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May
24
RNA-seq Showdown
Filed Under Information, Library Preparation | Leave a Comment
by Kelly Rae Chi – Biotechniques
Five RNA-seq library preparation methods go head-to-head in terms of performance in low-quality and low quantity RNA samples. Which method came out on top?
In a side-by-side comparison of five different transciptome sequencing (RNA-seq) library preparation methods, the RNase H technique outperformed others for analysis of low-quality RNA samples and was among the least expensive; SMART and NuGEN methods worked well for small amounts of RNA. The findings are published online May 19, 2013 in Nature Methods (1).
“We’d been looking for a way to deal with the issue of degraded RNA for a while. We tried different methods, and finally we found this RNase H method that works well and gives consistent results,” said co-author Xian Adiconis, senior research associate at the Broad Institute of Harvard and the Massachusetts Institute of Technology (MIT) in Cambridge, MA.
A strategy for analyzing the transcriptome, RNA-seq uses high-throughput sequencing to sequence and quantify RNA. But results suffer when researchers use samples that are degraded or present in small amounts. Numerous methods and commercial kits are available for the analysis of low quality and low quantity samples, but until now, the choice relied somewhat on guesswork, said Adiconis. Read more
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- low complexity rnaseq
Apr
11
A new method for stranded whole transcriptome RNA-seq
Filed Under Library Preparation | Leave a Comment
This report describes an improved protocol to generate stranded, barcoded RNA-seq libraries to capture the whole transcriptome. By optimizing the use of duplex specific nuclease (DSN) to remove ribosomal RNA reads from stranded barcoded libraries, researchers at Indiana University School of Medicine demonstrate improved efficiency of multiplexed next generation sequencing (NGS). This approach detects expression profiles of all RNA types, including miRNA (microRNA), piRNA (Piwi-interacting RNA), snoRNA (small nucleolar RNA), lincRNA (long non-coding RNA), mtRNA (mitochondrial RNA) and mRNA (messenger RNA) without the use of gel electrophoresis. The improved protocol generates high quality data that can be used to identify differential expression in known and novel coding and non-coding transcripts, splice variants, mitochondrial genes and SNPs (single nucleotide polymorphisms).
- Miller DF, Yan PS, Buechlein A, Rodriguez BA, Yilmaz AS, Goel S, Lin H, Collins-Burow B, Rhodes LV, Braun C, Pradeep S, Rupaimoole R, Dalkilic M, Sood AK, Burow ME, Tang H, Huang TH, Liu Y, Rusch DB, Nephew KP. (2013) A new method for stranded whole transcriptome RNA-seq. Methods [Epub ahead of print]. [abstact]
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- Streamlined Poly-A Tailing-based Strategy for Single-cell RNA-sequencing
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Mar
21
Accuracy of RNA-Seq and its dependence on sequencing depth
Filed Under Publications, Sequencing Protocols | Leave a Comment
The cost of DNA sequencing has undergone a dramatical reduction in the past decade. As a result, sequencing technologies have been increasingly applied to genomic research. RNA-Seq is becoming a common technique for surveying gene expression based on DNA sequencing. As it is not clear how increased sequencing capacity has affected measurement accuracy of mRNA, we sought to investigate that relationship.
Researchers at the University of Texas MD Anderson Cancer Center have empirically evaluated the accuracy of repeated gene expression measurements using RNA-Seq. They identifed library preparation steps prior to DNA sequencing as the main source of error in this process. Studying three datasets, they show that the accuracy indeed improves with the sequencing depth. However, the rate of improvement as a function of sequence reads is generally slower than predicted by the binomial distribution. They therefore used the beta-binomial distribution to model the overdispersion. The overdispersion parameters they introduced depend explicitly on the number of reads so that the resulting statistical uncertainty is consistent with the empirical data that measurement accuracy increases with the sequencing depth. The overdispersion parameters were determined by maximizing the likelihood. They show that their modified beta-binomial model had lower false discovery rate than the binomial or the pure beta-binomial models.
- Cai G, Li H, Lu Y, Huang X, Lee J, Müller P, Ji Y, Liang S. (2012) Accuracy of RNA-Seq and its dependence on sequencing depth. BMC Bioinformatics 13 Suppl 13, S5. [article]
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- Libraray preparation in ngs
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Jan
1
Strand-specific libraries for high throughput RNA sequencing (RNA-Seq) prepared without poly(A) selection
Filed Under Library Preparation | Leave a Comment
High throughput DNA sequencing technology has enabled quantification of all the RNAs in a cell or tissue, a method widely known as RNA sequencing (RNA-Seq). However, non-coding RNAs such as rRNA are highly abundant and can consume >70 % of sequencing reads. A common approach is to extract only polyadenylated mRNA; however, such approaches are blind to RNAs with short or no poly(A) tails, leading to an incomplete view of the transcriptome. Another challenge of preparing RNA-Seq libraries is to preserve the strand information of the RNAs.
Here, scientists at the University of Massachusetts Medical School, describe a procedure for preparing RNA-Seq libraries from 1 to 4 mug total RNA without poly(A) selection. Thier method combines the deoxyuridine triphosphate (dUTP)/uracil-DNA glycosylase (UDG) strategy to achieve strand specificity with AMPure XP magnetic beads to perform size selection. Together, these steps eliminate gel purification, allowing a library to be made in less than two days. They barcode each library during the final PCR amplification step, allowing several samples to be sequenced in a single lane without sacrificing read length. Libraries prepared using this protocol are compatible with Illumina GAII, GAIIx and HiSeq 2000 platforms.
The RNA-Seq protocol described here yields strand-specific transcriptome libraries without poly(A) selection, which provide approximately 90 % mappable sequences. Typically, more than 85 % of mapped reads correspond to protein-coding genes and only 6 % derive from non-coding RNAs. The protocol has been used to measure RNA transcript identity and abundance in tissues from flies, mice, rats, chickens, and frogs, demonstrating its general applicability.
Zhang Z, Theurkauf WE, Weng Z, Zamore PD. (2012) Strand-specific libraries for high throughput RNA sequencing (RNA-Seq) prepared without poly(A) selection. Silence [Epub ahead of print]. [abstract]
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Dec
12
An alternative to depleting rRNA sequences prior to cDNA library synthesis for RNA-Seq applications
Filed Under Library Preparation, Publications | Leave a Comment
An alternative to depleting rRNA sequences prior to cDNA library synthesis is to apply cDNA normalization (also called Cot filtration) approaches that remove highly abundant sequences from cDNA libraries. In normalization, double-stranded DNA (dsDNA) populations are first denatured and then allowed to re-anneal at an elevated temperature. Highly abundant sequences hybridize at higher rates (proportional to the square of their concentration) and, if the re-annealing reaction is stopped at a suitable time point (e.g., 4–24 h), these will comprise the majority of double-stranded species. If double-stranded and single-stranded cDNA can then be separated, representation of the highest abundance species in the resulting ss fraction can be significantly reduced. The two common approaches for separating ss-cDNA and ds-cDNA populations include enzymatic digestion of ds-cDNA using a duplex specific nuclease (DSN) and physical separation of ds-cDNA from ss-cDNA through methods such as hydroxyapatite chromatography (HAC). Read more
Incoming search terms:
- duplex-specific nuclease
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Jun
22
Incoming search terms:
- rna-seq protocol
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Jun
4
A high-throughput method for Illumina RNA-Seq library preparation
Filed Under Information, Publications | 1 Comment
With the introduction of cost effective, rapid and superior quality next generation sequencing (NGS) techniques, gene expression analysis has become viable for labs conducting small projects as well as large-scale gene expression analysis experiments. However, the available protocols for construction of RNA-Sequencing (RNA-Seq) libraries are expensive and/or difficult to scale for high-throughput applications. Also, most protocols require isolated total RNA as a starting point.
Presented here is a cost-effective RNA-Seq library synthesis protocol that is fast, starts with tissue, and is high-throughput from tissue to synthesized library. A set of 96 unique barcodes have been designed for library adapters that are amenable to high-throughput sequencing by a large combination of multiplexing strategies. This protocol has more power to detect differentially expressed genes when compared to the standard Illumina protocol, probably owing to less technical variation amongst replicates. The authors also address the problem of gene-length biases affecting differential gene expression calls and demonstrate that such biases can be efficiently minimized during mRNA isolation for library preparation. (read more… )
Kumar R, Ichihashi Y, Kimura S, Chitwood DH, Headland LR, Peng J, Maloof JN, Sinha NR. (2012) A high-throughput method for Illumina RNA-Seq library preparation. Front in Plant Genet and Genom [Epub ahead of print]. [abstract]
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Oct
31
Video Protocol – Single Read and Paired End mRNA-Seq Illumina Libraries from 10 Nanograms Total RNA
Filed Under Information | Leave a Comment
The authors describe a protocol for the Illumina Genome Analyzer II platform for mRNA-Seq sequencing for library preparation that avoids significant PCR amplification and requires only 10 nanograms of total RNA. While this protocol has been described previously and validated for single-end sequencing, where it was shown to produce directional libraries without introducing significant amplification bias, here they validate it further for use as a paired end protocol.
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SEQanswers – RNA Sequencing- 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
- 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
- HT Seq Count stranded options 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"?