Detecting selection in low-coverage high-throughput sequencing data using principal component analysis
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2105
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Detecting selection in low-coverage high-throughput sequencing data using principal component analysis ; volume:22 ; number:1 ; day:29 ; month:9 ; year:2021 ; pages:1-13 ; date:12.2021
BMC bioinformatics ; 22, Heft 1 (29.9.2021), 1-13, 12.2021
- Urheber
-
Meisner, Jonas
Albrechtsen, Anders
Hanghøj, Kristian
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1186/s12859-021-04375-2
- URN
-
urn:nbn:de:101:1-2021121819111196898334
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:24 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Meisner, Jonas
- Albrechtsen, Anders
- Hanghøj, Kristian
- SpringerLink (Online service)
Ähnliche Objekte (12)
Super-sparse principal component analyses for high-throughput genomic data
Detecting genomic deletions from high-throughput sequence data with unsupervised learning
High-throughput transcriptomics
Detecting circular RNA from high-throughput sequence data with de Bruijn graph
State-of-the-Art Targeted High-Throughput Sequencing for Detecting Inherited Platelet Disorders
Detecting differentially methylated loci for multiple treatments based on high-throughput methylation data
The importance of study design for detecting differentially abundant features in high-throughput experiments
Combining High‐Throughput Synthesis and High‐Throughput Protein Crystallography for Accelerated Hit Identification
Combining High‐Throughput Synthesis and High‐Throughput Protein Crystallography for Accelerated Hit Identification
High‐Throughput Contact Flow Lithography
High-throughput HEVC CABAC decoding
High-Throughput BitPacking Compression
Super-sparse principal component analyses for high-throughput genomic data
Detecting genomic deletions from high-throughput sequence data with unsupervised learning
High-throughput transcriptomics
Detecting circular RNA from high-throughput sequence data with de Bruijn graph
State-of-the-Art Targeted High-Throughput Sequencing for Detecting Inherited Platelet Disorders
Detecting differentially methylated loci for multiple treatments based on high-throughput methylation data
The importance of study design for detecting differentially abundant features in high-throughput experiments
Combining High‐Throughput Synthesis and High‐Throughput Protein Crystallography for Accelerated Hit Identification
Combining High‐Throughput Synthesis and High‐Throughput Protein Crystallography for Accelerated Hit Identification
High‐Throughput Contact Flow Lithography
High-throughput HEVC CABAC decoding
High-Throughput BitPacking Compression
Super-sparse principal component analyses for high-throughput genomic data
Detecting genomic deletions from high-throughput sequence data with unsupervised learning
High-throughput transcriptomics
Detecting circular RNA from high-throughput sequence data with de Bruijn graph
State-of-the-Art Targeted High-Throughput Sequencing for Detecting Inherited Platelet Disorders
Detecting differentially methylated loci for multiple treatments based on high-throughput methylation data
The importance of study design for detecting differentially abundant features in high-throughput experiments
Combining High‐Throughput Synthesis and High‐Throughput Protein Crystallography for Accelerated Hit Identification
Combining High‐Throughput Synthesis and High‐Throughput Protein Crystallography for Accelerated Hit Identification
High‐Throughput Contact Flow Lithography
High-throughput HEVC CABAC decoding