Mix2 Software

Transcriptional Responses to IFN-γ Require Mediator Kinase-Dependent Pause Release and Mechanistically Distinct CDK8 and CDK19 Functions

Iris Steinparzer, Vitaly Sedlyarov, Jonathan D. Rubin, Kevin Eislmayr, Matthew D. Galbraith, Cecilia B. Levandowski, Terezia Vcelkova, Lucy Sneezum, Florian Wascher, Fabian Amman, Renata Kleinova, Heather Bender, Zdenek Andrysik, Joaquin M. Espinosa, Giulio Superti-Furga, Robin D. Dowell, Dylan J. Taatjes, Pavel Kovarik

Molecular Cell, doi:10.1016/j.molcel.2019.07.034

Transcriptional responses to external stimuli remain poorly understood. Using global nuclear run-on followed by sequencing (GRO-seq) and precision nuclear run-on sequencing (PRO-seq), we show that CDK8 kinase activity promotes RNA polymerase II pause release in response to interferon-γ (IFN-γ), a universal cytokine involved in immunity and tumor surveillance. The Mediator kinase module contains CDK8 or CDK19, which are presumed to be functionally redundant. We implemented cortistatin A, chemical genetics, transcriptomics, and other methods to decouple their function while assessing enzymatic versus structural roles. Unexpectedly, CDK8 and CDK19 regulated different gene sets via distinct mechanisms. CDK8-dependent regulation required its kinase activity, whereas CDK19 governed IFN-γ responses through its scaffolding function (i.e., it was kinase independent). Accordingly, CDK8, not CDK19, phosphorylates the STAT1 transcription factor (TF) during IFN-γ stimulation, and CDK8 kinase inhibition blocked activation of JAK-STAT pathway TFs. Cytokines such as IFN-γ rapidly mobilize TFs to “reprogram” cellular transcription; our results implicate CDK8 and CDK19 as essential for this transcriptional reprogramming.

Features Mix2 RNA-Seq Data Analysis Software

RNA sequencing (RNA‐Seq) has been frequently used in genomic studies and has generated a vast amount of data. The RNA‐Seq data are composed of two parts: (a) a sequence of nucleotides of the genome; and (b) a corresponding sequence of counts, standing for the number of short reads whose mapped positions start at each position of the genome. One common feature of these count data is that they are typically nonuniform; recent studies have revealed that the nonuniformity is partially owing to a systematic bias resulted from the sequencing preference. Existing works in the literature model the nonuniformity with a single component Poisson linear model that incorporates the effects of the sequencing preference. However, we observe consistently that the short reads mapped to a gene may have a mixture structure and can be zero‐inflated. A single component model may not suffice to model the complexity of such data. In this paper, we propose a zero‐inflated mixture Poisson linear model for the RNA‐Seq count data and derive a fast expectation–maximisation‐based algorithm for estimating the unknown parameters. Numerical studies are conducted to illustrate the effectiveness of our method.

Features Mix2 RNA-Seq Data Analysis Software

Accuracy of transcript quantification with RNA-Seq is negatively affected by positional fragment bias. This article introduces Mix2 (rd. “mixquare”), a transcript quantification method which uses a mixture of probability distributions to model and thereby neutralize the effects of positional fragment bias. The parameters of Mix2 are trained by Expectation Maximization resulting in simultaneous transcript abundance and bias estimates. We compare Mix2 to Cufflinks, RSEM, eXpress and PennSeq; state-of-the-art quantification methods implementing some form of bias correction. On four synthetic biases we show that the accuracy of Mix2 overall exceeds the accuracy of the other methods and that its bias estimates converge to the correct solution. We further evaluate Mix2 on real RNA-Seq data from the Microarray and Sequencing Quality Control (MAQC, SEQC) Consortia. On MAQC data, Mix2 achieves improved correlation to qPCR measurements with a relative increase in R2 between 4% and 50%. Mix2 also yields repeatable concentration estimates across technical replicates with a relative increase in R2 between 8% and 47% and reduced standard deviation across the full concentration range. We further observe more accurate detection of differential expression with a relative increase in true positives between 74% and 378% for 5% false positives. In addition, Mix2 reveals 5 dominant biases in MAQC data deviating from the common assumption of a uniform fragment distribution. On SEQC data, Mix2 yields higher consistency between measured and predicted concentration ratios. A relative error of 20% or less is obtained for 51% of transcripts by Mix2, 40% of transcripts by Cufflinks and RSEM and 30% by eXpress. Titration order consistency is correct for 47% of transcripts for Mix2, 41% for Cufflinks and RSEM and 34% for eXpress. We, further, observe improved repeatability across laboratory sites with a relative increase in R2 between 8% and 44% and reduced standard deviation.

Features Mix2 RNA-Seq Data Analysis Software

Tristetraprolin binding site atlas in the macrophage transcriptome reveals a switch for inflammation resolution

Vitaly Sedlyarov, Jörg Fallmann, Florian Ebner, Jakob Huemer, Lucy Sneezum, Masa Ivin, Kristina Kreiner, Andrea Tanzer, Claus Vogl, Ivo Hofacker, Pavel Kovarik

Molecular Systems Biology (2016), doi: 10.15252/msb.20156628

Precise regulation of mRNA decay is fundamental for robust yet not exaggerated inflammatory responses to pathogens. However, a global model integrating regulation and functional consequences of inflammation‐associated mRNA decay remains to be established. Using time‐resolved high‐resolution RNA binding analysis of the mRNA‐destabilizing protein tristetraprolin (TTP), an inflammation‐limiting factor, we qualitatively and quantitatively characterize TTP binding positions in the transcriptome of immunostimulated macrophages. We identify pervasive destabilizing and non‐destabilizing TTP binding, including a robust intronic binding, showing that TTP binding is not sufficient for mRNA destabilization. A low degree of flanking RNA structuredness distinguishes occupied from silent binding motifs. By functionally relating TTP binding sites to mRNA stability and levels, we identify a TTP‐controlled switch for the transition from inflammatory into the resolution phase of the macrophage immune response. Mapping of binding positions of the mRNA‐stabilizing protein HuR reveals little target and functional overlap with TTP, implying a limited co‐regulation of inflammatory mRNA decay by these proteins. Our study establishes a functionally annotated and navigable transcriptome‐wide atlas (http://ttp-atlas.univie.ac.at) of cis‐acting elements controlling mRNA decay in inflammation.

Features SENSE mRNA‐Seq Library Prep Kit
Features Mix2 RNA-Seq Data Analysis Software

Quantification of RNA transcripts with RNA-Seq is inaccurate due to positional fragmentation bias, which is not represented appropriately by current statistical models of RNA-Seq data. Another, less investigated, source of error is the inaccuracy of transcript start and end annotations. This article introduces the Mix2 (rd. ”mixquare”) model, which uses a mixture of probability distributions to model the transcript specific positional fragment bias. The parameters of the Mix2 model can be efficiently trained with the EM algorithm and are tied between similar transcripts. Transcript specific shift and scale parameters allow the Mix2 model to automatically correct inaccurate transcript start and end annotations. Experiments are conducted on synthetic data covering 7 genes of different complexity, 4 types of fragment bias and correct as well as incorrect transcript start and end annotations. Abundance estimates obtained by Cufflinks 2.2.0, PennSeq and the Mix2 model show superior performance of the Mix2 model in the vast majority of test conditions.

Features Mix2 RNA-Seq Data Analysis Software