Cargando…

Computational analysis of bacterial RNA-Seq data

Recent advances in high-throughput RNA sequencing (RNA-seq) have enabled tremendous leaps forward in our understanding of bacterial transcriptomes. However, computational methods for analysis of bacterial transcriptome data have not kept pace with the large and growing data sets generated by RNA-seq...

Descripción completa

Detalles Bibliográficos
Autores principales:	McClure, Ryan, Balasubramanian, Divya, Sun, Yan, Bobrovskyy, Maksym, Sumby, Paul, Genco, Caroline A., Vanderpool, Carin K., Tjaden, Brian
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Oxford University Press 2013
Materias:	Methods Online
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3737546/ https://www.ncbi.nlm.nih.gov/pubmed/23716638 http://dx.doi.org/10.1093/nar/gkt444

Ejemplares similares

The small RNA SgrS: roles in metabolism and pathogenesis of enteric bacteria
por: Bobrovskyy, Maksym, et al.
Publicado: (2014)

iRNA-seq: computational method for genome-wide assessment of acute transcriptional regulation from total RNA-seq data
por: Madsen, Jesper Grud Skat, et al.
Publicado: (2015)

Identification of sRNAs expressed by the human pathogen Neisseria gonorrhoeae under disparate growth conditions
por: McClure, Ryan, et al.
Publicado: (2014)

ARH-seq: identification of differential splicing in RNA-seq data
por: Rasche, Axel, et al.
Publicado: (2014)

Detection and visualization of differential splicing in RNA-Seq data with JunctionSeq
por: Hartley, Stephen W., et al.
Publicado: (2016)

APERO: a genome-wide approach for identifying bacterial small RNAs from RNA-Seq data
por: Leonard, Simon, et al.
Publicado: (2019)

Assessment of kinship detection using RNA-seq data
por: Blay, Natalia, et al.
Publicado: (2019)

Disentangling sRNA-Seq data to study RNA communication between species
por: Bermúdez-Barrientos, José Roberto, et al.
Publicado: (2020)

Computational identification of cell-specific variable regions in ChIP-seq data
por: Andreani, Tommaso, et al.
Publicado: (2020)

Improving the value of public RNA-seq expression data by phenotype prediction
por: Ellis, Shannon E, et al.
Publicado: (2018)

Cell lineage inference from SNP and scRNA-Seq data
por: Ding, Jun, et al.
Publicado: (2019)

RUV-III-NB: normalization of single cell RNA-seq data
por: Salim, Agus, et al.
Publicado: (2022)

Accurate quantification of transcriptome from RNA-Seq data by effective length normalization
por: Lee, Soohyun, et al.
Publicado: (2011)

dropClust: efficient clustering of ultra-large scRNA-seq data
por: Sinha, Debajyoti, et al.
Publicado: (2018)

Linnorm: improved statistical analysis for single cell RNA-seq expression data
por: Yip, Shun H., et al.
Publicado: (2017)

Network embedding-based representation learning for single cell RNA-seq data
por: Li, Xiangyu, et al.
Publicado: (2017)

Using neural networks for reducing the dimensions of single-cell RNA-Seq data
por: Lin, Chieh, et al.
Publicado: (2017)

MLG: multilayer graph clustering for multi-condition scRNA-seq data
por: Lu, Shan, et al.
Publicado: (2021)

seqMINER: an integrated ChIP-seq data interpretation platform
por: Ye, Tao, et al.
Publicado: (2011)

Flexible expressed region analysis for RNA-seq with derfinder
por: Collado-Torres, Leonardo, et al.
Publicado: (2017)

Phospho-RNA sequencing with circAID-p-seq
por: Del Piano, Alessia, et al.
Publicado: (2021)

Insplico: effective computational tool for studying splicing order of adjacent introns genome-wide with short and long RNA-seq reads
por: Gohr, André, et al.
Publicado: (2023)

A two-parameter generalized Poisson model to improve the analysis of RNA-seq data
por: Srivastava, Sudeep, et al.
Publicado: (2010)

Rapid identification of methylase specificity (RIMS-seq) jointly identifies methylated motifs and generates shotgun sequencing of bacterial genomes
por: Baum, Chloé, et al.
Publicado: (2021)

Duplex-specific nuclease efficiently removes rRNA for prokaryotic RNA-seq
por: Yi, Hana, et al.
Publicado: (2011)

Isoform-level microRNA-155 target prediction using RNA-seq
por: Deng, Nan, et al.
Publicado: (2011)

PennSeq: accurate isoform-specific gene expression quantification in RNA-Seq by modeling non-uniform read distribution
por: Hu, Yu, et al.
Publicado: (2014)

Diverse cell stresses induce unique patterns of tRNA up- and down-regulation: tRNA-seq for quantifying changes in tRNA copy number
por: Pang, Yan Ling Joy, et al.
Publicado: (2014)

Canonical correlation analysis for RNA-seq co-expression networks
por: Hong, Shengjun, et al.
Publicado: (2013)

RNASequel: accurate and repeat tolerant realignment of RNA-seq reads
por: Wilson, Gavin W., et al.
Publicado: (2015)

Analysis of subcellular transcriptomes by RNA proximity labeling with Halo-seq
por: Engel, Krysta L, et al.
Publicado: (2021)

miRspring: a compact standalone research tool for analyzing miRNA-seq data
por: Humphreys, David T., et al.
Publicado: (2013)

Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package
por: Tarazona, Sonia, et al.
Publicado: (2015)

Entropy subspace separation-based clustering for noise reduction (ENCORE) of scRNA-seq data
por: Song, Jia, et al.
Publicado: (2020)

CRISPRroots: on- and off-target assessment of RNA-seq data in CRISPR–Cas9 edited cells
por: Corsi, Giulia I, et al.
Publicado: (2021)

Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data
por: Liu, Wei, et al.
Publicado: (2022)

MATS: a Bayesian framework for flexible detection of differential alternative splicing from RNA-Seq data
por: Shen, Shihao, et al.
Publicado: (2012)

LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data
por: Wan, Changlin, et al.
Publicado: (2019)

RNASEQR—a streamlined and accurate RNA-seq sequence analysis program
por: Chen, Leslie Y., et al.
Publicado: (2012)

A new strategy to reduce allelic bias in RNA-Seq readmapping
por: Vijaya Satya, Ravi, et al.
Publicado: (2012)

Cannot write session to /tmp/vufind_sessions/sess_uiidvc9kkujlbc398e435ncf0u