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Mar
18
Next-Gen Sequencing Conferences this Week
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CHI’s 3rd Annual RNA-Sequencing conference is only weeks away but there is still time to register and reserve your place.
RNA-Sequencing : Differential Expression in Depth conference is part of the 4th annual X-Gen Congress & Expo 2013
(http://www.xgencongress.com/RNA-Seq), March 18-20 in San Diego, CA.
X-Gen Congress is comprised of three scientific conferences
focused on RNA-Sequencing, DNA-Sequencing and Genomic Data Analysis. Your registration allows access to all three conferences.
6th international qPCR & Next Generation Sequencing Event
Symposium & Industrial Exhibition & Application Workshops
18th – 22 March 2013, Technical University of Munich, Freising, Weihenstephan, Germany
Main topic: Next Generation Thinking in Molecular Diagnostics
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Jan
20
Next-Generation Sequencing vs. Microarrays
Filed Under Information, News | 1 Comment
from genengnews.com – by Shawn C. Baker, Ph.D, CSO BlueSEQ (www.blueseq.com)- Reprinted with permission from Genetic Engineering & Biotechnology News (GEN)
Is it time to switch?
With recent advancements and a radical decline in sequencing costs, the popularity of next generation sequencing (NGS) has skyrocketed. As costs become less prohibitive and methods become simpler and more widespread, researchers are choosing NGS over microarrays for more of their genomic applications.
Rising maturity in NGS systems and ancillary protocols such as library preparation and data analysis tools have certainly contributed to the increasing popularity among the research community. Whether it’s a need for more accurate data, better resolution, pressure from granting agencies, or just plain fear of being left behind the technology forefront, it’s clear that the demand for revolutionary sequencing technologies that deliver fast, inexpensive, and accurate genomic information has never been greater. Read more
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Dec
7
Advances in next-generation sequencing suggest that RNA-Seq is poised to supplant microarray-based approaches for transcriptome analysis. This article briefly reviews the use of microarrays in the brain-behavior context and then illustrates why RNA-Seq is a superior strategy. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and non-coding RNAs, is superior for gene network construction, detects alternative spliced transcripts, detects allele specific expression and can be used to extract genotype information, e.g., non-synonymous coding single nucleotide polymorphisms.
Examples of where RNA-Seq has been used to assess brain gene expression are provided. Despite the advantages of RNA-Seq, some disadvantages remain. These include the high cost of RNA-Seq and the computational complexities associated with data analysis. RNA-Seq embraces the complexity of the transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior relationship is substantial.
- Hitzemann R, Bottomly D, Darakjian P, Walter N, Iancu O, Searles R, Wilmot B, McWeeney S. (2012) Genes, Behavior, and Next-Generation RNA Sequencing. Genes Brain Behav [Epub ahead of print]. [abstract]
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May
23
2nd Next Generation Sequencing
Boston, MA – May 30th – 31st
GTC’s 2nd Next Generation Sequencing Conference is a 2-day meeting that will bring together leading technology and diagnostic companies, national laboratories and academic institutions from across the globe to discuss applications of NGS platforms, novel sequencing strategies, developments in nanopore and single molecule sequencing and the implementation of NGS in the clinical space. (read more…)
Next Generation Sequencing
Prague – June 12th – 13th
Informa Life Sciences – Our Next Generation Sequencing conference provides you with the opportunity to hear the latest and most exciting developments in NGS technological advances (including 3rd and 4th generation platforms), sample preparation strategies for difficult samples, and how research, drug/diagnostic discovery and development are utilising next generation sequencing. (read more…)
Next-Gen Sequencing Applications & Translational Technologies Summit
San Francisco, CA – August 6th – 8th
ibc Life Sciences – How are others leveraging the power of next-generation sequencing in everyday research, and for clinical and diagnostic applications? Are you looking to accelerate your discovery and development efforts using translational technologies? (read more…)
NGX: Applying Next-Generation Sequencing
Providence, RI – Aug 13th – 15th
Cambridge Healthtech Institute’s NGX: Applying Next-Generation Sequencing investigates the expanding applications of NGS. Learn from experienced researchers from large sequencing centers, core laboratories, and specialized groups as they share their practical knowledge, real-world experiences and solutions. (read more…)
Next- Generation Sequencing Summit
London, September 17th – 18th
SMi – A data interpretation and analytical perspective. Identifying potential therapeutic drug targets and validating their suitability is a complex process involving numerous experimental platforms, including DNA sequence analysis. (read more…)
Beyond the Genome 2012
Boston, MA – September 27th 29th
Genome Medicine and Genome Biology – The aim of the conference is to discuss next-generation sequencing and other new technologies, informatic tools, and how these are being used to identify common and rare disease-causing mutations in the research laboratory and toward the clinic. Themes will include cancer genomics from discovery sequencing to genome guided therapy, epigenomic technologies and applications, and inherited disease beyond the candidate gene approach. There will also be an informatics workshop. (read more…)
Next Generation Sequencing Congress
London – November 15th-16th
Oxford Global – 2 day conference showcasing cutting edge next generation sequencing platforms, technologies, applications and computational tools for the analysis of NGS sequencing data. (read more…)
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May
17
Upcoming RNA-Seq Webinar
Filed Under Events | Leave a Comment
Next-Gen Sequencing: Novel Approaches to Automated Sample Prep and Data Analysis for RNA-Seq
| Date: | Wednesday, May 30, 2012 |
| Time: | 1 p.m. Eastern, 10 a.m. Pacific, 7 p.m. Central Europe |
| Duration: | 1 hour |
Webinar Description:
Next Generation Sequencing of DNA provides direct access to genomic information that has enabled researchers to transition away from more complex systems for the interrogation of this information, to direct sequencing of genomes and exomes. More recently the cost-effectiveness and accessibility of such data through the power of next-generation sequencing is being leveraged for other applications such as RNA-Seq, that allows the characterization of the transcriptome at an unprecedented level.
In this webinar, we will present an automated platform for the high throughput processing of extracted RNA via the preparation of high-quality libraries, and describe an easy-to-use, best-practices solution for the analysis of the overwhelming mass of gene expression data generated from Next Gen Sequencing.
Speakers:
Andrew Barry
Product Marketing Manager
PerkinElmer
Hugh Arnold, PhD
Senior Applications Scientist, Geospiza
PerkinElmer
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Sep
21
Pacific Biosciences Lays off 130 Employees
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On September 20, 2011, Pacific Biosciences of California, Inc. (the “Company”) implemented a reduction in its workforce of approximately 130 employees, or approximately 28% of its total workforce. The actions taken were in consideration of uncertainties associated with the economic environment and to position the Company for long-term success. The Company’s current infrastructure was staffed to support a faster adoption rate for its products. The reduction implemented will allow the Company to continue support of its growing customer base with improved service and continued product enhancements, while at the same time conserving cash.
The company reported the layoffs in a document filed with the US Securities and Exchange Commission.
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Sep
12
Review of Transcriptome Assembly Strategies & Tools
Filed Under Transcriptome Assembly Tools | Leave a Comment
Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalogue of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches — reference-based, de novo and combined strategies — along with some perspectives on transcriptome assembly in the near future.
- Martin JA, Wang Z. (2011) Next-generation transcriptome assembly. Nat Rev Genet [Epub ahead of print]. [abstract]
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Aug
25
NIH-Oxford collaboration uses RNA-Seq to produce detailed map of gene activity in mouse brain
Filed Under Publications | Leave a Comment
The researchers used a new sequencing technology called RNA-seq, a technology related to the latest DNA sequencers used to decode our genomes, to map gene activity in the different layers of the mouse cerebral cortex.
The technique is also able to detect ‘non-coding RNAs’, ie RNAs produced from DNA in between known genes that doesn’t code for proteins but may play a critical role in regulating genes and controlling biological processes.
‘We see a vast array of non-coding RNAs – hundreds that have never been seen before, but presumably have a biological role to play in the brain,’ says Professor Molnár. ‘One of the most abundant RNAs produced in the mouse brain is a noncoding RNA.’
The approach also reveals RNAs which, once read off from our DNA code, are stitched together in different ways through a process called ‘alternative splicing’. The process results in different proteins that can have different biological roles, despite coming from the same gene.
Oxford University Press Release
- T. Grant Belgard, Ana C. Marques, Peter L. Oliver, Hatice Ozel Abaan, Tamara M. Sirey, Anna Hoerder-Suabedissen, Fernando García-Moreno, Zoltán Molnár, Elliott H. Margulies, Chris P. Ponting (2011) A Transcriptomic Atlas of Mouse Neocortical Layers. Neuron 71(4), 605-16. [abstract]
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May
11
Bacterial Transcriptomics
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RNA-Seq or high-throughput sequencing of transcriptomes is sweeping through clinical microbiology, transforming the discipline in its wake. Early studies have uncovered a wealth of novel coding sequences and non-coding RNA, and are revealing a transcriptional landscape that increasingly mirrors that of eukaryotes.
Croucher NJ, Thomson NR. (2010) Studying bacterial transcriptomes using RNA-seq. Curr Opin Microbiol 13(5), 619-24. [abstract]
van Vliet AH. (2010) Next generation sequencing of microbial transcriptomes: challenges and opportunities. FEMS Microbiol Lett 302(1), 1-7. [abstract]
Pallen MJ, Loman NJ, Penn CW. (2010) High-throughput sequencing and clinical microbiology: progress, opportunities and challenges. Curr Opin Microbiol 13(5), 625-31. [abstract]
Skvortsov TA, Azhikina TL. (2010) Transcriptome analysis of bacterial pathogens in vivo: problems and solutions. Bioorg Khim 36(5), 596-606. [abstract]
Passalacqua KD, Varadarajan A, Ondov BD, Okou DT, Zwick ME, Bergman NH. (2009) Structure and complexity of a bacterial transcriptome. J Bacteriol 191(10), 3203-11. [abstract]
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May
3
Two recent studies (published as early access papers from the Journal of Experimental Botany) describe the combined use of RNA-Seq and custom microarrays to uncover more transcriptomics information about agriculturally important species.
MicroRNAs (miRNAs) are small, non-coding RNAs that play essential roles in plant growth, development, and stress response.
1In the first study, researchers characterize the miRNA profile of the shoot apical meristem (SAM) of an important legume crop, soybean, by integrating high-throughput sequencing data with miRNA microarray analysis.
A total of 8423 non-redundant sRNAs were obtained from two libraries derived from micro-dissected SAM or mature leaf tissue. Sequence analysis allowed the identification of 32 conserved miRNA families as well as 8 putative novel miRNAs.
A custom soybean miRNA microarray was designed containing miRNA and several miRNA* sequences derived from this RNA-Seq data as well as other soybean miRNAs available in the public miRNA database (miRBase). This microarray was subsequently utilized to compare the repertoire of miRNAs in the SAM and mature leaf as well as to verify the expression of novel miRNA candidates identified.
2The second study presents an efficient method for genome-wide discovery of new drought stress responsive miRNAs in Populus euphratica, a typical abiotic stress-resistant woody species, through the combined use of RNA-Seq and miRNA microarray profiling data.
High-throughput sequencing of P. euphratica leaves found 197 conserved miRNAs between P. euphratica and Populus trichocarpa. Additionally, 58 new miRNAs belonging to 38 families were identified, an increase in the number of P. euphratica miRNAs.
Comparison of high-throughput sequencing with miRNA microarray profiling data indicated that 104 miRNA sequences were up-regulated, whereas 27 were down-regulated under drought stress. The method of combining high-throughput sequencing and microarray technologies allowed the successful discovery of new and stress responsive miRNAs and will serve as a basis for future comparative functional genomic analyses using syntenic orthologues.
- Chui E. Wong, Ying-Tao Zhao, Xiu-Jie Wang, Larry Croft, Zhong-Hua Wang, Farzad Haerizadeh, John S. Mattick, Mohan B. Singh, Bernard J. Carroll, and Prem L. Bhalla. (2011) MicroRNAs in the shoot apical meristem of soybean. J Exp Bot [Epub ahead of print]. [article]
- Bosheng Li, Yurong Qin, Hui Duan, Weilun Yin, and Xinli Xia (2011) Genome-wide characterization of new and drought stress responsive microRNAs in Populus euphratica. J Exp Bot [Epub ahead of print]. [article]
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Apr
28
A list of the RNA-Seq data analysis software packages available from commercial providers. Again, if I’ve missed something, please send me a note and I will be happy to add to this list.
Avidis NGS – http://www.avadis-ngs.com/applications/rna_seq – Find fused genes via spliced and paired reads spanning the fused genes. Analyze coverage patterns to detect novel genes and exons not present in NCBI, UCSC, and Ensembl annotations. Seamlessly create and manipulate genomic region lists. Filter region lists based on different attributes. Determine gene and deconvoluted transcript expression profiles; identify alternative splicing patterns. Identify differential gene profiles via statitistical tests, cluster genes with similar profiles. Carry out GO analysis to identify significant GO terms.
CLC Genomics Workbench – http://www.clcbio.com/index.php?id=1240 – Our RNA-seq analysis now supports the use of paired-end data for RNA-seq. A combination of single reads and paired reads can also be used, and expression values can now be stratified into transcript level expression values, both for single and paired reads, allowing users to compare two different samples across transcripts. Another important new feature is our batching functionality of all our high-throughput sequencing tools, enabling researchers to perform the same analysis on several elements in one batch, which is an easy way to analyze multiple datasets in one go and thereby save time for setting up and running the same type of analysis multiple times.
DNAnexus - https://dnanexus.com/features/researcher/analyses – tag-based applications. RNA-seq is now a cost-effective way to measure gene expression, discover alternative splicing, and identify previously unassayed transcriptional activity. ChIP-seq provides a general way to study interactions between DNA and other molecules. With DNAnexus, these and other tag-based applications can be analyzed through our cloud-based infrastructure with the click of a button. Visualize the results in the DNAnexus browser, or export the results in tabular format and integrate your data across multiple experiment types, for example combining RNA-seq expression data with ChIP-seq binding affinity on the same genes.
DNASTAR QSeq – http://www.dnastar.com/t-products-qseq.aspx – QSeq is DNASTAR’s Next-Gen application for RNA-Seq, ChIP-Seq, and miRNA alignment and analysis. QSeq is fully integrated with ArrayStar, enabling you to take advantage of its powerful visualization and analytical tools, including using Gene Ontology (GO) annotations for ontology comparisons and gene characterization. Using QSeq, researchers can select gene sets and export associated reads through the rest of the DNASTAR software pipeline for sequence assembly, alignment, and detailed analysis.
GeneSifter – http://www.geospiza.com/Products/default.shtml – Analysis Kits offer a solution for scientists faced with large volumes of unprocessed data from AB SOLiD or Illumina Genome Analyzer DNA sequencing. Through Geospiza’s rapid analysis service, you can complete Next Generation Data Analysis as a service per file or per run. GeneSifter Analysis Edition now supports Whole Transcriptome Analysis with a more detailed view of each gene, including predicted splicing events and exon usage – increasing your access to the depth of next generation sequencing. This new analysis capability also includes an integrated gene viewer – to see your data as it relates to the genome and then tie it to publicly available information.
Geomatix Genome Analyzer – http://www.genomatix.de/en/produkte/genomatix-genome-analyzer.html – The Genomatix Genome Analyzer is our integrated solution for comprehensive second-level analysis of Next Generation Sequencing (NGS) data from ChIP-Seq, RNA-Seq or genotyping experiments. Each analyzer is brimming with state-of-the-art technology that sheds light on biological context – essential to help you understand the big picture.
GenomeQuest RNA Seq – http://www.genomequest.com/science/workflows/rna-seq/ – RNA-Seq stands to replace existing transcript profiling technologies as it measures all RNA in a sample, not just the RNA that can be probed for using traditional chips. GenomeQuest provides a powerful workflow that integrates best-of-breed open source technologies in a commercially supported environment to measure gene expression and to discover novel splice variants.
Ingenuity IPA 9.0 – http://www.ingenuity.com/products/pathways_analysis.html – IPA will support RNA-Seq processed datasets containing Ensembl, RefSeq or UCSC IDs. Researchers will now be able to analyze and interpret their RNA-Seq data. IPA helps you understand biology at multiple levels by integrating data from a variety of experimental platforms and providing insight into the molecular and chemical interactions, cellular phenotypes, and disease processes of your system. Even if you don’t have experimental data, you can use IPA to intelligently search the Ingenuity® Knowledge Base for information on genes, proteins, chemicals, drugs, and molecular relationships to build biological models or get up to speed in a relevant area of research.
Partek Genomics Suite (Partek GS) – http://www.partek.com/ngs#rnaseq Using a whole-transcriptome sequencing approach, sequence data are analyzed for differential expression and alternative splicing based on known mRNA annotation. Sequencing reads that are not mapped to any known mRNA annotation are used to uncover the novel transcriptional regions. Partek GS will identify and quantify sequence variants (coding SNPs) within RNA-seq samples. With a collection of SNPs identified, Partek GS will then find allele specific expression that drive phenotypic change within the transcriptome. Both paired end and junction reads are supported.
RNA Baser Sequence Assembler – http://www.rnabaser.com/ – RNA Baser Sequence Assembler represents an extension of DNA Baser Sequence Assembler, specialized in processing rRNA sequences. It is optimized for early integration of contextual metadata. This way, the metadata will stay attached to the primary sequence information throughout the complete workflow of sequence analysis, until final submission. In addition, RNA Baser Sequence Assembler is optimized for data exchange with standard non-commercial software used for rRNA sequence analysis and submission.
SeqSolve – http://www.integromics.com/ngs – for Next Generation Sequencing: a unique approach for the downstream analysis of RNA-seq data. – characterization of the reads, distributions into annotated genic regions, coverage profile within genes, read density over genomic sites, statistical support for differential gene expression, antisense transcription analysis, quality controls of the samples with library complexity, strand specificity, coverage asymmetry… and much more! Get the best from your RNA-seq data with SeqSolve.
Softgenetics NextGENe – http://www.softgenetics.com/NextGENe_11.html – NextGENe Software includes analysis tools designed for RNA-Seq analysis of data DNA sequencing data from Roche/454 GS FLX, FLX Titanium & Junior, Applied Biosystems’ SOLiD System and Illumina GA & Hi-Seq systems. Transcriptome sequencing data is aligned to the reference sequence to allow for detection of alternative splice sites, gene fusions, exon skipping and intron retention. NextGENe detects which exons are present in the sample data and chooses the appropriate reference transcripts. Accurate expression levels can be evaluated and mutations are reported. Alignment of RNA-Seq data with NextGENe uses a specialized algorithm to accurately align reads along exon boundaries.
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Mar
3
By Vicki Glaser – Genetic Engineering News
Advanced Technological Approach Generates Genomic Data Better, Faster, and Cheaper
In the next-gen sequencing (NGS) arena, the focus over the past several years has been on technological advances, moving from second-generation to third-generation sequencing strategies and producing research instruments capable of delivering whole-genome sequences in parallel at increasing speed. More recently, as read lengths and coverage continue to increase, throughputs rise, and costs decline, the expanding range of applications of NGS has taken center stage.
…Genomic Health announced results from its next-gen sequencing-driven biomarker discovery program in breast cancer at the recent “Advances in Genome Biology and Technology” (AGBT) meeting. Based on sequencing of the whole human transcriptome in formalin-fixed paraffin-embedded (FFPE) tumor and normal breast tissue samples, the company found hundreds of differences in both coding and noncoding transcripts between the two sample populations. Genomic Health reported an association between specific genes and some non-coding RNAs and risk of breast cancer recurrence.
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Oct
13
RNA-Seq Application for NGS
Filed Under Information | 2 Comments
More than half of survey respondents said they are using NGS for RNA-Seq applications.
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SEQanswers – RNA Sequencing- DESeq; can I omit timepoints during dispersal estimation? May 24, 2013I have a bacterial timecourse with 2 biological replicates per timepoint. There is a fair bit of variance between my replicates. I have spent the... […]amcloon
- HT Seq Count stranded options May 24, 2013I 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, 2013I 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, 2013we 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, 2013Hi, 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, 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
- DESeq; can I omit timepoints during dispersal estimation? May 24, 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"?