UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy
about
Advances and challenges in the detection of transcriptome-wide protein-RNA interactions.Overview of methodologies for T-cell receptor repertoire analysis.Technical Advances in the Measurement of Residual Disease in Acute Myeloid Leukemia.Single molecule counting and assessment of random molecular tagging errors with transposable giga-scale error-correcting barcodesThe efficacy and further functional advantages of random-base molecular barcodes for absolute and digital quantification of nucleic acid molecules.PureCLIP: capturing target-specific protein-RNA interaction footprints from single-nucleotide CLIP-seq data.CRISPR-UMI: single-cell lineage tracing of pooled CRISPR-Cas9 screens.Optimized targeted sequencing of cell-free plasma DNA from bladder cancer patients.RNA-dependent RNA targeting by CRISPR-Cas9.The Human Cell Atlas: Technical approaches and challenges.Next-Generation Sequencing of Antibody Display Repertoires.In vivo insertion pool sequencing identifies virulence factors in a complex fungal-host interaction.Analyzing Immunoglobulin Repertoires.TRUmiCount: Correctly counting absolute numbers of molecules using unique molecular identifiers.Using single-cell genomics to understand developmental processes and cell fate decisions.Systematic identification of factors mediating accelerated mRNA degradation in response to changes in environmental nitrogen.RepSeq Data Representativeness and Robustness Assessment by Shannon Entropy.A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data.dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments.zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs.AmpUMI: design and analysis of unique molecular identifiers for deep amplicon sequencing.Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools databaseASAP - A Webserver for Immunoglobulin-Sequencing Analysis PipelineCOMRADES determines in vivo RNA structures and interactionsomniCLIP: probabilistic identification of protein-RNA interactions from CLIP-seq dataHigh-throughput characterization of genetic effects on DNA-protein binding and gene transcriptionFACT Sets a Barrier for Cell Fate Reprogramming in Caenorhabditis elegans and Human Cellsfastp: an ultra-fast all-in-one FASTQ preprocessorUnique Molecular Identifiers reveal a novel sequencing artefact with implications for RNA-Seq based gene expression analysisQsRNA-seq: a method for high-throughput profiling and quantifying small RNAsscPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data
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UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy
description
2017 nî lūn-bûn
@nan
2017年の論文
@ja
2017年論文
@yue
2017年論文
@zh-hant
2017年論文
@zh-hk
2017年論文
@zh-mo
2017年論文
@zh-tw
2017年论文
@wuu
2017年论文
@zh
2017年论文
@zh-cn
name
UMI-tools: modeling sequencing ...... mprove quantification accuracy
@en
type
label
UMI-tools: modeling sequencing ...... mprove quantification accuracy
@en
prefLabel
UMI-tools: modeling sequencing ...... mprove quantification accuracy
@en
P2860
P50
P356
P1433
P1476
UMI-tools: modeling sequencing ...... mprove quantification accuracy
@en
P2860
P304
P356
10.1101/GR.209601.116
P577
2017-01-18T00:00:00Z