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© 2019 by the Krishnan Lab

PUBLICATIONS

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Selected Publications

[* Joint primary author] [† Corresponding author]

Preprints

  1. Supervised-learning is an accurate method for network-based gene classification.
    Lau R*, Mancuso CA*, Yannakopoulos A, Johnson KA, Krishnan A.
    bioRxiv (2019) doi:10.1101/721423
     

  2. SANe: The seed active network for mining transcriptional regulatory programs of seed development.
    Gupta C, Krishnan A, Collakova E, Wolinski P, Pereira A.
    [web-interface: SANe] 
    bioRxiv (2017) doi:10.1101/165894

Peer-Reviewed Journal Papers

  1. A computational framework for genome-wide characterization of the human disease landscape.
    Lee Y, Krishnan A, Oughtred  R, Rust J, Chang CS, Ryu J, Kristensen VN, Dolinski K, Theesfeld CL, Troyanskaya OG.
    Cell Systems (2019) 8:152-162.e6.
    [pubmed]
    [web-interface: URSAHD]
     

  2. Pervasive genetic interactions modulate neurodevelopmental defects of autism-associated 16p11.2 deletion in Drosophila melanogaster.
    Iyer J, Singh MD, Jensen M, Patel P, Pizzo L, Huber E, Koerselman H, Weiner AT, Lepanto P, Vadodaria K, Kubina A, Wang Q, Talbert A, Yennawar S, Badano J, Manak R, Rolls MM, Krishnan A, Girirajan S.
    Nature Communications (2018) 9:2548.
    [bioRxiv] [pubmed] [press: psu | spectrum]
     

  3. GIANT 2.0: genome-scale integrated analysis of gene networks in tissues.
    Wong AK, Krishnan A, Troyanskaya OG.
    Nucleic Acids Research (2018) 46:W65–W70.
    [pubmed]
    [web-interface: GIANT2]
     

  4. RECoN: Rice environment coexpression network for systems level analysis of abiotic-stress response.
    Krishnan A, Gupta C, Ambavaram MMR, Pereira A.
    Frontiers in Plant Science (2017) 8:1640.
    [bioRxiv] [pubmed]
    [web-interface: RECoN]
     

  5. Integrative networks illuminate biological factors underlying gene-disease associations
    Krishnan A†, Taroni JN, Greene CS†.
    Current Genetic Medicine Reports (2016) 4:155-162.
    [bioRxiv] [journal]
     

  6. Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder.
    Krishnan A*, Zhang R*, Yao V, Theesfeld CL, Wong AK, Tadych A, Volfovsky N, Packer A, Lash A, Troyanskaya OG.
    Nature Neuroscience (2016) 19:1454-1462.
    [bioRxiv] [pubmed] [press: princeton | simons foundation | popular science | researchgate]
    [web-interface: ASD]
     

  7. Understanding multi-cellular function and disease with human tissue-specific gene interaction networks.
    Greene CS*, Krishnan A*, Wong AK*, Ricciotti E, Zelaya R, Himmelstein D, Chasman D, Fitzgerald G, Dolinski K, Grosser T, Troyanskaya OG.
    Nature Genetics (2015) 47:569-576.
    [pubmed] [commentary: nature biotechnology]
    [web-interface: GIANT]
     

  8. Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms.
    Park C, Krishnan A, Zhu Q, Wong AK, Lee Y, Troyanskaya OG.
    Bioinformatics (2015) 31:1093-1101.
    [pubmed]
    [web-interface: PathwayNet]
     

  9. Coordinate regulation of photosynthetic carbon metabolism for yield and environmental stress response in rice.
    Ambavaram MM, Basu S, Krishnan A, Venkategowda R, Batlang U, Rahman L, Baisakh N, Pereira A.
    Nature Communications (2014) 5:5302.
    [pubmed]
     

  10. Ontology-aware classification of tissue and cell-type signals in gene expression profiles across platforms and technologies.
    Lee Y, Krishnan A, Zhu Q, Troyanskaya OG.
    Bioinformatics (2013) 29:3036-3044.
    [pubmed]
    [web-interface: URSA]
     

  11. Coordinated activation of cellulose and repression of lignin biosynthesis pathways in rice.
    Ambavaram MM*, Krishnan A*, Trijatmiko KR, Pereira A.
    Plant Physiology (2011) 155:916-931.
    [pubmed]
     

  12. Molecular and physiological analysis of drought stress in Arabidopsis reveals early responses leading to acclimation in plant growth.
    Harb A, Krishnan A, Pereira A.
    Plant Physiology (2010) 154:1254-1271.
    [pubmed]
     

  13. Diversity of En/Spm transposons in maize and rice.
    Krishnan A, Greco R, Pereira A.
    Maydica (2009) 53:181-187.
     

  14. Mutant resources in rice for functional genomics of the grasses.
    Krishnan A, Guiderdoni E, An G, Hsing YC, Han C, Lee MC, Yu SM, Upadhyaya N, Ramachandran S, Zhang Q, Sundaresan V, Hirochika H, Leung H, Pereira A.
    Plant Physiology (2009) 149:165-170.
    [pubmed]
     

  15. Integrative approaches for mining transcriptional regulatory programs in Arabidopsis.
    Krishnan A, Greco R, Pereira A.
    Briefings in Functional Genomics and Proteomics (2008) 7:264-274.
    [pubmed]

Book Chapters

  1. Microarray data analysis.
    Mohapatra SK*, Krishnan A*.
    Plant Reverse Genetics (2009) The Humana Press Inc., Totowa NJ, USA.

     
  2. Genetic networks underlying plant abiotic stress responses.
    Krishnan A, Ambavaram MMR, Harb A, Batlang U, Wittich PE, Pereira A.
    Genes for Plant Abiotic Stress (2009) John Wiley & Sons, Inc., Ames IA, USA.

Other Publications

  1. Rare variants in the genetic background modulate the expressivity of neurodevelopmental disorders.
    Pizzo L, Jensen M, Polyak A, Rosenfeld JA, Mannik K, Krishnan A, …, Girirajan S
    Genetics in Medicine (2018).
    [biorxiv] [pubmed]
     

  2. A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data.
    Rangan AV , McGrouther CC, Kelsoe J, Schork N, Stahl E, Zhu Q, Krishnan A, Yao V, Troyanskaya OG, Bilaloglu S, Raghavan P, Bergen S, Jureus A, Landen M, Bipolar Disorders Working Group of the Psychiatric Genomics Consortium.
    PLoS Computational Biology  (2018) 14: e1006105.
    [pubmed]
     

  3. IMP 2.0: A multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks.
    Wong AK, Krishnan A, Yao V, Tadych A, Troyanskaya OG.
    Nucleic Acids Research (2015) 43:W128-133.
    [pubmed]
     

  4. FNTM: a server for predicting Functional Networks of Tissues in Mouse.
    Goya J*, Wong AK*, Yao V*, Krishnan A, Homilius M, Troyanskaya OG.
    Nucleic Acids Research (2015) 43:W182-187.
    [pubmed]
     

  5. Low variance RNAs identify Parkinson’s disease molecular signature in blood.
    Chikina MD, Gerald CP, Li X, Ge Y, Pincas H, Nair VD, Wong AK, Krishnan A, Troyanskaya OG, Raymond D, Saunders-Pullman R, Bressman SB, Yue Z, Sealfon SC.
    Movement Disorders (2015) 30:813-821.
    [pubmed]
     

  6. Targeted exploration and analysis of large cross-platform human transcriptomic compendia.
    Zhu Q, Wong AK, Krishnan A, Aure MR, Tadych A, Zhang R, Corney DC, Greene CS, Bongo LA, Kristensen VN, Charikar M, Li K, Troyanskaya OG.
    Nature Methods (2015) 12:211-214.
    [pubmed]
     

  7. Drought responsive genes and their functional terms identified by GS FLX Pyro sequencing in maize.
    Batlang U, Ambavaram MMR, Krishnan A, Pereira A.
    Maydica 59: 306-314.
     

  8. Rice GROWTH UNDER DROUGHT KINASE is required for drought tolerance and grain yield under normal and drought stress conditions.
    Venkategowda R, Basu S, Krishnan A, Pereira A.
    Plant Physiology (2014) 166:1634-1645.
    [pubmed]
     

  9. Reconciling differential gene expression data with molecular interaction networks.
    Poirel CL, Rahman A, Rodrigues RR, Krishnan A, Addesa JR, Murali TM.
    Bioinformatics (2013) 29:622-629.
    [pubmed]
     

  10. Stochastic modeling of dwell-time distributions during transcriptional pausing and initiation.
    Xu X, Kumar N, Krishnan A, Kulkarni R
    52nd IEEE Conference on Decision and Control (2013) 4068-4073.
    [ieee]
     

  11. Effects of drought on gene expression in maize reproductive and leaf meristem tissue revealed by RNA-Seq.
    Kakumanu A, Ambavaram MM, Klumas C, Krishnan A, Batlang U, Myers E, Grene R, Pereira A.
    Plant Physiology (2012) 160:846-867.
    [pubmed]
     

  12. Mechanisms of action and medicinal applications of abscisic acid.
    Bassaganya-Riera J, Skoneczka J, Kingston DG, Krishnan A, Misyak S, Carter A, Pereira A, Guri AJ, Tumarkin R, Hontecillas R.
    Current Medicinal Chemistry (2009) 17:467-478.
    [pubmed]
     

  13. Improvement of water use efficiency in rice by expression of HARDY, an Arabidopsis drought and salt tolerance gene.
    Karaba A, Dixit S, Greco R, Aharoni A, Trijatmiko KR, Marsch-Martinez N, Krishnan A, Nataraja KN, Udayakumar M, Pereira A.
    Proceedings of the National Academy of Sciences USA (2007) 104:15270-15275.
    [pubmed]