• Subscribe to the RNA-Seq Blog

  
  

  
  Or subscribe by RSS Reader
  
• 6,176 feed subscribers
• 
  

• Featured Events

  

• 

• 

• Media Partners

  
  

• 

• Polls

  How long have you been involved with RNA Sequencing?

  • Just now learning about RNA-Seq
  • less 1 year
  • more than 1 year
  • more than 2 years
  • more than 3 years
  • more than 4 years
  • more than 5 years

  View Results

   Loading ...
• Search for:

• Data Analysis Tools

  Transcriptome Assembly Tools

  Expression & Quantification

  Unpliced Mapping Tools

  Splicing and Junction Mapping

  Analysis Pipelines

  Clouding Platforms

  Databases

  Other Tools

• Tag Cloud

  allele-specific expression alternative splicing bioconductor bioinformatics breast cancer chip-seq cloud computing cufflinks Data Analysis data analysis pipeline deep sequencing de novo assembly differential expression encode ensembl expression analysis galaxy gene expression high-throughput sequencing illumina ion torrent junction mapping library preparation life technologies lncrna microarray microarrays microrna mirna next-generation sequencing next-gen sequencing pacific biosciences poll results RNA-Seq rnaseq rna sequencing roche small rna tophat transcriptome transcriptome analysis transcriptome assembly transcriptomes transcriptome sequencing transcriptomics

  WP Cumulus Flash tag cloud by Roy Tanck requires Flash Player 9 or better.

• Categories

  Select Category Advertisers Analysis Pipelines Annotation Clouding Platforms Controls Data Analysis Data Analysis Data Sets Databases Events Expression and Quantification Grants / Funding Industry News Information Jobs Library Preparation Methods News Other Tools Patents Pathway Analysis Poll Results Presentations Press Release Publications Reader Conributions Request for RNA-Seq Services Review Sequencing Protocols SNP Detection Splicing and Junction Mapping Statistical Analysis Transcriptome Assembly Tools Transcriptome Sequenced Uncategorized Unspliced Mapping Tools Web Tools Workflow
• 
  
  

• Recent Comments

  • Brian Haas on Interview – Brian Haas Manager of Genome Annotation, Outreach, Bioinformatics and Analysis Broad Institute
  • Bret on The Magic of RNA-Seq
  • Bob Copeman on RobiNA – a user-friendly, integrated software solution for RNA-Seq-based transcriptomics
  • Oz Solomon on Expression Analysis Announces 2013 Grant Program
  • Dr. Jayakishan Meher on Bioinformatics postdoctoral research fellowship available

• Popular Search Terms

  • grape
  • tirthadipa pradhan
  • rna seq analysis
  • rna-seq analysis
  • next generation sequencing ppt
  • rna seq data analysis
  • rna seq vs microarray
  • wine grapes
  • RSEM
  • mangrove

• Recent Search Terms

  • how many type of bios pdf
  • deseq vs cufflinks
  • RNA-Seq Blog Poll Results
  • custom microarray
  • hla typing methods ppt
  • RNAseq blog poll DAVID
  • RNAseq blog poll GO enrichment DAVID
  • RNAseq blog poll GO enrichment
  • cirsium adans
  • rna 스플라이싱

• Archives

  • May 2013
  • April 2013
  • March 2013
  • February 2013
  • January 2013
  • December 2012
  • November 2012
  • October 2012
  • September 2012
  • August 2012
  • July 2012
  • June 2012
  • May 2012
  • April 2012
  • March 2012
  • February 2012
  • January 2012
  • December 2011
  • November 2011
  • October 2011
  • September 2011
  • August 2011
  • July 2011
  • June 2011
  • May 2011
  • April 2011
  • March 2011
  • February 2011
  • January 2011
  • December 2010
  • November 2010
  • October 2010
  • September 2010
  • August 2010
  • July 2010
  • June 2010
  • May 2010
  • April 2010
  • February 2010
  • January 2010
  • January 2009

DNA sequencing technology is becoming more accessible to a variety of researchers as costs continue to decline. As researchers begin to sequence novel transcriptomes, most of these datasets lack a reference genome and will have to rely on de novo assemblers. Making comparisons across assemblies can be difficult: each program has its strengths and weaknesses and no tool exists to comparatively evaluate these datasets.

Now, a team led by researchers at the University of Rhode Island have developed software in R, called Sequence Comparative Analysis using Networks (SCAN) to perform statistical comparisons between distinct assemblies. SCAN uses a reference dataset to identify the most accurate de novo assembly and the ‘good’ transcripts in the user’s data. They tested SCAN on 3 publicly available transcriptomes, each assembled using 3 assembly programs. Moreover, they sequenced the transcriptome of the oomycete Achlya hypogyna and compared de novo assemblies from Velvet, ABySS, and the CLC Genomics Workbench assembly algorithms. One thousand one hundred and twenty eight (1,128) of the CLC transcripts were statistically similar to the reference, compared to 49 of the Velvet transcripts and 937 of the ABySS transcripts. SCAN’s strength is providing statistical support for transcript assemblies in a biological context. However, SCAN is designed to compare distinct node sets in networks, therefore it can also easily be extended to perform statistical comparisons on any network graph regardless of what the nodes represent.

Availability – Two versions of SCAN were developed: “SCAN” and “SCAN stringent,” that can run either in single or multiprocessor nodes, and are available from http://evol-net.fr .

• Misner I, Bicep C, Lopez P, Halary S, Bapteste E, Lane CE. (2013) Sequence Comparative Analysis using Networks (SCAN): software for evaluating de novo transcript assembly from next generation sequencing. Mol Biol Evol [Epub ahead of print]. [abstract]

Incoming search terms:

• sequence comparative analysis using networks (scan) – software for evaluating de novo transcript assembly from rna-seq data
• software for evaluating scanned im
• liang liang@uky edu
• nugen illumina indexes comparison
• rna-seq r package plot
• www rna-seqblog com sequence-comparative-analysis-using-networks-scan-software-for-evaluating-de-novo-transcript-assembly-from-rna-seq-data

Giardia intestinalis is a common cause of diarrheal disease and it consists of eight genetically distinct genotypes or assemblages (A-H). Only assemblages A and B infect humans and are suggested to represent two different Giardia species. Correlations exist between assemblage type and host-specificity and to some extent symptoms. Phenotypical differences have been documented between assemblages and genome sequences are available for A, B and E.

Now, researchers at the Karolinska Institutet, Sweden have characterized and compared the polyadenylated transcriptomes of assemblages A, B and E. Four genetically different isolates were studied (WB (AI), AS175 (AII), P15 (E) and GS (B)) using paired-end, strand-specific RNA-Seq. Most of the genome was transcribed in trophozoites grown in vitro, but at vastly different levels. RNA-Seq confirmed many of the present annotations and refined the current genome annotation. Gene expression divergence was found to recapitulate the known phylogeny, and uncovered lineage-specific differences in expression. Polyadenylation sites were mapped for over 70% of the genes and revealed many examples of conserved and unexpectedly long 3′ UTRs. 28 open reading frames were found in a non-transcribed gene cluster on chromosome 5 of the WB isolate. Analysis of allele-specific expression revealed a correlation between allele-dosage and allele expression in the GS isolate. Previously reported cis-splicing events were confirmed and global mapping of cis-splicing identified only one novel intron. These observations can possibly explain differences in host-preference and symptoms, and it will be the basis for further studies of Giardia pathogenesis and biology.

Franzén O, Jerlström-Hultqvist J, Einarsson E, Ankarklev J, Ferella M, Andersson B, Svärd SG. (2013) Transcriptome Profiling of Giardia intestinalis Using Strand-specific RNA-Seq. PLoS Comput Biol 9(3), e1003000. [article]

Incoming search terms:

• seed transcriptome torrent
• rna-seq wheat tolerance
• rna-seq asthma
• ppt RNA transcriptome
• transcriptomes sequenced
• understanding transcriptomes
• Ananas comosus RNAseq
• tcga rna seq depth
• tcga rna-seq 100 million
• t cell subpopulations and rna-seq

Evolutionary studies benefit from deep sequencing technologies that generate genomic and transcriptomic sequences from a variety of organisms. Genome sequencing and RNAseq have complementary strengths. In this study, researchers from the University of Geneva, Switzerland present the assembly of the most complete Hydra transcriptome to date along with a comparative analysis of the specific features of RNAseq and genome-predicted transcriptomes currently available in the freshwater hydrozoan Hydra vulgaris.

To produce an accurate and extensive Hydra transcriptome, they combined Illumina and 454 Titanium reads, giving the primacy to Illumina over 454 reads to correct homopolymer errors. This strategy yielded an RNAseq transcriptome that contains 48’909 unique sequences including splice variants, representing approximately 24’450 distinct genes. Comparative analysis to the available genome-predicted transcriptomes identified 10’597 novel Hydra transcripts that encode 529 evolutionarily-conserved proteins. The annotation of 170 human orthologs points to critical functions in protein biosynthesis, FGF and TOR signaling, vesicle transport, immunity, cell cycle regulation, cell death, mitochondrial metabolism, transcription and chromatin regulation. However, a majority of these novel transcripts encodes short ORFs, at least 767 of them corresponding to pseudogenes. This RNAseq transcriptome also lacks 11’270 predicted transcripts that correspond either to silent genes or to genes expressed below the detection level of this study.

The researchers established a simple and powerful strategy to combine Illumina and 454 reads and they produced, with genome assistance, an extensive and accurate Hydra transcriptome. The comparative analysis of the RNAseq transcriptome with genome-predicted transcriptomes lead to the identification of large populations of novel as well as missing transcripts that might reflect Hydra-specific evolutionary events.

• Wenger Y, Galliot B. (2013) RNAseq versus genome-predicted transcriptomes: a large population of novel transcripts identified in an Illumina-454 Hydra transcriptome. BMC Genomics 14(1), 204. [Epub ahead of print]. [abstract]

Incoming search terms:

• difference between exome and transcriptome
• miame rna-seq publication
• rna seq homopolymer
• RNA seq Long read roche 454
• rna sequencing transcription
• tophat fusion -r parameter
• torrent transcriptome sequencing rna
• transctrip rna seq

The only therapeutic options that exist for squamous cell lung carcinoma (SCC) are standard radiation and cytotoxic chemotherapy. Cancer stem cells (CSCs) are hypothesized to account for therapeutic resistance, suggesting that CSCs must be specifically targeted. Here, we analyze the transcriptome of CSC and non-CSC subpopulations by RNA-seq to identify new potential therapeutic strategies for SCC.

Researchers at the University of California San Diego sorted a SCC into CD133- and CD133+ subpopulations and then examined both by copy number analysis (CNA) and whole genome and transcriptome sequencing. They analyzed The Cancer Genome Atlas (TCGA) transcriptome data of 221 SCCs to determine the generality of their observations.

Both subpopulations highly expressed numerous mRNA isoforms whose protein products are active drug targets for other cancers; 31 (25%) correspond to 18 genes under active investigation as mAb targets and an additional 4 (3%) are of therapeutic interest. Moreover, they found evidence that both subpopulations were proliferatively driven by very high levels of c-Myc and the TRAIL long isoform (TRAILL) and that normal apoptotic responses to high expression of these genes was prevented through high levels of Mcl-1L and Bcl-xL and c-FlipL-isoforms for which drugs are now in clinical development. SCC RNA-seq data (n = 221) from TCGA supported the findings. Their analysis is inconsistent with the CSC concept that most cells in a cancer have lost their proliferative potential. Furthermore, this study suggests how to target both the CSC and non-CSC subpopulations with one treatment strategy.

This study is relevant to SCC in particular for it presents numerous potential options to standard therapy that target the entire tumor. In so doing, it demonstrates how transcriptome sequencing provides insights into the molecular underpinnings of cancer propagating cells that, importantly, can be leveraged to identify new potential therapeutic options for cancers beyond what is possible with DNA sequencing.

• Barrett CL, Schwab RB, Jung H, Crain B, Goff DJ, Jamieson CH, Thistlethwaite PA, Harismendy O, Carson DA, Frazer KA. (2013) Transcriptome sequencing of tumor subpopulations reveals a spectrum of therapeutic options for squamous cell lung cancer. PLoS One 8(3), e58714. [article]

Incoming search terms:

• Transcriptome Sequencing of Tumor Subpopulations Reveals a Spectrum of Therapeutic Options for Squamous Cell Lung Cancer

The Total Integrated Archive of short-Read and Array (TIARA) database stores and integrates human genome data generated from multiple technologies including next-generation sequencing and high-resolution comparative genomic hybridization array. The TIARA genome browser is a powerful tool for the analysis of personal genomic information by exploring genomic variants such as SNPs, indels and structural variants simultaneously. As of September 2012, the TIARA database provides raw data and variant information for 13 sequenced whole genomes, 16 sequenced transcriptomes and 33 high resolution array assays. Sequencing reads are available at a depth of ∼30× for whole genomes and 50× for transcriptomes. Information on genomic variants includes a total of ∼9.56 million SNPs, 23 025 of which are non-synonymous SNPs, and ∼1.19 million indels. In this update, by adding high coverage sequencing of additional human individuals, the TIARA genome database now provides an extensive record of rare variants in humans. Following TIARA’s fundamentally integrative approach, new transcriptome sequencing data are matched with whole-genome sequencing data in the genome browser. Users can here observe, for example, the expression levels of human genes with allele-specific quantification. Improvements to the TIARA genome browser include the intuitive display of new complex and large-scale data sets.

Availability: TIARA database is available online at – http://tiara.gmi.ac.kr

• Hong D, Lee J, Bleazard T, Jung H, Ju YS, Yu SB, Kim S, Park SS, Kim JI, Seo JS. (2013) TIARA genome database: update 2013. Database (Oxford) [Epub ahead of print]. [article]

Incoming search terms:

• beta binomial trac nbic
• Integrated genome database
• rna-seq software database
• nextera rna seq
• tiara seq
• trinity 20in 2013 torrent
• understanding rna sequencing results
• www rna-seqblog com tiara-genome-database-update-2013

from The Scientist by Ed Yong

Transcriptome studies reveal new insights about unusual animals whose genomes have not been sequenced.

The red spotted newt (Notophthalmus viridescens) could sit in the palm of your hand, but its genome is ten times the size of yours—up to 10 billion base pairs. This daunting amount of DNA has kept this species off the radar of any genome sequencing projects, despite plummeting costs. It has also prevented newts and salamanders from becoming regular model organisms, despite their remarkable and medically-relevant ability to regenerate severed limbs and damaged organs.

Recently, a team of German scientists circumvented the difficulties posed by the newt’s huge genome by sequencing its transcriptome instead—the set of RNA produced from its genes. Since some of an animal’s genome is never transcribed, transcriptomes can often be decoded at a fraction of the cost and effort of a full genome, and the newt results, published last month (February 20) in Genome Biology, are part of a growing trend of using transcriptomes to understand lesser-known species. Read more

Incoming search terms:

• cummerbund r heatmap
• rna-seq breast cancer classification heatmaps
• transcriptome sequencing pdf
• using cummerbund to create 3d maps
• what is the difference between transcriptome and exome

Long intergenic non-coding RNAs (lincRNAs) are emerging as a novel class of non-coding RNAs and potent gene regulators. High-throughput RNA-sequencing combined with de novo assembly promises quantity discovery of novel transcripts. However, the identification of lincRNAs from thousands of assembled transcripts is still challenging due to the difficulties of separating them from protein coding transcripts (PCTs).

A team of scientists at The Chinese University of Hong Kong have developed iSeeRNA, a support vector machine (SVM)-based classifier for the identification of lincRNAs. iSeeRNA shows better performance compared to other software.

iSeeRNA demonstrates high prediction accuracy and runs several magnitudes faster than other similar programs. It can be integrated into the transcriptome data analysis pipelines or run as a web server, thus offering a valuable tool for lincRNA study.

Availability – iSeeRNA is available as a user-friendly web server with free accessibility at http://www.myogenesisdb.org/iSeeRNA

• Sun K, Chen X, Jiang P, Song X, Wang H, Sun H. (2013) iSeeRNA: identification of long intergenic non-coding RNA transcripts from transcriptome sequencing data. BMC Genomics 14(supp 2). [article]

Incoming search terms:

• lnc RNA NGS
• difference between rna seq and exome sequencing
• long non coding rna svm cancer
• transcriptome blog
• long non coding rna protocol
• transcriptome sequencing of lung cancers
• long noncoding RNA 2013
• long noncoding rna seq
• long noncoding rna enhancer
• linc noncdoing rna analysis

Deep sequencing of the cancer transcriptome has revealed an unexpected complexity including the presence of proteins that are unique to the cancer. As more than 60% of the genome is now believed to be transcribed, understanding the changes in the coding genes as well as the noncoding regulatory transcriptome is critical for a clear elucidation of the dysfunctional cancer genome. The RNA seq method provides a comprehensive amount of information on the cancer transcriptome: highly sensitive and accurate gene-expression levels, alternative splice forms, novel fusion transcripts, and mutational changes that have a direct impact on protein function. In this webinar, presenters will review transformative discoveries from RNA-seq, and describe a highly effective approach for isolation and characterization of circulating tumor cells using RNA-seq at single cell resolution. Parameters that affect quantification of expressed nucleotide sequence polymorphisms (SNVs) will also be discussed, as will the development and implementation of tools for the identification of fusion transcripts.

Incoming search terms:

• tumor rna-seq

The capsaicinoids are a group of compounds produced by chili pepper fruits and are used widely in many fields, especially in medical purposes. The capsaicinoid biosynthetic pathway has not yet been established clearly. To understand more knowledge in biosynthesis of capsaicinoids, researchers at South China Agricultural University, Guangzhou applied RNA-seq for the mixture of placenta and pericarp of pungent pepper (Capsicum frutescens L.).

The researchers have assessed the effect of various assembly parameters using different assembly software, and obtained one of the best strategies for de novo assembly of transcriptome data. They obtained a total 54,045 high-quality unigenes (transcripts) using Trinity software. About 92.65% of unigenes showed similarity to the public protein sequences, genome of potato and tomato and pepper (C. annuum) ESTs databases. Their results predicted 3 new structural genes (DHAD, TD, PAT), which filled gaps of the capsaicinoid biosynthetic pathway predicted by Mazourek, and revealed new candidate genes involved in capsaicinoid biosynthesis based on KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis. A significant number of SSR (Simple Sequence Repeat) and SNP (Single Nucleotide Polymorphism) markers were predicted in C. frutescens and C. annuum sequences, which will be helpful in the identification of polymorphisms within chili pepper populations.

These data will provide new insights to the pathway of capsaicinoid biosynthesis and subsequent research of chili peppers. In addition, this strategy of de novo transcriptome assembly is applicable to a wide range of similar studies.

• Liu S, Li W, Wu Y, Chen C, Lei J. (2013) De Novo Transcriptome Assembly in Chili Pepper (Capsicum frutescens) to Identify Genes Involved in the Biosynthesis of Capsaicinoids. PLoS One 8(1), e48156. [article]

Incoming search terms:

• RNA-seq strategy
• RNA seq de novo
• asia kuczek
• chili pepper lncRNA
• unigene annotation rna-seq and clc bio
• unigenes using trinity assembler

The cellular and molecular pathways in asthma are highly complex. Increased understanding can be obtained by unbiased transcriptomic analysis (RNA-Seq). Scientists at Academic Medical Centre, The Netherlands investigated the feasibility to obtain RNA from whole endobronchial biopsies suitable for RNA-Seq and examined the difference in transcriptomic profiles between asthma and controls.

This cross-sectional study compared 4 steroid-free atopic asthma patients and 5 healthy non-atopic controls. Total RNA of 4 biopsies per subject was prepared for RNA-Seq. Comparison of the numbers of reads per gene in asthma and controls was based on the Poisson distribution.Forty-six genes were differentially expressed between asthma and controls, including pendrin, periostin, and BCL2. Ten gene networks were found involved in cellular morphology, movement, and development.RNA isolated from whole human endobronchial biopsies is suitable for RNA-Seq, showing different transcriptomic profiles between asthma and controls. Novel and confirmative genes were found to be linked to asthma. These results indicate that biological processes in the airways of asthma patients are differently regulated compared to controls, which may be relevant for the pathogenesis and treatment of the disease.

• Yick CY, Zwinderman AH, Kunst PW, Grünberg K, Mauad T, Dijkhuis A, Bel EH, Baas F, Lutter R, Sterk PJ. (2013) Transcriptome sequencing (RNA-Seq) of human endobronchial biopsies: asthma versus controls. Eur Respir J [Epub ahead of print]. [abstract]

Incoming search terms:

• biopsy tumor rna-seq
• file type: pdf thesis 2010:2013

Scientists at Raffaele Scientific Institute, Italy report a genome-wide characterization of sRNAs in M. tuberculosis integrating experimental and computational analyses. Global RNA-seq analysis of exponentially growing cultures of M. tuberculosis H37Rv had previously identified 1373 sRNA species. In the present report they show that 258 (19%) of these were also identified by microarray expression. This set included 22 intergenic sRNAs, 84 sRNAs mapping within 5′/3′ UTRs, and 152 antisense sRNAs. Analysis of promoter and terminator consensus sequences identified sigma A promoter consensus sequences for 121 sRNAs (47%), terminator consensus motifs for 22 sRNAs (8.5%), and both motifs for 35 sRNAs (14%). Additionally, 20/23 candidates were visualized by Northern blot analysis and 5′ end mapping by primer extension confirmed the RNA-seq data.

Additionally, the researchers used a computational approach utilizing functional enrichment to identify the pathways targeted by sRNA regulation. They found that antisense sRNAs preferentially regulated transcription of membrane-bound proteins. Genes putatively regulated by novel cis-encoded sRNAs were enriched for two-component systems and for functional pathways involved in hydrogen transport on the membrane.

• Miotto P, Forti F, Ambrosi A, Pellin D, Veiga DF, Balazsi G, Gennaro ML, Di Serio C, Ghisotti D, Cirillo DM. (2012) Genome-Wide Discovery of Small RNAs in Mycobacterium tuberculosis. PLoS One 7(12), e51950. [article]

Incoming search terms:

• mirauto small rna
• rna seq mycobacterium
• rna seq mycobacterium tuberculosis
• tb rnaseq

Quantitative real-time PCR is a powerful tool for quantifying gene expression, but correct normalization of expression levels requires identification of control genes that have stable expression across tissues and life stages.

Researchers from Institute for Sustainable Agriculture, Spain have evaluated the suitability of six candidate housekeeping genes across key life stages of Striga hermonthica (a root parasitic weed that attacks many of the staple crops in Africa, India and Southeast Asia) from seed conditioning to flower initiation using qRT-PCR and high-throughput cDNA sequencing. Based on gene expression analysis by qRT-PCR and RNA-Seq across heterogeneous Striga life stages, they determined that using the combination of three genes, UBQ1, PP2A and TUB1 provides the best normalization for gene expression throughout the parasitic life cycle. The housekeeping genes characterized here provide robust standards that will facilitate powerful descriptions of parasite gene expression patterns.

• Fernández-Aparicio M, Huang K, Wafula EK, Honaas LA, Wickett NJ, Timko MP, Depamphilis CW, Yoder JI, Westwood JH. (2012) Application of qRT-PCR and RNA-Seq analysis for the identification of housekeeping genes useful for normalization of gene expression values during Striga hermonthica development. Mol Biol Rep [Epub ahead of print]. [abstract]

Incoming search terms:

• qrt-pcr
• qrt-pcr analysis
• qrt-pcr and rna-sequence
• rna seq deno assembly qrt pcr
• rna-seq qrt-pcr

Bud dormancy is a critical developmental process that allows perennial plants to survive unfavorable environmental conditions. Pear is one of the most important deciduous fruit trees in the world, but the mechanisms regulating bud dormancy in this species are unknown. Because genomic information for pear is currently unavailable, transcriptome and digital gene expression data for this species would be valuable resources to better understand the molecular and biological mechanisms regulating its bud dormancy. Read more

Incoming search terms:

• pear
• fruit tree of the world
• pear dormancy next generation seuqncing china
• Suli pear pyrus pyrifolia white pear group
• transcriptomic analysis of pear

Next Page →

• 

• 

• 

• 

• Social Networking Pages

  

• 

• SEQanswers – RNA Sequencing

  • The Transcript Length from Cufflinks May 25, 2013
    Hi Guys, I'm doing a fungus RNA-Seq.However, the merged transcriptome gave me very long transcripts (generally >2K). I used GeneMarES to do... […]
    hchang10
  • DESeq; can I omit timepoints during dispersal estimation? May 24, 2013
    I 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, 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

• 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 […]