Quantifying single nucleotide variant detection sensitivity in exome sequencing
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2105
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Quantifying single nucleotide variant detection sensitivity in exome sequencing ; volume:14 ; number:1 ; day:18 ; month:6 ; year:2013 ; pages:1-10 ; date:12.2013
BMC bioinformatics ; 14, Heft 1 (18.6.2013), 1-10, 12.2013
- Urheber
-
Meynert, Alison M.
- Beteiligte Personen und Organisationen
-
Bicknell, Louise S.
Hurles, Matthew E.
Jackson, Andrew P.
Taylor, Martin S.
SpringerLink (Online service)
- DOI
-
10.1186/1471-2105-14-195
- URN
-
urn:nbn:de:1111-2016021742024
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 15.08.2025, 07:23 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
- Meynert, Alison M.
- Bicknell, Louise S.
- Hurles, Matthew E.
- Jackson, Andrew P.
- Taylor, Martin S.
- SpringerLink (Online service)
Ähnliche Objekte (12)
Variant detection sensitivity and biases in whole genome and exome sequencing
Global inference of disease-causing single nucleotide variants from exome sequencing data
Improving somatic variant identification through integration of genome and exome data
Quantifying sensitivity to droughts – an experimental modeling approach
Impact of post-alignment processing in variant discovery from whole exome data
An integrative variant analysis suite for whole exome next-generation sequencing data
Achieving single nucleotide sensitivity in direct hybridization genome imaging
Quantifying ABCA3 deficiency and ABCA3 variant-specific response to hydroxychloroquine
Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis
Empirical Bayes single nucleotide variant-calling for next-generation sequencing data
Single-nucleotide variant calling in single-cell sequencing data with Monopogen
DECA: scalable XHMM exome copy-number variant calling with ADAM and Apache Spark
Variant detection sensitivity and biases in whole genome and exome sequencing
Global inference of disease-causing single nucleotide variants from exome sequencing data
Improving somatic variant identification through integration of genome and exome data
Quantifying sensitivity to droughts – an experimental modeling approach
Impact of post-alignment processing in variant discovery from whole exome data
An integrative variant analysis suite for whole exome next-generation sequencing data
Achieving single nucleotide sensitivity in direct hybridization genome imaging
Quantifying ABCA3 deficiency and ABCA3 variant-specific response to hydroxychloroquine
Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis
Empirical Bayes single nucleotide variant-calling for next-generation sequencing data
Single-nucleotide variant calling in single-cell sequencing data with Monopogen
DECA: scalable XHMM exome copy-number variant calling with ADAM and Apache Spark
Variant detection sensitivity and biases in whole genome and exome sequencing
Global inference of disease-causing single nucleotide variants from exome sequencing data
Improving somatic variant identification through integration of genome and exome data
Quantifying sensitivity to droughts – an experimental modeling approach
Impact of post-alignment processing in variant discovery from whole exome data
An integrative variant analysis suite for whole exome next-generation sequencing data
Achieving single nucleotide sensitivity in direct hybridization genome imaging
Quantifying ABCA3 deficiency and ABCA3 variant-specific response to hydroxychloroquine
Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis
Empirical Bayes single nucleotide variant-calling for next-generation sequencing data
Single-nucleotide variant calling in single-cell sequencing data with Monopogen