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Apr
30
RNA-Seq Enabling Proteomic Studies
Filed Under Publications | Leave a Comment
Effective proteome profiling is generally considered to depend heavily on the availability of a high-quality DNA reference database. As such, proteomics has long been taxonomically restricted, with limited inroads being made into the proteomes of “non-model” organisms. However, next generation sequencing (NGS), and particularly RNA-Seq, now allows deep coverage detection of expressed genes at low cost, which in turn potentially facilitates the matching of peptide mass spectra with cognate gene sequence.
A team led by researchers at Cornell University used a custom RNA-Seq database, created through 454 pyrosequencing, to perform a quantitative analysis of the proteomes of domesticated and wild varieties of tomato (Solanum lycopersicum). More than 1200 proteins were identified, with subsets showing expression differences between genotypes or in the accumulation of the corresponding transcripts. Importantly, no major qualitative or quantitative differences were observed in the characterized proteomes when mass spectra were used to interrogate either a highly curated community database of tomato sequences generated through traditional sequencing technologies, or the RNA-Seq database.
RNA-Seq provides a cost-effective and robust platform for protein identification and will be increasingly valuable to the field of proteomics.
- Lopez-Casado G, Covey PA, Bedinger PA, Mueller LA, Thannhauser TW, Zhang S, Fei Z, Giovannoni JJ, Rose JK. (2012) Enabling proteomic studies with RNA-Seq: The proteome of tomato pollen as a test case. Proteomics 12(6), 761-74. [abstract]
Incoming search terms:
- express rna-seq
- RNA-seq slide
- clipping profile of rna seq
- rna-seq coverage uneven transcript
- rna seq wikipedia
- pollen rna seq
- number of alignable reads does not match rsem
- Hepatocyte RNA-Seq\
- data rna seq proteomic
- clipping profile of rna-seq
Apr
30
RNA-SeQC for RNA-Seq Datasets
Filed Under Data Analysis, Other Tools | Leave a Comment
RNA-SeQC is a program which provides key measures of data quality for RNA-seq datasets. These metrics include yield, alignment and duplication rates; GC bias, rRNA content, regions of alignment (exon, intron, intragenic), continuity of coverage, 3’/5’ bias, and count of detectable transcripts, among others. The software provides multi-sample evaluation of library construction protocols, input materials and other experimental parameters. The modularity of the software enables pipeline integration and the routine monitoring of key measures of data quality such as the number of alignable reads, duplication rates and rRNA contamination. RNA-SeQC allows investigators to make informed decisions about sample inclusion in downstream analysis. In summary, RNA-SeQC provides quality control measures critical to experiment design, process optimization and downstream computational analysis.
Availability and Implementation: See www.genepattern.org to run online, or www.broadinstitute.org/rna-seqc/ for a command line tool.
- DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire MD, Williams C, Reich M, Winckler W, Getz G. (2012) RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics [Epub ahead of print]. [abstract]
Incoming search terms:
- rna seq datasets
- accepted_hits bam RNA-SeQC
- rna seq dataset
Apr
27
Upcoming RNA-Seq Workshop
Filed Under Events | Leave a Comment
Berkeley *Seq I: Tools and Workflows for RNA-Seq Analysis
Saturday, June 30, 2012
9 a.m. – 5 p.m.
105 Stanley Hall
University of California, Berkeley
http://qb3.berkeley.edu/qb3/starseq/
REGISTER BY JUNE 7
Berkeley *Seq I, the inaugural workshop on analysis of Next Generation sequencing data at UC Berkeley, will take place on June 30, 2012. This first year, the workshop is being organized by Lior Pachter and will focus on analytical tools and workflows for RNA-Seq experiments. The one-day meeting features a morning of talks, an on-site lunch, and afternoon live demonstrations of Cufflinks and eXpress software packages.
Sponsored by the California Institute for Quantitative Biosciences (QB3) and the Berkeley Center for Computational Biology (CCB).
Incoming search terms:
- rna-seq workshops 2013
- mirna-seq analysis workshops - 2013
- qb3 rna seq
- rna seq workshop in toronto
- rna-seq workshops
- rnaseq analysis workshop 2013
- workshops rna sequencing analysis
Apr
26
KISSPLICE: de-novo calling alternative splicing events from RNA-seq data
Filed Under Splicing and Junction Mapping | Leave a Comment
KISSPLICE is an algorithm for identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. KISSPLICE is based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn graph constructed from the RNA-seq reads.
kisSplice calls splicing events from one to n sets of NGS/HTS reads. It takes as input one to n sets of NGS raw reads or an already created de-Bruijn graph. It first constructs the de-Bruijn graph if it is not provided and then detects all patterns in the de Bruijn graph which correspond to alternative splicing events.
KISSPLICE is available for download at http://alcovna.genouest.org/kissplice/
- Sacomoto GA, Kielbassa J, Chikhi R, Uricaru R, Antoniou P, Sagot MF, Peterlongo P, Lacroix V. (2012) KISSPLICE: de-novo calling alternative splicing events from RNA-seq data. BMC Bioinformatics 13(Suppl 6), S5. [article]
Incoming search terms:
- kissplice
- identifying alternative splicing from rna-seq data
- kissplice presentation
- kissplice ppt
- kissplice peterlongo
- alternative splicing event RNAseq software
- identification of new splicing spaces by rna-seq technique
- differential splicing event rna-seq data analysis
- debruijn kissplice
- m ged deep sequencing analysis wiki space com file view
Apr
26
RNA-Seq Lecture – Center for Health Informatics & Bioinformatics
Filed Under Presentations | Leave a Comment
Incoming search terms:
- rna seq bioinformatics
- TopHat :: Center for Bioinformatics and Computational Biology
- tophat bioinformatics
- lecture rna seq
- rna seq lecture
- rna-seq and tophat and RNA size
- rnaseq slide
- rna-seq bioinformatics lecture
- rna seqppt
- online lectures for RNA seq
Apr
19
RNA-Seq on Linkedin
Filed Under Information | Leave a Comment
Incoming search terms:
- tophat unmapped bam assembly
- gsnap output gives alternative mate pairs
- tophat assemble unmapped bam
Apr
19
RSeQC – RNA-seq quality control package
Filed Under Other Tools | 2 Comments
RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. “Basic modules” quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while “RNA-seq specific modules” investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation.
Availability: http://code.google.com/p/rseqc/
Incoming search terms:
- rna seq qc
- rnaseq qc
- rna-seq quality control
- rseqc
- rseqc: quality control of rna-seq experiments
- rnaseq quality control
- RSeQC arabidopsis
- tophat result quality control
- rna seqqc
- RNA seq qc tophat
Apr
16
SUNDAY NUTRITION
4:00 128.5 RNA-Seq analysis of placental gene expression: effect of maternal obesity.
K. Shankar, Y. Zhong, P. Kang, M.J. Ronis and H. Gomez-Acevedo. Arkansas Children’s Nutr. Ctr., Little Rock.
SUNDAY NUTRITION
C307 II 647.10 An RNA-Seq approach to identify mechanisms by which the phytochemical sulforaphane acts to prevent prostate cancer.
L.M. Beaver, J.H. Chang, D.E. Williams, R.H. Dashwood and E. Ho. Oregon State Univ.
MONDAY PHARMACOLOGY
B126 850.9 RNA-Seq identifies novel alternative transcripts of cytochrome P450s in human hepatocytes. D. Li, L. Peng, I-H.
Lee, J. Li, M. Visvanathan and X-b. Zhong. Univ. of Kansas Med. Ctr. and Univ. of Kansas.
PHARMACOLOGY TUESDAY
B76 1044.6 Chronic LSD administration produces changes in mPFC gene and protein expression relevant to schizophrenia, as determined by RNA-Seq and DIGE.
D.A. Martin, D.E. Nichols and C.D. Nichols. LSU Hlth. Sci. Ctr., New Orleans and Purdue Univ.
Apr
13
SAVoR: a server for sequencing annotation and visualization of RNA structures
Filed Under Other Tools | Leave a Comment
RNA secondary structure is required for the proper regulation of the cellular transcriptome. This is because the functionality, processing, localization and stability of RNAs are all dependent on the folding of these molecules into intricate structures through specific base pairing interactions encoded in their primary nucleotide sequences. Thus, as the number of RNA sequencing (RNA-seq) data sets and the variety of protocols for this technology grow rapidly, it is becoming increasingly pertinent to develop tools that can analyze and visualize this sequence data in the context of RNA secondary structure.
Sequencing Annotation and Visualization of RNA structures (SAVoR) is a web server which seamlessly links RNA structure predictions with sequencing data and genomic annotations to produce highly informative and annotated models of RNA secondary structure. SAVoR accepts read alignment data from RNA-seq experiments and computes a series of per-base values such as read abundance and sequence variant frequency. These values can then be visualized on a customizable secondary structure model.
SAVoR is freely available at http://tesla.pcbi.upenn.edu/savor
- Li F, Ryvkin P, Childress DM, Valladares O, Gregory BD, Wang LS. (2012) SAVoR: a server for sequencing annotation and visualization of RNA structures. Nucleic Acids Res [Epub ahead of print]. [article]
Incoming search terms:
- rna structure
- Savor server rna
- visualize mapped reads on rna with secondary structure
Apr
13
MapAl – a tool for RNA-Seq expression profiling
Filed Under Expression and Quantification | Leave a Comment
MapAl is a tool for RNA-Seq expression profiling that builds on the established programs Bowtie and Cufflinks. In the post-processing of RNA-Seq reads, it incorporates gene models already at the stage of read alignment, increasing the number of reliably measured known transcripts consistently by 50%. Adding genes identified de novo then allows a reliable assessment of double the total number of transcripts compared to other available pipelines. This substantial improvement is of general relevance: Measurement precision determines the power of any analysis to reliably identify significant signals, such as in screens for differential expression, independent of whether the experimental design incorporates replicates or not.
The software is freely available at http://www.bioinf.boku.ac.at/pub/MapAl/
- Labaj PP, Linggi BE, Wiley HS, Kreil DP. (2012) Improving RNA-Seq Precision with MapAl. Front Genet [Epub ahead of print]. [article]
Incoming search terms:
- rna-seq mapping and detection of gene fusions with a suffix array algorithm
- expression profile analyzer tool
- rna gene expression profiling
- RNA-seq expression profile
- rna-seq expression profiling
Apr
13
Visual Exploration and Statistics to Promote Annotation (VESPA)
Filed Under Databases, Other Tools | Leave a Comment
VESPA is a desktop JavaTM application that integrates high-throughput proteomics data (peptide-centric) and transcriptomics (probe or RNA-Seq) data into a genomic context, all of which can be visualized at three levels of genomic resolution. Data is interrogated via searches linked to the genome visualizations to find regions with high likelihood of mis-annotation. Search results are linked to exports for further validation outside of VESPA or potential coding-regions can be analyzed concurrently with the software through interaction with BLAST.
VESPA is demonstrated on two use cases (Yersinia pestis Pestoides F and Synechococcus sp. PCC 7002) to demonstrate the rapid manner in which mis-annotations can be found and explored in VESPA using either proteomics data alone, or in combination with transcriptomic data.
The software is freely available at https://www.biopilot.org/docs/Software/Vespa.php
- Peterson ES, McCue LA, Schrimpe-Rutledge AC, Jensen JL, Walker H, Kobold MA, Webb SR, Payne SH, Ansong CK, Adkins JN, Cannon WR, Webb-Robertson BJ. (2012) VESPA: software to facilitate genomic annotation of prokaryotic organisms through integration of proteomic and transcriptomic data. BMC Genomics 13(1), 131. [article]
Incoming search terms:
- transcriptomics software vespa vs
Apr
13
A new normalization strategy for RNA-Seq datasets
Filed Under Expression and Quantification | Leave a Comment
Recent studies have demonstrated that the normalization step for RNA-seq data is critical for a more accurate subsequent analysis of differential gene expression. Development of a more robust normalization method is desirable for identifying the true difference in tag count data.
The key concept of this new strategy for normalizing tag count data is to remove data assigned as potential differentially expressed genes (DEGs) before calculating the normalization factor. Several R packages for identifying DEGs are currently available, and each package uses its own normalization method and gene ranking algorithm.
The new normalization strategy was compared with the default normalization settings of four R packages (edgeR, DESeq, baySeq, and NBPSeq). Many synthetic datasets under various scenarios were evaluated on the basis of the area under the curve (AUC) as a measure for both sensitivity and specificity. Results showed that the elimination of potential DEGs is essential for more accurate normalization of RNA-seq data. The concept of this normalization strategy can widely be applied to other types of tag count data and to microarray data.
- Kadota K, Nishiyama T, Shimizu K. (2012) A normalization strategy for comparing tag count data. Algorithms Mol Biol 7(1), 5. [article]
Incoming search terms:
- rna-seq median normalization
- tag count normalization
- rpkm fpkm normalization
- rna-seq normalization different cells counts
- allele specific expression normalized length
- normalization strategies for sirna data sets
- more than 1 normalization for RNAseq dataset
- high level diagram for gene analysis hadoop
- dnaseq algorithm explanation normalization
- tool to normalize RNA-seq data
Apr
12
SNVQ – A new bayesian method for SNV discovery and genotyping based on quality scores
Filed Under Splicing and Junction Mapping | Leave a Comment
RNA-Seq poses new technical and computational challenges compared to genome sequencing. In particular, mapping transcriptome reads onto the genome is more challenging than mapping genomic reads due to splicing. Furthermore, detection and genotyping of single nucleotide variants (SNVs) requires statistical models that are robust to variability in read coverage due to unequal transcript expression levels.
Presented here is a strategy to more reliably map transcriptome reads by taking advantage of the availability of both the genome reference sequence and transcript databases such as CCDS. The authors also present a novel Bayesian model for SNV discovery and genotyping based on quality scores.
Experimental results on RNA-Seq data generated from blood cell tissue of three Hapmap individuals show that our methods yield increased accuracy compared to several widely used methods.
The open source code for implementing these methods available at: http://dna.engr.uconn.edu/software/NGSTools/
- Duitama J, Srivastava PK. Măndoiu II, (2012) Towards accurate detection and genotyping of expressed variants from whole transcriptome sequencing data. BMC Genomics 13(Suppl 2), S6 [article]
Incoming search terms:
- RNA-seq snv
- calling genotypes from rna-seq data
- snv in sequencing
- snv genotype
- SNV discovery
- snv detection
- S N V WALLPEPAR
- rna-seq genotyping
- rna seq genotyping
- mirna snv
Social Networking Pages
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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"?