Literature DB >> 33391870

BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis.

Daniel Dimitrov1, Quan Gu1.   

Abstract

BACKGROUND: RNA sequencing is an indispensable research tool used in a broad range of transcriptome analysis studies. The most common application of RNA Sequencing is differential expression analysis and it is used to determine genetic loci with distinct expression across different conditions. An emerging field called single-cell RNA sequencing is used for transcriptome profiling at the individual cell level. The standard protocols for both of these approaches include the processing of sequencing libraries and result in the generation of count matrices. An obstacle to these analyses and the acquisition of meaningful results is that they require programing expertise. Although some effort has been directed toward the development of user-friendly RNA-Seq analysis analysis tools, few have the flexibility to explore both Bulk and single-cell RNA sequencing. IMPLEMENTATION: BingleSeq was developed as an intuitive application that provides a user-friendly solution for the analysis of count matrices produced by both Bulk and Single-cell RNA-Seq experiments. This was achieved by building an interactive dashboard-like user interface which incorporates three state-of-the-art software packages for each type of the aforementioned analyses. Furthermore, BingleSeq includes additional features such as visualization techniques, extensive functional annotation analysis and rank-based consensus for differential gene analysis results. As a result, BingleSeq puts some of the best reviewed and most widely used packages and tools for RNA-Seq analyses at the fingertips of biologists with no programing experience. AVAILABILITY: BingleSeq is as an easy-to-install R package available on GitHub at https://github.com/dbdimitrov/BingleSeq/.
© 2020 Dimitrov and Gu.

Entities:  

Keywords:  Differential expression; Functional annotation; R package; RNA-Seq; Rank-based consensus; Single-cell RNA-Seq

Year:  2020        PMID: 33391870      PMCID: PMC7761193          DOI: 10.7717/peerj.10469

Source DB:  PubMed          Journal:  PeerJ        ISSN: 2167-8359            Impact factor:   2.984


  45 in total

1.  The sva package for removing batch effects and other unwanted variation in high-throughput experiments.

Authors:  Jeffrey T Leek; W Evan Johnson; Hilary S Parker; Andrew E Jaffe; John D Storey
Journal:  Bioinformatics       Date:  2012-01-17       Impact factor: 6.937

2.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

Authors:  Evan Z Macosko; Anindita Basu; Rahul Satija; James Nemesh; Karthik Shekhar; Melissa Goldman; Itay Tirosh; Allison R Bialas; Nolan Kamitaki; Emily M Martersteck; John J Trombetta; David A Weitz; Joshua R Sanes; Alex K Shalek; Aviv Regev; Steven A McCarroll
Journal:  Cell       Date:  2015-05-21       Impact factor: 41.582

3.  Gene ontology analysis for RNA-seq: accounting for selection bias.

Authors:  Matthew D Young; Matthew J Wakefield; Gordon K Smyth; Alicia Oshlack
Journal:  Genome Biol       Date:  2010-02-04       Impact factor: 13.583

4.  Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.

Authors:  Itay Tirosh; Benjamin Izar; Sanjay M Prakadan; Marc H Wadsworth; Daniel Treacy; John J Trombetta; Asaf Rotem; Christopher Rodman; Christine Lian; George Murphy; Mohammad Fallahi-Sichani; Ken Dutton-Regester; Jia-Ren Lin; Ofir Cohen; Parin Shah; Diana Lu; Alex S Genshaft; Travis K Hughes; Carly G K Ziegler; Samuel W Kazer; Aleth Gaillard; Kellie E Kolb; Alexandra-Chloé Villani; Cory M Johannessen; Aleksandr Y Andreev; Eliezer M Van Allen; Monica Bertagnolli; Peter K Sorger; Ryan J Sullivan; Keith T Flaherty; Dennie T Frederick; Judit Jané-Valbuena; Charles H Yoon; Orit Rozenblatt-Rosen; Alex K Shalek; Aviv Regev; Levi A Garraway
Journal:  Science       Date:  2016-04-08       Impact factor: 47.728

5.  Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors.

Authors:  Alexandra-Chloé Villani; Rahul Satija; Gary Reynolds; Siranush Sarkizova; Karthik Shekhar; James Fletcher; Morgane Griesbeck; Andrew Butler; Shiwei Zheng; Suzan Lazo; Laura Jardine; David Dixon; Emily Stephenson; Emil Nilsson; Ida Grundberg; David McDonald; Andrew Filby; Weibo Li; Philip L De Jager; Orit Rozenblatt-Rosen; Andrew A Lane; Muzlifah Haniffa; Aviv Regev; Nir Hacohen
Journal:  Science       Date:  2017-04-21       Impact factor: 47.728

6.  FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data.

Authors:  David DeTomaso; Nir Yosef
Journal:  BMC Bioinformatics       Date:  2016-08-23       Impact factor: 3.169

7.  Risk-conscious correction of batch effects: maximising information extraction from high-throughput genomic datasets.

Authors:  Yalchin Oytam; Fariborz Sobhanmanesh; Konsta Duesing; Joshua C Bowden; Megan Osmond-McLeod; Jason Ross
Journal:  BMC Bioinformatics       Date:  2016-09-01       Impact factor: 3.169

8.  Cell composition analysis of bulk genomics using single-cell data.

Authors:  Amit Frishberg; Naama Peshes-Yaloz; Ofir Cohn; Diana Rosentul; Yael Steuerman; Liran Valadarsky; Gal Yankovitz; Michal Mandelboim; Fuad A Iraqi; Ido Amit; Lior Mayo; Eran Bacharach; Irit Gat-Viks
Journal:  Nat Methods       Date:  2019-03-18       Impact factor: 28.547

9.  Benchmark and integration of resources for the estimation of human transcription factor activities.

Authors:  Luz Garcia-Alonso; Christian H Holland; Mahmoud M Ibrahim; Denes Turei; Julio Saez-Rodriguez
Journal:  Genome Res       Date:  2019-07-24       Impact factor: 9.043

10.  A systematic performance evaluation of clustering methods for single-cell RNA-seq data.

Authors:  Angelo Duò; Mark D Robinson; Charlotte Soneson
Journal:  F1000Res       Date:  2018-07-26
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