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Background: Next-generation sequencing promises the de novo genomic and transcriptomic analysis of samples of interests. However, there are only a few organisms having reference genomic sequences and even fewer having well-defined or curated annotations. For transcriptome studies focusing on organisms lacking proper reference genomes, the common strategy is de novo assembly followed by functional annotation. However, things become even more complicated when multiple transcriptomes are compared. Results: Here, we propose a new analysis strategy and quantification methods for quantifying expression level which not only generate a virtual reference from sequencing data, but also provide comparisons between transcriptomes. First, all reads from the transcriptome datasets are pooled together for de novo assembly. The assembled contigs are searched against NCBI NR databases to find potential homolog sequences. Based on the searched result, a set of virtual transcripts are generated and served as a reference transcriptome. By using the same reference, normalized quantification values including RC (read counts), eRPKM (estimated RPKM) and eTPM (estimated TPM) can be obtained that are comparable across transcriptome datasets. In order to demonstrate the feasibility of our strategy, we implement it in the web service PARRoT. PARRoT stands for Pipeline for Analyzing RNA Reads of Transcriptomes. It analyzes gene expression profiles for two transcriptome sequencing datasets. For better understanding of the biological meaning from the comparison among transcriptomes, PARRoT further provides linkage between these virtual transcripts and their potential function through showing best hits in SwissProt, NR database, assigning GO terms. Our demo datasets showed that PARRoT can analyze two paired-end transcriptomic datasets of approximately 100 million reads within just three hours. Conclusions: In this study, we proposed and implemented a strategy to analyze transcriptomes from non-reference organisms which offers the opportunity to quantify and compare transcriptome profiles through a homolog based virtual transcriptome reference. By using the homolog based reference, our strategy effectively avoids the problems that may cause from inconsistencies among transcriptomes. This strategy will shed lights on the field of comparative genomics for non-model organism. We have implemented PARRoT as a web service which is freely available at http://parrot.cgu.edu.tw. [ABSTRACT FROM AUTHOR]

Background: Recent developments in high-throughput sequencing (HTS) technologies have made it feasible to sequence the complete transcriptomes of non-model organisms or metatranscriptomes from environmental samples. The challenge after generating hundreds of millions of sequences is to annotate these transcripts and classify the transcripts based on their putative functions. Because many biological scientists lack the knowledge to install Linux-based software packages or maintain databases used for transcript annotation, we developed an automatic annotation tool with an easy-to-use interface. Methods: To elucidate the potential functions of gene transcripts, we integrated well-established annotation tools: Blast2GO, PRIAM and RPS BLAST in a web-based service, FastAnnotator, which can assign Gene Ontology (GO) terms, Enzyme Commission numbers (EC numbers) and functional domains to query sequences. Results: Using six transcriptome sequence datasets as examples, we demonstrated the ability of FastAnnotator to assign functional annotations. FastAnnotator annotated 88.1% and 81.3% of the transcripts from the well-studied organisms Caenorhabditis elegans and Streptococcus parasanguinis, respectively. Furthermore, FastAnnotator annotated 62.9%, 20.4%, 53.1% and 42.0% of the sequences from the transcriptomes of sweet potato, clam, amoeba, and Trichomonas vaginalis, respectively, which lack reference genomes. We demonstrated that FastAnnotator can complete the annotation process in a reasonable amount of time and is suitable for the annotation of transcriptomes from model organisms or organisms for which annotated reference genomes are not avaiable. Conclusions: The sequencing process no longer represents the bottleneck in the study of genomics, and automatic annotation tools have become invaluable as the annotation procedure has become the limiting step. We present FastAnnotator, which was an automated annotation web tool designed to efficiently annotate sequences with their gene functions, enzyme functions or domains. FastAnnotator is useful in transcriptome studies and especially for those focusing on non-model organisms or metatranscriptomes. FastAnnotator does not require local installation and is freely available at http://fastannotator.cgu.edu.tw. [ABSTRACT FROM AUTHOR]

Background: Gene duplication provides resources for developing novel genes and new functions while retaining the original functions. In addition, alternative splicing could increase the complexity of expression at the transcriptome and proteome level without increasing the number of gene copy in the genome. Duplication and alternative splicing are thought to work together to provide the diverse functions or expression patterns for eukaryotes. Previously, it was believed that duplication and alternative splicing were negatively correlated and probably interchangeable. Results: We look into the relationship between occurrence of alternative splicing and duplication at different time after duplication events. We found duplication and alternative splicing were indeed inversely correlated if only recently duplicated genes were considered, but they became positively correlated when we took those ancient duplications into account. Specifically, for slightly or moderately duplicated genes with gene families containing 2 - 7 paralogs, genes were more likely to evolve alternative splicing and had on average a greater number of alternative splicing isoforms after long-term evolution compared to singleton genes. On the other hand, those large gene families (contain at least 8 paralogs) had a lower proportion of alternative splicing, and fewer alternative splicing isoforms on average even when ancient duplicated genes were taken into consideration. We also found these duplicated genes having alternative splicing were under tighter evolutionary constraints compared to those having no alternative splicing, and had an enrichment of genes that participate in molecular transducer activities. Conclusions: We studied the association between occurrences of alternative splicing and gene duplication. Our results implicate that there are key differences in functions and evolutionary constraints among singleton genes or duplicated genes with or without alternative splicing incidences. It implies that the gene duplication and alternative splicing may have different functional significance in the evolution of speciation diversity. [ABSTRACT FROM AUTHOR]

Background: Orthologs are genes derived from the same ancestor gene loci after speciation events. Orthologous proteins usually have similar sequences and perform comparable biological functions. Therefore, ortholog identification is useful in annotations of newly sequenced genomes. With rapidly increasing number of sequenced genomes, constructing or updating ortholog relationship between all genomes requires lots of effort and computation time. In addition, elucidating ortholog relationships between distantly related genomes is challenging because of the lower sequence similarity. Therefore, an efficient ortholog detection method that can deal with large number of distantly related genomes is desired. Results: An efficient ortholog detection pipeline DODO (DOmain based Detection of Orthologs) is created on the basis of domain architectures in this study. Supported by domain composition, which usually directly related with protein function, DODO could facilitate orthologs detection across distantly related genomes. DODO works in two main steps. Starting from domain information, it first assigns protein groups according to their domain architectures and further identifies orthologs within those groups with much reduced complexity. Here DODO is shown to detect orthologs between two genomes in considerably shorter period of time than traditional methods of reciprocal best hits and it is more significant when analyzed a large number of genomes. The output results of DODO are highly comparable with other known ortholog databases. Conclusions: DODO provides a new efficient pipeline for detection of orthologs in a large number of genomes. In addition, a database established with DODO is also easier to maintain and could be updated relatively effortlessly. The pipeline of DODO could be downloaded from http://140.109.42.19:16080/dodo_web/home.htm [ABSTRACT FROM AUTHOR]