Evidence map›Paper›PMID 34805991›Full record

ArticleNAR genomics and bioinformatics2021

TSSr: an R package for comprehensive analyses of TSS sequencing data.

Zhaolian Lu, Keenan Berry, Zhenbin Hu, Yu Zhan, Tae-Hyuk Ahn, Zhenguo Lin

Abstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zhaolian LuDepartment of Biology, Saint Louis University, St. Louis, MO 63103, USA.
Keenan BerryProgram of Bioinformatics and Computational Biology, Saint Louis University, St. Louis, MO 63103, USA.
Zhenbin HuDepartment of Biology, Saint Louis University, St. Louis, MO 63103, USA.
Yu ZhanDepartment of Biology, Saint Louis University, St. Louis, MO 63103, USA.
Tae-Hyuk AhnProgram of Bioinformatics and Computational Biology, Saint Louis University, St. Louis, MO 63103, USA.
Zhenguo LinDepartment of Biology, Saint Louis University, St. Louis, MO 63103, USA.ORCID https://orcid.org/0000-0002-8400-9138

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Identifiers

PMID34805991
PMCPMC8598296

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.