 108(4). 1513–1518. 1037 indexed citations breakdown →
11.
Chiang, Hou‐Yu, Lori W. Schoenfeld, J. Graham Ruby, et al.. (2010). Mammalian microRNAs: experimental evaluation of novel and previously annotated genes. Genes & Development. 24(10). 992–1009. 655 indexed citations breakdown →
12.
Levin, Joshua Z., Moran Yassour, Xian Adiconis, et al.. (2010). Comprehensive comparative analysis of strand-specific RNA sequencing methods. PMC. 1 indexed citations
13.
Hall, Sarah E., et al.. (2010). A Cellular Memory of Developmental History Generates Phenotypic Diversity in C. elegans. Current Biology. 20(2). 149–155. 78 indexed citations
14.
Schornack, Sebastián, Edgar Huitema, Liliana M. Cano, et al.. (2009). Ten things to know about oomycete effectors. Molecular Plant Pathology. 10(6). 795–803. 125 indexed citations
15.
Garber, Manuel, Michael C. Zody, Harindra Arachchi, et al.. (2009). Closing gaps in the human genome using sequencing by synthesis. Genome biology. 10(6). R60–R60. 19 indexed citations
16.
Zhang, Yong, Tao Liu, Clifford A. Meyer, et al.. (2008). Model-based Analysis of ChIP-Seq (MACS). Genome biology. 9(9). R137–R137. 11435 indexed citations breakdown →
17.
Haberer, Georg, Sarah Young, Arvind K. Bharti, et al.. (2005). Structure and Architecture of the Maize Genome. PLANT PHYSIOLOGY. 139(4). 1612–1624. 116 indexed citations
18.
Jaffe, David B., Keith O'Neill, Elinor K. Karlsson, et al.. (2005). Assembly of polymorphic genomes: Algorithms and application to Ciona savignyi. Genome Research. 15(8). 1127–1135. 134 indexed citations
19.
Donaldson, Deirdre, D.R. Rosen, J. O'Regan, et al.. (1992). Two independent dinucleotide repeat polymorphisms at the D21S235 locus (21q22.1). Human Molecular Genetics. 1(8). 651–651. 2 indexed citations
20.
Nusbaum, Chad & Barbara J Meyer. (1989). The Caenorhabditis elegans gene sdc-2 controls sex determination and dosage compensation in XX animals.. Genetics. 122(3). 579–593. 70 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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