A pipeline for RNA-seq data processing and quality assessment from Bioinformatics - current issue

A pipeline for RNA-seq data processing and quality assessment

from Bioinformatics - current issue by Goncalves, A., Tikhonov, A., Brazma, A., Kapushesky, M.

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Summary: We present an R based pipeline, ArrayExpressHTS, for pre-processing, expression estimation and data quality assessment of high-throughput sequencing transcriptional profiling (RNA-seq) datasets. The pipeline starts from raw sequence files and produces standard Bioconductor R objects containing gene or transcript measurements for downstream analysis along with web reports for data quality assessment. It may be run locally on a user's own computer or remotely on a distributed R-cloud farm at the European Bioinformatics Institute. It can be used to analyse user's own datasets or public RNA-seq datasets from the ArrayExpress Archive.
Availability: The R package is available at www.ebi.ac.uk/tools/rcloud with online documentation at www.ebi.ac.uk/Tools/rwiki/, also available as supplementary material.

RNA-Seq Analysis Tools from the Broad Institute from RNA-Seq Blog

RNA-Seq Analysis Tools from the Broad Institute

from RNA-Seq Blog by admin

GenePattern offers a suite of tools to support a wide variety of RNA-seq analyses, including short-read mapping, identification of splice junctions, transcript and isoform detection, quantitation, and differential expression. The modules have been adapted from widely-used tools. GenePattern also provides pipelines that allow you to perform a number of multi-step RNA-seq analyses automatically.

• Alignment: Bowtie.aligner
• Differential Expression: Cufflinks.cuffdiff
• Genome Annotation: Cufflinks, Cufflinks.cuffcompare, Scripture
• Isoform Detection: Tophat
• RNA Quantitation: Cufflinks, Scripture
• Utilities: Bowtie.indexer, BamToSam, ExprToGct, SamToBam, SortSam
• Visualizers: IGV

RNA-Seq Analysis Tools from the Broad Institute is a post from: RNA-Seq Blog More information about RNA-Seq can be here.

Incomplete lineage sorting patterns among human, chimpanzee, and orangutan suggest recent orangutan speciation and widespread selection [RESEARCH]

Incomplete lineage sorting patterns among human, chimpanzee, and orangutan suggest recent orangutan speciation and widespread selection [RESEARCH]

from Genome Research current issue by Hobolth, A., Dutheil, J. Y., Hawks, J., Schierup, M. H., Mailund, T.

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We search the complete orangutan genome for regions where humans are more closely related to orangutans than to chimpanzees due to incomplete lineage sorting (ILS) in the ancestor of human and chimpanzees. The search uses our recently developed coalescent hidden Markov model (HMM) framework. We find ILS present in ~1% of the genome, and that the ancestral species of human and chimpanzees never experienced a severe population bottleneck. The existence of ILS is validated with simulations, site pattern analysis, and analysis of rare genomic events. The existence of ILS allows us to disentangle the time of isolation of humans and orangutans (the speciation time) from the genetic divergence time, and we find speciation to be as recent as 9–13 million years ago (Mya; contingent on the calibration point). The analyses provide further support for a recent speciation of human and chimpanzee at ~4 Mya and a diverse ancestor of human and chimpanzee with an effective population size of about 50,000 individuals. Posterior decoding infers ILS for each nucleotide in the genome, and we use this to deduce patterns of selection in the ancestral species. We demonstrate the effect of background selection in the common ancestor of humans and chimpanzees. In agreement with predictions from population genetics, ILS was found to be reduced in exons and gene-dense regions when we control for confounding factors such as GC content and recombination rate. Finally, we find the broad-scale recombination rate to be conserved through the complete ape phylogeny.

Improving Detection of Genome Structural Variation

Improving Detection of Genome Structural Variation

from MassGenomics by Dan Koboldt

Large-scale structural variation (SV) is pervasive in the human genome, both in healthy individuals and in tumor cells. Numerous methods have been developed to detect such variants, most of which rely on the information provided by molecularly paired reads. Even the most sophisticated methods, however, still generate numerous false positives. A new study in Nature Genetics describes an innovative, population-based method to improve the accuracy of SV calling. In their introduction, Handsaker et al offer four main causes underlying false positives in SV calls:

1. Sequencing errors, which occur more frequently in next-generation sequencing data and exhibit both random and platform-specific bias distributions.
2. Chimeric molecules, in which read pairs linking two non-continguous segments of DNA masquerade as SVs. Sequencing libraries can contain millions of such fragments, which represent ~1% of sequence reads.
3. Read depth variation, which fluctuates across the genome for both biological and technical reasons.
4. Genome repeats, which confound most short-read aligners even when read pairing information is available.

These issues are exacerbated in population-scale sequencing, which often yields lower coverage across large numbers of samples. As more genomes are sequenced, false positives accumulate faster than real variants do. However, the authors hypothesized that population-scale sequencing might enable new analytical approaches. Here, they describe three strategies to do just that: allele sharing, population heterogeneity, and allelic substitution.

References
Handsaker RE, Korn JM, Nemesh J, & McCarroll SA (2011). Discovery and genotyping of genome structural polymorphism by sequencing on a population scale. Nature genetics, 43 (3), 269-76 PMID: 21317889

A comparison of single molecule and amplification based sequencing of cancer transcriptomes.

1. PLoS One. 2011 Mar 1;6(3):e17305.

A comparison of single molecule and amplification based sequencing of cancer transcriptomes.
Sam LT, Lipson D, Raz T, Cao X, Thompson J, Milos PM, Robinson D, Chinnaiyan AM, 
Kumar-Sinha C, Maher CA.

Michigan Center for Translational Pathology, University of Michigan, Ann Arbor,
Michigan, United States of America.

The second wave of next generation sequencing technologies, referred to as
single-molecule sequencing (SMS), carries the promise of profiling samples
directly without employing polymerase chain reaction steps used by
amplification-based sequencing (AS) methods. To examine the merits of both
technologies, we examine mRNA sequencing results from single-molecule and
amplification-based sequencing in a set of human cancer cell lines and tissues.
We observe a characteristic coverage bias towards high abundance transcripts in
amplification-based sequencing. A larger fraction of AS reads cover highly
expressed genes, such as those associated with translational processes and
housekeeping genes, resulting in relatively lower coverage of genes at low and
mid-level abundance. In contrast, the coverage of high abundance transcripts
plateaus off using SMS. Consequently, SMS is able to sequence lower- abundance
transcripts more thoroughly, including some that are undetected by AS methods;
however, these include many more mapping artifacts. A better understanding of the
technical and analytical factors introducing platform specific biases in high
throughput transcriptome sequencing applications will be critical in cross
platform meta-analytic studies.


PMID: 21390249 [PubMed - in process]

Solution to unmappable NGS reads - as a web service!

UMARS: Un-MAppable Reads Solution.
Li SC, Chan WC, Lai CH, Tsai KW, Hsu CN, Jou YS, Chen HC, Chen CH, Lin WC.
BMC Bioinformatics. 2011 Feb 15;12 Suppl 1:S9.
PMID: 21342592 [PubMed - in process]











Kevin: Don't you just love programs that say what they do?

Abstract

ABSTRACT : BACKGROUND : Un-MAppable Reads Solution (UMARS) is a user-friendly web service focusing on retrieving valuable information from sequence reads that cannot be mapped back to reference genomes. Recently, next-generation sequencing (NGS) technology has emerged as a powerful tool for generating high-throughput sequencing data and has been applied to many kinds of biological research. In a typical analysis, adaptor-trimmed NGS reads were first mapped back to reference sequences, including genomes or transcripts. However, a fraction of NGS reads failed to be mapped back to the reference sequences. Such un-mappable reads are usually imputed to sequencing errors and discarded without further consideration. METHODS : We are investigating possible biological relevance and possible sources of un-mappable reads. Therefore, we developed UMARS to scan for virus genomic fragments or exon-exon junctions of novel alternative splicing isoforms from un-mappable reads. For mapping un-mappable reads, we first collected viral genomes and sequences of exon-exon junctions. Then, we constructed UMARS pipeline as an automatic alignment interface. RESULTS : By demonstrating the results of two UMARS alignment cases, we show the applicability of UMARS. We first showed that the expected EBV genomic fragments can be detected by UMARS. Second, we also detected exon-exon junctions from un-mappable reads. Further experimental validation also ensured the authenticity of the UMARS pipeline. The UMARS service is freely available to the academic community and can be accessed via http://musk.ibms.sinica.edu.tw/UMARS/. CONCLUSIONS : In this study, we have shown that some un-mappable reads are not caused by sequencing errors. They can originate from viral infection or transcript splicing. Our UMARS pipeline provides another way to examine and recycle the un-mappable reads that are commonly discarded as garbage.

Next-Generation Sequencing without a Reference: Interview w/ Frank You of U.C. Davis/USDA-ARS

Chanced upon this interview in the SOLiD community but the thread was missing two days after. Oh well here's the google cache link
P.S. if there's a valid reason why it was taken off please inform me, will do the same..
Update: the page is up again

Recently we had the chance to talk with Frank You from the Department of Plant Sciences at U. C. Davis and the Genomics and Gene Dsicovery Research Unit of the USDA-ARS about his publication in BMC Genomics, Annotation-based genome-wide SNP discovery in the large and complex Aegilops tauschii genome using next-generation sequencing without a reference genome sequence.*

You are discovering genome wide SNPs in plant species. What is the reason you want to discover these SNPs?

Genome-wide SNPs are important resources for marker-assisted selection in breeding and high-dense genetic mapping construction which is required for map-based cloning and whole genome sequencing. In our study, we discovered genome-wide SNPs in Aegilops tauschii, the diploid ancestor of the wheat D genome, with a genome size of 4.02 Gb, of which 90% is repetitive sequences. We are using these SNPs to construct a high-dense genetic map for wheat D genome sequencing.

Please briefly explain the challenges you faced, and the solution you came to, in order to do this in the absence of a reference.

We have no reference sequences available for Ae. tauschii SNP discovery. For short reads generated by next-generation sequencing platforms, especially SOLiD™ and Solexa, the major challenge is mapping errors, when short reads in one genotype are mapped to short reads in another genotype in highly repetitive, complex genomes. Our idea is to reduce the complexity of the genome.

It is assumed that most genes are in a single-copy dose in a genome, and sequences of duplicated genes are usually diverged to such an extent that most of their reads do not cluster together. Therefore, the read depth (number of reads of the same nucleotide position) mapped to coding sequences of known genes estimates the expected read depth of all single-copy sequences in a genome. Sequences showing greater read depth are assumed to be from duplicated or repeated sequences. To implement this rationale, shallow genome coverage by long Roche 454 sequences is used to identify genic sequences by homology search against gene databases. Multiple genome coverages of short SOLiD™ or Solexa sequences are then used to estimate the read depth of genic sequences in a population of SOLiD™ or Solexa reads. The estimate is in turn used to identify (annotate) the remaining single-copy Roche 454 reads.

This combination of Roche 454 and SOLiD™ or Solexa platforms combines the long length of Roche 454 reads with the high coverage of the SOLiD™/Solexa sequencing platforms, thus reducing costs associated with the development of reference sequence. Short SOLiD™ or Solexa reads are mapped and aligned to the Roche 454 reads or contigs with short-read mapping tools. After the annotation of all sequences, SNPs are called and filtered.

 

An important part of your pipeline is the ability to call SNPs in repeat junctions. What is this, and why is it important?

Transposable elements (TE) make up large proportions of many eukaryotic genomes. For example, they represent ~35% of the rice genome, and ~90% of the hexaploid wheat genome, and significantly contribute to the size, organization and evolution of plant genomes. Repeat junctions (RJs) are created by insertions of TEs into each other, into genes, or into other DNA sequences. Previous studies showed that those repeat junctions are commonly unique and genome-specific. They can be therefore treated as single copy markers in the genome.

The genome specificity of TE junction-based markers makes them particularly useful for mapping of polyploid species including many important crops, such as wheat and cotton. Because repeat junctions are also abundant and randomly distributed along chromosomes, they have a great potential in development of genome-wide molecular markers for high-throughput mapping and diversity studies in large and complex genomes.

In this paper you used both base space and color space data. Did you find any challenges with mixing these data types?

We had difficulty using base space and color space data together in read mapping. No academic command-line-based programs for hybrid read mapping are currently available. Thus, this is still a challenge for hybrid data mapping. Instead, we can perform short read mapping separately for color-space and base-space data, and then merge the results in the pipeline.

Given the highly repetitive sequence you were working with, will this method work even better with less repetitive genomes?

Yes. I can expect that the method proposed in this paper will work even better with less repetitive genomes.

*Annotation-based genome-wide SNP discovery in the large and complex Aegilops tauschii genome using next-generation sequencing without a reference genome sequence. You FM, Huo N, Deal KR, Gu YQ, Luo MC, McGuire PE, Dvorak J, Anderson OD BMC Genomics 2011, 12:59 (25 January 2011)