Genetic differences between Arabidopsis thaliana accessions underlie the plant’s extensive phenotypic variation, and until now these have been interpreted largely in the context of the annotated reference accession Col-0. Here we report the sequencing, assembly and annotation of the genomes of 18 natural A. thaliana accessions, and their transcriptomes. When assessed on the basis of the reference annotation, one-third of protein-coding genes are predicted to be disrupted in at least one accession. However, re-annotation of each genome revealed that alternative gene models often restore coding potential. Gene expression in seedlings differed for nearly half of expressed genes and was frequently associated with cis variants within 5 kilobases, as were intron retention alternative splicing events. Sequence and expression variation is most pronounced in genes that respond to the biotic environment. Our data further promote evolutionary and functional studies in A. thaliana, especially the MAGIC genetic reference population descended from these accessions.

The nonsense-mediated decay (NMD) surveillance pathway can recognize erroneous transcripts and physiological mRNAs, such as precursor mRNA alternative splicing (AS) variants. Currently, information on the global extent of coupled AS and NMD remains scarce and even absent for any plant species. To address this, we conducted transcriptome-wide splicing studies using Arabidopsis thaliana mutants in the NMD factor homologs UP FRAMESHIFT1 (UPF1) and UPF3 as well as wild-type samples treated with the translation inhibitor cycloheximide. Our analyses revealed that at least 17.4% of all multi-exon, protein-coding genes produce splicing variants that are targeted by NMD. Moreover, we provide evidence that UPF1 and UPF3 act in a translation-independent mRNA decay pathway. Importantly, 92.3% of the NMD-responsive mRNAs exhibit classical NMD-eliciting features, supporting their authenticity as direct targets. Genes generating NMD-sensitive AS variants function in diverse biological processes, including signaling and protein modification, for which NaCl stress-modulated AS-NMD was found. Besides mRNAs, numerous noncoding RNAs and transcripts derived from intergenic regions were shown to be NMD responsive. In summary, we provide evidence for a major function of AS-coupled NMD in shaping the Arabidopsis transcriptome, having fundamental implications in gene regulation and quality control of transcript processing.

Background: For splice site recognition, one has to solve two classification problems: discriminating true from decoy splice sites for both acceptor and donor sites. Gene finding systems typically rely on Markov Chains to solve these tasks.

MotivationSeveral molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression.ResultsWe developed NetICS (Network-based Integration of Multi-omics Data), a new graph diffusion-based method for prioritizing cancer genes by integrating diverse molecular data types on a directed functional interaction network. NetICS prioritizes genes by their mediator effect, defined as the proximity of the gene to upstream aberration events and to downstream differentially expressed genes and proteins in an interaction network. Genes are prioritized for individual samples separately and integrated using a robust rank aggregation technique. NetICS provides a comprehensive computational framework that can aid in explaining the heterogeneity of aberration events by their functional convergence to common differentially expressed genes and proteins. We demonstrate NetICS’ competitive performance in predicting known cancer genes and in generating robust gene lists using TCGA data from five cancer types.Availability and implementationNetICS is available at https://github.com/cbg-ethz/netics.Supplementary information Supplementary data are available at Bioinformatics online.

Plants use light as source of energy and information to detect diurnal rhythms and seasonal changes. Sensing changing light conditions is critical to adjust plant metabolism and to initiate developmental transitions. Here we analyzed transcriptome-wide alterations in gene expression and alternative splicing (AS) of etiolated seedlings undergoing photomorphogenesis upon exposure to blue, red, or white light. Our analysis revealed massive transcriptome reprograming as reflected by differential expression of ~20% of all genes and changes in several hundred AS events. For more than 60% of all regulated AS events, light promoted the production of a presumably protein-coding variant at the expense of an mRNA with nonsense-mediated decay-triggering features. Accordingly, AS of the putative splicing factor REDUCED RED-LIGHT RESPONSES IN CRY1CRY2 BACKGROUND 1 (RRC1), previously identified as a red light signaling component, was shifted to the functional variant under light. Downstream analyses of candidate AS events pointed at a role of photoreceptor signaling only in monochromatic but not in white light. Furthermore, we demonstrated similar AS changes upon light exposure and exogenous sugar supply, with a critical involvement of kinase signaling. We propose that AS is an Plant Cell Advance Publication. Published on November 1, 2016November 1, , doi:10.1105 ©2016 American Society of Plant Biologists. All Rights Reserved 2 integration point of signaling pathways that sense and transmit information regarding the energy availability in plants.

Motivation: High-throughput sequencing of mRNA (RNA-Seq) has led to tremendous improvements in the detection of expressed genes and reconstruction of RNA transcripts. However, the extensive dynamic range of gene expression, technical limitations and biases, as well as the observed complexity of the transcriptional landscape, pose profound computational challenges for transcriptome reconstruction.Results: We present the novel framework MITIE (Mixed Integer Transcript IdEntification) for simultaneous transcript reconstruction and quantification. We define a likelihood function based on the negative binomial distribution, use a regularization approach to select a few transcripts collectively explaining the observed read data and show how to find the optimal solution using Mixed Integer Programming. MITIE can (i) take advantage of known transcripts, (ii) reconstruct and quantify transcripts simultaneously in multiple samples, and (iii) resolve the location of multi-mapping reads. It is designed for genome- and assembly-based transcriptome reconstruction. We present an extensive study based on realistic simulated RNA-Seq data. When compared with state-of-the-art approaches, MITIE proves to be significantly more sensitive and overall more accurate. Moreover, MITIE yields substantial performance gains when used with multiple samples. We applied our system to 38 Drosophila melanogaster modENCODE RNA-Seq libraries and estimated the sensitivity of reconstructing omitted transcript annotations and the specificity with respect to annotated transcripts. Our results corroborate that a well-motivated objective paired with appropriate optimization techniques lead to significant improvements over the state-of-the-art in transcriptome reconstruction.Availability: MITIE is implemented in C++ and is available from http://bioweb.me/mitie under the GPL license.Contact: Jonas_Behr@web.de and raetsch@cbio.mskcc.orgSupplementary information: Supplementary data are available at Bioinformatics online.

We present a highly accurate gene-prediction system for eukaryotic genomes, called mGene. It combines in an unprecedented manner the flexibility of generalized hidden Markov models (gHMMs) with the predictive power of modern machine learning methods, such as Support Vector Machines (SVMs). Its excellent performance was proved in an objective competition based on the genome of the nematode Caenorhabditis elegans. Considering the average of sensitivity and specificity, the developmental version of mGene exhibited the best prediction performance on nucleotide, exon, and transcript level for ab initio and multiple-genome gene-prediction tasks. The fully developed version shows superior performance in 10 out of 12 evaluation criteria compared with the other participating gene finders, including Fgenesh++ and Augustus. An in-depth analysis of mGene's genome-wide predictions revealed that approximate to 2200 predicted genes were not contained in the current genome annotation. Testing a subset of 57 of these genes by RT-PCR and sequencing, we confirmed expression for 24 (42%) of them. mGene missed 300 annotated genes, out of which 205 were unconfirmed. RT-PCR testing of 24 of these genes resulted in a success rate of merely 8%. These findings suggest that even the gene catalog of a well-studied organism such as C. elegans can be substantially improved by mGene's predictions. We also provide gene predictions for the four nematodes C. briggsae, C. brenneri, C. japonica, and C. remanei. Comparing the resulting proteomes among these organisms and to the known protein universe, we identified many species-specific gene inventions. In a quality assessment of several available annotations for these genomes, we find that mGene's predictions are most accurate

Large-scale genomic data highlight the complexity and diversity of the molecular changes that drive cancer progression. Statistical analysis of cancer data from different tissues can guide drug repositioning as well as the design of targeted treatments. Here, we develop an improved Bayesian network model for tumour mutational profiles and apply it to 8198 patient samples across 22 cancer types from TCGA. For each cancer type, we identify the interactions between mutated genes, capturing signatures beyond mere mutational frequencies. When comparing mutation networks, we find genes which interact both within and across cancer types. To detach cancer classification from the tissue type we perform de novo clustering of the pancancer mutational profiles based on the Bayesian network models. We find 22 novel clusters which significantly improve survival prediction beyond clinical information. The models highlight key gene interactions for each cluster potentially allowing genomic stratification for clinical trials and identifying drug targets.