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A Survey of Alternative Splicing in Allotetraploid Cotton ( Gossypium hirsutum L.) | Min | Computational Molecular Biology

Research Article

A Survey of Alternative Splicing in Allotetraploid Cotton (Gossypium hirsutum L.)  

Xiang Jia Min
Department of Biological Sciences, Youngstown State University, Youngstown, OH 44555, USA
Author    Correspondence author
Computational Molecular Biology, 2018, Vol. 8, No. 1   doi: 10.5376/cmb.2018.08.0001
Received: 10 Apr., 2018    Accepted: 23 May, 2018    Published: 27 Jul., 2018
© 2018 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Min X.J., 2018, A survey of alternative splicing in allotetraploid cotton (Gossypium hirsutum L.), Computational Molecular Biology, 8(1): 1-13 (doi: 10.5376/cmb.2018.08.0001)


Allotetraploid cotton (Gossypium hirsutum L.), accounting for more than 90% of cultivated cotton worldwide, provides textile fibers and seeds. Alternative splicing (AS) is a post-transcriptional process that generates more than one RNA isoforms from a single pre-mRNA transcript, increasing the diversity of functional proteins and RNAs. We surveyed the alternatively spliced genes in cotton using expressed sequence tag (EST) and mRNA sequences available in the public databases. A total of 56,080 AS events, including 41,150 (73.4%) basic events and 14,930 (26.6%) complex events were identified, which were generated from approximately 23,930 genes. Intron retention was the most frequent event, accounting for 34.8%, followed by alternative acceptor site events (18.8%) and alternative donor site events (11.8%), and exon skipping being the least frequent event (8.0%). Complex types, which are formed by more than one basic event, are accounted for 26.6%. The estimated AS rates of genes generating AS isoforms was 27.1% in cotton. Gene Ontology and protein family analysis showed that the products of alternatively spliced genes were involved in many biological processes with diverse molecular functions. The transcripts to cotton genome mapping information can be used to improve the predicted gene models in cotton. The annotation information of AS isoforms of these genes provides a basis for future investigation on the functions of these AS genes in cotton biology. The data can be accessed at Plant Alternative Splicing Database (http://proteomics.ysu.edu/altsplice/). 

Alternative splicing; Cotton; Gene expression; Gossypium hirsutum; mRNA; Plant


The most widely cultivated upland cotton (Gossypium hirsutum L.) is an allotetraploid species (AtAtDtDt), consisting of both A sub-genomes and D sub-genomes (Lubbers and Chee, 2009; Li et al., 2015; Zhang et al., 2015). G. hirsutum accounts for more than 90% of commercial cotton production worldwide and is the main sources of renewable textile fibers and seeds (Wendel and Grover, 2015). The genomes of the two extant progenitor relatives, G. arboreum (AA) and G. rainondii (DD), and G. hirsutum have been sequenced (Wang et al., 2012; Li et al., 2014a; Li et al., 2015; Zhang et al., 2015). Sequencing these genomes provides insights on the genome evolution, gene contents, regulatory elements, genomic signatures of selection and domestication in these species. The genome sequences of G. hirsutum (acc. TM-1) have been reported independently by two teams with 66,434 and 76,943 genes annotated from the assembled genomes, respectively (Li et al., 2015; Zhang et al., 2015).


Plant gene expression is a tightly controlled process in regulating growth and development as well as in response to changing environments. In addition to alternative transcription initiation site and polyadenylation site that generate different transcript isoforms, alternative splicing (AS) is a common process in plants that generates two or more transcript isoforms from one pre-mRNA sequence (Reddy et al., 2013). Thus, the diversities of mRNAs and proteins in the organism are significantly increased by AS. There are already well documented experimental data showing AS plays critical roles in many biological processes in plants such as photosynthesis, defense responses, flowering timing, grain quality, and responses to stresses (Reddy et al., 2013; Staiger et al., 2013). There are four basic types of AS, including exon skipping (ES), alternative donor site (AltD), alternative acceptor (AltA) site, and intron retention (IR). Various complex types can be found in transcript isoforms by combination of basic events (Sablok et al., 2011). AS isoforms may encode a distinct functional protein or become non-functional due to harboring a premature termination codon in protein coding regions. The nonfunctional isoforms are degraded by a process known as nonsense-mediated decay (NMD) (Lewis et al., 2003). 


Arabidopsis thaliana, a model plant species, has been intensively investigated and were reported with ~60-70% of multi-exon genes undergoing AS (Filichkin et al., 2010; Zhang et al., 2010; Marquez et al., 2012; Syed et al., 2012; Carvalho et al., 2013; Yu et al., 2016; Zhang et al., 2017). AS in other plant species also has been examined including Oryza sativa (rice) (Wang and Brendel, 2006; Min et al., 2015; Wei et al., 2017; Kater et al., 2018), Nelumbo nucifera (sacred lotus) (VanBuren et al., 2013), Vitis vinifera (grape) (Vitulo et al., 2014; Sablok et al., 2017), Brachypodium distachyon (Sablok et al., 2011; Walters et al., 2013), Zea mays (maize) (Thatcher et al., 2014; Min et al., 2015; Thatcher et al., 2016; Mei et al., 2017; Min, 2017), and Sorghum bicolor (sorghum) (Panahi et al., 2014; Min et al., 2015; Abdel-Ghany et al., 2016), etc. Approximately 60-75% of AS events occur within the protein coding regions of mRNAs, resulting changes in binding properties, intracellular localization, protein stability, enzymatic, and signaling activities (Stamm et al., 2005). IR has been shown to be the most frequent AS event in plants with AS rates in the intron containing genes ranged from ~30% to > 60% depending on available transcriptome data (Sablok et al., 2011; Reddy et al., 2013; Sablok et al., 2017). Genome-wide conserved alternatively spliced genes among different plant species have been identified in cereal plants and fruit plants (Min et al., 2015; Sablok et al., 2017). Further, genome-wide conserved AS events across a wide range of plant species such as in flowering plant species as well as in monocot species have also been analyzed (Chamala et al., 2015; Mei et al., 2017). These works lay the foundation for identifying and studying conserved AS genes as well as conserved AS events across evolutionally related plant species (Min et al., 2015; Mei et al., 2017).


There were only three reports related to genome-wide AS analysis in cotton so far. Using RNA-sequencing (RNA-seq) data from G. raimondii, 16,437 AS events in 10,197 genes were identified (Li et al., 2014b). Similar RNA-seq analysis identified 14,172 AS events in 6,797 genes G. davidsonii growing under salt stress conditions (Zhu et al., 2018). Most recently, Wang et al. (2018) reported that using Pacific Biosciences single molecule long-read isoform sequencing (Iso-Seq) identified 176,849 full-length transcript isoforms, detected a total of 133,229 AS events, from 27,229 gene loci, with 15,102 fiber-specific AS events in G. barbadense, an allotetraploid cotton species. In all three reports, the prevalent type of AS events was retained introns. In this work, we report a survey of AS events using currently available expressed sequence tags (ESTs) and mRNA sequences with an aim to generate a preliminary catalog of alternatively spliced genes in the cultivated upland cotton species, G. hirsutum


1 Materials and Methods

1.1 Sequence datasets and sequence assembly

Two draft genome sequences of allotetraploid cotton (G. hirsutum L. acc. TM-1) have been generated independently (Li et al., 2015; Zhang et al., 2015). In this work we used the genome sequences (assembly ASM98774v1) generated by Li et al. (2015) as they were available for downloading from the National Center for Biotechnology Information (NCBI) genome database (https://www.ncbi.nlm.nih.gov/genome/?term=cotton). We also downloaded a total of 432,161 nucleotide sequences of G. hirsutum including 94,350 mRNA sequences and 337,811 EST sequences. For simplicity of description the term “cotton” only means G. hirsutum in the context, otherwise, full species names were specified.


1.2 Transcripts assembly, mapping to genome, and identification AS events

The EST and mRNA sequences were processed to remove contaminants, vector and repetitive sequences using a procedure we implemented previously (Min et al., 2015). The procedure was briefly outlined below: EMBOSS trimmest tool was used to trim the polyA or polyT end (Rice et al., 2000); then trimmed ESTs and mRNA sequences were used to search against UniVec and E. coli database using BLASTN for removal of vector and E. coli contaminants; finally BLASTN searches against the plant repeat database which was built with TIGR gramineae repeat data, sorghum, maize, and rice repeat data (available from ftp://ftp.plantbiology.msu.edu/pub/data/TIGR_Plant_Repeats/). A total of 430,541 cleaned EST and mRNA sequences were assembled using CAP3 with the following parameters: -p 95 -o 50 -y 20 (Huang and Madan, 1999). A total of 279,050 putative unique transcripts (PUTs) including 28,316 contigs and 250,734 singlets were obtained for mapping to the genome sequences.


The assembled PUTs were mapped to their corresponding chromosomes using ASFinder (http://proteomics.ysu.edu/tools/ASFinder.html/) (Min, 2013). We applied the threshold values: a minimum of 95% identity, a minimum of 80 bp aligned length, and > 75% of a PUT sequence aligned to the genome (Walters et al., 2013). ASFinder uses SIM4 program (Florea et al., 1998) to align PUTs to the genome, and then subsequently identifies those PUTs that are mapped to the same genomic location and have variable exon-intron boundaries as AS isoforms. To avoid chimeric PUT assemblies, mapped PUTs having an intron size > 100 kb were removed for AS identification. The output file (AS. gtf) from ASFinder was submitted to AStalavista server (http://genome.crg.es/astalavista/) for AS event classification (Foissac and Sammeth, 2007). The rate of alternative splicing genes was estimated as the ratio of genomic loci having alternative splicing PUT isoforms over total genomic loci having at least one mapped PUT sequence.


1.3 Functional annotation of PUTs and data availability

The PUT sequences were functionally annotated, including prediction protein coding region and domain search. The coding region of each PUT was predicted using the ORFPredictor (Min et al., 2005a) and the full-length transcript coverage was assessed using TargetIdentifier (Min et al., 2005b). Functional classification was based on the BLASTX search with an E-value threshold of 1e-5 against UniProtKB/Swiss-Prot. In addition, predicted protein sequences from ORFPredictor were further annotated for functional domains using rpsBLAST against the PFam database (http://pfam.xfam.org/). The assembled PUTs were further compared with transcripts of predicted gene models using BLASTN with a cut off E-value of 1e-10, ≥ 95% identity and a minimum aligned length of 80 bp. Gene Ontology (GO) information was extracted from the UniProt ID mapping table based on the BLASTX search of PUTs sequences against the UniProtKB/Swiss-Prot (ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/). The GO categories were further analyzed using GO Slim Viewer using plant specific GO terms (http://www.agbase.msstate.edu/cgi-bin/tools/goslimviewer_select.pl) (McCarthy et al., 2006). 


1.4 Availability of data

The assembled PUTs and AS events identified in this study along with the predicted gene models, as well as data reported previously in our group, are available from Plant Alternative Splicing Database (http://proteomics.ysu.edu/altsplice/) (VanBuren et al., 2013; Walters et al., 2013; Min et al., 2015; Wai et al., 2016; Min, 2017; Sablok et al., 2017). BLAST search is also available for searching the PUTs and AS isoforms. The datasets supporting the conclusions of this article including the data used for database construction and the supplementary data are publicly available at: http://bioinformatics.ysu.edu/publication/data/Cotton/.


2 Results and Discussion

2.1 Transcripts assembling and annotation

After removing contaminant and low complexity sequences of the combined ESTs and mRNAs of cotton (G. hirsutum L.), we used CAP3 program to assemble the cleaned data. A total of 279,050 putative unique transcripts (PUTs) including 28,316 contigs and 250,734 singlets were obtained for further annotation and mapping to the genome sequences. The PUTs had a length ranged from 100 bp to 20,499 bp and an average length of 975 bp (Table 1). All PUTs were structurally and functionally annotated including ORF prediction, coding region completeness assessment, a putative function and PFam prediction. These basic features were summarized in Table 1. A total of 278,650 ORFs were predicted using OrfPredictor including 201,924 of them were predicted using the frame values obtained from BLASTX search against the UniProt Swiss-Prot dataset and 72,726 ORFs were predicted based the intrinsic sequence signals in the sequences (Min et al., 2005a). Among them 128,505 PUTs were predicted encoding full-length proteins by TargetIdentifier (Min et al., 2005b). Among the predicted ORFs, 166,174 were annotated with a protein family (Pfam) match (Table 1). Further, using BLASTN search with a cutoff of 95% identity 247,871 (88.8%) PUTs matched with predicted mRNA sequences of predicted protein coding gene models (Li et al., 2015). 


Table 1 Basic features of the assembled putative unique transcripts (PUTs) of cotton plant


2.2 Mapping transcripts to cotton genome

We used relatively strict mapping parameters to map PUTs to the genome as described in the method section. The identity threshold of 95% prevented PUTs mismatching to the genome segments with lower similarities due to ancient genome or gene duplications. In other hand, it allowed accurate mapping by tolerating errors in PUT sequences that might be generated in original ESTs or in the assembling process. In addition, there might be sequence errors in the assembled genome sequences or variations in different varieties or ecotypes of the same species. We have used the same procedure of PUTs mapping to the genomes in other plant species including cereal plants and fruit plants (Min et al., 2015; Min, 2017; Sablok et al., 2017). A total of 196,098 (70.3% of the total assembled PUTs) PUTs were mapped to G. hirsutum genome, including 113,180 PUTs were mapped to a single genomic locus and 82,918 PUTs were mapped to two or more genomic loci (Table 2). The reason for the relative larger number of PUTs (42.3% of mapped PUTs) having more than one mapping loci was apparently due to G. hirsutum AtDt genome consisting of both A subgenome and D subgenome (Li et al., 2015; Zhang et al., 2015). Diploid genomes of G. arboretum (AA) and G. raimondii (DD), which were diverged about 5-10 million years ago (MYA), have been sequenced (Wang et al., 2012; Li et al., 2014a). Our analysis show that the homologues mRNA sequences of diploid G. arboretum (AA) and G. raimondii (DD), still share 97-100% identity.


Table 2 Mapping of putative unique transcripts (PUTs) to cotton genome


The PUTs were mapped to a total of 88,420 genomic loci (Table 2). This number was higher than the number of genes reported by the genome sequencing projects, as 76,943 genes were reported by Li et al. (2015) and 66,434 genes were reported by Zhang et al. (2015). The mapped PUTs that were located in the regions outside of the predicted genes may contain genes remained to be annotated.


It should be noted that there were 29.7% of the PUTs not being mapped to the draft genome sequences (Table 2). The reasons for these PUTs not being mapped may include incompleteness of the genome sequences and possible errors in the PUTs or genomic sequences including sequencing errors and misassembling. However, these unmapped PUTs were annotated and available from our database, the information might be useful for identifying new genes from cotton species.


2.3 Detection and classification of alternative splicing events

The PUTs to genome mapping gtf (gene transfer format) file generated by ASFinder was submitted to the AStalavista server for identification and classification of AS events (Foissac and Sammeth, 2007; Min, 2013). A total of 56,080 AS events were detected and classified, including 41,150 (73.4%) basic events and 14,930 (26.6%) complex events which had more than one basic event (Figure 1). These AS events were generated from 23,930 genomic loci (clusters) with 44,239 unique transcripts (Table 2). As a total of 88,150 genomic loci have at least one PUT mapped, thus, the estimated AS rates of genes generating AS isoforms (AS genes) was 27.1% in cotton (Table 2). However, based on the PUTs mapping data, there were 25,427 genomic loci having PUTs not having an intron. Thus, only considering the genomic loci mapped with PUTs having an introns or introns, the AS rate was 40.0% in this dataset in cotton. The AS rate in cotton is lower than the rate in Arabidopsis (~60%) and in maize (55%) reported previously (Marquez et al., 2012; Mei et al., 2017; Min, 2017), this apparently due to relative lower number of available EST and mRNA sequences used in current analysis. Recently, RNA-seq analysis in G. raimondii and G. davidsonii revealed 31.6% and 32.0% AS rates, respectively, in intron-containing genes (Li et al., 2014b; Zhu et al., 2018). Thus, more cotton genes undergoing AS are expected to be identified when more gene expression data are available.


Figure 1 Landscape of alternative splicing events in cotton


Among the AS events, IR (34.8%) was the prevalent type, followed by AltA (18.8%) and AltD (11.8%), with ES (8.0%) as the least type of AS events (Figure 1). Though there were some variations in distributions of AS types, this pattern of AS events was consistent in all cotton species (Li et al., 2014; Zhu et al., 2018) as well as in other plant species so far we have investigated as well as plant species examined by others including Arabidopsis, Brachypodium distachyon, cereal plants, and fruit plants (Wang and Brendel, 2006; Baek et al., 2008; Labadorf et al., 2010; Sablok et al., 2012; VanBuren et al., 2013; Walter et al., 2013; Thatcher et al., 2014; Min et al., 2015; Sablok et al., 2017). We also observed that the proportion of complex events varied in different plant species or different analysis of the same species, and the ratio was positively correlated with the average length of assembled transcripts (Min et al., 2015; Min, 2017; Sablok et al., 2017).


One interesting finding of the role of transposons played during AS in plant species was reported recently by Li et al. (2014). Transposable elements (TEs) were found in only 2.9% of all introns, however, 43% of the retained introns were found to have TEs in the AS transcript isoforms. Such an enrichment of TEs in the retained introns in the AS isoforms suggested TE-insertion may play an important role during AS (Li et al., 2014b). In our datasets we retrieved 12,774 retained introns with a length > 30 bp and found only 263 TEs, about 2.1% having TEs in retained introns. Such a discrepancy of TEs in the retained introns of AS isoforms might be resulted by the data processing procedure because in our data cleaning steps, for avoiding misassembling, we purposely removed plant repetitive DNA elements including TEs from the ESTs and mRNA sequences prior to assembling PUTs.


It should be noted that the mapping of PUTs in this work used a cutoff of 95% sequence identity for the aligned regions, this cutoff value could not distinguish homoeologous genes between two subgenomes or homologous genes from recent gene duplications. Full-length mRNA sequences including both 5’-and 3’ untranslated regions (UTRs) with strict sequence identity, i.e., 100%, should be able to distinguish transcript isoforms of AS generated from two subgenomes. Recent work using single molecule long-read isoform sequencing (Iso-Seq) identified full-length transcript isoforms and was able to distinguish isoforms from two subgenomes in G. barbadense, an allotetraploid cotton species (Wang et al., 2018). It was estimated that ~51.4% of homoeologous genes produced divergent isoforms in each subgenome (Wang et al., 2018). 


2.4 Functional classification of PUTs and AS genes

All PUTs including both mapped and unmapped PUTs were annotated functionally as described in section 3.1. To simplify description, predicted gene models having AS transcript isoforms are referred as AS genes, and gene models not having AS transcripts in the current analysis are referred as non-AS genes. To obtain a general picture of protein family distribution in AS genes and non-AS genes, the predicted protein sequences of the PUTs were used to search the Pfam database. For genomic loci having more than one isoform PUT, only one Pfam annotation was selected from each genomic locus. A total of 57,900 Pfam matches from a total of 3,505 protein families were obtained from encoded proteins of a total of 88,420 loci. Among 23,930 genomic loci having AS isoforms, 18,218 genes encoded proteins had Pfam matches to a total of 2,454 protein families. The top protein families in the whole cotton proteome and proteins encoded by genes undergoing AS were listed in Table 3. Among the protein families, many of them were found having AS isoforms in other plant species including cereal plants and fruit plants (Min et al., 2015; Wai et al., 2016; Sablok et al., 2017). These protein families include Pkinase (protein kinase domain), RRM_1 (RNA recoginition motif), Pkinase_Tyr (protein tyrosine kinase), P450 (cytochrome P450), Ras family, UQ_con (uniquitin-conjugating enzyme), etc., suggesting an evolutionally systematic conservation of AS in plant species (Min et al., 2015; Sablok, 2017). We noticed that among 100 genomic loci encoded cellulose synthase (Pfam03552) 39 of them had alternative splicing. In considering the important role played by this enzyme in fiber formation, the functional significance of AS of these genes is warranted for further examination. 


Table 3 Protein family distributions in the proteins encoded by all genes and by genes with pre-mRNA alternative splicing (AS genes) in cotton

Note: This is only a partial list; The information for accessing the complete list can be found in the main text


Genes undergoing AS during post-transcriptional process produce functional isoforms or non-functional isoforms. We evaluated the impact of AS on the functionalities of the gene products by comparing their Pfam annotation. Among a total of 50,680 isoform pairs generating AS events, 14,214 (25.3%) pairs had no Pfam hit, 30,202 (53.9%) isoform pairs had identical Pfam, 9,046 (16.1%) pairs had one isoform with a Pfam hit and the other not having a Pfam hit, indicating the functional loss of gene products, and 2,708 (4.8%) pairs had different Pfam hits. Thus, about 20.9% of AS events generated isoforms with functional loss or change. Similar results were obtained in our previous analysis with pineapple and maize data (Wai et al., 2016; Min et al., 2017). The Pfam loss or change in the gene products is most likely caused by the translation frame changes in AS isoforms. The MADS-box genes were alternatively spliced in cotton and some of the alternatively spliced isoforms potentially encoded proteins with altered K-domain and/or C-terminal regions (Lightfoot et al., 2008). The genes were expressed in developing fiber cells suggesting a role in cotton fiber biosynthesis. The biological significance of the change in protein family functional domains in these genes certainly is interesting for further investigation. 


2.5 Gene Ontology (GO) analysis of gene products

GO categories provide an overview of the gene products involved in the biological processes, molecular functions, and cellular components. As GO annotation is fairly complex with variable available information for different gene products, thus the analysis is not intended for an accurate quantification but rather providing a broad picture of the functionalities of the gene products. Among the whole set of 27,9031 cotton PUTs sequences a total of 201,924 (72.3%) PUTs had a BLASTX hit (E-value < 1e-5) against the Swiss-Prot database. Then using the Swiss-Prot identifiers we retrieved a total of 1,324,154 GO identifiers. These GO identifiers were further grouped into top categories using GO Slim Viewer (McCarthy et al., 2006). The isoforms from AS genes were also analyzed using the same procedure and a total of 234,362 GO identifiers were obtained. Our previous analysis showed that GO cellular component analysis based on BLASTX method was not accurate, thus we only summarized the GO classification of biological process and molecular function in the whole set of PUTs and isoforms generated by AS genes (Table 4; Table 5). The top categories of molecular functions include binding, catalytic activity, nucleotide binding, transferase activity, hydrolase activity, protein binding etc. (Table 4). These top categories of molecular functions of gene products are more or less similar in all plant species we have examined (Min et al., 2015). The top categories of biological processes include cellular process, metabolic process, biosynthetic process, nucleobase-containing compound metabolic process, response to stress, etc. (Table 5). As expected, the distribution patterns of these processes were also similar in all the plant datasets we analyzed (Min et al., 2015).


Table 4 Gene Ontology classification of molecular functions of gene products in the whole set of assembled transcripts and in isoforms generated by alternatively spliced genes in cotton


Table 5 Gene Ontology classification of biological processes of gene products in the whole set of assembled transcripts and in isoforms generated by alternatively spliced genes in cotton


GO analysis showed AS gene products were involved in all the biological processes with various molecular functions. In average 46.3% in GO molecular functions and 47.1% in GO biological processes were obtained from the gene products of the AS genes. There are well characterized genes undergoing AS with demonstrated functional significance in regulation of plant growth, development, as well as stress responses (Reddy et al., 2013; Staiger and Brown, 2013). Therefore, the biological roles of AS genes in cotton growth and development need to be examined further.



The work was supported by the Youngstown State University Research Professorship award and a University Research Council (URC) grant to XJM. 



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PMid:15980561 PMCid:PMC1160155


Min X.J., Butler G., Storms R., and Tsang A., 2005b, TargetIdentifier: a web server for identifying full-length cDNAs from EST sequences, Nucleic Acids Res., 33: W669-W672


PMid:15980559 PMCid:PMC1160197


Min X.J., Powell B., Braessler J., Meinken J., Yu F., and Sablok G., 2015, Genome-wide cataloging and analysis of alternatively spliced genes in cereal crops, BMC Genomics, 16: 721


PMid:26391769 PMCid:PMC4578763


Panahi B., Abbaszadeh B., Taghizadeghan M., and Ebrahimie E., 2014, Genome-wide survey of alternative splicing in Sorghum bicolor, Physiol Mol Biol Plants, 20: 323-329


PMid:25049459 PMCid:PMC4101146


Reddy A.S.N., Marquez Y., Kalyna M., and Barta A., 2013, Complexity of the alternative splicing landscape in plants, Plant Cell, 25: 3657-3683


PMid:24179125 PMCid:PMC3877793


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Sablok G., Gupta P.K., Baek J.M., Vazquez F., and Min X.J., 2011, Genome-wide survey of alternative splicing in the grass Brachypodium distachyon: an emerging model biosystem for plant functional genomics, Biotechnology Letters, 33: 629-636




Sablok G., Powell B., Braessler J., Yu F., and Min X.J., 2017, Comparative landscape of alternative splicing in fruit plants, Current Plant Biology


Staiger D., and Brown J.W., 2013, Alternative splicing at the intersection of biological timing, development, and stress responses, Plant Cell, 25: 3640-3656


PMid:24179132 PMCid:PMC3877812


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PMid:22743067 PMCid:PMC3466422


Thatcher S.R., Danilevskaya O.N., Meng X., Beatty M., Zastrow-Hayes G., Harris C., Allen B.V., Habben J., and Li B., 2016, Genome-wide analysis of alternative splicing during development and drought stress in maize, Plant Physiology, 170: 586-599


PMid:26582726 PMCid:PMC4704579


Thatcher S.R., Zhou W., Leonard A., Wang B.B., Beatty M., Zastrow-Hayes G., Zhao X.Y., Baumgarten A., and Li B., 2014, Genome-wide analysis of alternative splicing in Zea mays: landscape and genetic regulation, Plant Cell, 26: 3472-3487


PMid:25248552 PMCid:PMC4213170


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PMid:24739459 PMCid:PMC4108029


Wai C.M., Powell B., Ming R., and Min X.J., 2016, Analysis of alternative splicing landscape in pineapple (Ananas comosus), Tropical Plant Biology, 9: 150-160



Walters B., Lum G., Sablok G., and Min X.J., 2013, Genome-wide landscape of alternative splicing events in Brachypodium distachyon, DNA Res., 20: 163-171


PMid:23297300 PMCid:PMC3628446


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Wang M., Wang P., and Liang F. et al., 2018, A global survey of alternative splicing in allopolyploid cotton: landscape, complexity and regulation, New Phytologist, 217: 163-178




Wei H., Lou Q., Xu K., Yan M., Xia H., Ma X., Yu X., and Luo L., 2017, Alternative splicing complexity contributes to genetic improvement of drought resistance in the rice maintainer HuHan2B, Sci. Rep., 7: 11686


PMid:28916800 PMCid:PMC5601427


Wendel J.F., and Grover C.E., 2015, Taxonomy and evolution of the cotton genus, Gossypium, Cotton, (agronmonogr57): 25-44


Yu H., Tian C., Yu Y., and Jiao Y., 2016, Transcriptome survey of the contribution of alternative splicing to proteome diversity in Arabidopsis thaliana, Mol. Plant, 9: 749-752




Zhang P.G., Huang S.Z., Pin A.L., and Adams K.L.,2010, Extensive divergence in alternative splicing patterns after gene and genome duplication during the evolutionary history of Arabidopsis, Mol. Biol. Evol., 27: 1686-1697




Zhang R., Calixto C.P., and Marquez Y. et al., 2017, A high quality Arabidopsis transcriptome for accurate transcript-level analysis of alternative splicing, Nucleic Acids Res., 45: 5061-73


PMid:28402429 PMCid:PMC5435985


Zhang T., Hu Y., and Jiang W. et al., 2015, Sequencing of allotetraploid cotton (Gossypium hirsutum L. acc. TM-1) provides a resource for fiber improvement, Nat. Biotech., 33: 531




Zhu G., Li W., Zhang F., and Guo W., 2018, RNA-seq analysis reveals alternative splicing under salt stress in cotton, Gossypium davidsonii, BMC Genomics, 19: 73


PMid:29361913 PMCid:PMC5782385

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