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May
7
Bacterial RNA sequencing workflow for PGM™
Filed Under Reader Conributions, Sequencing Protocols | Leave a Comment
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.
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Mar
29
EDGE-pro – Estimated Degree of Gene Expression in PROkaryots
Filed Under Expression and Quantification | Leave a Comment
The expression levels of bacterial genes can be measured directly using next-generation sequencing (NGS) methods, offering much greater sensitivity and accuracy than earlier, microarray-based methods. Most bioinformatics software for estimating levels of gene expression from NGS data has been designed for eukaryotic genomes, with algorithms focusing particularly on detection of splicing patterns. These methods do not perform well on bacterial genomes.
Here, researchers at Johns Hopkins University School of Medicine describe the first software system designed explicitly for quantifying the degree of gene expression in bacteria and other prokaryotes. EDGE-pro (Estimated Degree of Gene Expression in PROkaryotes) processes the raw data from an RNA-seq experiment on a bacterial or archaeal species and produces estimates of the expression levels for each gene in these gene-dense genomes.
Availability – The EDGE-pro tool is implemented as a pipeline of C++ and Perl programs and is freely available as open-source code at http://www.genomics.jhu.edu/software/EDGE/index.shtml.
- Magoc T, Wood D, Salzberg SL. (2013) EDGE-pro: Estimated Degree of Gene Expression in Prokaryotic Genomes. Evol Bioinform Online 9, 127-36. [article]
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Mar
8
Results Presented at the 14th Annual AGBT Meeting Reveal Novel Method for RNA-Sequencing and Mutation Identification
Filed Under Press Release | Leave a Comment
Genomic Health Announces Results from Two Studies Demonstrating Innovations in Next Generation Sequencing From Paraffin Tissue, Enhancing Understanding of Tumor Biology
REDWOOD CITY, Calif., Feb. 25, 2013 /PRNewswire/ – Genomic Health, Inc. (Nasdaq: GHDX) today announced the results of two studies demonstrating that DNA strand-of-origin information can help further refine the identification of prognostic biomarkers, and that tumor specific gene mutations can be effectively examined using archival fixed paraffin embedded tumor (FPET) tissue, enabling an improved and more practical process of tumor analysis. These new findings were presented at the 14th Annual Advances in Genome Biology and Technology (AGBT) meeting in Marco Island, Fla. Read more
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Nov
2
Microarray technology is a powerful tool for studying genome-wide gene expression. As the genome of many fish has not yet been determined, however, cDNA microarrays can only be designed from limited known sequence data.
In this study, researchers at Tokyo University of Marine Science and Technology, Japan designed a microarray based on the sequencing data (337,466 reads) obtained by next-generation sequencing of RNA extracted from rainbow trout (Oncorhynchus mykiss) embryonic genital ridge, testis, and ovary.
These data (307,264 reads) were assembled into 28,668 contigs. Based on this information, 55,928 microarray probes were designed for a microarray, which was validated by hybridizing experiments with RNA extracted from type A spermatogonia (A-SG) and testicular somatic cells. The rainbow trout gonad microarray developed in this study therefore appears to be a useful tool to understand gametogenesis in rainbow trout.
- Hayashi M, Sato M, Iwasaki Y, Terasawa M, Tashiro M, Yokoyama S, Katayama N, Sadaie S, Miwa M, Yoshizaki G. (2012) Combining next-generation sequencing with microarrays for transcriptome analysis in rainbow trout gonads. Mol Reprod Dev [Epub ahead of print]. [abstract]
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Aug
13
Largest RNA-Seq study of individuals with schizophrenia to date
Filed Under Publications | Leave a Comment
Upregulation of the immune response may be involved in the pathogenesis of schizophrenia with changes occurring in both peripheral blood and brain tissue. To date, microarray technology has provided a limited view of specific inflammatory transcripts in brain perhaps due to sensitivity issues. Now, a team led by researchers at Neuroscience Research Australia has used SOLiD Next Generation Sequencing to quantify neuroimmune mRNA expression levels in the dorsolateral prefrontal cortex of 20 individuals with schizophrenia and their matched controls. Read more
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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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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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Apr
9
Global Next Generation Sequencing (NGS) Market Worth $2,343 Million by 2016
Filed Under News, Press Release | Leave a Comment
From: Next Generation Sequencing (NGS) Market – Global Trends and Forecasts (2011-2016), marketsandmarkets.com, April 2012
The global NGS market was valued at $842.5 million in the year 2011, growing at a CAGR of 22.7% from 2012 to 2016. North America commanded the largest share of 51.8% of the overall NGS technology market in 2011.
The major factors driving the NGS Market is the decrease in the cost of sequencing with increase in the number of applications of sequencing in various fields of science such as cancer research, bio-fuels, marine sciences, live stock research, agricultural, and veterinary research. Thus, there is more demand for the equipment to understand the genome sequencing and utilize it further for human and environmental benefits.
The high pace of NGS research is likely to boost the market for personal genome sequencing in the coming years. Manufacturers predominantly have an inclination towards diagnostic applications both as a supplier and manufacturer. Key players in this market include Illumina (U.S.), Life Technologies (U.S.), 454 Roche (Switzerland), Oxford Nanopore Technologies (U.K.), and Pacific Biosciences (U.S.).
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Aug
17
Study Finds Array and Sequencing Combo Yields Novel Info on Gene Expression
Filed Under Information | Leave a Comment
from In Sequence – By Monica Heger
Microarray technology and next-generation sequencing, when used in conjunction, could improve on using either method alone to evaluate gene expression, according to researchers from the University of Barcelona.
The team combined three different microarray platforms — from Agilent, Operon, and Illumina — with digital gene-expression profiling on the Illumina Genome Analyzer to study genes related to the epidermal growth factor, a regulatory growth factor related to cell proliferation and survival. They found that the combination of the techniques was able to establish a validated gene set — including novel genes previously unrelated to EGF — as well as help piece together the network of how those genes interact.
The study, published in BMC Genomics this month, suggests that using the two different technologies could help generate more reliable data sets than when either method is used alone, and should help improve the confidence of functional analysis.
Llorens F, Hummel M, Pastor X, Ferrer A, Pluvinet R, Vivancos A, Castillo E, Iraola S, Mosquera AM, González E, Lozano J, Ingham M, Dohm JC, Noguera M, Kofler R, Del Río JA, Bayés M, Himmelbauer H, Sumoy L. (2011) Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis. BMC Genomics 12, 326. [article]
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May
24
FusionMap – detecting fusion genes from next-generation sequencing data at base-pair resolution
Filed Under Splicing and Junction Mapping | Leave a Comment
A novel method, FusionMap, is presented which aligns fusion reads directly to the genome without prior knowledge of potential fusion regions. FusionMap can detect fusion events in both single- and paired-end datasets from either RNA-Seq or gDNA-Seq studies and characterize fusion junctions at base-pair resolution.
FusionMap achieved high sensitivity and specificity in fusion detection on two simulated RNA-Seq datasets which contained 75nt paired-end reads. FusionMap achieved substantially higher sensitivity and specificity than the paired-end approach when the inner distance between read pairs was small. Using FusionMap to characterize fusion genes in K562 chronic myeloid leukemia cell line, its accuracy was further demonstrated in fusion detection in both single-end RNA-Seq and gDNA-Seq datasets. These combined results show that FusionMap provides an accurate and systematic solution to detecting fusion events through junction-spanning reads.
AVAILABILITY: FusionMap includes reference indexing, read filtering, fusion alignment and reporting in one package. The software is free for non-commercial use at (http://www.omicsoft.com/fusionmap). CONTACT: ge@amgen.com SUPPLEMENTARY INFORMATION: Supplementary data are available at the journal’s web site.
- Ge H, Liu K, Juan T, Fang F, Newman M, Hoeck W (2011) FusionMap: detecting fusion genes from next-generation sequencing data at base-pair resolution. Bioinformatics [Epub ahead of print]. [abstract]
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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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Apr
21
RNA-Seq News…
Filed Under News | Leave a Comment
New Software
Estimation of alternative splicing isoform frequencies from RNA-Seq data
This algorithm, referred to as IsoEM, is based on disambiguating of information provided by the distribution of insert sizes generated during sequencing library preparation, and takes advantage of base quality scores, strand and read pairing information when available. The open source Java implementation of IsoEM is freely available at http://dna.engr.uconn.edu/software/IsoEM/
New Species
BMC Genomics reports the transcriptome sequencing of two new species: Alfalfa and Guppy
Alfalfa, (Medicago sativa [L.] sativa), a widely-grown perennial forage has potential for development as a cellulosic ethanol feedstock. However, the genomics of alfalfa, a non-model species, is still in its infancy. Using the Illumina GA-II platform, a total of 198,861,304 expression sequence tags (ESTs, 76 bp in length) were generated from cDNA libraries derived from elongating stem (ES) and post-elongation stem (PES) internodes of 708 and 773. [abstract]
The guppy (Poecilia reticulata) transcriptome, assembled do novo using 454 sequence reads, is presented and the authors evaluate potential uses of this transcriptome, including detection of sex-specific transcripts and deployment as a reference for gene expression analysis in guppies and a related species. Guppies have been model organisms in ecology, evolutionary biology, and animal behaviour for over 100 years. An annotated transcriptome and other genomic tools will facilitate understanding the genetic and molecular bases of adaptation and variation in a vertebrate species with a uniquely well known natural history. [abstract]
New Deals
Integromics and Ingenuity expand their co-operation with the integration of a fourth Integromics product to Ingenuity¹s IPA
The SeqSolve analysis software, exclusively designed for Next Generation Sequencing (NGS), performs tertiary level analysis of RNA-seq data at the gene and transcript level for biologically relevant results. Integromics has already integrated three solutions with Ingenuity’s IPA: Integromics Biomarker Discovery(R) for microarray data analysis, RealTime StatMiner(R) for the analysis of qPCR data as well as OmicsHub(R) Proteomics for the analysis and storage of proteomics data.
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Apr
19
Construction of Normalized RNA-Seq Libraries for Next-Generation Sequencing
Filed Under Information | Leave a Comment
RNA-seq is a method for studying the transcriptome of cells or tissues by massively parallel sequencing of tens of millions of short DNA fragments. However, the broad dynamic range of gene expression levels, which span more than five orders of magnitude, necessitates considerable over-sequencing to characterize low-abundance RNAs at sufficient depth. Here, the authors describe a method that enables efficient sequencing of low-abundance RNAs by normalizing or reducing the range spanned by the most abundant RNA species to the least abundant RNA species. This normalization is achieved using an approach that was developed for generating expressed sequence tag (EST) libraries that uses the crab duplex-specific nuclease and exploits the kinetics of DNA annealing. That is, double-stranded cDNA is denatured, then allowed to partially re-anneal, and the most abundant species, which re-anneal most rapidly, are digested with crab duplex-specific nuclease. This procedure substantially decreases the proportion of sequence reads from highly expressed RNAs, facilitating assessment of the full spectrum of the sequence and structure of transcriptomes.
Christodoulou DC, Gorham JM, Herman DS, Seidman JG. (2011) Construction of Normalized RNA-seq Libraries for Next-Generation Sequencing Using the Crab Duplex-Specific Nuclease. Curr Protoc Mol Biol. Chapter 4:Unit4.12. [abstract]
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SEQanswers – RNA Sequencing- 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
- How to increase rowsize in heatmap? May 16, 2013Hi, 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, 2013Hi, 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, 2013Hi, I am performing an RNA-seq experiment to look at differential expression. The design is as follows: 2 populations x 3 biological... […]jbono
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- 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 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 expressionI 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 […]
- What are the best practices for SNP identification in RNA seq transcriptome data