Muutke küpsiste eelistusi

E-raamat: Transcriptome Data Analysis

Edited by
  • Formaat - PDF+DRM
  • Hind: 246,99 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

This detailed volume presents a comprehensive exploration of the advances in transcriptomics, with a focus on methods and pipelines for transcriptome data analysis. In addition to well-established RNA sequencing (RNA-Seq) data analysis protocols, the chapters also examine specialized pipelines, such as multi-omics data integration and analysis, gene interaction network construction, single-cell trajectory inference, detection of structural variants, application of machine learning, and more. As part of the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that leads to best results in the lab. 





 





Authoritative and practical, Transcriptome Data Analysis serves as an ideal resource for educators and researchers looking to understand new developments in the field, learn usage of the protocols for transcriptome data analysis, and implement the tools or pipelines to address relevant problemsof their interest.





 





Chapter 4 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
An RNA-Seq Data Analysis Pipeline.- Inferring Interaction Networks from Transcriptomic Data: Methods and Applications.- EMPathways2: Estimation of Enzyme Expression and Metabolic Pathway Activity Using RNA-Seq Reads.- Efficient and Powerful Integration of Targeted Metabolomics and Transcriptomics for Analyzing the Metabolism Behind Desirable Traits in Plants.- A RNAseq Data Analysis for Differential Gene Expression Using HISAT2-stringTie-Ballgown Pipeline.- RNA-Sequencing Experimental Analysis Workflow Using Caenorhabditis elegans.- Inferring Novel Cells in Single Cell RNA Sequencing Data.- Unsupervised Single-Cell Clustering with Asymmetric Within-Sample Transformation and Per Cluster Supervised Features Selection.- Inferring Tree-Shaped Single-Cell Trajectories with Totem.- Zebrafish Thrombocyte Transcriptome Analysis and Functional Genomics.- Plant Transcriptome Analysis with HISAT-StringTie-Ballgown and TopHat-Cufflinks Pipelines.- Cotton Meristem Transcriptomes: Constructing an RNA-Seq Pipeline to Explore Crop Architecture Regulation.- Detecting Somatic Insertions/Deletions (Indels) Using Tumor RNA-Seq Data.- A Protocol for the Detection of Fusion Transcripts Using RNA-Sequencing Data.- GAN Learning Methods for Bulk RNA-Seq Data and Their Interpretive Application in the Context of Disease Progression.- Protocol for Analyzing Epigenetic Regulation Mechanisms in Breast Cancer.- Identification of Virus-Derived Small Interfering RNAs (vsiRNAs) from Infected sRNA-Seq Samples.- Incorporating Sequence-Dependent DNA Shape and Dynamics into Transcriptome Data Analysis.- Utilizing RNA-Seq Data to Infer Bacterial Transcription Termination Sites and Validate Predictions.- RNA-Seq Analysis of Mammalian Prion Disease.- In Silico Identification of tRNA Fragments, Novel Candidates for Cancer Biomarkers, and Therapeutic Targets.