Integrative analyses of transcriptome data reveal the mechanisms of post-transcriptional regulation

Rna Binding ◽

Rna Binding Proteins ◽

Rapid Development ◽

Sequencing Data ◽

Public Resources ◽

Integrative Analyses ◽

Post Transcriptional Regulation

Abstract Post-transcriptional processing of RNAs plays important roles in a variety of physiological and pathological processes. These processes can be precisely controlled by a series of RNA binding proteins and cotranscriptionally regulated by transcription factors as well as histone modifications. With the rapid development of high-throughput sequencing techniques, multiomics data have been broadly used to study the mechanisms underlying the important biological processes. However, how to use these high-throughput sequencing data to elucidate the fundamental regulatory roles of post-transcriptional processes is still of great challenge. This review summarizes the regulatory mechanisms of post-transcriptional processes and the general principles and approaches to dissect these mechanisms by integrating multiomics data as well as public resources.

HTSeq - A Python framework to work with high-throughput sequencing data

10.1101/002824 ◽

2014 ◽

Cited By ~ 242

Author(s):

Simon Anders ◽

Paul Theodor Pyl ◽

Wolfgang Huber

Keyword(s):

High Throughput ◽

Rapid Development ◽

Differential Expression Analysis ◽

Rna Seq ◽

Sequencing Data ◽

Standard Work ◽

Data Formats ◽

Python Package

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard work flows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data such as genomic coordinates, sequences, sequencing reads, alignments, gene model information, variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability: HTSeq is released as open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index, https://pypi.python.org/pypi/HTSeq

rMAPS2: an update of the RNA map analysis and plotting server for alternative splicing regulation

Nucleic Acids Research ◽

10.1093/nar/gkaa237 ◽

2020 ◽

Vol 48 (W1) ◽

pp. W300-W306 ◽

Cited By ~ 2

Author(s):

Jae Y Hwang ◽

Sungbo Jung ◽

Tae L Kook ◽

Eric C Rouchka ◽

Jinwoong Bok ◽

...

Keyword(s):

Alternative Splicing ◽

High Throughput ◽

Splice Site ◽

Rna Binding ◽

Rna Binding Protein ◽

Sequencing Data ◽

Retained Intron ◽

Map Analysis

Abstract The rMAPS2 (RNA Map Analysis and Plotting Server 2) web server, freely available at http://rmaps.cecsresearch.org/, has provided the high-throughput sequencing data research community with curated tools for the identification of RNA binding protein sites. rMAPS2 analyzes differential alternative splicing or CLIP peak data obtained from high-throughput sequencing data analysis tools like MISO, rMATS, Piranha, PIPE-CLIP and PARalyzer, and then, graphically displays enriched RNA-binding protein target sites. The initial release of rMAPS focused only on the most common alternative splicing event, skipped exon or exon skipping. However, there was a high demand for the analysis of other major types of alternative splicing events, especially for retained intron events since this is the most common type of alternative splicing in plants, such as Arabidopsis thaliana. Here, we expanded the implementation of rMAPS2 to facilitate analyses for all five major types of alternative splicing events: skipped exon, mutually exclusive exons, alternative 5′ splice site, alternative 3′ splice site and retained intron. In addition, by employing multi-threading, rMAPS2 has vastly improved the user experience with significant reductions in running time, ∼3.5 min for the analysis of all five major alternative splicing types at once.

TERIUS: accurate prediction of lncRNA via high-throughput sequencing data representing RNA-binding protein association

BMC Bioinformatics ◽

10.1186/s12859-018-2013-9 ◽

2018 ◽

Vol 19 (S1) ◽

Cited By ~ 7

Author(s):

Seo-Won Choi ◽

Jin-Wu Nam

Keyword(s):

High Throughput ◽

Rna Binding ◽

Binding Protein ◽

Rna Binding Protein ◽

Accurate Prediction ◽

Sequencing Data ◽

Protein Association ◽

High Throughput Sequencing Data

Faculty Opinions recommendation of Coalescent Inference Using Serially Sampled, High-Throughput Sequencing Data from Intrahost HIV Infection.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726132071.793531014 ◽

2017 ◽

Author(s):

Sarah Rowland-Jones ◽

Sophie Andrews

Keyword(s):

Hiv Infection ◽

High Throughput ◽

Sequencing Data ◽

High Throughput Sequencing Data

BlindCall: ultra-fast base-calling of high-throughput sequencing data by blind deconvolution

Bioinformatics ◽

10.1093/bioinformatics/btu010 ◽

2014 ◽

Vol 30 (9) ◽

pp. 1214-1219 ◽

Cited By ~ 6

Author(s):

C. Ye ◽

C. Hsiao ◽

H. Corrada Bravo

Keyword(s):

High Throughput ◽

Blind Deconvolution ◽

Sequencing Data ◽

Base Calling ◽

High Throughput Sequencing Data

Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

MycoKeys ◽

10.3897/mycokeys.39.28109 ◽

2018 ◽

Vol 39 ◽

pp. 29-40 ◽

Cited By ~ 21

Author(s):

Sten Anslan ◽

R. Henrik Nilsson ◽

Christian Wurzbacher ◽

Petr Baldrian ◽

Leho Tedersoo ◽

...

Keyword(s):

High Throughput ◽

Computation Time ◽

Potential Effect ◽

Data Sets ◽

Sequencing Data ◽

Operational Taxonomic Units ◽

Recent Developments

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.