A de novo transcriptome assembly of the zebra bullhead shark, Heterodontus zebra.
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ABSTRACT:
SUBMITTER: Onimaru K
PROVIDER: S-EPMC6174923 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The shrimp Palaemon serratus is a coastal decapod crustacean with a high commercial value. It is harvested for human consumption. In this study, we used Illumina sequencing technology (HiSeq 2000) to sequence, assemble and annotate the transcriptome of P. serratus. RNA was isolated from muscle of adults individuals and, from a pool of larvae. A total number of 4 cDNA libraries were constructed, using the TruSeq RNA Sample Preparation Kit v2. The raw data in this study was deposited in NCBI SRA database with study accession number of SRP090769. The obtained data were subjected to de novo transcriptome assembly using Trinity software, and coding regions were predicted by TransDecoder. We used Blastp and Sma3s to annotate the identified proteins. The transcriptome data could provide some insight into the understanding of genes involved in the larval development and metamorphosis. SPECIFICATIONS:[Table: see text].
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Project description:MotivationDe novo transcriptome assembly is an integral part for many RNA-seq workflows. Common applications include sequencing of non-model organisms, cancer or meta transcriptomes. Most de novo transcriptome assemblers use the de Bruijn graph (DBG) as the underlying data structure. The quality of the assemblies produced by such assemblers is highly influenced by the exact word length k As such no single kmer value leads to optimal results. Instead, DBGs over different kmer values are built and the assemblies are merged to improve sensitivity. However, no studies have investigated thoroughly the problem of automatically learning at which kmer value to stop the assembly. Instead a suboptimal selection of kmer values is often used in practice.ResultsHere we investigate the contribution of a single kmer value in a multi-kmer based assembly approach. We find that a comparative clustering of related assemblies can be used to estimate the importance of an additional kmer assembly. Using a model fit based algorithm we predict the kmer value at which no further assemblies are necessary. Our approach is tested with different de novo assemblers for datasets with different coverage values and read lengths. Further, we suggest a simple post processing step that significantly improves the quality of multi-kmer assemblies.ConclusionWe provide an automatic method for limiting the number of kmer values without a significant loss in assembly quality but with savings in assembly time. This is a step forward to making multi-kmer methods more reliable and easier to use.Availability and implementationA general implementation of our approach can be found under: https://github.com/SchulzLab/KREATIONSupplementary information: Supplementary data are available at Bioinformatics online.Contactmschulz@mmci.uni-saarland.de.
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Project description:Apricot (Prunus armeniaca) belonging to the Prunus species is a popular kind of stone fruit tree. Apricot is native to Armenia and is currently cultivated in many countries with climates adaptable for apricot growth. In general, fresh fruits as well as dried apricot are produced. However, the information associated with genes and genetic markers for apricot is very limited. In this study, we carried out de novo transcriptome assembly for two selected apricot cultivars referred to as Harcot and Ungarische Beste, which are commercially important apricot cultivars in the world, using next generation sequencing. We obtained a total of 9.31 GB and 8.88 GB raw data from Harcot and Ungarische Beste (NCBI accession numbers: SRX1186946 and SRX1186893), respectively. De novo transcriptome assembly using Trinity identified 147,501 and 152,235 transcripts for Harcot and Ungarische Beste, respectively. Next, we identified 113,565 and 126,444 proteins from Harcot and Ungarische Beste using the TransDecoder program. We performed BLASTP against an NCBI non-redundant (nr) dataset to annotate identified proteins. Taken together, we provide transcriptomes of two different apricot cultivars by RNA-Seq.
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Project description:Cucurbita pepo (squash, pumpkin, gourd), a worldwide-cultivated vegetable of American origin, is extremely variable in fruit characteristics. However, the information associated with genes and genetic markers for pumpkin is very limited. In order to identify new genes and to develop genetic markers, we performed a transcriptome analysis (RNA-Seq) of two contrasting pumpkin cultivars. Leaves and female flowers of cultivars, 'Big Moose' with large round fruits and 'Munchkin' with small round fruits, were harvested for total RNA extraction. We obtained a total of 6 GB (Big Moose; http://www.ncbi.nlm.nih.gov/Traces/sra/?run=SRR3056882) and 5 GB (Munchkin; http://www.ncbi.nlm.nih.gov/Traces/sra/?run=SRR3056883) sequence data (NCBI SRA database SRX1502732 and SRX1502735, respectively), which correspond to 18,055,786 and 14,824,292 150-base reads. After quality assessment, the clean sequences where 17,995,932 and 14,774,486 respectively. The numbers of total transcripts for 'Big Moose' and 'Munchkin' were 84,727 and 68,051, respectively. TransDecoder identified possible coding regions in assembled transcripts. This study provides transcriptome data for two contrasting pumpkin cultivars, which might be useful for genetic marker development and comparative transcriptome analyses.
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Project description:Sorghum (Sorghum bicolor), also known as great millet, is one of the most popular cultivated grass species in the world. Sorghum is frequently consumed as food for humans and animals as well as used for ethanol production. In this study, we conducted de novo transcriptome assembly for sorghum variety Taejin by next-generation sequencing, obtaining 8.748 GB of raw data. The raw data in this study can be available in NCBI SRA database with accession number of SRX1715644. Using the Trinity program, we identified 222,161 transcripts from sorghum variety Taejin. We further predicted coding regions within the assembled transcripts by the TransDecoder program, resulting in a total of 148,531 proteins. We carried out BLASTP against the Swiss-Prot protein sequence database to annotate the functions of the identified proteins. To our knowledge, this is the first transcriptome data for a sorghum variety derived from Korea, and it can be usefully applied to the generation of genetic markers.
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Project description:De novo transcriptome assembly from billions of RNA-seq reads is very challenging due to alternative splicing and various levels of expression, which often leads to incorrect, mis-assembled transcripts. BayesDenovo addresses this problem by using both a read-guided strategy to accurately reconstruct splicing graphs from the RNA-seq data and a Bayesian strategy to estimate, from these graphs, the probability of transcript expression without penalizing poorly expressed transcripts. Simulation and cell line benchmark studies demonstrate that BayesDenovo is very effective in reducing false positives and achieves much higher accuracy than other assemblers, especially for alternatively spliced genes and for highly or poorly expressed transcripts. Moreover, BayesDenovo is more robust on multiple replicates by assembling a larger portion of common transcripts. When applied to breast cancer data, BayesDenovo identifies phenotype-specific transcripts associated with breast cancer recurrence.
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Project description:Foxtail millet (Setaria italica) belonging to the family Poaceae is an important millet that is widely cultivated in East Asia. Of the cultivated millets, the foxtail millet has the longest history and is one of the main food crops in South India and China. Moreover, foxtail millet is a model plant system for biofuel generation utilizing the C4 photosynthetic pathway. In this study, we carried out de novo transcriptome assembly for the foxtail millet variety Taejin collected from Korea using next-generation sequencing. We obtained a total of 8.676 GB raw data by paired-end sequencing. The raw data in this study can be available in NCBI SRA database with accession number of SRR3406552. The Trinity program was used to de novo assemble 145,332 transcripts. Using the TransDecoder program, we predicted 82,925 putative proteins. BLASTP was performed against the Swiss-Prot protein sequence database to annotate the functions of identified proteins, resulting in 20,555 potentially novel proteins. Taken together, this study provides transcriptome data for the foxtail millet variety Taejin by RNA-Seq.
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Project description:Correct quantification of transcript expression is essential to understand the functional elements in different physiological conditions. For the organisms without the reference transcriptome, de novo transcriptome assembly must be carried out prior to quantification. However, a large number of erroneous contigs produced by the assemblers might result in unreliable estimation. In this regard, this study investigates how assembly quality affects the performance of quantification based on de novo transcriptome assembly. We examined the over-extended and incomplete contigs, and demonstrated that assembly completeness has a strong impact on the estimation of contig abundance. Then we investigated the behavior of the quantifiers with respect to sequence ambiguity which might be originally presented in the transcriptome or accidentally produced by assemblers. The results suggested that the quantifiers often over-estimate the expression of family-collapse contigs and under-estimate the expression of duplicated contigs. For organisms without reference transcriptome, it remains challenging to detect the inaccurate estimation on family-collapse contigs. On the contrary, we observed that the situation of under-estimation on duplicated contigs can be warned through analyzing the read proportion of estimated abundance (RPEA) of contigs in the connected component inferenced by the quantifiers. In addition, we suggest that the estimated quantification results on the connected component level have better accuracy over sequence level quantification. The analytic results conducted in this study provides valuable insights for future development of transcriptome assembly and quantification.
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Project description:Monotropa hypopitys (pinesap) is a non-photosynthetic obligately mycoheterotrophic plant of the family Ericaceae. It obtains the carbon and other nutrients from the roots of surrounding autotrophic trees through the associated mycorrhizal fungi. In order to understand the evolutionary changes in the plant genome associated with transition to a heterotrophic lifestyle, we performed de novo transcriptomic analysis of M. hypopitys using next-generation sequencing. We obtained the RNA-Seq data from flowers, flower bracts and roots with haustoria using Illumina HiSeq2500 platform. The raw data obtained in this study can be available in NCBI SRA database with accession number of SRP069226. A total of 10.3 GB raw sequence data were obtained, corresponding to 103,357,809 raw reads. A total of 103,025,683 reads were filtered after removing low-quality reads and trimming the adapter sequences. The Trinity program was used to de novo assemble 98,349 unigens with an N50 of 1342 bp. Using the TransDecoder program, we predicted 43,505 putative proteins. 38,416 unigenes were annotated in the Swiss-Prot protein sequence database using BLASTX. The obtained transcriptomic data will be useful for further studies of the evolution of plant genomes upon transition to a non-photosynthetic lifestyle and the loss of photosynthesis-related functions.
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