Flexible analysis of TSS mapping data and detection of TSS shifts with TSRexploreR.
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ABSTRACT:
SUBMITTER: Policastro RA
PROVIDER: S-EPMC8265037 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND: Differential RNA sequencing (dRNA-seq) is a high-throughput screening technique designed to examine the architecture of bacterial operons in general and the precise position of transcription start sites (TSS) in particular. Hitherto, dRNA-seq data were analyzed by visualizing the sequencing reads mapped to the reference genome and manually annotating reliable positions. This is very labor intensive and, due to the subjectivity, biased. RESULTS: Here, we present TSSAR, a tool for automated de novo TSS annotation from dRNA-seq data that respects the statistics of dRNA-seq libraries. TSSAR uses the premise that the number of sequencing reads starting at a certain genomic position within a transcriptional active region follows a Poisson distribution with a parameter that depends on the local strength of expression. The differences of two dRNA-seq library counts thus follow a Skellam distribution. This provides a statistical basis to identify significantly enriched primary transcripts.We assessed the performance by analyzing a publicly available dRNA-seq data set using TSSAR and two simple approaches that utilize user-defined score cutoffs. We evaluated the power of reproducing the manual TSS annotation. Furthermore, the same data set was used to reproduce 74 experimentally validated TSS in H. pylori from reliable techniques such as RACE or primer extension. Both analyses showed that TSSAR outperforms the static cutoff-dependent approaches. CONCLUSIONS: Having an automated and efficient tool for analyzing dRNA-seq data facilitates the use of the dRNA-seq technique and promotes its application to more sophisticated analysis. For instance, monitoring the plasticity and dynamics of the transcriptomal architecture triggered by different stimuli and growth conditions becomes possible.The main asset of a novel tool for dRNA-seq analysis that reaches out to a broad user community is usability. As such, we provide TSSAR both as intuitive RESTful Web service ( http://rna.tbi.univie.ac.at/TSSAR) together with a set of post-processing and analysis tools, as well as a stand-alone version for use in high-throughput dRNA-seq data analysis pipelines.
| S-EPMC4098767 | biostudies-literature
Project description:SUMMARY: Accurate and complete mapping of short-read sequencing to a reference genome greatly enhances the discovery of biological results and improves statistical predictions. We recently presented RNA-MATE, a pipeline for the recursive mapping of RNA-Seq datasets. With the rapid increase in genome re-sequencing projects, progression of available mapping software and the evolution of file formats, we now present X-MATE, an updated version of RNA-MATE, capable of mapping both RNA-Seq and DNA datasets and with improved performance, output file formats, configuration files, and flexibility in core mapping software. AVAILABILITY: Executables, source code, junction libraries, test data and results and the user manual are available from http://grimmond.imb.uq.edu.au/X-MATE/.
| S-EPMC3035802 | biostudies-other
Project description:Transcription initiation is regulated in a highly organized fashion to ensure proper cellular functions. Accurate identification of transcription start sites (TSSs) and quantitative characterization of transcription initiation activities are fundamental steps for studies of regulated transcriptions and core promoter structures. Several high-throughput techniques have been developed to sequence the very 5'end of RNA transcripts (TSS sequencing) on the genome scale. Bioinformatics tools are essential for processing, analysis, and visualization of TSS sequencing data. Here, we present TSSr, an R package that provides rich functions for mapping TSS and characterizations of structures and activities of core promoters based on all types of TSS sequencing data. Specifically, TSSr implements several newly developed algorithms for accurately identifying TSSs from mapped sequencing reads and inference of core promoters, which are a prerequisite for subsequent functional analyses of TSS data. Furthermore, TSSr also enables users to export various types of TSS data that can be visualized by genome browser for inspection of promoter activities in association with other genomic features, and to generate publication-ready TSS graphs. These user-friendly features could greatly facilitate studies of transcription initiation based on TSS sequencing data. The source code and detailed documentations of TSSr can be freely accessed at https://github.com/Linlab-slu/TSSr.
| S-EPMC8598296 | biostudies-literature
Project description:Allostery is a fundamental mechanism of regulation in biology. The residues at the end points of long-range allosteric perturbations are commonly identified by the comparative analyses of structures and dynamics in apo and effector-bound states. However, the networks of interactions mediating the propagation of allosteric signals between the end points often remain elusive. Here we show that the covariance analysis of NMR chemical shift changes caused by a set of covalently modified analogs of the allosteric effector (i.e., agonists and antagonists) reveals extended networks of coupled residues. Unexpectedly, such networks reach not only sites subject to effector-dependent structural variations, but also regions that are controlled by dynamically driven allostery. In these regions the allosteric signal is propagated mainly by dynamic rather than structural modulations, which result in subtle but highly correlated chemical shift variations. The proposed chemical shift covariance analysis (CHESCA) identifies interresidue correlations based on the combination of agglomerative clustering (AC) and singular value decomposition (SVD). AC results in dendrograms that define functional clusters of coupled residues, while SVD generates score plots that provide a residue-specific dissection of the contributions to binding and allostery. The CHESCA approach was validated by applying it to the cAMP-binding domain of the exchange protein directly activated by cAMP (EPAC) and the CHESCA results are in full agreement with independent mutational data on EPAC activation. Overall, CHESCA is a generally applicable method that utilizes a selected chemical library of effector analogs to quantitatively decode the binding and allosteric information content embedded in chemical shift changes.
| S-EPMC3076865 | biostudies-literature
Project description:BackgroundAlthough transcription in mammalian genomes can initiate from various genomic positions (e.g., 3'UTR, coding exons, etc.), most locations on genomes are not prone to transcription initiation. It is of practical and theoretical interest to be able to estimate such collections of non-TSS locations (NTLs). The identification of large portions of NTLs can contribute to better focusing the search for TSS locations and thus contribute to promoter and gene finding. It can help in the assessment of 5' completeness of expressed sequences, contribute to more successful experimental designs, as well as more accurate gene annotation.MethodologyUsing comprehensive collections of Cap Analysis of Gene Expression (CAGE) and other transcript data from mouse and human genomes, we developed a methodology that allows us, by performing computational TSS prediction with very high sensitivity, to annotate, with a high accuracy in a strand specific manner, locations of mammalian genomes that are highly unlikely to harbor transcription start sites (TSSs). The properties of the immediate genomic neighborhood of 98,682 accurately determined mouse and 113,814 human TSSs are used to determine features that distinguish genomic transcription initiation locations from those that are not likely to initiate transcription. In our algorithm we utilize various constraining properties of features identified in the upstream and downstream regions around TSSs, as well as statistical analyses of these surrounding regions.ConclusionsOur analysis of human chromosomes 4, 21 and 22 estimates ?46%, ?41% and ?27% of these chromosomes, respectively, as being NTLs. This suggests that on average more than 40% of the human genome can be expected to be highly unlikely to initiate transcription. Our method represents the first one that utilizes high-sensitivity TSS prediction to identify, with high accuracy, large portions of mammalian genomes as NTLs. The server with our algorithm implemented is available at http://cbrc.kaust.edu.sa/ddm/.
| S-EPMC2981523 | biostudies-literature
Project description:Quantitative proton density (PD) maps measure the amount of free water, which is important for non-invasive tissue characterization in pathology and across lifespan. PD mapping requires the estimation and subsequent removal of factors influencing the signal intensity other than PD. These factors include the T1, T2* relaxation effects, transmit field inhomogeneities, receiver coil sensitivity profile (RP) and the spatially invariant factor that is required to scale the data. While the transmit field can be reliably measured, the RP estimation is usually based on image post-processing techniques due to limitations of its measurement at magnetic fields higher than 1.5 T. The post-processing methods are based on unified bias-field/tissue segmentation, fitting the sensitivity profile from images obtained with different coils, or on the linear relationship between T1 and PD. The scaling factor is derived from the signal within a specific tissue compartment or reference object. However, these approaches for calculating the RP and scaling factor have limitations particularly in severe pathology or over a wide age range, restricting their application. We propose a new approach for PD mapping based on a multi-contrast variable flip angle acquisition protocol and a data-driven estimation method for the RP correction and map scaling. By combining all the multi-contrast data acquired at different echo times, we are able to fully correct the MRI signal for T2* relaxation effects and to decrease the variance and the entropy of PD values within tissue class of the final map. The RP is determined from the corrected data applying a non-parametric bias estimation, and the scaling factor is based on the median intensity of an external calibration object. Finally, we compare the signal intensity and homogeneity of the multi-contrast PD map with the well-established effective PD (PD*) mapping, for which the RP is based on concurrent bias field estimation and tissue classification, and the scaling factor is estimated from the mean white matter signal. The multi-contrast PD values homogeneity and accuracy within the cerebrospinal fluid (CSF) and deep brain structures are increased beyond that obtained using PD* maps. We demonstrate that the multi-contrast RP approach is insensitive to anatomical or a priori tissue information by applying it in a patient with extensive brain abnormalities and for whole body PD mapping in post-mortem foetal imaging.
| S-EPMC7611405 | biostudies-literature
Project description:Cap analysis of gene expression (CAGE) is a high-throughput method for transcriptome analysis that provides a single base-pair resolution map of transcription start sites (TSS) and their relative usage. Despite their high resolution and functional significance, published CAGE data are still underused in promoter analysis due to the absence of tools that enable its efficient manipulation and integration with other genome data types. Here we present CAGEr, an R implementation of novel methods for the analysis of differential TSS usage and promoter dynamics, integrated with CAGE data processing and promoterome mining into a first comprehensive CAGE toolbox on a common analysis platform. Crucially, we provide collections of TSSs derived from most published CAGE datasets, as well as direct access to FANTOM5 resource of TSSs for numerous human and mouse cell/tissue types from within R, greatly increasing the accessibility of precise context-specific TSS data for integrative analyses. The CAGEr package is freely available from Bioconductor at http://www.bioconductor.org/packages/release/bioc/html/CAGEr.html.
| S-EPMC4417143 | biostudies-literature
Project description:Giardia lamblia is a protozoan parasite that is found worldwide and has both medical and veterinary importance. We applied the transcription start sequence (TSS-seq) and RNA sequence (RNA-seq) techniques to study the transcriptome of the assemblage A WB strain trophozoite. We identified 8000 transcription regions (TR) with significant transcription. Of these regions, 1881 TRs were more than 500 nucleotides upstream of an annotated ORF. Combining both techniques helped us to identify 24 ORFs that should be re-annotated and 60 new ORFs. From the 8000 TRs, we were able to identify an AT-rich consensus that includes the transcription initiation site. It is possible that transcription that was previously thought to be bidirectional is actually unidirectional.
| S-EPMC3792122 | biostudies-literature
Project description:Proteogenomics leverages information derived from proteomic data to improve genome annotations. Of particular interest are "novel" peptides that provide direct evidence of protein expression for genomic regions not previously annotated as protein-coding. We present a modular, automated data analysis pipeline aimed at detecting such "novel" peptides in proteomic data sets. This pipeline implements criteria developed by proteomics and genome annotation experts for high-stringency peptide identification and filtering. Our pipeline is based on the OpenMS computational framework; it incorporates multiple database search engines for peptide identification and applies a machine-learning approach (Percolator) to post-process search results. We describe several new and improved software tools that we developed to facilitate proteogenomic analyses that enhance the wealth of tools provided by OpenMS. We demonstrate the application of our pipeline to a human testis tissue data set previously acquired for the Chromosome-Centric Human Proteome Project, which led to the addition of five new gene annotations on the human reference genome.
| S-EPMC5703597 | biostudies-literature
Project description:BackgroundNeuropeptide Y is a key neurotransmitter of the central nervous system which plays a vital role in the feed energy homeostasis in mammals. Mutations in the regulatory and coding regions of the bovine NPY gene can potentially affect the neuronal regulation of appetite and feeding behaviour in cattle. The objectives of this experiment were to: a) fully characterize the bovine NPY gene transcript and b) identify the SNP diversity in both coding and non-coding regions of the NPY gene in a panel of Bos taurus and B. indicus cattle.ResultsBovine NPY gene consists of four exons (99, 188, 81 and 195 nucleotides) and three introns. The promoter region of the NPY gene consists of TATA and GC boxes which are separated from the transcription start site (TSS) by 29 and ~100 nt, respectively. Analyses of the tissue specific expression of the bovine NPY gene revealed the presence of highly abundant NPY gene transcripts in the arcuate nucleus, cerebral and cerebellar regions of the bovine brain. We identified a total of 59 SNPs in the 8.4 kb region of the bovine NPY gene. Seven out of nine total SNPs in the promoter region affect binding of putative transcription factors. A high level of nucleotide diversity was evident in the promoter regions (2.84 x 10(-3)) compared to the exonic (1.44 x 10(-3)), intronic (1.30 x 10(-3)) and 3' untranslated (1.26 x 10(-3)) regions.ConclusionThe SNPs identified in different regions of bovine NPY gene may serve as a basis for understanding the regulation of the expression of the bovine NPY gene under a variety of physiological conditions and identification of genotypes with high feed energy conversion efficiency.
| S-EPMC2657160 | biostudies-literature