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Oct
31
Detecting DE in RNA-Seq Data Using Quasi-likelihood with Shrunken Dispersion Estimates
Filed Under Data Analysis, Expression and Quantification | Leave a Comment
Next generation sequencing technology provides a powerful tool for measuring gene expression (mRNA) levels in the form of RNA-sequence data. Method development for identifying differentially expressed (DE) genes from RNA-Seq data, which frequently includes many low-count integers and can exhibit severe overdispersion relative to Poisson or binomial distributions, is a popular area of ongoing research.
Here, researchers at National Institute of Standards and Technology present quasi-likelihood methods with shrunken dispersion estimates based on an adaptation of Smyth’s (2004) approach to estimating gene-specific error variances for microarray data. The suggested methods are computationally simple, analogous to ANOVA and compare favorably versus competing methods in detecting DE genes and estimating false discovery rates across a variety of simulations based on real data.
An R package called QuasiSeq, used to implement the suggested methods of this article is available from the CRAN website: http://cran.r-project.org/web/packages/QuasiSeq/index.html
- Lund SP, Nettleton D, McCarthy DJ, Smyth GK. (2012) Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates. Stat Appl Genet Mol Biol. 11(5). [article]
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Oct
30
Comparison of the Gene Expression Profiles Between Smokers With and Without Lung Cancer Using RNA-Seq
Filed Under Publications | Leave a Comment
Lung cancer is the most common cause of cancer-related death in both men and women throughout the world. Cigarette smoke contains over 60 known carcinogens (Hech, 2003) and tobacco smoke is the main contributor to lung cancer (Biesalski et al., 1998) . Therefore, the tissues of those smokers with and without lung cancer provide great resources to study their gene expression changes and find out those lung cancer related genes to help corresponding treatment.
To better understand the gene expression differences between smokers with and without lung cancer, researchers at the Third Medical Military University, China analyzed two related datasets from short read archive (Beane, 2011).
They quantified the expression of human genes in these two samples of smokers with and without lung cancer. Differential expression analysis revealed a number of differentially expressed genes. The results show some interesting phenomenon of the gene expression profiles between smokers with and without lung cancer, and highlight that RNA-Seq technologies are powerful tools to study the characteristics of human transcriptome.
- Cheng P, Cheng Y, Li Y, Zhao Z, Gao H, Li D, Li H, Zhang T. (2012) Comparison of the Gene Expression Profiles Between Smokers With and Without Lung Cancer Using RNA-Seq. Asian Pac J Cancer Prev13(8), 3605-9. [article]
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Oct
30
PacBio Books Four Systems in Q3 – Plans Q4 Chemistry Upgrade to Enable Longer Average Read Length
Filed Under News | Leave a Comment
By Monica Heger from GenomeWeb News
Pacific Biosciences booked four new systems in the third quarter of 2012, an increase from just one in the previous quarter, but down from six in the year-ago quarter. The Q3 orders bring its backlog to five systems.
Additionally, the company is planning to launch new chemistry in the fourth quarter that it says will increase average read lengths to 5,000 bases with five percent of reads above 13,000 bases. PacBio’s current C2 chemistry yields average reads of 3,000 bases with five percent above 8,000 bases.
“Long reads are especially valuable for de novo assembly applications and targeted sequencing of large repeat regions and regions with complex structural variation,” CEO Mike Hunkapiller said during a conference call discussing the company’s 2012 third-quarter earnings. Read more
Oct
30
Featured RNA-Seq Job
Filed Under Jobs | Leave a Comment
Bioinformatician to join new Biomedical Sequencing Facility – Vienna, Austria
from naturejobs.com
We are looking for an experienced bioinformatician to join the new Biomedical Sequencing Facility at the CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences and the Medical University of Vienna. A strong background in software engineering and extensive programming experience are mandatory. Read more
Oct
29
STAR – an ultrafast universal RNA-Seq aligner
Filed Under Splicing and Junction Mapping, Transcriptome Assembly Tools | Leave a Comment
Spliced Transcripts Alignment to a Reference (STAR)
Accurate alignment of high-throughput RNA-Seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-Seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases.
Now, researchers at Cold Spring Harbor Laboratory, NY have developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously un-described RNA-Seq alignment algorithm which utilizes sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure.
They have used STAR to align their large (exceeding 80 billon reads) ENCODE Transcriptome RNA-Seq dataset.
STAR outperforms other aligners by more than a factor of 50 in mapping speed, aligning to the human genome 550 Million 2x76bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full length RNA sequences.
Implementation and Availability: STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/
- Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. (2012) STAR: ultrafast universal RNA-seq aligner. Bioinformatics [Epub ahead of print]. [abstract]
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Oct
29
UZH/ETH – FGCZ – Functional Genomics Center Zurich
www.fgcz.ch/
The Functional Genomics Center Zurich (FGCZ) is a joint state-of-the-art research and training facility of the ETH Zurich and the University of Zurich. With latest technologies and expert support in genomics, transcriptomics, proteomics, metabolomics, and bioinformatics, the FGCZ carries out research projects and technology development in collaboration with the Zurich Life Science research community.
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- splice event RNA-seq
Oct
26
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Abstract/Presentation Information |
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Title: Quantitative trait locus analysis for next-generation sequencing with the functional linear models.(20) (11:30AM-11:45AM on Wed) (Platform) |
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Title: Discovering SNPs Regulating Human Gene Expression Using Allele Specific Expression from RNA-Seq data.(39) (11:45AM-12:00NOON on Wed) (Platform) |
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Title: RNA-Seq identifies differentially expressed genes and mutations in oligodendrogliomas.(1236W) (3:15PM-4:15PM on Wed) (Poster) |
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Title: Widespread novel RNA editing in human brain tissue, identified by RNA-Seq.(2622W) (3:15PM-4:15PM on Wed) (Poster) |
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Oct
26
Sex-biased genes are thought to drive phenotypic differences between males and females. The recent availability of high-throughput gene expression data for many related species has led to a burst of investigations into the genomic and evolutionary properties of sex-biased genes. In Drosophila, a number of studies have found that X chromosomes are deficient in male-biased genes (demasculinized) and enriched for female-biased genes (feminized), and that male-biased genes evolve faster than female-biased genes. However, studies have yielded vastly different conclusions regarding the numbers of sex-biased genes and forces shaping their evolution. Read more
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Oct
25
RNA-Seq Blog Poll Results
Filed Under Poll Results | Leave a Comment
Looks like there is high confidence among the RNA sequencing community that the MiSeq will be the dominant benchtop sequencer. It is also interesting that this poll reached 100 voters much more quickly than most of our previous polls. Is that because researchers are more sure of their answer to this question or possibly because they feel more strongly about it?
Total Number of Voters = 133
In our next poll, we thought is would be interesting to see how the political views of the RNA sequencing community compare with the general public. We asked who will you vote for (or would you vote for if not a US citizen) in the upcoming presidential election. We’ll check back after the actual election is held.
Check it out in the left hand side-bar and cast your vote!
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Oct
25
Sequencing of the Pineapple Fruit Transcriptome
Filed Under Transcriptome Sequenced | Leave a Comment
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Pineapple (Ananas comosus var. comosus), is an important tropical non-climacteric fruit with high commercial potential. Understanding the mechanism and processes underlying fruit ripening would enable scientists to enhance the improvement of quality traits such as, flavor, texture, appearance and fruit sweetness. Although, the pineapple is an important fruit, there is insufficient transcriptomic or genomic information that is available in public databases.
Now researchers at Universiti Malaysia Sabah have performed RNA-Seq of ripe yellow pineapple fruit flesh using Illumina RNA sequencing technology. About 4.7 million paired-end reads were generated and assembled using the Velvet de novo assembler. The assembly produced 28,728 unique transcripts with a mean length of approximately 200 bp. Sequence similarity search against non-redundant NCBI database identified a total of 16,932 unique transcripts (58.93%) with significant hits. Out of these, 15,507 unique transcripts were assigned to gene ontology terms. Functional annotation against Kyoto Encyclopedia of Genes and Genomes pathway database identified 13,598 unique transcripts (47.33%) which were mapped to 126 pathways. The assembly revealed many transcripts that were previously unknown.
- Ong WD, Voo LY, Kumar VS. (2012) De Novo Assembly, Characterization and Functional Annotation of Pineapple Fruit Transcriptome through Massively Parallel Sequencing. PLoS One 7(10), e46937. [article]
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Oct
25
Interpreting Personal Transcriptomes: Personalized Mechanism-Scale Profiling of RNA-Seq Data
Filed Under Other Tools, Statistical Analysis | Leave a Comment
Despite thousands of reported studies unveiling gene-level signatures for complex diseases, few of these techniques work at the single-sample level with explicit underpinning of biological mechanisms. This presents both a critical dilemma in the field of personalized medicine as well as a plethora of opportunities for analysis of RNA-Seq data. Read more
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Oct
24
A physical, genetic and functional sequence assembly of the barley genome
Filed Under Transcriptome Sequenced | Leave a Comment
Barley (Hordeum vulgare L.) is among the world’s earliest domesticated and most important crop plants. It is diploid with a large haploid genome of 5.1 gigabases (Gb). Here, the International Barley Genome Sequencing Consortium presents an integrated and ordered physical, genetic and functional sequence resource that describes the barley gene-space in a structured whole-genome context. They developed a physical map of 4.98 Gb, with more than 3.90 Gb anchored to a high-resolution genetic map. Projecting a deep whole-genome shotgun assembly, complementary DNA and deep RNA sequence data onto this framework supports 79,379 transcript clusters, including 26,159 ‘high-confidence’ genes with homology support from other plant genomes. Abundant alternative splicing, premature termination codons and novel transcriptionally active regions suggest that post-transcriptional processing forms an important regulatory layer. Survey sequences from diverse accessions reveal a landscape of extensive single-nucleotide variation. Their data provide a platform for both genome-assisted research and enabling contemporary crop improvement.
- The International Barley Genome Sequencing Consortium. (2012) A physical, genetic and functional sequence assembly of the barley genome. Nature [Epub ahead of print]. [article]
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Oct
24
A standard format for representing gene fusion data
Filed Under Splicing and Junction Mapping | Leave a Comment
There currently exists no community standardization approach to organize and represent genomic structural variation data with the essential attributes in an interchangeable manner. As transcriptome studies have been widely used for gene fusion discoveries, the current non-standard mode of data representation could potentially impede data accessibility, critical analyses, and further discoveries in the near future.
Now, researchers at the University of Michigan Medical School propose a prototype, Gene Fusion Markup Language (GFML) as an initiative to provide a standard format for organizing and representing the significant features of gene fusion data. GFML will offer the advantage of representing the data in a machine-readable format to enable data exchange, automated analysis interpretation, and independent verification. As this database-independent exchange initiative evolves it will further facilitate the formation of related databases, repositories, and analysis tools.
Availability: The GFML prototype is made available at http://code.google.com/p/gfml-prototype/
- Kalyana-Sundaram S, Shanmugam A, Chinnaiyan AM. (2012) Gene Fusion Markup Language: a prototype for exchanging gene fusion data. BMC Bioinformatics 13(1), 269. [article]
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- rna-seq gene fusion structure prediction
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Social Networking Pages
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SEQanswers – RNA Sequencing- CuffDiff strange output May 23, 2013Hi, 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, 2013Hi,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
- TopHat extremely low paired mapping rate. PLS HELP! May 22, 2013Hey guys, I have some problems with my paried-end RNA seq analysis on Galaxy. As you can see in the bam flagstat output, my tophat alignment rate is... […]Felix.Lee
- Identifying small RNA sequence within whole genome sequence May 21, 2013Hi all, I want to know if there are any useful bioinformatic tool to find small RNA sequence within a whole bacteria genome. Thank you in... […]Inma
- standard of clean data May 21, 2013Hi all I recently got my prokaryotes RNA-seq data report back. the standard filter steps of the raw data set by our local sequencing center is as... […]Pengfei Liu
- Problem with cummeRbund diffData() May 20, 2013Hi all, I'm running Tophat/cufflinks/cuffdiff for differential gene expression and analysis with cummeRbund (v 2.0.0). I'm having an issue with... […]Enrique Zudaire
- CuffDiff strange output May 23, 2013
- 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 dataI 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 […]
- Why am I getting so many unmapped reads in STAR, classified as "too short"?