Genome-wide characterization of intergenic polyadenylation sites redefines gene spaces in Arabidopsis thaliana

Background:Messenger RNA polyadenylation is an essential step for the maturation of most eukaryotic mRNAs.Accurate determination of poly(A) sites helps define the 3’-ends of genes, which is important for genome annotation and gene function research. Genomic studies have revealed the presence of poly(A) sites in intergenic regions, which may be attributed to 3’-UTR extensions and novel transcript units. However, there is no systematically evaluation of intergenic poly(A) sites in plants. Results:Approximately 16,000 intergenic poly(A) site clusters (IPAC) in Arabidopsis thaliana were discovered and evaluated at the whole genome level. Based on the distributions of distance from IPACs to nearby sense and antisense genes, these IPACs were classified into three categories. About 70 % of them were from previously unannotated 3’-UTR extensions to known genes, which would extend 6985 transcripts of TAIR10 genome annotation beyond their 3’-ends, with a mean extension of 134 nucleotides. 1317 IPACs were originated from novel intergenic transcripts, 37 of which were likely to be associated with protein coding transcripts. 2957 IPACs corresponded to antisense transcripts for genes on the reverse strand, which might affect 2265 protein coding genes and 39 non-protein-coding genes, including long non-coding RNA genes. The rest of IPACs could be originated from transcriptional read-through or gene mis-annotations. Conclusions:The identified IPACs corresponding to novel transcripts, 3’-UTR extensions, and antisense transcription should be incorporated into current Arabidopsis genome annotation. Comprehensive characterization of IPACs from this study provides insights of alternative polyadenylation and antisense transcription in plants.Funding supports were in part from US National Science Foundation (No. 1541737 to QQL), the Hundred Talent Plans of Fujian Province and Xiamen City (to QQL). This project was also funded by the National Natural Science Foundation of China (Nos. 61201358 and 61174161), the Natural Science Foundation of Fujian Province of China (No. 2012J01154), and the specialized Research Fund for the Doctoral Program of Higher Education of China (Nos. 20120121120038 and 20130121130004), and the Fundamental Research Funds for the Central Universities in China (Xiamen University: Nos. 2013121025, 201412G009, and 2014X0234)

Aging Shapes the Population-Mean and -Dispersion of Gene Expression in Human Brains

Human aging is associated with cognitive decline and an increased risk of neurodegenerative disease. Our objective for this study was to evaluate potential relationships between age and variation in gene expression across different regions of the brain. We analyzed the Genotype-Tissue Expression (GTEx) data from 54 to 101 tissue samples across 13 brain regions in post-mortem donors of European descent aged between 20 and 70 years at death. After accounting for the effects of covariates and hidden confounding factors, we identified 1446 protein-coding genes whose expression in one or more brain regions is correlated with chronological age at a false discovery rate of 5%. These genes are involved in various biological processes including apoptosis, mRNA splicing, amino acid biosynthesis, and neurotransmitter transport. The distribution of these genes among brain regions is uneven, suggesting variable regional responses to aging. We also found that the aging response of many genes, e.g., TP37 and C1QA, depends on individuals' genotypic backgrounds. Finally, using dispersion-specific analysis, we identified genes such as IL7R, MS4A4E, and TERF1/TERF2 whose expressions are differentially dispersed by aging, i.e., variances differ between age groups. Our results demonstrate that age-related gene expression is brain region-specific, genotype-dependent, and associated with both mean and dispersion changes. Our findings provide a foundation for more sophisticated gene expression modeling in the studies of age-related neurodegenerative diseases

scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data

Motivation: Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 30 enriched strategy in library construction, the most commonly used scRNA-seq protocol—10 Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data. Results: Here, we present a package scDAPA for detecting and visualizing dynamic APA from scRNA-seq data. Taking bam/sam files and cell cluster labels as inputs, scDAPA detects APA dynamics using a histogram-based method and the Wilcoxon rank-sum test, and visualizes candidate genes with dynamic APA. Benchmarking results demonstrated that scDAPA can effectively identify genes with dynamic APA among different cell groups from scRNA-seq data.This research was supported in part by the Fundamental Research Funds for the Central Universities in China [Xiamen University: 20720170076 and 20720190106], and the National Natural Science Foundation of China [61802323, 31801268 and 61573296]

Identification of Plant Messenger RNA Polyadenylation Sites Using Length-Variable Second Order Markov Model

In this paper we adopted a length-variable second order Markov model to identify plant messenger RNA poly(A) sites, and provided a common method that only relies on the experimental sequences. The efficacy of our model is showed up to 92% sensitivity and 79% specificity. This method is particularly suitable for the prediction of the poly(A) site which is lack of biological priori knowledge and has poor conservative signal characteristic, as well as for the identification of the alternative poly(A) sites in different genetic regions. Compared with other algorithms, generalized hidden Markov model needed the signal distributions and AdaBoost required the construction of signal features around the sites, our model is more versatile

Genome level analysis of rice mRNA 3′-end processing signals and alternative polyadenylation

The position of a poly(A) site of eukaryotic mRNA is determined by sequence signals in pre-mRNA and a group of polyadenylation factors. To reveal rice poly(A) signals at a genome level, we constructed a dataset of 55 742 authenticated poly(A) sites and characterized the poly(A) signals. This resulted in identifying the typical tripartite cis-elements, including FUE, NUE and CE, as previously observed in Arabidopsis. The average size of the 3′-UTR was 289 nucleotides. When mapped to the genome, however, 15% of these poly(A) sites were found to be located in the currently annotated intergenic regions. Moreover, an extensive alternative polyadenylation profile was evident where 50% of the genes analyzed had more than one unique poly(A) site (excluding microheterogeneity sites), and 13% had four or more poly(A) sites. About 4% of the analyzed genes possessed alternative poly(A) sites at their introns, 5′-UTRs, or protein coding regions. The authenticity of these alternative poly(A) sites was partially confirmed using MPSS data. Analysis of nucleotide profile and signal patterns indicated that there may be a different set of poly(A) signals for those poly(A) sites found in the coding regions. Based on the features of rice poly(A) signals, an updated algorithm termed PASS-Rice was designed to predict poly(A) sites

WebTraceMiner: a web service for processing and mining EST sequence trace files

Expressed sequence tags (ESTs) remain a dominant approach for characterizing the protein-encoding portions of various genomes. Due to inherent deficiencies, they also present serious challenges for data quality control. Before GenBank submission, EST sequences are typically screened and trimmed of vector and adapter/linker sequences, as well as polyA/T tails. Removal of these sequences presents an obstacle for data validation of error-prone ESTs and impedes data mining of certain functional motifs, whose detection relies on accurate annotation of positional information for polyA tails added posttranscriptionally. As raw DNA sequence information is made increasingly available from public repositories, such as NCBI Trace Archive, new tools will be necessary to reanalyze and mine this data for new information. WebTraceMiner (www.conifergdb.org/software/wtm) was designed as a public sequence processing service for raw EST traces, with a focus on detection and mining of sequence features that help characterize 3′ and 5′ termini of cDNA inserts, including vector fragments, adapter/linker sequences, insert-flanking restriction endonuclease recognition sites and polyA or polyT tails. WebTraceMiner complements other public EST resources and should prove to be a unique tool to facilitate data validation and mining of error-prone ESTs (e.g. discovery of new functional motifs)

Predictive modeling of plant messenger RNA polyadenylation sites

BACKGROUND: One of the essential processing events during pre-mRNA maturation is the post-transcriptional addition of a polyadenine [poly(A)] tail. The 3'-end poly(A) track protects mRNA from unregulated degradation, and indicates the integrity of mRNA through recognition by mRNA export and translation machinery. The position of a poly(A) site is predetermined by signals in the pre-mRNA sequence that are recognized by a complex of polyadenylation factors. These signals are generally tri-part sequence patterns around the cleavage site that serves as the future poly(A) site. In plants, there is little sequence conservation among these signal elements, which makes it difficult to develop an accurate algorithm to predict the poly(A) site of a given gene. We attempted to solve this problem. RESULTS: Based on our current working model and the profile of nucleotide sequence distribution of the poly(A) signals and around poly(A) sites in Arabidopsis, we have devised a Generalized Hidden Markov Model based algorithm to predict potential poly(A) sites. The high specificity and sensitivity of the algorithm were demonstrated by testing several datasets, and at the best combinations, both reach 97%. The accuracy of the program, called poly(A) site sleuth or PASS, has been demonstrated by the prediction of many validated poly(A) sites. PASS also predicted the changes of poly(A) site efficiency in poly(A) signal mutants that were constructed and characterized by traditional genetic experiments. The efficacy of PASS was demonstrated by predicting poly(A) sites within long genomic sequences. CONCLUSION: Based on the features of plant poly(A) signals, a computational model was built to effectively predict the poly(A) sites in Arabidopsis genes. The algorithm will be useful in gene annotation because a poly(A) site signifies the end of the transcript. This algorithm can also be used to predict alternative poly(A) sites in known genes, and will be useful in the design of transgenes for crop genetic engineering by predicting and eliminating undesirable poly(A) sites