|
|
|
|
| LEADER |
00000nam a22000005i 4500 |
| 001 |
978-1-0716-3886-6 |
| 003 |
DE-He213 |
| 005 |
20250808093140.0 |
| 007 |
cr nn 008mamaa |
| 008 |
240728s2024 xxu| s |||| 0|eng d |
| 020 |
|
|
|a 9781071638866
|9 978-1-0716-3886-6
|
| 024 |
7 |
|
|a 10.1007/978-1-0716-3886-6
|2 doi
|
| 050 |
|
4 |
|a QH450.2
|
| 072 |
|
7 |
|a PSD
|2 bicssc
|
| 072 |
|
7 |
|a SCI029000
|2 bisacsh
|
| 072 |
|
7 |
|a PSD
|2 thema
|
| 082 |
0 |
4 |
|a 572.8845
|2 23
|
| 245 |
1 |
0 |
|a Transcriptome Data Analysis
|h [electronic resource] /
|c edited by Rajeev K. Azad.
|
| 250 |
|
|
|a 1st ed. 2024.
|
| 264 |
|
1 |
|a New York, NY :
|b Springer US :
|b Imprint: Humana,
|c 2024.
|
| 300 |
|
|
|a XI, 394 p. 79 illus., 76 illus. in color.
|b online resource.
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 341 |
0 |
|
|b Table of contents navigation
|2 onix
|
| 341 |
0 |
|
|b Single logical reading order
|2 onix
|
| 341 |
0 |
|
|b Short alternative textual descriptions
|2 onix
|
| 341 |
0 |
|
|b Use of color is not sole means of conveying information
|2 onix
|
| 341 |
0 |
|
|b Use of high contrast between text and background color
|2 onix
|
| 341 |
0 |
|
|b Next / Previous structural navigation
|2 onix
|
| 341 |
0 |
|
|b All non-decorative content supports reading without sight
|2 onix
|
| 347 |
|
|
|a text file
|b PDF
|2 rda
|
| 490 |
1 |
|
|a Methods in Molecular Biology,
|x 1940-6029 ;
|v 2812
|
| 505 |
0 |
|
|a 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.
|
| 520 |
|
|
|a 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.
|
| 532 |
8 |
|
|a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com.
|
| 532 |
8 |
|
|a No reading system accessibility options actively disabled
|
| 532 |
8 |
|
|a Publisher contact for further accessibility information: accessibilitysupport@springernature.com
|
| 650 |
|
0 |
|a Genetic transcription.
|
| 650 |
|
0 |
|a Quantitative research.
|
| 650 |
1 |
4 |
|a Gene Transcription.
|
| 650 |
2 |
4 |
|a Data Analysis and Big Data.
|
| 700 |
1 |
|
|a Azad, Rajeev K.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
|
| 710 |
2 |
|
|a SpringerLink (Online service)
|
| 773 |
0 |
|
|t Springer Nature eBook
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9781071638859
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9781071638873
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9781071638880
|
| 830 |
|
0 |
|a Methods in Molecular Biology,
|x 1940-6029 ;
|v 2812
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-0716-3886-6
|
| 912 |
|
|
|a ZDB-2-PRO
|
| 950 |
|
|
|a Springer Protocols (Springer-12345)
|