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from flybase.org

Isoform reconstruction is a key step in RNA-Seq analysis. Tools such as CEM, iReckon, NSMAP, and MonteBello use maximum likelihood for isoform reconstruction. The maximum likelihood approach has been observed to be computationally expensive. Here, researchers from Tsinghua University, China show that isoform reconstruction using short RNA-Seq reads by maximum likelihood is NP-hard.

• Li T, Jiang R, Zhang X. (203) Isoform reconstruction using short RNA-Seq reads by maximum likelihood is NP-hard. arXiv:1305.0916 [q-bio.QM]. [article]

from BioCompare by Josh P. Roberts

Knowledge of gene expression is crucial to understanding the molecular underpinnings of biology and medicine. As our ability to query the transcriptome grows—as new instrumentation comes online and becomes refined, along with the techniques necessary to use it—what was once the dominion of a few is becoming a workhorse in diverse labs.

“The era of RNA-Seq is definitely here,” says Christopher Mason Assistant Professor at Weill Cornel Medical College. “With RNA-Seq—especially for certain medium and high expressers—you get all of the same specificity of an array, plus greater sensitivity. You also get, not just expression by gene, by exon, by junction, but … also … SNP [single nucleotide polymorphism] information. Since it’s actual sequence data, you can look at the genetic variation present. You can look for things like gene fusion events or allele-specific expression. You can look for other rearrangements or new transcribed regions that are, by definition, novel, so they wouldn’t be on an array.” Mason further comments that the remainder of the RNA-Seq reads are sometimes from another species, what some might consider a “contaminant,” but which might also provide interesting and valuable information. In short, “You get a real wealth of information from the RNA-Seq data.” Read more

Bioo Scientific introduces the NEXTflex™ qRNA-Seq™ Kit for high precision gene expression analysis by RNA-Seq. Developed in conjunction with Cellular Research Inc., this new kit efficiently generates libraries equivalent to conventional RNA-Seq libraries, but with the added feature of Molecular Indexing™ technology. Similar to conventional RNA-Seq, sample RNA is converted to cDNA fragments.  But prior to any PCR amplification steps, all DNA fragment ends are ligated to a pair of adaptors chosen at random from a total set of 9,216 molecular indices. Individual DNA molecules of identical sequence become distinct through indexing, allowing for differentiation between re-sampling of the same molecule and sampling of a different molecule of identical sequence. Analysis using molecular indexing information provides an absolute, digital measurement of gene expression levels, irrespective of common amplification distortions observed in many RNA-Seq experiments. Read more

We asked – When it comes to RNA-Seq library prep protocols, which parameters are the most important to you?

Thanks to all who participated in this poll!

As far as Next-Gen Sequencing application go, RNA-Seq is hot and has been getting over the last few years.  We thought it would be interesting to know for how long our readers have been interested and/or involved with RNA sequencing.  Check out our latest poll in the left-hand sidebar and cast your vote!

Transcriptome analysis is a valuable tool for identification and characterization of genes and pathways underlying plant growth and development. A team led by scientists at University of Wisconsin previously published a microarray-based maize gene atlas from the analysis of 60 unique spatially and temporally separated tissues from 11 maize organs. To enhance the coverage and resolution of the maize gene atlas, they have analyzed 18 selected tissues representing five organs using RNA sequencing (RNA-Seq). For a direct comparison of the two methodologies, the same RNA samples originally used for our microarray-based atlas were evaluated using RNA-Seq. Both technologies produced similar transcriptome profiles as evident from high Pearson’s correlation statistics ranging from 0.70 to 0.83, and from nearly identical clustering of the tissues.

RNA-Seq provided enhanced coverage of the transcriptome, with 82.1% of the filtered maize genes detected as expressed in at least one tissue by RNA-Seq compared to only 56.5% detected by microarrays. Further, from the set of 465 maize genes that have been historically well characterized by mutant analysis, 427 show significant expression in at least one tissue by RNA-Seq compared to 390 by microarray analysis. RNA-Seq provided higher resolution for identifying tissue-specific expression as well as for distinguishing the expression profiles of closely related paralogs as compared to microarray-derived profiles. Co-expression analysis derived from the microarray and RNA-Seq data revealed that broadly similar networks result from both platforms, and that co-expression estimates are stable even when constructed from mixed data including both RNA-Seq and microarray expression data. The RNA-Seq information provides a useful complement to the microarray-based maize gene atlas and helps to further understand the dynamics of transcription during maize development.

• Sekhon RS, Briskine R, Hirsch CN, Myers CL, Springer NM, et al. (2013) Maize Gene Atlas Developed by RNA Sequencing and Comparative Evaluation of Transcriptomes Based on RNA Sequencing and Microarrays. PLoS ONE 8(4), e61005. [article]

Bioinformatician
1370806
CLARiENT, a GE Healthcare company

At Clarient, we combine innovative diagnostic technologies with world-class pathology expertise to assess and characterize cancer. Our mission is to become the leader in cancer diagnostics by dedicating ourselves to collaborative relationships with the healthcare community to translate cancer discovery and research into better patient care. Our principal customers include pathologists, oncologists, hospitals and biopharmaceutical companies. Read more

Please feel free to contact me at bhamory@theranos.com

Theranos is actively building a world-class team. Ideal candidates are currently employed or have been employed in similar types of positions and want to be part of a paradigm-shifting company doing innovative work. Candidates must be hard-working with an unfaltering determination to excel in an intense start-up environment and a desire to gain tremendous personal and career growth. Read more

Over the years, we at Ambion receive a number of requests for information about the best way to isolate and ribo-deplete prokaryotic total RNA for next-gen sequencing.  I just wanted to give a brief overview and some suggestions on RNA bacterial sequencing using the Ion platform (specifically, the PGM™).  After trying several methods to extract and purify total RNA from E. coli dh10b, we found that lysing and extracting using TRIzol® Reagent and isolating/purifying with the mirVana™ miRNA  isolation glass-fiber filter results in the most complete recovery of total RNA.  Specifically, we followed the TRIzol® Reagent protocol and homogenized using trizol then phase separated with chloroform. We then transferred the aqueous phase containing the RNA into a new RNase-free 1.5mL microcentrifuge tube and followed the mirVana™ miRNA Isolation Kit protocol starting with the Total RNA Isolation Procedure section and eluted with 100uL of Elution Solution.  This method is able to recover both large and small RNA molecules including miRNA, siRNA, snRNA, and other small RNA transcripts of yet unknown functions. The total RNA went through DNase I digestion to remove any genomic DNA contamination. Before proceeding to rRNA removal we checked the quality of the total RNA on an Agilent 2100 Bioanalyzer using the RNA 6000 Nano kit (see Figure 1). It is important that the RNA be high quality for maximum rRNA removal efficiency.

Figure 1. Agilent 2100 Bioanalyzer electropherogram of total RNA using TRIzol® Reagent for lysis/extraction and mirVana™ miRNA isolation glass-fiber filter for purification.

Read more

In this review, the authprotocols that have been developed and methods for analysis and provide an overview of studies that have examined the reproducibility and accuracy of these methods, as well as studies showing the advantages offered by the high resolution and dynamic range of high-throughput sequencing over previous methods. The authors go on to review a number of applications in the literature, from predicting genes essential for in vitro growth to directly assaying requirements for survival under infective conditions in vivo. They also highlight recent progress in assaying non-coding regions of the genome in addition to known coding sequences, including the combining of RNA-Seq with high-throughput transposon mutagenesis.

• Barquist L, Boinett CJ, Cain AK. (2013) Approaches to querying bacterial genomes with transposon-insertion sequencing. RNA Biol 10(7). [abstract]

Cambridge Healthtech Institute and Bio-IT World’s Inaugural
RNA-Seq and Transcriptome Analysis
Part of the Fifth International Clinical Genomics & Informatics Europe event
5-6 December 2013 | Sheraton Lisbon Hotel & Spa | Lisbon, Portugal

Transcriptome sequencing (RNA-Seq) is a robust approach steadily replacing microarrays as the choice method to accurately profile and quantify overall gene expression levels, while simultaneously allowing unbiased discovery of alternative splicing variants, rare and novel transcripts, miRNA precursors, differential isoforms and de novo analysis of samples. The ability to obtain a complete picture of disease transcriptomes provides clearer understanding of how the underlying genome is converted into the functional proteins, allowing for clinical utility in patient classification, diagnosis, and individualized treatment. However, a myriad of advanced bioinformatics tools and techniques for quality control, pre-processing, alignment, quantitative analysis and differential expression pose significant hurdles for many biologists and clinicians. Cambridge Healthtech Institute and Bio-IT World’s RNA-Seq and Transcriptome Analysis is designed to navigate the many bioinformatics challenges encountered by transcriptome sequencing, while highlighting its recent clinical applications. Read more

From Raw Sequence Data To mining Functional Information From Gene Lists Using Galaxy And R

The workshop is currently full. However, if there is enough demand we will offer it again. So if you are interested in attending, please fill in the information here. Also, people on the waiting list will receive the first opportunity to sign up for the next workshop!

Dates: June 3^rd, 4^th, 6^th & 7^th, 2013

Time: 1 pm – 5 pm

Location: 607 IGB – University of Illinois High-Performance Biological Computing

Workshop description: This is a 4-day workshop for RNA-Seq data analysis using the Galaxy interface and R. It will cover experimental design, evaluation of sequencing data, genome alignment, gene count extraction, statistical analysis to find differential expression and data mining. Data mining will include annotation of gene lists, Venn diagrams, heatmaps. The workshop will use the local Galaxy instance and open-source R and Bioconductor packages (no prior experience with Galaxy or R is necessary). Read more

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• SEQanswers – RNA Sequencing

  • Identifying small RNA sequence within whole genome sequence May 21, 2013
    Hi 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, 2013
    Hi 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, 2013
    Hi 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
  • How to increase rowsize in heatmap? May 16, 2013
    Hi, I am a complete newbie to all things cummeRbund and am currently fighting with generating readable heatmaps. When I use ... […]
    Mags
  • novoalign mapping May 15, 2013
    Hi, I want to use novoalign to map reads - allowing up to 15 mismatches for 100 bp paired-end reads I am new to novoalign(went through the... […]
    abh
  • Design of expt across multiple lanes May 15, 2013
    Hi, I am performing an RNA-seq experiment to look at differential expression. The design is as follows: 2 populations x 3 biological... […]
    jbono

• Biostar – RNA-Seq

  • 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 tag, which is useful for subsequent analyses, such as annotation. However, does this information influence the "mappability" of reads, or is this unaffected? My guess is that the information will be considered for mapping […]
  • 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 […]
  • Cell Type composition in a tissue based on gene marker expression
    I am not sure if the following would even make sense.... Tissues are composed of composite cell types, and often there are studies such as microarray/NGS where we perform a collective sampling of cells from these tissues. Information about the composition (say percentage of cell type) is not taken into consideration. In some case (such as brain/cancer), ther […]