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  • Brian Haas on Interview – Brian Haas Manager of Genome Annotation, Outreach, Bioinformatics and Analysis Broad Institute
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Evolution of Next Generation Sequencing
September 27^th – 29^th, 2010
Rhode Island Convention Center – Providence, RI
Cambridge Healthtech

Second-Generation Applications to Third-Generation Progress

Beyond the Genome: The true gene count, human evolution and disease genomics
October 11^th – 13^th, 2010
Joseph B. Martin Conference Center, Harvard Medical School – Boston, MA
BioMed Central

This international conference brings together leading researchers and industry representatives who will review recent progress in key areas of post-genomic research in biology and medicine and chart future developments, including the Human Microbiome Project and the resequencing of matched tumour and normal genomes from specific types of cancers. A cloud computing workshop, which will be open to all delegates, will provide an exciting opportunity to discuss recent and forthcoming developments in this critical and fast-moving field with policy makers and commercial and academic representatives of the genomics community and cloud platforms.

Next Generation Sequencing Congress 2010
November 15^th – 16^th, 2010
Radisson Edwardian, Heathrow – London, UK
Oxford Global Conference

Next generation sequencing technologies are revolutioising biology by allowing for genome wide transcription factor binding-site profiling, transcriptome sequencing and more recently, as an endpoint to applications ranging fromchromatin immunoprecipitation, mutation mapping and polymorphism discovery to noncoding RNA discovery.

Over the 2 days, the conference provides an overview of the current options of next generation sequencing platform, technologies, applications and the newest computational tools for the analysis of next-generation sequencing data.

X-Gen Congress and Expo
March 14^th – 18^th, 2011
Hilton – San Diego, CA
Cambridge Healthtech

Welcome to the Decade of the Genomics Revolution!  Technological advances are now enabling faster and cheaper DNA/RNA mapping, creating genomic comparisons and accelerating genomic discoveries.
NeXt-GENeration sequencing platforms create sequence reads of DNA fragments for genome variation studies, RNA for transcriptome studies, DNA-protein interactions for epigenetic studies, and chromosomal DNA of  large genome nucleotide variations for copy number studies.  The X-Gen Congress and Expo is uniquely designed to facilitate the cross fertilization of established and emerging genomic technologies, along with exciting applications. In addition, you will learn why data is the driving force that enables genomic discoveries. 

Next-Gen Sequencing Congress
April 26^th – 27^th, 2011
Boston Park Plaza Hotel & Towers – Boston, MA
Select Biosciences

• Sequencing Platforms and Methods
• 3rd Generation Sequencing Technologies
• Data Analysis and Bioinformatics
• Applications of Sequencing Data
• Toxicogenomics

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A systematic comparison of RNA-Seq and High-Density Exon Array for detecting differential gene expression between closely related species (a panel of human/chimpanzee/rhesus cerebellum RNA samples).

• Results indicate that RNA-Seq has significantly improved gene coverage and increased sensitivity for differentially expressed genes compared with the high-density array.
• Low expression level DEGs detected by array/qPCR were missed by RNA-Seq.
• RNA-Seq analysis showed an increase in both the false-negative rate and the false-positive rate for lowly expressed genes.

Liu S, Lin L, Jiang P, Wang D, Xing Y. (2010) A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species. Nucleic Acids Res [Epub ahead of print].  [abstract]

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RNA-Seq is a powerful technology for transcriptome analysis that is predicted to replace microarrays. Using second generation sequencing technology, millions of (relatively) short reads are sequenced from RNA samples. By analyzing these reads, more accurate estimation of both gene and isoform expression levels can be obtained. However, we need to conquer several computational challenges before we can obtain such estimation. One critical challenge is how to deal with reads that map to multiple locations.

We propose a generative probabilistic model of sequencing process to handle this challenge. The corresponding algorithm, RSEM(RNA-Seq by Expectation Maximization) is the first algorithm that handles both gene level and isoform level multireads in a statistically well founded way. Our simulation results show that RSEM has superior or comparable quantification accuracy to other currently available methods.

Using RSEM, we evaluate that, given a fixed sequencing throughput, if longer reads and paired-end reads can provide better accuracy than short reads and single-end reads. The simulation results suggest that in fact short reads and single-end reads are better for a fixe throughput, which is contrary to the common sense in the community. We also find that quality scores provide little additional information for improving quantification accuracy. Our findings have the potential of guiding RNA-Seq experimental design and technology development.

RSEM package is publicly available at http://deweylab.biostat.wisc.edu/rsem.

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The transcriptome of the human pathogen Trypanosoma brucei at single-nucleotide resolution.
Kolev NG, Franklin JB, Carmi S, Shi H, Michaeli S, Tschudi C. (2010)
PLoS Pathog. 2010 Sep 9;6(9). pii: e1001090.

Comprehensive annotation of the transcriptome of the human fungal pathogen Candida albicans using RNA-seq.
Bruno VM, Wang Z, Marjani SL, Euskirchen GM, Martin J, Sherlock G, Snyder M. (2010)
Genome Res. 2010 Sep 1. [Epub ahead of print].

RNA-Seq Atlas of Glycine max: a guide to the soybean transcriptome.
Severin AJ, Woody JL, Bolon YT, Joseph B, Diers BW, Farmer AD, Muehlbauer GJ, Nelson RT, Grant D, Specht JE, Graham MA, Cannon SB, May GD, Vance CP, Shoemaker RC. (2010)
BMC Plant Biol. 2010 Aug 5;10:160.

Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seq.
Lu T, Lu G, Fan D, Zhu C, Li W, Zhao Q, Feng Q, Zhao Y, Guo Y, Li W, Huang X, Han B. (2010)
Genome Res. 2010 Sep;20(9):1238-49.

 Using deep RNA sequencing for the structural annotation of the Laccaria bicolor mycorrhizal transcriptome.
Larsen PE, Trivedi G, Sreedasyam A, Lu V, Podila GK, Collart FR. (2010)
PLoS One. 2010 Jul 6;5(7):e9780.

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GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts.

The authors have developed a statistical methodology that enables the application of GO analysis to RNA-seq data by properly incorporating the effect of selection bias. Using published RNA-seq data, they show that accounting for this effect leads to significantly different results, which agree much better with previous microarray studies and the known biology than the results of an uncorrected analysis. (read more… )

Young MD, Wakefield MJ, Smyth GK, Oshlack A. (2010) Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol 11(2), R14. [article]

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Alternative expression analysis by sequencing (ALEXA-seq)

A method to analyze massively parallel RNA sequence data in order to catalog transcripts and assess differential and alternative expression of known and predicted mRNA isoforms in cells and tissues. The authors compared fluorouracil-resistant and -nonresistant human colorectal cancer cell lines and observed global disruption of splicing in fluorouracil-resistant cells characterized by expression of new mRNA isoforms resulting from exon skipping, alternative splice site usage and intron retention. Read more

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from genome.gov

Bethesda, Md., Mon., Sept. 13, 2010 — More than $18 million in grants to spur the development of a third generation of DNA sequencing technologies was announced today by the National Human Genome Research Institute (NHGRI). The new technologies will sequence a person’s DNA quickly and cost-effectively so it routinely can be used by biomedical researchers and health care workers to improve the prevention, diagnosis and treatment of human disease.  (Read more … )

$1,000 Genome Grants

NHGRI’s Revolutionary Genome Sequencing Technologies grants have as their goal the development of breakthrough technologies that will enable a human-sized genome to be sequenced for $1,000 or less. Grant recipients and their approximate funding are:

Adam Abate, Ph.D., GnuBIO Inc., New Haven, Conn.
$240,000 (1 year)
Microfluidic DNA Sequencing

Jeremy S. Edwards, Ph.D., University of New Mexico Health Sciences Center, Albuquerque
$2.7 million (3 years)
Polony Sequencing and the $1000 Genome

Javier A. Farinas, Ph.D., Caerus Molecular Diagnostics Inc., Los Altos, Calif.
$500,000 (2 years)
Millikan Sequencing by Label-Free Detection of Nucleotide Incorporation

M. Reza Ghadiri, Ph.D., Scripps Research Institute, La Jolla, Calif.
$5.1 million (4 years)
Single-Molecule DNA Sequencing with Engineered Nanopores

Steven J. Gordon, Ph.D., Intelligent Bio-Systems Inc., Waltham, Mass.
$2.6 million (2 years)
Ordered Arrays for Advanced Sequencing Systems

Xiaohua Huang, Ph.D., University of California San Diego
$800,000 (2 years)
Direct Real-Time Single Molecule DNA Sequencing

Stuart Lindsay, Ph.D., Arizona State University, Tempe
$860,000 (3 years)
Tunnel Junction for Reading All Four DNA Bases with High Discrimination

Amit Meller, Ph.D., Boston University
$4.1 million (4 years)
Single Molecule Sequencing by Nanopore-Induced Photon Emission

Murugappan Muthukumar, Ph.D., University of Massachusetts, Amherst
$800,000 (3 years)
Modeling Macromolecular Transport for Sequencing Technologies

Dean Toste, Ph.D., University of California, Berkeley
$430,000 (2 years)
Base-Selective Heavy Atom Labels for Electron Microscopy-Based DNA Sequencing

To read the grant abstracts go to Advanced Sequencing Technology Awards 2010. For more details about the full technology development program, go to: Genome Technology Program.

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The miRBase microRNA Sequence Database provides a searchable online repository for published microRNA sequences and associated annotation.  The database is updated 2 to 3 times per year based on publication of any new experimentally verified microRNA sequences.  The first version of the database was released in December of 2002 with only 218 database entries.  Monday, the database was updated to version 16 with more than 15,000 hairpin precursor sequence entries.  Many advances in RNA discovery and profiling technologies have contributed to the rapid growth of the microRNA database.  Custom microarrays have been used to verify microRNA sequences that were computationally predicted using folding algorithms and other software.  More recently, advances in RNA sequencing technology have accelerated the discovery of new microRNA sequences in both rare and model species.  In just the past year, the number of database entries for expressed, mature microRNA sequences has jumped from 10,581 to 17,341.  That’s an increase of 64% in just a year.  The latest update (to version 16) was released on September 9^th and included major updates for Human and the model species, mouse and rat.  9 new species were added to the database, including 6 new plants which accounted for 460 new hairpin precursor sequences.  Since these periodic updated have been continuous now for the past several years, this suggests that there are many more as yet undiscovered microRNA sequences. (Read the entire update summary here… )

From GenomeWeb – By Matthew Dublin

Using a grant from Amazon Web Services and the National Institutes of Health, researchers at the Johns Hopkins Bloomberg School of Public Health have developed an RNA sequencing data analysis program for the cloud called Myrna. The new software calculates differential gene expression in large RNA-seq datasets by using Bowtie, an ultrafast, memory-efficient short read aligner, and R/Bioconductor for statistical calculations. These tools are combined in an automatic, parallel pipeline that runs in the cloud using Elastic MapReduce, on a local Hadoop cluster. Read more

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The accurate mapping of reads that span splice junctions is a critical component of all analytic techniques that work with RNA-seq data. Here is a second generation splice detection algorithm, MapSplice, whose focus is high sensitivity and specificity in the detection of splices as well as CPU and memory efficiency. 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 […]