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Literature DB >> 34324187

Transcriptional Profiling During Neural Conversion.

Yohannes Afeworki¹, Hannah Wollenzien^2,3, Michael S Kareta^4,5,6,7,8,9.

Abstract

The processes that underlie neuronal conversion ultimately involve a reorganization of transcriptional networks to establish a neuronal cell fate. As such, transcriptional profiling is a key component toward understanding this process. In this chapter, we will discuss methods of elucidating transcriptional networks during neuronal reprogramming and considerations that should be incorporated in experimental design.

Entities: Chemical

Keywords: Induced neurons; Neuronal conversion; RNA-seq; Transcriptional profiling

Mesh：

Year: 2021 PMID： 34324187 PMCID： PMC9131516 DOI： 10.1007/978-1-0716-1601-7_12

Source DB: PubMed Journal: Methods Mol Biol ISSN： 1064-3745

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References

55 in total

1. The Gene Ontology (GO) database and informatics resource.

Authors: M A Harris; J Clark; A Ireland; J Lomax; M Ashburner; R Foulger; K Eilbeck; S Lewis; B Marshall; C Mungall; J Richter; G M Rubin; J A Blake; C Bult; M Dolan; H Drabkin; J T Eppig; D P Hill; L Ni; M Ringwald; R Balakrishnan; J M Cherry; K R Christie; M C Costanzo; S S Dwight; S Engel; D G Fisk; J E Hirschman; E L Hong; R S Nash; A Sethuraman; C L Theesfeld; D Botstein; K Dolinski; B Feierbach; T Berardini; S Mundodi; S Y Rhee; R Apweiler; D Barrell; E Camon; E Dimmer; V Lee; R Chisholm; P Gaudet; W Kibbe; R Kishore; E M Schwarz; P Sternberg; M Gwinn; L Hannick; J Wortman; M Berriman; V Wood; N de la Cruz; P Tonellato; P Jaiswal; T Seigfried; R White
Journal: Nucleic Acids Res Date: 2004-01-01 Impact factor: 16.971

Review 2. RNA sequencing: the teenage years.

Authors: Rory Stark; Marta Grzelak; James Hadfield
Journal: Nat Rev Genet Date: 2019-07-24 Impact factor: 53.242

3. STAR: ultrafast universal RNA-seq aligner.

Authors: Alexander Dobin; Carrie A Davis; Felix Schlesinger; Jorg Drenkow; Chris Zaleski; Sonali Jha; Philippe Batut; Mark Chaisson; Thomas R Gingeras
Journal: Bioinformatics Date: 2012-10-25 Impact factor: 6.937

4. Direct reprogramming of adult human fibroblasts to functional neurons under defined conditions.

Authors: Rajesh Ambasudhan; Maria Talantova; Ronald Coleman; Xu Yuan; Saiyong Zhu; Stuart A Lipton; Sheng Ding
Journal: Cell Stem Cell Date: 2011-07-28 Impact factor: 24.633

5. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

Authors: Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov
Journal: Proc Natl Acad Sci U S A Date: 2005-09-30 Impact factor: 11.205

6. Diverse reprogramming codes for neuronal identity.

Authors: Rachel Tsunemoto; Sohyon Lee; Attila Szűcs; Pavel Chubukov; Irina Sokolova; Joel W Blanchard; Kevin T Eade; Jacob Bruggemann; Chunlei Wu; Ali Torkamani; Pietro Paolo Sanna; Kristin K Baldwin
Journal: Nature Date: 2018-05-09 Impact factor: 49.962

Transcriptional Profiling During Neural Conversion.

1. The Gene Ontology (GO) database and informatics resource.

Review 2. RNA sequencing: the teenage years.

3. STAR: ultrafast universal RNA-seq aligner.

4. Direct reprogramming of adult human fibroblasts to functional neurons under defined conditions.

5. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

6. Diverse reprogramming codes for neuronal identity.

7. HTSeq--a Python framework to work with high-throughput sequencing data.

8. Single-cell mRNA quantification and differential analysis with Census.

9. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.

10. Clustering gene expression time series data using an infinite Gaussian process mixture model.