Here is the link to the new 150 deeply Illumina- sequenced 1000 Genomes samples…
https://www.ebi.ac.uk/ena/data/view/PRJEB20654
https://github.com/Illumina/Polaris/wiki/HiSeqX-Diversity-Panel
There is also a planned trio panel (51 trios) and a repeat expansion project.
TIGRA: Targeted Iterative Graph Routing Assembler for breakpoint[s ]http://GENOME.CSHLP.org/content/24/2/310.long key steps: read extraction & de Bruijn #assembly
This presents a breakpoint assembler used for many projects including 1000 Genomes. It uses a targeted iterative graph routing approach. The program consists of two steps: read extraction and then assembly. The assembly step uses a de Bruin graph-based approach to create contigs from the selected reads. A shortcoming of TIGRA is it depends on the success of the first step of the program, selection of reads that span breakpoints. Thus TIGRA is sensitive to the breakpoint annotation accuracy input. Breakpoints determined from discordant paired-end or split-end alignments and by predictors like breakdancer, delly, genomestrip are excellent for TIGRA, but those determined only by read-depth such as CNVnator and RDX are poor performers.
As input TIGRA requires putative breakpoints annotation/prediction (preferably at nucleotide level or at least within 100bp resolution) and BAM files (sequence reads aligned to reference genome).
In the read extraction TIGRA tries to select all the reads that are likely associated with the breakpoint as long ass they have at least one ned or subsegment that is confidently mapped. For known SV types, TIGRA extract reads selectively to reduce the over representation of the reference allele. The assembly step uses the a de Bruin graph-based approach to create contigs from the selected reads. For this TIGRA first uses an iterative procedure to explore multiple k-mers and thus increases the chance of assembling of low coverage reads. Next it records alternative path in the contain graph
PacBio-LITS: a…targeted sequencing method for characterization of…disease-associated SVs
http://www.biomedcentral.com/1471-2164/16/214 Capture reagent+@PacBio
Characterization of #SVs w. single molecule & hybrid sequencing http://bioinformatics.oxfordjournals.org/content/30/24/3458.abstract Probabilistic read mapping, re-evaluating adjacencies
QT:{{"
We present MultiBreak-SV, an algorithm to detect structural variants
(SVs) from single molecule sequencing data, paired read sequencing
data, or a combination of sequencing data from different platforms.
"}}
Viz of datasets
https://bitbucket.org/remills/svelter
predict bkpts & assess