Aziz is a computational biologist in Curtis lab at the Stanford Cancer Institute, where he develops reproducible pipelines and machine learning methods for integrative analysis of multi-omics data at bulk and single-cell resolution to understand tumor evolution and chromatin regulatory dynamics of tumor growth.

Aziz completed his PhD in Bioinformatics at Tsinghua University, China in 2016 followed by a three year postdoctoral training at the University of Oslo, Norway. During PhD and Postdoc his primary research emphasis was on regulatory genomics and epigenomics. He developed computational methods, tools, and resources to understand the (epi)genomic control of gene regulation in development and disease.

Apart from research, he is advocating for open science, open source, preprints, and reproducibility in research. He is a contributor for Bioconda and also developed several open source tools and resources such as JASPAR. He is ASAPbio and eLife Community Ambassador and co-founded ECRcentral (, a community initiative for early-career researchers.

Honors & Awards

  • CSC fully-funded PhD Scholarship, Chinese Scholarship Council (2012 – 2016)
  • Erasmus+ mobility grant, European Commission (2019)
  • OBF Travel Fellowship, ISMB/ECCB 2019, Basel, Switzerland (May 2019)
  • Biocuration Travel Fellowship, Biocuration Conference 2019, Cambridge, UK (Apr 2019)
  • Biocuration Travel Fellowship, Biocuration Conference 2018, Shanghai, China (Apr 2018)
  • Research Travel Grant, Higher Education Commission (HEC), Pakistan (2016)
  • TWAS BIOVISION.Next Fellowship, BioVision conference, Lyon, France (2014)
  • TWAS BIOVISION.Next Fellowship, BioVision conference, Lyon, France (2013)
  • Research Travel Grant, Higher Education Commission (HEC), Pakistan (2012)

Education & Certifications

  • PhD, Tsinghua University, China, Bioinformatics (2016)
  • Postdoctoral Fellow, NCMM, University of Oslo, Norway, Computational Biology (2019)

All Publications

  • The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution. Cell Rozenblatt-Rosen, O., Regev, A., Oberdoerffer, P., Nawy, T., Hupalowska, A., Rood, J. E., Ashenberg, O., Cerami, E., Coffey, R. J., Demir, E., Ding, L., Esplin, E. D., Ford, J. M., Goecks, J., Ghosh, S., Gray, J. W., Guinney, J., Hanlon, S. E., Hughes, S. K., Hwang, E. S., Iacobuzio-Donahue, C. A., Jane-Valbuena, J., Johnson, B. E., Lau, K. S., Lively, T., Mazzilli, S. A., Pe'er, D., Santagata, S., Shalek, A. K., Schapiro, D., Snyder, M. P., Sorger, P. K., Spira, A. E., Srivastava, S., Tan, K., West, R. B., Williams, E. H., Human Tumor Atlas Network, Aberle, D., Achilefu, S. I., Ademuyiwa, F. O., Adey, A. C., Aft, R. L., Agarwal, R., Aguilar, R. A., Alikarami, F., Allaj, V., Amos, C., Anders, R. A., Angelo, M. R., Anton, K., Ashenberg, O., Aster, J. C., Babur, O., Bahmani, A., Balsubramani, A., Barrett, D., Beane, J., Bender, D. E., Bernt, K., Berry, L., Betts, C. B., Bletz, J., Blise, K., Boire, A., Boland, G., Borowsky, A., Bosse, K., Bott, M., Boyden, E., Brooks, J., Bueno, R., Burlingame, E. A., Cai, Q., Campbell, J., Caravan, W., Cerami, E., Chaib, H., Chan, J. M., Chang, Y. H., Chatterjee, D., Chaudhary, O., Chen, A. A., Chen, B., Chen, C., Chen, C., Chen, F., Chen, Y., Chheda, M. G., Chin, K., Chiu, R., Chu, S., Chuaqui, R., Chun, J., Cisneros, L., Coffey, R. J., Colditz, G. A., Cole, K., Collins, N., Contrepois, K., Coussens, L. M., Creason, A. L., Crichton, D., Curtis, C., Davidsen, T., Davies, S. R., de Bruijn, I., Dellostritto, L., De Marzo, A., Demir, E., DeNardo, D. G., Diep, D., Ding, L., Diskin, S., Doan, X., Drewes, J., Dubinett, S., Dyer, M., Egger, J., Eng, J., Engelhardt, B., Erwin, G., Esplin, E. D., Esserman, L., Felmeister, A., Feiler, H. S., Fields, R. C., Fisher, S., Flaherty, K., Flournoy, J., Ford, J. M., Fortunato, A., Frangieh, A., Frye, J. L., Fulton, R. S., Galipeau, D., Gan, S., Gao, J., Gao, L., Gao, P., Gao, V. R., Geiger, T., George, A., Getz, G., Ghosh, S., Giannakis, M., Gibbs, D. L., Gillanders, W. E., Goecks, J., Goedegebuure, S. P., Gould, A., Gowers, K., Gray, J. W., Greenleaf, W., Gresham, J., Guerriero, J. L., Guha, T. K., Guimaraes, A. R., Guinney, J., Gutman, D., Hacohen, N., Hanlon, S., Hansen, C. R., Harismendy, O., Harris, K. A., Hata, A., Hayashi, A., Heiser, C., Helvie, K., Herndon, J. M., Hirst, G., Hodi, F., Hollmann, T., Horning, A., Hsieh, J. J., Hughes, S., Huh, W. J., Hunger, S., Hwang, S. E., Iacobuzio-Donahue, C. A., Ijaz, H., Izar, B., Jacobson, C. A., Janes, S., Jane-Valbuena, J., Jayasinghe, R. G., Jiang, L., Johnson, B. E., Johnson, B., Ju, T., Kadara, H., Kaestner, K., Kagan, J., Kalinke, L., Keith, R., Khan, A., Kibbe, W., Kim, A. H., Kim, E., Kim, J., Kolodzie, A., Kopytra, M., Kotler, E., Krueger, R., Krysan, K., Kundaje, A., Ladabaum, U., Lake, B. B., Lam, H., Laquindanum, R., Lau, K. S., Laughney, A. M., Lee, H., Lenburg, M., Leonard, C., Leshchiner, I., Levy, R., Li, J., Lian, C. G., Lim, K., Lin, J., Lin, Y., Liu, Q., Liu, R., Lively, T., Longabaugh, W. J., Longacre, T., Ma, C. X., Macedonia, M. C., Madison, T., Maher, C. A., Maitra, A., Makinen, N., Makowski, D., Maley, C., Maliga, Z., Mallo, D., Maris, J., Markham, N., Marks, J., Martinez, D., Mashl, R. J., Masilionais, I., Mason, J., Massague, J., Massion, P., Mattar, M., Mazurchuk, R., Mazutis, L., Mazzilli, S. A., McKinley, E. T., McMichael, J. F., Merrick, D., Meyerson, M., Miessner, J. R., Mills, G. B., Mills, M., Mondal, S. B., Mori, M., Mori, Y., Moses, E., Mosse, Y., Muhlich, J. L., Murphy, G. F., Navin, N. E., Nawy, T., Nederlof, M., Ness, R., Nevins, S., Nikolov, M., Nirmal, A. J., Nolan, G., Novikov, E., Oberdoerffer, P., O'Connell, B., Offin, M., Oh, S. T., Olson, A., Ooms, A., Ossandon, M., Owzar, K., Parmar, S., Patel, T., Patti, G. J., Pe'er, D., Pe'er, I., Peng, T., Persson, D., Petty, M., Pfister, H., Polyak, K., Pourfarhangi, K., Puram, S. V., Qiu, Q., Quintanal-Villalonga, A., Raj, A., Ramirez-Solano, M., Rashid, R., Reeb, A. N., Regev, A., Reid, M., Resnick, A., Reynolds, S. M., Riesterer, J. L., Rodig, S., Roland, J. T., Rosenfield, S., Rotem, A., Roy, S., Rozenblatt-Rosen, O., Rudin, C. M., Ryser, M. D., Santagata, S., Santi-Vicini, M., Sato, K., Schapiro, D., Schrag, D., Schultz, N., Sears, C. L., Sears, R. C., Sen, S., Sen, T., Shalek, A., Sheng, J., Sheng, Q., Shoghi, K. I., Shrubsole, M. J., Shyr, Y., Sibley, A. B., Siex, K., Simmons, A. J., Singer, D. S., Sivagnanam, S., Slyper, M., Snyder, M. P., Sokolov, A., Song, S., Sorger, P. K., Southard-Smith, A., Spira, A., Srivastava, S., Stein, J., Storm, P., Stover, E., Strand, S. H., Su, T., Sudar, D., Sullivan, R., Surrey, L., Suva, M., Tan, K., Terekhanova, N. V., Ternes, L., Thammavong, L., Thibault, G., Thomas, G. V., Thorsson, V., Todres, E., Tran, L., Tyler, M., Uzun, Y., Vachani, A., Van Allen, E., Vandekar, S., Veis, D. J., Vigneau, S., Vossough, A., Waanders, A., Wagle, N., Wang, L., Wendl, M. C., West, R., Williams, E. H., Wu, C., Wu, H., Wu, H., Wyczalkowski, M. A., Xie, Y., Yang, X., Yapp, C., Yu, W., Yuan, Y., Zhang, D., Zhang, K., Zhang, M., Zhang, N., Zhang, Y., Zhao, Y., Zhou, D. C., Zhou, Z., Zhu, H., Zhu, Q., Zhu, X., Zhu, Y., Zhuang, X. 2020; 181 (2): 236–49


    Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

    View details for DOI 10.1016/j.cell.2020.03.053

    View details for PubMedID 32302568

  • JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic acids research Fornes, O., Castro-Mondragon, J. A., Khan, A., van der Lee, R., Zhang, X., Richmond, P. A., Modi, B. P., Correard, S., Gheorghe, M., Baranasic, D., Santana-Garcia, W., Tan, G., Cheneby, J., Ballester, B., Parcy, F., Sandelin, A., Lenhard, B., Wasserman, W. W., Mathelier, A. 2019


    JASPAR ( is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) for TFs across multiple species in six taxonomic groups. In this 8th release of JASPAR, the CORE collection has been expanded with 245 new PFMs (169 for vertebrates, 42 for plants, 17 for nematodes, 10 for insects, and 7 for fungi), and 156 PFMs were updated (125 for vertebrates, 28 for plants and 3 for insects). These new profiles represent an 18% expansion compared to the previous release. JASPAR 2020 comes with a novel collection of unvalidated TF-binding profiles for which our curators did not find orthogonal supporting evidence in the literature. This collection has a dedicated web form to engage the community in the curation of unvalidated TF-binding profiles. Moreover, we created a Q&A forum to ease the communication between the user community and JASPAR curators. Finally, we updated the genomic tracks, inference tool, and TF-binding profile similarity clusters. All the data is available through the JASPAR website, its associated RESTful API, and through the JASPAR2020 R/Bioconductor package.

    View details for DOI 10.1093/nar/gkz1001

    View details for PubMedID 31701148

  • Modeling RNA-Binding Protein Specificity In Vivo by Precisely Registering Protein-RNA Crosslink Sites MOLECULAR CELL Feng, H., Bao, S., Rahman, M., Weyn-Vanhentenryck, S. M., Khan, A., Wong, J., Shah, A., Flynn, E. D., Krainer, A. R., Zhang, C. 2019; 74 (6): 1189-+


    RNA-binding proteins (RBPs) regulate post-transcriptional gene expression by recognizing short and degenerate sequence motifs in their target transcripts, but precisely defining their binding specificity remains challenging. Crosslinking and immunoprecipitation (CLIP) allows for mapping of the exact protein-RNA crosslink sites, which frequently reside at specific positions in RBP motifs at single-nucleotide resolution. Here, we have developed a computational method, named mCross, to jointly model RBP binding specificity while precisely registering the crosslinking position in motif sites. We applied mCross to 112 RBPs using ENCODE eCLIP data and validated the reliability of the discovered motifs by genome-wide analysis of allelic binding sites. Our analyses revealed that the prototypical SR protein SRSF1 recognizes clusters of GGA half-sites in addition to its canonical GGAGGA motif. Therefore, SRSF1 regulates splicing of a much larger repertoire of transcripts than previously appreciated, including HNRNPD and HNRNPDL, which are involved in multivalent protein assemblies and phase separation.

    View details for DOI 10.1016/j.molcel.2019.02.002

    View details for Web of Science ID 000472231600010

    View details for PubMedID 31226278

    View details for PubMedCentralID PMC6676488

  • A map of direct TF-DNA interactions in the human genome NUCLEIC ACIDS RESEARCH Gheorghe, M., Sandve, G., Khan, A., Cheneby, J., Ballester, B., Mathelier, A. 2019; 47 (4): e21


    Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the most popular assay to identify genomic regions, called ChIP-seq peaks, that are bound in vivo by transcription factors (TFs). These regions are derived from direct TF-DNA interactions, indirect binding of the TF to the DNA (through a co-binding partner), nonspecific binding to the DNA, and noise/bias/artifacts. Delineating the bona fide direct TF-DNA interactions within the ChIP-seq peaks remains challenging. We developed a dedicated software, ChIP-eat, that combines computational TF binding models and ChIP-seq peaks to automatically predict direct TF-DNA interactions. Our work culminated with predicted interactions covering >4% of the human genome, obtained by uniformly processing 1983 ChIP-seq peak data sets from the ReMap database for 232 unique TFs. The predictions were a posteriori assessed using protein binding microarray and ChIP-exo data, and were predominantly found in high quality ChIP-seq peaks. The set of predicted direct TF-DNA interactions suggested that high-occupancy target regions are likely not derived from direct binding of the TFs to the DNA. Our predictions derived co-binding TFs supported by protein-protein interaction data and defined cis-regulatory modules enriched for disease- and trait-associated SNPs. We provide this collection of direct TF-DNA interactions and cis-regulatory modules through the UniBind web-interface (

    View details for DOI 10.1093/nar/gky1210

    View details for Web of Science ID 000467961200003

    View details for PubMedID 30517703

    View details for PubMedCentralID PMC6393237

  • Integrative modeling reveals key chromatin and sequence signatures predicting super-enhancers SCIENTIFIC REPORTS Khan, A., Zhang, X. 2019; 9: 2877


    Super-enhancers (SEs) are clusters of transcriptional enhancers which control the expression of cell identity and disease-associated genes. Current studies demonstrated the role of multiple factors in SE formation; however, a systematic analysis to assess the relative predictive importance of chromatin and sequence features of SEs and their constituents is lacking. In addition, a predictive model that integrates various types of data to predict SEs has not been established. Here, we integrated diverse types of genomic and epigenomic datasets to identify key signatures of SEs and investigated their predictive importance. Through integrative modeling, we found Cdk8, Cdk9, and Smad3 as new features of SEs, which can define known and new SEs in mouse embryonic stem cells and pro-B cells. We compared six state-of-the-art machine learning models to predict SEs and showed that non-parametric ensemble models performed better as compared to parametric. We validated these models using cross-validation and also independent datasets in four human cell-types. Taken together, our systematic analysis and ranking of features can be used as a platform to define and understand the biology of SEs in other cell-types.

    View details for DOI 10.1038/s41598-019-38979-9

    View details for Web of Science ID 000459799800026

    View details for PubMedID 30814546

    View details for PubMedCentralID PMC6393462

  • High OGT activity is essential for MYC-driven proliferation of prostate cancer cells THERANOSTICS Itkonen, H. M., Urbanucci, A., Martin, S. S., Khan, A., Mathelier, A., Thiede, B., Walker, S., Mills, I. G. 2019; 9 (8): 2183–97


    O-GlcNAc transferase (OGT) is overexpressed in aggressive prostate cancer. OGT modifies intra-cellular proteins via single sugar conjugation (O-GlcNAcylation) to alter their activity. We recently discovered the first fast-acting OGT inhibitor OSMI-2. Here, we probe the stability and function of the chromatin O-GlcNAc and identify transcription factors that coordinate with OGT to promote proliferation of prostate cancer cells. Methods: Chromatin immunoprecipitation (ChIP) coupled to sequencing (seq), formaldehyde-assisted isolation of regulatory elements, RNA-seq and reverse-phase protein arrays (RPPA) were used to study the importance of OGT for chromatin structure and transcription. Mass spectrometry, western blot, RT-qPCR, cell cycle analysis and viability assays were used to establish the role of OGT for MYC-related processes. Prostate cancer patient data profiled for both mRNA and protein levels were used to validate findings. Results: We show for the first time that OGT inhibition leads to a rapid loss of O-GlcNAc chromatin mark. O-GlcNAc ChIP-seq regions overlap with super-enhancers (SE) and MYC binding sites. OGT inhibition leads to down-regulation of SE-dependent genes. We establish the first O-GlcNAc chromatin consensus motif, which we use as a bait for mass spectrometry. By combining the proteomic data from oligonucleotide enrichment with O-GlcNAc and MYC ChIP-mass spectrometry, we identify host cell factor 1 (HCF-1) as an interaction partner of MYC. Inhibition of OGT disrupts this interaction and compromises MYC's ability to confer androgen-independent proliferation to prostate cancer cells. We show that OGT is required for MYC-mediated stabilization of mitotic proteins, including Cyclin B1, and/or the increased translation of their coding transcripts. This implies that increased expression of mRNA is not always required to achieve increased protein expression and confer aggressive phenotype. Indeed, high expression of Cyclin B1 protein has strong predictive value in prostate cancer patients (p=0.000014) while mRNA does not. Conclusions: OGT promotes SE-dependent gene expression. OGT activity is required for the interaction between MYC and HCF-1 and expression of MYC-regulated mitotic proteins. These features render OGT essential for the androgen-independent, MYC-driven proliferation of prostate cancer cells. Androgen-independency is the major mechanism of prostate cancer progression, and our study identifies OGT as an essential mediator in this process.

    View details for DOI 10.7150/thno.30834

    View details for Web of Science ID 000464623500005

    View details for PubMedID 31149037

    View details for PubMedCentralID PMC6531294

  • Making genome browsers portable and personal GENOME BIOLOGY Khan, A., Zhang, X. 2018; 19: 93


    GIVE is a framework and library for creating portable and personalized genome browsers. It makes visualizing genomic data as easy as building a laboratory homepage.

    View details for DOI 10.1186/s13059-018-1470-9

    View details for Web of Science ID 000439134200002

    View details for PubMedID 30016986

    View details for PubMedCentralID PMC6050684

  • JASPAR RESTful API: accessing JASPAR data from any programming language BIOINFORMATICS Khan, A., Mathelier, A. 2018; 34 (9): 1612–14


    JASPAR is a widely used open-access database of curated, non-redundant transcription factor binding profiles. Currently, data from JASPAR can be retrieved as flat files or by using programming language-specific interfaces. Here, we present a programming language-independent application programming interface (API) to access JASPAR data using the Representational State Transfer (REST) architecture. The REST API enables programmatic access to JASPAR by most programming languages and returns data in eight widely used formats. Several endpoints are available to access the data and an endpoint is available to infer the TF binding profile(s) likely bound by a given DNA binding domain protein sequence. Additionally, it provides an interactive browsable interface for bioinformatics tool developers.This REST API is implemented in Python using the Django REST Framework. It is accessible at and the source code is freely available at under GPL v3 or data are available at Bioinformatics online.

    View details for DOI 10.1093/bioinformatics/btx804

    View details for Web of Science ID 000431509200033

    View details for PubMedID 29253085

  • Put science first and formatting later EMBO REPORTS Khan, A., Montenegro-Montero, A., Mathelier, A. 2018; 19 (5)

    View details for DOI 10.15252/embr.201845731

    View details for Web of Science ID 000431633800013

    View details for PubMedID 29650529

    View details for PubMedCentralID PMC5934772

  • JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework NUCLEIC ACIDS RESEARCH Khan, A., Fornes, O., Stigliani, A., Gheorghe, M., Castro-Mondragon, J. A., van der Lee, R., Bessy, A., Cheneby, J., Kulkarni, S. R., Tan, G., Baranasic, D., Arenillas, D. J., Sandelin, A., Vandepoele, K., Lenhard, B., Ballester, B., Wasserman, W. W., Parcy, F., Mathelier, A. 2018; 46 (D1): D260–D266


    JASPAR ( is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) and TF flexible models (TFFMs) for TFs across multiple species in six taxonomic groups. In the 2018 release of JASPAR, the CORE collection has been expanded with 322 new PFMs (60 for vertebrates and 262 for plants) and 33 PFMs were updated (24 for vertebrates, 8 for plants and 1 for insects). These new profiles represent a 30% expansion compared to the 2016 release. In addition, we have introduced 316 TFFMs (95 for vertebrates, 218 for plants and 3 for insects). This release incorporates clusters of similar PFMs in each taxon and each TF class per taxon. The JASPAR 2018 CORE vertebrate collection of PFMs was used to predict TF-binding sites in the human genome. The predictions are made available to the scientific community through a UCSC Genome Browser track data hub. Finally, this update comes with a new web framework with an interactive and responsive user-interface, along with new features. All the underlying data can be retrieved programmatically using a RESTful API and through the JASPAR 2018 R/Bioconductor package.

    View details for DOI 10.1093/nar/gkx1126

    View details for Web of Science ID 000419550700040

    View details for PubMedID 29140473

    View details for PubMedCentralID PMC5753243

  • Super-enhancers are transcriptionally more active and cell type-specific than stretch enhancers EPIGENETICS Khan, A., Mathelier, A., Zhang, X. 2018; 13 (9): 910–22


    Super-enhancers and stretch enhancers represent classes of transcriptional enhancers that have been shown to control the expression of cell identity genes and carry disease- and trait-associated variants. Specifically, super-enhancers are clusters of enhancers defined based on the binding occupancy of master transcription factors, chromatin regulators, or chromatin marks, while stretch enhancers are large chromatin-defined regulatory regions of at least 3,000 base pairs. Several studies have characterized these regulatory regions in numerous cell types and tissues to decipher their functional importance. However, the differences and similarities between these regulatory regions have not been fully assessed. We integrated genomic, epigenomic, and transcriptomic data from ten human cell types to perform a comparative analysis of super and stretch enhancers with respect to their chromatin profiles, cell type-specificity, and ability to control gene expression. We found that stretch enhancers are more abundant, more distal to transcription start sites, cover twice as much the genome, and are significantly less conserved than super-enhancers. In contrast, super-enhancers are significantly more enriched for active chromatin marks and cohesin complex, and more transcriptionally active than stretch enhancers. Importantly, a vast majority of super-enhancers (85%) overlap with only a small subset of stretch enhancers (13%), which are enriched for cell type-specific biological functions, and control cell identity genes. These results suggest that super-enhancers are transcriptionally more active and cell type-specific than stretch enhancers, and importantly, most of the stretch enhancers that are distinct from super-enhancers do not show an association with cell identity genes, are less active, and more likely to be poised enhancers.

    View details for DOI 10.1080/15592294.2018.1514231

    View details for Web of Science ID 000450445600002

    View details for PubMedID 30169995

    View details for PubMedCentralID PMC6284781

  • Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature methods Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J. 2018; 15 (7): 475–76

    View details for DOI 10.1038/s41592-018-0046-7

    View details for PubMedID 29967506

  • Intervene: a tool for intersection and visualization of multiple gene or genomic region sets BMC BIOINFORMATICS Khan, A., Mathelier, A. 2017; 18: 287


    A common task for scientists relies on comparing lists of genes or genomic regions derived from high-throughput sequencing experiments. While several tools exist to intersect and visualize sets of genes, similar tools dedicated to the visualization of genomic region sets are currently limited.To address this gap, we have developed the Intervene tool, which provides an easy and automated interface for the effective intersection and visualization of genomic region or list sets, thus facilitating their analysis and interpretation. Intervene contains three modules: venn to generate Venn diagrams of up to six sets, upset to generate UpSet plots of multiple sets, and pairwise to compute and visualize intersections of multiple sets as clustered heat maps. Intervene, and its interactive web ShinyApp companion, generate publication-quality figures for the interpretation of genomic region and list sets.Intervene and its web application companion provide an easy command line and an interactive web interface to compute intersections of multiple genomic and list sets. They have the capacity to plot intersections using easy-to-interpret visual approaches. Intervene is developed and designed to meet the needs of both computer scientists and biologists. The source code is freely available at , with the web application available at .

    View details for DOI 10.1186/s12859-017-1708-7

    View details for Web of Science ID 000402839800002

    View details for PubMedID 28569135

    View details for PubMedCentralID PMC5452382

  • dbSUPER: a database of super-enhancers in mouse and human genome. Nucleic acids research Khan, A., Zhang, X. 2016; 44 (D1): D164–71


    Super-enhancers are clusters of transcriptional enhancers that drive cell-type-specific gene expression and are crucial to cell identity. Many disease-associated sequence variations are enriched in super-enhancer regions of disease-relevant cell types. Thus, super-enhancers can be used as potential biomarkers for disease diagnosis and therapeutics. Current studies have identified super-enhancers in more than 100 cell types and demonstrated their functional importance. However, a centralized resource to integrate all these findings is not currently available. We developed dbSUPER (, the first integrated and interactive database of super-enhancers, with the primary goal of providing a resource for assistance in further studies related to transcriptional control of cell identity and disease. dbSUPER provides a responsive and user-friendly web interface to facilitate efficient and comprehensive search and browsing. The data can be easily sent to Galaxy instances, GREAT and Cistrome web-servers for downstream analysis, and can also be visualized in the UCSC genome browser where custom tracks can be added automatically. The data can be downloaded and exported in variety of formats. Furthermore, dbSUPER lists genes associated with super-enhancers and also links to external databases such as GeneCards, UniProt and Entrez. dbSUPER also provides an overlap analysis tool to annotate user-defined regions. We believe dbSUPER is a valuable resource for the biology and genetic research communities.

    View details for DOI 10.1093/nar/gkv1002

    View details for PubMedID 26438538

    View details for PubMedCentralID PMC4702767