@article{Anders2012, abstract = {RNA-seq is a powerful tool for the study of alternative splicing and other forms of alternative isoform expression. Understanding the regulation of these processes requires sensitive and specific detection of differential isoform abundance in comparisons between conditions, cell types, or tissues. We present DEXSeq, a statistical method to test for differential exon usage in RNA-seq data. DEXSeq uses generalized linear models and offers reliable control of false discoveries by taking biological variation into account. DEXSeq detects with high sensitivity genes, and in many cases exons, that are subject to differential exon usage. We demonstrate the versatility of DEXSeq by applying it to several data sets. The method facilitates the study of regulation and function of alternative exon usage on a genome-wide scale. An implementation of DEXSeq is available as an R/Bioconductor package.}, author = {Anders, Simon and Reyes, Alejandro and Huber, Wolfgang}, doi = {10.1101/gr.133744.111}, journal = {Nature Precedings}, month = {apr}, pages = {2008-2017}, title = {Detecting differential usage of exons from RNA-seq data}, url = {http://doi.org/10.1038/npre.2012.6837}, volume = {22}, year = {2012} } @article{Anders2013, abstract = {RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations) while optionally adjusting for other systematic factors that affect the data-collection process. There are a number of subtle yet crucial aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a state-of-the-art computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and, in particular, on two widely used tools, DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be <1 h, with computation time <1 d using a standard desktop PC.}, author = {Anders, Simon and Chen, Yunshun and McCarthy, Davis J. and Okoniewski, Michal and Smyth, Gordon K. and Huber, Wolfgang and Robinson, Mark D.}, doi = {10.1038/nprot.2013.099}, journal = {Nature Protocols}, month = {jan}, pages = {1765-1786}, title = {Count-based differential expression analysis of RNA sequencing data using R and Bioconductor}, url = {http://www.nature.com/articles/nprot.2013.099.pdf}, volume = {8}, year = {2013} } @article{Anders2014, abstract = {Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed.}, author = {Anders, Simon and Pyl, Paul Theodor and Huber, Wolfgang}, doi = {10.1093/bioinformatics/btu638}, journal = {Bioinformatics}, month = {sep}, pages = {166-169}, title = {HTSeq—a Python framework to work with high-throughput sequencing data}, url = {https://academic.oup.com/bioinformatics/article-pdf/31/2/166/7000027/btu638.pdf}, volume = {31}, year = {2014} } @article{Dao Thi2020, abstract = {A colorimetric isothermal RNA amplification method was shown to detect SARS-CoV-2 RNA in clinical samples with excellent sensitivity and specificity.}, author = {Dao Thi, Viet Loan and Herbst, Konrad and Boerner, Kathleen and Meurer, Matthias and Kremer, Lukas Pm and Kirrmaier, Daniel and Freistaedter, Andrew and Papagiannidis, Dimitrios and Galmozzi, Carla and Stanifer, Megan L. and Boulant, Steeve and Klein, Steffen and Chlanda, Petr and Khalid, Dina and Barreto Miranda, Isabel and Miranda, Isabel Barreto and Schnitzler, Paul and Kräusslich, Hans-Georg and Knop, Michael and Anders, Simon}, doi = {10.1126/scitranslmed.abc7075}, journal = {Science Translational Medicine}, month = {jul}, title = {A colorimetric RT-LAMP assay and LAMP-sequencing for detecting SARS-CoV-2 RNA in clinical samples}, url = {https://oadoi.org/10.1126/scitranslmed.abc7075}, volume = {12}, year = {2020} } @article{Dietrich2017, author = {Dietrich, Sascha and Oleś, Małgorzata and Lu, Junyan and Sellner, Leopold and Anders, Simon and Velten, Britta and Wu, Bian and Hüllein, Jennifer and da Silva Liberio, Michelle and Walther, Tatjana and Wagner, Lena and Rabe, Sophie and Ghidelli-Disse, Sonja and Bantscheff, Marcus and Oleś, Andrzej K. and Słabicki, Mikołaj and Mock, Andreas and Oakes, Christopher C. and Wang, Shihui and Oppermann, Sina and Lukas, Marina and Kim, Vladislav and Sill, Martin and Benner, Axel and Jauch, Anna and Sutton, Lesley Ann and Young, Emma and Rosenquist, Richard and Liu, Xiyang and Jethwa, Alexander and Lee, Kwang Seok and Lewis, Joe and Putzker, Kerstin and Lutz, Christoph and Rossi, Davide and Mokhir, Andriy and Oellerich, Thomas and Zirlik, Katja and Herling, Marco and Nguyen-Khac, Florence and Plass, Christoph and Andersson, Emma and Mustjoki, Satu and von Kalle, Christof and Ho, Anthony D. and Hensel, Manfred and Dürig, Jan and Ringshausen, Ingo and Zapatka, Marc and Huber, Wolfgang and Zenz, Thorsten}, doi = {10.1172/jci93801}, journal = {Journal of Clinical Investigation}, month = {dec}, pages = {427-445}, title = {Drug-perturbation-based stratification of blood cancer}, url = {https://doi.org/10.1172/jci93801}, volume = {128}, year = {2017} } @article{Fu2016, author = {Fu, Yu and Gaelings, Lana and Söderholm, Sandra and Belanov, Sergei and Nandania, Jatin and Nyman, Tuula A. and Matikainen, Sampsa and Anders, Simon and Velagapudi, Vidya and Kainov, Denis E.}, doi = {10.1016/j.antiviral.2016.07.008}, journal = {Antiviral Research}, month = {sep}, pages = {23-31}, title = {JNJ872 inhibits influenza A virus replication without altering cellular antiviral responses}, url = {https://helda.helsinki.fi/bitstream/10138/228312/1/1_s2.0_S0166354216302583_main.pdf}, volume = {133}, year = {2016} } @article{Herbst2022, abstract = {AbstractCancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic-based diagnostics. The relevance of proteomics for disease classifications remains to be established in clinically heterogeneous cancer entities such as chronic lymphocytic leukemia (CLL). Here, we characterize the proteome and transcriptome alongside genetic and ex-vivo drug response profiling in a clinically annotated CLL discovery cohort (n = 68). Unsupervised clustering of the proteome data reveals six subgroups. Five of these proteomic groups are associated with genetic features, while one group is only detectable at the proteome level. This new group is characterized by accelerated disease progression, high spliceosomal protein abundances associated with aberrant splicing, and low B cell receptor signaling protein abundances (ASB-CLL). Classifiers developed to identify ASB-CLL based on its characteristic proteome or splicing signature in two independent cohorts (n = 165, n = 169) confirm that ASB-CLL comprises about 20% of CLL patients. The inferior overall survival in ASB-CLL is also independent of both TP53- and IGHV mutation status. Our multi-omics analysis refines the classification of CLL and highlights the potential of proteomics to improve cancer patient stratification beyond genetic and transcriptomic profiling.}, author = {Herbst, Sophie A. and Vesterlund, Mattias and Helmboldt, Alexander J. and Jafari, Rozbeh and Siavelis, Ioannis and Stahl, Matthias and Schitter, Eva C. and Liebers, Nora and Brinkmann, Berit J. and Czernilofsky, Felix and Roider, Tobias and Bruch, Peter-Martin and Iskar, Murat and Kittai, Adam and Huang, Ying and Lu, Junyan and Richter, Sarah and Mermelekas, Georgios and Umer, Husen Muhammad and Knoll, Mareike and Kolb, Carolin and Lenze, Angela and Cao, Xiaofang and Österholm, Cecilia and Wahnschaffe, Linus and Herling, Carmen and Scheinost, Sebastian and Ganzinger, Matthias and Mansouri, Larry and Kriegsmann, Katharina and Kriegsmann, Mark and Anders, Simon and Zapatka, Marc and Del Poeta, Giovanni and Zucchetto, Antonella and Bomben, Riccardo and Gattei, Valter and Dreger, Peter and Woyach, Jennifer and Herling, Marco and Müller-Tidow, Carsten and Rosenquist, Richard and Stilgenbauer, Stephan and Zenz, Thorsten and Huber, Wolfgang and Tausch, Eugen and Lehtiö, Janne and Dietrich, Sascha}, doi = {10.1038/s41467-022-33385-8}, journal = {Nature Communications}, month = {oct}, title = {Proteogenomics refines the molecular classification of chronic lymphocytic leukemia}, url = {https://doi.org/10.1038/s41467-022-33385-8}, volume = {13}, year = {2022} } @article{Klein2020, abstract = {Rapid large-scale testing is essential for controlling the ongoing pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The standard diagnostic pipeline for testing SARS-CoV-2 presence in patients with an ongoing infection is predominantly based on pharyngeal swabs, from which the viral RNA is extracted using commercial kits, followed by reverse transcription and quantitative PCR detection. As a result of the large demand for testing, commercial RNA extraction kits may be limited and, alternatively, non-commercial protocols are needed. Here, we provide a magnetic bead RNA extraction protocol that is predominantly based on in-house made reagents and is performed in 96-well plates supporting large-scale testing. Magnetic bead RNA extraction was benchmarked against the commercial QIAcube extraction platform. Comparable viral RNA detection sensitivity and specificity were obtained by fluorescent and colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) using a primer set targeting the N gene, as well as RT-qPCR using a primer set targeting the E gene, showing that the RNA extraction protocol presented here can be combined with a variety of detection methods at high throughput. Importantly, the presented diagnostic workflow can be quickly set up in a laboratory without access to an automated pipetting robot.}, author = {Klein, Steffen and Müller, Thorsten G. and Khalid, Dina and Sonntag-Buck, Vera and Heuser, Anke-Mareil and Glass, Bärbel and Meurer, Matthias and Morales, Ivonne and Schillak, Angelika and Freistaedter, Andrew and Ambiel, Ina and Winter, Sophie L. and Zimmermann, Liv and Naumoska, Tamara and Bubeck, Felix and Kirrmaier, Daniel and Ullrich, Stephanie and Barreto Miranda, Isabel Barreto and Miranda, Isabel Barreto and Anders, Simon and Grimm, Dirk and Schnitzler, Paul and Knop, Michael and Kräusslich, Hans-Georg and Dao Thi, Viet Loan Dao and Thi, Viet Loan Dao and Börner, Kathleen and Chlanda, Petr}, doi = {10.3390/v12080863}, journal = {Viruses}, month = {aug}, pages = {863}, title = {SARS-CoV-2 RNA Extraction Using Magnetic Beads for Rapid Large-Scale Testing by RT-qPCR and RT-LAMP}, url = {https://doi.org/10.3390/v12080863}, volume = {12}, year = {2020} } @article{Love2014, abstract = {Abstract In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2 , a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html .}, author = {Love, Michael I. and Huber, Wolfgang and Anders, Simon}, doi = {10.1186/s13059-014-0550-8}, journal = {Genome Biology}, month = {jan}, title = {Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.}, url = {http://link.springer.com/content/pdf/10.1186/s13059-014-0550-8.pdf}, volume = {15}, year = {2014} } @article{Love2015, abstract = {Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.}, author = {Love, Michael I. and Anders, Simon and Kim, Vladislav and Huber, Wolfgang}, doi = {10.12688/f1000research.7035.2}, journal = {F1000Research}, month = {oct}, pages = {1070}, title = {RNA-Seq workflow: gene-level exploratory analysis and differential expression}, url = {https://doi.org/10.12688/f1000research.7035.2}, volume = {4}, year = {2015} } @article{Porcellato2022, abstract = {Protein S-palmitoylation, the addition of a long-chain fatty acid to target proteins, is among the most frequent reversible protein modifications in Metazoa, affecting subcellular protein localization, trafficking and protein-protein interactions. S-palmitoylated proteins are abundant in the neuronal system and are associated with neuronal diseases and cancer. Despite the importance of this post-translational modification, it has not been thoroughly studied in the model organism Drosophila melanogaster. Here we present the palmitoylome of Drosophila S2R+ cells, comprising 198 proteins, an estimated 3.5% of expressed genes in these cells. Comparison of orthologs between mammals and Drosophila suggests that S-palmitoylated proteins are more conserved between these distant phyla than non-S-palmitoylated proteins. To identify putative client proteins and interaction partners of the DHHC family of protein acyl-transferases (PATs) we established DHHC-BioID, a proximity biotinylation-based method. In S2R+ cells, ectopic expression of the DHHC-PAT dHip14-BioID in combination with Snap24 or an interaction-deficient Snap24-mutant as a negative control, resulted in biotinylation of Snap24 but not the Snap24-mutant. DHHC-BioID in S2R+ cells using 10 different DHHC-PATs as bait identified 520 putative DHHC-PAT interaction partners of which 48 were S-palmitoylated and are therefore putative DHHC-PAT client proteins. Comparison of putative client protein/DHHC-PAT combinations indicates that CG8314, CG5196, CG5880 and Patsas have a preference for transmembrane proteins, while S-palmitoylated proteins with the Hip14-interaction motif are most enriched by DHHC-BioID variants of approximated and dHip14. Finally, we show that BioID is active in larval and adult Drosophila and that dHip14-BioID rescues dHip14 mutant flies, indicating that DHHC-BioID is non-toxic. In summary we provide the first systematic analysis of a Drosophila palmitoylome. We show that DHHC-BioID is sensitive and specific enough to identify DHHC-PAT client proteins and provide DHHC-PAT assignment for ca. 25% of the S2R+ cell palmitoylome, providing a valuable resource. In addition, we establish DHHC-BioID as a useful concept for the identification of tissue-specific DHHC-PAT interactomes in Drosophila.}, author = {Porcellato, Elena and González-Sánchez, Juan Carlos and Ahlmann-Eltze, Constantin and Elsakka, Mahmoud Ali and Shapira, Itamar and Fritsch, Jürgen and Navarro, Juan Antonio and Anders, Simon and Russell, Robert B. and Wieland, Felix T. and Metzendorf, Christoph}, doi = {10.1371/journal.pone.0261543}, journal = {PLoS ONE}, month = {aug}, pages = {e0261543}, title = {The S-palmitoylome and DHHC-PAT interactome of Drosophila melanogaster S2R+ cells indicate a high degree of conservation to mammalian palmitoylomes}, url = {https://doi.org/10.1371/journal.pone.0261543}, volume = {17}, year = {2022} } @article{Reyes2013, abstract = {Alternative usage of exons provides genomes with plasticity to produce different transcripts from the same gene, modulating the function, localization, and life cycle of gene products. It affects most human genes. For a limited number of cases, alternative functions and tissue-specific roles are known. However, recent high-throughput sequencing studies have suggested that much alternative isoform usage across tissues is nonconserved, raising the question of the extent of its functional importance. We address this question in a genome-wide manner by analyzing the transcriptomes of five tissues for six primate species, focusing on exons that are 1:1 orthologous in all six species. Our results support a model in which differential usage of exons has two major modes: First, most of the exons show only weak differences, which are dominated by interspecies variability and may reflect neutral drift and noisy splicing. These cases dominate the genome-wide view and explain why conservation appears to be so limited. Second, however, a sizeable minority of exons show strong differences between tissues, which are mostly conserved. We identified a core set of 3,800 exons from 1,643 genes that show conservation of strongly tissue-dependent usage patterns from human at least to macaque. This set is enriched for exons encoding protein-disordered regions and untranslated regions. Our findings support the theory that isoform regulation is an important target of evolution in primates, and our method provides a powerful tool for discovering potentially functional tissue-dependent isoforms.}, author = {Reyes, Alejandro and Anders, Simon and Weatheritt, Robert J. and Gibson, Toby J. and Steinmetz, Lars M. and Huber, Wolfgang}, doi = {10.1073/pnas.1307202110}, journal = {Proceedings of the National Academy of Sciences}, month = {sep}, pages = {15377-15382}, title = {Drift and conservation of differential exon usage across tissues in primate species}, url = {http://www.pnas.org/content/110/38/15377.full.pdf}, volume = {110}, year = {2013} } @article{Segal2020, author = {Segal, Eran and Zhang, Feng and Lin, Xihong and King, Gary and Shalem, Ophir and Shilo, Smadar and Allen, William E. and Alquaddoomi, Faisal and Altae-Tran, Han and Anders, Simon and Balicer, Ran and Bauman, Tal and Bonilla, Ximena and Booman, Gisel and Chan, Andrew T. and Cohen, Ori and Coletti, Silvano and Davidson, Natalie and Dor, Yuval and Drew, David A. and Elemento, Olivier and Evans, Georgina and Ewels, Phil and Gale, Joshua and Gavrieli, Amir and Geiger, Benjamin and Grad, Yonatan H. and Greene, Casey S. and Hajirasouliha, Iman and Jerala, Roman and Kahles, Andre and Kallioniemi, Olli and Keshet, Ayya and Kocarev, Ljupco and Landua, Gregory and Meir, Tomer and Muller, Aline and Nguyen, Long H. and Oresic, Matej and Ovchinnikova, Svetlana and Peterson, Hedi and Prodanova, Jana and Rajagopal, Jay and Rätsch, Gunnar and Rossman, Hagai and Rung, Johan and Sboner, Andrea and Sigaras, Alexandros and Spector, Tim and Steinherz, Ron and Stevens, Irene and Vilo, Jaak and Wilmes, Paul}, doi = {10.1038/s41591-020-0929-x}, journal = {Nature Medicine}, month = {jun}, pages = {1161-1165}, title = {Building an international consortium for tracking coronavirus health status}, url = {http://www.nature.com/articles/s41591-020-0929-x.pdf}, volume = {26}, year = {2020} } @article{Söderholm2016, abstract = {Human influenza A viruses (IAVs) cause global pandemics and epidemics. These viruses evolve rapidly, making current treatment options ineffective. To identify novel modulators of IAV–host interactions, we re-analyzed our recent transcriptomics, metabolomics, proteomics, phosphoproteomics, and genomics/virtual ligand screening data. We identified 713 potential modulators targeting 199 cellular and two viral proteins. Anti-influenza activity for 48 of them has been reported previously, whereas the antiviral efficacy of the 665 remains unknown. Studying anti-influenza efficacy and immuno/neuro-modulating properties of these compounds and their combinations as well as potential viral and host resistance to them may lead to the discovery of novel modulators of IAV–host interactions, which might be more effective than the currently available anti-influenza therapeutics.}, author = {Söderholm, Sandra and Fu, Yu and Gaelings, Lana and Belanov, Sergey and Yetukuri, Laxman and Berlinkov, Mikhail and Cheltov, Anton V. and Cheltsov, Anton and Anders, Simon and Aittokallio, Tero and Nyman, Tuula A. and Matikainen, Sampsa and Kainov, Denis E.}, doi = {10.3390/v8100269}, month = {sep}, title = {Multi-Omics Studies towards Novel Modulators of Influenza A Virus–Host Interaction}, url = {http://doi.org/10.3390/v8100269}, year = {2016} } @article{Velten2015, author = {Velten, Lars and Anders, Simon and Pekowska, Aleksandra and Järvelin, Aino I. and Huber, Wolfgang and Pelechano, Vicent and Steinmetz, Lars M.}, doi = {10.15252/msb.20156198}, journal = {Molecular Systems Biology}, month = {jun}, pages = {812}, title = {Single-cell polyadenylation site mapping reveals 3′ isoform choice variability}, url = {http://doi.org/10.15252/msb.20156198}, volume = {11}, year = {2015} } @article{Wang2020, author = {Wang, Zhong-Yi and Leushkin, Evgeny and Liechti, Angélica and Ovchinnikova, Svetlana and Mößinger, Katharina and Brüning, Thoomke and Rummel, Coralie and Grützner, Frank and Cardoso-Moreira, Margarida and Janich, Peggy and Gatfield, David and Diagouraga, Boubou and de Massy, Bernard and Gill, Mark E. and Peters, Antoine H. F. M. and Anders, Simon and Kaessmann, Henrik}, doi = {10.1038/s41586-020-2899-z}, journal = {Nature}, month = {nov}, pages = {642-647}, title = {Transcriptome and translatome co-evolution in mammals}, url = {http://www.nature.com/articles/s41586-020-2899-z.pdf}, volume = {588}, year = {2020} } @article{Wilkening2013, abstract = {The use of alternative poly(A) sites is common and affects the post-transcriptional fate of mRNA, including its stability, subcellular localization and translation. Here, we present a method to identify poly(A) sites in a genome-wide and strand-specific manner. This method, termed 3′T-fill, initially fills in the poly(A) stretch with unlabeled dTTPs, allowing sequencing to start directly after the poly(A) tail into the 3′-untranslated regions (UTR). Our comparative analysis demonstrates that it outperforms existing protocols in quality and throughput and accurately quantifies RNA levels as only one read is produced from each transcript. We use this method to characterize the diversity of polyadenylation in Saccharomyces cerevisiae, showing that alternative RNA molecules are present even in a genetically identical cell population. Finally, we observe that overlap of convergent 3′-UTRs is frequent but sharply limited by coding regions, suggesting factors that restrict compression of the yeast genome.}, author = {Wilkening, Stefan and Pelechano, Vicent and Järvelin, Aino I. and Tekkedil, Manu M. and Anders, Simon and Benes, Vladimir and Steinmetz, Lars M.}, doi = {10.1093/nar/gks1249}, journal = {Nucleic Acids Research}, month = {jan}, pages = {e65-e65}, title = {An efficient method for genome-wide polyadenylation site mapping and RNA quantification}, url = {https://doi.org/10.1093/nar/gks1249}, volume = {41}, year = {2013} } @article{Wilkening2013_2, abstract = {Abstract Dissecting the molecular basis of quantitative traits is a significant challenge and is essential for understanding complex diseases. Even in model organisms, precisely determining causative genes and their interactions has remained elusive, due in part to difficulty in narrowing intervals to single genes and in detecting epistasis or linked quantitative trait loci. These difficulties are exacerbated by limitations in experimental design, such as low numbers of analyzed individuals or of polymorphisms between parental genomes. We address these challenges by applying three independent high-throughput approaches for QTL mapping to map the genetic variants underlying 11 phenotypes in two genetically distant Saccharomyces cerevisiae strains, namely (1) individual analysis of &gt;700 meiotic segregants, (2) bulk segregant analysis, and (3) reciprocal hemizygosity scanning, a new genome-wide method that we developed. We reveal differences in the performance of each approach and, by combining them, identify eight polymorphic genes that affect eight different phenotypes: colony shape, flocculation, growth on two nonfermentable carbon sources, and resistance to two drugs, salt, and high temperature. Our results demonstrate the power of individual segregant analysis to dissect QTL and address the underestimated contribution of interactions between variants. We also reveal confounding factors like mutations and aneuploidy in pooled approaches, providing valuable lessons for future designs of complex trait mapping studies.}, author = {Wilkening, Stefan and Lin, Gen and Fritsch, Emilie S. and Tekkedil, Manu M. and Anders, Simon and Kuehn, Raquel and Nguyen, Michelle and Aiyar, Raeka S. and Proctor, Michael and Sakhanenko, Nikita A. and Galas, David J. and Gagneur, Julien and Deutschbauer, Adam and Steinmetz, Lars M.}, doi = {10.1534/genetics.113.160291}, journal = {Genetics}, month = {dec}, pages = {853-865}, title = {An Evaluation of High-Throughput Approaches to QTL Mapping in Saccharomyces cerevisiae}, url = {http://www.genetics.org/content/genetics/196/3/853.full.pdf}, volume = {196}, year = {2013} } @article{Zarnack2013, abstract = {There are ∼650,000 Alu elements in transcribed regions of the human genome. These elements contain cryptic splice sites, so they are in constant danger of aberrant incorporation into mature transcripts. Despite posing a major threat to transcriptome integrity, little is known about the molecular mechanisms preventing their inclusion. Here, we present a mechanism for protecting the human transcriptome from the aberrant exonization of transposable elements. Quantitative iCLIP data show that the RNA-binding protein hnRNP C competes with the splicing factor U2AF65 at many genuine and cryptic splice sites. Loss of hnRNP C leads to formation of previously suppressed Alu exons, which severely disrupt transcript function. Minigene experiments explain disease-associated mutations in Alu elements that hamper hnRNP C binding. Thus, by preventing U2AF65 binding to Alu elements, hnRNP C plays a critical role as a genome-wide sentinel protecting the transcriptome. The findings have important implications for human evolution and disease.}, author = {Zarnack, Kathi and König, Julian and Tajnik, Mojca and Martincorena, Iñigo and Eustermann, Sebastian and Stévant, Isabelle and Reyes, Alejandro and Anders, Simon and Luscombe, Nicholas M. and Ule, Jernej}, doi = {10.1016/j.cell.2012.12.023}, journal = {Cell}, month = {jan}, pages = {453-466}, title = {Direct Competition between hnRNP C and U2AF65 Protects the Transcriptome from the Exonization of Alu Elements}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23374342}, volume = {152}, year = {2013} } @article{Zinkevičienė2016, abstract = {<b><i>Background:</i></b> Allergic contact dermatitis (ACD) is an inflammatory skin disease caused by repeated skin exposure to contact allergens. The severity and duration of this disease are associated with many different factors. Some of these factors may represent markers for monitoring disease activity and the individual response to an intervention. <b><i>Methods:</i></b> We used a targeted metabolomics approach to find such factors in the serum of individuals with ACD. Metabolomics profiles were examined and compared in the acute phase of the disease and also in the absence of disease activity. <b><i>Results:</i></b> Our study identified a significant remission phase of ACD-associated systemic biochemical shifts in 2 metabolic pathways: tryptophan-kynurenine and phenylalanine-tyrosine. <b><i>Conclusions:</i></b> Although the responsible mechanisms are unclear, these results suggest that the remission phase of ACD is linked to tryptophan metabolism via kynurenine and phenylalanine-tyrosine pathways. However, further replication studies with a larger number of subjects and their subgroups are necessary to validate our results. These studies may provide a new perspective with which to understand the mechanism of and find potential biomarkers of ACD, as well as a new reference for personalized treatment.}, author = {Zinkevičienė, Auksė and Kainov, Denis and Girkontaitė, Irutė and Lastauskienė, Eglė and Kvedarienė, Violeta and Fu, Yu and Anders, Simon and Velagapudi, Vidya}, doi = {10.1159/000450789}, journal = {International Archives of Allergy and Immunology}, month = {jan}, pages = {262-268}, title = {Activation of Tryptophan and Phenylalanine Catabolism in the Remission Phase of Allergic Contact Dermatitis: A Pilot Study}, url = {https://oadoi.org/10.1159/000450789}, volume = {170}, year = {2016} }