PLoS Genet. 2012 Oct;8(10):e1002944. doi: 10.1371/journal.pgen.1002944. Epub 2012 Oct 4.

A likelihood-based framework for variant calling and de novo mutation detection in families.

Li B, Chen W, Zhan X, Busonero F, Sanna S, Sidore C, Cucca F, Kang HM, Abecasis GR.

Source

Center for Human Genetics Research, Department of Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America.

Abstract

Family samples, which can be enriched for rare causal variants by focusing on families with multiple extreme individuals and which facilitate detection of de novo mutation events, provide an attractive resource for next-generation sequencing studies. Here, we describe, implement, and evaluate a likelihood-based framework for analysis of next generation sequence data in family samples. Our framework is able to identify variant sites accurately and to assign individual genotypes, and can handle de novo mutation events, increasing the sensitivity and specificity of variant calling and de novo mutation detection. Through simulations we show explicit modeling of family relationships is especially useful for analyses of low-frequency variants and that genotype accuracy increases with the number of individuals sequenced per family. Compared with the standard approach of ignoring relatedness, our methods identify and accurately genotype more variants, and have high specificity for detecting de novo mutation events. The improvement in accuracy using our methods over the standard approach is particularly pronounced for low-frequency variants. Furthermore the family-aware calling framework dramatically reduces Mendelian inconsistencies and is beneficial for family-based analysis. We hope our framework and software will facilitate continuing efforts to identify genetic factors underlying human diseases.

PMID:

 

23055937

 

[PubMed - in process] 

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1.	Bioinformatics. 2012 Oct 11. [Epub ahead of print] CD-HIT: accelerated for clustering the next generation sequencing data. Fu L, Niu B, Zhu Z, Wu S, Li W. Source Center for Research in Biological Systems, University of California San Diego, La Jolla, California, USA. Abstract SUMMARY: CD-HIT is a widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. In response to the rapid increase in the amount of sequencing data produced by the next generation sequencing technologies, we have developed a new CD-HIT program accelerated with a novel parallelization strategy and some other techniques to allow efficient clustering of such datasets. Our tests demonstrated very good speedup derived from the parallelization for up to ~24 cores and a quasi-linear speedup for up to ~8 cores. The enhanced CD-HIT is capable of handling very large datasets in much shorter time than previous versions. AVAILABILITY: http://cd-hit.org CONTACT: liwz@sdsc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
	PMID: 23060610 [PubMed - as supplied by publisher]

[Pub] CLEVER: Clique-Enumerating Variant Finder

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1.	Bioinformatics. 2012 Oct 11. [Epub ahead of print] CLEVER: Clique-Enumerating Variant Finder. Marschall T, Costa I, Canzar S, Bauer M, Klau GW, Schliep A, Schönhuth A. Source Centrum Wiskunde & Informatica, Amsterdam, the Netherlands, Federal University of Pernambuco, Recife, Brazil, Illumina, Cambridge, UK, Rutgers, The State University of New Jersey, Piscataway, NJ, USA. Abstract MOTIVATION: Next-generation sequencing techniques have facilitated large scale analysis of human genetic variation. Despite the advances in sequencing speed, the computational discovery of structural variants is not yet standard. It is likely that many variants have remained undiscovered in most sequenced individuals. RESULTS: Here we present a novel internal segment size based approach, which organizes all, including concordant, reads into a read alignment graph where max-cliques represent maximal contradiction-free groups of alignments. A novel algorithm then enumerates all max-cliques and statistically evaluates them for their potential to reflect insertions or deletions. For the first time in the literature, we compare a large range of state-of-the-art approaches using simulated Illumina reads from a fully annotated genome and present relevant performance statistics. We achieve superior performance in particular for indels of length 20-100nt. This has been previously identified as a remaining major challenge in structural variation discovery, in particular for insert size based approaches. In this size range we outperform even split read aligners. We achieve competitive results also on biological data where our method is the only one to make a substantial amount of correct predictions, which, additionally, are disjoint from those by split-read aligners. AVAILABILITY: CLEVER is open source (GPL) and available from http://clever-sv.googlecode.com. CONTACT: tobias.marschall@tu-dortmund.de.
	PMID: 23060616 [PubMed - as supplied by publisher]

Article: BMC Research Notes | Abstract | Rapid phylogenetic and functional classification of short genomic fragments with signature peptides

BMC Research Notes | Abstract | Rapid phylogenetic and functional classification of short genomic fragments with signature peptides
http://www.biomedcentral.com/1756-0500/5/460/abstract

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CummeRbund 2.0.0 released

---------- Forwarded message ----------
From: Loyal Goff

CummeRbund 2.0.0 release 10/3/2012

Following the release of Bioconductor 2.11, CummeRbund 2.0.0 is now available and is a recommended update for all CummeRbund users.  

Version 2.0.0 adds a host of new options/features/bugfixes and is the first stable release version to fully support Cuffdiff2.  

More information, details, vignettes, and downloads can be found at http://compbio.mit.edu/cummeRbund/.  As always, thank you for your support.

======================================================================

CHANGE LOG

======================================================================

v2.0.0

Stable public release (Bioconductor 2.11)

v1.99.6

Notes:

- 'annotation' and "annotation<-" generics were moved to BiocGenerics 0.3.2. Now using appropriate generic function, but requiring BiocGenerics >= 0.3.2

v1.99.5

Bugfixes:

- Added replicates argument to csDistHeat to view distances between individual replicate samples.

- Appropriately distinguish now between 'annotation' (external attributes) and features (gene-level sub-features).

- csHeatmap now has 'method' argument to pass function for any dissimilarity metric you desire. You must pass a function that returns a 'dist' object applied to rows of a matrix. Default is still JS-distance.

v1.99.3

New Features:

- Added diffTable() method to return a table of differential results broken out by pairwise comparison. (more human-readable)

- Added sigMatrix() method to CuffSet objects to draw heatmap showing number of significant genes by pairwise comparison at a given FDR.

- A call to fpkm() now emits calculated (model-derived) standard deviation field as well.

- Can now pass a GTF file as argument to readCufflinks() to integrate transcript model information into database backend

* Added requirement for rtracklayer and GenomicFeatures packages.

* You must also indicate which genome build the .gtf was created against by using the 'genome' argument to readCufflinks.

- Integration with Gviz:

* CuffGene objects now have a makeGeneRegionTrack() argument to create a GeneRegionTrack() from transcript model information

* Can also make GRanges object

* ONLY WORKS IF YOU READ .gtf FILE IN WITH readCufflinks()

- Added csScatterMatrix() and csVolcanoMatrix() method to CuffData objects.

- Added fpkmSCVPlot() as a CuffData method to visualize replicate-level coefficient of variation across fpkm range per condition.

- Added PCAplot() and MDSplot() for dimensionality reduction visualizations (Principle components, and multi-dimensional scaling respectively)

- Added csDistHeat() to create a heatmap of JS-distances between conditions.  

Bugfixes:

- Fixed diffData 'features' argument so that it now does what it's supposed to do.

- added DB() with signature(object="CuffSet") to NAMESPACE

Notes:

- Once again, there have been modifications to the underlying database schema so you will have to re-run readCufflinks(rebuild=T) to re-analyze existing datasets.

- Importing 'defaults' from plyr instead of requiring entire package (keeps namespace cleaner).

- Set pseudocount=0.0 as default for csDensity() and csScatter() methods (This prevents a visual bias for genes with FPKM <1 and ggplot2 handles removing true zero values).

v1.99.2

Bugfixes:

- Fixed bug in replicate table that did not apply make.db.names to match samples table.

- Fixed bug for missing values in *.count_tracking files.

- Now correctly applying make.db.names to *.read_group_tracking files.

- Now correctly allows for empty *.count_tracking and *.read_group_tracking files

v1.99.1

- This represents a major set of improvements and feature additions to cummeRbund.

- cummeRbund now incorporates additional information emitted from cuffdiff 2.0 including:

- run parameters and information.

- sample-level information such as mass and scaling factors.

- individual replicate fpkms and associated statistics for all features.

- raw and normalized count tables and associated statistics all features.

New Features:

- Please see updated vignette for overview of new features.

- New dispersionPlot() to visualize model fit (mean count vs dispersion) at all feature levels.

- New runInfo() method returns cuffdiff run parameters.

- New replicates() method returns a data.frame of replicate-level parameters and information.

- getGene() and getGenes() can now take a list of any tracking_id or gene_short_name (not just gene_ids) to retrieve

a gene or geneset. 

- Added getFeatures() method to retrieve a CuffFeatureSet independent of gene-level attributes.  This is ideal for looking at sets of features

outside of the context of all other gene-related information (i.e. facilitates feature-level analysis)

- Replicate-level fpkm data now available.

- Condition-level raw and normalized count data now available.

- repFpkm(), repFpkmMatrix, count(), and countMatrix are new accessor methods to CuffData, CuffFeatureSet, and CuffFeature objects.

- All relevant plots now have a logical 'replicates' argument (default = F) that when set to TRUE will expose replicate FPKM values in appropriate ways.

- MAPlot() now has 'useCount' argument to draw MA plots using count data as opposed to fpkm estimates.

Notes:

- Changed default csHeatmap colorscheme to the much more pleasing 'lightyellow' to 'darkred' through 'orange'.

- SQLite journaling is no longer disabled by default (The benefits outweigh the moderate reduction in load times).

Bugfixes:

- Numerous random bug fixes to improve consistency and improve performance for large datasets.

v1.2.1

Bugfixes:

-Fixed bug in CuffFeatureSet::expressionBarplot to make compatible with ggplot2 v0.9.

New Features:

- Added 'distThresh' argument to findSimilar.  This allows you to retrieve all similar genes within a given JS distance as specified by distThresh.

- Added 'returnGeneSet' argument to findSimilar.  [default = T] If true, findSimilar returns a CuffGeneSet of genes matching criteria (default). If false, a rank-ordered data frame of JS distance values is returned.

- findSimilar can now take a 'sampleIdList' argument. This should be a vector of sample names across which the distance between genes should be evaluated.  This should be a subset of the output of samples(genes(cuff)).

Notes:

- Added requirement for 'fastcluster' package.  There is very little footprint, and it makes a significant improvement in speed for the clustering analyses.

=================================================================================

Cheers,

Loyal 

_________________________________________________

Loyal A. Goff, Ph.D

NSF Postdoctoral Fellow

Computer Science and Artificial Intelligence Laboratory - MIT 

Stem Cell and Regenerative Biology - Harvard

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1.	PLoS One. 2012;7(9):e46211. Epub 2012 Sep 27. Paired-End Sequencing of Long-Range DNA Fragments for De Novo Assembly of Large, Complex Mammalian Genomes by Direct Intra-Molecule Ligation. Asan, Geng C, Chen Y, Wu K, Cai Q, Wang Y, Lang Y, Cao H, Yang H, Wang J, Zhang X. Source BGI-Shenzhen, Shenzhen, Guangdong, China. Abstract BACKGROUND: The relatively short read lengths from next generation sequencing (NGS) technologies still pose a challenge for de novo assembly of complex mammal genomes. One important solution is to use paired-end (PE) sequence information experimentally obtained from long-range DNA fragments (>1 kb). Here, we characterize and extend a long-range PE library construction method based on direct intra-molecule ligation (or molecular linker-free circularization) for NGS. RESULTS: We found that the method performs stably for PE sequencing of 2- to 5- kb DNA fragments, and can be extended to 10-20 kb (and even in extremes, up to ∼35 kb). We also characterized the impact of low quality input DNA on the method, and develop a whole-genome amplification (WGA) based protocol using limited input DNA (<1 µg). Using this PE dataset, we accurately assembled the YanHuang (YH) genome, the first sequenced Asian genome, into a scaffold N50 size of >2 Mb, which is over100-times greater than the initial size produced with only small insert PE reads(17 kb). In addition, we mapped two 7- to 8- kb insertions in the YH genome using the larger insert sizes of the long-range PE data. CONCLUSIONS: In conclusion, we demonstrate here the effectiveness of this long-range PE sequencing method and its use for the de novo assembly of a large, complex genome using NGS short reads.
	PMID: 23029438 [PubMed - as supplied by publisher]

[Pub] SNVerGUI: a desktop tool for variant analysis of next-generation sequencing data

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1.	J Med Genet. 2012 Sep 28. [Epub ahead of print] SNVerGUI: a desktop tool for variant analysis of next-generation sequencing data. Wang W, Hu W, Hou F, Hu P, Wei Z. Source 1Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA. Abstract BACKGROUND: Advances in next generation sequencing (NGS) technology have made it possible to interrogate comprehensively genome-wide genetic variations. However, most existing tools for variation detection are based on command-line interface, which discourages the main end users of NGS data, such as biologists, geneticists and clinicians, from utilising the software. METHOD AND RESULTS: We have developed the SNVerGUI, a graphical user interface (GUI) based tool for variant detection and analysis. Compared with other methods for variant calling, our approach is unique in that it is applicable to both individual and pooled sequencing data. With friendly GUI, end users can easily adjust running parameters to optimise variant calling for their specific needs. SNVerGUI supports commonly used input and output file formats that allows SNVerGUI to be seamlessly integrated into common NGS data analysis pipelines. SNVerGUI is implemented in Java, which is platform-independent and therefore easy to install and run on the commonly used operating systems, such as Linux, Mac, and Windows. Using two real datasets, we have shown that SNVerGUI is capable of analysing very high volume NGS data in a feasible time on personal computers. CONCLUSIONS: SNVerGUI is a fast and easy desktop GUI tool for the identification of genomic variants from pooled sequencing and individual sequencing data. Using this software, users can perform sophisticated variant detection by simply configuring several parameters in a friendly graphical user interface. SNVerGUI makes variant analysis as simple and effortless as possible, and we expect it to become popular among geneticists, clinicians, and biologists. SNVerGUI can be freely downloaded from http://snver.sourceforge.net/snvergui/  and will be continuously updated upon users' feedback.
	PMID: 23024288 [PubMed - as supplied by publisher]