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High accuracy de novo assembly of the short sequencing reads from RNA-Seq technology is very challenging. A team led by researchers at Asia University, Taiwan have developed a de novo assembly algorithm, EBARDenovo, which stands for Extension, Bridging And Repeat-sensing Denovo. This algorithm employs an efficient chimera-detection function to abrogate the effect of aberrant chimeric reads in RNA-Seq data.

EBARDenovo resolves the complications of RNA-Seq assembly arising from sequencing errors, repetitive sequences and aberrant chimeric amplicons. In a series of assembly experiments, this algorithm was found to be the most accurate among the examined programs including de Bruijn graph assemblers, Trinity and Oases.

AVAILABILITY: EBARDenovo is freely available at http://ebardenovo.sourceforge.net/

CONTACT: chu@live.asia.edu.tw; postergrey@gmail.com; cykao@csie.ntu.edu.tw

Chu HT, Hsiao WW, Chen JC, Yeh TJ, Tsai MH, Lin H, Liu YW, Lee SA, Chen CC, Tsao TT, Kao CY. (2013) EBARDenovo: Highly accurate de novo assembly of RNA-Seq with efficient chimera-detection. Bioinformatics [Epub ahead of print]. [abstract]

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Transcriptome reconstruction is an important application of RNA-Seq, providing critical information for further analysis of transcriptome. Although RNA-Seq offers the potential to identify the whole picture of transcriptome, it still presents special challenges. To handle these difficulties and reconstruct transcriptome as completely as possible, current computational approaches mainly employ two strategies: de novo assembly and genome-guided assembly.

Researchers at the Center for Bioinformatics and Computational Biology, East China Normal University, Shanghai chose five representative assemblers belonging to the two classes respectively, then investigated and compared their algorithm features in theory and real performances in practice.

The researchers found that all the methods can be reduced to graph reduction problems, yet they have different conceptual and practical implementations, thus each assembly method has its specific advantages and disadvantages, performing worse than others in certain aspects while outperforming others in anther aspects at the same time. Finally they merged assemblies of the five assemblers and obtained a much better assembly. Additionally they evaluated an assembler using genome-guided de novo assembly approach, and achieved good performance. Based on these results, they suggest that to obtain a comprehensive set of recovered transcripts, it is better to use a combination of de novo assembly and genome-guided assembly.

• Lu B, Zeng Z, Shi T. (2013) Comparative study of de novo assembly and genome-guided assembly strategies for transcriptome reconstruction based on RNA-Seq. Sci China Life Sci 56(2):143-55. [abstract]

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The capsaicinoids are a group of compounds produced by chili pepper fruits and are used widely in many fields, especially in medical purposes. The capsaicinoid biosynthetic pathway has not yet been established clearly. To understand more knowledge in biosynthesis of capsaicinoids, researchers at South China Agricultural University, Guangzhou applied RNA-seq for the mixture of placenta and pericarp of pungent pepper (Capsicum frutescens L.).

The researchers have assessed the effect of various assembly parameters using different assembly software, and obtained one of the best strategies for de novo assembly of transcriptome data. They obtained a total 54,045 high-quality unigenes (transcripts) using Trinity software. About 92.65% of unigenes showed similarity to the public protein sequences, genome of potato and tomato and pepper (C. annuum) ESTs databases. Their results predicted 3 new structural genes (DHAD, TD, PAT), which filled gaps of the capsaicinoid biosynthetic pathway predicted by Mazourek, and revealed new candidate genes involved in capsaicinoid biosynthesis based on KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis. A significant number of SSR (Simple Sequence Repeat) and SNP (Single Nucleotide Polymorphism) markers were predicted in C. frutescens and C. annuum sequences, which will be helpful in the identification of polymorphisms within chili pepper populations.

These data will provide new insights to the pathway of capsaicinoid biosynthesis and subsequent research of chili peppers. In addition, this strategy of de novo transcriptome assembly is applicable to a wide range of similar studies.

• Liu S, Li W, Wu Y, Chen C, Lei J. (2013) De Novo Transcriptome Assembly in Chili Pepper (Capsicum frutescens) to Identify Genes Involved in the Biosynthesis of Capsaicinoids. PLoS One 8(1), e48156. [article]

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For non-model organisms lacking well-defined genomes, de novo assembly is typically required for downstream RNA-Seq analyses, including SNP discovery and identification of genes differentially expressed by phenotypes. Although RNA-Seq has been successfully used to sequence many non-model organisms, the results of de novo assembly from short reads can still be improved by using recent bioinformatic developments. Read more

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With a Genome

FANSe¹ is a new, fast and accurate algorithm for nucleic acid sequence analysis with adjustable mismatch allowance settings and ability to handle indels to accurately and quantitatively map millions of reads to small or large reference genomes. It is a seed-based algorithm which uses the whole read information for mapping and high sensitivity and low ambiguity are achieved by using short and non-overlapping reads. Furthermore, FANSe uses hotspot score to prioritize the processing of highly possible matches and implements modified Smith-Watermann refinement with reduced scoring matrix to accelerate the calculation without compromising its sensitivity. The FANSe algorithm stably processes datasets from various sequencing platforms, masked or unmasked and small or large genomes. It shows a remarkable coverage of low-abundance mRNAs which is important for quantitative processing of RNA-Seq datasets.

AVAILABILITY: The FANSe algorithm is accessible at http://bioinformatics.jnu.edu.cn/software/fanse/. The web site contains a detailed tutorial and the source code for download

Without a Genome

Oases² is a software package designed to heuristically assemble RNA-seq reads in the absence of a reference genome, across a broad spectrum of expression values and in presence of alternative isoforms. It achieves this by using an array of hash lengths, a dynamic filtering of noise, a robust resolution of alternative splicing events, and the efficient merging of multiple assemblies. It was tested on human and mouse RNA-seq data and is shown to improve significantly on the transABySS and Trinity de novo transcriptome assemblers.

AVAILABILITY: Oases is freely available under the GPL license at www.ebi.ac.uk/~zerbino/oases/

1. Zhang G, Fedyunin I, Kirchner S, Xiao C, Valleriani A, Ignatova Z. (2012) FANSe: an accurate algorithm for quantitative mapping of large scale sequencing reads. Nucleic Acids Res [Epub ahead of print]. [article]
2. Schulz MH, Zerbino DR, Vingron M, Birney E. (2012) Oases: Robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics [Epub ahead of print]. [article]

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By Fabrizio Ghiselli

Despite their biological, ecological and economical importance, very little is known about bivalve genetics/genomics and until recently (see for example Boutet et al. 2008¹; Craft et al. 2010²; Milan et al. 2011³), the structure and gene content of bivalve genomes have been poorly understood even in the most important aquacultured organisms. The Manila clam (Ruditapes philippinarum) represents, after oysters, the most important species for Global Aquaculture production. Read more

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RNA-Seq has emerged as a powerful tool for studying transcriptomes. It aims to provide a comprehensive list of all transcripts and their expression levels from a given cell or cell population under a particular condition. RNA-Seq data analysis typically involves aligning the short read sequences to a reference genome to reveal reads from exons, splicing junctions, or polyA ends. This information is used to derive novel gene models or refine existing gene models, including exon structure and untranslated regions (UTRs) and to determine gene expression levels from read count statistics Read more

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For species that lack reference genome sequences, or whose genomes are poorly annotated, de novo short-read transcriptome assembly may be a practical alternative to conventional expressed sequence tag–based approaches and to methods that depend on short-read alignments. Read more

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• SEQanswers – RNA Sequencing

  • HT Seq Count stranded options May 24, 2013
    I am very new to bioinformatics, so I would be really grateful for some help! I have been using *HTSeq Count v0.5.3* and I am bit confused about... […]
    qwrissie
  • Tophat 2.0.8b installation error May 24, 2013
    I install tophat-2.0.8b to rerun the mapping. but when i make it, the error appears like this. make[1]: Entering directory... […]
    canhu
  • reason for low mapping rate?? May 23, 2013
    we did RNASeq using HiSeq 2000 100PE. When the data were back, I mapping them to the reference sequence, but got very low mapping rate (30-40%). I... […]
    miaom
  • cross-species data - questions about normalization May 23, 2013
    Hi, I have some data form various samples (cell types) in different species. I want to compare and analyze gene expression variability across the... […]
    trelek2
  • CuffDiff strange output May 23, 2013
    Hi, I hope that someone can be so gentle to help me. I'm analizing some data from RNA-Seq with TopHat and Cufflinks and I focus my attention on... […]
    Pruexel
  • cannot away with cuffdiff,incredible May 23, 2013
    Hi,all I have 4(A,B,C,D) sample in 4 times(increasing time),I got diff result in 3 different cuffdiff 1.cuffdiff 3(A,B,C) individual... […]
    upper

• Biostar – RNA-Seq

  • Why am I getting so many unmapped reads in STAR, classified as "too short"?
    I am currently using STAR to map several Hi-SEQ mRNA runs. I'm having trouble getting a decent amount of reads to map, but I don't really understand why. I'm hoping you can shed some light :) In the final log, only about 50% (or less) of the reads map to the reference. I'm using a GTF in addition to the genome. The unmapped bin that most […]
  • What are the best practices for SNP identification in RNA seq transcriptome data
    I have 20 RICE RNA seq tranascriptome data hiseq 2000 platform paired end reads. I aligned fasta reads with BWA and remove PCR duplicates with PICARD. Later I call SNP with samtools using various parameters. I would like to clarify what parameters should I used while alinging to reference rice genome for looking SNP location 100 bp upstream and 250 bp downst […]
  • How do TopHat options -g , --supress-hits, and Bowtie options interplay?
    Hi, I am currently using TopHat2 to map RNA-seq runs. I think there have been some changes pertaining the -g option. Does anyone know how it works now? I used to think that setting -g would look for n alignments for a given read, report them [if top-scoring] and discard those reads that had more than g [top scoring] alignments. Now, the description sounds mo […]
  • What happened to -k in TopHat for multiple-mapping reads?
    Selecting -g n in tophat does not discard reads mapping more than n, but instead only reports n alignments for those out all all their TOP scoring alignments. I think there used to be an option -k that would allow one to discard reads that topped x alignments -- whatever happened to that? I only see -g in the tophat 2 manual, no reporting options like before […]
  • Does tophat use the library-type information for mapping, or just for the XS flag?
    When I specify library-type to TopHat, i.e., first-strand, second-strand, unstranded, TopHat appends a value + or - to the XS:A flag, which is useful for subsequent analyses, such as annotation. However, does this information actually influence the "mappability" of reads, or is this unaffected? My thinking is that the information would be considere […]
  • Purpose of Y-shaped adapters in Illumina Sequencing?
    Hi all, Y adapters different sequences to be annealed to the 5' and 3' ends of each molecule in a library. The arms of the Y are unique, and the middle part, connected to the DNA fragment, is complementary. What are the advantages of this? My take of this over having fully-complementary adapters (ADAPTER1 - - - - - ADAPTER1) is that: -Upon primer a […]