• Alternative splicing
• Cancer stem cell
• Expression analysis
• Stem cell
• Transcriptome

• alternative splicing
• colorectal carcinoma
• regulation mechanism

• RNA sequencing
• alternatively spliced transcript detection microarray
• coefficient value
• rice
• transcriptome

• Alternative Splicing/genetics
• Exons/genetics
• Gene Expression Regulation, Plant
• Genome, Plant
• Introns/genetics
• Microarray Analysis
• Oryza/genetics
• Plant Leaves/genetics
• Plant Leaves/growth & development
• Plant Roots/genetics
• Plant Roots/metabolism
• RNA Splicing/genetics
• Seeds/genetics
• Seeds/growth & development
• Transcriptome/genetics

Standard 3′ Affymetrix gene expression arrays have contributed a significantly higher volume of existing gene expression data than other microarray platforms. These arrays were designed to identify differentially expressed genes, but not their alternatively spliced transcript forms. No resource can currently identify expression pattern of specific mRNA forms using these microarray data, even though it is possible to do this.

We report a web server for expression profiling of alternatively spliced transcripts using microarray data sets from 31 standard 3′ Affymetrix arrays for human, mouse and rat species. The tool has been experimentally validated for mRNAs transcribed or not-detected in a human disease condition (non-obstructive azoospermia, a male infertility condition). About 4000 gene expression datasets were downloaded from a public repository. ‘Good probes’ with complete coverage and identity to latest reference transcript sequences were first identified. Using them, ‘Transcript specific probe-clusters’ were derived for each platform and used to identify expression status of possible transcripts. The web server can lead the user to datasets corresponding to specific tissues, conditions via identifiers of the microarray studies or hybridizations, keywords, official gene symbols or reference transcript identifiers. It can identify, in the tissues and conditions of interest, about 40% of known transcripts as ‘transcribed’, ‘not-detected’ or ‘differentially regulated’. Corresponding additional information for probes, genes, transcripts and proteins can be viewed too. We identified the expression of transcripts in a specific clinical condition and validated a few of these transcripts by experiments (using reverse transcription followed by polymerase chain reaction). The experimental observations indicated higher agreements with the web server results, than contradictions. The tool is accessible at http://resource.ibab.ac.in/TIPMaP.

The newly developed online tool forms a reliable means for identification of alternatively spliced transcript-isoforms that may be differentially expressed in various tissues, cell types or physiological conditions. Thus, by making better use of existing data, TIPMaP avoids the dependence on precious tissue-samples, in experiments with a goal to establish expression profiles of alternative splice forms – at least in some cases.

Prior to the completion of the human genome project, the human genome was thought to have a greater number of genes as it seemed structurally and functionally more complex than other simpler organisms. This along with the belief of “one gene, one protein”, were demonstrated to be incorrect. The inequality in the ratio of gene to protein formation gave rise to the theory of alternative splicing (AS). AS is a mechanism by which one gene gives rise to multiple protein products. Numerous databases and online bioinformatic tools are available for the detection and analysis of AS. Bioinformatics provides an important approach to study mRNA and protein diversity by various tools such as expressed sequence tag (EST) sequences obtained from completely processed mRNA. Microarrays and deep sequencing approaches also aid in the detection of splicing events. Initially it was postulated that AS occurred only in about 5% of all genes but was later found to be more abundant. Using bioinformatic approaches, the level of AS in human genes was found to be fairly high with 35-59% of genes having at least one AS form. Our ability to determine and predict AS is important as disorders in splicing patterns may lead to abnormal splice variants resulting in genetic diseases. In addition, the diversity of proteins produced by AS poses a challenge for successful drug discovery and therefore a greater understanding of AS would be beneficial.

• Alternative splicing (AS)
• Bioinformatics
• Database
• Expressed sequence tags (ESTs)
• Microarray
• Protein
• Splice variants.
• mRNA

• Biomarkers, Tumor
• Breast Neoplasms/genetics
• Breast Neoplasms/pathology
• Breast Neoplasms/physiopathology
• Cell Line, Tumor
• Cell Movement
• Cell Proliferation
• Cell Shape
• Drug Resistance, Neoplasm/physiology
• Epithelial-Mesenchymal Transition/physiology
• Female
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Gene Silencing
• Humans
• Microarray Analysis
• Neoplasm Invasiveness
• RNA, Small Interfering/genetics
• RNA, Small Interfering/metabolism
• Receptors, Estrogen/genetics
• Receptors, Estrogen/metabolism

• Alternative Splicing
• Animals
• Expressed Sequence Tags
• Humans
• Metabolism/genetics
• Phylogeny
• Polymorphism, Single Nucleotide/genetics
• Sequence Analysis, DNA

• Alternative Splicing/genetics
• Base Sequence/physiology
• Binding Sites
• Computational Biology
• DNA, Complementary/metabolism
• Genome, Plant/genetics
• Genome, Plant/physiology
• Introns
• Leucine-Rich Repeat Proteins
• Molecular Sequence Data
• Oryza/genetics
• Oryza/metabolism
• Proteins/metabolism
• Tissue Distribution

Understanding the molecular mechanisms responsible for the regulation of the transcriptome present in eukaryotic cells is one of the most challenging tasks in the postgenomic era. In this regard, alternative splicing (AS) is a key phenomenon contributing to the production of different mature transcripts from the same primary RNA sequence. As a plethora of different transcript forms is available in databases, a first step to uncover the biology that drives AS is to identify the different types of reflected splicing variation. In this work, we present a general definition of the AS event along with a notation system that involves the relative positions of the splice sites. This nomenclature univocally and dynamically assigns a specific “AS code” to every possible pattern of splicing variation. On the basis of this definition and the corresponding codes, we have developed a computational tool (AStalavista) that automatically characterizes the complete landscape of AS events in a given transcript annotation of a genome, thus providing a platform to investigate the transcriptome diversity across genes, chromosomes, and species. Our analysis reveals that a substantial part—in human more than a quarter—of the observed splicing variations are ignored in common classification pipelines. We have used AStalavista to investigate and to compare the AS landscape of different reference annotation sets in human and in other metazoan species and found that proportions of AS events change substantially depending on the annotation protocol, species-specific attributes, and coding constraints acting on the transcripts. The AStalavista system therefore provides a general framework to conduct specific studies investigating the occurrence, impact, and regulation of AS.

The genome sequence is said to be an organism's blueprint, a set of instructions driving the organism's biology. The unfolding of these instructions—the so-called genes—is initiated by the transcription of DNA into RNA molecules, which subsequently are processed before they can take their functional role. During this processing step, initially identical RNA molecules may result in different products through a process known as alternative splicing (AS). AS therefore allows for widening the diversity from the limited repertoire of genes, and it is often postulated as an explanation for the apparent paradox that complex and simple organisms resemble in their number of genes; it characterizes species, individuals, and developmental and cellular conditions. Comparing the differences of AS products between cells may help to reveal the broad molecular basis underlying phenotypic differences—for instance, between a cancer and a normal cell. An obstacle for such comparisons has been that, so far, no paradigm existed to delineate each single quantum of AS, so-called AS events. Here, we describe a possibility of exhaustively decomposing AS complements into qualitatively different groups of events and a nomenclature to unequivocally denote them. This typological catalogue of AS events along with their observed frequencies represent the AS landscape, and we propose a procedure to automatically identify such landscapes. We use it to describe the human AS landscape and to investigate how it has changed throughout evolution.

Most human genes produce several transcripts with different exon contents by using alternative promoters, alternative polyadenylation sites and alternative splice sites. Much effort has been devoted to describing known gene transcripts through the development of numerous databases. Nevertheless, owing to the diversity of the transcriptome, there is a need for interactive databases that provide information about the potential function of each splicing variant, as well as its expression pattern.

After setting up a database in which human and mouse splicing variants were compiled, we developed tools (1) to predict the production of protein isoforms from these transcripts, taking account of the presence of open reading frames and mechanisms that could potentially eliminate transcripts and/or inhibit their translation, i.e. nonsense-mediated mRNA decay and microRNAs; (2) to support studies of the regulation of transcript expression at multiple levels, including transcription and splicing, particularly in terms of tissue specificity; and (3) to assist in experimental analysis of the expression of splicing variants. Importantly, analyses of all features from transcript metabolism to functional protein domains were integrated in a highly interactive, user-friendly web interface that allows the functional and regulatory features of gene transcripts to be assessed rapidly and accurately.

In addition to identifying the transcripts produced by human and mouse genes, fast DB provides tools for analyzing the putative functions of these transcripts and the regulation of their expression. Therefore, fast DB has achieved an advance in alternative splicing databases by providing resources for the functional interpretation of splicing variants for the human and mouse genomes. Because gene expression studies are increasingly employed in clinical analyses, our web interface has been designed to be as user-friendly as possible and to be readily searchable and intelligible at a glance by the whole biomedical community.

• Alternative Splicing
• Animals
• Expressed Sequence Tags
• Female
• Genomics/statistics & numerical data
• Humans
• Male
• Mice
• Pregnancy
• RNA, Messenger/genetics
• Software Design
• Species Specificity
• Tissue Distribution

• Alternative Splicing/genetics
• Chromosome Mapping/methods
• DNA Mutational Analysis/methods
• Genetic Variation/genetics
• Polyadenylation/genetics
• Software
• Transcription Factors/genetics

Human genes use various mechanisms to generate different transcripts having different exon content, which in turn generate multiple protein isoforms having differential and even opposite biological activities. To understand the biological consequences of gene transcriptional activity modulation, it is necessary to integrate the capability of genes to generate distinct functional products, particularly because transcriptional stimuli also affect the exon content of their target gene products. For this purpose, we have developed a bioinformatics suite, FAST DB, which defines easily and accurately the exon content of all known transcripts produced by human genes. In addition, several tools have been developed, including a graphical presentation of all gene products, a sequence multi-alignment of all gene transcripts and an in silico PCR computer program. The FAST DB interface also offers extensive links to website resources for promoter analysis and transcription factor binding site prediction, splicing regulatory sequence prediction, as well as 5′- and 3′-untranslated region analysis. FAST DB has been designed to facilitate studies that integrate transcriptional and post-transcriptional events to investigate the expression regulation of human gene products.

• Alternative Splicing
• Computational Biology
• Computer Graphics
• DNA Primers
• DNA, Complementary/chemistry
• Databases, Nucleic Acid
• Exons
• Expressed Sequence Tags/chemistry
• Gene Expression Regulation
• Humans
• Internet
• Polymerase Chain Reaction
• RNA, Messenger/chemistry
• RNA, Messenger/metabolism
• Sequence Alignment
• Sequence Analysis, RNA
• Software

• Actinin/biosynthesis
• Actinin/genetics
• Alternative Splicing
• Base Sequence
• DNA Primers
• DNA, Complementary/analysis
• Exons
• Humans
• Microfilament Proteins/biosynthesis
• Microfilament Proteins/genetics
• Molecular Sequence Data
• Nucleic Acid Heteroduplexes/analysis
• Polymerase Chain Reaction
• Protein Isoforms/analysis
• Protein Isoforms/genetics
• Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
• Vascular Endothelial Growth Factor A/biosynthesis
• Vascular Endothelial Growth Factor A/genetics

We have developed an information repository named SpliceInfo to collect the occurrences of the four major alternative-splicing (AS) modes in human genome; these include exon skipping, 5′-alternative splicing, 3′-alternative splicing and intron retention. The dataset is derived by comparing the nucleotide and protein sequences available for a given gene for evidence of AS. Additional features such as the tissue specificity of the mRNA, the protein domain contained by exons, the GC-ratio of exons, the repeats contained within the exons, and the Gene Ontology are annotated computationally for each exonic region that is alternatively spliced. Motivated by a previous investigation of AS-related motifs such as exonic splicing enhancer and exonic splicing silencer, this resource also provides a means of identifying motifs candidates and this should help to identify potential regulatory mechanisms within a particular exonic sequence set and its two flanking intronic sequence sets. This is carried out using motif discovery tools to identify motif candidates related to alternative splicing regulation and together with a secondary structure prediction tool, will help in the identification of the structural properties of such regulatory motifs. The integrated resource is now available on http://SpliceInfo.mbc.NCTU.edu.tw/.

• Alternative Splicing
• Databases, Genetic/statistics & numerical data
• Exons
• Genome, Human
• Humans
• Internet
• Nucleic Acid Conformation
• RNA, Messenger/chemistry
• RNA, Messenger/metabolism
• User-Computer Interface

ECgene provides annotation for gene structure, function and expression, taking alternative splicing events into consideration. The gene-modeling algorithm combines the genome-based expressed sequence tag (EST) clustering and graph-theoretic transcript assembly procedures. The website provides several viewers and applications that have many unique features useful for the analysis of the transcript structure and gene expression. The summary viewer shows the gene summary and the essence of other annotation programs. The genome browser and the transcript viewer are available for comparing the gene structure of splice variants. Changes in the functional domains by alternative splicing can be seen at a glance in the transcript viewer. We also provide two unique ways of analyzing gene expression. The SAGE tags deduced from the assembled transcripts are used to delineate quantitative expression patterns from SAGE libraries available publically. Furthermore, the cDNA libraries of EST sequences in each cluster are used to infer qualitative expression patterns. It should be noted that the ECgene website provides annotation for the whole transcriptome, not just the alternatively spliced genes. Currently, ECgene supports the human, mouse and rat genomes. The ECgene suite of tools and programs is available at http://genome.ewha.ac.kr/ECgene/.

• Algorithms
• Alternative Splicing
• Animals
• Databases, Genetic
• Gene Expression Profiling
• Genome
• Genomics
• Humans
• Internet
• Mice
• RNA, Messenger/chemistry
• Rats
• Software
• User-Computer Interface

Alternative splicing (AS) is now considered as a major actor in transcriptome/proteome diversity and it cannot be neglected in the annotation process of a new genome. Despite considerable progresses in term of accuracy in computational gene prediction, the ability to reliably predict AS variants when there is local experimental evidence of it remains an open challenge for gene finders.

We have used a new integrative approach that allows to incorporate AS detection into ab initio gene prediction. This method relies on the analysis of genomically aligned transcript sequences (ESTs and/or cDNAs), and has been implemented in the dynamic programming algorithm of the graph-based gene finder EuGÈNE. Given a genomic sequence and a set of aligned transcripts, this new version identifies the set of transcripts carrying evidence of alternative splicing events, and provides, in addition to the classical optimal gene prediction, alternative optimal predictions (among those which are consistent with the AS events detected). This allows for multiple annotations of a single gene in a way such that each predicted variant is supported by a transcript evidence (but not necessarily with a full-length coverage).

This automatic combination of experimental data analysis and ab initio gene finding offers an ideal integration of alternatively spliced gene prediction inside a single annotation pipeline.

• Algorithms
• Alternative Splicing
• Arabidopsis/genetics
• Codon
• Computer Graphics
• DNA, Complementary/metabolism
• Databases, Genetic
• Databases, Nucleic Acid
• Databases, Protein
• Exons
• Expressed Sequence Tags
• Gene Expression Profiling
• Genes, Plant
• Genome
• Genome, Human
• Genomics
• Humans
• Introns
• Models, Genetic
• Proteomics/methods
• RNA Splice Sites
• Sequence Alignment
• Sequence Analysis, Protein
• Sequence Analysis, RNA
• Software
• Transcription, Genetic
• User-Computer Interface

• Exons
• Humans
• Introns
• RNA Precursors/metabolism
• RNA Splicing

• Alternative Splicing
• Computer Graphics
• Databases, Nucleic Acid
• Genome, Human
• Humans
• Internet
• RNA Splice Sites
• Sequence Alignment
• Sequence Analysis, DNA
• Sequence Analysis, RNA
• Software
• Transcription, Genetic

• Alternative Splicing
• Animals
• Apoptosis/genetics
• Evolution, Molecular
• Gene Expression Regulation
• Genetic Techniques
• Humans
• Mice
• Protein Biosynthesis
• Untranslated Regions

• K22 HG00056 NHGRI NIH HHS
• T32 GM07127 NIGMS NIH HHS
• T32 HG00047 NHGRI NIH HHS

• Environment
• Genotype
• Health
• Humans
• Models, Biological
• Nutritional Physiological Phenomena
• Proteome/physiology
• Proteomics/methods
• Systems Biology/methods

• Humans
• Caco-2 Cells
• Cell Differentiation
• Cell Division
• Cytosol/metabolism
• Epithelial Cells/metabolism
• Epithelial Cells/physiology
• Fluorescent Antibody Technique
• Intestine, Small/cytology
• Intestine, Small/metabolism
• Microtubule-Associated Proteins/chemistry
• Microtubule-Associated Proteins/metabolism
• Microtubules/metabolism
• Protein Binding
• Protein Isoforms/chemistry