Dataset Information

Flexible analysis of TSS mapping data and detection of TSS shifts with TSRexploreR.

ABSTRACT: Heterogeneity in transcription initiation has important consequences for transcript stability and translation, and shifts in transcription start site (TSS) usage are prevalent in various developmental, metabolic, and disease contexts. Accordingly, numerous methods for global TSS profiling have been developed, including most recently Survey of TRanscription Initiation at Promoter Elements with high-throughput sequencing (STRIPE-seq), a method to profile transcription start sites (TSSs) on a genome-wide scale with significant cost and time savings compared to previous methods. In anticipation of more widespread adoption of STRIPE-seq and related methods for construction of promoter atlases and studies of differential gene expression, we built TSRexploreR, an R package for end-to-end analysis of TSS mapping data. TSRexploreR provides functions for TSS and transcription start region (TSR) detection, normalization, correlation, visualization, and differential TSS/TSR analyses. TSRexploreR is highly interoperable, accepting the data structures of TSS and TSR sets generated by several existing tools for processing and alignment of TSS mapping data, such as CAGEr for Cap Analysis of Gene Expression (CAGE) data. Lastly, TSRexploreR implements a novel approach for the detection of shifts in TSS distribution.

SUBMITTER: Policastro RA

PROVIDER: S-EPMC8265037 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Flexible analysis of TSS mapping data and detection of TSS shifts with TSRexploreR.

Policastro Robert A RA McDonald Daniel J DJ Brendel Volker P VP Zentner Gabriel E GE

NAR genomics and bioinformatics 20210601 2

Heterogeneity in transcription initiation has important consequences for transcript stability and translation, and shifts in transcription start site (TSS) usage are prevalent in various developmental, metabolic, and disease contexts. Accordingly, numerous methods for global TSS profiling have been developed, including most recently Survey of TRanscription Initiation at Promoter Elements with high-throughput sequencing (STRIPE-seq), a method to profile transcription start sites (TSSs) on a genom ...[more]

PMID: 34250478

Similar Datasets

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.

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/.

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.

Dataset Information

Flexible analysis of TSS mapping data and detection of TSS shifts with TSRexploreR.

Publications

Flexible analysis of TSS mapping data and detection of TSS shifts with TSRexploreR.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets