Pepper EST database: comprehensive in silico tool for analyzing the chili pepper (Capsicum annuum) transcriptome

Kim, Hyun Jin; Baek, Kwang Hyun; Lee, Seungwon; Kim, Jung Eun; Lee, Bong Woo; Cho, Hye Sun; Kim, Woo Taek; Choi, Doil; Hur, Cheol Goo

doi:10.1186/1471-2229-8-101

Cited by 53 publications

(36 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, SNPs can be identified by simply comparing a candidate sequence to a reference sequence (Nicolai et al, 2012), by whole genome sequencing (WGS; Goff et al, 2002;The Arabidopsis Genome Initiative, 2000), or by sequence alignment of expressed sequence tags (ESTs) to a reference sequence (Jones, 2009;Kota et al, 2001;Labate and Baldo, 2005). For the crops like hot pepper, in which the genome is huge, ESTs have been adopted as an alternative to WGS and as a substrate for cDNA array-based expression analyses (Kim et al, 2008;Rudd, 2003). ESTs are a few hundred base pairs of sequence derived from randomly selected cDNA clones prepared from specific tissues, and EST sequencing is inexpensive compared to WGS.…”

Section: Introductionmentioning

confidence: 99%

Single Nucleotide Polymorphism Marker Discovery from Transcriptome Sequencing for Marker-assisted Backcrossing in Capsicum

Kang¹,

Yang²,

Jeong³

et al. 2014

HST

View full text Add to dashboard Cite

Backcross breeding is the method most commonly used to introgress new traits into elite lines. Conventional backcross breeding requires at least 4-5 generations to recover the genomic background of the recurrent parent. Marker-assisted backcrossing (MABC) represents a new breeding approach that can substantially reduce breeding time and cost. For successful MABC, highly polymorphic markers with known positions in each chromosome are essential. Single nucleotide polymorphism (SNP) markers have many advantages over other marker systems for MABC due to their high abundance and amenability to genotyping automation. To facilitate MABC in hot pepper (Capsicum annuum), we utilized expressed sequence tags (ESTs) to develop SNP markers in this study. For SNP identification, we used Bukang F1-hybrid pepper ESTs to prepare a reference sequence through de novo assembly. We performed large-scale transcriptome sequencing of eight accessions using the Illumina Genome Analyzer (IGA) IIx platform by Solexa, which generated small sequence fragments of about 90-100 bp. By aligning each contig to the reference sequence, 58,151 SNPs were identified. After filtering for polymorphism, segregation ratio, and lack of proximity to other SNPS or exon/intron boundaries, a total of 1,910 putative SNPs were chosen and positioned to a pepper linkage map. We further selected 412 SNPs evenly distributed on each chromosome and primers were designed for high throughput SNP assays and tested using a genetic diversity panel of 27 Capsicum accessions. The SNP markers clearly distinguished each accession. These results suggest that the SNP marker set developed in this study will be valuable for MABC, genetic mapping, and comparative genome analysis.Additional key words: expressed sequence tag, linkage map, marker-assisted breeding *Corresponding author: bk54@snu.ac.kr † These authors contributed equally to this work.※

show abstract

Section: Introductionmentioning

confidence: 99%

Single Nucleotide Polymorphism Marker Discovery from Transcriptome Sequencing for Marker-assisted Backcrossing in Capsicum

Kang¹,

Yang²,

Jeong³

et al. 2014

HST

View full text Add to dashboard Cite

show abstract

“…One example of such a scenario in count data is detection of differentially expressed genes, where even subtle changes in gene expression levels can be indicators of biologically crucial processes [1]. When replicas are costly to obtain one can attempt to use the limited data at one's disposal to make the relevant inferences, as for example in the Audic and Claverie approach [2][3][4][5][6]. Another situation where available count data can be extremely sparse is estimation of time delay in non-stationary gravitationally lensed photon streams.…”

Section: Introductionmentioning

confidence: 99%

“…We consider a model-based Bayesian approach that averages over possible Poisson models with weighting determined by the posterior over the models, given the single observation. In fact, such a Bayesian approach has been considered in the bioinformatics literature under the assumption of flat improper prior over the Poisson rate parameter [2][3][4][5][6]. One can, of course, be excused for being highly sceptical about the relevance of such inferences, yet the methodology has apparently been used in a number of successful studies.…”

Section: Introductionmentioning

confidence: 99%

Pushing for the Extreme: Estimation of Poisson Distribution from Low Count Unreplicated Data—How Close Can We Get?

Tiňo

2013

Entropy

View full text Add to dashboard Cite

Studies of learning algorithms typically concentrate on situations where potentially ever growing training sample is available. Yet, there can be situations (e.g., detection of differentially expressed genes on unreplicated data or estimation of time delay in non-stationary gravitationally lensed photon streams) where only extremely small samples can be used in order to perform an inference. On unreplicated data, the inference has to be performed on the smallest sample possible-sample of size 1. We study whether anything useful can be learnt in such extreme situations by concentrating on a Bayesian approach that can account for possible prior information on expected counts. We perform a detailed information theoretic study of such Bayesian estimation and quantify the effect of Bayesian averaging on its first two moments. Finally, to analyze potential benefits of the Bayesian approach, we also consider Maximum Likelihood (ML) estimation as a baseline approach. We show both theoretically and empirically that the Bayesian model averaging can be potentially beneficial.

show abstract

“…To better understand fruit development and ripening mechanisms, numerous studies have focused on measuring transcript and metabolite levels in climacteric fruits, such as tomato (Alba et al, 2005;Vriezen et al, 2008;Enfissi et al, 2010;Karlova et al, 2011;Osorio et al, 2011a) and peach (Borsani et al, 2009;Zhang et al, 2010;Lombardo et al, 2011;Li et al, 2012), and in nonclimacteric fruits, such as strawberry (Aharoni and O'Connell, 2002;Fait et al, 2008;Bombarely et al, 2010;Osorio et al, 2011b;Zhang et al, 2011), pepper (Kim et al, 2008;Lee et al, 2010;Osorio et al, 2012;Liu et al, 2013;Wahyuni et al, 2013), and grape Grimplet et al, 2007). These studies have been complemented by the investigation of transcriptomics, proteomics, and metabolomics data in the three dominant ripening mutants of tomato, ripening-inhibitor (rin), nonripening (nor), and never-ripe (Nr), along the developmental and ripening periods.…”

mentioning

confidence: 99%

Conserved Changes in the Dynamics of Metabolic Processes during Fruit Development and Ripening across Species

et al. 2013

View full text Add to dashboard Cite

Computational analyses of molecular phenotypes traditionally aim at identifying biochemical components that exhibit differential expression under various scenarios (e.g. environmental and internal perturbations) in a single species. High-throughput metabolomics technologies allow the quantification of (relative) metabolite levels across developmental stages in different tissues, organs, and species. Novel methods for analyzing the resulting multiple data tables could reveal preserved dynamics of metabolic processes across species. The problem we address in this study is 2-fold. (1) We derive a single data table, referred to as a compromise, which captures information common to the investigated set of multiple tables containing data on different fruit development and ripening stages in three climacteric (i.e. peach [Prunus persica] and two tomato [Solanum lycopersicum] cultivars, Ailsa Craig and M82) and two nonclimacteric (i.e. strawberry [Fragaria 3 ananassa] and pepper [Capsicum chilense]) fruits; in addition, we demonstrate the power of the method to discern similarities and differences between multiple tables by analyzing publicly available metabolomics data from three tomato ripening mutants together with two tomato cultivars. (2) We identify the conserved dynamics of metabolic processes, reflected in the data profiles of the corresponding metabolites that contribute most to the determined compromise. Our analysis is based on an extension to principal component analysis, called STATIS, in combination with pathway overenrichment analysis. Based on publicly available metabolic profiles for the investigated species, we demonstrate that STATIS can be used to identify the metabolic processes whose behavior is similarly affected during fruit development and ripening. These findings ultimately provide insights into the pathways that are essential during fruit development and ripening across species.

show abstract

Pepper EST database: comprehensive in silico tool for analyzing the chili pepper (Capsicum annuum) transcriptome

Cited by 53 publications

References 20 publications

Single Nucleotide Polymorphism Marker Discovery from Transcriptome Sequencing for Marker-assisted Backcrossing in Capsicum

Single Nucleotide Polymorphism Marker Discovery from Transcriptome Sequencing for Marker-assisted Backcrossing in Capsicum

Pushing for the Extreme: Estimation of Poisson Distribution from Low Count Unreplicated Data—How Close Can We Get?

Conserved Changes in the Dynamics of Metabolic Processes during Fruit Development and Ripening across Species

Contact Info

Product

Resources

About