Benchmarking RNA-Seq quantification tools

Chandramohan, Raghu; Wu, Po-Yen; Phan, John H.; Wang, May D.

doi:10.1109/embc.2013.6609583

Cited by 35 publications

(26 citation statements)

References 14 publications

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“…The quality of read datasets (Sanger FastQ format) was determined using FastQC (Ramirez‐Gonzalez et al ., 2013); data were filtered and trimmed using Biopieces (biopieces.org) to select for reads containing the Tn‐seq barcodes and Krmit ITR ends. Reads were then de‐multiplexed and count tables generated using SamTools (Li et al ., 2009) and HTseq (Chandramohan et al ., 2013). Reads were mapped to the GAS 5448 or NZ131 genome using Bowtie (Langmead et al ., 2009) and data relevant to the gacA‐L locus visualized using the Integrative Genomics Viewer (IGV) browser (broadinstitute.org/igv/home).…”

Section: Methodsmentioning

confidence: 99%

GacA is essential for Group A Streptococcus and defines a new class of monomeric dTDP‐4‐dehydrorhamnose reductases (RmlD)

Beek

Breton

Ferenbach

et al. 2015

Molecular Microbiology

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SummaryThe sugar nucleotide dTDP‐L‐rhamnose is critical for the biosynthesis of the Group A Carbohydrate, the molecular signature and virulence determinant of the human pathogen Group A S treptococcus (GAS). The final step of the four‐step dTDP‐L‐rhamnose biosynthesis pathway is catalyzed by dTDP‐4‐dehydrorhamnose reductases (RmlD). RmlD from the Gram‐negative bacterium S almonella is the only structurally characterized family member and requires metal‐dependent homo‐dimerization for enzymatic activity. Using a biochemical and structural biology approach, we demonstrate that the only RmlD homologue from GAS, previously renamed GacA, functions in a novel monomeric manner. Sequence analysis of 213 Gram‐negative and Gram‐positive RmlD homologues predicts that enzymes from all Gram‐positive species lack a dimerization motif and function as monomers. The enzymatic function of GacA was confirmed through heterologous expression of gac A in a S. mutans rml D knockout, which restored attenuated growth and aberrant cell division. Finally, analysis of a saturated mutant GAS library using Tn‐sequencing and generation of a conditional‐expression mutant identified gac A as an essential gene for GAS. In conclusion, GacA is an essential monomeric enzyme in GAS and representative of monomeric RmlD enzymes in Gram‐positive bacteria and a subset of Gram‐negative bacteria. These results will help future screens for novel inhibitors of dTDP‐L‐rhamnose biosynthesis.

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Section: Methodsmentioning

confidence: 99%

GacA is essential for Group A Streptococcus and defines a new class of monomeric dTDP‐4‐dehydrorhamnose reductases (RmlD)

Beek

Breton

Ferenbach

et al. 2015

Molecular Microbiology

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“…For analysis of RNA-seq datasets, we used Tophat, SAMtools, Cufflinks, Cuffcompare, HTseq-count, and DESeq2 to perform differential expression analysis for strand-specific RNA-seq datasets (Li et al, 2009;Trapnell et al, 2009;Anders and Huber, 2010;Trapnell et al, 2010;Chandramohan et al, 2013). Read numbers on each gene were aligned and calculated using Tophat, SAMtools, and HTseq-count based on a GTF-formatted file produced by Cufflinks and Cuffcompare (Li et al, 2009;Trapnell et al, 2009;Trapnell et al, 2010;Chandramohan et al, 2013).…”

Section: Differential Expression Analysismentioning

confidence: 99%

“…Read numbers on each gene were aligned and calculated using Tophat, SAMtools, and HTseq-count based on a GTF-formatted file produced by Cufflinks and Cuffcompare (Li et al, 2009;Trapnell et al, 2009;Trapnell et al, 2010;Chandramohan et al, 2013). Then, we applied DESeq2 to normalize expression levels and perform differential expression analysis based on the negative binomial distribution (Anders and Huber, 2010).…”

Section: Differential Expression Analysismentioning

confidence: 99%

CURLY LEAF Regulates Gene Sets Coordinating Seed Size and Lipid Biosynthesis

et al. 2016

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ORCID IDs: 0000-0003-1338-523X (J.L.); 0000-0001-6872-9373 (S.D.).CURLY LEAF (CLF), a histone methyltransferase of Polycomb Repressive Complex 2 (PRC2) for trimethylation of histone H3 Lys 27 (H3K27me3), has been thought as a negative regulator controlling mainly postgermination growth in Arabidopsis (Arabidopsis thaliana). Approximately 14% to 29% of genic regions are decorated by H3K27me3 in the Arabidopsis genome; however, transcriptional repression activities of PRC2 on a majority of these regions remain unclear. Here, by analysis of transcriptome profiles, we found that approximately 11.6% genes in the Arabidopsis genome were repressed by CLF in various organs. Unexpectedly, approximately 54% of these genes were preferentially repressed in siliques. Further analyses of 118 transcriptome datasets uncovered a group of genes that was preferentially expressed and repressed by CLF in embryos at the mature-green stage. This observation suggests that CLF mediates a large-scale H3K27me3 programming/reprogramming event during embryonic development. Plants of clf-28 produced bigger and heavier seeds with higher oil content, larger oil bodies, and altered long-chain fatty acid composition compared with wild type. Around 46% of CLF-repressed genes were associated with H3K27me3 marks; moreover, we verified histone modification and transcriptional repression by CLF on regulatory genes. Our results suggest that CLF silences specific gene expression modules. Genes operating within a module have various molecular functions, but they cooperate to regulate a similar physiological function during embryo development.

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“…In many programs, the accuracy of estimating isoform abundances is still debatable. It is important to detect splice site junctions with as much accuracy as possible (Chandramohan et al, 2013), particularly when a priori splice site annotations are unavailable. Programs discussed here include Tophat (Kim et al, 2013), MapSplice (Wang et al, 2010), SOAPsplice (Huang et al, 2011), GSNAP (Wu and Nacu, 2010) and CRAC (Philippe et al, 2013).…”

Section: Box 82 Mapping and Splice Site Determinationmentioning

confidence: 99%

Dual RNA-Sequencing to Elucidate the Plant-Pathogen Duel

Naidoo¹,

Visser²,

Zwart³

et al. 2018

Current Issues in Molecular Biology

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RNA-sequencing technology has been widely adopted to investigate host responses during infection with pathogens. Dual RNA-sequencing (RNA-seq) allows the simultaneous capture of pathogen-specific transcripts during infection, providing a more complete view of the interaction. In this review, we focus on the design of dual RNAseq experiments and the application of downstream data analysis to gain biological insight into both sides of the interaction. Recent literature in this area demonstrates the power of the dual RNA-seq approach and shows that it is not limited to model systems where genomic resources are available. Sequencing costs continue to decrease and single cell transcriptomics is becoming more feasible. In combination with proteomics and metabolomics studies, these technological advances are likely to contribute to our understanding of the temporal and spatial aspects of dynamic plant-pathogen interactions. A dual approach in plantaThe interaction between plants and pathogens is an active and dynamic process that can be likened to a duel. Plants have complex defence mechanisms that can be rendered ineffective when pathogens interfere with one of the various processes required for host defence. These processes include penetration resistance, recognition by Pattern Recognition Receptors (PRRs), phytohormone signalling pathways, secretory pathways, secondary metabolite production, and plant cell death (Dou and Zhou, 2012). Until recently, transcriptomic approaches have been applied in the host and pathogen separately to obtain the gene expression profile of each organism and gain insight into infection biology or host defence mechanisms.RNA sequencing (RNA-seq) is a powerful technology that does not rely on any prior knowledge of transcripts and can generate vast quantities of data with much smaller costs involved than for older techniques such as microarrays (Pareek et al., 2011;Wilhelm and Landry, 2009). An advantage of RNA-seq in the field of plant-pathogen interactions is that both plant and pathogen transcripts can be detected simultaneously and accurately in the same sample. This tactic, known as dual RNAseq, in planta RNA-seq, simultaneous RNA-seq, or comparative RNA-seq, is a relatively new technique both in the plant and medical fields. In plants, it allows for the study of plant-pathogen interactions in herbaceous crops Kunjeti et al., 2012;Lowe et al., 2014) as well as trees (Hayden et al., 2014;Liang et al., 2014;Teixeira et al., 2014). This review outlines technical considerations for dual RNA-seq experiments, summarizes recent insights drawn from such approaches in plant-pathogen interactions, and provides an

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Benchmarking RNA-Seq quantification tools

Cited by 35 publications

References 14 publications

GacA is essential for Group A Streptococcus and defines a new class of monomeric dTDP‐4‐dehydrorhamnose reductases (RmlD)

GacA is essential for Group A Streptococcus and defines a new class of monomeric dTDP‐4‐dehydrorhamnose reductases (RmlD)

CURLY LEAF Regulates Gene Sets Coordinating Seed Size and Lipid Biosynthesis

Dual RNA-Sequencing to Elucidate the Plant-Pathogen Duel

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