Article: PLoS ONE: myKaryoView: A Light-Weight Client for Visualization of Genomic Data

PLoS ONE: myKaryoView: A Light-Weight Client for Visualization of Genomic Data
http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026345

 a web tool for visualization of genomic data specifically designed for direct-to-consumer genomic data that uses publicly available data distributed throughout the Internet. It does not require data to be held locally and it is capable of rendering any feature as long as it conforms to DAS specifications. Configuration and addition of sources to myKaryoView can be done through the interface. Here we show a proof of principle of myKaryoView's ability to display personal genomics data with 23andMe genome data sources. The tool is available at: http://mykaryoview.com.

Vim the ubiquitous ide

Vim: revisited Read it online: http://mislav.uniqpath.com/2011/12/vim-revisited/ My love affair with vim started with a futile effort in locating a perl ide. Then I chanced on a debate between vim and emacs I tried both but turns out vim is installed by default and emacs wasn't so I started on a journey of being used to vim and unable to use notepad forever. Right now I am playing with cream and wondering if it's worth the effort just for using crtl s for pasting clipboard items.

multiBamCov (BAM) Counts sequence coverage for multiple position-sorted bams at specific loci defined in a BED/GFF/VCF file BedTools

multiBamCov will report a count of alignments for each BAM at each  interval

Program: multiBamCov (v2.14.2)
Summary: Counts sequence coverage for multiple bams at specific loci.

Usage:   multiBamCov [OPTIONS] -bams aln.1.bam aln.2.bam ... aln.n.bam -bed

Options:
        -bams   The bam files.

        -bed    The bed file.

        -q      Minimum mapping quality allowed. Default is 0.

        -D      Include duplicate-marked reads.  Default is to count non-duplicates only

        -F      Include failed-QC reads.  Default is to count pass-QC reads only

        -p      Only count proper pairs.  Default is to count all alignments with MAPQ
                greater than the -q argument, regardless of the BAM FLAG field.
 

 the sample output looks like

  chr20 0 100000 19312 16844
  chr20 100000 200000 43910 37579
  chr20 200000 300000 43245 43215
  chr20 300000 400000 41653 47556
  chr20 400000 500000 42929 43165
  chr20 500000 600000 44265 45325 

The last two columns are the count  of alignments in the first and second BAM, respectively.

FAQ - How to get average depth of coverage for targeted seq regions using BedTools

Question
If I have a list of targeted resequencing regions and a bam file is
there any easy way to get the average depth of coverage for each region?
I could calculate it from all of the values in the histogram output from
coverageBed -hist but that seems messy. In addition is there any way to
adjust the minimum number of hits at a location for it to be included in
the fraction of bases in B that had non-zero coverage into bases in B
that had more-than-X coverage?

Reply
There is no option for directly computing the mean coverage. However, one can combine the -d option (reports the depth at each base in each interval) with the groupBy tool found in the filo package (https://github.com/arq5x/filo

) to compute the mean.  For example, here's a 5 column BED of the UCSC simple repeat track:

$ head simRep.chr22.bed
22      16052163        16052199        trf     65
22      16059465        16059496        trf     53
22      16060479        16060524        trf     90
22      16060479        16060592        trf     120
22      16060517        16060609        trf     129
22      16071925        16071962        trf     74
22      16078616        16078686        trf     86
22      16079642        16079731        trf     61
22      16079644        16079696        trf     59
22      16079648        16079724        trf     52

I can compute the coverage at each base in each of these intervals as follows (column 6 is the position in the interval, column 7 is the depth):

$ coverageBed -abam aln.bam -b simRep.22.bed -d | head
22      17825583        17825839        trf     372     1       2
22      17825583        17825839        trf     372     2       2
22      17825583        17825839        trf     372     3       2
22      17825583        17825839        trf     372     4       2
22      17825583        17825839        trf     372     5       2
22      17825583        17825839        trf     372     6       2
22      17825583        17825839        trf     372     7       3
22      17825583        17825839        trf     372     8       3
22      17825583        17825839        trf     372     9       3
22      17825583        17825839        trf     372     10      3

Now, I can compute the mean by grouping on columns 1-5 of the output (the orig. BED entry) and computing the mean on column 7 for each grouped interval (the last column in the output below is the mean):

$ coverageBed -abam aln.bam -b simRep.22.bed -d | groupBy -g 1,2,3,4,5 -c 7 -o mean | head
22      17825583        17825839        trf     372     2.96875
22      16506905        16558419        trf     20099   7.6543852156695271205
22      16506905        16558419        trf     23903   7.6543852156695271205
22      16506941        16537880        trf     14401   7.47002165551569241586
22      16515001        16516375        trf     832     4.88427947598253275885
22      17039134        17049375        trf     5493    6.38755980861244054836
22      17039134        17049375        trf     4544    6.38755980861244054836
22      17039134        17049375        trf     5071    6.38755980861244054836
22      18087713        18088319        trf     759     5.42904290429042912791
22      19529662        19529964        trf     469     5.91721854304635730415

Note that coverageBed does not maintain the order of the intervals in the original file, so you might want to resort the output first:

$ coverageBed -abam aln.bam -b simRep.22.bed -d | sort -k1,1 -k2,2n | groupBy -g 1,2,3,4,5 -c 7 -o mean

>  In addition is there any way to
> adjust the minimum number of hits at a location for it to be included in
> the fraction of bases in B that had non-zero coverage into bases in B
> that had more-than-X coverage?

There is no way to do this except by taking the raw output of the -d or -hist and computing this yourself.

Best,
Aaron

Dissecting plant genomes with the PLAZA comparative genomics platform.

http://www.ncbi.nlm.nih.gov/pubmed/22198273

Plant Physiol. 2011 Dec 23. [Epub ahead of print]

Dissecting plant genomes with the PLAZA comparative genomics platform.

Van Bel M, Proost S, Wischnitzki E, Mohavedi S, Scheerlinck C, Van de Peer Y, Vandepoele K.

Source

1 VIB - Ghent University;

Abstract

With the arrival of low-cost, next-generation sequencing a multitude of new plant genomes is being publicly released, providing unseen opportunities and challenges for comparative genomics studies. Here, we present PLAZA 2.5, a user-friendly online research environment to explore genomic information from different plants. This new release features updates to previous genome annotations and a substantial number of newly available plant genomes, as well as various new interactive tools and visualizations. Currently, PLAZA hosts 25 organisms covering a broad taxonomic range, including 13 eudicots, five monocots, one Lycopod, one moss, and five algae. The available data consist of structural and functional gene annotations, homologous gene families, multiple sequence alignments, phylogenetic trees, and colinear regions within and between species. A new Integrative Orthology Viewer, combining information from different orthology prediction methodologies, was developed to efficiently investigate complex orthology relationships. Cross-species expression analysis revealed that the integration of complementary data types extended the scope of complex orthology relationships, especially between more distantly related species. Finally, based on phylogenetic profiling, we propose a set of core gene families within the green plant lineage that will be instrumental to assess the gene space of draft or newly sequenced plant genomes during the assembly or annotation phase.

ART: a next-generation sequencing read simulator.

http://bioinformatics.oxfordjournals.org/content/early/2011/12/23/bioinformatics.btr708.long

Bioinformatics. 2011 Dec 23. [Epub ahead of print]

ART: a next-generation sequencing read simulator.

Huang W, Li L, Myers JR, Marth GT.

Source

Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA.

Abstract

SUMMARY:

ART is a set of simulation tools that generate synthetic next-generation sequencing reads. This functionality is essential for testing and benchmarking tools for next-generation sequencing data analysis including read alignment, de novo assembly, and genetic variation discovery. ART generates simulated sequencing reads by emulating the sequencing process with built-in, technology-specific read error models and base quality value profiles parameterized empirically in large sequencing datasets. We currently support all three major commercial next-generation sequencing platforms: Roche's 454, Illumina's Solexa, and Applied Biosystems' SOLiD.ART also allows the flexibility to use customized read error model parameters and quality profiles.

AVAILABILITY:

Both source and binary software packages are available at http://www.niehs.nih.gov/research/resources/software/art.

NAR RNASEQR a RNA seq pipeline written in python

RNASEQR was written in Python 2.7 and runs on 64-bit Linux systems. It employs a Burrows–Wheeler transform (BWT)-based and a hash-based indexing algorithm. Briefly, there are three sequential processing steps: the first step is to align RNA-Seq sequences to a transcriptomic reference; the second step is to detect novel exons; the third step is to identify novel splice junctions using an anchor-and-align strategy.

http://m.nar.oxfordjournals.org/content/early/2011/12/22/nar.gkr1248.full