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UTAP: User-friendly Transcriptome Analysis Pipeline

Refael Kohen, Jonathan Barlev, Gil Hornung, Gil Stelzer, Ester Feldmesser, Kiril Kogan, Marilyn Safran, Dena Leshkowitz
2019 BMC Bioinformatics  
RNA-Seq technology is routinely used to characterize the transcriptome, and to detect gene expression differences among cell types, genotypes and conditions. Advances in short-read sequencing instruments such as Illumina Next-Seq have yielded easy-to-operate machines, with high throughput, at a lower price per base. However, processing this data requires bioinformatics expertise to tailor and execute specific solutions for each type of library preparation. Results: In order to enable fast and
more » ... er-friendly data analysis, we developed an intuitive and scalable transcriptome pipeline that executes the full process, starting from cDNA sequences derived by RNA-Seq [Nat Rev Genet 10:57-63, 2009] and bulk MARS-Seq [Science 343:776-779, 2014] and ending with sets of differentially expressed genes. Output files are placed in structured folders, and results summaries are provided in rich and comprehensive reports, containing dozens of plots, tables and links. Conclusion: Our User-friendly Transcriptome Analysis Pipeline (UTAP) is an open source, web-based intuitive platform available to the biomedical research community, enabling researchers to efficiently and accurately analyse transcriptome sequence data.
doi:10.1186/s12859-019-2728-2 fatcat:6ytquwm2nbgmzntbiknqaluaei

Elucidating tissue specific genes using the Benford distribution

Deepak Karthik, Gil Stelzer, Sivan Gershanov, Danny Baranes, Mali Salmon-Divon
2016 BMC Genomics  
The RNA-seq technique is applied for the investigation of transcriptional behaviour. The reduction in sequencing costs has led to an unprecedented trove of gene expression data from diverse biological systems. Subsequently, principles from other disciplines such as the Benford law, which can be properly judged only in datarich systems, can now be examined on this high-throughput transcriptomic information. The Benford law, states that in many count-rich datasets the distribution of the first
more » ... nificant digit is not uniform but rather logarithmic. Results: All tested digital gene expression datasets showed a Benford-like distribution when observing an entire gene set. This phenomenon was conserved in development and does not demonstrate tissue specificity. However, when obedience to the Benford law is calculated for individual expressed genes across thousands of cells, genes that best and least adhere to the Benford law are enriched with tissue specific or cell maintenance descriptors, respectively. Surprisingly, a positive correlation was found between the obedience a gene exhibits to the Benford law and its expression level, despite the former being calculated solely according to first digit frequency while totally ignoring the expression value itself. Nevertheless, genes with low expression that exhibit Benford behavior demonstrate tissue specific associations. These observations were extended to predict the likelihood of tissue specificity based on Benford behaviour in a supervised learning approach. Conclusions: These results demonstrate the applicability and potential predictability of the Benford law for gleaning biological insight from simple count data.
doi:10.1186/s12864-016-2921-x pmid:27506195 pmcid:PMC4979126 fatcat:g3zl3wlw2zesnpyqgh5taaaxbi

GIFtS: annotation landscape analysis with GeneCards

Arye Harel, Aron Inger, Gil Stelzer, Liora Strichman-Almashanu, Irina Dalah, Marilyn Safran, Doron Lancet
2009 BMC Bioinformatics  
Gene annotation is a pivotal component in computational genomics, encompassing prediction of gene function, expression analysis, and sequence scrutiny. Hence, quantitative measures of the annotation landscape constitute a pertinent bioinformatics tool. GeneCards ® is a gene-centric compendium of rich annotative information for over 50,000 human gene entries, building upon 68 data sources, including Gene Ontology (GO), pathways, interactions, phenotypes, publications and many more. Results: We
more » ... esent the GeneCards Inferred Functionality Score (GIFtS) which allows a quantitative assessment of a gene's annotation status, by exploiting the unique wealth and diversity of GeneCards information. The GIFtS tool, linked from the GeneCards home page, facilitates browsing the human genome by searching for the annotation level of a specified gene, retrieving a list of genes within a specified range of GIFtS value, obtaining random genes with a specific GIFtS value, and experimenting with the GIFtS weighting algorithm for a variety of annotation categories. The bimodal shape of the GIFtS distribution suggests a division of the human gene repertoire into two main groups: the high-GIFtS peak consists almost entirely of protein-coding genes; the low-GIFtS peak consists of genes from all of the categories. Cluster analysis of GIFtS annotation vectors provides the classification of gene groups by detailed positioning in the annotation arena. GIFtS also provide measures which enable the evaluation of the databases that serve as GeneCards sources. An inverse correlation is found (for GIFtS>25) between the number of genes annotated by each source, and the average GIFtS value of genes associated with that source. Three typical source prototypes are revealed by their GIFtS distribution: genome-wide sources, sources comprising mainly highly annotated genes, and sources comprising mainly poorly annotated genes. The degree of accumulated knowledge for a given gene measured by GIFtS was correlated (for GIFtS>30) with the number of publications for a gene, and with the seniority of this entry in the HGNC database. Conclusion: GIFtS can be a valuable tool for computational procedures which analyze lists of large set of genes resulting from wet-lab or computational research. GIFtS may also assist the scientific community with identification of groups of uncharacterized genes for diverse applications, such as delineation of novel functions and charting unexplored areas of the human genome.
doi:10.1186/1471-2105-10-348 pmid:19852797 pmcid:PMC2774327 fatcat:e7jfqzhntrfpnongo5zgulj72m

The enigma of ATCE1, an acrosome-associated transcription factor

Stelzer Gil, Dicken Yosef, Niv Golan, Jeremy Don
2006 Developmental Biology  
., 1993) while CREM is more dominant in germ cells (for a review, see Don and Stelzer, 2002) .  ... 
doi:10.1016/j.ydbio.2006.06.029 pmid:16925989 fatcat:ww72so7qznh7rhkiedf5bpnmrm

Molecular disease presentation in diabetic nephropathy

Andreas Heinzel, Irmgard Mühlberger, Gil Stelzer, Doron Lancet, Rainer Oberbauer, Maria Martin, Paul Perco
2015 Nephrology, Dialysis and Transplantation  
A B S T R AC T Diabetic nephropathy, as the most prevalent chronic disease of the kidney, has also become the primary cause of end-stage renal disease with the incidence of kidney disease in type 2 diabetics continuously rising. As with most chronic diseases, the pathophysiology is multifactorial with a number of deregulated molecular processes contributing to disease manifestation and progression. Current therapy mainly involves interfering in the renin-angiotensin-aldosterone system using
more » ... otensin-converting enzyme inhibitors or angiotensin-receptor blockers. Better understanding of molecular processes deregulated in the early stages and progression of disease hold the key for development of novel therapeutics addressing this complex disease. With the advent of high-throughput omics technologies, researchers set out to systematically study the disease on a molecular level. Results of the first omics studies were mainly focused on reporting the highest deregulated molecules between diseased and healthy subjects with recent attempts to integrate findings of multiple studies on the level of molecular pathways and processes. In this review, we will outline key omics studies on the genome, transcriptome, proteome and metabolome level in the context of DN. We will also provide concepts on how to integrate findings of these individual studies (i) on the level of functional processes using the gene-ontology vocabulary, (ii) on the level of molecular pathways and (iii) on the level of phenotype molecular models constructed based on proteinprotein interaction data. Kidney disease is a common problem in patients with diabetes. Depending on the degree of GFR impairment and/or albuminuria, roughly 30% of subjects are affected. As with most diabetes-associated comorbidities, the pathophysiology is multifactorial and the molecular pathways involved in the initiation and progression constitute a wide and complex as well as redundant network of regulators. Considerable success has been achieved in uncovering certain aspects of molecular regulation of these processes. The most prominent examples are the identification of the causal contribution of the renin-angiotensin-aldosterone system (RAAS) as well as the matrix metalloproteinase network (MMP) [1, 2] . However, this gain in knowledge did not lead to an equal success in the discovery of actual drugs that could prevent or slow down the pace of this progressive disease. The only exception is the invention of the blockade of the RAAS at several different sites by using angiotensin-converting enzyme (ACE) inhibitors or angiotensin-receptor blockers (ARBs) [3] [4] [5] . There are several valid explanations for this uncomfortable situation but probably the most striking cause lies in the multifactorial and highly redundant nature of the molecular pathways of this disease. The revolutions in information technology as well as the omics technologies, however, offer nowadays new opportunities to uncover these complex molecular signal cascades thus laying the ground for potentially uncovering diagnostic and prognostic as well as therapeutic leads to tackle this highly prevalent clinical problem. This review highlights the most recent research in this area and provides rational strategies of how to proceed in the fight against diabetic nephropathy (DN). If at
doi:10.1093/ndt/gfv267 pmid:26209734 fatcat:umjf3m74wjfg3o4wwbawoyjoge

GeneDecks: Paralog Hunting and Gene-Set Distillation with GeneCards Annotation

Gil Stelzer, Aron Inger, Tsviya Olender, Tsippi Iny-Stein, Irina Dalah, Arye Harel, Marilyn Safran, Doron Lancet
2009 Omics  
PubMed Ids for a keyword were more in common, on average (X10, p ¼ 9Â10 À61 ), with the genes in the set created from the term in comparison to random gene sets. 484 STELZER ET AL.  ...  The least contributing are generally pathways. 482 STELZER ET AL. microarray-based gene sets originated from the ArrayExpress database ( and appearing in Chandran  ... 
doi:10.1089/omi.2009.0069 pmid:20001862 fatcat:nmldxe24mzbylfspjqt3ozvpxu

MalaCards: A Comprehensive Automatically-Mined Database of Human Diseases

Noa Rappaport, Michal Twik, Noam Nativ, Gil Stelzer, Iris Bahir, Tsippi Iny Stein, Marilyn Safran, Doron Lancet
2014 Current Protocols in Bioinformatics  
., 2009; Stelzer et al., 2009; Safran et al., 2010; Stelzer et al., 2011; Belinky et al., 2013) .  ...  Related diseases subsection: Displays a unified scored list of diseases obtained in two ways: first, by GeneDecks set analysis (Stelzer et al., 2009) c.  ... 
doi:10.1002/0471250953.bi0124s47 pmid:25199789 fatcat:vowmssjxt5c5pnc56rviyj4ot4

PathCards: multi-source consolidation of human biological pathways

Frida Belinky, Noam Nativ, Gil Stelzer, Shahar Zimmerman, Tsippi Iny Stein, Marilyn Safran, Doron Lancet
2015 Database: The Journal of Biological Databases and Curation  
The study of biological pathways is key to a large number of systems analyses. However, many relevant tools consider a limited number of pathway sources, missing out on many genes and gene-to-gene connections. Simply pooling several pathways sources would result in redundancy and the lack of systematic pathway interrelations. To address this, we exercised a combination of hierarchical clustering and nearest neighbor graph representation, with judiciously selected cutoff values, thereby
more » ... ting 3215 human pathways from 12 sources into a set of 1073 SuperPaths. Our unification algorithm finds a balance between reducing redundancy and optimizing the level of pathway-related informativeness for individual genes. We show a substantial enhancement of the SuperPaths' capacity to infer gene-to-gene relationships when compared with individual pathway sources, separately or taken together. Further, we demonstrate that the chosen 12 sources entail nearly exhaustive gene coverage. The computed SuperPaths are presented in a new online database, PathCards, showing each SuperPath, its constituent network of pathways, and its contained genes. This provides researchers with a rich, searchable systems analysis resource. Database URL: Database, 2015, 1-13
doi:10.1093/database/bav006 pmid:25725062 pmcid:PMC4343183 fatcat:mf37aem2avhe7nhcqrcdgk32se

Defining murine monocyte differentiation into colonic and ileal macrophages

Mor Gross-Vered, Sébastien Trzebanski, Anat Shemer, Biana Bernshtein, Caterina Curato, Gil Stelzer, Tomer-Meir Salame, Eyal David, Sigalit Boura-Halfon, Louise Chappell-Maor, Dena Leshkowitz, Steffen Jung
2020 eLife  
Gross-Vered M, Trzebanski S, Shemer A, Bernshtein B, Curato C, Stelzer G, Salame T, David E, Boura-Halfon S, Chappell-Maor L, Dena Leshkowitz, Steffen Jung 2020 Defining murine monocyte differentiation  ... 
doi:10.7554/elife.49998 pmid:31916932 fatcat:s4xnf5xnnva4poynssuplxeuta

In-silico human genomics with GeneCards

Gil Stelzer, Irina Dalah, Tsippi Stein, Yigeal Satanower, Naomi Rosen, Noam Nativ, Danit Oz-Levi, Tsviya Olender, Frida Belinky, Iris Bahir, Hagit Krug, Paul Perco (+4 others)
2011 Human Genomics  
Since 1998, the bioinformatics, systems biology, genomics and medical communities have enjoyed a synergistic relationship with the GeneCards database of human genes ( This human gene compendium was created to help to introduce order into the increasing chaos of information flow. As a consequence of viewing details and deep links related to specific genes, users have often requested enhanced capabilities, such that, over time, GeneCards has blossomed into a suite of
more » ... s (including GeneDecks, GeneALaCart, GeneLoc, GeneNote and GeneAnnot) for a variety of analyses of both single human genes and sets thereof. In this paper, we focus on inhouse and external research activities which have been enabled, enhanced, complemented and, in some cases, motivated by GeneCards. In turn, such interactions have often inspired and propelled improvements in GeneCards. We describe here the evolution and architecture of this project, including examples of synergistic applications in diverse areas such as synthetic lethality in cancer, the annotation of genetic variations in disease, omics integration in a systems biology approach to kidney disease, and bioinformatics tools.
doi:10.1186/1479-7364-5-6-709 pmid:22155609 pmcid:PMC3525253 fatcat:szb36ui2mnbyzoup6jvadonurm

Induction of CD4 T cell memory by local cellular collectivity

Michal Polonsky, Jacob Rimer, Amos Kern-Perets, Irina Zaretsky, Stav Miller, Chamutal Bornstein, Eyal David, Naama Meira Kopelman, Gil Stelzer, Ziv Porat, Benjamin Chain, Nir Friedman
2018 Science  
CD4 T cell memory by local cellular collectivity Michal Polonsky, Jacob Rimer, Amos Kern-Perets, Irina Zaretsky, Stav Miller, Chamutal Bornstein, Eyal David, Naama Kopelman, Gil Stelzer, Ziv Porat, Benjamin  ... 
doi:10.1126/science.aaj1853 pmid:29903938 fatcat:gjef3mvqtveejicn7h2a5u6t6e

Non-redundant compendium of human ncRNA genes in GeneCards

Frida Belinky, Iris Bahir, Gil Stelzer, Shahar Zimmerman, Naomi Rosen, Noam Nativ, Irina Dalah, Tsippi Iny Stein, Noa Rappaport, Toutai Mituyama, Marilyn Safran, Doron Lancet
2012 Computer applications in the biosciences : CABIOS  
GeneCards is an integrated human gene compendium, which strives to consolidate information about all human genes (Safran et al., 2010; Stelzer et al., 2011) .  ...  ., 2010; Stelzer et al., 2011) . Interestingly, these databases vary greatly in their human ncRNA gene counts, partly because of the use of different sources and integration mechanisms.  ... 
doi:10.1093/bioinformatics/bts676 pmid:23172862 fatcat:eva7zsn2bbg3zfevzxohslvdxu

MalaCards: an integrated compendium for diseases and their annotation

Noa Rappaport, Noam Nativ, Gil Stelzer, Michal Twik, Yaron Guan-Golan, Tsippi Iny Stein, Iris Bahir, Frida Belinky, C. Paul Morrey, Marilyn Safran, Doron Lancet
2013 Database: The Journal of Biological Databases and Curation  
., Stelzer,G., et al. MalaCards: an integrated compendium for diseases and their annotation.  ... 
doi:10.1093/database/bat018 pmid:23584832 pmcid:PMC3625956 fatcat:ju462m3cezgqtkai2jjrdvatuq

Aym1, a mouse meiotic gene identified by virtue of its ability to activate early meiotic genes in the yeast Saccharomyces cerevisiae

Mira Malcov, Karen Cesarkas, Gil Stelzer, Sarah Shalom, Yosef Dicken, Yaniv Naor, Ronald S. Goldstein, Shira Sagee, Yona Kassir, Jeremy Don
2004 Developmental Biology  
Our understanding of the molecular mechanisms that operate during differentiation of mitotically dividing spermatogonia cells into spermatocytes lags way behind what is known about other differentiating systems. Given the evolutionary conservation of the meiotic process, we screened for mouse proteins that could specifically activate early meiotic promoters in Saccharomyces cerevisiae yeast cells, when fused to the Gal4 activation domain (Gal4AD). Our screen yielded the Aym1 gene that encodes a
more » ... short peptide of 45 amino acids. We show that a Gal4AD-AYM1 fusion protein activates expression of reporter genes through the promoters of the early meiosis-specific genes IME2 and HOP1, and that this activation is dependent on the DNA-binding protein Ume6. Aym1 is transcribed predominantly in mouse primary spermatocytes and in gonads of female embryos undergoing the corresponding meiotic divisions. Aym1 immunolocalized to nuclei of primary spermatocytes and oocytes and to specific type A spermatogonia cells, suggesting it might play a role in the processes leading to meiotic competence. The potential functional relationship between AYM1 and yeast proteins that regulate expression of early meiotic genes is discussed. D
doi:10.1016/j.ydbio.2004.08.026 pmid:15531368 fatcat:6kcchbt7bzevvoxmef57ztw4qi

VarElect: the phenotype-based variation prioritizer of the GeneCards Suite

Gil Stelzer, Inbar Plaschkes, Danit Oz-Levi, Anna Alkelai, Tsviya Olender, Shahar Zimmerman, Michal Twik, Frida Belinky, Simon Fishilevich, Ron Nudel, Yaron Guan-Golan, David Warshawsky (+9 others)
2016 BMC Genomics  
Next generation sequencing (NGS) provides a key technology for deciphering the genetic underpinnings of human diseases. Typical NGS analyses of a patient depict tens of thousands non-reference coding variants, but only one or very few are expected to be significant for the relevant disorder. In a filtering stage, one employs family segregation, rarity in the population, predicted protein impact and evolutionary conservation as a means for shortening the variation list. However, narrowing down
more » ... rther towards culprit disease genes usually entails laborious seeking of gene-phenotype relationships, consulting numerous separate databases. Thus, a major challenge is to transition from the few hundred shortlisted genes to the most viable disease-causing candidates.
doi:10.1186/s12864-016-2722-2 pmid:27357693 pmcid:PMC4928145 fatcat:zbmio3et65h23phhmmgyve46z4
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